BULLETIN OF THE

NATIONAL NATURAL RESOURCES MANAGEMENT SYSTEMNNRMS (B) - 38

Satellite Remote Sensing & GIS Applications in Water Resources

December 2013

NNRMSDepartment of SpaceAntariksh Bhavan, New BEL RoadBangalore - 560 231INDIA

Editorial AdvisorsKiran Kumar AS, Director, SACDadhwal VK, Director, NRSCShivakumar SK, Director, ISAC

Editorial BoardRaghava Murthy DVA, Director, EOS Diwakar PG, Deputy Director (RSA), NRSC Saha SK, Dean (Academics), IIRS

Technical GuidanceShantanu Bhatawdekar, Associate Director (A), EOS

Technical Support and CompilationPaul MA, Scientist/Engineer SF, EOSBandyopadhyay S, Scientist/ Engineer SF, EOS Sameena M, Scientist/ Engineer SE, EOS

For details and inputs, please write to Director Earth Observations SystemISRO HeadquartersAntariksh BhavanNew BEL RoadBangalore 560 231

Email: raghava@isro.gov.in

Fax: 91-80-2341 3806

Published byP&PR Unit, ISRO Headquarters on behalf of National Natural Resources Management System (NNRMS)Antariksh Bhavan, New BEL RoadBangalore 560 231

Designed byImagic Creatives Pvt. Ltd., Bangalore 560 071

Printed atBrilliant Printers, Bangalore

P R E F A C E

Water is a vital component of the planet earth and an essential resource for human life,

health, economic growth, and the vitality of ecosystems. All the human civilizations are built

around the major rivers. One of the major tasks in 21st century is to conserve and protect the

water resources form the threat of pollution, unsustainable use and unscientific wastages

towards providing water security and establishing foundation for sustainable biomass-based

economy. Therefore, achieving water security is very essential for existence of future humanity

on the earth surface.

In India, water resources vary unevenly with space and time. India is known for varied climatic

zones, covering the areas of highest to lowest rainfall and areas covered by cold desert as

well as hot deserts. Further, it is coupled with rapid population growth, intensive agricultural

and industrial activities, expanding urbanization - creating enormous pressure on surface

and ground water resources. Managing water resources is, therefore, a major challenge to

protect resources from unsustainable use in order to ensure water security.

Satellite remote sensing has really become a boon for the management, monitoring and

exploration of both surface water as well as ground water scientifically. The wide spread use

of Information Technology (IT) tools and Geographic Information System (GIS) also helps to

create an up-to-date information on water resources to manage, monitor and to conserve the

nature to improve the quality of life. Extraction of hydrological parameters from multi-spectral

Earth Observation data and their interpretations in terms of soil and water conservation,

watershed development and ground water prospecting have made considerable impacts in

ensuring water security especially in the dryland areas of the country. In these circumstances,

this issue of ‘NNRMS Bulletin on water resources’ attracts great significance.

This issue comprises articles related to various aspects of water resources covering, status of

water resources in India, water geography, water resources management, water resources

development and modeling, flood monitoring and early warning system, drought assessment

and creation of water resources information system.

I take this opportunity to thank all those who have contributed articles, and others who

have helped in bringing out this issue of Bulletin on Water resources. The efforts made by

Dr. J. R. Sharma, Chief General Manger, RCs & OSD, New Delhi and Dr. P. G. Diwakar,

Deputy Director (RSA), NRSC for their support in bringing out this issue is also greatly

acknowledged.

D. V. A. Raghava MurthyDirector

Earth Observations System

Page No.

1 Water Resources of India - 1-9 Critical Issues and Satellite Technology Options Venkateshwar Rao V, Sharma JR and Dadhwal VK

2 Rainfall Estimation using Satellite Data 11-22 Pal PK, Atul Kumar Varma and Gairola RM

3 Inventory, Mapping and Monitoring of Surface Water Bodies 23-33 Suresh Babu AV, Shanker M and Venkateshwar Rao V

4 Geospatial Technology for Inventory and Monitoring of 34-42 Glacial Lakes and Water Bodies in the Himalayan Region Abdul Hakeem K and Siva Sankar E

5 Monitoring of Irrigation Projects 43-50 using High Resolution Cartosat Satellite Data Shanker M, Suresh Babu AV, Simhadri Rao B and Venkateshwar Rao V

6 Remote Sensing and GIS for River Morphology Studies 51-56 Manjusree P, Satyanarayana P, Bhatt CM, Sharma SVSP and Srinivasa Rao G

7 Water Resources and Hydrology of the Western Ghats: 57-66 Their Role and Significance in South India Mysooru R. Yadupathi Putty and Madhusoodhanan CG

8 Use of Earth Observation Data to Unearth Sub-Surface 67-74 Drainages: Potential Groundwater Source in Arid Region of North West India Bhadra BK and Sharma JR

9 Modeling Hydrological Water Balance in the 75-80 Forested Watershed for Water Management Gupta PK, Singh RP, Panigrahy S, Chauhan JS, Sonakia A and Parihar JS

10 Flood Monitoring and Management using Remote Sensing 81-88 Srinivasa Rao G, Bhatt CM, Manjusree P, Sharma SVSP and Asiya Begum

C O N T E N T S

Page No.

11 Geospatial and Hydro-Met Approach for 89-92 Flood Management in Assam Diganta Barman, Kundu SS, Jonali Goswami, Ranjit Das, Singh NGR, Arup Borgohain, Rekha Bharali Gogoi, Victor Saikhom, Suranjana B Bora and Sudhakar S

12 Agricultural Drought: Assessment & Monitoring 93-105 Sesha Sai MVR, Murthy CS, Chandrasekar K, Mohammed Ahamed J and Prabir Kumar Das

13 Irrigation Command Area Management using 107-116 Remote Sensing Raju PV, Abdul Hakeem K and Venkateswar Rao V

14 Remote Sensing Inputs for Feasibility Assessment Studies of 117-123 Proposed Water Resources Projects Simhadri Rao B, Suresh Babu AV, Shanker M and Venkateswar Rao V

15 Modeling the Impact of Land Use/Cover Change on the 124-129 Runoff Water Availability: Case Study for the Narmada River Basin Gupta PK, Punalekar S, Singh RP, Panigrahy S and Parihar JS

16 Hydrological Modeling Approach for Annual 130-140 Water Resources Assessment- A Pilot Study in the Godavari and Brahmani-Baitarani Basins, India Durga Rao KHV, Raju PV, Simhadri Rao B, Venkateshwar Rao V and Sharma JR

17 Snow Melt Runoff Modeling in Himalayan River Basins 141-151 Siva Sankar E, Abdul Hakeem K and Simhadri Rao B

18 Remote Sensing in Groundwater Modeling 152-162 Sudhir Kumar and Sanjay Kumar Jain

19 Role of Earth Observation for Grass Root Level 163-169 Water Resources Planning- Technology Demonstration for a Cluster of Villages in Semi-Arid Region of Rajasthan Rama Subramoniam S, Manoj Joseph, Bera AK and Sharma JR

20 India-WRIS WebGIS Design and Development of 170-179 web Enabled Water Resources Information System of India Sharma JR and Project Team

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

WATER RESOURCES OF INDIA - CRITICAL ISSUES AND SATELLITE TECHNOLOGY OPTIONS

Venkateshwar Rao V, Sharma JR and Dadhwal VK National Remote Sensing Centre ISRO, Department of Space, Hyderabad - 500 037, IndiaEmail:vvrao@nrsc.gov.in

IntroductionWater is the most important renewable and finite natural resources. It regenerates

and redistributes through evaporation and rainfall. In India, there is a mismatch between the

endowment of natural resources and the population to be supported (Table 1). Based on per

capita renewable water availability, India – the second most populous country in the world

has water enough to meet its peoples’ needs. The per capita availability of fresh water in

the country, which was a healthy 5,177 cubic meters in 1951, has dropped to 1,869 cubic

meters in 2001. It is estimated to further decline to 1,341 by 2025 and 1,140 by 2050. The

demand by 2050 AD is likely to reach the level of the full utilisable quantum.

With the ever-increasing population, there is staggering increase in water

requirements for agriculture, domestic and industrial sectors.

Table 1: Water Availability (In Billion Cubic Metre)

Total Precipitation 4000

Total Water Availability 1869

Total Utilisable Water 1122 (690 Surface water + 432 Ground Water)

India’s Population 16% of World Population

India’s Water Resources 4% of Global Water Resources

India’s Land Resources 2.5% of Global Land Resources

Source: Annual report 2004-05 of Ministry of Water Resources, Govt. of India.

The spectacular growth in food production in India (from merely 50.8 million

tonnes in 1950-51 to an estimated production of more than 235 million tonnes in

2010- 11) is mainly attributed to three major factors; namely, development of high

yielding varieties, large expansion in irrigated area and fertilizer use. Among these,

irrigation may be considered as a key driver of modern intensive agriculture, since it

not only ensures physiological water needs of crop plants for desired growth of photo

synthetically efficient new genotypes but also supports efficient utilization of plant

nutrients. Realizing the fact that spatial and temporal distribution of monsoon rains is

highly uneven and that development of agriculture is very difficult without irrigation,

the Government laid major emphasis on creation of irrigation potential during different

plan periods.

Agriculture sector is the largest consumer of water (80%). With growing population, urbanization and

industrialization in the country the requirements of water from competing sectors like domestic and industrial needs,

are increasing.

Precipitation VariabilityThe long-term average annual rainfall for the country is 1160 mm, which is the highest anywhere in the world

for a country of comparable size. The annual rainfall in India however fluctuates widely. The highest rainfall in India of

about 11,690 mm is recorded at Mousinram near Cherrapunji in Meghalaya in the northeast. In this region rainfall as

much as 1040 mm is recorded in a day. At the other extreme are places like Jaisalmer, in the west, which receives barely

150 mm of rain. Though the average rainfall is adequate, nearly three-quarters of the rain pours down in less than 120

days, from June to September. As much as 21% of the area of the country receives less than 750 mm of rain annually while

15% receives rainfall in excess of 1500 mm. Precipitation generally exceeds 1000 mm in areas to the east of Longitude

780E. It reaches nearly to 2500 mm along almost the entire west coast and over most of Assam and sub-Himalayan

West Bengal. Large areas of peninsular India receive rainfall less than 600 mm. Annual rainfall of less than 500 mm

is experienced in western Rajasthan and adjoining parts of Gujarat, Haryana and Punjab. Rainfall is equally low in the

interior of the Deccan plateau, east of the Sahyadris. A third area of low precipitation is around Leh in Kash. The rest of

the country receives moderate rainfall. Snowfall is restricted to the Himalayan region (Rakesh Kumar, et al., 2005).

Surface Water Resources of India From the point of view of surface water resources, India has been divided into 20 river basins. These comprise

of 12 major basins each having catchment areas exceeding 20,000 sq km and 8 composite river basins combining

suitably together all the other remaining medium and small river systems. The total water potential of these basins is

estimated at 187.9 mha.m. A break up of this resource reveals that 105 mha.m. is the runoff from rainfall that flows into

rivers and streams including reservoir and tanks. Additional water is received from snow melt (10 mha.m.), flow from

outside India (20 mha.m.), from groundwater (37 mha.m.) and from irrigated areas (11 mha.m.) making a total of about

183 mha.m. The largest potential of water is available in Ganga/ Brahmaputra/Barak and others making a total of 117

mha.m. followed by Godavari and by west flowing rivers from Tapi to Tadri each having an average annual potential of

more than 10 mha.m. Due to extreme variability in precipitation, which disallows assured storage of all the water, due

to non-availability of storage space in hills and plains, evaporation losses and water going to the sea and outside India,

it is anticipated that utilizable surface water resources would be 69 mha.m. which will be utilized by the year 2025. It is

assessed that on full development, 76 mha.m. area would be irrigated through surface water resource

Water Resources Management – IssuesPlanning and development of water resources related aspects need to be governed by

National Perspectives. India is endorsed with a large network of 12 major r iver basins covering

256 mha, 46 medium river basins covering about 25 mha besides other water bodies like tanks and ponds covering 7

mha with the ultimate irrigation potential of 140 mha. Significantly large gaps exist in the ultimate irrigation potential,

creation and the utilization and the water-use efficiency in the country is reported to be only 25-30 per cent, which has

adequate scope for improvement by employing various modern techniques.

Floods and drought affect vast areas of the country, transcending state boundaries. A third of country’s geographical

area is drought-prone. Floods affect an average area of around 9 mha.

The drinking water needs of people and livestock have also to be met. Domestic and industrial water needs

have largely been concentrated in or near the principal cities, but the demand from rural society is expected to increase

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

3

sharply as the development programmes improve economic conditions in the rural areas. The demand

for water for Hydro and Thermal power generation and for other industrial uses is also likely to increase

substantially.

Another important aspect is water quality. It is required to eliminate the pollution of surface and

ground water sources, to improve water quality and to step up the recycling and re-use of water.

Projections show, in the coming decade there may be acute water crisis in terms of availability

and supply. The problems associated with the water resources development are varied and diversified.

They are; spatial and temporal variations in water availability of the country; Falling per capita water

availability of the country; Rising multi-sectoral water demand; Complex nature of command area

problems; Significantly large gaps exist in the ultimate irrigation potential, creation and the utilization;

Rapid rate of reservoir sedimentation; Frequent floods severely affecting the development process in

flood prone areas; Recurring drought in certain regions; Over-exploitation and depletion of the ground

water resources; Deteriorating water quality and environment

To address the above issues, it calls for use of available water resources through surface water

capture and storage, long distance conveyance and inter-basin transfer, ground water exploitation,

watershed management, conjunctive use of surface and ground water and de-salinisation.

Assessment of Water Resources

At present, the natural water resource of India is estimated at around 1869 km3, and out of

this 690 km3 is considered useable from surface waters and another 433 km3 is considered useable

from the ground water. The main deficiency of the current assessment is that it ignores the principle

of the unified nature of the water resources. The important interactions between the surface waters

and the ground waters, through artificial recharge from irrigated areas and possible recharge from

river beds on one hand, and the contribution of the groundwater to the surface source through base

flows on the other hand are somewhat ignored.

A re-assessment, based on the complete modeling of the hydrologic cycle in the land phase

is very important on its own and has to be done by setting up a hydrologic model for the whole

basin. The model either has to be distributed one, or is to be made as a distributed model through an

assembly of a large number of lumped models. The NRSC and CWC have come together to develop

a tailor made model for the Indian water assessments using a simplified Thornthwaite and Mather

model with satellite derived spatial inputs.

Agricultural Water Management

Irrigation Status in IndiaOut of the 141.84 mha of net sown area in the country, 63.19 mha (44.5%) is under

irrigation in 2009-10. Out of this 35% is under canal irrigation and more than 55% is through

tube wells and other wells. Tank irrigation is only less than 10% and confined mainly to the

peninsular portion of the country. Amongst the different states, the net irrigated area varies from

12.3% in Mizoram to as high as 98% in Punjab. In Assam, Himachal Pradesh, Odisha and other

North Eastern States, it is predominantly canal system while in Gujarat, Punjab, Rajasthan and Uttar Pradesh the

ground water is the major source. In Tamil Nadu, tank irrigation is an important irrigation source with almost a quarter

of the irrigated area under this system. In the other southern states as well as Odisha, tank system is also in vogue to a

considerable extent.

As regards groundwater, though only 58% ground water development of the utilizable resources is used at

present, water tables are progressively declining at an alarming rate of 1 m per year due to excessive extraction. Presently

about 435 blocks/ Mandals/ watersheds are grouped into critical/ dark category and 422 blocks/ mandal/ watersheds semi

critical/ grey category. Construction of new wells in 435 dark areas is not allowed while a cautious slower development

is required in 422 Grey areas.

Over different Five Year Plans (FYPs), Net Sown Area (NSA) in India has increased from 125.95 mha in first FYP

to 139.66 mha in Fourth FYP and remained almost constant thereafter at 141.84 mha . However, the Gross Sown Area

(GSA) in India has increased consistently from about 140 mha in first FYP to 189.84 mha in tenth FYP with the growth

of 0.58 % per year. Consistent increase in GSA was due to improvement in the cropping intensity over the planning

periods from 111.17% in first FYP to 136.05% in tenth FYP. One of the reasons for improvement in cropping intensity

might be assured irrigation supply through various major, medium and minor irrigation projects. This is reflected through

significant increase in the net as well as gross irrigated area of the country with the growth of 2.08 and 2.52% per

annum, respectively, during 1950-2007. Comparatively higher growth in Net Irrigated Area (NIA) and Gross Irrigated

Area (GIA) led to their increasing share in NSA and GSA, respectively over different FYPs. At the end of tenth FYP, about

42% of the GSA as well as NSA was irrigated. Irrigation intensity also witnessed increasing trend during the period under

consideration, Cropping intensity, irrigation intensity and share of NIA and GIA in NSA and GSA, respectively witnessed

increasing trend in all the regions of the country over different FYPs reflecting overall improvement in irrigation status

and consequently, agriculture.

Strategies to Enhance Water Productivity in Irrigated AgricultureMost of the irrigation projects are operating at dismally low efficiency of 35%, wasting enormous amounts

of water and increasing waterlogged and saline areas and depriving the tail end farmers of their legitimate right.

With increasing pressure on land and water, immediate action is needed to improve and increase irrigation efficiency

to 60% for surface water and 75-80% for groundwater. The data presented above shows that, with almost no

increase in NSA likely in near and distant future there will be greater pressure to cultivate the available land intensively

by increasing the cropping intensity from 132% to 145% by 2025 to step up Gross Cultivated Area (GCA) from

194 mha to 210 mha. In fact in the last 25 years the increase in GCA has been for non-food grain crops and area under

grain hovering between 123 mha and 128 mha. Likewise, not much change in switch over from rainfed to irrigated

conditions could be expected and around 80 mha areas would continue to be rainfed.

Groundwater Resources ManagementIn India more than 85% of India’s rural domestic water requirements, 50% of its urban water requirements and

more than 50% of its irrigation requirements are being met from ground water resources. The increasing dependence on

ground water as a reliable source of water has resulted in its large-scale and often indiscriminate development in various

parts of the country, without due regard to the recharging capacities of aquifers and other environmental factors.

Ground water extraction for various uses and evapotranspiration from shallow water table areas constitute the

major components of ground water draft. In general, the irrigation sector remains the main consumer of ground water.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

5

The ground water draft for the country as a whole has been estimated as 231 bcm (Central Ground

Water Board, 2006), about 92 % of which is utilized for irrigation and the remaining 8% for domestic

and industrial uses. Hence, the stage of ground water development, computed as the ratio of ground

water draft to total replenishable resource, works out at about 58% for the country as a whole.

However, the development of ground water in the country is highly uneven and shows considerable

variations from place to place. Though the overall stage of ground water development is about 58%,

the average stage of ground water development in North Western Plain States is much higher (98%)

when compared to the Eastern Plain States (43%) and Central Plain States (42%).

As a part of the resource estimation following the Groundwater Estimation Committee (GEC)

norms, the assessment units have been categorized based on the stage of ground water development

and long term declining trend of ground water levels. As per the assessment, out of the total of 5723

assessment units in the country, ground water development was found to exceed more than 100 %

of the natural replenishment in 839 units ( 14.7%) which have been categorized as ‘Over-exploited’.

Ground water development was found to be to the extent of 90 to 100 percent of the utilizable

resources in 226 assessment units ( 3.9 %), which have been categorized as ‘Critical’. 550 assessment

units with stage of ground water development in the range of 70 to 100 % and long-term decline of

water levels either during pre- or post-monsoon period have been categorized as ‘Semi-Critical’ and

4078 assessment units with stage of ground water development below 70% have been categorized

as ‘Safe’. 30 assessment units have been excluded from the assessment due to the salinity of ground

water in the aquifers in the replenishable zone.

In addition to the resources available in the zone of water level fluctuation, extensive ground

water resources have been proven to occur in the deeper confined aquifers in the country, a major

chunk of which is in the Ganga-Brahmaputra alluvial plains (Romani, 2006). Such resources are also

available in the deltaic and coastal aquifers to a lesser extent. These aquifers have their recharge

zones in the upper reaches of the basins. The resources in these deep-seated aquifers are termed ‘In-

storage ground water resources’. The quantum of these resources has been tentatively estimated as

~10,800 bcm. Though the ground water resources in these aquifers are being exploited to a limited

extent in parts of Punjab, Haryana and western Uttar Pradesh, detailed studies are to be taken up to

fully understand the yield potentials and characteristics of these aquifers (Jha, 2009).

Adoption of Space Technology in Water Resources ManagementSpace borne multispectral measurements at regular intervals have helped evaluating the

performance in many irrigation projects across the country. The anticipated increase in irrigated area,

equitable distribution, crop productivity, extent and severity of water logging and salinity/alkalinity have

been studied in major irrigation command projects in India (NRSA, 1998). High resolution satellite data

(LISS IV) is used in evaluating the impact of National Project for Repair, Restoration and Renovation

(NPRRR) program aimed at restoration of lost irrigation potential under minor irrigation schemes. Use

of high resolution remote sensing data for monitoring the irrigation infrastructure is a major recent

initiative. Country’s program of Accelerated Irrigation Benefit Program ( AIBP) of large scale creation of

irrigation facilities towards achieving food security is being monitored through high resolution satellite

data from Cartosat-1 & Cartosat-2 for its effective implementation (NRSC, 2010).

Realistic appraisal of reservoir capacity is essential for appropriate utilisation plans. Multi temporal satellite

data have been used as an aid for capacity surveys of many reservoirs in a cost and time effective manner in India.

A National action plan of sedimentation survey of 124 reservoirs using remote sensing technology is

being implemented. Airborne Laser based Terrain Mapping (ALTM), in conjunction with detailed mapping through

digital camera provide valuable information on terrain characteristics in terms of topography, association, detailed land

use/ cover, geological features, etc., which were effectively used in water resources infrastructure projects viz. Interlinking

of Rivers Project (ILR) .

Space technology derived terrain (DEM) and land use-land cover information along with hydro-met data

are being used in hydrological modeling for a fresh assessment of national water resources in the country (NRSC,

2012). Space inputs are operationally used in hydrologic modeling for flood forecasting and flood inundation

simulation purposes. River Basin-specific flood forecasting models have been developed and are used in real-time by the

concerned departments.

Snow and glacier investigations and snow melt runoff forecasting are yet other areas where satellite remote

sensing imagery is providing information on retreating glaciers as well as possible potential snow melt run-off. Seasonal

and short term (weekly) forecasts of snowmelt runoff are being provided for Sutlej and Beas and Parabati basins in Western

Himalayas by the NRSC since1980s. Development of snow melt runoff models for five major Himalayan river basins is

under development for providing both short term (fortnightly) and seasonal snow melt runoff forecasts. Inventory and

monitoring of glacial lakes and water bodies in entire Himalayas covering Indian river basins is underway.

Satellite Remote Sensing in conjunction with collateral field data has made possible to provide drinking water

sources to the rural India using groundwater prospects maps under the umbrella of Rajiv Gandhi Drinking Water Mission

(RGDWM). So far ~ 3,00,000 bore wells and 12,000 recharge structures have been implemented by the respective State

Governments (NRSC, 2012). Multi-year satellite data is also used to monitor the impact of the implementation of watershed

management programmes. Implementation of appropriate rain water harvesting structures in selected watersheds under

“ Integrated Mission for Sustainable Development (IMSD) programme has demonstrated the significant benefits by way of

increased ground water recharge and agricultural development of once barren areas.

Institutional arrangements for use of Remote Sensing for Water Resources Management

The space technology application activities in the country are co-coordinated by the National Natural Resources

Management System (NNRMS) established under the Planning Commission, for which Department of Space (DOS) is the

nodal agency. NNRMS has been facilitating inventory of the country’s natural resources for its optimal utilization through

application of Remote Sensing technology in conjunction with the conventional methods. The Planning Committee of

NNRMS (PC-NNRMS) is the apex of NNRMS, which is chaired by Member (Science) Planning Commission with Secretaries

of Ministries/Departments as members. Ten thematic Standing Committees support the PC-NNRMS, of which Standing

Committee on Water Resources (SC-W) is one.

Standing Committee on Water Resources (SC-W) since it’s formation in the year 1984 has been providing the

essential thrust and guidelines for many important projects in water resources sector and operationalisation of remote

sensing using IRS satellite data. Ministry is periodically implementing the plan scheme on remote sensing applications in

water resources development and management, through which several important remote sensing projects are taken up

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

7

in the fields of reservoir sedimentation, irrigation command area monitoring, river basin development,

creation of national digital water data bases, development of national water resources information

system, snow & glacier studies.

Use of high resolution remote sensing data for monitoring the irrigation infrastructure and

feasibility studies for interlinking of river basins are major recent initiatives. Development of a national

Water Resources Information system (India WRIS) is underway jointly by the Department of Space and

Ministry of Water Resources.

There is a need to institutionalise the space technology applications in Water Resources by

integrating and strengthening the existing Central and State level agencies. This initiative would help

to adopt improved methodological framework for measuring/monitoring the progress of the country’s

water sector towards sustainable development and management

Strategies for Improved Water ManagementStructural and non-structural measures are required for mitigating the floods and droughts.

Mathematical models are needed for forecasting the monsoon rainfall accurately, which may be

utilized by the decision makers and farmers for adopting appropriate strategies for management of

droughts and floods.

There is a need for increasing the availability of water by better management of existing storages and

creation of additional storages by constructing small, medium and large sized dams, considering the

economical, environmental and social aspects.

The availability of water resources may be further enhanced by rejuvenation of dying lakes, ponds and

tanks and increasing the artificial means of groundwater recharge. In addition to these measures,

Inter-basin transfer of water provides one of the options for mitigating the problems of the surplus

and deficit basins. However, for inter-basin transfer of water, scientific studies need to be carried out

for establishing its technical and economic feasibility considering the environmental, social and eco-

hydrological aspects.

Integrated and coordinated development of surface water and groundwater resources and their

conjunctive use should be envisaged right from the project planning stage and should form an integral

part of the project implementation.

Mathematical modeling of hydrological processes would provide an opportunity to both the research

hydrologists and the water resources engineers involved in developing the integrated approaches for

planning, development and management of water resources projects for sustainable development as

well for preserving the ecosystems.

Concerted and holistic efforts are required in increasing the overall water use efficiency at system

level which would be achieved through various measures like timely execution of projects, AIBP

monitoring, minimizing the losses, better operational efficiency through stake holders participation,

implementation of on – farm water management technologies, conjunctive use of water and changes

in irrigation policy.

Regular water quality monitoring programme has to be launched for identifying the areas likely

to be affected the water quality problems. For maintaining the quality of freshwater, water quality

management strategies are required to be evolved and implemented. Minimum flow must be maintained in the rivers

for meeting the criteria of Environmental Flow Requirements (EFR).

As the country has embarked upon creating national digital water data bases and with availability of high resolution

satellite data, the stage is now set for developing locale-specific strategies for benefiting at the grass-root level. Such

systems provide an integrated approach for water resources management considering the various water-related disciplines

together with socio-economic aspects.

Recent studies have brought out the adverse impact of climate change on water resources in the country.

Realistic estimation of water resources in spatial and temporal domain is required. Hence, in the coming years efforts

would be made to address the above issues with advanced technologies. Periodic Basin-wise water resources assessment,

flood forecasting and flood inundation modeling of all flood prone areas, periodic monitoring of Irrigation projects

for monitoring Irrigation infrastructure and irrigated area assessment, development of Decision Support System (DSS)

for real-time irrigation water management, micro level ground water aquifer mapping and modeling for sustainable

management of groundwater resources, snow cover dynamics monitoring and snow melt runoff forecasting, glacial

inventory & monitoring and climate change impact are some of the key areas requiring critical inputs from satellite

remote sensing in the near future

ConclusionSpace borne multi spectral measurements have in some cases replaced ground based observations and in others

complemented at varying levels.The various issues related to topographical surveys, irrigation infrastructure, water

resource assessment in both rainfed and snowfed baisns, information on command area expansion, planning of new

storage reservoirs, stabilizing existing enroute command areas, reservoir sedimentation, glaciers, river configuration,

flood forecasting and flood inundation studies, etc. have been suitably addressed through satellite data during the last

two and half decades. The water and water related data bases generated using satellite data over the last two decades

under various programmes under NNRMS umbrella were used together with large hydromet and environmental data

from CWC, for developing a standardized national information system viz.India-WRIS jointly by ISRO and CWC. With

the improved field observational networks and Remote sensing and GIS capabilities including India-WRIS web portal,

the focus would be on scientific assessment, development and monitoring the available water resources.

AcknowledgementsAuthors place on record various ministries viz. Ministry of Water Resources, Ministry of Environment and Forest, Ministry

of Drinking water Sanitation and Ministry of Rural Development for the valuable support in executing several projects

mentioned in the manuscript. Authors also acknowledge the support and help provided by CWC, NIH and other

organizations in preparing the manuscript. Authors sincerely place on record the contributions of all the concerned

scientists across various ISRO centers who have executed several projects which have been mentioned in the manuscript

and their contribution is acknowledged.

ReferencesCGWB. (2006). Dynamic groundwater Resources of India, Central Groundwater Board, New Delhi.

Jha, B.M., (2009). Management of Groundwater resources for ensuring food security in India, National Groundwater

Congress, New Delhi.

NRSA, (1998). Project report : Performance Evaluation of 13 irrigation command areas in 5 States, Study commissioned

by, Ministry of Water Resources, GOI.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

9

NRSC, (2010). Project Report on ‘Assessment of irrigation potential through mapping of irrigation

infrastructure using high resolution satellite data in 53 AIBP Projects.

NRSC, (2011). Assessment of Water Resources at Basin Scale using Space Inputs (December, 2011):

NRSC / RS&GISAA / WRG / WRD / NRSC-CWC Pilot Study / R1 /Dec 2011/ TR 369.

NRSC, (2012). Project Report on” Groundwater Prospects mapping Under Rajiv Gandhi Drinking Water Mission.

Study commissioned by Ministry of Rural Development, GOI.

Rakesh Kumar, Singh R.D. and Sharma K.D., (2005). Water Resources of India, Current science,vol.89, No.5

10 September 2005.

Romani, (2006). Groundwater management- Emerging challenges, Groundwater Governance-

ownership of Groundwater and it’s pricing. Proceedings of the 12th National Symposium on Hydrology,

November 14-15, New Delhi.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

RAINFALL ESTIMATION USING SATELLITE DATA Pal PK, Atul Kumar Varma and Gairola RMSpace Applications CentreISRO, Department of Space, Ahmedabad - 380015, IndiaEmail: varma.atul@gmail.com

IntroductionRainfall is one of the most important atmospheric parameters, which affects the

lives and economies of a majority of the earth’s population. Its amount and distribution

during summer and winter monsoon seasons are crucial to sustain the rural economy of

the country. While the deficiency of rain causes drought and failure of the crop, excessive

rainfall is often a curse that causes flood, property and crop damages, loss of human lives

and other livestock. Rainfall is an indispensable component of global hydrological cycle and

the energy exchange that maintains the planet liveable.

Conventionally rainfall over the ground is measured using rain gauges and radars.

Rain gauges with inadequate ground network, which provide point measurements at discrete

random locations and are ineffective to capture the variability of the precipitation and vary

from few meters to several kilometers on spatial scale and few seconds to several days on

temporal scale (Piyush et al., 2012). The sparsely available distribution of the rain gauges

makes it difficult to measure areal averaged rainfall, which is necessary to study various

rain-induced events/processes, like flash flood, dam failure, river catchments, etc. On the

other hand, radars are better representative of the areal rain, but their coverage is limited

due to their sophistication and high cost. The radar measurements often suffer due to poor

calibration of radar reflectivity and also of relationship between effective radar reflectivity

(Ze) and rain rate (Ze-R relationship). The usability of radars is also marred by ground clutter,

anomalous propagation, uncertainty in the drop size distribution, melting precipitation,

attenuation, etc. Both gauge and radar observations mostly absent over the vast oceans

and often fail during severe weather conditions.

The most convenient means to measure the precipitation over large area is by using

the satellite-based methods that offer frequent uniform coverage over large area. However,

the satellite measurements suffer from large errors. The satellite based rainfall estimation is

carried out using observations in Visible/IR and microwave spectral bands. While Visible/IR

based methods suffer from their inability to sense hydrometeors directly (Barrett and Martin,

1981, Bhandari and Varma, 1995), microwave measurements suffer due to so called beam

filling problem, and uncertainty in the rain type, drop-size distribution, drop temperature, fall

velocity and shape and orientation of the drops, etc. (Varma et al., 2003, 2004 and Varma and

Liu, 2006, 2010, Mishra et al., 2010, Varma and Pal, 2012). The clouds are essentially opaque

at IR frequencies, and the IR measurement of brightness temperature from satellite refers only to Cloud Top Brightness

Temperature (CTT). Thus their use for the measurement of precipitation suffers from lack of a direct physical relationship

between CTT and rain falling below the cloud base. Inspite of these limitations, it is still possible to develop a statistical

relationship between spatially and temporally averaged CTT and precipitation. On the other hand, microwaves directly

interact with the hydrometeors and thus offer more direct measurement of precipitation, but they have large footprint

(~ 25-50 km at 19 GHz) from low earth orbiting satellites that also restrict their frequency of observation over any given

area to 1-2 observations a day. In this paper, an attempt has been made to provide an account of all the rainfall estimation

techniques from both IR and microwave observations developed and employed at Space Applications Centre.

IR based rain estimation techniquesIn the IR based methods, the CTT is the key parameter to identify and estimate the rainfall. The basic assumption

is that a cloud with low CTT is a deep convective cloud and hence it gives higher rain rate. Unfortunately neither all cold

clouds rain nor the rain always comes from cold clouds (Barrett and Martin, 1981). In this section an account of the all

the major rain measurement techniques employed for rain estimation using IR observations is provided.

Quantitative Precipitation Estimation (QPE) with Arkin’s Method This is a very simple technique, yet gives satisfactory results for rainfall measurement over larger spatial and

temporal domains. This technique is not meant for finer scale rain estimation. It was initially proposed by Arkin (1979)

and Richard and Arkin (1981) when they established a linear relationship between fractional rain-cover over a large area

and rain rate. They used data from GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE)

to show that for large spatial scales (e.g., 1.5o X 1.5o or 2.5o X 2.5o) the fraction of an area covered by cold clouds with

CTT < 235oK in hourly imagery is highly correlated (>0.7) with area averaged hourly rainfall accumulations in the same

area. The temporal averaging for 24 hr. or more improved the correlations to as high as 0.9. According to Arkin and

Meisner (1987), the probability of rainfall is a step function of equivalent black body brightness temperature (EBBT) for

each pixel and the rain rate is a constant. The probability is 0 for EBBT > 235oK and 1 for lower temperatures. Using

3 hourly images, they found that maximum rain rate for a day is 71.2 mm/day. Arkin et al., (1989) applied their scheme

to INSAT-2B observations over Indian region and found that correlation between weekly meteorological sub-divisional

rainfall from Arkin’s method and ground

observations varies from 0.33 to 0.91 for

different subdivisions.

The quantitative precipitation

estimation of rainfall using Arkin’s method

is employed for operational rain estimation.

Figure 1 shows the rainfall estimation on

7 June 2007 using IR observations from

Kalpana-1 VHRR. Mishra et al., (2010) have

compared daily averaged rain from Meteosat

using Global Precipitation Index (GPI) with

surface observations in 0.25oX0.25o over

Indian region and reported correlation

coefficient of 0.59 and 0.47 and RMS

difference of 28.67 and 16.53 mm/day for

S-W monsoon and N-E monsoon seasons,

respectively. A more advanced variant of

Quantitative Precipitation Estimation (QPE) Fig. 1: QPE for 7 June 2007 using Arkin’s method

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

13

with inclusion of precipitable water and relative humidity from National Weather Prediction (NWP)

model is also attempted and found to provide improved results.

Hydro-estimatorWhile QPE technique, holds good for large scale rain measurement for climatic studies, for

precipitation measurements at finer scale the indirect relationship between CTT and rain rate is always

an inhibiting factor. Also the QPE measured with Arkin’s method still has the known problem with warm

rain and orographic rain. The QPE method described above is also not able to provide corrections due to

evaporation of rain drops below the cloud base which is important when air below cloud base is dryer.

In order to overcome these problems, Scofield (1987) suggested blending of IR measurements with

ancillary knowledge/observations of the cloud environment and provided a scheme for instantaneous

rainfall estimation and outlooks for next 30 minutes called IFFA (Interactive Flash Flood Analyzer). The

scheme is build upon a hypothetical model of precipitation utilizing skill of a trained meteorologist

along with complementary information about environment from Numerical Weather Prediction (NWP)

model fields. This scheme was a good success but required experience and skill of a meteorologist

who is able to calculate the precipitation estimates. In order to eliminate human intervention in IFFA,

Vicente et al. (1998) proposed a new scheme called Auto-Estimator (A-E). Though it simplified IFFA,

it often produced false rain from cirrus clouds. It also required radar observations in near real time,

which are not readily available in many parts of the world. This has restricted the use of A-E outside

North America, and also over the vast oceans. In order to further improve the A-E method, Scofield

et al., (2005) proposed further modifications to A-E called Hydro-Estimator (H-E), which is completely

automated and eliminates the limitations associated with A-E.

In H-E method the cirrus clouds are avoided by considering the pixel under consideration

with respect to its neighboring pixels. Rain from convective and non-convective cores is identified and

different rain rates (R) versus brightness temperature (Tb) relationships are suggested for them. This

allows higher precipitation rates for the convective cores. For convective core, the following relationship

between brightness temperature (Tb) in 0K and convective rain (Rc) (mm h-1) is given:

The coefficients ‘a’ and ‘b’ are dynamically calculated for each pixel for given value of

Precipitable Water (PW) from National Centre for Environmental Prediction (NCEP/NOAA) model. Also,

in H-E method, the maximum possible rain value at any pixel is limited depending upon availability of

PW. Thus the maximum precipitation becomes a function of available moisture in the environment.

For a non-convective core, the relationship between brightness temperature (Tb) and stratiform

rain (Rs) is given as:

and

In H-E method, the precipitation at a pixel is allowed to be combination of both convective and

non-convective core. This is worked out by considering an array of 101 X 101 pixels surrounding the

pixel under consideration, and the mean (µ) and standard deviation (σ) of Tb in this box is determined.

The µ and σ are used to determine standard score (also called Z score) for each pixel, where,

The maximum value of Z score is restricted to 1.5. If Z < 0; R = 0, i.e., pixel either cirrus or inactive

convective,

Otherwise,

If Z=1.5, the pixel rain rate R reduces to convective type only. On the other hand, if Z=0, the pixel rain rate R is

determined by purely non-convective rain. The above relation provides the first guess of precipitation amounts, which

is further modified for the wetness/dryness of the atmosphere and for warm rain situations. The following corrections

are applied in H-E estimations:

• CorrectionforWet/dryEnvironment

• CorrectionforEquilibriumLevel/WarmTop

• Orographycorrection:

A block diagram of H-E method is

shown in Figure 2. Figure 3 shows hourly rain

measurement using H-E by SAC and NOAA/

NESDIS. The H-E at NOAA is run on MaCIDAS

(Man Computer Interactive Data Assessment

System), whereas it has been coded in

FORTRAN on Linux / windows. In SAC,

Kalpana thermal IR observations are used,

whereas NOAA uses Meteosat observations

at TIR1 channel (10.7 µm band). Also SAC

uses the 6 hourly forecast fields from NOAA

Global Forecasting System (GFS), whereas

the NOAA uses the derived fields from high

resolution North America Model (NAM).

Figure 3 shows a good quantitative and

qualitative comparison in the rain measured

at SAC and NOAA using H-E method on

13th August 2008. The minor differences in

two are possibly due to difference in the input

parameters from satellite and NWP model

fields as discussed above. Figure 4 shows a

more recent rain measurement from H-E on

21st August 2012 at 2200 Z during Jaipur

intense rain event. The figure shows a very high

rain rate of >100 mm/h at 2200 Z on 21st August

2012, which according to meteorological

reports continued till 22nd August and caused

flash floods, and casualties of human and

other lives.

INSAT Multispectral Rainfall AlgorithmApart from H-E, the other IR based algorithm that is presently operational at NOAA/ NESDIS is referred as

GOES Multispectral Rainfall Algorithm (GMSRA) which makes use of all the 5 channels of GOES for rain

Fig. 2: A schematic diagram of H-E method

Simplified Block Diagram of Hydro-Estimator

NWP derived T and RH profile

NWP derived 850 mb wind

ElevationModel

NWP derived TPW

NWP derived 1000-500 RH

Generating DewPoint TempProfiles

Atmospheric Thermodynamic

Model

Equilibrium LevelCorrection (EL)extrapolated ateach Kalpana Pixel

OrographicCorrection (ORO)interploated at eachKalpana Pixel

TPW Correction(TPW) interpolatedat each KalpanaPixel

RH Correction(RH) interpolated at each Kalpana Pixel

Kalpana IR Tb

No Rain

No Rain

Tb corrections for EL, ORO, RH and TPW (Teff)

If Teff<235 KNo

Yes

Tmin, σ, mean surrounding 101x101 pixels

Apply EL correction procedure to Tb

Apply ORO correction procedure to Tb

Apply TPW correction procedure to Tb

Next Pixel

Get corrected Tadj

Get core rain (Rc) by function fit with Rn=12.7mm/h at 240 k and Rc=f(TPW) at 210k

Get non-core rain (Rn); Rn = f(TPW, Tadj) and Rn < Rc Rn < 12 mm/h

Zi=(mean-Tadj) σ, for101x101 and 30x30 windows

Apply Rh correction procedure Rain Rain

Done

Zi=o

No

Yes

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3estimation (Ba and Gruber, 2001). These

channels are used for removing the thin

cirrus clouds, identifying overshooting tops

from anvil cirrus, determining cloud

particle size, and cloud growth rate, etc.

The GMSRA offers different relationships

for determining the rain for different

regions. Inspired by GMSRA, Gairola et

al., (2010) deloped a scheme called INSAT

Multispectral Rainfall Algorithm (IMSRA).

Technically the IMSRA is not inclusive of

all the features of GMSRA. In IMSRA, a

regression based non-linear relationship

between the brightness temperature and

the rain rate is established using near

concurrent observations from Kalpana and

TRMM Precipitation Radar (PR). Prior to

rain measurement, a cloud classification

scheme is applied which essentially screens a

pixel for probable rain using brightness

temperature measured at water vapour

absorption band (6.7 µm) and thermal

infrared band (10.5-12.5 µm). The IMSRA

is used for daily/ 3-hourly averaged rain

measurement in 0.25o x 0.25o grid.

Unlike H-E method, the IMSRA method

lacks in its ability to identify orographic/warm

rain, for example, those observed in the

N-E region and Western Ghats during

monsoon season. Figure 5 shows rain

measurement on 21st and 22nd August 2012

using IMSRA method.

The GPI and IMSRA are operationally

implemented at SAC and products are being

regularly distributed through MOSDAC

(Meteorology and Oceanography Satellite

Data Archival Centre) website (www.mosdac.

gov.in). The H-E products will also be very

soon available through this site. It is also

proposed to provide through website the high

rain areas with rain rate, affected districts and

met-subdivisions, etc. As an example, a typical

H-E based such information is provided in

Figure 6 for rain occurred during last one 15

Fig. 3: Comparison of H-E hourly rain using Kalpana data and NOAA Meteosat data

Fig. 4: H-E hourly Rain on 21st August 2012 at 2200 Z during intense Jaipur intense rain event

Fig. 5: Daily rain measurement using IMSRA method on (a) 21st and (b) 22nd August 2012

hour on 3rd September 2012 at 1400 Z. This

information may be useful for assessing of

flashflood situations for better coordination

of relief work. Further developments are

underway to provide 3 hour outlook of intense

rain using half-hourly H-E rain estimates.

Passive Microwave based Rain Estimation Techniques

In the microwave regime, at low

frequencies (e.g., 10 and 19 GHz) the

emission from the hydrometeors dominates

over the scattering, whereas at higher

frequencies (e.g., 85 GHz) the scattering is

dominating. Thus at low frequencies the

brightness temperature usually increases with

rain rate, whereas at higher frequencies it

decreases. Both scattering and emission based

algorithms are used for rain measurement

from passive microwave observations. An

account of the rain estimation techniques

employed by SAC using measurements from

microwave instruments onboard Indian

satellite missions is provided herein.

Oceansat -1 MSMR Ra in Algorithm

The Oceansat-1 launched in May

1999 carried onboard a passive microwave

radiometer called Multichannel Scanning

Microwave Radiometer (MSMR) and an

Ocean Colour Monitor (OCM). The MSMR provided measurements of brightness temperatures at 6.6, 10, 18 and

21 GHz frequencies in both horizontal and vertical polarisations. The operational geophysical parameters available from

MSMR measurements are wind speed, cloud liquid water, water vapour and surface temperatures over the global oceans

(Gohil et al., 2000). After the satellite was launched, a detailed campaign for validation of MSMR derived geophysical

parameters was carried out using both in-situ and other satellite data (Varma et al., 1999, 2002a). Varma et al., (2000,

2002b, and 2003), developed a non-linear empirical algorithm for rain measurement by regression using concurrent TMI

rain rate with MSMR brightness temperatures measured at 10 and 18 GHz V and H polarized channels. Their algorithm

explained a multi-correlation of 0.82 and error of estimation of 1.61 mm/h. Figure 7 shows the monthly rain map from

MSMR of global oceans. The MSMR could not be used for rain estimation over land as it did not carry high frequency

channels for rain estimation over land.

MT-MADRAS Rain AlgorithmA more recent launch of Indo-French Megha-Tropiques (MT) satellite on 12th October, 2011 provided a strong

impetus for precipitation measurement over tropical regions. The MT satellite carries a microwave radiometer called

MADRAS (Microwave Analysis and Detection of Rain and Atmospheric Systems) that is specifically designed to measure

Fig. 6: The location of high rain events during an hour ending at 1500 Z on 3rd Sep 2012, along with tabular report on area of each rain event of > 10 mm/h with highest rain and its geolocation (Table 1), and name of the district along with corresponding metrological subdivision experienced high rain (> 5 mm/h) and the actual rain amount.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

17

Fig. 7: Monthly Rain from (a) MSMR and (b) SSM/I for the month of August 2000

Fig. 8: A schematic diagram of rain estimation from MT-MADRAS as proposed by Varma et al. (2011)

rainfall over the land and oceans, along with

other geophysical parameters over tropical

oceans (Varma et al., 2011). In addition

to MADRAS, MT mission also carries a

number of other instruments referred as

radiation budget instrument (SCAnner for

RAdiative Budget: SCRAB), an atmospheric

sounding instrument Sounder for Probing

Vertical Profiles of Humidity (SAPHIR), and

a GPS receiver for occultation based profile

measurements. The MT MADRAS measures

the Earth’s radiation at 18.7, 23.8, 36.5, 89

and 157 GHz V and H polarized frequencies

(except 23.8 GHz which is received with

V polarization only) over the entire global

tropics (within ~ ±28o latitudes).

Gohil et al., (2013) described the

operational rain algorithm for MSMR which is based on Mishra et al. (2009). Following Grody (1991)

and Ferraro and Mark (1995), Mishra et al., (2009) used scattering index for rain measurement. The

Scattering Index (SI) is defined as depression in brightness temperature at 85 GHz SSM/I channel due

to rain. The SI is calculated by taking difference of the observed brightness temperature from the

expected brightness temperature during rain free conditions. The expected brightness temperature at

85 GHz during rain free conditions is obtained by regression involving non-scattering (low-frequency)

channels (19 and 21 GHz). Mishra et al., (2009) proposed new set of coefficients for the Indian region

for calculating expected 85 GHz brightness temperature for SI. They further correlated SSM/I brightness

temperatures with surface rain observations from TRMM PR, and established relationship between SI

and rain rate for Indian region.

A radiative transfer approach is

adopted by Varma et al., (2011), in which an

attempt was made to radiometrically calibrate

MT-MADRAS channels with corresponding

SSM/I channels using radiative transfer model

simulations. After calibrating MT-MADRAS to

SSM/I channels, they used the rain retrieval

scheme of Ferraro et al., (1996). The steps

involved in their scheme are as follows;

first establishment of radiative transfer (RT)

simulation based relationship between SI

for SSM/I 85 GHz channel (SI-SSMI) with SI

for MADRAS 89 GHz channel (SI-MADRAS).

A very high correlation is found between

SI-SSMI and SI-MADRAS. In the second

step, SI-SSMI and SI proposed by Grody (1991) are calculated using actual SSM/I observations and

a relationship is established between them which accounted for any difference in the development

procedure of the two SI models (e.g., difference in the RT model, etc.). Further procedure for rain estimation as developed

by Ferraro and Mark (1995) are applied. Over the land area the identification of deserts and arid land as proposed by

Ferraro and Mark (1995), required calibration of MT-MADRAS 18.7 V, 18.7 H and 89V channels to corresponding SSM/I

channels, which is carried out using RT simulated measurements for SSM/I and MT channels. Over the oceans, the Cloud

Liquid Water (CLW) along with SI is used by Ferraro and Mark (1995) for identifying the rain. CLW from operational MT

products (Varma et al., 2011) was considered. The scheme developed by Varma et al. (2011) for rainfall measurement is

shown in Figure 8.

Active Microwave based Rain Estimation TechniquesThe Precipitation Radar onboard TRMM is known to provide most accurate measurement of the precipitation.

The PR rain is available as a finished product in TRMM operational products, e.g., 2A23 and 2A25. PR observations

for development as well as validation of various rain algorithms have been extensively used. An account of the rain

algorithms that have been developed in the past using active microwave instrument, the scatterometer and the altimeter

are provided herein.

R a i n f r o m O c e a n s a t - 2 Scatterometer

Scatterometer is essentially a Radar

that measures the radar cross-section at larger

incidence angles. The dominant mechanism

for radar return in scatterometer is Bragg

scattering from the wind generated capillary

waves on the ocean surface. In September

2009, India launched a Ku-band pencil beam

radar scatterometer onboard Oceansat-II

satellite for global wind vector measurements.

The Oceansat-II scatterometer has an antenna with dual feed assembly generating two rotating beams at two different

incidence angles and polarizations. The Oceansat-II scatterometer is operating at 13.515 GHz frequency in VV and HH

polarizations at incidence angles of 48.9o and 57.6o while sweeping a swath of 1400 km and 1840 km by its inner and

outer beams, respectively. The scatterometer instrument also provides the apparent brightness temperature measurements

through measurement of instrument noise using an appropriate model. Ghosh et al. (2012) developed a technique to detect

and estimate precipitation over the global oceans using the radar back scattering coefficient and brightness temperature

measurements from Oceansat-II scatterometer along with rain sensitive parameters from NWP model through a neural

network (NN) based setup. Rain / No-Rain label was produced by using concurrent Tropical Rainfall Measuring Mission

(TRMM) and Advanced Microwave Scanning Radiometer for Earth Observation Satellite (AMSR-E) rain measurements.

NN was applied in two stages: (1) rain identification and (2) rain measurement with training samples from five different

geographical regions over 5 different latitudinal bands. Rain identification accuracy of about 93%, 87.2%, 90.1%, 78.9%

and 85.5% and no-rain detection accuracy of about 96.9%, 87.4%, 87.5%, 84.1% and 85.5% for these 5 regions was

reported. It was found that the missing rain cases are few compared to the size of no-rain samples and are largely from

the low rain regime. RMS error of rain estimation for regions I to V (rain rates varying from > 0 to approximately 45, 25,

25, 45, and 20 mm/h) to 1.86, 0.69, 0.47, 0.56, 0.46 mm/h, respectively was reported. The qualitative comparisons of

rain rates from scatterometer and AMSR-E demonstrate a good agreement between them (Figure 9).

Rain from Radar AltimeterTOPEX – an ocean topography mission – launched in 1992 carried onboard a dual frequency Radar Altimeter

(RA) working in C and Ku band. The primary objective of the radar altimeter was to provide ocean surface topography.

Fig. 9: 3-day averaged (10-12 Dec. 2012) rainfall from (a) scatterometer (b) AMSRE

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3The two frequencies were used to account

for estimating Total Electron Content (TEC)

and thereby estimating the ionospheric delay

in the altimetric measurements for better

accuracy of the sea surface height. The

TOPEX satellite also carried a 3 frequency

(18, 21 and 37 GHz) nadir looking TOPEX

Microwave Radiometer (TMR) for wet

tropospheric correction to sea surface height

measurements. Using differential attenuation

at C and Ku band, Bhandari and Varma (1996) demonstrated the use of altimeter for rain measurement.

The study was further extended by combining both TMR and RA for rain measurement (Gairola et al.,

2005). Figure 10 shows a monthly rain map over ocean for July 2000 using TMR and RA combined

algorithm. The study is useful in view of forthcoming dual frequency AltiKa mission.

ConclusionA number of rain estimation schemes developed at SAC/ISRO using thermal IR, passive and active

microwave observations are presented in this paper. These schemes are presently employed with

IR observations from Kalpana satellite, but will also be employed with future INSAT-3D mission. All

schemes have their own advantages and disadvantages. For example, while IR based QPE technique is

simple to implement, which, provides good estimation at climatic scales. It is not suitable for providing

high rain rates that occur over small area for short time. On the other hand, the H-E is too complex,

but is worth pursuing as that can provide measurement of even very intense rain at pixel-scale and

at the frequency of satellite-image-acquisition. The passive microwave observations are more direct

in nature but suffer due to their coarser time sampling and spatial scale. Both IR and microwave

have poorer estimation over land where measurements are most desirable for applications like crop

monitoring, flash flood, surface hydrology, etc. The most challenging task over land is to account for

warm rain and the rain falling over hilly terrain. Perhaps, the H-E method is the only satellite based

rain measurement scheme which is designed to take care of warm rain and rain over topography by

blending the NWP model fields and earth elevation model.

Keeping in view the advantages and disadvantages of IR and passive microwave measurements, it

is thus desirable to develop a comprehensive rain algorithm that takes into account advantages of

IR (e.g., its spatial resolution and high frequency of observations), passive microwave (e.g., its direct

interaction with hydrometeors) and the ancillary datasets from other sources (e.g., NWP fields and

earth elevation model) to optimally generate the estimation. A number of algorithms that blend IR

and microwave observations are already developed and made operational by many other agencies

(e.g., TRMM 2B42 by NASA; GsMAP by JAXA). It is found that the existing IR-Microwave blended

algorithms do not consider any of the NWP model fields into the algorithm. With the experience

gained towards blending IR measurements with such observations in H-E method, an algorithm that

combines IR, Microwave and model fields for the best rain estimation under an approved NASA-GPM

(Global Precipitation Mission) project is being pursued at present..

The rain algorithms presented herein for scatterometer can also be combined with other measurements

in the comprehensive rain algorithm as discussed above. Like passive microwave radiometers (e.g.,

SSM/I, MADRAS, etc.), the wind scatterometer has a large swath that provides a very good spatial

Fig. 10: Rain for the month of July 2000 from TOPEX -TMR and RA

19

coverage. However, the operational continuity of the scatterometer similar to one onboard Oceansat-2 has to be ensured

for making it meaningful for blending it in any comprehensive rain algorithm. The presently available scatterometer

based rain is useful for providing better sampling of rain for climatic studies and for flagging the scatterometer wind

measurements. Unlike scatterometer with very large swath, the Radar Altimeter provides rain only at the nadir which may

be of limited use. However, the altimeter rain is essential for possible correction and/or flagging off the rain affected sea

surface height measurements. Recently, with the launch of Indo-French AltiKa mission with a dual frequency altimeter

operating at Ku and Ka band rain measurements with better expected accuracy is being attempted.

The most advanced sensors for rain measurement till date is flying on TRMM satellite since 1997. The TRMM carries

onboard a passive microwave radiometer (TMI), a precipitation radar (PR), and other instruments. The TRMM is still

providing useful observations to global scientific community. The continuity of TRMM has been ensured through more

advanced Global Precipitation Mission (GPM) with proposed launch in 2013. The GPM is a constellation with a core and

several candidate satellites with each of them to carry a passive microwave radiometer to ensure passive microwave

observation of most parts of the globe in < 3 hour. The core satellite will be carrying a very advanced radiometer called

GPM Microwave Imager (GMI) with frequencies ranging from 10 to 157 GHz in both V and H polarizations and a two-

frequency (Ku and Ka band) precipitation radar. The dual frequency radar will help retrieving the vertical rain profile with

differential attenuation. The availability of GPM and the INSAT-3D simultaneously will open up possibility of providing

more accurate rain estimations.

ReferencesArkin, P.A., (1979). The relationship between fractional coverage of high cloud and rainfall accumulation during GATE

over the B- scale array, Monthly Weather Review, 107, 1382 –1387

Arkin, P. A., and Meisner, B.N., (1987). The relationship between large-scale convective rainfall and cold cloud over the

western hemisphere during 1982-84, Monthly Weather Review, 115, 51-74.

Arkin, P.A., Krishna Rao, A. V. R., and Kelker, R. R., (1989). Large Scale Precipitation and Outgoing Longwave Radiation

from INSAT-1B during the 1986 Southwest Monsoon Season, Journal of Climate, 2, 619-618.

Ba, M. B., and Gruber, A., (2001). GOES multispectral rainfall algorithm (GMSRA). J. Appl. Meteor., 40, 1500–1514.

Barrett, E. C. and Martin, D. W., (1981). The use of satellite data in rainfall monitoring, Academic Press, London.

Bhandari, S.M and Varma, A.K. , (1995). On Estimation of Large Scale Monthly Rainfall Estimation Over the Indian Region

Using Minimal INSAT-VHRR Data, International Journal of Remote Sensing, 16, 2023-2030.

Bhandari, S.M and Varma, A.K., (1996). Potential of Simultaneous Dual-Frequency Radar Altimeter Measurements from

TOPEX/Poseidon for Rainfall Estimation Over Ocean, Remote Sensing of Environment, 58 (1), 13-20.

Ferraro R. R, Weng F., Grody, N.C., and Basist, A., (1996). An eight-year (1987-1994) time series of rainfall, clouds,

water vapour, snow cover and sea ice derived from SSM/I measurements, Bulletin of American Meteorological Society,

77 (5), 891-905.

Ferraro R .R, and Mark, G. F., (1995). The development of SSM/I rain rate algorithms using ground-based radar

measurements, Journal of Atmospheric and Oceanic Technology, 12, 755-770.

Gairola, R.M., Pokhrel, S., Varma, A.K., and Agarwal, V.K., (2005). A combine passive-active microwave retrieval of quantitative

rainfall from TOPEX/Poseidon altimeter and TMR, International Journal of Remote Sensing, 26 (8), 1729-1753.

Gairola R.M, Mishra A., Prakash S., and Mahesh C., (2010). Development of INSAT multi-spectral rainfall algorithm (SRA)

for monitoring rainfall events over India using alpana-IR and TRMM-precipitation radar observations, SAC/ISRO Report,

SAC/EPSA/AOSG/INSAT/SR-39/2010.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Ghosh, A., Varma, A.K., Shivani Shah, Gohil, B.S., and Pal, P.K., (2012). Rain identification and

measurement using Oceansat-II Scatterometer observations, Remote Sensing of Environment, under

revision.

Gohil, B S, Gairola, R.M., Mathur, A.K., Varma, A.K., Mahesh, C., Gangwar R.K., and Pal, P.K.,

(2013). Algorithms for retrieval of Geophysical Parameters from MADRAS and SAPHIR sensors of

Megha-Tropiques Satellite – Indian Scenario, Quarterly Journal of Royal Meteorological Society, 139:

954–963.

Gohil, B.S., Mathur A.K., and Varma, A.K., (2000). Geophysical Parameters Retrieval over Oceans from

IRS-P4 MSMR, Proc. of Pacific Ocean Remote Sensing Conference (PORSEC-2000), 5-8 Dec, 2000 at

NIO, Goa, 207-211.

Grody, N.C., (1991). Classification of snow cover and precipitation using the special sensor microwave

imager (SSM/I), J. Geophys. Res., 96, 7423-7435.

Mishra, A, Gairola, R.M., Varma, A K., Sarkar, A., and Agarwal, V.K., (2009). Rainfall retrieval over

Indian land and oceanic regions from SSM/I microwave Data, Advances in Space Research, 44, 815-

823, doi: 10.1016/j.asr.2009.05.010.01.

Mishra, A., Gairola, R.M., Varma, A.K., and Agarwal, V.K., (2010). Remote Sensing of precipitation over

Indian Land and Oceanic regions by synergistic use of multi satellite sensors, Journal of Geophysical

Research, 115, D06106, doi:10.1029/2009JD012157.

Piyush, D N, Varma, A.K., Pal, P.K., and Liu, G., (2012). An Analysis of Rainfall Measurements over

Different Spatio-Temporal Scales and Potential Implications for Uncertainty in Satellite Data Validation,

Journal of Meteorological Society of Japan, 90 (4), 439 - 448, DOI: 10.2151/JMSJ.2012-408.

Richard, F. and Arkin, P.A., (1981). On the relationship between satellite observed cloud cover and

precipitation, Monthly Weather Review, 109, 1081 – 1093.

Scofield, R. A., (1987). The NESDIS Operational Convective Precipitation Estimation Technique, Mon.

Wea Rev., 115, 1773-1792.

Scofield, R.A., Kuligowskiand R.J., Davenport, J.C., (2005). The satellite-derived Hydro-Estimator and

Hydro-Nowcaster for Mesoscale Convective Systems and Landfalling Tropical Systems, in Applications

with Weather Satellites II, edited by W. Paul Menzel, Toshiki Iwasaki, Proceedings of SPIE Vol. 5658

(SPIE,Bellingham,WA,2005)0277-786X/05/$15•doi:10.1117/12.577850

Varma, A.K., and Pal, P.K., (2012). Use of TRMM Precipitation Radar to address the problem of rain detection

in Passive Microwave Measurements, Indian Journal of Radio and Space Physics, 41 411-420.

Varma, A K, Gohil, B.S., Prashant Kumar and Pal, P.K., (2011). Retrieval of Ocean Surface Wind Speed,

Total Precipitable Water and Cloud Liquid Water from Megha-Tropiques MADRAS: Initial Brightness

Temperature Observations and Retrievals, internal report no.: SAC/EPSA/AOSG/MT/SR/75/2011, p 25.

Varma, A.K, Gohil, B.S., Gairola R.M., and Pal, P.K., (2011). A new radiative transfer based rain retrieval

algorithm for rain identification and measurement from MT-MADRAS: Initial Results, internal report

no.: SAC/EPSA/AOSG/MT/SR/76/2011, p 26.

Varma, A.K., and Liu, G., (2010). On Classifying Rain Types Using Satellite Microwave Observations,

Journal of Geophysical Research, 115, D07204, doi:10.1029/2009JD012058.21

Varma, A.K., and Liu, G., (2006). Small-Scale Horizontal Rainrate Variability by Satellite, Monthly Weather Review,

134 (10), 2722-2733.

Varma, A.K., Liu, G., and Noh, Y. J., (2004). Sub-Pixel Scale Variability of Rainfall and Its Application to Mitigate the

Beam-Filling Problem, Journal of Geophysical Research, 109, D18210, doi:10.1029/2004JD004968,.

Varma, A.K., Gairola, R.M., Kishtawal, C.M., Pandey, P.C., and Singh, K.P., (1999). Rain Rate Estimation from Nadir Looking

Microwave Radiometer (TMR) for Correction of Radar Altimetric Measurements, IEEE Transactions on Geosciences and

Remote Sensing, 35 (5), 2556-2568.

Varma, A.K., Gairola, R.M., Mathur, A.K., Gohil B.S., and Agarwal, V.K., (2002a). Intercomparison of IRS-P4-MSMR

derived geophysical products with DMSP-SSM/I and TRMM-TMI finished products, Proceedings of Indian Academy of

Sciences – Earth and Planetary Sciences, 111 (3), 247-25,.

Varma, A.K., Gairola, R.M., Samir Pokhrel, Mathur, A.K., Gohil B.S., and Agarwal, V.K., (2002b). Rain Rate Measurements

over global oceans from IRS-P4 MSMR, Proceedings of Indian Academy of Sciences – Earth and Planetary Sciences,

111 (3), 257-266.

Varma, A.K., Samir Pokhrel, Rakesh Mohan Gairola, and Vijay K. Agarwal, (2003). An Empirical Algorithm for Cloud

Liquid Water From MSMR and Its Utilization in Rain Identification, IEEE Transactions on Geosciences and Remote Sensing,

41 (8), 1853-1858.

Varma, A.K., Pokhrel, S., Gairola, R.M., and Agarwal, V.K., (2000). Rain Rate Measurements from IRS-P4 MSMR, Proc. of

5th Pacific Ocean Remote Sensing Conference (PORSEC-2000), 5-8 Dec, 2000 at NIO, Goa, India, 240-243.

Vicente, G. A., Scofield, R. A., and Menzel W. P., (1998). The operational GOES infrared rainfall estimation technique.

Bulletin of the American Meteorological Society, 79, 1883–1898.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

INVENTORY, MAPPING AND MONITORING OF SURFACE WATERBODIES Suresh Babu AV, Shanker M and Venkateshwar Rao VWater Resource Group, National Remote Sensing Centre ISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: sureshbabu_av@nrsc.gov.in

Introduction and ObjectiveSurface water bodies are essential water storage units and play an important

role in efficient tapping of huge water quantities from rainfall, runoff events. For irrigated

agriculture in India, water is drawn from surface water bodies such as major/medium

reservoirs, all irrigation tanks, river reaches where lifting of water is possible, etc. Huge

infrastructure / establishments were created in India for creation of irrigation facilities.

Irrigation potential was created about ~102.77 Mha out of ultimate irrigation potential of

~140 Mha [Annual Report –MoWR, 2011]. There is continuous development and increase

in number of large dams from 3600 in 1999 to 4710 in 2009; 382 large dams are also

under construction [Dam Registry, CWC, MoWR]. In addition, there are huge number of

smaller irrigation tanks / water bodies, and also many are under construction apart from

the above number of major reservoirs in the country. Mapping and monitoring of surface

water bodies is necessary as they are dynamic in nature in terms of water spread area and

volume of water. The dynamic nature results into inter/intra annual/ seasonal/ monthly

variations in surface water spread and there by the availability of water for agricultural use

varies (Roberts et al., 1993, Voeroesmarty et al., 1997, Bastiaanssen et al., 2000 and Eric

et al., 2003). The utilisation of satellite images for the inventory, mapping and monitoring

of surface water bodies would lead to the assessment of inter and intra seasonal as well

as annual surface water spread dynamics vis-à-vis progress of cropping area and irrigated

agriculture and water utilisation patterns.

This article describes the need for spatio-temporal information on surface water

bodies, brief overview of the existing scenario of popular satellite data interpretation /

image processing / classification / methods for extraction of surface water bodies, need for

development of automated image processing methods for extraction of surface water bodies

from satellite images etc. Possibilities for the use of automated methods for quick processing

of satellite images and, further opportunities for surface water area dissemination in

near real time through web services is discussed along with demonstrated results. Details

of automated algorithm for extraction of water bodies from optical and microwave data

are explained and demonstrated, combined use of these datasets in a crop year to capture

the dynamic range of surface water spreads. Results from the demonstrative studies are

presented to represent the national scenario of surface water spread dynamics. The water

body information generated from these efforts would support the studies on spatio-temporal

surface water spread dynamics, water resources planning and management, etc.

Inventory, Mapping and Monitoring of Surface Water Bodies from Satellite Images – Present Scenario

The availability of reliable and higher frequency of earth observation capabilities from various satellite missions

has increased the scope for regular database generation and analysis on surface water area dynamics. There are several

techniques used for delineation of surface water pixels / surface water area from satellite images using both optical and

microwave sensors. This paper describes the overview of current scenario and typical case studies. Further, summary on

efforts made by NRSC / ISRO on development and utilization of quick image processing techniques and algorithms for

delineation of surface water bodies is also brought out.

Optical Satellite Images

There are traditional image classification algorithms for detection of water pixels, namely supervised classification,

unsupervised classification, applying band value thresholds, generation of popular spectral indices like Normalized

Difference Water Index (NDWI = Green-NIR / Green + NIR), Modified Normalized Difference Water Index (MNDWI =

Green-SWIR /Green + SWIR) etc. Several case studies are mentioned here. ISRO has mapped wet lands at national level

in India and atlas was released under Natural Resources Census (NRC) programme. Lenher, et .al, 2004 have created

National Global lakes and wetlands database at levels viz. large lakes, reservoirs, smaller water bodies, and wetlands.

Li et al., 2005 have published the maps of Canada’s wetlands using optical, radar and DEM data. Ma et al., 2011

published a dataset of China’ lakes, which were constructed using 11004 satellite images from CBRES CCD camera

data and Landsat TM/ETM sensors. There are several case studies in literature who have used multi temporal satellite

images for delineating the water spread area of several reservoirs at high frequency, and estimated the live storage

capacity useful for reservoir planning, operations, and sedimentation assessment (Frazer et al., 2000, Suresh Babu et.al.,

2003, Manavalan et.al., 1993, Hui, et al., 2008). Normalized Difference Water Index (NDWI) and Modified Normalized

Difference Water Index (MNDWI) were used by McFeeters et al., 1996 and Hanqiu, et al., 2006. Subramaniam et al.,

2011 have developed automated extraction algorithm for the delineation of surface water bodies using IRS Resourcesat

AWiFS, LISS III and implemented on national datasets.

Microwave Satellite Images

There were several case studies on use of microwave remote sensing datasets for delineation of surface water

bodies. The application potentials of microwave datasets for several applications including hydrological perspective was

described by Van der Sanden 2004, Sokol et al., 2004 and Brisco et al., 2008. There are several mapping techniques being

used by researchers considering various parameter viz. polarization ratios (Brisco et al., 2011, Schroeder et al., 2010),

concurrent use of optical and radar images (Laura Brown et al., 2006), ISODATA algorithm (Maria et al., 2002), adaptive

thresholding (Liu et al., 2004) and active contour model (Horritt et al., 2002, Thomas et al., 2010) for delineation of

surface water bodies from the microwave images. Rajiv Kumar Nath et al., 2010 have also described several techniques

for delineation of surface water from satellite data. There are several applications where in multi-polarization radar images

were utilized for flood inundation mapping and monitoring (Van der Sanden 2004, Hu et al., 2007 and Panchagnula

et al., 2012). Several wetland mapping applications were also conducted using optical and radar remotely sensed data

(Junhua et al., 2005 and Jean-Robert et al., 2010). The influence of incidence angle on backscatter varies according to

ground layer characteristics, including surface roughness, presence/absence of standing water, soil moisture, and the

forest structure (Megan et al., 2008). The sigma naught being chiefly a function of incidence angle and varying by about

5 dB for the smooth targets such as bare soil, surface water (Ross et al., 1998) and 10dB for other landuse features as the

incidence angle varies between 200 and 500 (Horritt et al., 2002). Separability between different classes was performed

by the visual investigation of, incident angle based HH and HV scatter plots (Brisco et al., 2011) to finalize the thresholds.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3RISAT-1 launched on April 26, 2012 is providing microwave datasets on regular basis for operational

hydrological applications. There is a need for automation of such data processing techniques. NRSC

has made initiation in this direction which is briefed in this paper.

Information Requirements vis-à-vis Image Processing for Delineation of Surface Water Bodies

Satellite data processing for extraction of surface water bodies is required for assessment of

water resources at regional level in different seasons. India is a large country and hence, huge number

of satellite datasets are to be processed. Development of techniques for quick processing of these

datasets and in specific automated algorithms will help in generation of near real time information

and would enable web based information service.

Most of the contemporary image processing softwares have modules for image processing.

Techniques involved in these softwares are extraction of inherent spectral pattern in satellite data and

categorization of pixels into various classes. Interpretation and selection of these classes representing

water surface are manually carried and grouped to derive water spread. For example, in supervised

classification, spectral signature sets are generated from water bodies and is used as basis for the

classification which may not accommodate all types of water bodies that might exist in other images

across the region. Similarly, in unsupervised classification, number of classes are defined and classified.

The corresponding classes pertaining to water pixels have to be determined by visual interpretation

techniques by the analyst to arrive at water layer. In band threshold method, digital number (DN) ranges

for a water pixel in different bands are defined and are used for extraction of water bodies. These

methods are however, scene specific, and may not be applicable to other areas. Assigning of these

parameters or threshold limits are highly subjective in nature and vary from image to image and across

the seasons / years and also in different geographical settings of India. This has lead to requirement

of manual intervention, processing time and commitment of errors in surface water area delineation.

Popular indices such as NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized

Difference Water Index) are being used in general which uses spectral information available in any

of two spectral bands such as Green, SWIR or Green, NIR and range for water pixel varies from one

water body to other and also from one image to another image. In this method, spectral information

available in other bands of a sensor are not fully utilized.

Concept of automatic extraction of water features from Resourcesat-1 AWiFS, LISS was

developed using spectrally knowledge based and further implementation on series of seasonal national

databases. The emphasis was made here for explaining the concept of automated delineation of

surface water bodies using an hierarchical multi-logic execution using all four spectral bands available

in AWiFS / LISS III.

Automated Techniques for Delineation of Surface Water Bodies

The increasing number of Satellites / Sensors available at present, provides data at higher

spatio-temporal resolutions and better repetivity. Improved computational infrastructure for handling

huge satellite data sets is enabling the development of quick image processing techniques facilitating

analysis of multi-date satellite data in near real time to derive dynamic thematic information. Surface

water spread is highly dynamic in nature and varies with rainfall pattern, storage capacity in reservoirs,

tanks, etc. of a region and water utilisation patterns. Hence, inventory, mapping and monitoring 25

is necessary for the assessment of water

resources planning and management with

respect to supply-demand. The availability

of medium spatial resolution data from

Advanced Wide Field Sensor (AWiFS) onboard

Resourcesat-1/ Resourcesat-2 (56m spatial

resolution, 5 days repetivity,740km swath)

enabled the prospects of near-real time

monitoring of water bodies. In addition, all

weather capable RISAT-1 MRS/CRS data is

also helping in capturing of water bodies

information in cloud condition and has also

increased the frequency of observation.

Logical steps followed for the development of

automatic algorithm for quick processing of

satellite images for extraction of water surface

are briefly described in this section.

Various reservoirs, tanks are depicted

through satellite images (Figure.1a, Figure.1b)

from Resourcesat AWiFS and RISAT-1

MRS data .

Automated Extraction of Water Bodies from Optical Satellite Images

AWiFS/LISS III sensor onboard

Resourcesat missions provide data in four

spectral bands Green (0.52-0.59 µm), Red

(0.62-0.68 µm), NIR (0.77-0.86 µm), SWIR

(1.5-1.70 µm). Identification of distinct

spectral reflectance characteristics of water

surface in these four bands has led to

development of the automatic extraction

algorithm for extraction of water bodies.

A new knowledge based algorithm was

developed (Subramaniam, et al., 2011) using

multi-temporal spectral information available

in four bands of AWiFS / LISS III.

Knowledge base was created from

collection and analysis of visually known

water pixels (extracted from various water

bodies) from multiple satellite data sets of

different time periods and regions. Spectral

characteristics of water in the visible-SWIR

Fig. 1a: Satellite image showing water bodies (Resourcesat AWiFS -Optical data)

Fig. 1b: Satellite image showing water bodies (Risat-1 MRS-Microwave data)

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3spectral bands are (i) lower reflectance in

Visible-SWIR spectral region compared

to other surface cover features due to

the absorption of NIR and SWIR radiation

(ii) high spectral contrast between green and

NIR /SWIR. Distinct spectral behaviour of

water pixels are identified and a knowledge

based hierarchical algorithm was developed

with band thresholds, band ratios, spectral

indices for quick processing of satellite data

and extraction of water bodies. Hierarchical,

logical steps were developed for building

algorithm and utilise all the information

available in all four bands of the sensor.

Quantitative evaluation of water

spread obtained using the algorithm and by

visual analysis was carried out with respect

to size and shape by comparing the results.

Visual interpretation was chosen as it is well established technique to capture the land–water boundary,

though one or two pixels along the periphery could be ambiguous. The accuracy of delineation of

water bodies using automated algorithm vis-à-vis visual interpretation has been found to be in the

range of 92 - 97% for large water bodies and varied for the other water bodies depending on the

size, area, area /perimeter. Figure.2 shows satellite image and corresponding water body information

derived from automatic extraction algorithm.

Automated Extraction of Water Bodies from Microwave Satellite Images

Microwave satellite images are useful to capture the information during cloud cover. An

experimental study was conducted using

RISAT -1 MRS data (dual polarization-HH,

HV, 18 m spatial resolution) and developed an

automated algorithm for classification of water

bodies with the development of knowledge

base on sigma naught of HH, HV values. The

methodology consists of characterization of

microwave response to various types of water

features, observations on behaviour of back

scatter coefficient (σo) from these features,

generation of spectral plots, identification of

sigma naught thresholds, identifying the back

scatter coefficient thresholds which are distinct

to water surface. The accuracy was improved

by referring to legacy water bodies mask. Initial

results are shown in Figure 3.

Fig. 2: Water spread derived from Resourcesat AWiFS Images

Fig. 3: Water spread derived from RISAT-1 MRS Images27

Monthly Monitoring of Surface Water Bodies Water spread is dynamic in nature and requires continuous monitoring, but there are limitations due to cloud

cover. Experimental studies indicated that the cloud free data is available during October month only, where as, capturing

the same in Kharif season (Jun, Jul, Aug, Sep) months is difficult with optical data and hence there is a need to make

use microwave data to fill the gap. In view of this, the automated approach presented in above sections would provide

scope for capturing the dynamics of water spread in India at monthly interval.

Operational Utilization of A u t o m a t e d E x t r a c t i o n Algorithm for Surface Water Bodies

R e s u l t s f r o m o p e r a t i o n a l

Implementation of automated extraction

algorithm revealed that the quick processing

of optical satellite data is feasible. Algorithm

was implemented on multiple datasets in

different time scales. Resourcesat -2 AWiFS

derived Water Body Area (WBA) in different

months (Sep, 2011-May 2012) is shown

in Figure 4.

Ut i l i zat ion of Sate l l i te Derived Surface Water Bodies Information

Huge number of satellite data

sets were used for extraction of dynamic

water body area information in addition to

inventory of surface water bodies. The focus

is being made for specific utilization of such

dynamic surface water bodies. The immediate

applications are : Monitoring of surface water

bodies at regional level at defined interval (fortnight/month) and analysis of the same variations and development of

hydrological drought indices, input to hydrological modeling, climate models as surface water area is one of critical

parameter in land surface process modeling, representation of grid wise surface water area fraction, capacity estimation

of major & medium reservoirs, etc.

Utilisation at National Scale

A scenario of water body area dynamics is described here along with the satellite derived information. Resourcesat

AWiFS data sets were used for the implementation of automated water spread extraction algorithm for the months of

Oct, Feb, May of 2004-2011 for the demonstration of the concept and its operational utilization. Analysis was carried

out in two categories namely, Water Bodies Area (WBA) and WBA-Res (only reservoirs, tanks, lakes, ponds excluding

rivers/streams).

The dynamic behaviour of WBA was estimated at national scale and statistics revealed that WBA was ranging

from 2.386 M.ha (May) to 5.107 M.ha (October) during 2004-2011. The dynamic behaviour of WBA was quantified

Fig. 4: Monthly surface water bodies area using Resourcesat-2 AWiFS Sensor data

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3at national level and revealed that WBA

was ranging from 0.72%-1.55% of total

geographical area during 2004-2011 in India.

WBA was estimated maximum in October,

reduced by 4-17% in February, 30-46% in

May analyzed over 7 years period.

Overall scenario indicates that WBA

is around 35%, 15%, 50% from larger

water bodies (>500 ha), small water bodies,

rivers / streams respectively. The rainfall was

higher during Jun-Sep in all the years which is

70-87% of annual RF (890 mm-1212 mm)

and the rest in Oct-Dec, and rainfall was

negligible during Jan-May months and WBA

variations were found to be in correspondence

with rainfall variations.

The results shown in Figure 5

indicate that, the automated methods for

consistent information generation on surface

water bodies can be operationally utilized.

Though, the study was done for three months

representing 3 seasons namely Kharif, Rabi

and summer, the same can be extended at

higher frequency with the combination of

microwave and optical datasets.

Development of Water Spread

I n d i c a t o r s R e p r e s e n t i n g

Hydrological Drought at Regional

Scale

Hydrological drought is expressed

in terms of availability of water - change /

deviations in discharges which are ultimately

linked to rainfall intensity and amounts.

However, the impact can be observed from

surface water spread in various water bodies.

The concept demonstrated here is the use

of satellite derived surface water spread

information to understand the deviations

from normal water spread area of water bodies and also the visibility of new water bodies in the

region. An example shown in Figure 6 indicates the regional analysis of surface water spread over

Vidarbha region of Maharashtra State.

Fig. 5: Variations in surface water spread during 2004-2011 over India

29

Fig. 6: Spatial variations in satellite derived surface water bodies – current & historic data

WSA: Total Waterbodies

WSA: Excluding new Waterbodies after 2006

WSA: New Waterbodies after 2006

Note : Number of new water bodies formed after 2006 are Indicated within brackets

Wat

er s

prea

d ar

ea (H

a)

140000

120000

100000

80000

60000

20000

40000

0

Oct 2010 Oct 2009 Oct 2008 Oct 2007 Oct 2006Oct 2011

Reference year

126336

112779

115971

105272

66749

61433

8434180232

109302105829

121801

3473

(10)(15)

4109(21)

5316

10699

(28)

15557

(37)

Inputs for Land Surface Process Modeling

Water Body Fraction (WBF) was

estimated as fraction of Water Body Area

(WBA) and Grid Area (3’ x 3’ grid) over

India. An example is shown in Figure 7. WBF

derived over India during 2004-2011 indicated

that majority of grids are having <5% of

WBF and significant number grids are having

WBF in the range of 5-30%. Grid (3’x3’) wise

surface water area fraction was generated,

which is useful as an input to climate and

weather studies.

Capacity Estimation of Major

Reservoirs from Satellite Data

Monitoring the changes in capacities

of several reservoirs in cropping seasons is

essential in India as we have huge number

of reservoirs and irrigated agriculture. This

will help us to estimate reservoir capacities

from time to time for better water resources

planning and management. Utilisation of

satellite data for extraction of water spread

area of reservoir(s) at frequent intervals and

linking the water spread area to the Area-

Capacity curves of the concerned reservoir for

capacity estimation in near real time through automation would provide the scope for estimation of reservoir capacities

as soon as satellite data is acquired. In this direction, an integrated set of software tools were developed for extraction of

water layer from satellite data, tagging the identification to cluster of reservoirs available in satellite image, linking lookup

table for capacity estimation (pre defined area – capacity curve from the field data). The development of these tools

enabled the use of Resourcesat AWiFS / LISS III effectively . Operational utilisation of such tools is in progress. The concept

will also be useful for effective utilization of RISAT-1 CRS/MRS dual polarization data as we can overcome cloud cover

problem in monsoon season when there are

more dynamic changes in water spread due

to rainfall and subsequent water withdrawals.

Figure 8, depicts the demonstration of the

concept. Blue line indicates the capacity of

reservoir based field based observations. The

magenta dots on the blue curve indicates the

satellite derived water spread based capacity

of reservoir.

Fig. 7: Example showing the satellite derived Water Body Fraction (WBF) at 3’ x 3’ grid interval

Fig. 8: Water Spread Area, Capacity of a reservoir derived from GIS Data model

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Conclusion The scope for use of optical and microwave data for the purpose of inventory, mapping and

monitoring of surface water bodies has been improved with the frequency of data available with the

combination of optical and microwave satellite data sets. The updated scenario on the techniques

being used and the demonstration on the utilization and implementation of quick data processing

methodologies, derived results and examples on the utilization of datasets are explained. It can be

concluded that, the surface water bodies can be delineated from satellite data at fortnight interval

with the use Resourcesat-2 AWIFS data and RISAT-1 MRS/ CRS datasets. The datasets can be served

through web services like ISRO Bhuvan and India WRIS.

ReferencesAnnual Report, Ministry of Water Resources, India. 2011.

Bastiaanssen, W.G.M., Molden, D.J., and Makin, I.W., (2000). Remote sensing for irrigated agriculture:

examples from research of possible applications. Agricultural Water Management, vol.46, pp.

137–155.

Brisco, B., Touzi, R., van der Sanden, J.J., Charbonneau, F., Pultz, T.J., D’Iorio, M. (2008). Water resource

applications with RADARSAT-2 – a preview, Int. J. of Digital Earth. 1(1):130-147

Brisco, B., Kapfer, M., Hirose, T., Tedford, B., and Liu, J. (2011). Evaluation of C-band polarization

diversity and polarimetry for wetland mapping, Can. J. Remote Sensing. 37(1):82-92

Dam Registry, Central Water Commission, Ministry of Water Resources, Govt. of India.

Eric, S. Kasischke, Kevin B. Smith, Laura L. Bourgeau-Chavez, Edwin A. Romanowicz, Suzy Brunzell,

Curtis J. Richardson (2003); Effects of seasonal hydrologic patterns in south Florida wetlands on radar

backscatter measured from ERS-2 SAR imagery, Remote Sensing of Environment 88, 423–441.

Frazier, P.S. and Page K.J., (2000). Water Body Detection and Delineation with Landsat TM Data,

Photogrammetric Engineering & Remote Sensing, vol. 66, pp.1461-1468.

Hanqiu and Xu., (2006). Modification of normalize difference water index (NDWI) to enhance open

water features in remotely sensed imagery. Int. J. of Remote Sensing, vol.27, pp.3025-3033.

Horritt D Mason., and Luckman, A J., (2002). Flood boundary delineation from Synthetic Aperture

Radar imagery using a statistical active contour model, Int. J. of Remote Sensing. 22(13):2489-2507

Hu Zhuowei., Gong Huili., Zhu Liying. (2007). Fast Flooding Information Extraction in Emergency

response of flood Disaster, ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The

5th ISPRS Workshop on DMGISs

Hui,F., Xu, B., Huang, H., Yu, Q,. and Gong, P., (2008). Modeling spatial-temporal change of Poyang

Lake using multitemporal Landsat imagery. Int. J. of Remote Sensing, vol.29, pp. 5767–5784.

Jean-Robert B Bwangoy., Matthew C Hansen., David P Roy., Gianfranco De Grandi., Christopher O

Justice. (2010). Wetland mapping in the Congo Basin using optical and radar remotely sensed data

and derived topographical indices, Remote Sensing of Environment. 114:73–86

Junhua, Li., and Wenjun, Chen. (2005). A rule-based method for mapping Canada’s wetlands using

optical., radar and DEM data, Int. J. of Remote Sensing. 26(22):5051-5069.31

Laura Brown., and Kathyl L Young. (2006). Assessment of Three Mapping Techniques to Delineate Lakes and Ponds in

a Canadian High Arctic Wetland Complex,ARCTIC. 59(3):283–293.

Lehner,B., and Doell,P. (2004) Development and validation of a global database of lakes, reservoirs and wetlands” J. of

Hydrology, vol. 296, pp. 1–22.

Liu, H., Jezek K C. (2004). Automated extraction of coastline from satellite imagery by integrating Canny edge detection

and e thresholding methods, Int. J. of Remote Sensing. 25(5):937-958

Li , J. and Chen, W. (2005).A rule-based method for mapping Canada’s wetland using optical, radar and DEM data. Int.

J. of remote sensing, vol.26, no.22, pp. 5051-5069.

McFeeters, S.K.(1996).The use of normalized difference water index (NDWI) in the delineation of open water features.

Int. J. of Remote Sensing, vol.17, pp. 1425-1432.

Ma, D.J. , Yang, G. , Duan, H. , Jiang, J. , Wang, S., Feng, X., Li, A., Kong, F., Xue, B., Wu, J. and Li, S. (2011). China’s

lakes at present: number, area and spatial distribution. Science China Earth Sciences, vol. 54, pp. 283–289.

Maria Gabriela Parmuchi., Haydee Karszenbaum., and Patricia Kandus. (2002). Mapping wetlands using multi-temporal

RADARSAT-1 data and a decision-based Classifier, Can. J. Remote Sensing. 28(2):175–186

Manavalan, P., Sathyanath, P., and Rajegowda, G.L. (1993). Digital Image Analysis Techniques to Estimate Water spread

for Capacity Evaluations of Reservoirs. Photogrammetric Engineering & Remote Sensing, vol. 59, pp.1389-1395.

Megan W. Lang, Philip A. Townsend, Eric S. Kasischk (2008). Influence of incidence angle on detecting flooded forests

using C-HH synthetic aperture radar data, Remote Sensing of Environment 112 pp. 3898–3907

Panchagnula Manjusree, Prasanna Kumar, L., Chandra Mohan Bhatt, Goru Srinivasa Rao, and Veerubhotla Bhanumurthy.

(2012). Optimization of Threshold Ranges for Rapid Flood Inundation Mapping by Evaluating Backscatter Profiles of High

Incidence Angle SAR Images . Int. J. Disaster Risk Sci. 2012, 3 (2): 113–122.

Rajiv Kumar Nath., Deb S.K. (2010). Water-Body Area Extraction from High Resolution Satellite Images-An Introduction,

Review, and Comparison, 2010. Int. J. of Image Processing. 3(6):353-372.

Roberts, N., Taieb, M., Barker, P., Damnati, B., Icole, M., and Williamson, D. (1993). Timing of the Younger Dryas event

in East Africa from lake-level changes. Nature, vol.366, pp. 146–148.

Ross, S., Brisco, B., Brown, R.J., Yun, S., and Staples, G. (1998). Temporal Signature Analysis of Rice Paddies using

RADARSAT-1, Preliminary Results. 20th Canadian Symposium on Remote Sensing, Calgary, May 11-14.

Schroeder, R., Rawlins, M A., McDonald, K C., Podest, E., Zimmermann, R., and Kueppers, M. (2010). Satellite microwave

remote sensing of North Eurasian inundation dynamics, development of coarse-resolution products and comparison with

high-resolution synthetic aperture radar data, Environ. Res. Lett. 5 015003.

Sokol, J., NcNairn, H., and Pultz, T J. (2004). Case studies demonstrating the hydrological applications of C-band multi

polarized and polarimetric SAR, Can. J. Remote Sensing. 30(3):470–483.

Subramaniam, S., Suresh Babu, A.V. and Roy, P.S.(2011). Automated Water Spread Mapping Using ResourceSat-1 AWiFS

Data for Water Bodies Information System. IEEE Journal of Selected Topics in Applied Earth Observations and Remote

Sensing, vol.4, pp. 205 – 215.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Suresh Babu, A.V., Shanker, M., Venkateshwar Rao, V., and Bhanumurthy, V. (2003). “Generation

of water Spread contours for Tungabhadra reservoir during low water levels of the year 2002 using

satellite remote sensing technique”, proc. GIS India, Jaipur, India.

Thomas Hahmann, Birgit Wessel (2010); Surface Water Body Detection in High-Resolution TerraSAR-X

Data using Active Contour Models, EUSAR 2010

Van der Sanden J J. (2004). Anticipated applications potential of RADARSAT-2 data, Technical Note,

Can. J. Remote Sensing. 30(3):369–379.

Voeroesmarty, C J., Sharma, K P., Fekete, B M., Copeland, A H., Holden, J.,. Marble, J., and

Lough, J A. (1997). The storage and aging of continental runoff in large reservoir systems of the

world, Ambio.26:210–219.

33

GEOSPATIAL TECHNOLOGY FOR INVENTORY AND MONITORING OF GLACIAL LAKES AND WATER BODIES IN THE HIMALAYAN REGIONAbdul Hakeem K and Siva Sankar EWater Resource Group, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: abdulhakeem_k@nrsc.gov.in and sivasankar_e@nrsc.gov.in

IntroductionThe Hindu-Kush Himalaya (HKH) region extends about 3500 km from Afghanistan in the

west to Myanmar and China in the east, and runs through Pakistan, Nepal, India, Bangladesh and

Bhutan (Nina Behrman, 2007). The HKH region is one of the most geologically active zones on

Earth. Such is the size, elevation and climate of the HKH region that it hosts the largest areas of

glaciers, snow and permafrost beyond high latitudes. These huge reservoirs of frozen fresh water

represent the sources of a number of the world’s greatest rivers, and is often described as the ‘water

tower of Asia’. The glaciers of the Hindu Kush–Himalayan (HKH) region are one of nature’s greatest

renewable storehouses of fresh water; and they benefit hundreds of millions of people downstream.

The common natural hazards in the region are earthquake, landslide (due to seismicity), landslide

induced flood, cloudburst, ice/snow avalanches, etc.

Glacial Lakes and Water Bodies in HimalayasIn general, the area higher than 4,000 m in elevation is mostly covered by snow and ice

throughout the year. The glaciers, some of which consist of a huge amount of perpetual snow and

ice, are found to create many glacial lakes. These glaciers as well as glacial lakes are the sources

of the headwaters of many great rivers in the region. Most of these lakes are located in the down

valleys close to the glaciers. They are formed by the accumulation of vast amounts of water from

the melting of snow and ice cover and by blockage of end moraines.

A glacial lake is a lake with origins in a melted glacier. Towards the end of the last glacial

period, roughly 10,000 years ago, glaciers began to retreat. A retreating glacier often left behind

large deposits of ice in hollows between drumlins or hills. As the ice age ended, these melted to

create lakes. These lakes are often surrounded by drumlins, along with other evidence of the glacier

such as moraines, eskers and erosional features such as striations and chatter marks.

A water body is any significant accumulation of water, usually covering the Earth. It most

often refers to large accumulations of water, such as oceans, seas, and lakes, but it includes smaller

pools of water such as ponds, puddles or wetlands. Some bodies of water are man-made (artificial),

such as reservoirs or harbors, but most are naturally occurring geographical features. In addition to

several glacial lakes, Himalayan region also hosts many water bodies of varying size.

Glacial Lake Outburst FloodsA Glacial Lake Outburst Flood (GLOF) is a type of outburst flood that occurs when the

dam containing a glacial lake fails. The dam can consist of glacier ice or a terminal moraine. Failure

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3can happen due to erosion, a build-up of water pressure, an avalanche of rock or heavy snow, an

earthquake or cryoseism, volcanic eruptions under the ice, or if a large enough portion of a glacier breaks

off and massively displaces the waters in a glacial lake at its base. Catastrophic failure of the containing

ice or glacial sediment can release this water over periods of minutes to days. Peak flows as high as

15,000 m3/sec have been recorded in such events, suggesting that the v-shaped canyon of a normally

small mountain stream could suddenly develop an extremely turbulent and fast-moving torrent about

50m deep. On a downstream floodplain, it suggests a somewhat slower inundation spreading as much

as 10 km wide. Both scenarios are significant threats to life, property and infrastructure.

Apart from landslides and river erosion, the mountainous Himalayan region is also quite

susceptible to disastrous hazards due to GLOF (Pradeep K. Mool et al., 2001). There have been several

occurrences of GLOF events in different parts of the Hindu Kush-Himalayan region. Downstream

impacts of these GLOFs are reported to be highly destructive in nature and to lead to long-term

secondary environmental degradation in the valleys, both physically and socio-economically. A report

by International Centre for Integrated Mountain Development (ICIMOD) (Ives, J.D. et al., 2010) lists

34 GLOF events that have occurred in Nepal, TAR/ China, and Bhutan.

The United Nations has adopted a series of monitoring efforts to help prevent death and

destruction in regions that are likely to experience GLOFs. The importance of this situation has

magnified over the past century due to increased population, and the increasing number of glacial

lakes that have developed due to glacier retreat. While all countries with glaciers are susceptible to

this problem, central Asia, the Andes regions of South America and those countries in Europe that

have glaciers in the Alps, have been identified as the regions at greatest risk (http://en.wikipedia.org,

2013). The study of glacial lakes is also very important for the planning and implementation of any

water resources development projects.

Geospatial Technology for Inventory of Glacial Lakes/ Water BodiesGlaciers and glacial lakes are generally located in remote areas, where access is through tough

and difficult terrain. Creating inventories and monitoring of the glacial lakes can be done quickly and

correctly using satellite images and aerial photographs. Visual and digital image analysis techniques

integrated with Geographic Information Systems (GIS) are very useful for the study of glacier, glacial

lakes. Satellite remote sensing offers several unique advantages quick data collection, reliability, more

accurate, repetitive collection, geometric integrity and digital storage, which makes it an ideal tool

for mapping, inventorying and monitoring the natural resources.

Earlier Studies on Glacial Lakes Inventory in Himalayan RegionMany studies have been carried out for inventorying glacial lakes in different parts of the

Himalayan region. International Centre for Integrated Mountain Development (ICIMOD) has carried out

inventory of glaciers and glacial lakes in the Himalayan regions in cooperation with other organisations

(Ives, J.D. et al., 2010). ICIMOD has created a comprehensive inventory and GIS database of glaciers

and glacial lakes in Nepal and Bhutan using available maps, satellite images, aerial photographs,

reports, and field data on different scales. Along with national partner institutions, ICIMOD has also

carried out similar studies in parts of India (Himachal Pradesh, Sikkim and Uttarakhand), and a few

basins in China. (http://www.icimod.org).

Space Applications Centre (SAC), ISRO has mapped glacial lakes in Himalayan region (SAC,

2011). National Remote Sensing Centre (NRSC), ISRO has also carried out inventory of glacial lakes 35

and water bodies in Sutlej basin. Mangde Chu River Basin and Tawang river basin using medium resolution satellite data

(NRSA, 2007a, NRSA, 2007b, & NRSC, 2009). GLOF study was carried out in Alaknanda valley through inventorying of

glacial lakes using satellite images (Sanjay K. Jain et al., 2012).

The efforts put by different organisations for inventory of glacial lakes in the Himalayan region were not covering

entire Himalayas. In addition to this, Landslide Lake Outburst Floods (LLOF) occurred during 2000 and 2005 caused by

the breaching of landslide lakes created in the Trans-Himalayan region along the Satluj River and Paree Chu (stream),

respectively, both in the Tibetan region of China (Vikram Gupta and M. P. Sah, 2008). This LLOF had severe impact on

the channel and infrastructure in the Kinnaur district of Himachal Pradesh. The lake formed due to the landslide across

the Pareechu river in 2004 and the subsequent breach in 2005 was continuously monitored by NRSC using satellite data

of different resolutions (NRSA, 2005).

The occurrence of LLOFs and threat of GLOFs lead to the need for a comprehensive inventory and monitoring

of glacial lakes and water bodies in the Himalayan region of Indian river basins. Based on the request from Central Water

Commission under Ministry of Water Resources, NRSC carried out a study (NRSC, 2011) during 2010-11 for inventorying

of glacial lakes/ water bodies in the Himalayan region of Indian River basins using satellite data of the recent year (2009).

Glacial lakes with spatial extent greater than 50 ha were considered and inventoried. In addition to this, NRSC also

monitored (NRSC, 2012) the spatial extent of the glacial lakes/ water bodies (identified/inventoried earlier) on monthly

basis from June to October during 2011 and 2012.

Inventory and Monitoring of Glacial Lakes and Water Bodies

The study was carried out for the

area covering Himalayas under the major river

basins of Indus, Ganga and Brahmaputra. The

study area extends across different countries

namely India, Nepal, Bhutan and China.

The index map showing study area is given

in Figure.1.

Data Used for the InventoryThe basic data required for the

inventory of glacial lakes and water bodies

are large-scale topographic maps, aerial

photographs, and multi-date satellite images. Large-scale topographic maps and aerial photographs are generally not

available for this area and also the study area extends across international boundaries. The study mainly used the satellite

images of the Advanced Wide Field Sensor (AWiFS) Resourcesat-1. Reports of earlier studies carried out by different

organizations like International Centre for Integrated Mountain Development (ICIMOD), Geological Survey of India (GSI),

on glaciers and glacial lakes inventory in the Himalayan region were also referred.

For glacial lake identification from satellite images, it is preferable to have images with least snow cover and

cloud cover. Generally the least snow cover occurs in the period between May and September in the Himalayas. If

snow precipitation is late in the year, winter images are also suitable except for the problem of long relief shadows

in the high mountain regions. The cloud free satellite data for the period of May-Dec, 2009 were procured and

orthorectified with the reference image (Figure 2). Apart from the above satellite data, historic AWiFS data were also used.

Fig. 1: Map showing the extent of Himalayan region of Indian River basins

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3The ortho-rectified images from Enhanced

Thematic Mapper (ETM) on-board Landsat-7

satellite available in the Global Land Cover

Facility website (http://www.landcover.org)

were also used as reference image as well

as in supporting the identification of glacial

lakes & water bodies.

Preparation of Input DataThe Shuttle Radar Topographic

Mission (SRTM) based digital elevation

model was used to delineate basin and

sub-basin boundaries with the help of AcrHydro tool. The SRTM Digital Terrain Elevation Data (DTED)

one degree tiles covering the study area were mosaiced and filled for sinks. Then flow direction and

flow accumulation images were generated. The default threshold value was used in delineating

catchment boundaries using the flow accumulation grid image. Drainage lines were subsequently

derived automatically. The southern boundary of the study area was restricted up to the Himalayan

foot hills & administrative/political boundary, whereas northern boundary follows the natural basin

boundaries of Indus, Ganga and Brahmaputra rivers as shown in Figure 1. The total geographical area

of the study area is 98.6 mha.

Since, the study area is highly undulating mountainous terrain, orthorectification of satellite

data is essential for accurate water spread area estimation as well as for better location accuracy. In

this study, orthorectification of AWiFS data was carried out using Projective Transform model available

in ERDAS Imagine software. The orthorectified Landsat ETM images were used as reference image for

collections of GCPs and the elevation values for GCPs were collected from SRTM DEM.

Identification & Mapping of Glacial Lakes/Water BodiesThe glacial lakes & water bodies were delineated based on the visual interpretation of satellite

images of Resourcesat AWiFS sensor. Identification of features was done through panchromatic mode

and/or different colour combinations of the multi-spectral bands namely green, red, near infrared

and shortwave infrared. To identify the glacial lakes & water bodies, different image enhancement

techniques are used to improve the visual interpretation. This method is complimented with the

knowledge and experience of the Himalayan terrain conditions for inventorying glacial lakes and water

bodies. With different spectral band combinations in False Colour Composite (FCC) and in individual

spectral bands, glacial lakes and water bodies can be identified. Figure 3 and Figure 4 shows how

typical water bodies and glacial lakes respectively are seen in satellite image. The water spread area

of the lakes in images ranges in appearance from light blue to blue to black. The frozen lakes appear

white in colour. They are generally associated with glaciers in the case of high lying areas, or rivers in

the case of low lying areas.

The boundary of glacial lakes and water bodies are digitized using on-screen digitisation

techniques as polygon feature. The polygons are geoprocessed and the water spread area of glacial

lakes/water bodies were computed digitally. The lakes are identified and digitized as on the date of

satellite data. There is a possibility that some lakes that are frozen or overlaid with snow might have

been omitted in this inventory. However, during monitoring phase of this study, the inventory was

Fig. 2: Mosaic of satellite images covering the study area

37

updated. Although it was planned to map

only glacial lakes & water bodies that are

larger than 50 ha in area, glacial lakes and

water bodies having area more than 10 ha

were also digitised.

Organisat ion of Digita l Database

A digital database is necessary for the

monitoring of these lakes & water bodies and

to identify the potentially dangerous lakes.

GIS is the most appropriate tool for spatial

data input and attributes data handling.

The digital database has the following

attributes (Table 1).

Glacial Lake Number: Each glacial lake

& water bodies will have unique number

in the digital database. The numbering is

done sequentially within each 1:250,000

reference grid. The first two digits indicate

the basin number (01 - Indus, 02 - Ganga

and 03 - Brahmaputra). The next three

characters depict the reference number of the

1:250,000 SOI toposheet. The last three digit

number indicates lake number within a grid

of 1:250,000 SOI toposheet. The attributes

of the databases are Latitude and Longitude, Area, Length, Width and Altitude

In addition to above, the attributes on basin, sub-basin name, country toposheet No. (1:250,000 scale) and

name of the glacial lake/water body (where ever available) were also populated.

Salient features of inventory of glacial lakes/water bodies

The inventory of glacial lakes and

water bodies in the Himalayan region of

Indian river basins carried out using satellite

images shows presence of 2028 glacial lakes

and water bodies within the study area

(Table 2). Out of these, 503 are glacial lakes

and 1525 are water bodies. Brahmaputra

basin part of the Himalayan region contains

294 glacial lakes and 1099 water bodies

whereas Indus basin has 31 glacial lakes and

321 water bodies. There are 178 glacial lakes

and 105 water bodies under Ganga basin

(Figure 5).

Fig. 3: Water bodies as seen in satellite image

Fig. 4: Glacial lakes as seen in satellite image

Table 1: Sample record showing the GIS database attributes

Basin_Name Indus

Lake ID 01_520_001

River Shyok

Country China

Toposheet No 52 O

Latitude 330 45’00”

Longitude 79014’23.93”

SRTM Elevation (m) 5064

Area (ha) 65825.15

Length 147347.41

Width 29207.60

GL/WB WB

Lake Name Pangong Tso

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

The distribution of glacial lakes and

water bodies based on its water spread area

is given in Table 3. It is observed that around

1600 glacial lakes/water bodies are having

water spread area between 10 and 50 ha

and around 200 water bodies have water

spread area between 50 and 100 ha. There

are 14 water bodies with water spread area

more than 10,000 ha. All the glacial lakes

are having water spread area less than 600

ha and 80% of them have less than 50 ha.

The largest water body (Pangong Tso) with

water spread area of 65,825 ha is located in

Shyok sub basin of Indus basin. The largest

glacial lake with water spread area of 542 ha

is located in Brahmaputra basin.

It is also observed from the Table

4 that more than 50% of glacial lakes/

water bodies (1169 nos.) are located within

the elevation range of 4,000 to 5,000 m.

The water body at the highest elevation of

5,810 m is located in the Karnal sub-basin

of Ganga basin. The glacial lake at the

highest elevation of 5,743 m is located in

Brahmaputra basin. A glacial lake at the lowest elevation of 2,744 m is located in the Gilgit sub-basin

of Indus basin.

The analysis of water spread area of glacial lakes/water bodies distributed within different

elevation zones (Table 5) shows the presence of more number of smaller water bodies at higher

elevation. It is also observed that 75% of the larger water bodies (water spread area > 1,000 ha) are

located in the higher altitude.

Monitoring of Glacial Lakes & Water BodiesThe monitoring of glacial lakes and water bodies in the Himalayan region of Indian river basins was

carried out through visual interpretation of satellite images from AWiFS sensor for the months of June to October

during 2011 and 2012. All the water bodies that are larger than 50 ha in size only were considered (433 nos).

Due to persistent cloud cover during the monsoon period, all the water bodies could not be monitored.

Table 2: Basin wise details of glacial lakes & water bodies

Basin NameGlacial Lakes Water Bodies Total

Count Area (ha) Count Area (ha) Count Area (ha)

Brahmaputra 294 11,371 1099 1,94,562 1393 2,05,935

Ganga 178 8,476 105 54,247 283 62,724

Indus 31 771 321 2,94,791 352 2,95,562

Total 503 20,619 1525 5,43,602 2028 5,64,221

39

Fig. 5: Distribution of glacial lakes/water bodies

Numbers

Table 3: Distribution of glacial lakes & water bodies based on area

Table 4: Elevation zone wise distribution of glacial lakes & water bodies

Table 5: Relation between elevation and water spread area

Water SpreadArea (ha)

Glacial Lakes Water Bodies Total

Count Area (ha) Count Area (ha) Count Area (ha)

10-50 404 8,999 1191 25,279 1595 34,278

50-100 62 4,370 139 9,891 201 14,261

100-1000 37 7,565 146 44,165 183 51,730

1000-10000 35 91,632 35 91,632

> 10000 14 3,72,635 14 3,72,635

Total 503 20,934 1525 5,43,602 2028 5,64,536

ElevationZone (m)

Glacial Lakes Water Bodies Total

Count Area (ha) Count Area (ha) Count Area (ha)

< 1000 20 54,244 20 54,244

1000-2000 13 2,717 13 2,717

2000-3000 1 12.85 14 1,626 15 1,639

3000-4000 11 1,099 236 14,393 247 15,493

4000-5000 212 7,578 957 3,36,452 1169 3,44,031

> 5000 279 12,243 285 1,34,168 564 1,46,412

Total 503 20,934 1525 5,43,602 2028 5,64,536

The maximum water spread area for each water body among the different dates of satellite for the month was considered

for the final analysis of the change in water spread. The following criteria were followed while monitoring the

water bodies.

Water SpreadArea (ha)

< 50 50-100 100-1000 1000-10000

> 10000 Total

Elevation Zone (m)

< 1000 5 3 4 5 3 20

1000-2000 6 3 3 1 13

2000-3000 11 2 1 1 15

3000-4000 205 21 19 2 247

4000-5000 929 104 104 24 8 1169

> 5000 439 68 52 2 3 564

Total 1595 201 183 35 14 2028

• Achangeinwaterspreadareawithin+/-5%isconsideredtobenormal(insignificant)inremotesensingderived

inventory studies.

• Partlyorfullycloudcoveredorfrozenwaterbodieshavenotbeenconsideredinmonitoring

• Onlythemaximumspatialextentofwaterspreadareaduringeachmonthhasbeenmappedandcomparedwith

the spatial extent of water spread area mapped for the inventory year (2009).

The status of monitoring carried out during the years 2011 and 2012 is given in Table 6.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Table 6: Status of glacial lakes/water bodies monitored

Month No. of glacial lakes/water

bodies monitored

Water Spread Area

Increased Decreased No Change

June 2011 178 49 20 109

July 2011 125 36 17 72

August 2011 153 73 23 57

September 2011 243 93 56 94

October 2011 360 114 97 149

June 2012 267 40 126 101

July 2012 217 48 73 96

August 2012 240 16 128 96

September 2012 305 5 200 100

October 2012 370 15 228 123

ConclusionThe inventory and monitoring of glacial lakes and water bodies in the Himalayan region was carried out

using satellite images from AWiFS sensor. The study reveals that the water spread area is fluctuating

over time due to either snow melt or rainfall. Regular monitoring the change in water spread area of

the glacial lakes will be useful in identifying the potential dangerous lakes prone for GLOF. The 56 m

resolution data of AWiFS is found to be sufficient for monitoring water bodies of size more than 50

ha. However, for monitoring other water bodies of size less than 50 ha, medium resolution images

with better repetivity will be useful.

ReferencesGeological Survey of India, (1999). Inventory of the Himalayan glaciers, a contribution to the

international Hydrological Programme, Special publication no 34, GSI, India.

http:// http://en.wikipedia.org/wiki/Glacial_lake_outburst_flood, Last accessed: 08-Aug-2013

Ives, JD., Shrestha, RB., Mool, P.K. (2010). Formation of glacial lakes in the Hindu Kush-Himalayas

and GLOF risk assessment. Kathmandu: ICIMOD - ISBN978 92 9115 137 0 (printed) 978 92 9115

138 7 (electronic)

Nina Behrman (Edited by), (2007). The Waters of the Third Pole: Sources of Threat, Sources of Survival:,

Published by Aon Benfield UCL Hazard Research Centre, University College London.

NRSA, (2005). Study of Pareechu Lake in Spiti basin using Satellite Remote Sensing Data, Technical

Report, Published by National Remote Sensing Agency, Hyderabad.

NRSA, (2007a). Inventory of Glacial Lakes and Water Bodies in Sutlej Basin, Technical Report, Published

by National Remote Sensing Agency, Hyderabad.

NRSA, (2007b). Inventory of Glaciers and Glacial Lakes in Mangde Chu River Basin, Technical Report,

Published by National Remote Sensing Agency, Hyderabad.

NRSC, (2009). Inventory of glacial lakes and water bodies in Tawang river basin, Technical Report,

Published by National Remote Sensing Centre, Hyderabad.

NRSC, (2011). Final Report of “Inventory and Monitoring of Glacial Lakes / Water Bodies in the

Himalayan Region of Indian River Basins”, Technical Report Published by National Remote Sensing

Centre, Hyderabad.41

NRSC, (2012). Report on “Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during

2011”, Technical Report Published by National Remote Sensing Centre, Hyderabad.

Pradeep K. Mool., Samjwal R., Bajracharya and Sharad P. Joshi, (2001). Inventory of Glaciers, Glacial Lakes and Glacial

Lake Outburst Floods - Monitoring and Early Warning Systems in the Hindu Kush-Himalayan Region Nepal, ISBN 92 9115

331 1 Published by ICIMOD, Kathmandu, Nepal.

Sanjay K. Jain., Anil K. Lohani., Singh R.D., Anju Chaudhary and Thakural, L.N. (2012). Glacial lakes and glacial lake

outburst flood in a Himalayan basin using remote sensing and GIS, Natural Hazards, Volume 62, Issue 3, pp 887-899

SAC, (2011). Snow and Glaciers of the Himalayas, Technical Report, ISBN 978-81-909978-7-4, Published by Space

Applications Centre, Ahmedabad.

Vikram Gupta and Sah, M.P. (2008). Impact of the Trans-Himalayan Landslide Lake Outburst Flood (LLOF) in the Satluj

catchment, Himachal Pradesh, India, Natural Hazards, Volume 35, Issue 3, pp 379-390

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

MONITORING OF IRRIGATION PROJECTS USING HIGH RESOLUTION CARTOSAT SATELLITE DATAShanker M, Suresh Babu AV, Simhadri Rao B and Venkateshwar Rao V Water Resource Group, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: shanker_m@nrsc.gov.in or muthyamshanker@gmail.com

IntroductionIrrigation development is the key for ensuring water and food security of the

country. Irrigation has been the major factor in increasing agricultural production in India.

The massive development of a vast irrigation network in India has been recognized as a

landmark in the history of agriculture. The development of water resources for irrigated

agriculture received high priority in the different Plan periods. Expansion of irrigation facilities,

along with consolidation of the existing systems, has been the main strategy for increasing

production of food grains.

The irrigation projects are classified into three categories viz major, medium and

minor. Projects which have a Cultivable Command Area (CCA) of more than 10,000 ha are

termed as major projects, those which have a CCA of less than 10,000 ha but more than

2,000 ha are termed as medium projects and those which have a CCA of 2,000 ha or less

are known as minor projects. Irrigation support is provided through major, medium and

minor irrigation projects and command area development in India.

Total irrigation potential of the country is 139.90 mha. (Figure 1). Of this, the

irrigation potential from Major and Medium (M&M) irrigation projects is 58.47 mha and

81.43 mha from Minor irrigation (64.05 mha from groundwater and 17.38 mha from surface

water). The inter-basin transfer of water from surplus to deficit basins envisaged to bring

additional 35 mha under irrigation.

Irrigation Infrastructure in IndiaMinor irrigation projects have both surface and ground water as their source,

while major and medium projects mostly exploit surface water resources. The plan-wise

proliferation of schemes in major and medium sector is provided in Figures 2a & 2b. There

are about 442 Major projects, 1230 Medium projects and another 215 Extension, Renovation

and Modernization (ERM) projects taken up, out of which 276, 1008 and 126 projects have

been completed respectively till the end of Xth Plan (INCID Publication, 2009).

Irrigation Potential Development in IndiaDuring the pre-plan period prior to1951, irrigation potential created through Major

and Medium (M&M) sectors was 9.70 mha. In the 1st FYP (1951-56) period, the country

launched a major irrigation programme and a number of multipurpose and major projects

were taken up. During II and III Plan periods, several new projects were started. During the

IV Plan (1969-74), emphasis was shifted

to the completion of ongoing projects,

integrated use of surface and ground water,

adoption of efficient management techniques

and modernization of existing schemes. In the

V Plan, Command Area Development Program

(CADP) was launched with the objective of

reducing the lag between potential created

and optimum utilization of available land and

water. During the VIII Plan, new projects were

restricted considerably and greater emphasis

was laid on completion of projects. By the

end of the X Plan, the projects completed,

along with minor irrigation and ground water

development, have created an estimated

potential of about 102.77 mha including

42.35 mha. under Major & Medium projects

(Figure 3). However, the target of 58.47 mha

is expected to be achieved by the end of the

XII Plan under Major & Medium projects.

Irrigation potential created in the

country from Major & Medium and Minor

irrigation projects, which stood at 22.60 mha

in 1951, has risen to 102.77 mha till the

end of X Plan period (2002-07). An irrigation

potential of 7.3 mha has been created by the

end of March, 2009 during XI plan. Thus,

the total cumulative potential created in

the country reached to 110.07 mha by the

end of March 2009. The temporal irrigation

potential creation and its utilization among

Major & Medium irrigation projects in India

is shown in Figure 4.

Accelerated Irrigation Benefit Program (AIBP)

A large number of river valley

projects, both multipurpose and irrigation

have spilled over from plan to plan mainly

because of financial constraints being faced

by the State Governments. As a result of this, despite huge investment having already been made on these projects,

the country was not able to derive the desired benefits. There were 171 major, 259 Medium and 72 ERM on-going

Irrigation projects in the country at various stages of construction with spillover cost of Rs. 75,690 crore, at the end of

VIII plan (i.e end of March 1997). This was a matter of great concern for the Union Government and remedial measures

for expeditious completion of some of the projects which were in advanced stage of completion became necessary

Fig. 1: Total irrigation potential in India

Fig. 2a: Status of completion of irrigation projects taken up in India (Major)

Fig. 2b: Status of completion of irrigation projects taken up in India (Medium)

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3(www.wrmin.nic.in). The lack of funds with

State Governments was identified as the single

most important factor for the inordinate delay

in irrigation projects execution.

To ove rcome the f i nanc i a l

resources constraints, a special scheme

named ‘Accelerated Irrigation Benefits

Programme (AIBP)’ was launched in

1996-97 by Government of India, for providing

Central Loan Assistance (CLA) to the States

for accelerating the implementation of large

irrigation and multipurpose projects. AIBP

was aimed at twin benefits of accelerating

the ongoing irrigation / multipurpose

water resources projects and realization

of bulk benefits from completed irrigation

projects quickly.

The loan assistance under this

scheme was extended to selected irrigation

projects in the country, with the objective

to accelerate the implementation of those

projects, which were beyond resource

capability of the States or were in advanced

stage of construction and could yield irrigation benefits in the next few agricultural seasons (A R -

MoWR 2007-08). Since its formulation, the terms of the program have been widened and liberalized

over the time. As per present pattern of assistance under the AIBP, the Central Government provide

grant to the irrigation projects as an incentive to the States for creating irrigation infrastructure in

the country.

As on date, Major, Medium and ERM projects are eligible for central assistance under AIBP.

The surface water minor irrigation schemes of special category States as well as schemes in

drought prone and tribal areas in non-special category. States are also eligible for central assistance

under AIBP.

So far, a total of 293 Major and Medium irrigation projects (with an I.P of 13.79077 mha)

have been included under AIBP, out of which, 140 projects have been completed, another 148

projects (with an I.P of 10.382989 M.ha) are ongoing as on March 2012 and remaining 5 projects

were deferred.

Conventional Monitoring Mechanism and Need for Spatial Information

A comprehensive physical and financial periodical monitoring of the Major and Medium

projects is being carried out by the Central Water Commission / Ministry of Water Resources with the

emphasis on quality control through CWC regional offices situated all over the country.

Fig. 3: Status of IP creation since 1951 to 2007 (Area in mha)

Fig. 4: Irrgation potential creation and utilisation among Major & Medium (mha)

45

Conventional monitoring reports are prepared based on inputs provided by field authorities without the spatial

content and random field checks necessitated by time constraint. Thus, the project monitoring is limited in nature,

lacking the synoptic view of the critical gaps and the quantitative progress in actual irrigation infrastructure creation.

However, the complete spatial information about the project status is essential for objective monitoring, identification

and prioritisation of critical gaps for efficient utilisation of resources.

Role of Satellite Remote Sensing data in Monitoring of Irrigation Projects Satellite remote sensing is an ideal tool for mapping, inventorying and monitoring purposes. High resolution

satellite data like Cartosat-1 provides excellent opportunities to capture the existing irrigation infrastructure and for

monitoring the project implementation progress. High resolution satellite data acquisition can be planned to match the

field reporting for effective monitoring of the project. Monitoring visit can be effectively planned and carried out with

the help of critical gap areas identified in irrigation infrastructure creation using satellite data and thus minimizing the

time required for each visit. Thus, satellite based monitoring addresses both inadequacy in number of monitoring visits

and total area monitoring rather than random checks carried out presently.

Conceptualisation and Methodology DevelopmentAt the instance of Planning Commission, NRSC, ISRO proposed the concept on “Satellite Technology Applications

in Irrigation Infrastructure Mapping” including

the scope of monitoring the progress made

and potential created through AIBP.

Subsequently, NRSC, ISRO has

developed the methodology for assessment of

irrigation potential created through inventory

and mapping of irrigation infrastructure using

high resolution satellite data through a pilot

study carried out in two selected irrigation

projects (Upper Krishna Project in Karnataka

and Teesta Project in West Bengal during

2004-05.

Basic approach (Figure 5) consists of

inventory and mapping of existing irrigation

infrastructure (such as canal network,

irrigation and other related structures) from

high resolution satellite data in an on-going

irrigation project and comparing the physical

progress & status to the design with proposed

irrigation infrastructure. Based on the

completion status of irrigation infrastructure

derived from the satellite data and considering

the hydraulic connectivity from source to the

outlet, the irrigation potential created in the

project is assessed.

The pilot study had captured the

ground reality of the irrigation infrastructure Fig. 5: Methodology for monitoring satellite based irrigation projects

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3and its status in spatial domain (Figures 6,7 &

8). Based on the satellite derived information,

percentage progress of AIBP works along

with critical gap areas were identified and an

assessment of irrigation potential created in

the project was carried out.

Thus, the satellite data can capture

the existing irrigation infrastructure and

its physical status for time stamping its

completion status. It also provides possibility

to monitor the entire irrigation project yet with

limited field checks overcoming the limitation

of physical visits to entire project area.

The encouraging and satisfactory

results of the pilot study (in terms of progress,

status of infrastructure and associated

irrigation potential created) were objective

in nature and compared well with ground

realities which were verified and reported

by CWC field offices and State Government

departments. Thus, the pilot study during

2004-2005 demonstrated the utility of high

resolution satellite data for monitoring the

progress of ongoing irrigation projects and

its application potential for assessment of

Irrigation Potential (I.P.) created.

OperationalisationThe availabil ity of Cartosat-1

(2.5 m resolution) data from 2005-06

provided a cost effective solution for upscaling

the study. In view of the importance and

utility of results arising out of satellite data

based pilot study, Planning Commission in

consultation with NRSC and MoWR decided

to upscale the study to national scale covering

all AIBP Projects with an estimated irrigation

potential of 10 mha spread across different

States in India in a phased manner.

Accord ing ly, under phase- I ,

“Assessment of Irrigation Potential Created in 53 AIBP funded Irrigation Projects in India using

Cartosat-1 Satellite data“ with an I.P. target area of 5.45 mha spread across 18 States in India during

2007-09 (Figure 9) was carried out.

Fig. 6: Canal (lined) as observed in Cartosat data

Fig. 7: Gaps / Pending work in Canal network as observed in Cartosat data

47

Cartosat data derived spatial

irrigation canal network and structures along

with assessed Irrigation Potential created

for Upper Wardha (Maharashtra) is shown

in Figure 10.

Satellite data based I.P. created

was compared with field based report for

50 projects (Summary Report, February 2010).

As per the field report, the I.P. created is about

25% more than the satellite data based study.

Assessment also indicated large deviation

(>25%) in field reporting in 15 projects out

of 50 projects studied (Figure 11).

CWC and MoWR utilized the study

result for reconciliation of figures on I.P

creation through verifications and clarifications

from respective State departments. The

spatial irrigation infrastructure information

generated in the study is utilized for further

monitoring by CWC.

Thus, the technology has been well

received by the Central Water Commission

(CWC) and MoWR and is being used

as a tool for effective AIBP programme

implementation. Further, the technology has

been recognized by the Planning Commission

and suggested for monitoring of all projects

funded under Accelerated Irrigation Benefit

Porgramme (AIBP) using high resolution

Cartosat satellite data.

Capacity BuildingTo create awareness among planners,

managers, engineers and other stake holders,

a two day workshop was conducted during

May 2011 on the use of high resolution

Cartosat data for monitoring irrigation

infrastructure and potential creation. The workshop recommended for adoption of technology among Central and State

Govt. departments after detailed deliberation on conventional monitoring mechanism and results of the operational

phase study by NRSC.

To further the capacity building process, NRSC has executed additional 50 irrigation projects (Figure 12) essentially

to transfer technology in the domain of satellite based monitoring of AIBP funded irrigation projects during 2011-12.

Fig. 8: Pending cross Drainage structure as observed in Cartosat data

Fig. 9: Distribution 53 projects in AIBP Phase-I

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3This study was carried out with 14 Partner

Institutions consisting of State Remote

Sensing Centres and academic institutions

located across different states.

During this phase, training and

guidance were provided to all teams from

Partner Institutes and the technology was

transferred to CWC by providing intensive

on the job training during execution of

two projects. The study was completed in

December 2012.

O n l i n e M o n i t o r i n g through ISRO-BHUVAN and Institutionalization

In continuation of demonstration

and operational use of application technology,

capacity building / technology transfer to

CWC and other institutions on the satellite

based monitoring by NRSC, adoption of

technology by line departments is next step

for institutionalization.

There are 148 irrigation projects

currently ongoing under AIBP in India.

CWC does monitoring of these projects twice

a year and needs two time period Cartosat

data (pre and post monsoon) in near real time

for use by their monitoring offices located

across the country.

In this regard, ISRO-BHUVAN platform

meant for Earth Observation visualisation

provided an excellent opportunity for online

monitoring of irrigation projects. It facilitates

hosting of satellite data with user access

control and provides multiple access facility

from various locations. NRSC demonstrated

the potential of online monitoring through

ISRO-BHUVAN platform using near real

time Cartosat data during 8th NNRMS SC-W

(National Natural Resources Monitoring

System – Standing Committee on Water

Resources) meeting held on 18th June 2012.

SC-W recommended for institutionalization

Fig. 10: Cartosat derived irrigation infrastructure and potential created in Upper Wardha project

Fig. 12: Distribution of 50 projects in AIBP Phase-II

Fig. 11: Satellite derived study result in Phase-I projects

49

of the technology among the Central and State Govt. line departments for online monitoring using Cartosat data through

ISRO-BHUVAN web services platform.

A team headed by Chief Engineer, Project Monitoring Organisation, CWC had thoroughly evaluated the ISRO-

BHUVAN services for online monitoring of AIBP projects and recommended for its implementation. Accordingly, CWC

(MoWR) had prepared a road map for implementation of satellite data based online monitoring of AIBP projects through

ISRO BHUVAN platform. NRSC has provided on the job training to 30 CWC officers through two training programs during

December 2012 and February 2013 for implementation of online monitoring of AIBP projects.

ConclusionSatellite based monitoring provides an excellent opportunity to monitor the entire irrigation project yet with limited field

checks overcoming the limitation of physical visits to entire project area. ISRO-BHUVAN platform is an excellent facility

for online monitoring of AIBP implementation by CWC. The adoption of this new application and using ISRO-BHUVAN

platform for online monitoring by CWC and other line departments would go a long way in institutionalization of

technology. This particular application will be one of the largest user driven utilization of high resolution Cartosat satellite

data for their operational program implementation.

ReferencesAnnual Report (2007-08), Central Water Commission, MoWR, Govt. of India

(http://cwc.nic.in/main/downloads/AR_07-08.pdf).

Annual Report (2007-08), Ministry of Water Resources (MoWR), Govt. of India.

Summary Report on Assessment of Irrigation Potential Created in AIBP funded irrigation projects in India using Cartosat

satellite data (53 projects) (NRSC–RS&GIS AA-WR&OG-WRD-February 2010-TR-153).

Website of Ministry of Water Resources (MoWR), Govt. of India (http://wrmin.nic.in).

Water Resources Development in India. INCID Publication, 2009.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

REMOTE SENSING AND GIS FOR RIVER MORPHOLOGY STUDIES

Manjusree P, Satyanarayana P, Bhatt CM, Sharma SVSP, Srinivasa Rao G Remote Sensing Applications Area, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: manjusree-p@nrsc.gov.in

IntroductionA river is the general term for a channel and develops various landforms through

channel processes. The main channel/ fluvial processes are erosion, transportation and

sedimentation. Erosion predominates in the upper reach area of a drainage basin and valleys

composed of channels and slopes are formed. The materials brought to the lower reaches

in a channel are sediment load. Weathering of rocks composing slopes is the main cause

of production of sediment load and it is deposited in the form of alluvial plains. River bank

erosion leads to sequential changes in the position of banklines as well as various changes

within the channel. Three basic channel patterns are detected in alluvial plains namely braided,

meandering and straight. River morphology is explained in channel patterns and channel

forms and is decided by factors which are inter-related to each other such as discharge, water

surface slope, water velocity, depth and width of channel and river bed materials etc.

For a scientific and rational approach to different river problems and proper planning

and design of water resources projects, an understanding of the morphology and behaviour

of the river is a pre-requisite. Morphology of river is a field of science which deals with the

change of river plan form and cross sections due to sedimentation and erosion. In this field,

dynamics of flow and sediment transport are the principal elements. The Morphological

studies, therefore, play an important role in planning, designing and maintaining river

engineering structures. In order to assist the engineers of the concerned departments and

other agencies, morphological study reports with broad guidelines are prepared.

Satellite Remote Sensing is being successfully used for various river morphological/

engineering studies. The present paper discusses about the role of remote space technology

in mapping and monitoring the shifts in river bank line and erosion/deposition with the help

of multi-sensor, multi-spectral, multi-date satellite images. The changes have been illustrated

by comparing pre and post event satellite images for better understanding.

Remote Sensing and GIS for River Morphological StudiesThere are direct and indirect methods for monitoring the river bank erosion. The

direct method is taking measurements from the field in terms of linear rates of erosion,

volumes of erosion and channel cross section. The indirect method is by analyzing the

archival sources that exist at various timescales with the sediment records. The archive sources

can be conventional survey maps, aerial photos or satellite images. Using multi-temporal

high-resolution satellite data, the latest river configuration, shift in the river courses, formation of new channels/oxbow

lakes, bank erosion/deposition, drainage-congested areas, etc. can be mapped at different scales. Since accurate river

configuration is obtained, it can be used for laying models for conducting river behaviour studies. Information derived from

remote sensing can be used for other river morphological application studies like monitoring the existing flood control

works and identification of vulnerable reaches, planning bank protection works, drainage improvement works etc.

Works have been carried out worldwide using remote sensing and GIS for river morphological studies. Sarma

and Basumallick (1980) studied the bank line migration of the Burhi Dihing River using topographic maps and field survey.

Bardhan (1993) studied the channel behavior of the Barak River using satellite imagery. Naik et al. (1999) studied the

erosion at Kaziranga National Park using remote sensing data. Goswami et al. (1999) carried out a study on river channel

changes of the Subansiri (northern tributary of Brahmaputra River) in Assam, India using information of topographic

sheet and satellite data.

MethodologyIn the present study application of

remote sensing and GIS for the identification

of the changes in the river bankline, erosion

and deposition and in quantification of the

changes occurred has been demonstrated

taking stretches along the Brahmaputra River,

Ganga River, Kosi River and Gandak River.

Figure 1 shows the methodology.

Brahmaputra River, AssamThe Brahmaputra River is one of the

largest alluvial rivers in the world characterized

by frequent bank erosion, exceedingly large

flow, enormous volume of sediment load,

continuous changes in channel morphology,

rapid bed aggradations and bank line recession

and erosion. According to Assam’s Water

Resources Department, Assam valley portion

of the Brahmaputra has lost approximately

7.4 per cent of its land area due to river bank

erosion and channel migration.

Satellite images for the year 2002 and 2010 have been used for mapping of the shift in the river bank

lines along the Brahmaputra River in Assam. It is observed that the Brahmaputra River has shifted its bank line

drastically, causing severe damage to agriculture as well as habitat areas on its both sides. The total land area

lost due to erosion has been estimated at 27,098 ha (15248 ha. on the north bank and 11850 ha. on the south

bank). On the south bank, the maximum impact of erosion was noticed in the Golaghat district (3221 ha. approx),

followed by Marigaon (2815 ha. approx) and Dibrugarh (1442 ha. approx) during the period. On the north bank,

Dhubri (3030 ha. approx) accounted for maximum area affected by erosion, followed by Sonitpur (2823 ha.

approx) and Dhemaji (2671 ha. approx). Deposition of silt has been very low, compared to the rate of erosion.

On the south bank, deposition has been 18% of the total land eroded, while on the north bank it is 24%. Maximum

deposition has been observed in Kamrup district (537 ha.) on the south bank and Tinsukia (1396 ha) on the north bank.

Fig. 1: Methodology flow chart

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Figures 2, 3 & 4 show the erosion and

deposition during 2002 and 2010 in parts of

Upper, Middle and Lower Assam.

Majuli Island, Assam : Majuli,

the largest inhabited river island bounded

by the river Subansiri to the north and

Brahmaputra River to the south, is one of

the subdivisions of the Jorhat district, Assam.

Erosion of the island is a continuous processes

and posses a significant concern. The extreme

braided nature of the Brahmaputra coupled

with silt and sand strata of the banks is the

main cause of erosion.

It was observed in 2010, the Majuli

Island reduced to 448.23 km2. Erosion in

the island was observed due to the shift in

Brahmaputra river at Goalgaon and Haldibari.

Deposition was observed at Mayadobi mainly

due to shift in the river Subansiri besides

erosion due to Brahmaputra. Figure 5 shows

the erosion and deposition during 1996, 2002

and 2010 in Majuli Island.

Ganga River, Bihar and West Bengal

Ganges is one of the largest perennial

river systems amongst the 14 major rivers in

India having its source in the Gangotri glacier

in the Himalayas at an elevation of about

4200 m above Mean Sea Level (MSL). River

dynamics is one of the major problems in

rivers draining the Ganga plains.

Near Ballia and Chappra, Bihar: The

present study was confined from West of

Ballia to the East of Chappra and the entire

region actually falls as part of U.P.- Bihar

border. The Ganga River in this region exhibits

a great deal of dynamicity thereby showing a

continuous change in the river morphology

as well as bank-line stability.

Near Khawashpur in Ballia, the

shift is mainly in the North-West direction by

around 850 m. South West of Chappra near Fig. 4: Map showing the erosion and deposition during 2002 and 2010 in parts of Lower Assam

Active river channel during 2010 and Water bodiesRiver Bank Erosion

Settlements

River bank

Other Major Roads

National Highway

Railway

District boundary

State boundary

River bankine of 2002

River Bank Deposition

Fig. 3: Map showing the erosion and deposition during 2002 and 2010 in parts of Middle Assam

Active river channel during 2010 and Water bodiesRiver Bank Erosion

Settlements

River bank

Other Major Roads

National Highway

Railway

District boundary

State boundary

River bankine of 2002

River Bank Deposition

Fig. 2: Map showing the erosion and deposition during 2002 and 2010 in parts of Upper Assam

Active river channel during 2010 and Water bodiesRiver Bank Erosion

Settlements

River bank

Other Major Roads

National Highway

Railway

District boundary

State boundary

River bankine of 2002

River Bank Deposition

53

Ghazipur,the river shows shifting towards

the Northern direction by 1.34 km. Near

Bakhorapur, the river shifted by 1.1 km

towards Southwest. A detailed mapping

regarding the area under aggradation and

degradation showed that both the bank-lines

in this region have undergone aggradation as

well as degradation with a varying amount.

From Figure 6, it can be seen that the

Northern bank (left bank) has undergone

more aggradation than degradation which

indicates the shrinking of the river bed

whereas conversely the Southern bank shows

much more degradation then aggradation

which indicates the widening of the river

bed. The river has undergone deposition and

the shifting of the river channel has occurred

mainly in the north direction.

The present study showed prominent changes in river bank-line as well as various fluvial features. It showed great

deal of variation in direction, dimension and magnitude. The shifting of the river channel in this region is mainly due to the

erosional and depositional processes of the river. The overall direction of shifting is in the South and South-East direction.

Amount of deposition near Kushwarapur in

Ballia and Ghazipur in 2004 was estimated

to be 491and 283 ha. respectively.

Near Jalangi in West Bengal:

Due to the shift in the river course of Ganga

River near Jalangi, substantial erosion

is observed during the last one decade

1987-1997. The analysis of IRS images of

1987-1992-1997, it is observed that the

river has shown shift towards south at two

locations (encircled with yellow colour).

Figure 7 shows the river bankline shift in

Ganga River near Jalangi in West Bengal.

Upstream of Jalangi about 722 ha. of erosion

was observed and near Jalangi about 5218

ha. of erosion was observed to have taken

place during last one decade.

The Kosi River has been responsible

for some of the most devastating floods in the North Bihar. It is also known as the “Sorrow of Bihar” due to the frequent

channel migration and the extensive flood damage it causes in the region. During the last two centuries the Kosi river

has shifted its course by about 150 km (Gole and Chitale, 1996; Wells and Dorr, 1987). The presence of a number of

palaeo channels all along the surface of Kosi alluvial fan clearly visible on the satellite images, bear testimony to the

dynamic nature of the Kosi river.

Fig. 5: Maps showing the erosion, deposition and the changes in the river banklines observed during 1996, 2002 and 2010 in Majuli island

Fig. 6: River bankline shift in Ganga River near Chappra in Bhojpur district, Bihar

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Kosi River Embankment Breach,

Bihar: The change in the course of the

Kosi River led to a breach in the eastern

embankment causing severe floods in parts

of North Bihar. Figure 8 shows the changes in

the river course of Kosi River and subsequent

flooding in parts of North Bihar. The breach

in the eastern embankment of the Kosi river

took place about 12 km upstream of Birpur

barrage, near Kusaha in Nepal on August

18, 2008.

The analysis of the satellite datasets

reveals that the Kosi river course 30 km

upstream of the Kosi barrage has been very

dynamic and frequently changing its course

as observed from the river course delineated

for IRS LISS III images of 1997, 2006 and

2011 (Figure 9).

At the breach location the river flow

was westwards (Figure 9a, location1 ) whereas

upstream the river was flowing eastwards

(Figure 9a, location A) during 1998. However

due to sedimentation and formation of large

sand bar along the main channel, the river

flow shifted towards east (Figure 9b, location

2) at the breach location whereas upstream of

it shifted towards west (Figure 9b, location B)

as visible from the images of 2001. Although

the flow had shifted eastwards, the major

portion of the discharge was still passing

down the west side (Figure 9c, location 1) of

the bar, until 2007. However, further sediment

deposition along the west side of the channel

blocked the free flow of water and the river

has almost abandoned the western channel,

with the entire discharge flowing down

the east side of the sand bar (Figure 9d,

locat ion 2) dur ing 2008. The f low

direction upstream of the breach location

had also gradually changed prior to the

breach, providing a more erosive angle of

attack against the eastern embankment

(Figure 9b-f, dotted lines). The river flow had

become almost perpendicular in the months

Fig. 8: Flooding caused due to change in the river course of Kosi River in Bihar

Fig. 7: River bankline shift in Ganga River near Jalangi in West Bengal

Fig. 9: Change in the river course of Kosi River near breach location during 1997-2011

55

prior to the breach (Figure 9e, dotted lines), thrusting pressure directly on the eastern embankment. From the satellite images

it can be seen that the river was flowing very close to the eastern embankment during 2007 (Figure 9c) and during, 2008

the river can be seen almost abutting the eastern embankment (Figure 9d & e). Measurements made along the river in this

stretch show that the river was flowing at a distance of less than 300 m from the eastern embankment.

From the satellite based study it is observed that the reduction in the cross-sectional area of the river and channel

carrying capacity due to the sedimentation together with the change in the angle of attack of Kosi river, upstream of the

breach location prior to the breach, may be one of the causes that led to Kosi river avulsion. The change in the angle

of attack upstream of breach location may be a surface manifestation of the sub-surface structural movements as the

higher Himalayas and the foothill zone through which the Kosi river emerges into the plains near Chatra constitutes

a tectonically active zone (Wells and Dorr, 1987, Agarwal and Bhoj, 1992, Sinha et al., 2005). Therefore, the flood

management of the Kosi river basin requires an integrated approach, which not only considers the hydrological factor,

but also the geological and geomorphological aspects of the river basin.

ConclusionRemote sensing technology due to its various advantages could be used by the planners for taking measures

required for channel stabilization and strengthening of embankments. It is essential to monitor the vulnerability of the

flood control structures, identify the changes in the river course, formation of new oxbow lakes and to understand the

behaviour of the river to lay physical models. Climate change would seriously affect the quality and quantity of water in the

rivers particularly due to extreme events like floods and droughts, seawater intrusion and anthropogenic contamination.

In this regard Remote Sensing and GIS technology can help in undertaking mitigation measures.

ReferencesAgarwal, R P, and Bhoj, R. (1992). Evolution of Kosi River fan, India: structural implications and geomorphic significance. International Journal of Remote Sensing, 13 (10): 1891–1901.

Bardhan, M. (1993). Channel stability of Barak river and its tributaries between Manipur-Assam and Assam- Bangladesh borders as seen from satellite imagery, Proc. Nat. Syrup. on Remote Sensing Applications for resource Management with special emphasis on N.E. region, held in Guwahati, Nov. 25-27, 481-485. Gole CV, and Chitale SV (1996) Inland delta building activity of Kosi River. Journal of the Hydraulics Division. American Society of Civil Engineers 92: 111-126.

Gole, C.V. and Chitale, S.V. (1996). Inland delta building activity of Kosi river. Journal of the Hydraulics Division, American Society of Civil Engineers 92: 111-126

Goswami, U., Sarma, J.N. and Patgiri, A.D. (1999). River channel changes of Subansiri in Assam. India. Geomorphology, 30: 227-244.

Naik, S.D., Chakravorty, S.K., Bora, T. and Hussain. (1999). Erosion at Kaziranga National Park, Assam, a study based on multitemporal satellite data. Project Report. Space Application Centre (ISRO) Ahmedabad and Brahmaputra Board, Guwahati.

Sarma, J.N. and Basumallick, S. (1980). Bankline Migration of Burhi Dihing river, Assam. IND. J. Ear. SCI., 11 (3&4): 199-206.

Sinha R, Jain V, Prasad B G, and Ghosh, S. (2005). Geomorphic characterisation and diversity of the fluvial systems of the Gangetic plains. Geomorphology, 70/3-4: 207–225.

Wells N.A, and Dorr, J.A. (1987). Shifting of the Kosi river, northern India. Geology, 15: 204-207.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

WATER RESOURCES AND HYDROLOGY OF THE WESTERN GHATS:THEIR ROLE AND SIGNIFICANCE IN SOUTH INDIAMysooru R. Yadupathi Putty1 and Madhusoodhanan CG2

1Dept. of Civil Engineering, The National Institute of Engineering, Mysuru - 570008, India2Dept. of Civil Engineering, Indian Institute of Technology, Bombay - 400076, IndiaEmail: puttyyadupathi@gmail.com

IntroductionThe Western Ghats, locally called the Sahyadri ranges, are a series of hill ranges

bordering the west coast of India, almost throughout its length. The region of Western

Ghats is one of the eight ‘hottest biodiversity hotspots’ in the world and has also been

recently acknowledged as a World Heritage Site (Myers et. al., 2000; UNESCO, 2012).

The Western Ghat mountains extend 80 19’8” - 210 16’ 24” N and 720 56’ 24” - 780 19’

40” E and encompass an area of approximately 1,29,000 km2, in the states of Kerala,

Tamil Nadu, Karnataka, Goa, Maharashtra and Gujarat (WGEEP, 2011). They run almost

parallel to the coast and have a 1600 km long north – south orientation, with an extremely

narrow east - west profile (Figure 1). The Western Ghats are also one of the world’s most

heavily populated biodiversity hotspots (Cincotta et al., 2000), providing for and supporting

245 million people with water for drinking, transport, irrigation and hydroelectric power,

together with food and resources to sustain livelihoods (WGEEP, 2011). As the repository

of an exceptional assemblage of life forms and human cultural diversity, the Western Ghats

have a global pre-eminence.

The remnant natural ecosystems of the Western Ghats are currently subject to a

plethora of threats that make the region a highly sensitive zone.Though there are numerous

studies related to the biodiversity and its conservation in this biologically rich hill tract, research

into the hydro-climatological aspects of the Western Ghats, which ultimately sustain these

unique ecosystems and the people in the whole of south India, has been negligible. This

paper presents a brief account on the available knowledge of the hydrological aspects of

the Sahyadris, highlighting the uniqueness of the region and bringing forth the probable

fields open for research.

Physical Settings: Physiography, Geology and Soils

The physiographic map of the region, derived from 1 km resolution SRTM DEM

data, is shown in Figure 2 (Jarvis et. al., 2008). The Sahyadris consist of mountain ranges

with an average elevation of 1000 m, displaying an extremely steep western face and gently

descending eastern slopes. The eastern slopes merge with Mysore plateau in the south

and Deccan plateau in the north, which are contiguous and located north of Palakkad gap

(between 10030’-110 N). The Palakkad gap is a major discontinuity in the Sahyadri ranges,

extending about 30 km with an average elevation of 200 m. South of Palakkad gap, the

eastern and western slopes are equally steep with drastically different ecological conditions

Fig. 1: Location and the extent of the Western Ghats

(Nair, 1991). The hills do not rise much

beyond 1500 m in the northern tract, while

in the south they tend to be rounded and rise

even beyond 2000 m. The Anaimudi peak in

Kerala is the highest peak (2695 m) south of

the Himalayas.

Geologically, the Sahyadris are a

stable mark of Archaean and Pre-Cambrian

formations, where the mountain building

has ceased in the Pre-Cambrian times

(Daniels, 2001). Most of the exposed Gneisses

of the Sahyadris are 2,500 million years old.

The non-metamorphic sedimentary formations

are very rare and found only along the coastal

belt (WGEEP, 2011) in north. Lithologically,

Sahyadris vary from basaltic Deccan Trap, with

relatively fragile rocks, North of 160 N, to the

Precambrian Archaean crystalline hard rocks

with flat hill tops in the south. The continental

drainage divide coincides with the crest almost

throughout the length of the Western Ghats.

However, in many places, the western faulting

has led to ‘river capture’ and diversion of the

easterly drainage to the west (Radhakrishna,

1991) is noticed.

The soils of Sahyadris consist mainly

of the derivatives of the basaltic lava and

ancient metamorphic rocks, rich in iron and

manganese (Pascal, 1988). There are seven

main soil groups found in the region, viz. laterites (high and low), red loam, medium black soils, hill soils, red gravelly soils

and alluvial soils including coastal alluvium, mixed red and black soils. Soils vary from humus rich peat in the montane

areas to laterite in the lower elevations on the western foothill belts. Soils are generally acidic. Isolated small expanses of

exposed lateritic rocks, that are mostly unfit for plant growth, characterise low lands along the coastal hills. The depth of

soils on the western slopes of south and central zones belong to the very deep and deep categories, while in the north,

they belong to shallow and very shallow categories. All over the ghats, soils are well drained with very high infiltration

rates (NBSS&LUP, 1996 a & b; Sivaprasad et al.,1998; Challa et al.,1999; Harindranath et al., 1999).

Land use and Land coverWith their high rainfall regime, the western slopes of the Ghats have a natural cover of evergreen forests, which

changes to moist deciduous types as one comes to the eastern slopes. The vegetation reaches its highest development

towards the southern tip in Kerala with rich tropical rain forests. Together, the forests cover approximately 20 percent

of the total area of the Sahyadris. The majority of the area under moist forest types falls within the southern states of

Kerala and Karnataka (IIRS, 2002).

The entire Western Ghats have now been declared an Ecologically Sensitive Area. It is estimated that not more than

about 7% of the area of the Western Ghats is presently under primary vegetation cover, though a much larger area is under

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

Fig. 2: Physiographic features of South India underlining dominance of the Western Ghats

secondary forest or some form of tree cover

(WGEEP, 2011). The traditional land use in

the non-forested tracts of the Ghats has been

Paddy cultivation in the valleys, supplemented

by cultivation of millets and legumes on

the hill slopes. A number of horticultural

and tuber crops such as tea, coffee, rubber,

cashew, tapioca and potato were introduced

to this region through European influence.

Pepper and cardamom, which are native to

the evergreen forests of the Sahyadris were

also taken up as plantation crops on a more

extensive scale in the modern times. These

changes have led to rampant conversion and

high fragmentation of the Western Ghats

forest landscape. At the same time, the deep

river valleys spread across the Ghats were

thrown open by various hydroelectric power

projects causing significant submersion of

virgin forest tracts. Developments in remote

sensing technology have revolutionised the

understanding of these changes in the region.

But, the implication of these large scale

landscape changes on the water resources of the region is yet to be studied.

Rainfall and ClimateThe heterogeneity in the physical features of the Sahyadris has a profound influence on

the climate of the region (Nair, 1991). The region lies in the tropical South Asian monsoon tract

characterised by wet summers and dry winters. The climate in general is hot and humid tropical, with

mean temperatures between 20 and 240 C. However, it frequently shoots beyond 300 C during April

– May and sometimes falls to 00 C during winter in the higher hills.

The Western Ghats present a combination of hydrological features which can be considered

very rare (or even unique) in the tropics. Characteristics of rainfall – the distribution over space and

time and the intensity pattern, are among the primary reasons for this uniqueness. The rainfall in the

region, despite being located in the tropics, is rather due to orographic lifting of South West Monsoon

winds, than due to convective activities. Since the Sahyadri ranges are close to the coast and run

nearly normal to the direction of the motion of moisture laden air from the vast Indian Ocean, rainfall

in the region is very heavy, and a major portion of the Ghats falls under the ‘wet’ category of climate

classification (Strahler and Strahler, 1992). According to WMO (1983), the complete region comes

under the meteorological division ‘humid tropics, with no cyclonic activity’.

The distribution of Normal Annual Rainfall (NARF) over the Western Ghats is presented in

Figure 3. It may be noted that there exists a systematic variation in rainfall across the region with a

gradual increase in magnitude towards the crests and a more gradual decrease towards the plains

in the east. The intra-annual distribution of rainfall varies over the region, depending on the cause 59

of precipitation. The area below 150 N is

influenced by both South West and North

East monsoons, North East monsoon being

more effective deeper south, where tropical

cyclones originating in the Bay of Bengal

bring substantial rains. The influence of South

West Monsoon increases gradually towards

north, parts of Kerala and the rest of the

Ghats getting more than 90% of their rains

during this season. The pre-monsoon rainfall

that occurs in the months of April and May

due to local convection is never widespread.

A very important and interesting feature of

rainfall in the Sahyadris is its intensity and

duration. Throughout the length of the Ghats

and the coast, daily intensities are very high.

In areas with NARF above 5000 mm, daily

rainfall exceeding 250 mm is quite a common

feature and even where NARF is less than

2000 mm, falls exceeding 100 mm contribute

about 25% of the annual rainfall. From a

hydrological perspective, a more important

feature of rainfall in the Western Ghats is

its short duration intensity. On very heavy

rainy days (>150 mm) in the region, it rains

during 20-23 hours and the contribution of

low intensity rainfall is always much greater

(Tipperudrappa, 2009). Even in a very heavy

rainfall area (NARF > 5000 mm) falls less

than 6 mm/15 minutes (24 mm/h) last 95 %

of the time and contribute 75% of the total

rain. This is a feature of only the extra-tropical

areas, but is found to characterise this tropical

region also. Hence, the common belief that

heavier rainfalls mean higher intensities does

not hold water in this region.

S t re a m f l o w a n d Wa t e r Resources

The importance of the Sahyadris

with regard to the water resources of South

India is evident from the fact that more than

60% of the surface waters in Karnataka

(Malhotra and Prasad, 1984), 70% in Kerala, 60% in Tamil Nadu, 50% in Maharashtra, 25% in Goa and 10% in Gujarat

are derived from the Western Ghats. While the western slopes of the Ghats, below Tapi, drain into 10 Medium basins

(> 200 km2, Rao, 1970) and 18 minor basins, the eastern slopes contribute water to the major basins of Godavari,

Krishna and Kaveri and to a couple of minor basins. Figure 4 shows all the basins draining to the west and parts of the

Fig. 3: Distribution of normal annual rainfall (in mm) over the Western Ghats

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3major basins draining to the east and getting

fed by the Western Ghats. Table 1 shows the

approximate quantities of yield contributed by

the Western Ghats to the east flowing rivers,

illustrating the importance of the Sahyadris.

Some of the important features of

streamflow in the Sahyadris are presented

in Figure 5. These figures show the flow

duration curves for daily flow within the

monsoon months (June-Sept.), the variation

of runoff coefficient for 10-daily flows in

the season and typical hydrographs of flow

from a very small catchment. These figures

illustrate the very special characteristic of

streamflow in the region - the predominance

of slow flow (commonly called baseflow).

This is in contradiction to the common belief

that surface runoff dominates streamflow

in heavy rainfall areas. But, the fact is that

it is the subsurface runoff which shapes the

hydrographs in the region (Putty and Prasad,

2000a). The experience is that the term

‘runoff’ needs to be defined differently in

these areas – if it is to be near reality, runoff

should be defined to encompass all forms of

flow in to the stream. However, this feature

is not an exceptional characteristic of the

Western Ghats, but has been found to be

the case in many mountainous and forested

catchments over the world (Ward and

Robinson, 2011). In such areas, the runoff

processes are explained by the Variable Source

Areas theory (Hewlette and Hibbert, 1967)

and the Jones’ Extended Variable Sources

Area theory (Jones, 1979). In the heavy

rainfall areas of the Sahyadris, flow in natural

pipes formed within the soil mantle forms

an important process contributing flow to the stream (Putty and Prasad, 2000b). Surface runoff due

to rainfall intensities being greater than infiltration rates is found only in limited areas with exposed

rocks, in high lands and those with non-weathered laterites in the foot of the hills towards the coast

and in areas where the human interference is too much.61

Fig. 4: River basins into which the Western Ghats drain

200N

180N

160N

140NAchancoil

Ambika

Bedthi

Bharathapuzha

Chalakudy

Chaliyar

Chandragiri

Daman Ganga

Kadalundi

Kali

Kaliada

Karuvannur

Mandovi

Muvattupuzha

Netravathi

Pamba

Par

Periyar

Purna

Savitri

Sharavathi

Shastri

Tadri

Ulhas

Vaitarna

Valapattanam

Vashishti

120N

100N

80N

740E 760E 780E

Legend

Kilometers

Remote sensing data is also being used in assessing runoff yield and quantities of soil loss from basins, using

rainfall data and adopting watershed modeling techniques. Most of the models used for these purposes consider land-

use and land-cover as a factor influencing runoff. Further, assessment of catchment wetness conditions, ground water

availability and river channel expansion are all parameters of hydrological importance, being accomplished with the help

RS data. However, the application of the commonly used methods for such hydrological studies in the Western Ghat

areas remains a challenging task because of two reasons – (i) runoff processes in the region are different from those for

which the commonly used models, like the Curve Number method, have been developed and (ii) non-availability of cloud

free data during the rainy season in sufficient quantities. The fact that not much has yet been understood concerning

the hydrology of the region provides ample scope for research in the areas of Western Ghats. Availability of microwave

remote sensing and imageries of finer spatial and temporal resolutions may be considered to have opened up new vistas

for hydrological research in the area.

Water Resources DevelopmentThe Western Ghats are obviously rich in water resources. The west flowing rivers are not harnessed to the extent

the east flowing rivers are. However, the hydroelectric potential of the west flowing rivers has been tapped to the extent

possible, further development having been impeded by reasons of ecological degradation of the region. Figure 6 shows

the reservoirs (both irrigation and hydroelectric) located in the region of the Ghats. These projects have led to destruction

of natural vegetation to an alarming extent in Kerala, while in other states, awareness in time has prevented destruction

to the point of no return. Open dug wells and springs are the other important water sources being extensively used for

irrigation and drinking water purposes in the Sahyadri region, even though the Western Ghats have been identified as a

region of very low groundwater potential, yielding less than 1 liter per second. Bore wells have made their entry in the

recent past due to intensive irrigation patterns and lowering of water tables in the plateaus and the foothill regions.

Inter Basin Water Transfers As mentioned earlier, there are some portions of the Sahyadris, the vast eastern slopes of which ultimately drain

to the west, just because of some minor geological changes like faulting. It is often felt that flow in rivers from such

areas can be diverted easily to the east, by technological interventions like damming, tunneling or lifting. Even gravity

diversions, by means of garland (contour) canals, have been suggested. The common man’s perception is that the yield

from the basins draining to the west is ‘being wasted’, since it is not harnessed completely for consumptive uses, while

the east draining basins adjacent to them have severe shortages. Hence, proposals for diverting flow from west flowing

rivers to the east are making rounds for a couple decades now. Even the National Water Development Authority, entrusted

with the task of planning and implementing the National Water Grid, has proposed three inter-basin transfer schemes

in the Western Ghats and feasibility studies are underway. On the other hand, inter-basin transfers already find a very

Table 1: Contribution of the Western Ghat region to flow in the east flowing rivers

River Total yield (Mm3)

Basin area considered

Up to Fraction of thetotal basin area (%)

Total yield (Mm3) Yield from WG region (Mm3)

Godavari 1,05,000 Nanded 15% 11,000 5,000

Krishna (excluding TB)

50,000 Raichur 70% 37,000 28,000

Tungabhadra 16,000 TB Dam 40% 12,000 5,000

Kaveri 20,000 Karnataka-TN Border

45% 15,000 6,500

Kaveri Bhavani 55% 18,000 8,000

Note: All figures in the table are indicative only

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Fig. 5: Characteristics of streamflow in Western Ghats – (i) Runoff coefficients during the monsoon; (ii) A typical flow duration curve of monsoon flow; and (iii) The hydrograph of flow in a small stream during a typical rainy day (rainfall amounting to about 100 mm)

prominent position in the water resources

map of Kerala and Tamil Nadu. There are four

interstate water diversions between Kerala

and Tamil Nadu, of which the Mullaperiyar

and the Parambikulam - Aliyar Project (PAP)

are the major ones. The Mullaperiyar project,

in operation since the 1890s, transfers

water from the Periyar basin in Kerala to the

Vaigai basin in Tamil Nadu. The project was

realised due to the political manoeuvring

by the British and since Travancore was a

Princely State of the British and not due to

any riparian rights of Tamil Nadu. PAP diverts

water from the Periyar, Bharathapuzha and

Chalakudy river basins, since some parts of

the upper catchments of these basins fall in

the state of Tamil Nadu. Ravi et al., (2004),

Madhusoodhanan and Sreeja (2010) and

Sreeja et al., (2012) discussed the varied

impacts of these diversions, including the

challenges to river basin governance in

the linked basins. Their findings show that

when not planned and implemented in true

spirit and with a high order of fairness and

justice, such projects not only lead to severe

water shortages downstream but also to

unending conflicts and tensions. In fact, the

experience of Kerala in more than 100 years

of interlinking, fraught with continuous

conflicts and power tussles with Tamil Nadu,

can be an eye opener to the planners and

a great deal of lessons can be learnt from

it. Inter-basin transfer projects are always

planned to divert only the so called ‘excess

waters’. It is argued with great credibility that if executed with restraint they would be a boon to the

water starved plains of South India. However, once implemented with great difficulty and with huge

investments, people and the state find many plausible reasons as to why the facilities created should

be operated even in drier months, to draw water flowing ‘waste into the sea’. The downstream effects

of such drastic summer diversion can spell acute water scarcities and ecological disasters in the donor

basin, which are conveniently forgotten in the analysis of benefits to the recipient basin. This is one

reason why the projects being planned in Karnataka are facing stiff opposition from the people in the

coastal belt. The possibility of small-scale diversions that do not infringe on the needs and the riparian

rights of the downstream users and the ecological needs of the river, which are always overlooked,

can only be realised through a basin-wise planning effort that would consider the requirements of all

the sections of the stake holders including the river itself.63

W a t e r S c a r c i t i e s a n d Solutions

The average rainfall of the region

of Western Ghats, throughout their extent,

exceeds 2500 mm and the amount of ground

water recharge is also very high in most parts of

the Ghats. Hence, possibilities of water scarcity

must have been low. However, demands for

domestic needs, energy, irrigation and industrial

purposes have been high in the downstream

areas of the Western Ghats. Most of the rivers

in the Sahyadris, except in Karnataka, are

already either dammed or diverted at several

sites for power generation in the upper reaches

and irrigation in the lower reaches, affecting

the natural flow regimes and downstream

regions. In addition, east flowing rivers like

Krishna and Kaveri barely reach the seas due

to over abstraction (Molle, et. al., 2010; Venot,

et. al., 2011), and demands for river diversions

are mounting. Many west draining basins are

also fast closing and the impacts are felt on

delta fishing, farming, livelihoods and ecology

(WGEEP, 2011). The aquifers in the coastal belt,

being lateritic, drain out fast during the post

monsoon season and as a result, sea water

intrusion into the fresh water aquifers is creating

serious problems. Indiscreet diversions and

injudicious use of land and water for agriculture

have left Kerala a water starved state. The

situation in Maharashtra is not much different –

the ever growing urban domestic and industrial

demands here have been responsible for the

need to resort to countless reservoirs storing

the flow from the Ghats. Yet, acute scarcities

of water are confined to the summer months,

while shortages in immediate post monsoon

season are mounting. It is commonly agreed

that measures like better management of water

and implementation of conservation practices in

the coastal areas should help alleviate scarcities

in the winter season, while more stringent

measures, presently not visible as a possibility,

may be necessary to wade through the

summer months. Fig. 6: Reservoirs in the Western Ghats

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3It is commonly agreed that factors contributing to shortage of water in areas where large scale

irrigation is practiced, in India, include illegal diversion and violation of indicative cropping patterns.

Unscientific methods of conveyance and rampant misuse of water add to these factors. It is true that

developments over the world during the last two decades, in the backdrop of the threat of a global

climate change that is detrimental to the very sustenance of life, have resulted in emergence of lobby

groups spreading environmental awareness and promoting sustained development even in the water

sector. But, not much seems to have happened in the Sahyadri region. One of the possible reasons

for such a state is the dearth of proper documentation and assessment of the evil practices and the

damage being caused as a result. The extent and the type of the terrain of the area seem to be a

major hurdle in mobilising a movement against forces responsible for the mess. The same is the case

also with the unabated expansion of tea, coffee and rubber plantations and illegal logging for timber

and firewood in those parts of the Sahyadris, which still remain fairly undisturbed. These activities

have often gone unnoticed because they are executed deep inside private or encroached land. It is in

these sectors, which call for urgent attention, that modern technologies of geo-information systems,

including remote sensing, do come in handy for the planners and the concerned groups of enlightened

citizens. Remote sensing technologies furnish the tools required for acquiring real time data from vast

areas, for better management of resources, but it remains a big question as to how these are made

use of and how useful the knowledge gained would be in combating the ever increasing needs.

ConclusionThe remnant natural ecosystems of the Western Ghats are currently subject to a plethora of threats

that vary widely from localised threats such as quarrying, livestock grazing, and forest fires to

landscape-level threats such as mining, hydel projects, large-scale agricultural expansion and creation

of monoculture plantations. Each of these must have directly or indirectly impacted upon the availability

of water resources and led to an accelerated impoverishment of this ancient landscape. Though there

are numerous studies related to the biodiversity and its conservation in these hill tracts, research

into the hydro-climatological aspects of the Ghats that ultimately sustain this unique ecosystem has

been negligible. Further, the available knowledge of the hydrological processes that characterise this

region establishes its uniqueness and underline the need for further studies. This paper calls for an

urgent need to take up detailed investigations and lists the possible research areas in hydrology and

water resources pertaining to the Western Ghats, where modern tools of geo-spatial studies could

be adopted with advantage.

References Challa, O., Gajbhiye, K.S. and Velayutham, M. (1999). Soil series of Maharashtra, NBSS Publication 79, National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra, 428 p.

Cincotta, R. P., Wisnewski, J. and Engelman, R. (2000). Human population in the biodiversity hotspots, Nature, 404, 990-992.

Daniels, R.J.R. (2001). National Biodiversity Strategy and Action Plan: Western Ghats Eco-region. Report submitted to Ministry of Environment and Forests, Government of India, New Delhi.

Harindranath, C.S., Venugopal, K.R., Raghumohan, N.G., Sehgal, J. and Velayutham, M. (1999). Soils of Goa for optimizing land use: Executive summary, NBSS Publication 74b (Soils of India series), National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra, 131 p.

Hewlett, J.D. and Hibbert, R.A. (1967). Factors affecting the response of small watersheds to precipitation in humid areas. In: Proc. Inter. Symp. on Forest Hydrology. Pennsylvania State Univ., 1965.Pergamom Press. N.Y., 275-289.

65

IIRS. (2002). Biodiversity Characterization at Landscape Level in Western Ghats India using Satellite Remote Sensing and Geographic Information Systems, Indian Institute of Remote Sensing, Indian Institute of Remote Sensing, Dept. of Space, Govt. of India, Dehradun.

Jarvis, A., Reuter, H.I., Nelson, A., Guevara., E. (2008). Hole-filled seamless SRTM data V3, International Centre for Tropical Agriculture (CIAT), Colombia, available at http://srtm.csi.cgiar.org.

Jones, J.A.A. (1979). Extending the Hewlette model of stream runoff generation. Area (Inst. of British Geographers), 11(2):110-114.

Madhusoodhanan, C.G. and Sreeja, K.G. (2010). The Mullaperiyar Conflict. NIAS Backgrounder on Conflict resolution, B4-2010, Bangalore: National Institute of Advanced Studies.

Malhotra, K. and Prasad, R. (1984). Some important aspects of water resources in Karnataka.Pub.Dept. Civil.Engg.,IISc., Bengaluru, India.

Molle, F., Wester, P. and Hirsch, P. (2010). River basin closure: Processes, implications and responses, Agricultural Water Management, 97: 569-577.

Myers, N, Mittermeier, R.A., Mittermeier C. G., da Fonseca, G. A. B. and Kent, J. (2000). Biodiversity hotspots for conservation priorities, Nature, 403, 853-858.

Nair, S.C.(1991). Southern Western Ghats. INTACH, New Delhi.

NBSS&LUP. (1996) (a). Kerala Soils.National Bureau of Soil Survey and Land Use Planning, Nagpur.

NBSS&LUP. (1996) (b).Tamil Nadu Soils. National Bureau of Soil Survey and Land Use Planning, Nagpur.

Pascal, J.P. (1988). Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession, French Institute of Pondicherry (FIP), Pondicherry, India.

Putty, M.R.Y. and Prasad, R. (2000a). Understanding runoff processes using a watershed model—a case study in the Western Ghats in South India, J. Hydrol., 228:215-227.

Putty, M.R.Y. and Prasad, R. (2000b). Runoff processes in headwater catchments – An experimental study in Western Ghats, South India, J. Hydrol., 235:63-71.

Radhakrishna, B.P. (1991). An excursion into the past – ‘the Deccan volcanic episode’, Curr. Sci., 61 (9&10): 641–647.

Rao, K.L. (1970). India’s Water Wealth. Orient Longman Ltd. New Delhi.

Ravi, S.P., Madhusoodhanan C.G., Latha, A., Unnikrishnan, S. and AmitaBachan, K.H. (2004). Tragedy of commons: The Kerala experience in river linking. New Delhi: SANDRP and Thrissur: RRC.

Sivaprasad, C.R., Reddy, R.S., Saigal, J. and Velayutham, M. (1998). Soil resources of Karnataka for Land Use Planning, Draft Final Copy, National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra.

Sreeja, K.G., Madhusoodhanan, C.G., Shetty, P.K., and Eldho, T.I. (2012). Inclusive spaces in Integrated River Basin Management: discerning multiple boundaries of resource relations. International Journal of River Basin Management, 10(4), 351-367.

Strahler, A.H. and Strahler, A.N. (1992). Modern Physical Geography.John Wiley & Sons, Inc. NY.

Thipperudrapa. (2009). Some studies on the intensity pattern of rainfall in Karnataka, with particular reference to Wetern Ghats. Unpub.Ph.D thesis, The National Inst. of Engg.,Mysuru, 570008.

UNESCO, (2012). Decisions adopted by the World Heritage Committee at its 36th session, Available from: http://whc.unesco.org/en/sessions/36COM/ [Accessed 16 August 2012].

Venot, J.P., Bharati, L., Giordano M. and Molle, F. (2011). Beyond water, beyond boundaries: spaces of water management in the Krishna river basin, South India. The Geographical Journal, 177 (2), 160-170.

Ward, R.C. and Robinson, M. 2011. Principles of Hydrology, 4thEdition, Tata McGrawHill, New Delhi.

WGEEP, (2011). Report of the Western Ghats Ecology Expert Panel, Submitted to the Ministry of Environment and Forests, Government of India, New Delhi.

WMO. (1983). (World Meteorological Organization).Operational Hydrology in the Humid Tropical Regions. In: Hydrol. of Humid Tropical regions with Particular reference to the Hydrological effects of Agriculture and Forestry practices, IAHS. Publ. No: 140: 1-25.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

USE OF EARTH OBSERVATION DATA TO UNEARTH SUB-SURFACE DRAINAGES: POTENTIAL GROUNDWATER SOURCE IN ARID REGION OF NORTH WEST INDIABhadra BK and Sharma JR RRSC-W, NRSC, ISRO, Dept. of Space, CAZRI Campus, Jodhpur 342003, IndiaEmail: bkbhadra63@gmail.com

IntroductionIn the ancient literature like Rigveda, the river Saraswati is described as the

Ambitame, Naditame and Devitame ie., the best of mother, the best of river and the best of

goddess. The Vedic Saraswati, a mighty and holy river of Northwest India during 6000 B.C.

stretched for 1600 km long and 3-12 km wide through Punjab, Haryana and Rajasthan and

finally discharged into the Rann of Kutchch in Gujarat coast and disappeared around 3000

B.C. (Chauhan, 1999; Kalyanraman, 1999; Radhakrishna, 1999, Valdiya, 2002; Lal, 2009).

It was a life line of the people of ancient India, mostly in the Vedic and Puranic ages. The

discovery of sites of Harappan civilization on the banks of Saraswati also indicates that the

river was mighty more than 8000 years ago. Two perennial rivers of the Himalaya viz. Yamuna

and Satluj might be the feeder drainage of the `Lost River Saraswati’ in the past (Figure 1).

But due to eastward shifting of Yamuna and westward deflection of Sutlej, the Saraswati

finally dried off. Compounding with the neo-tectonic activity and climatic changes in North West

India, the river Saraswati finally got lost under the aeolian sand cover. Several remnants of this

river still exist as palaeochannels in different parts of North West India. To unearth the sub-surface

drainage through modern tools and its great significance as Indian cultural heritage, study of

River Saraswati becomes a challenging task among the Geologists and Archeologists.

Remote Sensing Studies by Different AgenciesOver the past 30-35 years, taking advantage of the aerial and satellite remote

sensing data, palaeochannels have been systematically mapped by the agencies working in

this region mainly Central Arid Zone Research Institute (CAZRI), Jodhpur, Geological Survey of

India (GSI), Jaipur, Space Application Center (SAC/ISRO), Ahmedabad and Regional Remote

Sensing Center (RRSC-W/ISRO), Jodhpur to discover the course of lost Saraswati river. Most

of the earlier studies have used aerial photographs and satellite images of low resolution

from the Landsat MSS and IRS 1A/1B satellites to map the palaeo drainage courses in North-

west India. Mostly hardcopy in Black & White and colour prints of the satellite images have

been used in these studies. Probably, Ghosh et al., (1979, 1980) of CAZRI, Yash Pal et al.

(1980) of SAC/ISRO and Bakliwal and Sharma (1980) of GSI were the pioneering workers in

satellite base interpretation of Saraswati palaeochannels. Based on the image interpretations,

varying number of courses of river Saraswati have been suggested by these workers. Due to

the lack of clear image signatures in several areas, the continuity of the river courses could

not be maintained. Hence, there are discrepancies in delineating the number of Saraswati

Fig. 1: Existence of Saraswati River System(?) between Indus and Ganges River System in NW India

Fig. 2: SRTM derived DEM (90m) showing gradual change in slope along the major drainage systems of Northwest India

courses e.g. 5 courses by Ghosh et al., (1979,

1980); 7 courses by Bakliwal and Grover

(1988) and single course by Yash Pal et al.,

(1980). All the three organization continued

their palaeo-drainage work till 1990s with

the available Landsat images and aerial

photographs (Sood and Sahai, 1983; Kar

and Ghosh, 1984; Bakliwal and Grover,

1988). The pace of remote sensing work

gained momentum during 1991-2000 with

the availability of new optical sensors as well

as microwave sensors. It has been observed

that microwave data has advantageous

over optical data in delineating buried

channels below the sand cover due to having

penetration capability (Ramasamy et al.,

1991; Rajawat et al., 1999; Kar, 1999; Sharma

et al., 1999; Sahai 1999). Further, it became

necessary to map afresh of the palaeo-

drainage network of the Saraswati river basin

due the advancements in sensor resolution,

image processing and GIS techniques. Taking

advantages of the new data and methods,

re-interpretation of satellite images was

carried out by RRSC-W, Jodhpur during 2000-

2010 to arrive at the actual visible course of

river Saraswati and its tributaries (Gupta et

al., 2004; Bhadra et al., 2005; Bhadra et al.,

2009). Image interpretation was done bit

by bit to trace the entire palaeochannels of

River Saraswati, covering Punjab, Haryana,

Rajasthan and Gujarat States. Finally, the

entire palaeochannel network in Northwest

India were reconstructed by integrating

ground data such as archaeological sites,

historical maps, hydrogeology, sediment

character of aquifer formation through

drilling data, geochronology of trapped

water and sand samples (Bhadra and Sharma,

2011). Delineation of Sutlej palaeochannel

and the old course of River Drishadvati

helped in linking the present day `Lost River

Saraswati’’ with the Himalayan perennial

sources (Sharma and Bhadra, 2012).

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3DEM Study for Major Drainages in North West IndiaPhysiography and drainage pattern of North West India is depicted in Figure 2 through Digital Elevation Model

(DEM) from the SRTM data (90m resolution) for the year 2000. The images indicate that these channels served as passage

for huge volumes of water that rushed down from the Himalayas, made their way into the ocean. The flood plains of

the Saraswati River slope towards South West direction from Siwalik foothills (elevation > 270m) to the low lying marshy

stretch of the Great Rann of Kutchch. Beyond the sandy expanse of the Thar Desert in Cholistan (Bahawalpur) in Pakistan,

there exists the flood plain (500km long and 16-24km wide) of the dry channel of Hakra River. The flood plain was

built on the sunken part of the Indian crust

known as the Himalayan foredeep. Huge

piles of detritus were deposited by the rivers

originating from the Himalayas and some part

was also contributed by the Aravalli hills.

Methodology in Delineation of Palaeochannel

The present study has been carried

out to map the palaeochannels in the

Northwestern region and establish the

course of Vedic river Saraswati. Due to

large study area, the delineation work of

the palaeochannels is done in two phases

viz. Phase-I study in western Rajasthan and

Northern Gujarat during 1995-2000 and

Phase-II study in Haryana, Punjab and parts

of Himachal Pradesh and Uttarakhand during

2005-10. Image processing techniques such

as Piece-wise Histogram Stretching and

contrast enhancement have been applied in

both the phases. Further, SRTM DEM is used

to show the topography and present day

drainage system in North-west India.

(a) Phase-I Study in Rajasthan and

Gujarat

Initially, digital mosaic image of

IRS 1A LISS-I data of August, 1990 is generated

for detailed study of palaeochannels in the

Northwestern region (Figure 3).The linear

stretching is performed on the mosaiced LISS-I

FCC image. Palaeochannels are basically the

old course of river channels which appears

on the satellite image as serpentine drainage

course with high moisture content (dark

tone). The composited image showed the

course of palaeochannels (dark signature)

very clearly with naked eye. Further, satellite

Fig. 3: IRS-1A LISS-I image of Aug. 1990 showing the signatures of palaeochannels (dark tone) in Thar Desert

Fig. 4: (IRS P3 WiFS) of Dec. 1999 showing the palaeochannel network of Saraswati River in parts of Rajasthan and Gujarat

69

data from IRS P3 WiFS sensor of December 12, 1999 has been used for mapping of the prominent palaeochannels

(Figure 4), as it may be easy to locate broad channels (minimum ~200m wide) on the ground for their exploration. The large

swath of WiFS data of December, 1999 has made the mapping exercise more reliable and easier as the palaeochannels

are mapped from the data with same radiometric and geometric conditions over the entire study area. This has facilitated

avoiding the problems of radiometric and geometric corrections of large number of scenes.

“Piece-wise Histogram Stretching” technique has been used to enhance the palaeochannel signatures on the

image. This technique has been found unique in enhancing digital images to discriminate the moist sand (dark tone) in the

sandy alluvium tract. The feature enhancement is carried out by way of loading sub-scenes on computer terminal screen in

full resolution and improving the feature contrast by histogram stretching interactively. Palaeochannels have been mapped

online from the enhanced digital data using the vector module of EASI/PACE image processing software. At the same time,

IRS 1C LISS-III, data of November 1, 1996 and PAN scene of same date of pass have been used for selective areas in parts

of Jaisalmer district. For this purpose, digital

image processing techniques like hybrid

product generation (merged product of

LISS-III and Pan) have been used to enhance the

micro-geomorphological features indicating

palaeochannels. Thus, the entire course of

palaeochannels along Indo-Pakistan border

of Rajasthan State has been delineated.

These palaeochannels have been designated

as parts of `Lost Vedic Saraswati’ and linked

to present day Ghaggar River in Rajasthan-

Haryana Border States.

(b) Phase-II Study in Punjab and

Haryana

In Phase-II study, the work is

extended in upstream direction by further

delineation of palaeochannels of Ghaggar,

Saraswati, Drishadvati and Sutlej Rivers in

Haryana and Punjab. For this purpose, multi-

resolution data from IRS P6 AWiFS, LISS-III, LISS-IV of (February, 2004), Landsat ETM data and Radarsat-1 SAR data

(100m resolution) has been used in the study. For delineation of palaeochannels, digital image processing techniques like

histogram equalization, linear stretching, contrast and brightness enhancement etc. have been applied on a small area of

the satellite images (IRS P6 LISS-III). Drainage features are highlighted on applying local stretching on ~10X10 km2 area

out of the full LISS-III scene (141X141 km2). In this process, palaeochannels are delineated with proper care by avoiding

the canals, existing ephemeral drainages and water logged areas. The delineated course of palaeochannels pass through

Kurukshetra, Kaithal, Jind, Fatehbad, Hisar and Sirsa districts of northern Haryana from east to west (Figure 5). A large

number of discontinuous palaeochannels are found to lie in the above districts. In the northeast of Kurukshetra, these

palaeochannels could possibly have a link with the existing / abandoned channels which originate from the Siwalik Hills.

In the central Haryana, a sub-parallel drainage to Saraswati is also marked on the satellite image that passes through

Karnal, Jind, and Hisar districts and is known as Drishadvati River. Presently, Western Yamuna Canal (WYC) and Hansi

Branch Canal is constructed all along the natural depression of Drishadvati River. In the west of Hisar, Draishadvati River

is passing through Anupgarh and joins with Ghaggar River near Kalibangan in Hanumangarh district of Rajasthan.

Fig. 5: Satellite image showing the delineated palaeochannels using IRS P6 AWiFS and LISS-III images (Feb. 2004) in northern districts of Haryana. Inset shows highly moist zones with dark tone in Hisar and Sirsa districts

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3An attempt has been made to identify the continuity of Saraswati palaeochannels in eastern

Punjab. For this purpose, satellite images of IRS P6 LISS-III data of February, 2004 and Radarsat SAR

data (50m resolution) of December, 2002 have been used to delineate palaeochannels (Figure 6). In

this area, Radarsat SAR image was used to map moist zone (torquous channel with dark tone), possibly

indicating a subsurface palaeochannel. The delineated palaeochannel between Ropar and Patiala is

named as Sutlej palaeochannel which is a North-South trending tortuous palaeochannel extending

for a length of about 75 km with having width between ~1 to 6 km. The Sutlej palaeochannel is

connecting the present day Sutlej River and the Ghaggar River in the south of Patiala. Thus, the Sutlej,

Saraswati and Drishadvati palaeochannels possibly contributed to the main Vedic Saraswati to form

a mighty flowing River in the past.

Ground Validation as Supporting EvidenceMapped courses of Saraswati River within Indian Territory have been validated through data

from a variety of ground investigations viz. historic maps, geomorphic anomaly, archeological sites,

core drilling / litholog data, ground water quality-yield-depth, age of ground water etc. Study of a

number of historical maps of Indo-Pakistan region of Mughal period, prepared by the Italians, Dutch

and British authors during 14th –17th Century A.D., show the presence of the dry channels of Saraswati.

The merged satellite image (LISS-III+Pan) are used to identify corn-cob structures which are formed

due to deposition of sand over flowing as well as stagnant water bodies. They indicate the presence

of fluvial activity in this region in the past. Compilation of all the available archaeological sites of Early

Harappan, Mature Harappan and Late Harappan sites in northern states of India indicates 436 sites

in Haryana, 157 sites in Punjab, 100 sites in Gujarat and only 36 sites in Rajasthan. The association of

Harappan sites and the delineated palaeochannels points towards a close link with the Vedic Saraswati

civilization. Similarly, analysis of drilling data, groundwater quality and its isotopic ages support the

existence of palaeochannels in the entire region of North-west India.

Source of Groundwater along the Palaeochannels

Using the delineated palaeochannels maps as a guide, 14 tube wells have been drilled

by Ground Water Department, Rajasthan jointly with the Central Ground Water Board (CGWB)

along Tanot–Kishangarh–Longewala-Ghotaru section in Jaisalmer district of Rajasthan (Figure 6).

Observation of core drilled samples (Table-1)

suggests that the palaeochannels consist

of alternating layers of fine to medium

and coarse grained sand and sometimes

have gravel columns, indicating presence

of fluvial regime. The column thickness of

palaeochannels in this area ranges from

35 to 80 m. Coarser sediments are noticed

at a depth ranging from 40-125 m and

have been encountered in 9 out of the

14 tube wells. Water levels in most of the

bore wells drilled ranged in between 35-60 m.

Similarly, litholog data analysis of 304 drilling

sites (Source: Ground Water Cell, Govt. of

Haryana, 2005-06) in 9 districts of northern

Haryana indicates that most of the lithologs,

Fig. 6: Optical (IRS P6 LISS-III) and SAR (Radarsat-1) images showing the delineated Sutlej palaeochannels between Ropar and Shatrana in Punjab. A large number of archaeological sites can be noted lying along the palaeochannels.

71

lie near the palaeochannels, have medium to coarse grained sands and associated gravel and pebbles at a depth between 10

to 100 m. Analysis of ground water samples indicates that the water quality is quite good (potable water) for most of the drilled

tube wells, as compared to tube wells or dug wells away from the channel. For example, the well drilled at Tanot shows Total

Disseled Solids (TDS) in the order of 2650 ppm in comparison to the existing well at Tanot (TDS~9000 ppm), which is about

1.5 km away from the channel. The isotopic age analysis (H3, O18 and C14) by BARC, Mumbai of 17 ground water samples from

the existing wells along the palaeochannels in Jaisalmer district of Rajasthan shows ground water ages from 1340 to 18880

Before Present (BP) at different localities from NE to SW (Rao and Kulkarni, 1997; Nair et al., 1999).Ground Water Department,

Jodhpur has identified three major aquifer zones along palaeochannels of Saraswati River. Static groundwater reserve in these

aquifers is estimated as 590 Mm3 (A1 zone), 92 Mm3 (A2 zone) and 81 Mm3 (A3 zone) respectively.

Sl.No

Drilled well name / location

Yield (lph)

Quality(TDS)

DepthDrilled (m)

Static WaterLevel (m)

Aquifer material as observedin lithologs

1. Tanot (3.5 km from Ghantiyali to Tanot)

11250** 2650 125 33 Mainly fine grained sand, medium grained at certain levels

2. Ghotaru-I (12.5 km Ghotaru to Longewala)

13500** 6506 151 43 Medium to coarse sand and gravel (Out of main channel)

3. Ghotaru-II (14.5 km Ghotaru to Longewala)

Not Developed

151 Fine Grained sand / S.St – kankar (Out of main channel)

4. Ghotaru-III (10 km from Ghotaru to Longewala)

2250** 4337 151 48 Fine Grained sand and very coarse grained gravelly sand

5. Ghotaru-IV (3 km from Ghotaru to Longewala)

32400# 3554 151 45 Medium to fine and coarse grained sands

6. Ghotaru-V 150 m NE of Fort

33750# 1536 148 33 Coarse gravelly and fine to medium grained sands, occasional clayey

7. Ghotaru-VI (1.5 km from Ghotaru to Asutar)

22500# 934 125 46 Dominantly medium to coarse sands, fine grained and clayey sands

8. Dharmi Khu (3 km from Kishengarh to Dharmi Khu)

35100# 1024 153 40 Fine and medium grained sands

9. Ranau-I* (Ranau- Tanot Road)

9120** 1010 102 42 Fine grained sand and silt with kankar, fine to medium sand

10. Ranau-II* (Close to Ranau Village)

18240** 1000 120 58 N.A.

11. Karthai* (9.5 km from Ranau on Tanot Road)

12312* 1800 125 42 Mostly Fine sand medium at certain levels

12. Nathura Kua* (4.5 km from Tanot)

12768** 2656 120 36 Fine grained sand and silt with kankar

13. Kuria Beri* 12768** 1295 131 32 Mostly fine Sand

14. Ghantiyali I* (500 m from Ghantiyali Mandir to Tanot)

11400** 2200 130 62 Fine grained sand

*Wells are drilled by the CGWB, **Compressor yield # Pump test yield

Table 1: Data of the tube wells drilled along the palaeochannels in the Jaisalmer district, Rajasthan(Source: Ground Water Dept, Govt. of Rajasthan:Adopted from Gupta et al, 2004)

Linkage of Vedic Saraswati with Himalayan SourcesThe then mighty river, the Saraswati in Vedic Period might have been contributed by any major river system

of the Himalaya. Presently, Sutlej and Yamuna are the two perennial rivers which are likely to be the feeder channels

of Vedic Saraswati River in the past (Figure 7). Beyond the range of Siwalik and lesser Himalaya, these two rivers are

fed with the permanent glaciers in the higher Himalaya. It has been observed from the satellite images that the size of

the glacier of Sutlej River is much larger than the size of the Yamunotri and Bandarpunch glaciers. But, due to tectonic

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3changes in the past, these two perennial rivers

shift their courses viz. Sutlej to the west to

join river Indus and Yamuna to east to join

River Ganges near Allahabad.

Based on the analysis of several

satellite images the entire course of Saraswati

palaeochannels has been delineated from the

Himalayan foothills to the Rann of Kutchch,

passing through the Thar Desert in North

West India. Review of different literatures,

archaeological findings and synthesized

scientific evidences, two possible connectivity

of the Vedic Saraswati with the Himalayan

River sources have been emerged viz.

(a) Connectivity of Vedic Saraswati with Sutlej

River (b) Connectivity of Vedic Saraswati with Yamuna/Tons Rivers.

AcknowledgementsThe authors are extremely grateful to Dr. V. K. Dadhwal, Director, NRSC, Hyderabad for his valuable

guidance to carry out this research work. The authors are equally grateful to Dr. A. K. Gupta, Ex. ISRO

Scientist, Jodhpur, Dr. S. Kalyanraman of Saraswati Nadi Sodh Prakalp, Chennai; Shri Darshan Lal Jain

of Saraswati Nadi Sodh Sansthan, Yamunanagar; Late Dr. S P Gupta, Indian Archaeological Society,

New Delhi; Dr. Amol Kar, CAZRI, Jodhpur and Shri Rajesh Purohit, Archaeologist, Director, Allahabad

Museum and others for their active association, encouragement and fruitful discussion at various

stages of the project work.

ReferencesBakliwal, P.C. and Grover, A.K. (1988). Signature and migration of Saraswati River in Thar Desert,

Western India, Records Geol. Surv.India, V.116, Pt.3-8, pp.77-86.

Bakliwal, P.C. and Sharma, S.B. (1980). On the migration of river Yamuna.J. Geol. Soc. India, V.21,

pp.461-463.

Bhadra, B.K., Sharma, J.R. and Bharadwaj, Trilok (2005). Delineation of Sutlej Palaeochannel using

IRS LISS-III and Radarsat SAR data in parts of Punjab: Its possible linkage with “Lost River Saraswati”

in Northwest India. Published Abstract in Indian Geological Congress (IGC), National Conference at

Delhi University during 2-4 December, 2005, pp.8-9.

Bhadra, B.K., Gupta, A.K. and Sharma, J.R. (2009). Saraswati Nadi in Haryana and its linkage with the

Vedic Saraswati River – Integrated study based on satellite images and ground based information. J.

Geol. Soc. India (Springer Co-Publ.), V.73, pp.273-288.

Bhadra, B.K. and Sharma, J.R. (2011). Satellite Images as Scientific Tool for Saraswati Palaeochannel

and its Archaeological Affinity in NW India. In: Proceedings of Int. Seminar `How Deep are the Roots

of Indian Civilization’ by Draupadi Trust & Ministry of Culture, New Delhi, pp.30-49.

Chauhan, D.S. (1999). Mythological observations and scientific evaluation of the Lost Saraswati River.

In: Vedic Saraswati (Eds. B.P. Radhakrishna and S.S.Merh). MemoirGeol. Soc. India, V.42, pp.35-45.

Fig. 7: Mosaiced Landsat ETM+ image of 1999-2000 shows the Saraswati palaeochannel network and their link with the perennial sources of Sutlej and Yamuna Rivers

73

Ghosh, B., Kar, A. and Hussain, Z. (1979). The lost courses of the Saraswati River in the Great Indian Desert: new evidence

from LANDSAT imagery. Geograph. Jour., London, V.145(3); pp.446-451.

Ghosh B, Kar A and Hussain, Z. (1980). Comparative role of Aravalli and Himalayan river systems in the fluvial sedimentation

of Rajasthan desert. Man and Environment, V. 4, pp.8-12.

Gupta. A.K., Sharma, J.R. Sreenivasan, G. and Srivastava, K.S. (2004). New findings on the course of River Saraswati.

Jour. Ind. Soc. Remote Sensing, V.32 (1), pp.1-24.

Kalyanraman, S. (1999). Saraswati River, Goddess and Civilization. In: Vedic Saraswati (Eds. B.P. Radhakrishna and

S.S.Merh). MemoirGeol. Soc. India, V.42, pp.25-34.

Kar, A. (1999). A hitherto unknown palaeodrainage system from the Radar Imagery of southeastern Thar Desert and its

significance.Memoir Geol. Soc. India, No.42, pp.229-235.

Kar, A. and Ghose, B. (1984). The Drishadvati River System of India: An assessment and new Findings. The Geographical

Journal, V.150 No.2, pp. 221-229.

Lal, B. B. (2009). How deep are the Roots of Indian Civilisation? Archaeology Answers. Aryan Books International, New Delhi.

Nair, A.S., Navada, S.V. and Rao, S.M. (1999). Isotope study to investigate the origin and the age of ground water along

the palaeochannels in Jaisalmer and Ganganagar districts of Rajasthan. In: Vedic Saraswati (Eds.: B.P. Radhakrishna and

S.S. Merh). Memoir Geol. Soc. India, No.42, pp. 315-321.

Radhakrishna. (1999). Vedic Saraswati and the dawn of Indian Civilization. In: Vedic Saraswati (Eds. B.P. Radhakrishna

and S.S.Merh). Memoir.Geol. Soc. India, V.42, pp.5-13.

Rajawat A.S., Narain A., Navalgund, R.R., Pathak S., Sharma, J.R., Soni V., Babel, M.K., Srivastava, K.S. and Sharma, D.C.

(1999). Potentials of Radar (RES-1/2 SAR) and high Resolution IRS 1-C Data in reconstructing Palaeodrainage Network

of western Rajasthan. Momoir Geo. Soc. India, No.42, pp.245-258.

Ramasamy, S.M., Bakliwal, P.C. and Verma, R.P. (1991). Remote Sensing and river migration in western India.Int. JRemote

Sensing, V.12, pp.2597-2609.

Rao, S.M. and Kulkarni, K.M. (1997). Isotope hydrology studies on water resources in western Rajasthan. Curr. Sci., V.72

(1), pp. 55-61.

Sahai Baldev. (1999). Unraveling the ‘Lost’ Vedic Saraswati. In: Vedic Saraswati (Eds.: B.P. Radhakrishna and S.S. Merh).

Memoir Geol. Soc. of India, No.42, pp.121-142.

Sharma, J.R. and Bhadra, B.K. (2012). Signatures of Palaeo Rivers Network in Northwest India Using Satellite Remote

Sensing. In: Historicity of Vedic and Ramayan Eras: Scientific Evidence from the Depths of Oceans to the Heights of

Skies’, Book Eds. - SarojBala and Kulbhusan Mishra, Publ. Institute of Scientific Research on Vedas (I-SERVE), New Delhi,

pp.171-192.

Sharma J.R, Gupta A.K., Pathak S., Rajawat, A.S., Sharma D.C., Srivastava, K.S. and SoniVimal. (1999). Results of

Remote Sensing based studies on Reconstruction of the course of River Saraswati in Western India. In: Proceeding of

ISRS Symposium, Bangalore, pp.378-384.

Sood, R. K. and Sahai, B. (1983). Hydrographic changes in northwestern India. Man and Environment, V.7, pp.166-169.

Valdiya, K.S. (2002). Saraswati – The River That Disappeared. University Press (India), Hyderabad, 116p.

Yash Pal, Sahai, B., Sood, R.K. and Agarwal, D.P. (1980). Remote Sensing of the `Lost’ Saraswati River. Proc. Ind. Acad.

Science (Earth and Planetary Sciences), V.89, pp.317-331.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

MODELING HYDROLOGICAL WATER BALANCE IN THE FORESTED WATERSHED FOR WATER MANAGEMENTGupta PK1, Singh RP1, Panigrahy S2, Chauhan JS3, Sonakia A3 and Parihar JS1 1Environment and Hydrology Division, Space Applications Centre, Ahmedabad-3800152Former Group Director, Agriculture, Terrestrial Biosphere and Hydrology Group, Space Applications Centre, Ahmedabad-3800153Kanha National park, MP Forest Department, Mandla, MP India Email: pkgupta@sac.isro.gov.in

IntroductionSevere water shortages have already occurred in several parts of the world and this

situation is worsening each year. Freshwater withdrawals increase with increasing population

and increasing per capita needs and both ground water aquifers and low-season stream flows

are experiencing depletion. This water crisis will adversely affect water supplies for irrigated

agriculture, drinking, industries and other domestic uses. Forested catchments supply a

high proportion of the water for various purposes in both upstream and downstream areas.

There is an urgent need to understand the interactions between forests and water. Of late

studies are carried out on large-scale watershed hydrology, climate change impacts, and

application of hydrological models. But, the forest hydrology in India is still in an initial stage.

The information to define the entire hydrological system and water budget of a particular

forest system is not yet available.

Forests management practices have the potential to alter the quantity, quality and

timing of water moving through aquifers by altering the interception, evapotranspiration,

soil infiltration, base flow, runoff etc. (Anderson et al., 1976; Ice and Stednick, 2004). Forest

hydrology deals with the role of forest over precipitation and water yield production potential

of the forest. Therefore, forest influences on various hydrological parameters viz. rainfall,

interception, infiltration, soil moisture, evapotranspiration, groundwater, water yield, soil loss

and floods etc., forms an important area of hydrological studies. Some of the earlier studies

(Mehar Homji 1986; Dutt and Manikiam, 1987; Gupta et al., 2005) indicated that forests

and rainfall relationship are not linear on a regional scale. Interception studies carried out in

India (Dabral and Subbarao, 1969; Mathur, 1975) indicate that the canopy interception varies

from 15% to 35% of rainfall in the forest regions. There is evidence that interception varies

not only with canopy density but also with intensity of rainfall. The analysis of infiltration

data from small forests and agriculture watershed in Doon valley indicated that the rate

of infiltration was twice in forest watershed (Shorea Robusta) as compared to agriculture

watershed (Dhruvanayayana and Shastri 1983). It can be depicted that the infiltration rates

are relatively more in forested soils as compared to agricultural areas & grasslands. Much

effort has not been made to quantify soil moisture storages under forests. However, forested

soils have a better soil moisture retention capacity due to improved soil structure because of

more humus and organic matter content. In general, forests have high evapotranspiration

requirement as compared to other land uses. Groundwater relationship with forest is

yet to be examined scientifically on large scales. Studies conducted in India and abroad

(Hibbert, 1965; Lal and Subba Rao, 1981) have shown increase in stream flow due to cutting and reduction of density

of forests. Empirical modeling approach to quantify the accumulated hydrologic effects of watershed management is

limited due to its complex nature of soil and water conservation practices.

This paper describes a study under MIKE SHE/ MIKE 11 coupled model. The study has been done to estimate the

water balance components in the forest and neighbouring regions for the conservation and management of water.

Data used

Three types of data used were such as remote sensing, in-situ measurement and soil physical and channel

characteristics for the hydrological and hydraulic modeling. Brief summary of data used in modeling are as follows;

Remote Sensing data: The remote sensing derived parameters that have been used for model setup, delineation

of various thematic layers and model testing are (i) Digital Elevation Model (ASTER), (ii) Rainfall (NOAA Climatic Prediction

Centre), (iii) LULC (LISS III), (iv) LAI (LISS III and MODIS) and (v) River network (LISS III, Google earth)

In-Situ measurements: The measurements that were used for model initialization, calibration and validation are

(i) River water level (2 locations), (ii) Velocity, (iii) Cross sections, (iv) Groundwater (pre and post monsoon), (v) Resistivity

survey (groundwater, hydraulic conductivities, soil formations etc.), (vi) Rainfall, (vii) Temperature, (viii) Soil moisture and

(ix) Leaf Area Index

Other data: The other data that were used for setting up and as calibration parameters for the coupled hydrological

and hydraulic model are (i) Soil physical properties and (ii) Channel characteristics

Study area The watershed is located within and

the buffer regions of the Kanha National Park

along the south-east boundary of Madhya

Pradesh State (Figure 1). Geographic extent

is 800 42’E to 810 01’E and 220 02’N to

220 31’N. It covers 892 km2. Majority of the

area is covered by forest and agriculture.

Forest is mainly deciduous type and found

near the southern parts. Main forest types are

Sal, Sal mixed with bamboo and miscellaneous

etc. Agricultural area extends towards middle

and northern watershed area. Leaf area

index varies from 1.5 to 6 in the study area.

Elevation ranges from 523 to 900 m with

an average of 689 m and the terrain slopes

toward the northern side. The soil is mainly

loamy. In the study area during the summer,

the temperature ranges from 11°C to 43°C, whereas in winter it ranges from 2°C to 29°C. The southwest monsoon

prevails during late June to September. The maximum rainfall occurs in July and August. The average annual rainfall is

1,225 mm. Groundwater is shallow during monsoon season (surface to 20 feet) whereas pre-monsoon period groundwater

table variation ranges from 4 to 49 feet. Area mainly consist of basaltic (porphyritic and non-porphyritic), quartz (Mica

and k-feldspathic) and granite rock types. Forest area is dominated by the several perennial and semi perennial springs.

Fig. 1: Location map of the study area

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3These springs are located in the hilly regions and basically are geological lenses which store water

during the monsoon season and release it to the nearby rivers/channels as the delayed interflow.

Methodology

MIKE SHE/ MIKE 11 Coupled ModelMIKE SHE is a GIS-based distributed model. It is a spatially and temporally explicit, modularized

modeling system. This model simulates the complete terrestrial water cycle, including saturated water

movement in soils, 2-D water movement of overland flow, 1-D water movement in rivers / streams,

unsaturated water movement and evapotranspiration (ET). Saturated water movement in soils is

modeled using 3-D Finite Difference or Linear Reservoir. The 2-D water movement of overland flow is

simulated using Finite Difference or Subcatchment-based method. The diffusive wave version of Saint

Venant equations is used to simulate 1-D water movement in rivers / streams. The unsaturated water

movement is simulated using either Richards’s equation or Gravity Flow or Two-Layer water balance

method. Detailed descriptions of the model and algorithms can be found in many publications (Abbott

et al., 1986a, b; DHI, 2005).

M o d e l S e t u p a n d Parameterization

In this study, MIKE SHE was coupled

with the river flow routing model MIKE

11 (DHI, 2005; Sahoo et al., 2006), a one-

dimensional river/ channel water movement

model, to simulate the full hydrological cycle of

the watershed, including evapotranspiration,

infiltration, unsaturated flow, saturated flow,

overland flow and stream flow (Figure 2).

The main inputs for the model included

spatial data on topography, soils, vegetation,

and drainage network; and temporal

data on precipitat ion and Potentia l

Evapotranspiration (PET).

Unsaturated Flow: The Two-Layer Water Balance model (Yan and Smith, 1994; DHI, 2005),

which is designed for the areas with a shallow groundwater table, was used to simulate the unsaturated

flow for this study. The model divides the unsaturated zone into a root-zone where ET can occur, and

a below-root-zone where ET does not occur (Yan and Smith, 1994).

Saturated Flow: The 3-D Finite Difference method (DHI, 2005) and linear reservoirs methods

were used to simulate the saturated flow and delayed interflow for this study. The inputs needed

to simulate saturated flow were soil hydraulic properties, including horizontal and vertical hydraulic

conductivities, specific yield, and storage coefficient. Overland flow, subsurface flow (lateral flow) and

ground water table level are significantly affected by the values of vertical hydraulic conductivity.

Overland Flow and Stream Flow: Overland flow was simulated using diffusive wave

approximation. The inputs include initial water depth on the surface, surface detention storage, and

Manning number. The measured surface water depth was used to initialize the water depth above

Fig. 2: Processes for modeling hydrological cycle in the forest system

77

the ground surface for the model to run. Surface detention storage largely affects routing water toward the stream

and water table dynamics. Large values of surface detention storage reduce the overland flow reaching the stream, but

increase ponding water that may lead to a subsequent increase in water table level. Manning coefficients significantly

influences routing overland flow toward the stream and stream flow toward the outlet of the stream with higher values

leading to faster water movement.

Evapotranspiration (ET): In this study, daily actual evapotraspiration was estimated using the Kristensen

and Jensen model, 1975. Model is based on the effect of the LAI on transpiration and effect of soil and water on

evaporation.

Calibration and Validation: Model is calibrated and validated based on the modeled and observed

measurements on river water level/discharges and groundwater measurements.

Simulation time Steps and Period: MIKE SHE has the flexibility of using variable simulation time steps

for different hydrological modeling components and flow characteristics (DHI, 2005; Zhang et al., 2008). Simulation

period was taken during 1 June 2010 to 15 August 2012.

Results

M o d e l C a l i b r a t i o n a n d Validation

Daily Flows for the model calibration

(2010) and validation (2011 and 2012)

periods show that the model could capture

the dominant runoff process and stream

flow dynamics of the watershed (Figures 3

& 4). However, the model both over estimated

and underestimated stream flow during

the simulation period. This is because of

perturbations in the discharge values which

model could not handle and contributed for

uncertainty in the simulations. Coefficients

of determination (R2) of 0.82 and 0.78 and

RMSE of 10.81 and 32.10 were obtained

for the upstream (u/s) and downstream (d/s)

gauging sites during model testing period,

respectively. During the calibration period

R2 values were of 0.78 and 0.81, whereas

RMSE values were of 8.8 and 24.0 considering

u/s and d/s gauging sites, respectively.

A reasonable good match between modeled

and observed stream flows were obtained (u/s

gauge; R2 = 0.84 and RSME = 9.73 whereas d/s gauge; R2 = 0.78 and RSME = 39.13) for the validation period. The higher R2 values

between modeled and measured discharge during calibration and validation period for the u/s gauging site was mainly due to the

fact that fewer flow events with high peak flows were underestimated compared to d/s gauging site and less modeling

errors occurred. For the validation period, peak flow rates for a few more events were underestimated or overestimated

and resulting in much higher simulation errors.

Fig. 3: MIKE SHE Model simulated at daily time scale for calibration (2010) and validation (2011 and 2012) at u/s gauging site (Kurkuti).

Fig. 4: MIKE SHE Model simulated at daily time scale for calibration (2010) and validation (2011 and 2012) at d/s gauging site (Sijhora).

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Water Balance AnalysisWater balance components were

extracted for the whole watershed. Further,

results were extracted and analysed for

the forested and other land cover classes

separately (Figure 5) to see the inter linkages

between the water balance components

of forest regions with the neighbouring

areas. Also, water balance components

were extracted for the geological lenses

representing for springs located in the study area. Rainfall for all the three categories viz whole

catchment, forest and other classes was of 4230 mm. Out of which surface water which goes to the

river, base flow production, evaporation from ponded water, canopy water storage and transpiration

were of 36.6%, 22.4%, 3.6%, 12.5% and 24.9%, respectively for the whole catchment. Forest

area contribution was more for base flow (26.9%), transpiration (30.2%), canopy storage (16.6%)

whereas less contribution was for evaporation from ponded water (0.8%) and surface water (25.5%)

in comparison to other classes water balance components. Recharge to the aquifers beneath the spring

locations which are basically geological lenses was of 34.5% whereas other components contributions

were of 65.5% which includes interception, ET, overland water etc. This water seeps out in the flat

regions during the post monsoon period and contributes for the channel flow.

Water Volume EstimationsPercentage daily contributions to the

main river (Halon) from small rivers / streams

/ nala were extracted and are presented in

Figure 6. Volume of water contributed by

various streams to the main river were also

estimated and presented in Figure 7. Total

stream flow water among the different

streams ranges from 18 Million Cubic Metre

(MCM) (Stream- 9, lowest order stream)

to 338 MCM (Kashmiri Nadi with highest

order stream). Total volume of water within

the river system considering surface water

and base flow contributions is estimated of

1994 MCM. Out of that base flow water

from the forest region (514 MCM) which

flows through the river and recharged to

the groundwater may be utilized within the

forest and neighbouring areas for various

applications.

ConclusionThe objective of this study was to evaluate the ability of the distributed hydrologic-hydrodynamic

model, MIKE SHE to simulate different water balance components through hydrological experiment

Fig. 5: Water balance components for the catchment and land cover classes

Fig. 6: Daily percentage of water among different streams and small rivers / nala

Fig. 7: Volume of water produced in different streams and rivers

79

and integrating remote sensing inputs. Model calibration and validation suggested that the model could capture the

dominant runoff process of the large watershed. The physically based model required calibration at appropriate scales.

The model was useful for understanding the rainfall and its various partitioning parameters in the forest as well other

land cover classes and their interlinking mechanism. However, more datasets with higher temporal resolution are needed

to further apply the model for regional applications.

ReferencesAbbott, M. B., Bathrust, J. C., Cunge, J. A., O’Connell, P. E. and Rasmussen, J. (1986a). “An introduction to the European

Hydrological System—Systeme Hydrologique Europeen, ‘SHE.’ 1: History and philosophy of a physically-based, distributed

modeling system.” J. Hydrol., 87 (1), 45–59.

Abbott, M., Bathurst, J., Cunge, J., ’Connell, P. and Rasmussen, J. (1986b). An introduction to the european hydrological

system – systeme hydrologique europeen, “SHE”, 2: Structure of a physically-based, distributed modeling system, J.

Hydrol., 87, 61–77.

Anderson, W.M.D. and K.G. Hoover (1976). ‘Forest and Water effects of forest management on floods, sedimentation

and water supply’, USDA Forest Service, General Tech. Report.

Dabral, B.G. and Subbarao, B.K. (1969). ‘Interception Studies in Sal (Shoria Robusta) and Khair (Acacia-Catchu) Plantation

New Forest, Dehradun’, Ind. For., 96, pp. 313-323.

DHI: MIKE SHE Technical Reference, Version (2005). DHI Water and Environment, Danish Hydraulic Institute,

Denmark, 2005.

Dhruvanarayana, V.V. and Sastry, G. (1983). ‘Annual Report’, CSWCRTI, Dehradun.

Dutt, C.B.S.and Manikiam, B. (1987). ‘Forest Ecology and Related Weather Influences’. NNRMS, ISRO Hq., Tech. Report

ISRO-NNRMS-TR-66-87, Bangalore.

Gupta, A., Thapliyal, PK., Pal, PK, Joshi, PC (2005). Impact of deforestation on Indian Monsoon- A GCM Sensisitivity

study. J. Of Indian Geophysic. Union, 9(2), pp 97-104.

Hibbert, A.R. (1965). ‘Forest Treatment Effects on Water Yield, Proc. Int. Symp. on Forest Hydrology, Pennysylvania State

Univ., pp. 527-543.

Ice, G.G., and J. D. Stednick (eds.) (2004). A Century of Forest and Wildland Watershed Lessons. Bethesda, MD: Society

of American Foresters.

Kristensen, K. J., and Jensen, S. E. (1975). “A model for estimating actual evapotranspiration from potential transpiration.”

Nord. Hydrol., 6(3), 70–88.

Lal, V.B. and S.K. Subba Rao (1981). ‘Hydrological Influences of Vegetation Cover in Water shed Management’, paper

presented at Nat. Workshop. on Watershed Management,

Mathur. H.N. (1975). ‘Research in Soil Conservation Forestry’, in Soil & Water Conservation Research, 1956-71, ICAR,

Publication.

Mehar-Homji, V.M. (1986). ‘Trends of Rainfall in Relation to Forest Cover’, The French Institute, Pondichery, Memoir.

PSW-18.

Sahoo, G., Ray, C., and Carlo, E. (2006). Calibration and validation of a physically distributed hydro logical model, MIKE

SHE, to predict streamflow at high frequency in a flashy mountainous Hawaii stream, J. Hydrol., 327, 94–109.

Yan, J. and Smith, K. (1994): Simulation of integrated surface water and ground water systems –Model formulation,

Water Resour Bull., 30, 1–12.

Zhang, Z., Wang, S., Sun, G., McNulty, S., Zhang, H., Li, J., Zhang, M., Klaghofer, E., and Strauss, P. (2008). Evaluation

of the MIKE SHE model for application in the Loess Plateau, China, JAWRA, 44, 1108–1120.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

FLOOD MONITORING AND MANAGEMENT USING REMOTE SENSING

Srinivasa Rao G, Bhatt CM, Manjusree P, Sharma SVSP, Asiya Begum Remote Sensing Applications Area, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: srinivasarao_g@nrsc.gov.in

IntroductionIndia is one of the most flood prone countries in the world. India, due to its

geographical location, climate, topography and large population, has a greater impact

of flood disasters. Twenty-three of the 35 states and union territories in the country are

subject to floods. Around 40 million hectares (mha) or nearly 1/8 of Indian geographical

area is flood prone and the country’s vast coastline of 5700 km out of 7500 km is exposed

to tropical cyclones (National Flood Control Commission Report, 1980). The annual average

area affected by floods is about 7.57 mha and the crop area affected is about 3.5 mha. The

average loss is about Rs.13,000 million. On an average the human lives lost is about 1595

(Gopalakrishnan, 2002).

Floods occur in almost all major river basins in India. The Indo-Gangetic and

Brahmaputra river basins are the most chronic flood prone areas and are regarded as the

worst flood affected region in the world (Agarwal and Sunita, 1991). Every year states like

Assam located in Brahmaputra basin and Bihar, Uttar Pradesh and West Bengal located in

Indo-Gangetic basin face severe flood problems. Nearly 75 per cent of the total Indian rainfall

is concentrated over a short monsoon season of four months (June-September). As a result the

rivers witness a heavy discharge during these months, leading to widespread floods in Uttar

Pradesh, Bihar, West Bengal and Assam. The Himalayan Rivers also carry a large amount of

sediment, causing erosion of the banks in the upper reaches and over-topping in the lower

segments. Inadequate capacity of the rivers to contain within their banks the high flows

brought down from the upper catchment areas following heavy rainfall, leads to flooding.

The tendency to occupy the flood plains has been a serious concern over the years. Because

of the varying rainfall distribution, many a time, areas which are not traditionally prone to

floods also experience severe inundation. Areas with poor drainage facilities get flooded

by accumulation of water from heavy rainfall. Excess irrigation water applied to command

areas and increase in ground water levels due to seepage from canals and irrigated fields

also are factors that accentuate the problem of water-logging. The problem is exacerbated

by factors such as silting of the riverbeds, reduction in the carrying capacity of river channels,

erosion of beds and banks leading to changes in river courses, obstructions to flow due to

landslides, synchronisation of floods in the main and tributary rivers and retardation due to

tidal effects (NDMA, 2008). Drainage problems also arise concurrently if floods are prolonged

and the outfalls of major drainage arteries are blocked. One of the major reasons for the

floods is the massive indiscriminate deforestation, which leads to large amounts of topsoil

coming loose in the rains. Thus, the soil, instead of soaking up the rainfall, flows down into the river and in turn causes

the riverbeds and its tributaries to rise.

Phases of Flood ManagementFlood disaster management cycle has three main phases viz. flood preparedness (before flood occurs), flood

response (during a flood) and the last phase called flood mitigation (after flood has occurred). Flood preparedness involves

identification of chronically flood prone areas, identification of areas that are liable to be affected by a flood and planning

of optimum evacuation plans. Flood response involves the immediate action taken once the flood disaster has occurred in

terms of the identification of the region affected, spatial extent of inundation, flood damage statistics, flood progression

and recession etc which can help in carrying out the relief and rescue operations on ground. Flood mitigation phase starts

after the flood has occurred by identification of the changes in the river course due to flooding, status of flood control

works, river bank erosion, drainage congestion, flood hazard and risk vulnerability assessment .

Space Inputs for Flood Disaster Management

The most important element in

flood disaster management is the availability

of timely information for taking decisions

and actions by the authorities (Miranda

et al., 1988, Okamoto et al., 1998). NRSC

under the Disaster Management Support

(DMS) Programme of ISRO provides support

during the flood response phase by providing

flood based products like flood inundation

maps, damage statistics, flood progression,

flood recession and flood persistence to

state agencies. During the post disaster

phase monitors the flood situation, status

of the flood control works etc. Under the

mitigation phase studies towards flood

hazard zonation, flood risk and vulnerability and bank erosion are also carried out. Apart from this flood forecasting and

flood inundation simulation studies towards early warning are also initiated. Figure1 shows the different phases of flood

disaster management being addressed using remote sensing.

Early WarningThe Godavari river system is one of the major river systems in the country and one of the most flood-prone in

the Southern India. Heavy rains in the catchment of Godavari river during the first week of August, 2006 caused heavy

loss to lives and infrastructure in East Godavari, West Godavari and Khammam districts of Andhra Pradesh (India).

Flood ForecastingThough Central Water Commission (CWC) is the main nodal agency having the mandate to provide the flood

forecast, NRSC has collaborated with CWC for the development of medium-range Flood Forecast Model for the Godavari

Basin using space inputs (LU/LC, Soil, SRTM-DEM) and hydro-meteorological data through semi-distributed modeling

approach. Flood forecasting model is run by CWC during 2010 &11 using real time 3 hrs Hydro-meteorological data.

The discharge data generated as output would be very useful input for inundation simulation for developing Spatial

Early warning.

Fig. 1: Different phases of flood disaster management being addressed using remote sensing

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Flood Inundation SimulationFlood inundation simulation studies

for part of Sabari tributary from Konta to

Kunavaram (35 km) stretch in Godavari

River basin were carried out using discharge

data, land use/land cover and ALTM DTM

and the results were validated with satellite

data. Figure 2 shows the flowchart of the

methodology used for simulation of flood

inundation. The river geometry data extracted

using HEC-GeoRAS, together with the

discharge data and other parameters, spatial

flood modeling has been carried out for peak

flood situation using HEC-RAS, 1-D hydraulic

model. Hourly water level data was used to

generate different flood inundation patterns.

Flood inundation is simulated for the year

2010 and the maximum flood inundation

extent simulated by the model was compared

with the corresponding actual inundated

area obtained from Radarsat image. Figure 3

shows the comparison of the simulated and

observed flood extent. The results obtained

from the model for peak flood situation were

in well accordance with flood inundation

observed from corresponding satellite data.

Flood ResponseDecision Support Centre (DSC) keeps

a continuous watch on the flood situation

in the country through different sources.

Based on the cloud cover pattern observed

from meteorological satellite KALPANA-1,

rainfall pattern from Indian Meteorological

Department (IMD), hydrological data from

Central Water Commission (CWC), and

flood related information from websites,

news media and state departments satellite

data from various satellite sensor systems is

programmed. Presently emergency requests

are placed for programming of the Indian

satellites (Resourcesat-1 & 2, Cartosat-1

& 2 and RISAT-1 & 2) and as well as the foreign satellites (RADARSAT-1 & 2) for flood mapping

and monitoring and identification of embankment breaches. The extent of flood inundation is

extracted from the satellite data and flood maps at various scales i.e. state, district and detailed levels

are prepared for the flood affected states. The spatial inundation maps along with estimates on

Fig. 2: F lowchart of the methodology used for s imulat ion of flood inundation

Fig. 3: Comparison of the simulated and observed flood extent

83

submergence are generated within 5-6 hours after receiving raw satellite data product and disseminated to the State

and Central Governments and user departments through a VSAT based satellite communication network. In addition

to the floodmaps, the flood inundation layer in GIS compatible format are also transmitted to the state remote sensing

application centres for further value addition and dissemination as per the requirements of the user/line departments.

Successful operational use of remote sensing technology for near real time flood mapping and monitoring was done

for the Bihar floods of 2008, Andhra Pradesh floods of 2009, Uttar Pradesh floods of 2010, Orissa floods of 2011 and

the for recent Assam floods of 2012.

Assam Floods 2012During June, 2012, Assam State witnessed one of the devastating floods since 1988, due to high flood levels in

Brahmaputra river and its tributaries causing huge loss of human lives, cattle and infrastructure. Decision Support Centre (DSC)

kept a close watch on the flood situation. All

efforts are made to acquire the satellite data over

flood affected areas in Assam. In addition, DSC

also activated International Charter “Space and

Major Disasters” for frequent observations over

the flood affected areas.The satellite datasets

were analysed and flood inundation layer was

extracted. Flood maps and flood inundated area

statistics were generated and provided to State

& Central Government Departments.

Overview of Flood SituationDSC has acquired and analysed

satellite data of 27, 29 & 30 of June and

2 July 2012 and provided the overview

of the flood situation to the concerned.

Figure 4 shows the flood affected areas in

various districts of Assam State during 27-Jun

to 02-Jul, 2012. It was observed that about

4.65 lakh hectares of area was inundated.

Major flood inundation was observed in

the districts of Nowgong, Cachar, Marigaon,

Sonitpur, Marigaon, Lakhimpur, Barpeta,

Kamrup (Rura l ) , Jorhat, Kar imganj ,

Golaghat, Sibsagar, Dhemaji, Dibrugarh and

Darrang districts.

Affected Transport NetworkDue to the severity of the floods,

roads were damaged at several places. About

426 km length of major roads in Assam state

was observed to be affected by these floods as

on July 02, 2012. Significant damages to railway

track due to these floods were reported. About Fig. 5: Indian remote Sensing satellites, RISAT-1 & 2 showing the flood situation and the affected railway track in parts of Dibrugarh district

Fig. 4: Flood inundated areas in Assam State during 27-Jun to 02-Jul, 2012

Airport

Area covered by satellite

Flood Inundation

River bank

Normal river / Water bodies

Other Major Roads

National Highway

Railway

State boundary

District Hq

Settlement

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

388 km length of railway track in Assam state was observed to be affected by these floods as on July 02, 2012.

Figure 5 shows the affected railway track in parts of Dibrugarh district as observed by Indian microwave

remote sensing satellites, RISAT-1 & 2.

Marooned VillagesThe flood waters marooned several villages in the State and many villages were surrounded

by flood waters for several days. About 3829 villages were observed to be affected by flooding as on

July 02, 2012. In Nowgong district about 312 villages were affected by floods upto 02-July, 2012. It

is observed that about 77 villages are marooned for more than 6 days, 85 villages for 4-5 days and 75

villages for 1-3 days. Recession in flood was also observed in 75 villages in Nowgong district.

Flood Situation at Kaziranga National Park

Kaziranga National Park is one of

the most severely affected areas in Assam

during these floods. Loss of life of several

wild animals was reported. Figure 6 shows

the temporal changes in flood inundation

around Kaziranga National Park area during

June 27-30, 2012 and detailed view of

flood inundation around Kaziranga National

Park area (Figure 7). Recession in flood

inundation is observed in some parts during

June 27-30, 2012.

Flood MitigationTowards flood mitigation DSC has

carried out flood hazard zonation, flood risk

and vulnerability and bank erosion studies.

Flood Hazard Zonation (FHZ) is one of the

most important non-structural measures,

which facilitates appropriate regulation, and

development of floodplains thereby reducing

the flood impact. Flood hazard zonation maps

for Assam state are prepared, whereas for

Bihar state the flood hazard maps are being

validated and the atlas is under preparation.

Flood Hazard Zonation for Assam

A flood hazard zonation atlas is prepared based on the analysis of 93 satellite datasets acquired

during 1998-2007 flood season over Assam region and the atlas has been released (Figure 8). The flood

inundation layers generated from the analysis of the satellite data for different flood waves in a calendar

year were integrated in GIS environment to generate the maximum flood inundation extent observed

in that year. The maximum flood inundation layers corresponding to various years (1998-2007) were

integrated for assessing the frequency of inundation and subsequent generation of flood hazard layer.

Fig. 6: Satellite images showing temporal changes in flood inundation around Kaziranga National Park area during June 27-30, 2012

Fig. 7: Resourcesat-2 image showing the detailed view of flood inundation around Kaziranga National Park area as on July 01, 2012

85

The flood hazard has been classified into five

categories based on frequency of inundation

(Table 1). ‘Very Low’ category indicates the

areas which are inundated once or twice

during the 10-year period. Similarly, ‘Low’

indicates three to four times, ‘Moderate’

indicates five to six times, ‘High’ indicates

seven to eight times and ‘Very High’ indicates

the areas, which are regularly subjected to

inundation. Area under each category was

estimated and flood hazard maps at state and

district level were prepared. Further, cropped

area (from land use) was also integrated with

flood hazard layer to assess the impact. The

statistics for cropped area affected under each

hazard category was computed.

Table 1: Flood hazard area under various categories

Sl No Hazard Severity

FloodHazard Area (ha)

% Flood Hazard(with respectto StateGeographic Area)

% Flood Hazard(with respect toTotal FloodHazard Area)

Crop Area UnderDifferent FloodHazardCategories (ha)

1. Very High 1,28,687 1.64 5.79 83488

2. High 2,24,629 3.86 10.11 168802

3. Moderate 3,51,667 4.48 15.83 270558

4. Low 4,91,761 6.27 22.14 351356

5. Very Low 10,24,584 13.06 46.13 621367

Total 22,21,328 28.31 100.00 14,95,571

It is observed from the analysis that about 22.21 lakh hectares of land constituting about 28.31% of Total

Geographical Area (TGA) of Assam state is affected by flooding (Table 1). Out of the total flood affected area of

22.21 lakh hectares, about 1.28 lakh hectares of land falls under very high flood hazard category. This area is observed

to be continuously under submergence during the last ten years period. About 2.24 lakh hectares of land falls under

high flood hazard category, indicating that this area has been subjected to flooding for about 7-8 times during last ten

years. Area falling under moderate flood hazard category (area subjected to inundation 5-6 times during last ten years

period) is estimated to be about 3.51 lakh hectares, constituting about 4.48% of TGA of Assam state.

Development of Village-Wise Flood Risk Index MapVillage flood risk index map for Nagaon district has been generated using the flood hazard layer as primary input.

About 50 satellite datasets, optical (IRS) and microwave (Radarsat), acquired during the last 10-year period (1998–2007)

during the flood season have been analysed to extract flood inundation layer and generate composite flood hazard

layer. Flood hazard layer is considered as the primary input and is integrated with land use/ land cover, infrastructure

and population data and weightages are assigned to each class. Based on this, village flood risk index map for Nagaon

district has been generated. Figure 9 shows the flow chart of the methodology adopted for generating village-wise

flood risk index. The results of analyses indicate that about 267 villages are in the moderate–high risk index zone. About

35,354 ha of the district is in high flood hazard zone and about 25,281 ha of crop area is affected annually.

Fig. 8: Flood hazard zonation map for Assam state

Settlement

District Hq

District Road

National Highway

Railway

Village boundary

Taluk boundary

District boundaryVery low

River/Water bodies

Very High

High

Moderate

Low

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Bank Erosion StudiesBank erosion is one of the most

commonly experienced problems especially

in the Brahmaputra basin. Rate of erosion in

the Brahmaputra catchment, 953 ton per sq

km per year, is the highest in any catchment

system in the whole world (Venkatachary

et.al., 2001). Resourcesat-1 LISS-III satellite

data having a spatial resolution of 23 m

acquired during 2002 and 2010 were used

for the bank erosion and deposition study in

the Brahmaputra and Barak Rivers in Assam.

The satellite datasets of 2002 and 2010 were

geometrically rectified to the master base

map for positional accuracy. The individual

satellite data sets were mosaiced for the

individual years of 2002 and 2010. The

banklines of the rivers for the year 2002 and

2010 were delineated. Both the banklines

were intersected to identify and estimate the

amount of erosion and deposition at different

pockets along the main Brahmaputra and

Barak river stretch. Figure 10 shows the

pockets where bank erosion and deposition

has taken place from the comparison of the

satellite images acquired during 2002 and

2010 for part of Assam.

Conclusion

Space technology has emerged as the most

powerful tool for decision making in flood

disaster management. The technology can

help in disaster identification, response

prioritisation, damage assessment, inundation

monitoring, river course changes and

identification of vulnerable zones required for

flood disaster management. The near realtime

information provided during flood disaster

can be of immense help to decision makers

to evolve risk reduction strategies. Remote

sensing technology due to its repetitive and

synoptic coverage, cost-effectiveness prove

to be a viable tool for monitoring the river shifting, proximity of river course to the embankments

and channel aggradation by the planners for taking measures required for channel stabilization and

strengthening of embankments.

Fig. 10: Bank erosion and deposition delineated from satellite images in parts of Assam

Fig. 9: Flow chart of the methodology adopted for generating village-wise flood risk index

Planning and acquisition of satellite data during foods

Geo-rectification of satellite data

Land use/Land coverAgricultural land = 1(kharif and double crop)Other land use = 3

PopulationPopulation > 3000 = 1Population 1001-3000 = 0.6Population ≤ 1000 = 0.3

Transport networkNational and state highways = 1District roads = 0.6Other roads = 0.3

Hazard indexHigh road hazard = 1Moderate road hazard = 0.6Low road hazard = 0.3

Integration

Integration

Integration withvillage layer

Flood layers1998-2007

Flood layers1998-2007

Flood inundation layer

Land usevulnarability

Land usevulnarability index

(LVI)

Infrastructurevulnarability index

(IVI)

Populationvulnarability index

(PVI)

Vulnarability index (VI)

=LVI + IVI + PVI

Vulnarability index (VI)

Village-wise flood rish index (VFRI) = hazard index x

Vulnarability index

Hazard index

Infrastructurevulnarability

Populationvulnarability

Village layer

Pre-flood river and water bodies

Pre-flood river and water bodies

Extraction of vector layer

87

ReferencesAgarwal, A. and Sunita, N. (1991). Floods, floodplains and environmental myths. State of Indian environment: A citizen

report, Centre for Science and Environment, New Delhi.

Gopalakrishnan, M. (2002). Central Water Commission. The International Seminar on Disaster Preparedness and Mitigation,

21 November 2002 New Delhi.

Miranda, F.P., Fonseca, L. and Carr, J.R., (1988). Semivariogram textural classification of JERS-1 SAR data obtained over

a flooded area of the Amazon rainforest. International Journal of Remote Sensing, 19, pp. 549–556.

NDMA, (2008). National Disaster Management Guidelines: Management of Floods

National Flood Commission Report (Rashtriya Barh Ayog), 1980

Okamoto, K., Yamakawa, S. and Kawashima, H. (1998). Estimation of flood damage to rice production in North Korea

in 1995. International Journal of Remote Sensing, 19, pp.365–367.

Venkatachary, K.V., Bandhypadhyay, K., Bhanumurthy, V., Rao, G.S., Sudhakar, S., Pal, D.K., Das, R.K., Sarma, U., Manikiran, B.,

Rani, H.C.M. and Srivastava, S.K. (2001). Defining a space-based disaster management system for floods: A case study

for damage assessment due to 1998 Brahmaputra floods. Current Science. 80 3, 369-377

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

GEOSPATIAL AND HYDRO-METAPPROACH FOR FLOOD MANAGEMENT IN ASSAMDiganta Barman, Kundu SS, Jonali Goswami, Ranjit Das, Singh NGR, Arup Borgohain, Rekha Bharali Gogoi, Victor Saikhom, Suranjana B Bora and Sudhakar S North Eastern Space Applications Centre (NESAC)Umiam, Shillong, Meghalaya - 793 103, IndiaEmail: diganta_isro1@yahoo.co.in

IntroductionFlood is a chronic disaster occurring almost every year in the state of Assam. Along

with structural measures like the construction of embankment, non structural measures

like flood forecasting, flood plain zonation and regulation etc in recent past has gained

importance among researchers, technocrats and policy makers. In this context an attempt

has been made by North Eastern Space Applications Centre (NESAC) on a request from

Govt. of Assam to mitigate the flood damage by developing an operational flood warning

system for the state by using geospatial technology coupled with established relationships

among important hydro-meteorological parameters. Thus this exercise which was started

with Lakhimpur district, in upper Assam, as a pilot study in 2009 has been able to cover 14

flood prone districts in Brahmaputra valley and three south Assam districts in Barak valley.

Hydrological events require extensive datasets to understand its highly dynamic nature.

However, the scarcity in gauge data has been a challenge to the scientific community. So far,

this limitation has been addressed by various hydrological models attributing the hydrometry

and morphometry of the catchment. The strength of hydro-meteorological modeling in

geo-spatial domain for flood forecasting has been put to operational use covering such large

study area for the first time in any flood prone state of India.

ObjectivesThe study has been taken up to achieve following short and long term objectives

(1) Issue of alert for possible flood situation in District/ Revenue Circle/ Village level with

best possible lead time. (Operational).

(2) Submission of annual periodic report on post-flood status of existing flood protection

embankments in district level (Operational).

(3) Development of optimum methodology for rainfall prediction from satellite based

weather monitoring and numerical weather prediction models supported by insitu

ground data (Research).

(4) Development of river specific rainfall-runoff models for forecasting of flood (Research).

(5) Development of inundation simulation for flood plain zonation (Research).

MethodologyThe overall exercise of FLEWS (Flood Early Warning System) has got different multi-

disciplinary components under both operational as well as research objectives. Following

flow chart explains the operation work flow

leading to issue of early warning of flood. This

work flow comprises of a series of analytical

exercises involving hydro-meteorological

modeling along with real time monitoring

of critical hydro-meteorological observations

as input for decision support in issuing flood

warnings. The prominent components are

briefly explained in Figure 1.

T h e m e t e o r o l o g i c a l Component

This component of the FLEWS

workflow basically deals with two sub-

components viz Near real time weather

watch with daily collection insitu rainfall

reading from various sources like Automatic

Weather Stations, Automatic Rain Gauges

etc and Running of Numerical weather

prediction model such as Weather Research

Forecast (WRF) with 3 hourly rainfall forecasts

in grids of various spatial resolution such as

27 km x 27 km, 9 km x 9 km, 3 km x 3 km

etc. that goes as the daily input to the

run-off model. The MM5 and ARPS (Advanced

Regional Prediction System) models are also

being used to investigate their relative ability to

simulate different events, particularly intense

and localized rainfall events as shown in

Figure 2. The High Performance Computers

(HPC) at INCOIS, Hyderabad and at NARL,

Gadanki have been used through remote

access to operationally run the models during

the monsoon season. Moreover the Synoptic

weather analysis is being done to forecast

rainfall for the basins under study. Since many

of the basins under study is very small in size,

rainfall forecast is given at synoptic scale. The

forecasts are issued for four districts (for the

districts in eastern Assam, western Assam,

central Assam and Southern Assam) over the

NER. The preparation of synoptic weather

report involves studying the cloud properties

over and around the area, the prevailing

wind speed and direction at different

heights (pressure levels), the condition of

Fig. 1: FLEWS operational block

Fig. 2: WRF derived daily rainfall chart over the study area

Fig. 3: Synoptic weather monitoring block diagram

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3the atmosphere in terms of its ability to allow or suppress convection, etc. The overall flow chart for

preparation of synoptic scale rainfall forecast is shown in Figure 3.

The Hydrological ComponentThis component of the FLEWS workflow basically deals with two sub-components viz preparation

of all hydrology specific watershed layers such

as basin boundary, landuse, drainage, slope,

aspects, soil classification etc., leading to

calculation of various watershed parameters

such as time of concentration, co-efficient of

discharge etc., and generation of discharge

forecast with two approaches by using a

lumped and a distributed hydrological model

(Figure 4). Here the lumped approach

delivers the peak discharge forecast and

the distributed approach delivers a daily

hydrograph with single or multiple peaks.

The output of both the models together

finally leads to the decision to issue a flood

alert on the concerned river and the district.

The hydrological evaluations indicate that the average annual discharge of Ranganadi ranges from

72 to 163 cumecs (m3/sec) as based on observations (NEEPCO). The average monsoon discharge

of Subansiri for the year 2009 is 5745 cumecs (CWC) as observed at Chouldhuwaghat GD site. The

highest discharge of 18, 215 cumecs was recorded on 26th July.

High resolution hydrologic forecasts can provide information on flash flood situations;

however, an important question to consider in evaluating high resolution forecasts is whether or not

larger simulation uncertainties at smaller scales will diminish the utility of these forecasts, and, if so, to

what degree? As part of the FLEWS project, a “statistical-distributed hydrologic modeling” approach

on a fine grid scale is being developed to simulate flash flooding on small basins and account for

hydrologic modeling uncertainty. Hydrologic kinematic wave routing is adopted in parallel with the

existing Muskingum / Muskingum-Cunge method (Rozalis et al., 2010) in the model approach with an

objective to enhance our ability to predict the occurrence of flash flooding. The statistical-distributed

approach requires running a distributed model using archived and WRF-derived precipitation grids

at finer grid scales (less than 4 km) to derive flood probability characteristics of simulated flows for

all sub-watersheds in each grid cell in the distributed model. When running the distributed model in

forecast mode, the flooding flow threshold for each grid cell is defined in terms of a flood probability

level rather than an absolute value of flow (Yatheendradas et al., 2008). A hydrological model used

for flash flood modeling and prediction is inevitably an abstraction of reality.

The Post Flood River Embankment MonitoringThis component of FLEWS project deals with analysis of high resolution satellite data on an

annual basis after the completion of flood season in order to identify existing breaches in various

embankments created during the completed flood season and to be reported to the concerned

authority/ department to enable them to take corrective actions for the next season. An example of

river embankment breach is shown in Figure 5.

Fig. 4: FLEWS rainfall to runoff conversion component

91

Results and ConclusionThis project has been taken up on the joint

request of Assam State Disaster Management

Authority (ASDMA), Government of Assam

and the North Eastern Council (NEC), Ministry

of DONER (Development of North Eastern

Region), Government of India. Based on the

initial success achieved so far, fresh request

has come from Government of Assam for

implementing this project in all the flood

prone districts of Assam in phases. Based

on the above described operational work

components, flood warnings has been issued

to concerned district authorities several times

during 2010, 2011 monsoon seasons in

Assam with an average lead time of 12 to 18

hours. Majority of the significant flood events during these two years have been successfully forecasted and encouraging

feedback have been received from the user. By the year 2012, 14 severely flood prone districts of Assam covering 60

odd rivers, tributaries and rivulets have been brought under this project.

At the behest of NDMA, New Delhi states like Bihar and West Bengal have also officially corresponded to NESAC for

exploring avenues for joint collaboration for their respective states in the field of flood management. Due to the gradually

improving performance of FLEWS, in 2012 this project has been selected for professional documentation under “Good

governance initiative” by Department of Administrative Reforms & Public Grievances under Ministry of Personnel, Public

Grievances & Pensions, Govt. of India. Thus the FLEWS project has been able to start a holistic approach to look at the

overall flood management scenario in North Eastern Region of India.

ReferencesRozalis, Morin, Yair, price. (2010). “Flash flood prediction using an uncalibrated hydrological model and radar rainfall

data in a Mediterranean watershed under changing hydrological condition” Journal of Hydrology, 314 p. 245 – 255.

Yatheendradas, Wagener, Gupta, Unkrich, Goodrich, Scaffner, Stewart (2008).“ Understanding uncertainty in distributed

flash flood forecasting for semiarid regions” Water Resources Research, AGU, Vol 44, Issue 5,

Fig. 5: River embankment breach identified from Cartosat – I data

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

AGRICULTURAL DROUGHT: ASSESSMENT & MONITORING

Sesha Sai MVR, Murthy CS, Chandrasekar K, Mohammed Ahamed J and Prabir Kumar Das Agricultural Sciences & Applications GroupNational Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: seshasai_mvr@nrsc.gov.in

IntroductionDrought is a climatic anomaly characterized by deficient supply of moisture

resulting either from sub-normal rainfall, erratic rainfall distribution, higher water need or

a combination of all the three factors. Agricultural drought is a situation when rainfall and

soil moisture are inadequate during the crop season to support the timely cultural practices

and healthy crop growth from sowing to harvesting.

Drought results from adverse climatic conditions leading to deleterious impacts

on various sectors of the economy. The immediate impact of drought is on crop area, crop

production and farm employment. Less than normal crop sown area, delayed time of crop

sowing, poor crop growth caused by inadequate soil moisture availability ultimately lead to

decrease in crop yield or crop production.

Impacts of droughts on crop productivity are more intense in low rainfall regions

than in regions receiving higher quantum of rainfall. Droughts have a multiplier effect on

agricultural production during the subsequent year also due to non-availability of quality

seeds for sowing of crops, reduced use of inputs like fertilizers as the investment capacity of

the farmers’ decline, non-availability of raw material in agro-based industries etc.

Drought Occurrence in India

On an average, severe drought occurs once in five years in most of the tropical

countries, though often they occur on successive years causing misery to human life and

livestock. About two thirds of the geographic area of India receives low rainfall (<1000 mm),

which is also characterized by uneven and erratic distributions. Out of net sown area of

140 mh about 68% is reported to be vulnerable to drought conditions and about 50% of

such vulnerable area is classified as ‘severe’, where frequency of drought is almost regular.

India experiences localized drought almost every year in some region or other. In the post-

independence era, major droughts that affected more than 1/3rd of the country were reported

during 1951, 1966-67, 1972, 1979, 1987-88 and 2002-03 (Subbaih, 2004).

Drought Occurrence – Global Scenario

Nearly 50 per cent of the world’s most populated areas are highly vulnerable to

drought (USDA 1994). In the world’s two largest agricultural producers, the United States

and the former Soviet Union, drought occurs almost every year. More than 500 million

people live in the drought prone areas of the world and 30% of the entire continental

surface is affected by droughts or desertification process. In 2004, widespread drought in much of Asia resulted in loss

of significant agricultural production.

Meteorological DroughtNormal rainfall during south west monsoon season is 890 mm for the country as a whole and 611 mm

in north-west India, 994 mm in central India, 723 mm in southern India and 1427 mm in north east India. Out of

890 mm of normal rainfall in the country, June month accounts for 162 mm followed by 293 mm in July, 262 mm in

August and 175 mm in September (www.imd.gov.in). About 70% of the annual rainfall over India is contributed by

south west monsoon which commences in the month of May from the southern tip. The normal duration is roughly

100 days and its withdrawal starts from Punjab and Rajasthan by mid of September.

The onset of north east monsoon is not well defined and a general understanding is that the rainfall in the winter

months from October to December represents the northeast monsoon. Tamil Nadu state receives significant rainfall from

north east monsoon, and it is about 463 mm representing 48% of annual rainfall of the state (www.imd.gov.in).

Rainfall is the most important single factor influencing the incidence of drought and practically all definitions use

this variable either singly or in combination with other meteorological elements. Many studies have analysed the nature

and frequency of droughts based on simple relation between actual and average rainfall. Based on rainfall, temperature,

soil moisture and evaporation, various indicators of meteorological drought like Palmer’s index, Standardized Precipitation

Index, Crop Moisture Index etc. have been developed.

The India Meteorological Department (IMD) prepares rainfall maps on sub-divisional basis every week throughout

the year. These maps show the rainfall received during a week and corresponding departures from normal. During

monsoon season, these maps are indicative of development of drought. In addition, IMD also provides the information

on weekly rainfall and its deviation from normal at district level for the entire country. This data is useful to identify the

districts with deficit/scanty rainfall and the prevailing meteorological drought.

IMD also monitors drought using climatic water balance technique. The aridity index is calculated as a fraction

of water deficit/water need. The departure of aridity index from normal percentage terms is used to define the drought

severity. Anomaly upto 25% is attributed to mild drought, 26-50% to moderate drought and >50% to severe drought.

IMD has been bringing out weekly aridity anomaly charts, which show the departures of actual aridity from normal aridity

giving indication of the severity of water deficit to water demand relationship on weekly basis.

Monsoon and Tele-connections

The variability of summer monsoon rainfall in the country has been found to be closely linked to the variations

in Sea Surface Temperature (SST) over the equatorial Pacific and Indian Oceans (Gadgil et al., 2003). El-Nino Southern

Oscillation Index (ENSO) a climatic phenomenon signifying temperature and pressure patterns in the Peruvian coast of

South America and the central Pacific along the South American coast. Warm phase of ENSO called El-Nino is found

to be associated with reduced summer monsoon rainfall over India. Rajeevan and Pai (2006) indicated that most of the

severe droughts over India are associated with El-Nino events. But all the El-Nino years have not resulted in drought

occurrence. In addition to El Nino, Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINO) also

influences the summer monsoon rainfall in India. Severe droughts during 1958-2003 were associated with unfavorable

phases of either ENSO or EQUINO phenomenon. Negative phases of ENSO and EQUINO were evident in 2002. Such a

phenomenon was also observed in June 2009 but with lesser magnitude compared to 2002. The rainfall deficiency in

June 2009 was related to suppression of convection over Bay of Bengal with unfavourable SST gradient between Bay

(more cooler) and Eastern Equatorial Indian Ocean (EEIO). A similar suppression had also occurred in 1995 resulting in

a larger deficit rainfall in the country.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3The effect of these ocean variations is transmitted to remote areas of the globe through

recurrent, seasonally varying patterns of atmospheric circulation anomalies referred to as tele-

connections. These tele-connections affect the precipitation regime over much of the Tropics.

Observational studies and model experiments have also demonstrated a significant link between

Atlantic sea surface temperatures and precipitation over the drought-prone areas of the African Sahel

and northeast Brazil.

Agricultural Drought Agricultural drought results from the complex and nonlinear interactions between weather,

soil, crop and human actions and hence, the assessment of the intensity of agricultural drought

continues to be a challenging task for researchers, drought managers and policy makers. Meteorological

drought is measured by rainfall data recorded by weather stations and the hydrological drought is

assessed by inflows into the surface water bodies measured through gauging points. But, agricultural

drought assessment could not be accomplished by such direct and quantitative measurements. It

requires the quantitative information related to soil moisture, planting pattern and crop condition

along with their inter-relations which is not adequately provided through manual surveys or sparsely

located measuring stations.

The intensity of agricultural drought is largely determined by the crop growing environment

- diversity of crops grown in a given location, soil variability, rainfall variability etc. Since the water

requirement of a crop varies during different phenol-phases of growth, the impact of drought depends

on the crop growth stage. Central Arid Zone Research Institute (CAZRI) attempted classification of

agricultural drought considering the values of Actual Evaporation/Potential Evaporation (AE/PE) during

different phenol phases of crop growth – seedling, vegetative and reproductive which was later

improved with crop factor (Sastri et al., 1981).

Agriculture departments of different states collect information on crop sown areas, crop

development, pests and diseases occurrence etc. to assess the drought situation. A special task force

known as ‘Crop Weather Watch Group’ is constituted by Ministry of Agriculture, Government of

India. This group reviews the progress of monsoons, crop situations, water levels in reservoirs/dams,

availability of fertilizers etc.

Agricultural Drought Assessment – Geospatial Approach

Dynamic nature of droughts with complex phenomenon having multiple effects is a major

challenge in planning, monitoring, predicting, assessing impact and offering solutions to drought hit

areas. Because of these complexities, high quality data and improved tools are needed to capture the

spatial and temporal dimensions of drought intensity. Unlike point observations of ground data, satellite

sensors provide direct spatial information on vegetation stress caused by drought conditions. Satellite

remote sensing technology is widely used for monitoring crops and agricultural drought assessment.

Over the last 20 years, coarse resolution satellite sensors are being used routinely to monitor vegetation

and detect the impact of moisture stress on vegetation. The NOAA AVHRR NDVI has been extensively

used for drought/vegetation monitoring, detection of drought and crop yield estimation (Batista

et al., 1997, Beneditte and Rossini, 1993, Moulin et al., 1998 and Tucker et al., 1985).

United States Agency for International Development has evolved a Famine Early Warning

System NetWork (FEWSNET) by integrating the composite information on temperature, winds, humidity,

soil and topography, observations on conflict, civil interest, health, market prices, field observations 95

on agriculture, satellite derived rainfall and NDVI. FEWSNET is operationally issuing monthly food security reports for

decision makers in Africa and USA (http://ltpwww.gsfc. nasa.gov).

The Drought Monitor of USA using NOAA-AVHRR data (www.cpc.ncep.noaa.gov), Golbal Information and Early

Warning System (GIEWS) and Advanced Real Time Environmental Monitoring Information System (ARTEMIS) of FAO

using Meteosat derived cold cloud duration and SPOT – VGT derived decadal NDVI composites (Minamiguchi, 2005),

International Water Management Institute (IWMI)’s drought assessment in South west Asia using MODIS data (Thenkabail,

2004) and NADAMS drought monitoring in India with IRS–WiFS/AWiFS and NOAAAVHRR (Murthy et al., 2007) data

are the proven examples for successful application of satellite remote sensing for operational drought assessment

Central Research Institute for Dry Land Agriculture (CRIDA), Hyderabad, provides information on drought

conditions and their mitigations measures during the season. The weekly contingent crop plans for rain-fed regions over the

country during the crop growing seasons and at fortnightly interval in the rest of the period are prepared from the inputs

provided by the centers of both the coordinating projects and the general weather situations prevailed over the period.

These are updated regularly in the website “Crop-weather-outlook” maintained by CRIDA. In addition, weather based

agro-advisories prepared from the forecast of National Centre for Medium Range Weather Forecasting (NCMRWF) and

crop condition are also updated regularly in the above mentioned website to meet any aberrant weather conditions.

National Agricultural Drought Assessment and Monitoring System (NADAMS)

In India, National Agricultural Drought Assessment and Monitoring System (NADAMS) was initiated towards

the end of 1986, with the participation of National Remote Sensing Agency, Dept. of Space, Government of India, as

nodal agency for execution, with the support of India Meteorological Department (IMD) and various state departments

of agriculture. NADAMS was made operational in 1990 and has been providing agricultural drought information in

terms of prevalence, severity and persistence at state, district and sub-district level. Over a period of time, NADAMS

project has undergone many methodological improvements such as use of moderate resolution data for disaggregated

level assessment, use of multiple indices for drought assessment, augmentation of ground data bases, achieving synergy

between ground observations and satellite based interpretation, providing user friendly information, enhanced frequency

of information etc.

Monitoring of agricultural drought is limited to kharif season (June-Oct/November), since this season is

agriculturally more important and rainfall dependent. Resourcesat-1 / Resourcesat-2 AWiFS based sub-district level

agricultural drought assessment is carried-out in 4 states namely Andhra Pradesh, Karnataka, Maharashtra and Haryana.

AVHRR/MODIS based district level assessment is carried out in 9 states namely Bihar, Chhattisgarh, Gujarat, Madhya

Pradesh, Orissa, Rajasthan, Uttar Pradesh, Jharkhand and Tamil Nadu. Oceansat-2 OCM data was brought into operational

use in kharif 2011 by generating fortnightly and monthly Rayleigh corrected TOA NDVI composites for all the 13 states.

MODIS 250m derived NDVI and NDWI are also used for assessment. Coarse resolution products of soil moisture from

passive microwave data, rainfall estimates and rainfall forecasts are also used in the assessment. The Area Favourable for

Crop Sowing/crop sown area (AFCS), derived from Shortwave Angle Slope Index (SASI) and ancillary data is a recently

added product in NADAMS project. This product is useful to monitor the sowing period drought conditions. Overview of

NADAMS project is shown in Figure 1. From kharif 2012, NADAMS project is being carriedout by Mahalanobis National

Crop Forecast Centre (MNCFC), Department of Agriculture and Cooperation, Ministry of Agriculture, with the technical

support of NRSC. The satellite derived crop indices that are widely used in NADAMS project- NDVI, NDWI and SASI- are

described in subsequent sections.

Normalized Difference Vegetation Index (NDVI)

Among the various vegetation indices, Normalized Difference Vegetation Index (NDVI) is widely used for

operational drought assessment because of its simplicity in calculation, easy to interpret and its ability to partially

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3compensate for the effects of atmosphere, illumination geometry etc. (Malingreau 1986, Tucker

and Chowdhary 1987, Jhonson, et al., 1993). NDVI is a transformation of reflected radiation in the

visible and near infrared bands of a sensor system and is a function of green leaf area and biomass.

Computation of NDVI is given by:

NDVI = (NIR reflectance – Red reflectance) / (NIR reflectance + Red reflectance)

NDWI= (NIR reflectance – SWIR reflectance) / (NIR reflectance + SWIR reflectance)

The severity of drought situation is assessed by the extent of NDVI deviation from its long

term mean. Maps produced using relative greenness are quite useful to assess drought situation and

hence this indicator is being used widely (Johnson et al., 1993).

NDVI shows a lag correlation with rainfall and aridity anomaly. The lag time is about 2-4 weeks.

However, the correlation is not unique either through the season or between the areas. The rainfall

use efficiency varies in time and space making direct satellite monitoring of vegetation development

essential for reliable and objective monitoring of agricultural drought. However, conjunctive use of

rainfall/aridity anomaly and VI provides greater reliability.

The general crop growing period with regard to beginning, peak growth stage and senescence

can be identified through the seasonal NDVI profile. However, NDVI can be an indicator of crop

development/condition only after significant spectral emergence of crops, which occurs at about

2-4 weeks after sowings/transplantations.

Normalized Difference Water Index (NDWI)

Normalised Difference Water Index (NDWI) is derived from two bands including a moisture

sensitive SWIR band and insensitive NIR band (Gao 1996). In the beginning of the cropping season, when

soil back ground is dominant, SWIR is sensitive to surface wetness of top soil. As the crop progresses,

SWIR becomes sensitive to leaf moisture content. When the crop is grown up, the response in SWIR

band is mostly from canopy and not from the underlying soil. Indices based on the reflectance of

Shortwave Infrared (SWIR) bands have been found to be sensitive to moisture available in soil as well as

in crop canopy (Tucker and Choudhary 1987, Wang et al., 2008). SWIR band has got no penetrating

capability. It provides only surface information. When the crop is grown-up, SWIR response is only

from canopy and not from the underlying soil. NDWI using SWIR can complement NDVI for drought

assessment particularly in the beginning of the season. Computation of NDWI is given by:

NDWI has been a popular index for crop stress detection and for monitoring moisture condition

of crop/vegetation canopies over larger areas (Fensholt and Sandholt 2003, Jackson et al., 2004, Maki

et al., 2004, Chen et al., 2005, Gu et al., 2007). The response of NDWI to moisture is instantaneous

without any time lag. NDWI is more sensitive to both desiccation and wilting. NDVI is more sensitive

to canopy chlorophyll changes and tend to saturate at high biomass levels. In view of the limitations

associated with individual indicators either NDVI or NDWI, combination of both the indicators may

provide a robust approach for drought monitoring. Combination approach would amplify the anomalies

and become more responsive to the ground agricultural situation. Gu et al., 2007, combined NDVI

and NDWI to form Normalised Difference Drought Index (NDDI), which was found to be more sensitive

to drought conditions over grass lands. Combined use of NDVI and NDWI temporal anomalies has

better delineated the rice transplanting areas in China (Xiao et al., 2002). 97

Short-wave Angle Slope Index (SASI)

A new index - Shortwave Angle Slope Index (SASI), adopted from Khanna et al., (2007), is being used in NADAMS

project from kharif 2010 for generating a geo-spatial product called Area Favourable for Crop Sowing/crop sown area

(AFCS). This product provides the estimates of the potential kharif area that is either favourable for sowing or already

sown, progressively from June to August, at state level. Such crop sowing related information is a direct indicator of the

impact of early-season agricultural drought.

MODIS 500m 8-day composite images of NIR, SWIR 1 and SWIR 2 spectral bands are being used for computing

the index. SASI is very sensitive to surface wetness. SASI is calculated as product of the angle at SWIR1 (βSWIR1) and

the inclination of the line that passes through the NIR and SWIR2 of a triangle with vertices at R (NIR), R (SWIR1) and

R (SWIR2), where R is the reflectance at broad bands. The slope of line, (RSWIR2−RNIR) / (λSWIR2−λNIR) can be approximated

by the difference of the reflectance at NIR and SWIR2 since the wavelength difference between the two vertices is

constant. An advantage of angle indexes is that they are relatively insensitive to albedo differences for comparison

between spectra.

Prabir et al., (2011) demonstrated that weekly SASI profiles at district level revealed seasonality and strong

association with rainfall and sown area pattern.

In the absence of operational procedures for nearreal-time soil moisture estimation, SASI can act as surrogate

parameter to draw inferences on the commencement and progression of crop sowings and to characterize the agricultural

situation in the early part of the season.

Using multiple criteria – SASI, soil texture, rice/non-rice areas, Soil Moisture Index derived spatial soil water

balance, a procedure has been evolved to generate spatial product on crop sowing favourable area or already cropped

area in the season. This product called Area Favourable for Crop Sowing (AFCS), has been generated on fortnightly or

monthly scale from June-September. After validation with state level crop sown area statistics reported by respective

agriculture departments, the product is generated and used operationally from kharif 2010. The cloud covered pixels in

SASI images are resolved with the support of rainfall and water balance derived soil moisture index.

Extending SASI for rabi season monitoring, Murthy et al., 2012, mapped spatial patterns of surface wetness

in the transplantation period of rabi season of recent 10 years (2002-03 to 2011-12) in a rice dominant irrigated

command area.

OCM 2 NDVI

Ocean Colour Monitor (OCM) sensor onboard Oceansat-2 satellite provides data in eight spectral bands located

in blue, green, red and near Infrared regions of electromagnetic spectrum. The satellite covers entire India in two paths

with each path data being available on alternate days. Images of large swath (1400 km) of either of the two paths

are available every day. Although the satellite is mainly meant for Ocean applications, certain features of the data like

wide swath, high temporal repitivity and availability of visible and infrared channels data make it equally useful for land

applications particularly for agricultural drought monitoring. Keeping in view the advantages, OCM data is being used

operationally in NADAMS project, from kharif 2011.

The cloud free data from all the overpass scenes during the season have been procured. After geometric

correction of each scene, the radiance data is corrected for Rayleigh scattering using the data of sun and sensor zenith

and azimuth angles. Top-of-the-atmosphere (TOA) reflectance is generated for each and cloud masking is done using

the data of green, red and NIR bands. NDVI is generated with cloud masked TOA data of band 6 (red) and NIR (band 8)

for each overpass date. Fortnightly and monthly time composites of Rayleigh corrected TOA NDVI are generated using

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3MVC approach to minimize cloud cover in the NDVI. NDVI anomaly maps have been generated on

fortnightly/monthly scales.

Narrow spectral band width of OCM images causes saturation problem, particularly of red

reflectance,well before the crop reaches the maximum greenness. As a result NDVI tends to saturate

and reaches its maximum value by the end of August or first fortnight of September. Therefore, OCM2

NDVI anomalies need to be interpreted with caution during September/October months.

Soil Moisture Index (SMI) from Soil Water Balance (SWB) Model

A simple book keeping – bucket type – water tight model was developed to derive the top

30 cm profile soil moisture. This model considers the initial root depth of 30 cm throughout the season

to capture the soil water scenario for crops sown and germinating during any part of the cropping

season. The soil water balance in the upper layer is governed by daily values of rainfall, runoff,

Evapo-Transpiration (ET) and drainage to the second layer. When the upper layer saturates in excess

of Field Capacity (FC) due to rainfall, the excess water percolates to the lower passive root zone and

are instantaneously redistributed in that zone. The excess soil water in the passive zone, moves out as

deep percolation. Since the upper 30 cm is considered for the soil water assessment the lower limit of

soil water is the residual water content of the soil as the upper layer is exposed to the atmosphere and

subjected to upward flux due to the direct solar radiation. The climatic, soil and crop parameter are the

main inputs for the SWB. The daily near real time TRMM 3B42RT spatial rainfall product and the daily

global potential evapo-transpiration data are used as the rainfall and climatic input, respectively. The

soil information was derived from the 1: 0.5 million scale NBSS&LUP soil map. Since this model does

not take into account the irrigation applied from various sources, the results of the model should be

considered over rainfed areas alone. The Soil Moisture Index (SMI) derived is defined as the proportion

of the difference between the current soil moisture (SM) and the Permanent Wilting Point (PW) to the

Field Capacity (FC) and the Permanent Wilting Point. The index values range from 0 to 100 with 0 as

extreme dry condition and 100 as extreme wet condition.

SMI = (SM-PW / FC-PW) x 100

Gridbased Vegetation Index (VI) Products

Historically, coarser resolution VI products have been evolved for quick assessment of

vegetation changes and drought detection over larger areas – countries and continents. The LAC

data of AVHRR has given rise to GAC data of 4 km resolution for global change detection studies.

The GIMMS data of 8 km resolution has been widely used across the globe for various applications

related to agriculture, hydrology and climate change. In NADAMS project, monthly composite 1 km

NDVI images derived from NOAA AVHRR, 1 km NDWI images derived from Terra MODIS data and

OCM2 NDVI were transformed to generate 5 km Grid images. Grid VI images were generated for each

month from June to November for historic years (2009, 2010 and 2011) also along with current year.

Monthly NDVI/NDWI anomalies for different months of kharif 2012 were also computed. These Grid

images are useful for rapid assessment of agricultural situation during each month. These NDVI / NDWI

deviation images are to be integrated with rainfall, soils, cropping pattern for effective interpretation

of the agricultural situation. The grid images are also useful as inputs in the modeling tasks such

hydrological, land surface and energy balance models. All the grid VI products, their anomalies and

interpretation were disseminated to user community through Bhuvan portal. 99

Agricultural Drought Assessment in

NADAMS Project

The assessment of agricultural

drought situation in each district / block

/ taluk takes in to consideration the

following factors; (1) seasonal NDVI / NDWI

progression – i.e transformation of NDVI /

NDWI from the beginning of the season,

(2) comparison of NDVI / NDWI profile with

previous normal years – relative deviation

and vegetation condition index, (3) weekly

rainfall status compared to normal and (4)

weekly progression of sown area. The relative

deviation of NDVI / NDWI from that of normal

and the rate of progression of NDVI / NDWI

from month to month gives the first level

indication about the agricultural situation

in the district. Ancillary data on rainfall,

soil moisture index, crop sown area, cropping pattern, irrigation support is anlaysed to attribute the VI anomalies to

agricultural drought situation. The ground data from different states has been organized in to a data base along with

satellite derived NDVI / NDWI data.

During June to August, the extent of crop sowing favourable area/crop area against normal kharif area in each

state is assessed using AFCS product derived from SASI, modeled soil moisture and other ancillary data. AFCS based

crop sown area progression is useful to detect the intensity of crop-sowing period drought.

During June to August, drought warning information is issued in terms of “Watch, Alert and Normal” categories.

In case of ‘Watch’, external intervention is required, if similar drought like conditions persist during the successive month,

while ‘Alert’ warning calls for immediate external intervention, in terms of crop contingency plans. During September

and October, based on NDVI anomalies corroborated by ground situation, drought declaration is done in terms of Mild,

Moderate and Severe drought.

The mandals/taluks of mild agricultural drought category are characterised by about 10-20% reduction in

NDVI and NDWI persistently for more than a month. The agricultural situation of this class represents one or more of

the attributes - slightly reduced crop sown area or slightly reduced vigour of crops leading to slight reduction (about

10%) in crop yield.

For moderate agricultural drought category, mandals/taluks are characterised by more than 20-30% reduction

in NDVI and NDWI persistently for more than a month. The agricultural situation of this class represents one or more

of the following attributes – more than a month delayed sowing time, more than 25-50% reduction in crop area, poor

greenness/moisture levels of crop vegetation, significant reduction in crop yield.

In case of mandals/taluks under severe agricultural drought category, there would be more than 30% reduction

in NDVI and NDWI persistently for more than a month. The agricultural situation of this class represents one or more

of the following attributes – more than a month delayed sowing time, more than 50% reduction in crop area, poor

greenness/moisture levels of crop vegetation, significant reduction in crop yield.

Fig. 1: Overview of National Agricultural Drought Assessment and Monitoring System (NADAMS)

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Monthly drought reports from June to November are being prepared and disseminated to the

Departments of Agriculture and Relief of different states in addition to the Department of Agriculture

and Cooperation, Ministry of Agriculture, Government of India. Whenever need arises, drought

information is also disseminated on fortnightly basis, subject to availability of cloud free satellite data.

Feedback received from the states indicates that the drought reports are being used as inputs in their

review meetings on agricultural situation. The agricultural drought information of NADAMS reports

are being used as inputs in the development of contingency plans and in relief management. It was

also found that there is good correlation between NDVI images of NADAMS and aridity maps being

provided by India Meteorological Department.

NADAMS Assessment in Kharif 2012

During kharif 2012, drought

assessment under NADAMS project was

carried-out jointly by NRSC and MNCFC.

Satellite data analysis and generation of

different products was done by MNCFC.

SASI and water balance derived

geospatial product Area Favourable for

Crop Sowing/ Crop sown area (AFCS) has

progressively increased – 44 mha in June,

87 mha in July, 98 mha in August and 106 mha

in September. The normal kharif crop area in

the country is 109 mha. Thus 97% of normal

area was covered in the season, although it

was delayed in Andhra Pradesh, Karnataka,

Rajasthan, Gujarat and parts of Karnataka due to deficit rainfall. The unfavourable area of 3 mha

was mostly located in Karanataka, Gujarat and Rajasthan states.

Seasonal profiles of NDVI and NDWI from June to October indicate normal agricultural

situation in many parts of the country - Chhattisgarh, Orissa, Madhya Pradesh, Jharkhand, Uttar

Pradesh, Bihar, Tamil Nadu, Northern half of Andhra Pradesh, West coast of Karnataka, Vidarbha

region of Maharashtra, Eastern Rajasthan and Eastern Gujarat.

Less than normal NDVI and NDWI and reduced rate of transformation from June to October,

signifying delayed crop sowing/reduced crop area/poor crop growth are observed in southern parts

of Andhra Pradesh, northern and southern Karnataka, Marathwada and Madhya Maharashtra regions

of Maharashtra, West Gujarat, Southern Haryana and Western Rajasthan.

In many parts of Karnataka state, Marathwada and Madhya Maharashtra regions of

Maharashtra, West Gujarat, Southern Haryana, Western Rajasthan and in parts of Andhra Pradesh,

the extent of crop sown area and/or the vigour of already sown crops was significantly less than that

of kharif 2011 or kharif 2010 indicating agricultural drought situation.

The trends of different crop indices were normal in many states indicating progressive

improvement in agricultural situation from June. At the end of October month, Normal agricultural

situation was evident in 316 districts followed by Mild agricultural drought in 43 districts and moderate

agricultural drought in 51 districts (Figure 2).

Fig. 2: Agricultural drought assessment under NADAMS project, kharif 2012

101

Gujarat, Karnataka and Maharashtra were the states of concern, with significant number of districts under

“Moderate drought”, followed by Haryana, Rajasthan and Andhra Pradesh states.

Sub-district level assessment in four states namely, Andhra Pradesh, Karnataka, Maharashtra and Haryana,

agricultural drought assessment was done using Resourcesat AWiFS derived indices along with other indices, as shown

in Table 1:

Table 1 : Agricultural drought situation in kharif 2012 at sub-district unit level for 4 states

State Sub-district unit No. of sub-district units affected

Normal Mild Moderate

Andhra Pradesh Mandal 687 201 165

Haryana Blocks 52 30 39

Karnataka Taluk 55 53 68

Maharashtra Taluk 223 59 73

In Andhra Pradesh state, 165 mandals were categorised under “Moderate drought” class followed by 201

mandals in “Mild drought” class and 687 mandals in “Normal” class. In Haryana, 39 blocks were categorised under

“Moderate agricultural drought” class followed by 30 blocks under “Mild agricultural drought” class and 52 blocks in

“Normal” class. The “Moderate agricultural drought” class blocks mostly correspond to southern part of the state - Hisar,

Bhiwani, Mahendragarh, Rewari, Jajjar and Mewat districts. In Karnataka, 68 taluks were categorised under “Moderate

agricultural drought” class followed by 53 Taluks under “Mild agricultural drought” class and 55 Taluks under “Normal”

class. The Taluks under Moderate drought class were distributed all over the state except in the west coast region. In

Maharashtra, 73 taluks were categorised under “Moderate agricultural drought” class followed by 59 Taluks under “Mild

agricultural drought” class and 223 Taluks under “Normal” class. The Taluks under Moderate drought were distributed

mostly in Madhya Maharashtra and Marathwada regions of the state. Poor crop growth in parts of Karnataka, Gujarat,

Maharashtra, Andhra Pradesh Rajasthan and Haryana may result in the reduction of crop yield during kharif 2012.

Challenges in Drought AssessmentBecause of the complexities of drought, no single index has been adequate to capture the intensity and severity

of drought and its potential impacts. Different states are adopting different methodologies for drought assessment,

preparation of drought memorandum, drought declaration and relief assessment in India. The criteria adopted in different

states also vary depending on the rainfall and crops grown in the region. Keeping in view the need for rationalization

of drought criteria and adoption of uniform and integrated approach, the Ministry of Agriculture, Government of

India, broughtout a drought manual in 2010 after conducting wide range of discussions with different experts. The

manual is available at www.agricoop.gov.in. The main challenges in the operational drought assessment are discussed

in subsequent sections.

Integrated Approach

Development of a unified index for drought severity assessment by integrating the data from different sources

is an important challenge. There is a need to arrive at a scientifically acceptable indicator of drought for the country.

The index should give appropriate weightages to the rainfall, soil moisture and crop condition and to make the criteria

uniform irrespective of region or state. Standardized departures from normal of different indices need to be blended

to characterize the drought intensity.

Empirical models, process based models, satellite data and ground surveys with sampling techniques need to

be explored in this context.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Early Warning Systems

An early warning system should provide the information about the onset, progress and end

of drought conditions to the decision makers at all levels. Early warning systems help in formulating

drought intervention strategies that respond to the needs of the people and enables individuals/

community to face the risk with reduced damage. A good early warning system should have a composite

database on meteorological conditions, agricultural situation, production estimates, availability of

drinking water, fodder, price trends of food and feed etc. Vulnerability profile of the area should also

form an important component of early warning methodology.

Remote sensing data provides wide ranging inputs to drought early warning system. Time

series data on different vegetation indices are extremely useful to detect the response patterns of

agricultural crops to weather variations. Satellite meteorology provide inputs to all the three types

rainfall prediction systems currently in operation in India (Roy et al., 2006). In the long term rainfall

prediction, using global and regional atmosphere, land and ocean parameters, the remote sensing

data from geo-stationary and polar orbiting weather satellites, such as, INSAT, NOAA etc is being

ingested directly or through parameterization. In the medium range weather prediction, the NCMRWF

uses satellite based SST, NDVI, snow cover area and depth, surface temperature, altitude, roughness,

soil moisture at surface level and Tiros Operational Vertical Sounder (TOVS) and Radio sonde data on

water vapour, pressure and temperature at vertical profile data in the T86/NMC Model. However, at

present only global data with poor spatial resolution is being used. In the short range rainfall prediction

also INSAT based visible and thermal data is being used.

ConclusionAgricultural drought is a complex hydro-meteorological disaster. Occurrence of agricultural drought

is determined by a number of parameters – rainfall, soil moisture, cropping pattern, crop stage etc.

State departments of Agriculture and Relief are adopting a number of parameters and indices for

monitoring and management of drought situation. Geospatial approach for drought assessment

involving different satellite derived indices, ground measured data and derived indices and their

integration brought rationality, objectivity, spatial and temporal perspective to agricultural drought

assessment. NADAMS project of NRSC successfully incorporated different satellite derived indices,

geospatial products to represent soil moisture and crop status during the season, showcased the

operational robustness of geospatial approach and strengthened the drought assessment mechanism in

the country. Drought impact assessment and early warning are some of the yet-to-be operationalised

issues to take the drought monitoring endeavor to next level and to achieve weather resilient rain-fed

agriculture in India.

AcknowledgementsWe express our sincere gratitude to Dr. V.K. Dadhwal, Director, National Remote Sensing Centre for

his constant encouragement and guidance. Valuable suggestions offered by Dr. V. Raghavaswamy,

Deputy Director (Training), NRSC are sincerely acknowledged.

References Batista, T. T., Shimabukuro, Y.E. and Lawrence, W.T. (1997). The long term monitoring of vegetation

cover in the Amazonian region of northern Brazil using NOAA-AVHRR data, International Journal of

Remote Sensing, 18, pp.3195-3210.103

Chen, D., Huang, J. and Jackson, T.J. (2005). Vegetation water content estimation for corn and soyabeans using spectral

indices derived from MODIS near- and shrot-wave infrared bands, Remote Sensing of Environment, 98,pp.225-236

Fensholt, R. and Sandholt, I. (2003). Deviation of a shortwave infrared water stress index from MODIS near and short

wave infrared data in a semi-arid environment, Remote Sensing of Environment, 87, pp.111-121.

Gadgil, S., Vinayachandran, P.N. and Francis, P.A. (2003). Droughts of the Indian summer monsoon: Role of clouds over

the Indian ocean, Current Science, 85, pp.1713-1719

Gao, B.C. (1996). NDWI – A Normalised difference water index for remote sensing of vegetation liquid water from space,

Remote Sensing of Environment, 58,pp.257-266

GuYingxin, Brown F Jesslyn, Verdin P James and Wardlow Brain. (2007). A five year analysis of MODIS NDVI and NDWI

for grass land drought assessment over central Great Plains of the United States, Geophysical Research Letters, 34,

L06407, doi:10.1029/2006GL029127.

http://ltpwww.gsfc. nasa.gov/bsb/hqvisit/16MAR2004/tucker.GIMMS_FEWS.pdf.

Jackson, J. t., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doraisamy, P. and Hunt, E.R. (2004). Vegetation water

content mapping using Landsat data derived from normalized difference water index for corn and soybeans, Remote

Sensing of Environment, 92, pp.475-482.

Jhonson, E., Gary., Rao Achutuni, V., Thiruvengadachari, S. and Kogan Felix, (1993). The role of NOAA satellite data in

Drought early warning and monitoring: Selected studies, in Drought Assessment, Management and Planning: Theory

and case studies, edited by Donald A. Wilhite, Kulwer Academic Publishers.

Khanna, S., Palacios-Orueta, A., Whiting, M. L., Ustin, S. L., Riano, D. and Litago, J. (2007). Developement of angle

indexs for soil moisture estimation, dry matter detection and land-cover discrimination. Remote Sens. Environ. 109,

pp.154-165.

Maki, M., Ishiahra, M. and Tamura, M. (2004). Estimation of leaf water status to monitor the risk of forest fires by using

the remotely sensed data, Remote Sensing of Environment, 90, pp.441-450.

Malingreau, J.P. (1986). Global vegetation dynamics: Satellite observations over Asia, International Journal of Remote

Sensing, 7, 1121-1146.

Minamiguchi Naoki. (2005). The application of Geospatial and Disaster information for food insecurity and agricultural

drought monitoring and assessment by the FAO GIEWS and Asia FIVIMS, Workshop on Reducing Food Insecurity Associated

with natural Disasters in Asis and the Pacific, Bangkok, Thailand, 27-28 January 2005.

Moulin, S.A., A. Bondeau and R. Delecolle. (1998). Combining agricultural crop models and satellite observations: from

field to regional scales, International Journal of Remote Sensing, 19, pp.1021-1036.

Murthy, C.S., SeshaSai, M.V.R., BhanujaKumari. V. and Roy, P.S. (2007). Agricultural drought assessment at disaggregated

level using AWiFS/WiFS data of Indian Remote Sensing satellites, Geocarto International, 22, pp.127-140

Murthy, C.S., Abid S.M., Prabir Kumar Das and SeshaSai M. V. R. (2012). Tracking surface wetness and rice transplantation

using Shortwave Angle Slope Index (SASI), Proc. of ISRS Symposium, New Delhi 2012.

Prabir Kumar Das., Murthy C.S., SeshaSai M.V.R. and G Behera. (2011). Analyzing seasonal dryness/wetness conditions

during crop sowing period using Shortwave Angle Slope Index, Proc. of ISRS Symposium, Bhopal.

Rajeevan, M. and Pai, D.S. (2006). On El Nino-India monsoon predictive relationships, Research Report no.4/2006,

National Climate Centre, IMD, Pune.

Roy, P.S., Joshi, P.C., Murthy, C.S. and Kishtawal C.M. (2006). Geoinformatics for Drought Assessment, in Drought

Management Strategies in India (Eds. J.S. Samra, Gurbachan Singh & J.C. Dagar), pp. 23-60. ICAR, NEW Delhi, India.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Sastry, A.S.R.A.S., Ramakrishna, Y.S. and Ramana Rao, B.V. (1981). A new methods

of classification of agricultural droughts. Arch. Fur. Met.Geoph.And Biokl. Ser. B. 29:

pp.293-297.

Subbaiah, A.R., (2004). State of the Indian Farmer – A Millenium study, Natural Disaster

Management, vol. 21, Published by Department of Agriculture and Cooperation, Ministry

of Agriculture, Government of India, New Delhi. P226.

Thenkabail, P. S., Gamage, M.S.D.N., and Smakhtin, V.U. (2004). The use of remote sensing

data for drought assessment and monitoring in Southwest Asia, International Water

Management Institute (IWMI) Research Report no.85, Sri Lanka.

Tucker, C.J., Townshend, J.R.G. and Goff, T.E. (1985). African land covers classification using

satellite data, Science, 227, pp.369-375.

Tucker, C.J., and Chowdhary, B.J. (1987). Satellite remote sensing of drought conditions,

Remote Sensing of Environment, 23, pp.243-251

Wang, Lingli., Qu, John., J., Hao, Xianjun., and Zhu, Qingping. (2008). Sensitivity studies of

the moisture effects on MODIS SWIR reflectance and vegetation water studies, International

Journal of Remote Sensing, 29, pp.7065-7075

Xiao, X., Boles, S., Frolking. S., Salas, W., Moore III., and Li, C. (2002). Observation of flooding

and rice transplanting of paddy rice fields at the site to landscape scales in China using

Vegetation sensor data, International Journal of Remote Sensing, 23, pp.3009-3022.

105

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

IRRIGATION COMMAND AREA MANAGEMENT USING REMOTE SENSING

Raju PV, Abdul Hakeem K and Venkateswar Rao V Water Resource Group, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: raju_pv@nrsc.gov.in

IntroductionIrrigated agriculture accounts for more than 70 percent of water withdrawn and

is the major consumer of fresh water resources. Global population continues to swell,

increasing the demand for food and fibre, demanding agricultural intensification, increasing

water use, especially in arid and semi-arid regions. Arid and semi-arid regions, where in

precipitation occurs over few months, irrigation support is essential for achieving optimal

crop productivity.

Irrigated agriculture is under severe scrutiny to produce more with fewer inputs,

specifically, the water. The other sectors of water utilization are posing great challenge and

competition to irrigation to maximize its water use efficiency. Anticipated climate change

conditions are expected to alter water availability and demand conditions. Irrigated agriculture

is projected to be one of the most vulnerable sectors, requiring adoptive technologies and

management. Programs such as Accelerated Irrigation Benefit Programme (AIBP) augment the

irrigation potential creation. Programs such as Command Area Development (CAD), National

Water Management Project (NWMP), Water Resources Consolidation Project (WRCP), National

Pilot Project for Repair, Renovation and Restoration (NPRRR) were initiated for improving the

performance of existing irrigation systems to bridge the gap between potential created and

utilized and to improve overall water use efficiency/productivity.

Use of satellite remote sensing data for irrigation water management has been

demonstrated through many studies addressing: base line inventory, performance assessment

& monitoring, providing in-season inputs, monitoring physical progress of potential creation,

generating inputs for feasibility assessment of new projects, environmental impacts such

as water logging & soil salinity, reservoir management, etc. This would support the field

departments to cope up with water scarcity and augmenting the water use efficiency through

integration of geo-spatial information with their conventional practices.

Satellite Remote Sensing for Irrigated Command Area Management

Satellite Remote Sensing (SRS) & Geographic Information System (GIS) techniques

have many roles to supplement and complement the data needs of irrigation sector to equip

them for efficient management of existing schemes as well as for scientific planning of new

schemes (Bastiaanssen et al., 2000).

The high spatial resolution satellite data, of the order of few meters to sub-meter, (also equipped with stereo

capability) is providing inputs for evaluating the feasibility of various alternatives while planning for new irrigation schemes.

In ongoing projects, where new irrigation potential is under creation, high spatial resolution data is being used to capture

the irrigation infrastructure, thus equipping the funding agencies to monitor the physical progress and accomplishment

of the targets. Alternatively, such data is also found useful to generate geo-databases required for development of

information & decision support systems. The intermediate spatial resolution satellite data is being extensively applied to

existing irrigation systems to carry out baseline inventory, performance evaluation, impact assessment, near-real time

monitoring, in-season estimation of irrigation water demand, surface water logging, soil salinity/alkalinity, etc. The large

archives of historical satellite data provide valuable evidence of the responses and performance of irrigation system to

varying water availability conditions. Near real time satellite data provides the current information on irrigated agriculture,

which can be used to take appropriate in-season decisions in order to reduce the impact of water scarcity on agricultural

production. These inputs are assisting irrigation department to improve and stabilize the performance of the existing

irrigation systems.

Satellite Remote Sensing data captures the information both command level and at different spatial units, thus

providing the variability at sub-command units. This capability significantly enhances the usefulness of satellite data for

deriving decision variables at various hierarchal units. The repetitive coverage through time-series satellite data captures

the temporal dynamics of agriculture, thus providing opportunity to monitor and derive decision variables during

the season.

Baseline Inventory of Irrigation SystemsThere has been a steep decline of new investments for expanding irrigation sector due to rapidly increasing

capital investment and due to environmental problems associated with such infrastructure creation. In India, the funding

for irrigation sector has sharply declined from 23% in mid 60-70’s to a low of 9% in mid 90’s. The focus has shifted

towards improving the efficiency of existing irrigation systems instead of creating new systems. In most of the existing

irrigation systems there is a serious lack of systematic organizational structure providing/maintaining data pertaining to

the system. Effective irrigation water management needs reliable, comprehensive and objective data base in a timely

and cost-effective manner. Satellite data with its synoptic coverage coupled multi-spectral information and time series

data sets are suitable for inventorying the irrigation systems (Thenkabail et al., 2009).

Crops cultivated in irrigated command areas were identified and inventoried through analysis of

multi-spectral optical remote sensing data and through digital image processing algorithms (Jonna & Chari,

1992; Nageswar Rao and Mohankumar, 1994) and also using microwave radar data (Saindranath et al., 2000).

Baseline information on cropping pattern was generated using remote sensing data from command level to water

course level. Multi-temporal optical and microwave (Ozdogan, et. al., 2010) data were used to identify multiple crops

in irrigated agricultural system. Murthy et al., (2003) used advanced classifiers like ANN back-propagation technique for

classification of irrigated crops.

Satellite data derived spectral indices have been used to evaluate crop condition. Some of the indices like

Normalised Difference Vegetation Index (NDVI) were found to be directly related crop yield and thus were used for

estimation of crop yield of cereal crops (Murthy, et al., 1996). NDVI was also used for ground sampling of crop cutting

experiment in irrigation system (Murthy, et al., 1996). Satellite technology tools were applied to evaluate the schemes

such as National Water Management Project (NWMP) and Water Resources Consolidation Project (WRCP).

Case Study – Inventory of Bhakra Irrigation System, Haryana StateMulti-date satellite data of 1995-96 rabi season were analysed to assess irrigation system under Bhakra canal

low performing pockets, effectiveness and sustainability of improvement schemes, etc. Some of the performance indicators

generated from satellite data are crop intensity, equivalent crop area intensity, principal crop intensity, proportionate crop

intensity, crop condition, coefficient of variation in crop condition, tail-head ratio of cropping intensity, tail-head ratio of

crop condition and sustainability in crop intensity.

Remote sensing based performance indicators were used for evaluating the performance of various

irrigation systems in the country (Thiruvengadachari et al 1994 and Raju et al., 1997). Bastiaanssen et. al., (1999)

listed the performance indicators derived from RS algorithms supplemented by ground data. Ray et al., (2002) used

RS data has to compute three indices namely, adequacy, equity and water use efficiency for the evaluation of

performance of distributaries in an irrigation system. Panigrahy et al., (2005) attempted to derive crop indices

like Multiple Cropping Index, Area Diversity Index and Cultivated Land Utilization Index using satellite derived

parameters such as cropping pattern, crop rotation, and crop calendar, crop type, acreage, rotation and crop duration.

Command Area Development (CAD) scheme under 13 irrigation commands was evaluated using multi-year satellite

data (Anonymous, 2005).

Case Study – Bhadra C o m m a n d A r e a , Karnataka State

Study carried out in

Bhadra project command area

in Karnataka State demonstrated

the capability Satellite Remote

Sensing techniques in providing

spatial information on irrigated

area, cropping pattern, crop

productivity and crop water use

efficiency at micro level. Analysis

of multi-date satellite data (IRS

LISS I sensor) during 1992-93

rabi season along with field

data has indicated 91 percent

irrigation intensity with 66 percent

coverage under paddy crop.

Figures 2a and 2b depict canal-

wise paddy yield and paddy

water use efficiency respectively

during rabi 1992-93. The total

depth of water application was 0.799 m with a water use efficiency of 0.495 kg/m3 for paddy crop. Water distribution

was found to be more inequitable in Bhadravathi division and was also having low paddy water use efficiency of

0.394 kg/m3. Malebennur division reorded higher performance with more uniform water distribution, high

paddy productivity and high paddy water use efficiency. Distributaries with low irrigation intensity, paddy yield

and water use efficiency could be identified. The study established the usefulness satellite data for assessing the

performance of an irrigation system and in identifying the poorly performing pockets which require improvement

measures (Anonymous, 1994).

Fig. 2a: Canal-wise Paddy yield during rabi (1992-93)

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3C a s e S t u d y – C h a m b a l Command Area, Rajasthan State

Multi date satellite data of three

rabi seasons (1997-98, 1990-91 and 1986-

87) were used to assess the performance

of Chambal irrigation command, Rajasthan

state, at distributary/minor irrigation unit

level. The satellite derived spatial and

temporal information on cropping pattern,

crop intensity and crop condition formed

the basic inputs to develop the indicators

of agricultural performance of the irrigation

system. The total irrigated area during

1997-98 rabi season was higher than the

irrigated area during 1990-91 and 1986-87

(Figures 3a, 3b, 3c and 3d). The increase was

observed in both right main canal and left

main canal. However, the irrigation intensity

was observed low in Left Medium Canal

(LMC) indicating significant gap between

irrigation potential created and utilised. During

1997-98 rabi season, the water requirement

is increased by 31.98 percent (in terms of

Equivalent Wheat Area), mostly resulting

from the significant increase in wheat crop

extent, when compared with 1990-91 rabi

season. The supplies were increased by only

6.48 percent, resulting in highly variable

wheat crop condition during 1997-98 rabi

season (Anonymous, 2005)

I n - s e a s o n I n p u t s f o r Improved Irrigation Water Management

Indian irrigation systems have been

traditionally designed and constructed with

a minimal consideration of dynamic system

operation and control capabilities. With the

growing competition for fresh water resources from other sectors, irrigation management is faced

with increasing needs for more flexible, reliable and efficient supply regime in order to achieve

maximum efficiency. To maintain control over the process of delivering water, real time information

is to be obtained on various aspects, which control and influence the supply & utilization regimes.

Fig. 2b: Canal-wise irrigation intensity

Fig. 3a: Standard FCC of rabi (1998)

111

Fig. 3b: Crop map of rabi 1998

Fig. 3c: Canal-wise Irrigation Intensity

Irrigation managers are constrained by the

lack of real time information on - to what

extent irrigated agriculture is confirming

with their plans and the extent of deviations,

if any. Such information, when provided

during supply time, would equip the managers

to make real-time decisions and to sensitize

the release pattern in accordance with

demand variability and its sensitivity.

Field based studies have been

developed to estimate or forecast irrigation

water requirements (Li and Cui 1996,

Pulido-Calvo et al., 2003) and to optimize

water allocations (Vedula and Muzumdar

1992; Wardlaw and Barnes 1999; Westphal

et al., 2003). In most of these studies the

mixed cropping was either predetermined or

derived through optimization approaches and

irrigation water allocations were optimized

considering the system resources, operating

principles and associated constraints. It is

important to note that year to year variations

in water availability and corresponding

variations in distribution policies induce

significant variability in field conditions and

agricultural operations.

Near Real-Time Inputs from Remote Sensing Data

Satell ite remote sensing data

captures the information both at command

level and at different spatial units, thus

providing the spatial variability at sub-

command units. This capability significantly

enhances the usefulness of satellite data for deriving decision variables at various hierarchal units. The repetitive coverage

through time-series satellite data captures the temporal dynamics, thus providing opportunity to monitor and derive

decision variables during the season. Time-series satellite data during the irrigation season can provide various sets of

information: capturing the onset and extension of irrigation service; progression of cropped area; area under major crops/

crop-groups; crop and irrigation water requirements and crop condition/productivity.

Monitoring the onset and extension of irrigation service : Irrigation supplies provided through a

canal system, in general, get extended from head to tail reaches. In the initial time periods, supplies confine to main

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3canal and its immediate surrounding areas.

Progressively the supplies get extended to the

lateral canals and distribution system. The

pattern in which the access to the irrigation

supplies is taking place has a significant

influence on the amount of water usage

and corresponding efficiency. Irrigation

managers evolve a programmatic schedule

through which they plan to provide access to

irrigation to all parts of the command area.

It is important and desirable to monitor the

actual pattern of access to irrigation service

and verify whether it is as per designed plans

or not. Because, deviations, if any, results in

inefficient water usage and would call for a

revision allocation schedules.

Figure 4 shows the time series AWiFS data capturing the initiation of irrigation service and its

extension. The time-series data depict False Colour Composite (FCC ) images of NIR-SWIR-Red over

Hirakud command area, Orissa State during 2003-04 rabi season. The ability to characterize moisture

status of a soil by multi-spectral information through remote sensing has many practical applications.

The high frequency AWiFS data Resorcesat-1 visibly depicted the irrigation service initiation and

progress across the command area. Satellite

data of December shows fallow fields with

no significant residual soil moisture before

the irrigation service and no rainfall during

the preceding days. The irrigation supplies,

which commenced in the last week of

December, provided initial wetting of soil

(blue to purple colour areas) indicating the

access to irrigation service (Figure 4). This

was mostly confined to a few areas within

the vicinity of main canals. Subsequent dates

satellite data depict the progressive increase

in wetted area. The irrigation supplies were

observed to extend to tail portions of the

command and into lateral distribution system

by 10th Feb, The time-series AWiFS data clearly

captured pattern of irrigation service initiation and its extension to different parts of command area

(Raju et al., 2008).

The above information when generated on near-real time basis could assist the irrigation

department to verify with their actual plans and identify the deviations. A comparison with planned

Fig. 3d: CV of wheat crop condition

Fig. 4: Onset & extension of irrigation service as captured by multi-date AWiFS data (FCC using NIR-SWIR-red)

113

irrigation schedules would help the managers

to take necessary steps for minimizing the

deviations from original plans.

Estimation of Intra-seasonal Irrigation

Water Requirements:

Figure 5 shows the multi-date

AWiFS data over Hirakud command area

during 2003-04 rabi season. The temporal

satellite data set clearly depicts the paddy

crop area progression and its phenological

cycle beginning from field preparation/

transplantation, active vegetative phase,

heading, etc. From the progression of

rice crop acreage, the variability in rice

transplantation period was identified. Distinct

classes representing different periods of rice

transplantation are shown in Figure 6. The

satellite data analysis facilitated statistics

generation right up to distributary (tertiary)

canal thus providing the spatial variability

among irrigation units. This information was

integrated with agro-meteorological data,

to derive lateral canal-wise irrigation water

demand and the critical periods were also

identified. A comparison with the actual

supply pattern (Figure 7) indicated poor

correlation with the chronological variations

associated with crop water requirement, supplies were 15% excess during Dec-Jan and were 20.1% deficit during later

part of season (Feb to Apr).

The study has demonstrated the usefulness of time series AWiFS data to generate the irrigation water

requirement during the supply season and would support the irrigation managers to reschedule the irrigation

water supplies to achieve better synchronization between requirement and supply leading to improved water

use efficiency.

Water Logging and Soil Salinity in Irrigation SystemsWater logging and subsequent salinization and alkalization are the major land degradation processes operating

upon in the irrigation commands of the semiarid regions. The significant occurrence of salt affected soils lies in the arid

and semiarid regions reducing considerably (7–8%) the productive capacity of the land surface in the world. Due to

improper management of soil and water resources in the command areas, the problems of salinity/ alkalinity and water

logging are reported to be on the increase. Information on the nature, extent, spatial distribution and temporal behaviour

of areas under water logging and salinity/alkalinity is essential for proper management of irrigated lands. Satellite data

are being used regularly for mapping of salt affected soils (Singh & Dwivedi, 1989) and waterlogged areas (Sharma &

Bhargava, 1987; Command Area Development (CAD) programme, the Ministry of Water Resources, Government of

India, supported a programme to apply satellite remote sensing techniques to generate distributary-wise information

Fig. 5: Multi-date AWiFS data over Hirakud command area

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3on the status of water logging and salinity/

alkalinity periodically during selected years of

operation in selected command areas. The

information on nature, extent, and spatial

distribution of waterlogged area and salt-

affected soils was derived through systematic

interpretation of satellite data. State-wise salt

affected soils map of India on 1:250,000 scale

were prepared using remote sensing data

jointly with the Central Soil Salinity Research

Institute (ICAR) and National Bureau of Soil

Survey and Land Use Planning, Nagpur

(NBSS & LUP). The database contains maps

showing physiographic features, distribution

and extent of salt affected soils supported by

a base map and a descriptive dataset showing

nature and degree of salinity/sodicity.

ReferencesAnonymous, (1994). Performance evaluation

of Bhadra command area, Karnataka State,

Project Report, NRSA, Hyderabad, India

Anonymous, (1996). Remote Sensing Study of

Bhakra Canal Command Area, Haryana State,

Project Report, NRSA, Hyderabad, India

Anonymous, (2005). Satellite remote sensing

based evaluation study of Chambal irrigation

command area, Rajasthan State, Project

Report, NRSA, Hyderabad, India.

Bastiaanssen, W.G.M. and Bos, M.G. (1999).

Irrigation system performance indicators

based on remotely sensed data: a review

of literature. Irrigation Dranage Systems

13:291-311

Bastiaanssen, W.G.M., Molden, D.J. and

Makin, I.W. (2000). Remote Sensing for irrigated agriculture: examples from research and possible

applications. Agricultural Water Management 46, 137-155.

Jonna, S. and Chari, S.T. (1992). ‘Irrigated command area inventory using IRS 1A LISS I data’, Proceedings of

Interantional Space Year Conference on Remote Sensing and GIS, Feb 26-28, JNTU, Hyderabad, India.

Li, Y.H. and Cui, Y.L. (1996). ‘Real-time forecasting of irrigation water requirements of paddy fields’.

Agricultural Water Management 31, 185-193.

Murthy, C.S., Thiruvengadachari, Raju, P.V. and Jonna, S. (1996). ‘Improved ground sampling and crop yield

estiamtion using satellite data’, International Journal of Remote Sensing, Vol.17, No.5, pp. 945-956.

Fig. 6: : Variability in rice transplantation period

Fig. 7: Estimated irrigation requirement and actual supply under Attabira Canal command during Rabi 2003-04

115

Murthy, C.S., Thiruvengadachari, Raju, P.V. and Badrinath, K.V.S. (2003). ‘Classification of wheat crop with multi-temporal

images: performance of maximum likelihood and artificial neural networks., International Journal of Remote Sensing,

Vol.24, No.23, pp. 4871–4890.

Nageswar Rao, P.P. and Mohan Kumar, A. (1994). Crop land inventory in the command area of Krishnarajasagar project

using satellite data, International Journal of Remote Sensing, 15, 1295-1305

Panigrahy, S., Manjunath, K.R. and Ray, S.S. (2005). ‘Deriving cropping system performance indices using remote sensing

and GIS. IJRS 26 (12): 2595-2606.

Pulido-Calvo, I., Roldan, J., Lopez-Luque, R. and Gutierrez-Estrada, J.C. (2003). Demand forecasting for irrigation water

distribution systems. Jl. of Irrigation and Drainage Engineering 129 (6), 422-431.

Ozdogan Mutlu, Yang Yan, Allez George and Cervantes Chelsea. (2010). Remote Sensing of Irrigated Agriculture:

Opportunities and Challenges,Remote Sensing 2010, 2, 2274-2304.

Raju, P.V., Jonna, S., Murthy, C.S., Abdul Hakeem, K., Chari, S.T. (1997). Performance evaluation of a canal delivery

system using satellite and field data, Proc. of workshop on Remote Sensing and GIS Applications in Water Resources

Engineering, Bangalore, India, II, 11-20.

Raju P.V., Sesha Sai M.V.R. and Roy P.S. (2008). ‘In-season time series analysis of Resourcesat-1 AWiFS data for estimating

irrigation water requirement’, International Journal of Applied Earth Observation and Geoinformation:10 (2008),

220–228.

Ray, S.S., Dadhwal, V.K. and Navalgund, R.R. (2002). Performance evaluation of an irrigation command area using remote

sensing: A Case Study of Mahi Command, Gujarat, India, Agricultural Water Management, 56(2): 81-91.

Saindranath, J., Narasimha Rao, P.V. and Thiruvengadachari, S. (2000). Radarsat data analysis for monitoring and evaluation

of irrigation projects in the monsoon. International Journal of Remote Sensing, Vol. 21, No. 17,pp. 3219–3226.

Sharma, R.C. and Bhargava, G.P. (1988). Landsat imagery for mapping saline soils and wet lands in north-west India,

International Journal of Remote Sensing, 11, 39-44.

Singh, A.N. and Dwivedi, R.S. (1989). Delineation of salt-affected soils through digital analysis of Landsat-MSS data.

International Journal of Remote Sensing, 19(1), 83-92.

Thenkabail, P.S., Biradar, C.M., Noojipady, P., Dheeravath, V., Li, Y.J., Velpuri, M., Gumma,M., Gangalakuntag, O.R.P.,

Turral, H., Cai, X.L., Vithanage, J., Schull, M.A., Dutta, R. (2009). Global irrigated area map (GIAM), derived from remote

sensing, for the end of the last millennium. International Journal of Remote Sensing 30, 3679-3733.

Thiruvengadachari S., Jonna S., Raju P.V., Murthy C.S. & Hakeem K.A. (1994). System performance evaluation and

diagnostic analysis of canal irrigation projects. Asian Conference on Remote Sensing, Bangalore, India: B2-1 to B-2-6.

Vedula, S. and Muzumdar, P.P. (1992). Optimal reservoir operation for irrigation of multiple crops. Water Resources

Research 28(1), 1-9.

Wardlaw, R. and Barnes, J. (1999). Optimal allocation of irrigation water supplies in real time. Jl. of Irrigation Drainage.

125(6), 345-354.

Westphal, S.K., Vogei, R.M., Krishen, P. and Chapra, S.C. (2003). Decision support system for adaptive water supply

management. Jl. of Water resources planning and management May/June, 165-177.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

REMOTE SENSING INPUTS FOR FEASIBILITY ASSESSMENT STUDIES OF PROPOSED WATER RESOURCES PROJECTSSimhadri Rao B, Suresh Babu AV, Shanker M and Venkateswar Rao VWater Resource Group, National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: simhadrirao_b@nrsc.gov.in

IntroductionThe volume of freshwater resources in the world is about 35 M km3, which is

2.5% of total volume of water (1400 M km3). India has only 4% of world’s renewable water

resources but with 17% of world’s population. Water resources has become scare element

in the present day scenario with growing population and rising living standards of people.

To utilise the water resources in efficient manner, water resources projects are developed.

A number of water resources projects have been taken up to manage the available water

resources for different purposes such as irrigation, hydropower generation, domestic and

industrial consumption, navigation, flood control, recreation, etc. Before initiation of such

massive and expensive water resources projects calls for feasibility studies as prerequisite.

A feasibility study assesses the viability of the water resources project

with an emphasis on identifying potential problems and addressing the needs

of the project. Feasibility studies can be of technical and financial in nature. This paper

discusses only the technical feasibility of the projects. The aspects considered while assessing

the feasibility is the quantum of water available, demand that has to be meet, structural

requirements for storing the required water and availability of sufficient land area under the

downstream of the project. Traditionally the tasks that are carried out during the feasibility

study (Punmia, 1984) are (i) Hydrological Investigations, (ii) Engineering Surveys and (iii)

Geological Investigations.

Many studies were carried out for deriving the required information using remote

sensing techniques. Yusof et al., (2000) studied development of criteria for locating optimum

sites for reservoirs employing satellite remote sensing data in langkawi Island, Malaysia. Ravi

Shankar and Mohan (2005) attempted to identify zones for augmentation of groundwater

through IRS LISS-III data by deriving drainage pattern, drainage density, lineament density.

Based on the derived information and adopting suitable criteria, zones are identified. Singh

J.P. et al., (2009) conducted a study to identify suitable sites for water harvesting structures in

a watershed in Punjab using IRS LISS-III satellite data and Geographic Information System(GIS).

Ramakrishnan et al., (2009) used IRS LISS-III satellite data for identifying the potential water

harvesting sites in a watershed by adopting SCS-Curve Number approach. Balachandar et

al., (2010) used various satellite derived thematic maps and based on their weightages they

had identified suitable sites for artificial recharge.

This paper presents a case study showcasing the abilities of remote sensing in deriving the necessary information

for conducting the feasibility studies.

Case Study

Inter linking of rivers in India is aimed at reducing the regional imbalances in the availability of water by transferring

water from surplus basins to deficit basins. National Perspective Plan was envisaged for this purpose comprising of

two components viz. Himalayan rivers development and Peninsular rivers development. Himalayan rivers development

component consists of 14 river links and Peninsular river development component consists of 16 river links (www.nwda.

gov.in). Ken-Betwa river link is one such river link under peninsular river development component, envisages transfer of

surplus Ken river flows to Betwa river upstream of Parichha weir. About 659 M km3 of water is proposed to be transfered

to Betwa river to be utilised through construction of 8 new irrigation projects.

Study AreaThe study area covers the upper Betwa basin area and is located in Madhya Pradesh state. It is covered partly

by Bhopal, Guna, Vidisha, Sagar and Raisen districts (Figure 1).

Ken and Betwa RiversKen river is a major tributary of Yamuna river, flows in northern part of India and is an interstate

river between Uttar Pradesh and Madhya Pradesh. The total catchment area of the basin is 28,058 km2,

out of which 24,472 km2 lies in Madhya Pradesh and remaining 3,584 km2 in Uttar Pradesh. The Betwa river is

also an interstate river between Madhya Pradesh and Uttar Pradesh states which originates in Raisen district of

Madhya Pradesh near Barkhera village at an elevation of about 576 m above mean sea level. The total catchment

area of the basin is 43,895 km2., out of which 30,217 km2 lies in Madhya Pradesh and the remaining 13,768 km2 lies

in Uttar Pradesh.

Ken-Betwa River Link projectKen-Betwa Link Project envisages

the diversion of 1,020 MCM of surplus water

at Daudhan of Ken basin to Betwa basin.

This surplus water is proposed to be utilised

for irrigation and drinking water supply in

Madhya Pradesh. Out of which, 659 MCM

is transferred to Betwa river upstream of

Parichha weir and 312 MCM is utilised in

the enroute command. The Betwa command

proposed to utilise the diverted water

by constructing new projects. Eight new

projects namely Barari, Neemkheda, Richhan,

Makodia, Bebanai, Sindh, Tharr and Kesari

were proposed in the upper reaches of the

Betwa basin (Figure 1). An area of 1.27 lakh

ha in Raisen and Vidisha districts of Madhya

Pradesh will be benefited by utilising 659 MCM of water annually (NRSA Report, 2007).

Satellite Data UsedLISS IV MX data of IRS P6 was used to derive information on landuse/landcover and PAN data of Cartosat-1 was

used for generation of Digital Elevation Model (DEM).

Fig. 1: Study area of the proposed irrigation projects

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3MethodologyThe methodology involves three

major steps, the first step is derivation of

land use / land cover from IRS-P6 LISS IV MX

satellite data and second step is generation of

contours for selected proposed reservoir levels

from Cartosat derived DEM. The third step

involves carrying out reservoir submergence

analysis and command area analysis using

information derived from step 1 and step 2.

Figure 2 shows the methodology used for

the study.

Suitable cloud free IRS-P6 LISS IV

MX satellite data was selected for land use /

land cover mapping. Ortho-rectified Landsat

ETM+ data sets covering study area was

downloaded from the Internet. The LISS IV

MX satellite images were geometrically rectified with reference to Land ETM+ data using 2nd order

polynomial transformation with sub pixel accuracy using ERDAS Imagine image processing software.

Initially, all the images in a particular path are rectified and then mosaiced together. Later all the path-

wise mosaic images were mosaiced covering the entire study area.

The ancillary maps collected from

the project authorities were scanned at a

suitable spatial resolution and georeferenced

with satellite image of the study area. The

boundary of the study area is digitized from

the georeferenced scanned map. The base

layer information such as road network,

railway network, settlements, drainage

network, etc., land use / land cover classes

were derived from LISS IV MX satellite image

by visual image interpretation techniques. On

screen digitisation was done at appropriate

scale to generate ArcInfo coverage layers

in ERDAS Imagine software. Two levels of

land use / cover types were derived from the

satellite data as given below:

Level- I : Bui l tup, Agriculture,

Forest, Waste land, Waterbodies, other

waste and scrub. Level-II: Builtup-Urban,

Rural; Agriculture-Crop, Plantations,

Fallow; Forest-Dense, Open, Scrub, Blank, Wasteland-Gullied, Sandy area, Barren rock, Waterbodies,

lakes and streams.

Fig. 2: : Methodology flow chart

119

Fig. 3: Land Use / land cover of the study area

Built Up-Urban

Built Up-Rural

Agriculture-Fallow land

Forest-Dense/Closed

Forest-Open

Forest-Scrub

Forest-Blank

Guillied/Ravinous

Sandy Area

Barren Rocky

River/Stream

Lakes/Ponds

Other scrub land

Agriculture-Crop land

Legend

Landuse - Land cover

As the land use / land cover information was manually derived from satellite image ground truth field

visits were conducted to validate the information. Global Positing System (GPS) receivers are used to record the exact

location information. Figure 3 shows land use / land cover derived from the IRS-P6 LISS-IV MX satellite data over the

study area.

Cartosat DEM Generation and Derivation of Contours

The study area is covered by 40

Cartosat-1 stereo scenes which is about

22,000 km2. The accuracy of DEM derived

from satellite data depends on the accuracy of

Ground Control Points (GCP) used. Differential

GPS survey was carried out for 110 GCPs

within the study area. Levelling survey was

conducted at all GCP’s to find Mean Sea

Level (MSL) levels using surveyed bench mark.

These MSL heights were intended to be used

during the DEM generation process instead of

GPS heights. Figure 4 shows the sliced DEM

derived from Cartosat satellite data covering

the study area.

Selection of dam height and reservoir

levels play crucial role in optimum design of

the dam. Change in every metre of dam height

may vary the hydraulic particulars of the dam.

Hence, reservoir submergence analysis, at

every increment of 1 m contours of 1m interval

was generated. Also the information derived is

basically used for feasibility assessment, reservoir submergence analysis for every 1 m for the proposed reservoirs.

Reservoir Submergence and Command Area AnalysisFor each proposed dam site about 7 to 10 reservoir levels were chosen, out of which, one reservoir level has

been taken as the Full Reservoir Level (FRL) depending on the reservoir submergence analysis. The required reservoir

level contours were intersected with land use / land cover layer already prepared. Land use / land cover statistics within

each reservoir level were extracted. Reservoir elevation-capacity curves were prepared using the generated contours. The

reservoir capacity is estimated using contours generated from the DEM by Cone formula as given below.

where V = volume between two levels; h = contour interval;

A1 & A2 = area at two successive contours

The irrigation command area was delineated based on the Minimum Draw Down Level (MDDL) chosen for each

of the dam site using the Cartosat-1 DEM. Contours were generated for each of the selected MDDL of all the dam sites.

Land use / land cover statistics were extracted for MDDL by intersecting the derived contour and land use / land cover

layer. Geographical Command Area (GCA) and Cultivable Command Area (CCA) within the command for each dam site

were estimated.

Fig. 4: Cartosat DEM of the Study area

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Results and DiscussionThe land use / land cover results obtained from the satellite data analysis for the entire study

area are given below. For the 8 proposed dam sites namely Neemkheda, Barari barrage, Kesari barrage,

Makodia, Bebanai, Tharr, Sindh and Richhan, the reservoir submergence analysis and command area

analysis was carried out. Out of these dam sites only Bebanai results are produced in the following

section.

Land Use / Land Cover MappingThe study area is predominately rural in nature. The ground truth field visits were undertaken

for validation of satellite image interpretation. The satellite derived land use / land cover statistics shows

that within the study area agricultural land (8,91,416 ha) followed by forest (2,94,684 ha) are the

major land cover categories. The other land cover categories present within the study area are scrub

(31,973 ha), built-up (30,094 ha), waste land (24,038 ha) and water bodies (10,778 ha).

Reservoi r Submergence Analysis

The proposed Bebanai dam site is

located on Bebanai nadi in Madhya Pradesh

state. For this dam site reservoir levels of

438 m to 450 m (13 levels) above mean

sea level were chosen for analysis. Figure 5

shows the contours that were generated from

Cartosat-1 derived DEM.

Table 1 provides land use / land

cover area statistics derived from reservoir

submergence analysis. It can be observed

from the Table that agriculture followed

by built-up types are affected by reservoir

submergence.

Irrigation Command Area Analysis

The proposed irrigation command

area was delineated based on the Minimum

Draw Down Level (MDDL) value of 431 m chosen for the Bebanai dam site. Land use / land cover

statistics extracted with the MDDL contour shows that there are 3,548 ha. crop land and 6 ha. of

fallow land. The total agricultural crop area that can be commanded is 3,554 ha. Hence, The GCA

and CCA area estimated as 3,813 ha. and 3,554 ha. respectively.

Comparative AnalysisThe reservoir submergence and command area statistics generated for the eight proposed dam

sites provide the basis for project authorities to arrive at the feasibility of these projects. A comparative

analysis of the submergence and command area statistics shows that out of 8 proposed sites, 4 sites

with maximum proposed FRL was found to be feasible while other 4 sites the FRL can be decided

based on the remaining available water. Sindh, Richhan, Barari and Kesari sites found to feasible with

Fig. 5: Cartosat-1 DEM derived contours for various reservoir levels of Bebanai project

121

Table 1: Statistics of land use / land cover under submergence for different proposed reservoir levels

Proposed

Reservoir

Level (m)

Cumul-

ative

storage

(MCM)

Total

Area (ha)

Built-up

(ha)

Agriculture

(ha)

Forest

(ha)

Waste

land (ha)

Water

bodies

(ha)

Other

scrub

land (ha)

No. of

villages

Rural Culti-

vable

Fallow Scrub Barren

rocky

River

438 10.62 455 7 414 0 0 0 33 0 4

439 16.32 694 13 647 0 0 0 34 0 7

440 24.91 1,035 20 969 0 0 0 46 0 8

441 37.18 1,429 28 1,349 0 5 0 46 0.7 11

442 53.71 1,889 43 1,791 0 8 0 46 1.3 13

443 74.93 2,364 59 2,244 0.2 13 0 46 1.7 14

444 101.11 2,880 76 2,727 0.5 27 0 46 2.3 17

445 132.46 3,398 94 3,217 2.4 34 0 46 3.6 19

446 169.00 3,915 111 3,709 4.1 40 0.2 46 4.8 19

447 210.72 4,435 123 4,206 8.5 45 0.6 46 5.8 19

448 257.78 4,981 136 4,781 11.0 49 2.1 46 5.8 22

449 310.46 5,562 151 5,293 11.3 51 4.2 46 5.8 24

450 369.07 6,164 173 5,866 11.3 54 7.1 46 5.8 28

maximum proposed FRL and Neemkheda, Tharr, Bebanai and Makodia sites with a lower FRL level than maximum proposed.

The Sindh at maximum proposed FRL of 420 m has storage capacity of 14 MCM also has less area under submergence

(1,400 ha of crop land) and 16,000 ha of cultivable land under its command. The Richhan site at maximum proposed

FRL of 448 m has storage capacity of 1 MCM and has less area under submergence. Similarly, Barari and Kesari sites

have 79 MCM and 98 MCM of storage capacity respectively at maximum proposed FRL and have extensive area

under their command.

ConclusionWater resources projects involve huge expenditure, vast resources of land and manpower. Hence, such

projects are taken up for construction after feasibility assessment studies. In this regard, satellite remote sensing

plays important role in providing necessary information for carrying out the feasibility assessment studies. This was

demonstrated through case study of Ken-Betwa river link in which about 659 MCM of water is proposed to transfer

to Betwa river to be utilised through construction of 8 new irrigation projects. From IRS-P6 LISS IV MX satellite

image land use / land cover types and drainage network information was derived. Cartosat-1 stereo pairs are useful in

deriving DEM from which contour information was generated. Using these inputs, reservoir submergence analysis and

irrigation command analysis was carried out for all the proposed projects. The results of the analysis provide the decision

makers to arrive at selection of suitable sites among the proposed project sites and the final levels of the reservoir. This

study amply demonstrates the important role played by satellite remote sensing in deriving the necessary inputs for the

feasibility assessment studies.

AcknowledgementThe authors would like to acknowledge DD(RSAA), NRSC for encouragement and Former Group Director(WR) for

guidance of the study. The authors would also like to acknowledge National Water Development Agency, Ministry of

Water Resources, Govt. of India for sponsoring the study.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3ReferencesBalachandar, D., Alaguraja, P., Sundaraj, P., Rutharvelmurthy, K. and Kumaraswamy, K. (2010).

“Application of Remote sensing and GIS for Artificial Recharge zone in Sivaganga district, Tamil Nadu,

India”, International Journal of Geomatics and Geosciences, Vol. 1, Issue.1, pp.84-97.

Morris, Gregory, L. and Fan, Jiahua. (1998). Reservoir Sedimentation Handbook, McGraw-Hill Book

Co., New York.

NRSA Technical Report (2007). “Satellite Data Based Inputs for Feasibility Assessment of proposed

Irrigation Projects In Upper Betwa Basin as Part of Ken- Betwa River Link Project”, Dept. of Space.

Punmia, B.C., (1984 ). “Irrigation and Water Power Engineering”, Laxmi Publications (P) Limited, New

Delhi 110 002.

Ramakrihsnan, D., Bandyopadhyay, A. and Kusuma, K.N. (2009). “SCS-CN and GIS based approach

for identifying potential water harvesting sites in the Kali Watershed, Mahi River Basin, India”, Journal

of Earth System science, Vol. 118, No.4 pp.355-368.

Ravi Shankar, M.N. and Mohan, G. (2005 ). “A GIS based hydrogeomorphic approach for identification

of site-specific artificial recharge techniques in the Deccan Volcanic Province”, Journal of Earth System

Science, 114, No.5, pp.505-514.

Singh, J.P., Darshdeep Singh and Litoria.P.K. (2009). “Selection of suitable sites for water harvesting

structures in Soankhad watershed, Punjab using remote sensing and GIS approach - A case study”,

Journal of ISRS, 37:21-35.

Yusof, K.W., Serwan, M. and Baban, J. (2000 ). “Identifying optimum sites for locating Reservoirs

employing Remotely Sensed data and Geographical Information System”, Proceeding of 21st Asian

Conference on Remote Sensing, 4-8, Taipei, Taiwan.

123

MODELING THE IMPACT OF LAND USE/COVER CHANGE ON THE RUNOFFWATER AVAILABILITY: CASE STUDY FOR THE NARMADA RIVER BASINGupta PK, Punalekar S1, Singh RP, Panigrahy S and Parihar JS Space Applications Centre, Ahmedabad-380015, Gujarat, India 1PhD. Student, Department of Geography and Environmental Science University of Reading, UK Email: pkgupta@sac.isro.gov.in

IntroductionLand use change is an important characteristic in the runoff process that affects infiltration,

erosion, and evapotranspiration. Due to rapid development, land cover is subjected to changes causing

decrease in the soil infiltration rate and consequently increase in the amount of runoff. Deforestation,

urbanization, and other land-use activities can significantly alter the seasonal and annual distribution

of runoff (Dunne 1978). The conventional methods of detecting land use changes are costly and low

in accuracy. Remote sensing technique, because of its capability of synoptic viewing and repetitive

coverage provides useful information on land use dynamics. The changes in land use due to natural

and human activities can be observed using current and archived remotely sensed data. There is a

need to investigate the relationship between the landuse change and the runoff water availability.

With advances in computational power and the growing availability of spatial data, it is possible to

accurately describe watershed characteristics when determining runoff response to rainfall input (kite

and piteroniro, 1996; Singh and Woolhiser 2002). With the development of Geographic Information

System (GIS) and remote sensing techniques, it is possible to enumerate various interactive hydrological

processes considering spatial heterogeneity (Mohan and Shrestha 2000). Soil Conservation Service

Curve Number method (USDA 1985) is widely used conceptual rainfall-runoff model as the major

input parameters are defined in terms of land use, DEM, rainfall and soil types. The advantage of this

method is that the user can experiment with changes in land use and assess their impacts.

Study AreaNarmada river basin has undergone various changes in land cover due to establishment of

various dam projects. It has also been under controversies due to probable changes in forest cover

and submergence of agricultural areas under big multi-purpose projects like Sardar Sarovar, Indrasagar

etc. Hence, it is important to study the land cover dynamics in the area. Satellite images and image

analysis techniques have made it easier to undertake these kinds of land use/cover (LULC) dynamics

studies. Moreover, rainfall- runoff models can be used to help further understand and predict changes in

runoff water availability. In this paper, an integrated approach using RS, GIS and hydrological modeling

is presented to delineate and analyze the past and present land cover conditions of a basin and to

estimate the impacts of the detected changes on surface runoff. Study was undertaken to estimate

the change in LULC over Narmada river basin from 1970’s to 2004. Satellite imageries of the basin

during the decade of 1970 were available from Landsat mission. Whereas, for the recent scenario

images obtained from AWiFS sensor from IRS P6 was used which has similar spectral resolution.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Data Used Landsat 1 MSS (multi spectral scanner) images were used for the LULC mapping of 1972.

As limited data was available, different date images were used to obtain a complete mosaic of the

area. However, the images of single season i.e. November to January were preferred. Original spatial

resolution of the images by MSS was 79 m but resampled product for 57m was available. Dates used

were 16th December 1972 (2 images), 17th December 1972, 30th November 1972, 1st December

1972 (2 images), 8th November1977, 27th November1975, 29th January1977. AWiFS images were

used to prepare LULC map for the year 2004. Two images of same date 23rd November 2004 were

geo-registered and mosaicked to cover the study area.

Methodology

LULC map and change detection:

Landsat Images were mosaiced with

‘feathering at cutline’ option in ERDAS.

River basin area was then clipped out of it.

Unsupervised classification was done with

ISODATA algorithm. At first, 50 classes were

produced after 6 iterations. The classes

were then merged into four major classes –

agriculture, forest, water and other classes.

The other class category was a broad class

which covers barren areas, fallow land and

dried river coasts, etc. as, mosaic of the area

was obtained from multi date images, there

were some miss-classification observed. These

areas were then identified using toposheets.

The Areas of Interests (AOI) were selected

which showed some haziness in the original

images. AOIs were again reclassified using same method. The classes in the classified AOIs were

then recoded to merge them in the proper class as per the toposheets and Crop region map. These

classified AOIs were then merged with the rest of the map. It was observed that settlements were

also very small and inseparable from surrounding agricultural tone. Because the agriculture in this

area appears to be kharif type and hence, it had lost the entire canopy till November-January. So, the

river channel and major tributaries and lakes and settlements were digitized separately. This vector

file was then converted into raster and then integrated with the LULC map. Similar procedure was

adopted for the classification of AWiFS data to prepare LULC map for the year 2004. FCC images

for 1972 and 2004 are presented in Figures 1 and 2, respectively. Methodology for the LULC map

generation is presented in Figure 3.

Accuracy Assessment: In both the maps accuracy assessment was done using ERDAS post-

classification assessment tool. 130 random points were added using equally distributed (w.r.t. land

cover classes) option in 23rd Nov, 2004 map. About 160 points were added in 1970’s land cover map

to have sufficient number of points in settlement class which covered very small area. The 2004 map

was compared with the available LULC map (from NRDB library) as reference. In case of 1970’s map

assessment was done through visual interpretation of original satellite imagery, toposheets and crop

region map of India. Error matrix was produced by this method.

Fig. 1: False color composite of mosaiced image obtained from Landsat MSS (1972)

Fig. 2: False color composite of mosaiced image obtained from AWiFS (2004)

125

Runoff Estimation:

Modified curve number model

(Mishra et al., 2005 b; Gupta et. al., 2012;

Sharpley and Williams 1990) was used for

the estimation of runoff in the Narmada river

basin. Major inputs used in runoff modeling

were soil texture map (NRDB), DEM (SRTM),

rainfall (CPC NOAA), LULC maps. Runoff

model was evaluated for the year 2006, due

to non availability of observed runoff data for

the year 2004. Evaluated model was used to

study the impact of LULC change during 1972

to 2004 on the runoff production. Normal

rainfall was considered for both the years

(1972 and 2004) runoff estimations.

Result and Discussion

Land cover analysis: The overall

classification accuracy for the LULC map of

2004 was 92% and for 1970’s map it was

91.7%. The kappa statistics was 0.9245 for

1972 map and 0.9313 for 2004 map. The

producer and user accuracy for all the classes

for both the maps were significantly high.

The area statistics shows that the agriculture area has increased by 2.22% (130176.6 ha) whereas area under forest

has declined by -1.67% (39211.3 ha) (Table 1). The other class which includes barren, fallow and dried river channels

showed decline of -71.9% (209743.2 ha). Agriculture covers the largest area among all classes followed by forest. Area

under settlements greatly increased by 228.7% (18185.4 ha) and as expected water bodies area also show significant

increase of 124.9% (100592.5 ha). Classified land cover maps for 1972 and 2004 are presented in Figures 4 and 5,

respectively.

Fig. 3: Methodology flow chart for LULC map generation

Table 1: Area under different classes and overall change between 1972 and 2004

Class name Area covered in 1970’s (ha)

Area covered in 2004 (ha)

Total change

Agriculture 5855967.3 5986143.9 130176.6

Forest 2353372.9 2314161.7 -39211.3

Other classes 291372.3 81629.1 -209743.2

Water bodies 80522.1 181114.7 100592.5

Settlements 7949.1 26134.5 18185.4

Total Area 8589183.8 8589183.8 -

Table 2 shows net gain or loss of each LULC class to other classes. The statistics was generated by considering

both area increase and decrease between pair of classes from matrix shown in Table 2. The positive sign shows that the

area gained from the respective class and negative sign shows area lost to it. There was net loss of 62598 ha (78.7%)

from agriculture land under water bodies and 16930.6 ha (21.3%) for settlement expansion. In spite of these losses

there was overall increase (130176.6 ha) in the agricultural land. Forest on the other hand showed overall loss of area.

Though there was some degree of increase in the forest cover, especially over barren and uncultivated areas, area of

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

318121.7 ha (35%) under forest has

transformed into agriculture, 33316.2 ha

(64.4%) into water bodies and 267.2 (0.6%)

into settlements. It mostly consists of land

surrounding large reservoirs. The area under

other category showed mostly net loss to the

agricultural one. Settlements have expanded

over agricultural land by 16930.6 ha (94.53%),

and other classes by 980.9 ha (5.47%) and

marginally over forested areas. The area

under other classes has been undertaken

for agricultural development, settlements

and water bodies too. Settlements show

significant rise over surrounding agriculture

and other land cover classes.

The area statistics shows the overall

increasing trend of agricultural and urban

development in the river basin. These changes were mainly due to enhanced availability of water for agriculture

which was made available from many large reservoirs. This increase has been achieved at the cost of loss

of some forested area, but on the positive side, it was evident that mostly the area which was not

under any kind of utilization has been brought under some development. The multipurpose projects

developed in the basins have significantly proven useful to improve agriculture over the area. This

increase obviously has also been supported by increase in the water availability.

Fig. 4: Land cover map of Narmada river basin (1972)

Fig. 5: Land cover map of Narmada river basin (2004)

Class name Area gained or lost to classes (ha)

Agriculture Forest Other classes

Water bodies

Settlements

Agriculture - -18121.7 -191583.6 62598 16930.6

Forest 18121.7 - -12493.8 33316.2 267.2

Other Class 191583.6 12493.8 - 4684.9 980.9

Water bodies -62598 -33316.2 -4684.9 - 6.6

Settlements -16930.6 -267.2 -980.9 -6.6 -

Net change 130176.6 -39211.3 -209743.2 100592.5 18185.4

Table 2: Change detection matrix showing net gain and loss of area between all pairs of land cover classes

-ve sign indicates loss of area to respective class mentioned in column title

Runoff AnalysisModel calibration was done for 2006 by comparing the observed and modelled runoff. Scatter

plot between observed and modelled runoff is presented in Figure 6. A reasonably good match was

obtained for the calibration period with coefficient of determination 0.875

Evaluated model was used to see the impact of LULC change on the runoff during 1972 to

2004. There was overall increase in runoff by 2.2% (816 Mm3) due to change in the land cover during

1972 to 2004. Significant increase in runoff production in agriculture (1138 Mm3) and settlement

(84 Mm3) has been observed whereas there was decline in runoff for the forest (131 Mm3) and other 127

land cover classes (938 Mm3). These changes

are in parity with alterations in their extent.

Runoff water pattern for different land cover

classes during 1972 and 2004 is presented

in Figure 7.

Significant increase in runoff in

agriculture (1138 MCM) and settlement

(84 MCM) has been observed, whereas,

there was decline in runoff for the forest

(131 MCM) and other land cover classes

(938 MCM). These changes are in parity with

alterations in their extent. Decrease in runoff

over upstream sub-basins (Manot, Sandia)

whereas increase in runoff for the basins

falling down stream. This may be due to

gain and loss of forest cover in the upstream

and downstream, respectively. There was

7% increase in the runoff over Rajghat

(264 MCM) and Parts of Garudeshwar (392

MCM) whereas 6% decline in runoff over

Manot (95mcm) was estimated (Table 3).

Runoff change pattern for major sub-basins

of Narmada river is presented in Figure 8.

Upstream sub-basin such as Manot showed

increase in runoff contribution from forest

areas whereas reverse trend has been

obtained for the downstream sub-basins

such as Garudeswar. Rajghat sub-basin

has shown significant increase in runoff

contribution from agriculture during 1972 to

2004. Great reduction in runoff from other

land cover class has been observed for Sandia

and Rajghat sub-basins. Runoff contribution

from settlement has increased considerably

in Mandleswar sub-basin.

ConclusionIn this study, change pattern in the land use/

cover during 1972 to 2004 were delineated

and its impact on the runoff water availability

in the Narmada river basin has been modeled

and analysed. Significant change in the LULC

pattern has been observed. This is because

of construction of several new dams in the

Narmada river. Settlement (228.7%) and

Fig. 6: Relationship between observed and modeled runoff

Fig. 8: Runoff change (%) pattern for 2004 w.r.t. 1972 for different land cover classes under major sub-basins of Narmada river

Fig. 7: Runoff for different land cover classes (%) in the Narmada basin for the year (a) 1972 and (b) 2004

Agriculture

Forest

Other class

Water body

Settlement

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Table 3: Runoff change during 1972 to 2004 for major sub-basins of Narmada river

Sub-basin Runoff vol. ( Mm3) Year-1972

Runoff vol. ( Mm3) Year- 2004

Change (%)

Manot 1599 1504 -6

Sandia 5731 5590 -2

Hoshangabad 3773 3884 3

Mandleshwar 13029 13242 2

Rajghat 3596 3859 7

Garudeshwar 5327 5719 7

Water bodies (124.9%) area have increased significantly with a slight increase in the agriculture area

(2.2%). Forest and other class have been reduced by 71.9% and 1.7%, respectively. Increased water

availability due to large dam projects has acted as one of the encouraging factor for agricultural and

urban development. Overall loss in forest area to agriculture was 39211 ha. Expansion of settlements

was mainly over surrounding agriculture area. Runoff analysis show changes in contribution of

different land cover classes in total runoff in parity with increase or decrease in their extent. Significant

increase in runoff was obtained over settlements. There was overall increase in runoff was of 2.2%

(816 MCM).

ReferencesDunne, T. and Leopold. (1978). L.B. Water in Environmental Planning, W.H. Freeman& Co., New York,

NY, pp 818.

Gupta, P.K., Punalekar S., Panigrahy S., Sonakia, A. and Parihar, J.S. (2012). Runoff Modeling in an

Agro-Forested Watershed using Remote Sensing and GIS. Journal of Hydrologic Engineering, American

Society of Civil Engineers Vol. 17, No. 11, November 1, 2012. ASCE, ISSN 1084-0699/2012/11-

1255-1267.

Kite, G. W. and Piteroniro, A. (1996). “Remote sensing applications in hydrological modeling.” Hydrol.

Sci. J., 41 (4), 561–591.

Mishra, S.K., Jain, M.K., Pandey, R. P. and Singh, V.P. (2005 b). “Catchment area-based evaluation of

the AMC-dependent SCSCN- based rainfall–runoff models.” Hydrol. Processes, 19(14), 2701–2718.

Mohan and Madhav Narayan Shrestha (2000). A GIS based Integrated Model for Assessment of

Hydrological change due to Land use modifications, proceeding of symposium on Restoration of Lakes

and Wetlands, Indian Institute of Science, November 27-29, 2000, Banglore, India.

SCS (1972). SCS National Engineering handbook, Section 4. Hydrology, Soil Conservation service, USDA,

Washington, DC.

Sharpley, A.N. and Williams J.R. (1990). “EPIC—Erosion/Productivity Impact Calculator: 1. Model

Documentation. US Department of Agriculture Technical Bulletin No. 1768.” US Government Printing

Office: Washington, DC.

Singh, V.P. and Woolhiser, D.A. (2002). “Mathematical modeling of watershed hydrology.” J. Hydrol.

Eng., 7 (4), 270–292.

USDA, Soil Conservation Service (1985). National Engineering Handbook, Section 4, Hydrology. U.S.

Government Printing Office, Washington, DC.129

HYDROLOGICAL MODELING APPROACH FOR ANNUAL WATER RESOURCES ASSESSMENT - A PILOT STUDY IN THE GODAVARI AND BRAHMANI-BAITARANI BASINS, INDIADurga Rao KHV, Raju PV, Simhadri Rao B, Venkateshwar Rao V, and Sharma JR National Remote Sensing Centre, ISRO, Department of Space, Hyderabad - 500 037, India Email: khvdurgarao@yahoo.com

IntroductionProper assessment of the availability of water resources is the cornerstone for proper planning,

development and management. Natural flow in the river basin is reckoned as water resources of a

basin. The mean flow of a basin is normally obtained on pro-rata basis from the average annual flow

at the terminal site. However, at any point of time, the water resources in a river basin has already been

developed and utilized to some extent through construction of major or medium storage dams and

development of hydro-power, irrigation and other water supply systems. A large number of diversion

schemes and pumped storage schemes may have also been in operation. Assessment of natural flow

has, therefore, become complex in view of the upstream utilizations, reservoir storages, re-generated

flows and return flows. The natural flow at the location of any site is obtained by summing up

the observed flow, upstream utilization for irrigation, domestic and industrial uses both from

surface and ground water sources, increase in storage of reservoirs (surface and sub-surface) and

evaporation losses in reservoirs, and deducting return flows from different uses from surface and

ground water sources.

The first ever attempt to assess the average annual flow of all the river systems in India was

made by the Irrigation Commission of 1901-1903. The major constraint at that time was that while

records in respect of rainfall were available, data in respect of river flows were not available even for

most of the important river systems. The Commission, therefore, resorted to estimation of river flows by

adopting coefficients of runoff. The average annual flow of all the river systems in India was assessed

as 1443 BCM (Billion Cubic Meter). Dr. A.N Khosla developed an empirical relationship between mean

temperature (as an expression for mean evaporation loss) and mean runoff by studying the flows of

Sutlej, Mahanadi and other river systems. According to these studies, the total annual flow of all the

systems worked out to 1673 BCM. Central Water and Power Commission (1954-66), estimated the

surface water resources of different basins during the period 1952 to 1966. The study was mostly

based on the statistical analysis of the flow data wherever available and rainfall-runoff relationships

wherever data was meagre. The country was divided into 23 basins / sub-basins. According to these

studies, the water resources of various basins amounted to 1881 BCM (Rao, K L, 1979). National

Commission on Agriculture (1976) has estimated total annual water resources of the country as 1850

BCM (1800 BCM available in an average year) based on water balance approach taking into account

rainfall, percolation of water in soils, evaporation and evapotranspiration.

Central Water Commission (1988) has made estimates of water resources using lumped

approach. As per the report ‘Water Resources of India’, the natural run-off of a basin could be computed

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3by adding to the surface flow measured at the terminal site, the net export of the surface water out of

the basin, the net increase of the surface water storage, additional evapotranspiration caused by use

or storage of surface water, direct ground water flow from the river basin below or along the terminal

site, the net export of ground water out of the basin, the net increase in ground water storage, soil

moisture storage and the additional evapotranspiration caused by use or storage of ground water.

Basin wise reassessment of water resources potential in the country was carried out by

Central Water Commission (CWC) in 1999 and presented in the report entitled ‘Reassessment of

Water Resources Potential of India’ (CWC, 1999). The water resources potential of the country was

reassessed as 1869 BCM against an earlier assessment of 1880 BCM done by CWC in 1988.

While assessing the water resources of India, the country has been divided into 20 river basins

comprising 12 major basins and 8 composite river basins. The natural flow at any location on a river is

obtained by summing up the observed flow, upstream utilization for irrigation, domestic and industrial

uses both from surface and ground water sources, increase in storage of reservoirs (both surface and

sub-surface) and evaporation losses in reservoirs, and deducting return flows from different uses from

surface and ground water sources. The observed flows at terminal sites of the basins were corrected

for upstream abstractions to arrive at the natural flows. Based on this methodology, CWC assessed

the average annual water resources potential of the country as 1869 BCM in the year 1993. The

broad limitations of the CWC’s study are –

(1) Water resources assessment was done based on CWC’s gauge data only, no rainfall input is taken

into consideration.

(2) There is no cross check mechanism in the studies

(3) Data from major and medium reservoirs only taken into consideration for computation of irrigation

utilisation

(4) Study was fully lumped approach having the estimates at terminal sites of the basins only.

In this perspective, assessment of water resources in a comprehensive way is a very important

aspect in water resources development and management. Remote satellite data may be used in

studying the land use dynamics and its affect on hydrology. Keeping in view of these, a joint research

project has been executed by National Remote Sensing Centre (NRSC) and CWC for re-assessment of

water resources in the Godavari Basin, Brahmani-Baitarani Basin, India using hydrological modeling

approach. The main objectives of the study are to compute water resources in the basin during the last

20 years (1988-89 to 2007-08), mean annual water resources and the availability of water resources

during extreme wet and dry rainfall conditions through distributed hydrological modelling approach

using space inputs.

Study BasinsIn the present pilot study Godavari and Brahmani-Baitarani Basins are taken up as study basins

to demonstrate the methodology.

Godavari Basin extends over an area of 312,800 km2, covering nearly 9.5 percent of the total

geographical area of India. The Godavari River is perennial and is the second largest river of the India

and joins Bay of Bengal after flowing through a distance of 1470 km (CWC, 1999). It flows through

the Eastern Ghats and emerges into the plains after passing Polavaram. Pranahita, Sabari and Indravati 131

are the main tributaries of the Godavari River.

The basin receives the major part of its rainfall

during the Southwest monsoon period. More

than 85 percent of the rainfall takes place

from July to September. Annual rainfall of the

basin varies from 880 to 1395 mm and the

average annual rainfall is 1110 mm.

The combined Brahmani-Baitarani

river basin extends over a geographical area

of 50,768 km2 and the basin is bounded on

the north by the Chhotanagpur Plateau, on

the west and south by the ridge separating

it from Mahanadi basin and on the east by

the Bay of Bengal. The drainage area of the

basin lies in the States of Orissa (33,923 km2),

Jharkhand (15,479 km2) and Chhattisgarh

(1,367 km2). Geographic locations of the

study basins are shown in the Figure 1.

Spatial and Non-spatial Data Used

In this study, various spatial and non-

spatial database such as landuse, soils, digital

elevation model, command area boundary,

hydro-meteorological data, and groundwater

data were used in the study.

Land Use/land Cover: The rainfall-runoff relationship is one of the most complex hydrologic phenomena to

comprehend due to the tremendous spatial and temporal variability of watershed characteristics, precipitation patterns

and the number of variables involved in the physical processes. Rate of evapotranspiration mainly depends upon the

landuse/landcover pattern. Landuse/landcover maps of the years 2004-05, 2005-06, 2006-07, 2007-08 prepared using

AWiFS sensor data of IRS-P6 satellite (Source NRC project: ISRO) were used for runoff calculations in the study.

Soil Texture: In the hydrological cycle, infiltration is another major component after the evapotranspiration.

Infiltration at a given time depends upon the soil texture and the existing soil moisture. Soil maps (1:250,000 scale)

prepared by NBSS&LUP, India were used in the study. These soil maps were reclassified into soil textural grids and were

used in computing soil monthly moisture availability subsequently.

Digital Elevation Model (DEM): DEM is one of the main inputs for hydrological modeling studies. SRTM

DEM of 90 m resolution was used to delineate the watershed and sub-watershed boundaries of the study basins. Using

the DEM, flow direction for each cell was assigned based on the direction of the steepest slope from among the eight

possible directions to the adjacent cells. Based on the flow direction, flow accumulation towards the outlet of the

watershed was calculated. Godavari Basin and Brahmani-Baitarani Basin were divided into 23 and 8 sub-basins respectively

based on the drainage pattern and corrected with the satellite data. These sub-basins were used in aggregating the final

results at the identified gauge sites.

Fig. 1: Geographic location of the study basins

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Command Area Map: Estimation of actual evapotranspiration (AET) varies from irrigated

area to rain-fed areas. It was assumed that irrigation supplies are provided for all the agricultural areas

within the command boundaries. Kharif crop outside of the command area was considered as rain-

fed crop and rest is assumed as fully irrigated. Command area map of the study basins were obtained

(source: India WRIS project, ISRO) and used in the evapotranspiration computations and subsequently

in runoff computations.

Rainfall and Temperature Grids: Daily rainfall grids at 0.5 degree and temperature grids

at 1 degree resolution of the mentioned 20 years period (Rajeevan and Jyote, 2008) were obtained

from the India Meteorological Department (IMD) and used in the model for runoff computations after

converting them into monthly grids in Geographic Information System (GIS) domain.

Groundwater and Reservoir Flux Data: Ground water level data of wells spread across

both the basin and specific yield map were collected from the Central Ground Water Board (CGWB),

India. Annual ground water flux (recharge or withdrawal) for each observation was arrived at through

arithmetical difference between June / May month’s observations of the two consecutive years. A

point map was created and spatial interpolation was done in GIS environment to create groundwater

flux grids. Annual ground water recharge or withdrawal in each year was computed by integrating

groundwater flux grids with specific yield grid.

CWC monitors 11 major and medium reservoirs in the Godavari basin and 3 reservoirs in

Brahmani-Baitarani Basin. Water level data of these reservoirs was collected and reservoir flux in each

hydrological year (June-May) was computed.

Domestic, Livestock and Industrial Water Consumptive Use: Census data of

1991 and 2001 (www.Censusindia.gov.in) was used for estimating domestic and industrial water use.

Survey of India village administrative information was integrated with population attribute data to

prepare spatial population maps for each year by interpolating the above census data. For estimating

domestic requirements, it was assumed that, 70 litres per capita per day (lpcd) and 140 lpcd for rural

and urban consumption respectively (NCIWRD, 1999). Industrial demand was assumed being equivalent

to the domestic demand. Domestic & Livestock consumptive use is taken as 15% of its demand, and

industrial consumptive use is taken as 50% of the demand (NCIWRD). As per NCIWRD, average water

requirement by the livestock is about 30 litres/

livestock/day and its population is 50% to the

human population.

River Discharge Data: Monthly

observed river discharge data for 20 years of

various gauge stations in the study basins was

collected from CWC and used in the model

validation and calibration.

Modeling Framework and Methodology

The modeling frame work for the

present study (Figure 2) involves integration

of spatial data sets (DEM, LULC, soil texture, Fig. 2: Modeling framework133

village census) with hydro-meteorological data sets (Rainfall, temperature, GW flux, reservoir flux, river discharge) in GIS

environment to carry out water balance computations at hydrological response unit level. The model development and

calibration was carried out using the four years data sets (2004-2008) and the calibrated model was extended for all

the remaining years. The 20 years water balance outputs were averaged to arrive at Long-term Mean Annual Basin level

Water Resources. Broad modeling framework is shown in the Figure 2.

Thornthwaite & Mather MethodAfter examining various hydrological models, Thornthwaite and Mathor Model (Thornthwaite and Mathor,

1957) was selected for estimating runoff in the present study (NRSC, 2009; Ray, 2001; Sokolov and Champan, 1994; X

U CY and Singh, 1998). The water balance has been used for computing seasonal and geographic patterns of irrigation

demand, the soil moisture stresses under which crop and natural vegetation can survive. Water table calculated for a

single soil profile or for an entire catchment, refers to the balance between incoming of water by precipitation and

outflow of water by evapotranspiration, ground water recharge and stream flow. Among the several possible methods

of calculation, the one introduced by Thornthwaite and Mather (1957) generally has been accepted (Jianbiao, 2005).

This technique uses long term average monthly rainfall, long term average potential evapotranspiration (ET), and soil

& vegetation characteristics. The last two factors are combined in the water capacity of the root zone. Computation of

ET in this method is mainly based on temperature and day length factors. Day length factor grids were prepared in GIS

environment. Potential Evapotranspiration (PET) was calculated using the temperature and day length grids through

spatial modeling technique. These spatial PET maps of the country were prepared and subsequently PET grids of Godavari,

Brahmani - Baitarani were extracted for further use.

Vegetation Factors: The Thornthwaite method doesn’t account for vegetative effect which is most useful

parameter in water balance estimations (Peter E. Black). Monthly landuse factors have been derived for both the river

basins using satellite remote sensing data and integrated with PET to account for vegetation effect on PET. Thus the PET

revised has been calculated using the equation 1

PET revised = PET * vegetation factor Eq. 1

The crop coefficient integrates the effect of characteristics that distinguish a typical field crop from the grass reference,

which has a constant appearance and a complete ground cover. Consequently, different crops will have different crop

coefficients (Kc) The Kc values primarily depend on crop type, crop growth stage, soil evaporation. Uniform vegetation coefficient

during all the months has been considered for the vegetations like forest, scrub land etc.(Canadell et. al., 1996; Descheemaeker

et al., 2011). Whereas for agricultural lands, variable coefficients taken in different months according to the crop growth

stage and type of crop (FAO; Mohan and Arumugam, 1994). These vegetation coefficients are further calibrated using

the field discharge data.

After the calculation of PET revised, water loss in each month and accumulated water loss (La) were computed

based on PET revised and rainfall data. Next, the water storage capacity (SM), which depends upon the soil texture type,

rooting depth of vegetation and land use, in the root zone of the soil must be determined. Thus, from the readily available

tables, graphs and by using the Mather’s empirical formula soil moisture and the change in soil moisture in each month were

calculated. Actual evapotranspiration (AET) represents the actual transfer of moisture from the soil and vegetation to the

atmosphere. When the rainfall (P) exceeds PET revised, it was assumed that sufficient moisture exists in the soil within the

root depth to meet the climatic demands, in such case Actual Evapotranspiration (AET) will be equal to PET revised. In the

condition when P < PET revised, AET demand will be met from the P and change in soil moisture. In irrigated agricultural land

(canal and well irrigation) it is assumed that full irrigation support is provided to meet the AET requirements. Irrigation support

(P- PET revised) is added to rainfall to make AET equal to PET revised. Kharif crop outside the command area boundaries

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3is considered as rain-fed and the rest is considered as irrigated either by canal or well irrigation system.

The added irrigation support was subsequently adjusted while computing runoff. Monthly moisture

surplus & deficit and runoff were calculated based on P, PET revised, AET and SM.

Model Calibration and ValidationIf any unknown variable exists in the model, it can be calibrated using the observed/field data

during the calibration process. The vegetation coefficients are the main variable to be calibrated. Basically

the calibration process is a hit and trial method. Runoff computations were repeated by changing

the landuse coefficients till the computed runoff matches with the observed runoff. After calibrating

the model, the runoff calculations have to be revised using the calibrated (revised) coefficients. In the

present study the model has been calibrated during 2004-05, and 2006-07 since these two are dry

and wet years respectively.

Once the model is calibrated perfectly, it has to be validated with other set of field observations

to check the calibrated parameters. In the present study, model has been validated with the

hydro-meteorological data of 2005-06 and 2007-08. Calibration of the Model is done using the

equation 2.

R Calibrated/computed = (R Model - F GW - F R - F DIL ) ≈ R o Eq. 2

R Calibrated/computed = Calibrated/computed runoff

R Model = Model estimated runoff (output from Thornthwaite Mather Model)

F R = Reservoir Flux ( - ve sign for drawdown)

FGW = Ground water Flux (- ve sign for drawdown)

F DIL = Domestic, Industrial and Livestock consumption

R 0 = Observed runoff at gauge sites (CWC’s observed data is taken)

All water balance components are in volumetric units (BCM/MCM)

Water Resources Availability (WRA)Water resources of the basin comprises of runoff in the river at final outlet, upstream effective

utilisations for irrigation, domestic and Industrial, groundwater flux, and surface water flux. Thus, it

can be expressed as ;

WRA = R Calibrated/computed + IS + E +F DIL + FGW + F R Eq. 3

Where,

E = evaporation from the reservoirs (computed)

IS = Estimated Consumptive Irrigation Input Provided (computed)

Annual water resources availability during the 20 years (1988-89 to 2007-08) has been computed for

both the pilot study basins. Mean annual water resources have been further calculated. Rainfall during the last 135

35 years was analysed and the water resources

availability during the extreme minimum and

maximum rainfall years was analysed further.

It is noticed that these extreme events in both

the basins are falling in the study period of

1988-89 to 2007-08.

Results and DiscussionsGodavari Basin

From the analysis of meteorological

data, it is found that during the last 35 years

(1973-74 to 2007-08) maximum rainfall was

recorded in the Godavari Basin as 1393 mm

in 1994-95 and minimum as 881 mm in

2002-03 as shown in the Figure 3. Hence

these two are considered as meteorologically

wet and dry periods respectively during this

35 years span. Average monthly temperature

varies from 20oC to 35oC in a year which

causes lot of monthly variations in the

potential evapotranspiration in the basin.

From the landuse/landcover map

(2006-07), it is found that nearly 16 landuse

classes exist in the study area. Agriculture land

is the predominant class in the Godavari Basin

having more than 50% (including current

fallow) of the basin area; this extent varies

slightly from year to year. Next dominating

class in the study area is forest. Paddy, cotton

and pulses are the main crops in the basin.

Land use land cover derived from IRS P6 data

of the year 2004-05 is shown in the Figure 4. Clay, loam, loamy skeletal, clayey skeletal, sand and rock outcrop are found

to be main soil textural classes in the study basin. Predominant soil textures in the study area are clayey and loamy which

has the property of low infiltration rate and more runoff.

Annual groundwater flux in the basin is found to vary from + 10m to -10m. In some pockets these fluctuations

are more, otherwise stable flux is noticed. Specific yield of the basin varies from 1.5 to 16%, with maximum part

of the basin having the specific yield of 1.5%. The mean annual groundwater flux in the basin is estimated at 0.67

BCM (drawdown). Reservoir fluxes in individual sub-basins are aggregated. It is noticed that many reservoirs maintains

sustainable flux (less annual flux). The mean annual reservoir flux of all the 11 major and medium reservoirs is estimated

at 0.01 BCM (drawdown). The mean annual domestic, livestock and industrial consumption flux is estimated at 0.99

BCM in the basin. Landuse coefficients calibrated through trial and process are found to be ranging from 0.5 for bare

soil to 1.2 for paddy during peak crop stage. Landuse factors for scrubland, grassland and forest lands are found to be

0.65, 0.7 and 0.9 respectively.

Fig. 4: Landuse/landcover of Godavari Basin derived from IRS P6-AWiFS (2004-05)(Courtesy: NRC Project, NRSC)

Fig. 3: Annual rain fall of the Godavari basin

73-7

4

1400

1300

1200

1100

1000

900

800

700

Year

Rai

nfa

ll (m

m)

74-7

5

75-7

6

76-7

7

77-7

8

78-7

9

79-8

0

80-8

1

81-8

2

82-8

3

83-8

4

84-8

5

85-8

6

86-8

7

87-8

8

88-8

9

89-9

0

90-9

1

91-9

2

92-9

3

93-9

4

94-9

5

95-9

6

96-9

7

97-9

8

98-9

9

99-0

0

00-0

1

01-0

2

02-0

3

03-0

4

04-0

5

05-0

6

06-0

7

07-0

8

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Runoff in each sub-basin during all the years was aggregated separately. Computed runoff and

observed runoff was validated and calibrated at 5 prominent CWC gauge stations namely; Polavaram,

Asthi, Bamini, Patagudem and Tekra. Polavaram is the final gauge station in the Godavari Basin that

represents the hydrology of the complete Basin. It is found that at Polavaram computed runoff is very

well matching with the observed runoff. Deviation between average computed runoff and average

observed runoff is found to be 5.58 % only.

Mean water resources of the basin (1990-91 to 2007-08) computed is found to be 113.09

BCM. In this, evaporation from only 11 reservoirs (data provided by CWC) is considered. Mean water

resources availability in the Godavari Basin and its components are shown in the Figure 5.

Mean water resources of the basin during 1967-68 to 1984-85 as assessed by CWC was 110.54 BCM.

From the rainfall data analysis, it is found that nearly 8 BCM of rainfall has increased from the period 1973-1985 to

1988-2008; this may be one of the reasons for increase in the WRA of the basin during 1990 91 to

2007-08. From the rainfall data of 1973 to 2008 (35 years data) it has been inferred that 1994-95

and 2002-03 were maximum and minimum rainfall years having rainfall of 435.1 and 275.3 BCM

respectively. Hence, WRA during these two years were analysed separately. It is found that water

resources availability in maximum and minimum rainfall years are 178.7 BCM and 72.63 BCM

respectively.

Brahmani-Baitarani BasinRainfall varies both spatially and

temporally in the Brahmani-Baitarani basin.

Figure 6 shows the annual rainfall of 37 years

(1971-72 to 2007-08) in the basin. Among

these 37 years, the lowest annual rainfall

is 802 mm (2004-05) and highest annual

rainfall is 2022 mm (1994-95). The analysis of

rainfall during the study period of 1988-89 to

2007-08 (20 years) indicated that the average

annual rainfall is 1467 mm.

From the landuse/landcover map of

2004-05, it is found that 17 landuse classes

exist in the Brahmani-Baitarani Basin as

shown in the Figure 7. Forest cover forms the

major constituent (31.9%), followed by crop

area (29.15%) and current fallow (28.25%).

The remaining 10.7% of basin area is covered

by built up land, plantation, littoral swamp,

grassland, gullied land, scrubland, other

waste land and water bodies. The crop area

is further categorised as Kharif only (23.64%),

Rabi only (0.9%), Zaid only (0.26%) and

Double/Triple (4.35%) classes.

Fig. 5: Water Resources Availability in the Godavari Basin

137Fig. 6: Annual Rainfall of Brahmani-Baitarani basin from 1971-72 to 2007-08

71-7

2

72-7

3

73-7

4

74-7

5

75-7

6

76-7

7

77-7

8

78-7

9

79-8

0

80-8

1

81-8

2

82-8

3

83-8

4

84-8

5

85-8

6

86-8

7

87-8

8

88-8

9

89-9

0

90-9

1

91-9

2

92-9

3

93-9

4

94-9

5

95-9

6

96-9

7

97-9

8

98-9

9

99-0

0

00-0

1

01-0

2

02-0

3

03-0

4

04-0

5

05-0

6

06-0

7

07-0

8

Year

Rai

nfa

ll (m

m)

2100

1900

1700

1500

1300

1100

900

700

The main soil types found in the

basin are red and yellow soils, red sandy and

loamy soils, mixed red and black soils and

coastal alluvium. The coastal plains consist

of fertile delta area highly suited for intensive

cultivation. The soils are classified based on

the soil textural information as sandy, loamy,

clayey, loamy skeletal, clay skeletal.

Annual groundwater flux in the

basin is found to vary from + 7m to -7m. In

some pockets these fluctuations are more,

otherwise sustainable flux is noticed. Specific

yield of the basin was assumed as 3% as the

field data is not available. The mean annual

groundwater flux in the basin is estimated at

14 MCM (recharge). It is noticed that many

reservoirs maintain sustainable flux (less

annual flux). The mean annual reservoir flux

of all the 3 major and medium reservoirs is

estimated at 11 MCM (drawdown). The mean

annual domestic and industrial consumption

flux is estimated at 53 MCM in the basin.

On Brahmani River Tilga, Jaraikela,

Panposh, Gomlai and Jenapur discharge sites

are located and the model estimated runoff is

calibrated against the observed discharge at

all the locations. Similarly, on Baitarani River,

Champua and Anandpur discharge sites are

located and the model estimated runoff is

calibrated against the observed discharge

at these 2 locations. For the combined

Brahmani-Baitarani deltaic region observed

discharge data is not available. Hence, for

entire Brahmani-Baitarani basin, runoff is

computed by adding model estimated runoff

at Jenapur(entire upstream), Anandpur (entire

upstream) and Delta region (exclusive) and

computing calibrated runoff.

The average annual computed runoff of the basin is about 35,129 MCM. The maximum annual computed runoff is

60,429 MCM during 1994-95 which is wettest year in the 20 years. The minimum annual computed runoff is 12,003

MCM during 2004-05 which is the driest year in the 20 years. The maximum annual water resources available in the basin is

62,020 MCM during 1994-95 and the minimum annual water resources availability is 13,922 MCM during 2004-05.

The average annual available basin water resources is 35,129 MCM. The average available water resources of Brahmani-

Fig. 7: LandUse/Landcover Map of Brahmani-Baitarani basin (2004-05)

Fig. 8: Mean Water Resources Availability in the Brahmani-Baitarani Basin

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Baitarani basin accounts about 47% of mean annual rainfall. Mean annual water resources availability

and its components are shown in the Figure 8.

The mean annual rainfall during 1988-2008 is 6.6% more than the mean annual rainfall

during the period 1971-85 which was considered for 1993 estimate.

In 1993 estimates, the river discharge data was available only at Jenapur on Brahmani river

and at Birdi on Baitarani river. Discharge data at the outlet of the composite basin was not available.

Hence, area proportionate approach was adopted to estimate composite delta water resources. While

using this approach, the composite delta area was estimated at 3,595 km2 as against the delta area

estimated from present study is 7,887 km2 (which is based on geo-spatial data sets). As a result of

this, the water resources estimate of composite delta was 2,050 MCM during 1993 as against present

estimate of 4,780 MCM.

Following the successful completion of the present pilot project in the Godavari and Brahmani-

Baitarani Basins, it is proposed to extend the study to other river basins of the country as a joint project

by National Remote Sensing Centre and Central Water Commission, India.

ConclusionIn this study, a procedure was developed for realistic assessment of water resources at basin scale

using a simple Thornthwaite and Mather method by incorporating landuse coefficients derived from

the remote sensing data. This approach requires limited meteorological data and can be up-scaled to

other river basins in India. This study emphasises quantifying India’s water wealth by transformation

from presently adapted basin terminal gauge site runoff aggregation to meteorological based water

budgeting exercise through hydrological modeling approach. The spatial modeling approach can

help in quantifying water resources availability in any major tributary of the basin also. This modeling

approach can help in studying impact of future climate change in water resources of the basin. Different

components in the water balance such as; evapotranspiration from agriculture, forest area and other

landuses can be computed in spatial environment using this spatial modeling approach. Accuracy of

runoff computations are found to be more than 90% compared to the field observations.

AcknowledgmentsThe authors sincerely thank Dr. V K Dadhwal, Director, NRSC for providing constant support,

encouragement and guidance during the project. Thanks are due to Dr Raghava Swamy, DD, training,

NRSC and former Deputy Directors of RS&GIS AA, NRSC for providing continuous encouragement and

guidance during the project. The authors sincerely acknowledge the support and guidance provided

by Sri. M. E. Haque, Member (WP&P), Sri. Shankar Mahto, then CE (BPMO) and Sri. Rishi Sreevastava,

CWC, New Delhi. The authors deeply acknowledges the support provided by Sri. V. N. Wakpanjar and

Sri. M. Raghuram, CWC, Hyderabad for providing the field data. The authors sincerely acknowledge

Ashis Benarjee and Lalit Kumar, CWC, New Delhi for their contribution in executing the project and

participating in the discussions. The authors thank Smt. R. Jyothsna, JRF, NRSC and Smt. K Anusree

for providing technical support during the project.

References

Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E. and Schulze, E.D., (1996). Maximum

rooting depth of vegetation types at the global scale. Oecologia. 108, 583-595. 139

CWC: (1999) Reassessment of Water Resources Potential of India. CWC, New Delhi.

Descheemaeker, K. , Raes, D., Allen, R., Nyssen, J., Poesen, J., Muys, B., Haile, M. and Deckers, J. (2011). Two rapid

appraisals of FAO-56 crop coefficients for semiarid natural vegetation of the northern Ethiopian highlands. Jouranal of

Arid Environments. 75, 353-359.

FAO: Crop evapotranspiration – guidelines for computing crop water requirements. FAO CorporateDocument Repository.

www.fao.org.docrep/x0490e/x0490e0h.htm

Jianbiao Lu, Ge Sun, Steven G. McNulty and Devendra M. Amatya, (2005 ). “A comparison of six potential evapotranspiration

methods for regional use in the Southeastern United States”, Journal of the american Water Resources Association,

41(3):621-633.

Mohan, S. and Arumugam, N., (1994). Crop coefficients of major crops in South India. Agricultural Water Management.

26, 67-80.

NCIWRD, (1999). Integrated Water Resources Development, A plan for the action, Report of the national commission

for Integrated Water resources development, Volume 1, Ministry of Water Resources, Government of India.

NRSC, (2009). Water Resources Assessment the National Perspective- A Technical Guide for Research and Practice, NRSC-

RSGIS AA-WRG-WRD-Oct2009-TR98.

Peter E Black, Revising the Thornthwaite and Mather Water Balance, http://www.watershedhydrology.com/pdf/T&M%20

Revisited.pdf

Rajeevan M. and Jyote Bhate, (2008). “A high resolution daily gridded rainfall data set (1971-2005) for mesoscale

meteorological studies”, National Climate Centre Report, India Meteorological Department, Pune.

Rao, K.L., (1979). India’s Water Wealth, Its Assessment, Uses and Projections, Pub. Orient Longman Limited,

New Delhi.

Ray, S. S. and Dadhwal, V. K., (2001). Estimation of crop evapotranspiration of irrigation command area using remote

sensing and GIS. Agricultural Water Management, 49, 239-249.

Thronthwaite, C.W. and Mather, J.R. (1957). Instructions and tables for computing potentialevapotranspiration and water

balance, Laboratory of Climatology, Publication No. 10, Centerton, NJ.

Sokolov, A.A. and Champan, T.G., (edited 1994). Methods for water balance computations – an international guide for

research and practice. The Unesco Press, Paris

X U CY and Singh V P. (1998). A Review on Monthly Water Balance Models for Water Resources Investigations. Water

Resources Management 12: pp. 31–50.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

SNOW MELT RUNOFF MODELING IN HIMALAYAN RIVER BASINS Siva Sankar E, Abdul Hakeem K, Simhadri Rao B National Remote Sensing CentreISRO, Department of Space, Hyderabad - 500 037, IndiaEmail: sivasankar_e@nrsc.gov.in

IntroductionMore than half of humanity relies on the freshwater that accumulates in mountains.

The Himalaya mountains located in northern part of India holds one of the largest resources

of fresh water in the form of snow and ice and abode of many glaciers and glacial lakes. A

significant portion of the low flow contribution of Himalayan rivers during the dry season

is from snow and glaciers melt. The runoff supplies communities with water for drinking,

irrigation and industry, and is also vital for maintaining river and riparian habitat.

In Himalayas, the snow melt runoff occurring mostly during April, May and June

months constitutes a substantial part of the water resources of the major perennial rivers of

Northern and Eastern India, namely the Indus, Ganga, Brahmaputra and their tributaries.

However, the data availability situation in the Himalayas is quite critical. The snowcover

accumulated in winter months melts in lean summer months, which is vital during the period

of high demand for water and power. A timely forecast of snow melt water is very useful

in planning and managing the multi-purpose project. To forecast such runoff, information

on terrain, snow pack and meteorological parameters are required. But collection of such

information is not always possible by ground based systems due to rugged, hazardous and, in

most cases, inaccessible terrain and sometimes due to the areas extending across international

boundaries. Satellite remote sensing is therefore best suited for collecting information on

the snow-cover under such situations.

Snowmelt Runoff Forecast in Sutlej Basin Based on Depletion Concept

For more than a decade, National Remote Sensing Centre (NRSC) has been giving

long-term (during April-May-June) snow melt runoff forecast from Sutlej basin into Bhakra

reservoir (NRSC, 2010). The Sutlej river is one of the main tributaries of Indus and has its

origin near Manasarovar and Rakas lakes in Tibetan plateau. It travels about 300 km in

Tibetan plateau in North-Westerly direction and changes direction towards South-West

and covers another 320 km up to Bhakra gorge where 225 m high straight gravity dam has

been constructed. This western Himalayan basin is highly rugged terrain with abundant

natural water resource in the form of snow pack. It cover and area of about 51,000 km2.

Characteristics of the basin and inaccessibility of the major part of it make remote sensing

application ideal for hydrologists to monitor the snow cover information of the region and

assess the water resource.

The contribution of snow melt is quite significant in the Sutlej river. The snow melt exceeds the rainfall component

after middle of March leading to onset of depletion of snow pack in the basin. The main snow accumulation period

extends from October to March. Sometimes, intermittent snowfall events occur in April and May for short durations.

Snow Cover Area MappingThe satellite data from AVHRR sensor onboard NOAA satellite has been used to compute snow cover area in

the basin during the snow melt periods. Field data on discharge, snowfall and temperature were collected from stations

established by Bhakra Beas Management Board (BBMB), all located inside the Indian territory of the basin. Cloud free

images during accumulation and depletion period (generally between November to June every year) were used to derive

Snow Cover Area (SCA) for the basin. Snow cover area statistics were derived for three regions of the basin namely,

Spiti, Tibet and lower Sutlej.

Snow Cover Accumulation and DepletionSnow cover depletion curve is generated with SCA as a percentage of the total basin area versus the time scale.

This depletion curve graphically depicts the gradual change in the snow cover area over a time period. In physical terms,

it indicates the change in the Snow Water Equivalent (SWE) of the snow pack over time representing the rate at which

the snow pack and the embedded SWE change during the melting period, namely, April - May - June. This curve also

indicates the occurrence of temporary snowfall events during the melting period by way of sudden increase in the SCA

values. In the forecast situation, the satellite data is processed in near-real time as soon as the satellite overpasses the study

area. The percentage SCA as on 1 April is interpolated from depletion curve and it is used as input for forecasting.

Initial Snow Melt Runoff ForecastThe hypothesis of depletion analysis is that the thick snow pack having high water equivalent depletes later and

slower compared to a thin snow pack having low water equivalent which depletes early and faster. The SCA, the rate of

depletion of snow cover at the beginning of the melt period and the commencement of depletion help in characterizing

the current snow pack vis-a-vis the previous years qualitatively.

The depletion analysis helps to assess the type of snow pack existing in the basin and the likely trend it may

follow. It also indicates the relative/ qualitative status of the SWE with reference to the previous years. An empirical

relationship has been developed by regression analysis. The maximum snow cover in the basin does not necessarily yield

maximum runoff. Also, same snow cover area in the basin does not necessarily yield same runoff in two different years.

In view of the complex topography and varied hydro-meteorological settings within the basin, the spatial variation in

snow cover needs to be addressed to understand the snow melt process. The spatial distribution of snow cover in the

3 regions namely the Spiti, Tibet and lower Sutlej regions and their respective contributions to snow melt runoff need

to be modeled to explain the spatial variability. The historic discharge data of the three regions have been studied and

weightage factors have been arrived at to represent the contributions of the 3 regions to the total runoff. The weighted

snow cover area index has been computed with above factors. The inflows volume is regressed with weighted snow

cover area index.

Revised Snow Melt Runoff ForecastThe monitoring of snow cover in the Sutlej basin was continued during the summer months i.e. April, May,

June. The snow cover area in the regions of the Sutlej basin was computed. The snow cover depletion curves, the rate of

depletion of snow pack were analysed. Any deviation in the actual depletion pattern from the assumed depletion pattern

at the time of initial forecast is found through the monitoring of satellite derived snow cover and other meteorological

observations, or whenever any new information on the ground situation is obtained, and the initial forecast needs to

be revised.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3The seasonal snowfall accumulated in the basin during winter months in the form of snow

pack melts during the subsequent summer months while the intermittent snowfall occurring during

April-May-June is less significant. Hence, the snowfall accumulated in the basin till 31st March of the

year is considered for the purpose of computing the snow melt runoff. During the forecast period,

the snow cover in the basin depletes meaning the water embedded in the snow pack is depleting

with energy input. Therefore, the product of snow cover area on a particular day and the snow fall

index derived from ground measured snowfall data cumulated till 31st March works as an index of the

water volume embedded in the snow pack as on that day. This product does have a relation to the

snow melt runoff occurring from that day to 30th June of that season which is the potential snow

melting season.

The actual ground measured snowfall data and temperature data are usually received from

BBMB in the middle of May. The snowfall measured at 21 gauge stations within Indian portion of

Sutlej basin does not explain the complete spatial variability within the basin. The snowfall measured

at Namgia is assumed to represent the Tibet region while the snowfall measured at other stations

represent Spiti and lower Sutlej regions based on their physical location.

A model has been developed using regression analysis. The snow melt runoff occurring

between the date of satellite overpass and 30th June is taken as dependent variable. The sum of products

of SCA and the average snowfall in that region is taken as independent variable. SCA values derived

from satellite data of multiple dates during the melting season of the previous years have been used

in the regression analysis along with the corresponding snowfall data.

Q= c*A*(C1S1W1+C2S2W2+C3S3W3)

Q - Inflows between the date of SCA computed and 30th June in cusec-days

C1, C2, and C3 - Runoff coefficients for three regions

S1 - SCA in Spiti region as % of the region area

S2 - SCA in Tibet region as % of the region area

S3 - SCA in Lower Sutlej as % of the region area

W1 - Average snowfall in Spiti region measured till 31 March

W2 - Average snowfall in Tibet region measured till 31 March

W3 - Average snowfall in Lower Sutlej basin measured till 31 March

A - Basin area

c - Unit conversion factor

NRSC provides initial forecast in the 1st week of April and a revised forecast in the last

week of May during every year. The forecast period was specific 91 day period i.e. from 1st April to

30th June. The actual measured daily inflows into Bhakra reservoir were provided by BBMB and the

cumulated inflows up to 30th June were computed in lakh cusec-days. The deviation in the forecasts

were calculated with reference to actual measured inflows. The summary of forecast results given

for last three years is shown in Table 1.

Snowmelt Runoff Modeling Based on Energy Balance ApproachBased on the experience gained in Sutlej basin, Central Water Commission requested NRSC

for developing seasonal and short term snow melt runoff models for Sutlej, Beas, Yamuna, Chenab

and Ganga basins.143

Table 1: Forecast results (in lakh cusec-days)

Table 2: Drainage area for each basin

Year Forecast Actual Deviation

2007 Initial 16 +/- 10% 12.48 +28.0%

Revised 14 +/- 10% +12.1%

2008 Initial 12 +/- 10% 16.95 -29.0%

Revised 12 +/- 10% -29.0%

2009 Initial 12 +/- 10% 11.14 +7.7%

Revised 12 +/- 10% +7.7 %

The development of snowmelt runoff models is carried out for the following basins.

1. Beas up to Bhuntar

2. Sutlej basin up to Bhakra

3. Chenab up to Premnagar

4. Yamuna up to Hatnikund

5. In the case of Ganga basin, model shall be developed for

a. Bhagirathi up to Uttarkashi and

b. Alaknanda up to Rudraprayag

The location map of study area is shown in Figure 1. The drainage area up to the basin outlet for each basin

is given in Table 2.

Basin Area (km2)

Beas up to Bhuntar 3,160

Sutlej up to Bhakra 51,451

Chenab up to Premnagar 17,273

Yamuna up to Hatnikund 11,323

Alaknanda up to Rudraprayag 10,201

Bhagirathi up to Uttarkashi 4,527

Fig. 1: Location map of study area

Data UsedThe snowmelt runoff model has

been developed using satellite data based

inputs as the availability of field measured

hydrometeorological data is less in most of

the basins.

The hydrometeorological data

such as snowfall, temperature were received

from CWC and is available at fewer stations.

The elevation range of the study area is

between 500 m and 7500 m in Himalayan

region whereas the majority of hydromet

stations are located below 2500 m and

very few between 2500 m and 4000 m.

However transient snow line is generally

above 2000 m.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3In the present study, spatially distributed modeling approach is adopted. The hydromet point

data at few stations cannot be accurately interpolated spatially for the entire basin. Therefore it is

observed that the data measured at these stations is not representing the entire basin and its spatial

variability.

The data used in the present study are:

Snow Cover Area (SCA), Land Surface Temperature (LST), emissivity Snow Albedo and Snow

Persistence Index (SPI) were derived from MODIS satellite data, while Glacier Cover Area (GCA)

and classified Land Cover were derived from AWiFS satellite data. The other data included digital

Elevation Model, Slope, Aspect from SRTM DEM data, Hydrometeorological data such as rainfall

and discharge

MethodologyThe snow melt runoff is modeled either by lumped approach using degree day index or by

empirical models with skeleton data sets or by energy balance approach wherein the spatial distributed

modeling is adopted. The present study proposes to use energy balance based approach for modeling

the snow melt runoff process and thus for forecasting the snowmelt runoff. The runoff at the basin

outlet comprises of snowmelt, glacier melt, runoff due to rainfall and base flow components. The

details of energy balance principles are mentioned in the following sections.

Energy Balance PrinciplesThe exchange of energy between the snowpack and its environment ultimately determines

the rate of snowpack water losses due to melting and evaporation / sublimation. Energy exchange

primarily occurs at the snowpack surface through exchange of shortwave and longwave radiation

and turbulent or convective transfer of latent heat due to vapour exchange and sensible heat due to

difference in temperature between the air and the snow. These linkages between the different energy

components responsible for snowmelt are summarized in Figure 2.

The sources of energy that cause snowmelt include both shortwave (Qsn) and long-wave (Qln)

net radiation, convection from the air (sensible energy, Qh), vapor condensation (latent energy, Qe),

and conduction from the ground (Qg), as well as the energy contained in rainfall (Qp) as shown in the

upper left of Figure 2. These fluxes are usually

measured as energy per time per unit area

of snow. The energy budget equation that

describes the energy available for snowmelt

is given in Equation 1 below. The total energy

available for snowmelt is Qm.

Qm = Qsn + Qln + Qh + Qe + Qg + Qp - ΔQi ------- (1)

where ΔQi is the rate of change in

the internal energy stored in the snow per

unit area of snowpack. This term is composed

of the energy to melt the ice portion of the

snowpack, freeze the liquid water in the snow,

and change the temperature of the snow.Fig. 2: Schematic diagram showing snowmelt processes (After Price, Hendrie, and Dunne (1979)

145

Whenever sufficient energy is available, some snow (ice) will melt and form liquid water. Since the physical structure

of the snowpack is a porous matrix, this snowmelt will be held as liquid water (provided it does not refreeze) in the interstices

between the snow grains and will increase snow density and snow water content. The snowpack is commonly called “ripe”

when it is isothermal at 0 °C and saturated. Whenever the capacity of the snowpack interstices to hold the liquid water is

exceeded, some of the snowmelt will begin to move down-gradient to become a portion of the snowmelt runoff. Some of

the snowmelt may infiltrate into the ground depending on inherent soil characteristics, the soil moisture content, as well as

whether or not the ground surface is frozen. The infiltrated snowmelt later re-emerges as interflow into stream channels,

or it percolates into deeper groundwater storage.

Energy Balance of the Snow CoverEvaluating snowmelt theoretically is a problem of heat transfer involving radiation, convection, condensation, and

conduction. The relative importance of each of these heat transfer processes is highly variable, depending upon conditions

of weather and the local environment. Gray and Prowse (1992) tabulate selected results of the relative contributions of

each heat transfer process as a function of site environment. The basic equations and coefficients that describe snowmelt

at a point have been derived primarily from various laboratory and field experiments.

Equation 1 given above summarized the energy sources available to melt snow. The summation of all sources of

energy represents the total amount of energy available for melting the snowpack (Qm). The amount of snowmelt at a point

may be expressed by the general formula given as Eqn. 2

M =Qm

LρwB---------------- ----------------(2)

Where -

M = snowmelt, m of water equivalent

Qm= algebraic sum of all heat components, kJ/m2

B = thermal quality of the snow (e.g., ratio of heat required to melt a unit weight of the snow to that of ice at 0 °C)

L = latent heat of fusion of ice, 334.9 kJ/kg

ρw = density of water, kg/m3

A melting snowpack consists of a mixture of snow (ice) and a small quantity of free (liquid) water trapped in the

interstices between the snow grains. The relative proportion of a snowpack that consists of ice determines the thermal

quality (B) of the snowpack. A snowpack that contains no free water has a thermal quality of 1.0. However, after melt

has begun, there is some free water held within the snow matrix, yielding a thermal quality of less than 1.0. The heat

energy required to release 1 g of water is somewhat less than the latent heat of fusion of water (that is the energy

required to change state from ice to water; 334.9 kJ/kg or 80 cal/g for pure ice). For a melting snowpack, after free

drainage by gravity for several hours, the thermal quality normally averages between 0.95 and 0.97, corresponding to

3 to 5 percent liquid water in the snow. The thermal quality of snow may be far lower for “ripe” snows and in extreme

cases where the water cannot drain freely from the snowpack.

Radiational Energy ExchangeRadiational energy is the prime source of energy at the Earth’s surface. Some of this energy is classed as solar

or shortwave radiation (radiation having wavelengths (λ) between 0.2 and 2.2 µm) and terrestrial or long-wave radiation

(wavelengths between 6.8 and 100 µm). The first two terms of Equation 1 are sometimes referred to as net radiation

Qn, the sum of net shortwave Qsn and net long-wave Qln energy fluxes. As the net long-wave exchange is often a loss

from the snow surface, Qn is expressed as -

Qn = Qsn - Qln

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Shortwave Radiation : Shortwave radiation is the most important source of energy to the

snowpack. The net amount of radiant energy that is available to melt snow depends on how much of

the radiation is either reflected from or absorbed by the snowpack. The amount of heat transferred

to the snowpack by solar radiation varies with latitude, season, time of day, atmospheric conditions,

forest cover, and reflectivity of the snow (albedo).

The amount of energy available for snowmelt from the absorption of shortwave radiation

(Qsn) is given in equation for

Qsn = (1-α) Ii ------------(4)

Where-

α= albedo (expressed as a decimal fraction)

Ii= daily incident solar radiation (kJ/m2 per day)

The short wave energy is corrected for the influence of forest canopy using landuse and

landcover information.

Longwave Radiation : Some of the energy absorbed by the snowpack from solar radiation

is radiated from the snowpack to the atmosphere as long-wave radiation. Snow is nearly a perfect

blackbody, with respect to long-wave (terrestrial) radiation, absorbing all such radiation incident

upon it and emitting the maximum possible radiation in accordance with the Stefan-Boltzman law

(Equation 5).

Qln = εσTs4 ------------(5)

Where -

Qln= total longwave energy emitted by the snow, kJ/m2 per second

ε = emissivity, 0.99 for clean snow

σ = Stefan-Boltzman constant, 5.735 × 10-11 kJ/m2s K4

Ts= blackbody temperature in Kelvin (K) (temperature of the snow surface)

The long wave energy is corrected for the influence of forest canopy using landuse and

landcover information.

Turbulent TransferEnergy is also exchanged between the snow pack and atmosphere through the processes

of convection and condensation. Depending on the climatological and local weather conditions, the

relative importance of these processes differs widely. Turbulent exchange involves the transfer of

sensible heat from warm air advected over the snowfield (convection), and also the latent heat of

condensation of water vapor from the atmosphere condensed on the snow surfaces.

Qh = Dhuz(Ta-Ts) ------------(7)

Qe = Eeuz(eu-es) ------------(8)

Where -

Dh = bulk transfer coefficient for sensible heat transfer, kJ/m30C

Ee = bulk transfer coefficient for latent heat transfer, kJ/m3 Pa147

uz = wind speed at a chosen height above the snow surface, m/s

eu = vapour pressure of the air, Pa

es = vapour pressure of the snow surface, Pa

Ground HeatHeat entering the snow from the ground (Qg) by solid conduction is a very small component to the overall energy

budget, especially compared with the radiational and turbulent exchange at the air/snow interface. This ground heat

component can be neglected over short periods of time (less than 1 week). Although the daily melt caused by ground

heat is small, it can amount to a significant quantity of water over an entire snow season. Most lumped, conceptual

models use constant daily values in the range of 0-5 J/m per second. Ground heat flow can also be estimated using

soil temperature gradients measured near the surface in an equation for steady-state, one-dimensional heat flow by

conduction (equation 9):

Qg = k dTg /dz ------------(9)

where -

k = thermal conductivity of the soil

Tg = Soil temperature

dTg /dz = temperature gradient from soil to snow

Heat Convected by RainThe heat convected from the snow by rainfall is (equation 10)

Qp = CpρwPr(Tr-Ts)/1000 ------------(10)

Where-

Cp = specific heat of rain, kJ/kg °C

ρw = density of water, kg/m3

Pr = rain quantity, mm/unit time

Tr = temperature of the rain, °C

Ts = snow temperature, °C

The temperature of the ra in i s as sumed to be equa l to the a i r temperature or, i f

available, the wet-bulb temperature. The specific heat Cp is equal to 4.20 kJ/ (kg °C) for rainfall and

2.09 kJ/(kg °C) for snowfall.

Internal EnergyBy definition, if the cold content or heat deficit of the snowpack is positive, the snowpack’s temperature is

below freezing. The internal energy Qi can be changed and the heat deficit reduced by the heat released when melt or

rainwater freezes within the snow cover. This phenomenon is prominent during diurnal temperature cycles with refreezing

at night because of radiational cooling. Melt and rainwater will continue to freeze within the snow cover until the total

heat deficit reaches zero. When the total heat deficit reaches zero, the snow cover will become isothermal at 0 °C. This

internal energy is calculated by the following expression (Gray and Prowse 1992) equation 11:

Qi = ds(ρiCpi + ρlCpl + ρvCpv) Tm ------------(11)

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3where -

ds = depth of snow

ρ = density, ρi = 922 kg/m3 , ρl = 1000 kg/m3

Cp = specific heat, Cpi ≈ 2.1 kJ/kg °C; Cpl ≈ 4.2 kJ/kg °C

Tm = mean snow temperature, °C

The subscripts i, l, and v refer to the ice, liquid, and vapor phases, respectively. The contribution

of the vapor phase is assumed negligible.

Snowmelt RunoffBased on the energy balance

approach, the snow melt is calculated

spatially using the remote sensing based

inputs from various sources. The overview of

the methodology is shown in the flow chart

given in Figure 3.

Initial ly the basin boundaries

have been delineated using SRTM digital

elevation model and AWiFS satellite data

considering the basin outlet points at

which the runoff forecast is proposed to

be made. The AWiFS satellite data has

been used to map the land use and land cover

in the study area. The land cover is categorized primarily into open areas, forest, deciduous forest

and water bodies.

The snow cover present in a basin can be mapped using appropriate satellite data. As the

study necessitates frequent temporal observations, it was decided to use snow cover products available

as 8-day time composites on MODIS web site for free download. The 8-Day time composites are for

specific Julian 8-Day periods commencing from 1st Jan of a year. The maximum possible snow cover

during the 8-Day time period is mapped in this snow cover product. The data from 30-March to 30-

June were used for this purpose. The snow cover products are reprojected and subsets are prepared

for the study area. Snow cover in each basin is computed using basin mask.

The total net energy flux is primarily contributed by incoming solar radiation and outgoing

longwave radiation. The incoming solar radiation is a function of location of a pixel (latitude, longitude),

elevation, Julian day and time. The longwave radiation is a function of surface temperature, air

temperature, and emissivity.

In addition, to account for snow depth in an indirect manner, the snow cover depletion concept

is used. The snow cover depletion concept states that a thicker snowpack depletes slower and later

than a thinner snowpack, which depletes faster and earlier. Based on this concept, to account for

snow depth in an indirect manner, snow persistence index is computed for each year depending on

the snow residency period at each pixel. The index is scaled between 0.1 and 1 where 1 representing

a pixel containing snow for full 91 day period and 0.1 representing a pixel containing the snow for

minimum period. This persistence index is used to qualify the snowpack in runoff estimation.

Fig. 3: Overview of the methodology followed in snow melt runoff modeling

149

The snow melt runoff in a basin is computed using the total energy available for melt during that 8-day

time period and snow persistence index. The snow melt runoff for all the 12 time periods of 8-days between 1st April

and 30th June are computed individually with corresponding data inputs and then integrated to arrive at seasonal snow

melt runoff.

Glacier Melt RunoffThe presence of glaciers in a watershed or a basin significantly affects runoff volume. The partial glacierization

of a basin by as little as a few percent of cover can cause moderate to extreme variations in peak runoff magnitude and

frequency over days, year, and decades. Runoff is not directly related to precipitation within a glacierized basin, so it is,

at present, difficult to predict because of lack of glacio-hydrologic data and limited, rather rudimentary knowledge of

the glacio-hydrologic process controlling runoff.

Runoff from glacierized areas of a basin is generally greater than that from nonglacierized areas with similar

precipitation. Majority of runoff from ice-covered areas comes during the melt season, generally from mid-May to

mid-September.

The glacier melt runoff during summer months is a significant component of total runoff. The glacier melt runoff

depends on the extent of glacier area within a basin and the level of exposure of the glaciers and duration of exposure

during the progress of the snow melt season. The level of exposure of glaciers in the summer months depends on the

presence of seasonal snow cover. The probability of glacier melt and its magnitude is more when the seasonal snow

cover is less. The glacier melt runoff increases as the snow melt season progresses.

For the purpose of estimation of glacier melt runoff, it is necessary to map the glacier in each basin using satellite

data of the September - October months. The glacier map prepared from satellite data is used to estimate glacier melt

volume during summer period considering the net energy available for melt. The glacier melt component during the 3

months period is computed with energy available for melt and the level of exposure of glacier in each basin.

Qg = Cg * Glacier Area

Rainfall RunoffThe runoff due to rainfall constitutes significant proportion of the total runoff during the snow melt season in

Himalayan region. The rainfall in a region varies spatially and temporally and needs to be measured in systematic and

well representative manner. The accuracy of runoff estimation due to rainfall depends on the number of rain gauge

stations available and their distribution within the basin. Generally, in Himalayan mountainous region where elevation

is more than 4500 m only snowfall occurs and rainfall does not occur. Hence, in these study areas with elevation below

4500 m is considered as areas where rainfall contributes for generating runoff. In each basin, areas with elevation lower

than 4500 m is extracted using DEM.

Mean rainfall of a basin is estimated depending on the number of rain gauge stations available within a basin.

In basins where only one rain gauge station is available the rainfall at this station is assumed to be representative of the

entire basin. In basins where multiple rain gauge stations are available theissen polygon method is used to estimate the

mean rainfall of the basin.

The rainfall runoff is estimated using suitable runoff coefficient. In the present study, the scope is restricted to

runoff due to rainfall during snow melt period i.e. Apr-May-Jun months. An appropriate runoff coefficient is assumed

for each basin which varies temporally during the season. Basin level runoff from rainfall is computed by multiplying the

average rainfall with rainfall contribution area and runoff coefficient.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Qr = Cr * Mean Rainfall * Rainfed Area

Base FlowBase flow as defined by Hall (1968) is the portion of flow that comes from groundwater or

other delayed sources. During snow melt season, the base flow is a part of total runoff. When snow

melts, a significant portion of it percolates in the underlying ground and joins the stream within the

basin, sometimes with a time delay. In the initial part of snow melt season, depending on the soil

conditions, the snow melt water percolates until the ground is saturated before it becomes direct

runoff. The base flow varies marginally depending on the snow cover conditions prevailing in the basin.

The base flow during winter months is generally constant. The base flow during summer months

may be similar to that of winter months or it can vary marginally. In the present study, the base flow

is empirically estimated as a function of seasonal Snow Cover Available in each basin.

Qb = Cb * SCA

Total RunoffThe total runoff during snow melt season (Apr-May-Jun months) is the sum of snow melt

runoff, glacier melt runoff, rainfall runoff and baseflow components. The runoff measured in the

field at the outlet point represents total runoff and the same is compared with computed runoff. The

details are shown in the flow chart (Figure 4).

Q = Qs + Qg + Qr + Qb

ConclusionThe seasonal snow melt runoff model for

all the basins has been developed and the

experimental forecast has been provided to

CWC. The results are encouraging. Some

more finer modifications are being made to

the model based on the experimental forecast

and its verification.

ReferencesNRSC (2009). Inventory of glacial lakes and water bodies in Tawang river basin, Technical Report, National Remote Sensing Centre.

NRSC (2010). Snow melt runoff forecasting in Sutlej basin using remote sensing inputs, Technical Report, National Remote Sensing Centre.

Gray, D.M., and Prowse, T. D. (1992).”Snow and floating ice, “ in Hand book of Hydrology, D.R. Maidment.,ed., McGraw-Hill, Inc., Newyork:7.1-7.58.

Hall, F.R. (1968). Base flow recessions--a review. Water Resources Research., Vol.4 Issue 5: 973-983.

Fig. 4: Total runoff estimation

151

REMOTE SENSING IN GROUNDWATER MODELING

Sudhir Kumar and Sanjay Kumar Jain National Institute of Hydrology, Roorkee – 247667, India Email: sudhir.nih@gmail.com

IntroductionGroundwater is an important natural resource. Availability and utilisation of groundwater

plays a leading role in social and economic development of the nation. For scientific exploitation of

groundwater, proper and good quality data is required to be collected and well documented. Geological

and hydrological data are required to be collected before initiating studies on groundwater problems.

This includes information on surface and subsurface geology, aquifer type, aquifer parameters, water

tables, precipitation, evapotranspiration, stream flows, land use, vegetative cover on the surface,

extraction from study wells, aquifer boundaries, irrigation, aquifer characteristics etc. These data are

important not only for exploitation but also for planning, design and operation of the groundwater

structures. Since groundwater is a dynamic source, the accuracy and reliability of acquired data usually

increases with the time available for observation and interpretation.

Extensive groundwater exploitation during past two decades for meeting irrigation, water

supply and industrial needs, has drawn the attention of groundwater hydrologists towards maintaining

sufficient groundwater supply throughout the year. The assessment and management of groundwater

resources are more important in the sense that groundwater is more protected against pollution, has

natural system for its storage, does not have the problem of huge evaporation losses. In order to

arrive at the management alternatives, the assessment of groundwater potential and the behaviour

of groundwater system under various stress conditions are required besides the alternatives for the

creation of additional resources. All these require the collection of reliable data and their compilation

and processing in an intelligent manner.

Groundwater ModelingGroundwater models are tools that help in understanding the physical, chemical, and

biochemical processes taking place in groundwater systems. They also help in understanding the

intricate interactions between these processes, and they can provide the information needed in order

to manage these processes beneficially and without harm to the environment. Numerical models are

now used in virtually all areas of groundwater hydrology.

A series of mathematical formulas are linked together to explain the working of a particular

phenomenon. A good model has the ability to predict the outcome of a set of inputs, as they would

affect the real world.

A simulation model is used to analyze the known information about a data feature. This

could be a stretch of stream, a point-source of pollution, or a census tract. A simulation model uses

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3information from the data table associated with the object, plugs that information into a formula,

and creates a new result based on the information from one or more variables. These models deal

with extracting more information from what is already known about a feature.

A predictive model, on the other hand, is used to predict how a change in a variable will

affect other conditions. Once again, this can be applied to any data feature, but it applies more to

the attributes of a feature than the feature itself. For instance, such a model might be used to predict

the effect of any stress (pumping, discharge of pollutants etc.) on the groundwater conditions.

These models deal with predictive models deal with changes that will occur if certain variables of a

feature change.

Groundwater models are broadly grouped into two categories: water quantity and water

quality. In the water quantity category, models are used in aquifer management, well field design,

groundwater recharge enhancement, determination of optimum aquifer yield, well interference studies,

studies of groundwater-streamflow interactions, and similar problems. Models of this type have been

well proven in many years of use.

In the water quality category, groundwater models are used to study the consequences of

groundwater contamination, the measures that need be taken to prevent contamination, and the

design of remediation measures.

Natural groundwater systems are often highly complex and a complete description of the

physical characteristics in most cases is impossible. Instead, the physical properties of the system are

generally described either in terms of averages or in the form of statistical distributions. Also, physical/

chemical/biochemical processes can interact in complex ways and some of these interactions (i.e.

reaction kinetics) are not yet fully understood.

In view of these complexities, it is risky to use a model to predict in absolute terms, for example,

the extent of a contamination plume, or the status of a clean-up project. Such absolute predictions

are justified only in very simple scenarios. On the other hand, groundwater models are appropriately

and meaningfully used for purposes of (i) obtaining insight into complex processes, (ii) assessing the

relative importance of the various processes occurring in a given situation by means of sensitivity analyses, (iii)

analyzing “worst-case” or “what if” situations and (iv) making probabilistic predictions.

Constructing a Numerical ModelTo construct a numerical model of a groundwater system, the geological units and their

hydrogeologic properties within the domain of interest are defined. The domain should be selected

such that conditions along its boundaries can be defined unambiguously.

Next, the processes that may play a role in the transport of contaminants are defined. These

processes will include; groundwater flow, advective transport, dispersive/diffusive transport, chemical

interactions, radioactive or biological decay, gravity forces, thermal processes and capillarity.

The processes taking place in a groundwater system are subject to physical laws such as;

constitutive laws (Darcy’s law, Fick’s Law), conservation laws for fluid mass, solute mass, thermal energy 153

and force equilibrium laws. These physical laws can be expressed mathematically in terms of governing equations, which are

usually partial differential equations. In cases of linear processes with simple geometry, these equations can generally be solved

by analytical methods. In general cases involving nonlinear processes or complex geometry, the equations are

solved numerically.

Conceptualization of Groundwater ModelThe mathematical description of the relevant physical laws, together with the description of the hydrogeology

and the definition of the boundaries, constitute the conceptual model of the system. In order to solve the mathematical

equations numerically, the system is discretized and the partial differential equations are approximated by algebraic

equations at a finite number of points in the domain. The numerical solution then solves the resulting sets of algebraic

equations. Typically, several thousands of simultaneous equations are solved repeatedly in contaminant transport

problems.

The entire solution process is formally expressed in a computer code. Although often the computer code is seen

as “The Model”, the most important component is actually the conceptual model which has been selected to represent

the real physical system and the processes taking place within this system. Thus great care must be taken in defining

the conceptual model.

Before the model can be applied to a real system, the models should be calibrated and vaidated. Once the

model is properly calibrated and validated, it can be applied to simulate the processes occurring in a real system, to

perform sensitivity analysis, and to investigate “worst-case” situations. At this stage, the model should be used to obtain

the best possible insight into the system, and to use this insight in developing appropriate strategies for achieving the

overall objectives of the study.

Numerical Methods for Groundwater ModelsThe types of numerical methods that are most often used in groundwater modeling are Finite Difference, Finite

Volume, Finite Element, and Particle Tracking Methods. The first three of these approaches share the basic formulation

of the solution as a boundary value problem. The finite difference method in its elementary form, which is the oldest of

the above methods, has the advantage of being conceptually simple and easy to understand. The finite volume method

can be considered a variant of finite difference method and it has certain advantages with respect to the maintenance

of local mass balance. The finite element method is highly flexible and versatile in representing domains with irregular

geometry or anisotropic and heterogeneous media; it is somewhat more complicated than finite differences, but it provides

a very powerful and versatile tool to the user once the basic principles are mastered. In terms of overall accuracy, these

methods are essentially equivalent. Any of these methods can be applied to the solution of flow and transport problems,

which allows for the incorporation of chemical-biochemical interactions.

Particle Tracking method differs from the above methods as it does not solve a boundary value problem, but

instead considers the fate of tracer particles as they migrate through the system. It is therefore applicable to the solution

of transport problems only. Particle tracking models are usually coupled with finite difference models for determining

the flow field. The main advantage of this technique is a simple and understandable concept. Its disadvantage is that

chemical/biochemical interactions cannot be easily incorporated in a general way (although some advances in this area

are being made). The method is particularly useful for the simulation of hydraulic remediation measures and the definition

of well head protection zones.

The user of numerical models should be aware of certain pitfalls which may affect the quality of numerical

simulations. One of these pitfalls is numerical dispersion, which may arise in the solution of the transport equation by

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3either finite differences or finite elements (the particle tracking method is not affected by numerical

dispersion). This effect can be deceiving to the novice because modeling results may look perfectly

reasonable. Techniques for the control of numerical dispersion have been developed and are easy

to implement.

Excellent texts on groundwater modeling at the introductory level are Kinzelbach (1986), and

Wang and Anderson (1982). Huyakorn and Pinder (1983) give an advanced treatment of numerical

methods with a strong mathematical basis.

For developing a groundwater model, following basic steps are involved:

• Developaconceptualmodelofthesystem.

• Designagridtorepresentthemodelareawithasmuchdetailasneeded.

• Assignhydrologicpropertiestoeachgridcell.

• Selectanappropriatetimestepfortransientmodels.

• Setupboundaryconditions.

• Selectasolver.

• Runthemodelandcomparewithmeasureddatatoevaluatetheeffectivenessofthemodel.

• Ifnecessary,revisethemodel.

• Plottingthedata,and

• Trackingdownerrors.

Numerical Groundwater ModelsThe three dimensional unsteady movement of groundwater of constant density through

porous earth material in a heterogeneous anisotropic medium can be described by the following

partial differential equation:

x,y,z = Cartesian coordinates aligned along the major axes of conductivity Kxx, Kyy, Kzz

h = potentiometric head [L],

W = volumetric flux per unit volume and represents sources and/or sinks of water [T-1],

Ss = specific storage of the porous material [L-1] and,

t = time [T].

In general, Ss, Kxx, Kyy and Kzz are functions of space, for example; Ss = Ss(x,y,z), Kxx = Kxx(x,y,z),

etc. whereas W and h are functions of space and time i.e W = W(x,y,z) and h = h(x,y,z). Equation (1)

together with specification of flow conditions at the boundaries of an aquifer system and specification

of initial head conditions, constitutes a mathematical model of ground water flow.

Except for very simple systems, analytical solutions of equation (1) are rarely possible. Therefore,

various numerical methods are employed to obtain an approximate solution of the above equation.

One such approach is the finite-difference method. The continuous system described by equation

(1) is replaced by a finite set of discrete elements in space and time, and the set of finite difference

equations are solved numerically which yield values of head at specific points and times. These values

constitute an approximation to the time-varying head distribution that would be given by an analytical

solution of the flow equation.

Where,

155

Role of Remote Sensing Data for Use in Groundwater ModelingGroundwater resources assessment, modeling and management are hampered considerably by the lack of data.

Classical hydrological measurements provide only a limited number of point data, for example, at a weather station, a

gauging station, or a borehole. One of the main problems in hydrological research to transfer from point information

to regional distributed information. Regional hydrological models require spatial and temporal distributions of input

and calibration data. If such data are not available, models cannot play their proper role in decision support as they

are underdetermined and uncertain. Recent developments in remote sensing have opened new sources for distributed

spatial data. Remote sensing offers a possibility to do this for certain parameters required in groundwater modeling. A

very good account of use of remote sensing in groundwater modeling is given by Brunner et al., (2007).

In general, only l imited information on the spatial distribution of these input parameters

(K, S and W) is available. Yet, a model computation needs a complete set of parameters. There are different ways to

determine or estimate those. In traditional model calibration, the aquifer is divided into a limited number of zones.

Within these zones, aquifer properties are assumed to be constant. This means a strong reduction in degrees of freedom.

The zonation should be such that the parameters are expected to show little spatial variation within the defined zones.

Remote sensing can play a role in the definition of these zones. For subsurface features, structural elements as seen in

aerial geophysical surveys together with point data from drillings and pumping tests allow zoning. So the first main use of

remote-sensing data is seen in the spatial modulation and interpolation of input data, where otherwise a homogeneous

value or a purely mathematical interpolation function would have to be used. During the process of model calibration,

updated estimates of the missing parameters such as hydraulic conductivity (for the defined zones), are obtained such

that a historical record of head and/or flux observations can be reproduced. This process is non-unique.

Piezometric head data do not reduce the uncertainty of the estimated parameters of storativity, hydraulic

conductivity and recharge, in case those parameters are only known within large error intervals. If, however, the spatially

distributed input data can be constrained, the calibration problem stabilizes. If the spatial distribution of relative recharge

can be estimated from land use and soil type and the yearly regional variation can be estimated from local lysimeter

data, then the total function of recharge in space and time R(x,t) could be reconstructed as the product of the temporal-

spatial average of recharge Rav, i.e.

R (x,t) = Ravf(x) g(t)

Where,

f(x) = relative values on areas with different land use and a weighting factor and

g(t) = relative proportion of recharge in a certain time interval

If f(x) can be obtained from remote-sensing data and g(t) can be determined from point data at a few lysimeters

there is only one unknown parameter left and the large number of degrees of freedom residing in a temporal-spatial

distribution collapses into one single number, the temporal-spatial average value Rav.

Alternatively, remote sensing information on properties such as recharge could also be introduced in the traditional

model calibration in the form of prior knowledge. Carrera and Neuman (1986) have shown that disadvantages of the

model calibration can be mitigated by prior knowledge about the parameters to be estimated. Remote sensing can even

be introduced as a kind of soft (not exact) information into the traditional zone based model calibration strategy.

Remote sensing information intrinsically contains uncertainty because the correlation between remote-sensing

patterns and ground truth will not be perfect. The stochastic modeling approach is able to use this type of information.

The remote-sensing-based data and ground truth can be used to generate a series of equally likely images of the variable

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3of interest within the uncertainty of the correlation. The geostatistical algorithm developed for co-

simulation of exhaustive data on a regular grid is very useful for this purpose (Almeida and Frykman

1994). This algorithm simplifies the relation between the remote-sensing data and the variable of

interest to a linear correlation coefficient (Markov assumption) (e.g. Goovaerts 1997). The generated

images are conditioned to the two different data sources, and take into account their estimated errors,

a (linear) correlation between them, and a variogram estimated from the spatially distributed remote-

sensing data. These equally likely images of the variable of interest sample the high dimensional space

of possible spatial distributions of the variable of interest within the uncertainty bounds mainly given

by the mismatch between remote-sensing data and ground truth. In a case where the resolution of

the hydrological model coincides with that of the remote sensing raster, and further, if the remote-

sensing data are perfect (perfect correlation between variable of interest and the measured signal), this

space of possible spatial distributions would be reduced to one deterministic “truth”. As this truth will

never be known, the stochastic calibration of a groundwater flow model consists of the selection of

an ensemble of realizations of input data (combined from stochastic and deterministic information),

which reproduce hydraulic head, flux and possibly tracer data to a predefined degree.

Application of Remote Sensing Data in Groundwater ModelingAirborne geophysical surveys allow for the identification of faults and dykes, changes in

lithology and the depth of magnetic features. This information is helpful in constructing more realistic

conceptual models of aquifers. An aquifer that is compartmentalized by dykes and faults will behave

differently from an aquifer without such flow guides. Lineaments on the surface have been identified

early as conduits for groundwater flow in fractured aquifers and hence targeted for locating production

wells. Their use in geology is already widespread.

The overlaying of lineaments mapped from conventional remote-sensing techniques (aerial

photographs and satellite images) and those derived from airborne geophysical methods can be

implemented using geographical information systems (GIS) at both local and regional scales. Some

lineaments detectable by airborne geophysics may be due to deep-seated sources (up to several tens

of kilometers) and hence have no effect on groundwater flow in aquifers of interest, which are mostly

within a few hundreds of meters below the ground surface. Therefore, the depths to magnetic sources

must be estimated in order to retain only lineaments that are deemed relevant to groundwater flow. On

the other hand, lineaments identified with conventional methods give only information on structures

with surface expression and no information on depth and vertical continuity of the structures.

Space-borne gravitational surveys such as the Gravity Recovery and Climate Experiment

(GRACE) mission can be used to detect temporal changes in the total water storage (surface water,

soil water and groundwater). A 2 cm thick, infinitely extended layer of pure water located at any

depth below a gravimeter generates an incremental gravitational acceleration of 1×10−8 m/s2

or 1 μGal (microgal). The temporal change in total water storage (TWS) in the Earth system is therefore

directly proportional to the temporal change in the measured gravitational acceleration. The GRACE

twin satellites have dramatically improved the accuracy and resolution of regional observations. This

satellite mission delivers an accuracy of 0.4 μGal or 1 cm of groundwater on spatial scales larger than

1,300 km (Andersen and Hinderer 2005; Andersen et al., 2005) and delivers reliable observations

of the regional part of the global hydrological cycle. Although the spatial resolution is still less than

the size of typical groundwater systems, the prospects of this method for future use in verification of

models, especially for the determination of the storage coefficient, are bright. 157

For a phreatic aquifer, the surface of the terrain is also the upper boundary of the aquifer and constrains the

groundwater levels. Surface elevations can be determined by various remote-sensing techniques, from airborne platforms

(e.g. light detection and ranging LIDAR (Bufton et al. 1991), interpretation of stereo orthophotos, or satellite platforms

using, for example, radar interferometry (Slater et al. 2006). In the latter case, the phase differences in pixels seen from

different points in orbit allow a translation into differences in elevation. To obtain absolute elevation data and to verify their

relative distribution, accurate elevation data at ground control points are required. These can be obtained, for example,

with differential GPS (Global Positioning System). In many applications, the depth to groundwater is of importance for

environmental reasons, including water supply to vegetation or salinization by phreatic evaporation. This distance is the

difference between the surface elevation given by the Digital Elevation Model (DEM) and the groundwater level.

High-precision measurements of the surface elevation changes can reveal regional subsidence caused by

piezometric depression around well fields (e.g. Hoffmann et al., 2001) or seasonal variations of the groundwater level

(Chang et al., 2004). Once a relation is given between subsidence and drawdown, a spatial distribution of drawdown can

be obtained from the amount of surface subsidence observed. Differential GPS can also serve the purpose of determining

temporal variations in the ground level related to groundwater pumping or recharge.

Also, river and lake levels can be determined by using radar satellites (e.g. Berry et al., 2005; Jekeli and

Dumrongchai 2003). Such data are available close to real time and can be of relevance for subsurface hydrology if they

are indicative of groundwater levels (ESA, 2005).

The bulk of remote-sensing data relevant for groundwater modeling are data that allow for quantification of

the distribution of recharge or discharge. Recharge is one of the most important quantities for sustainable ground- water

management. In dry regions, its estimation has been, up to today, a challenge, as it may occur only sporadically at intervals

of several years. It may also be spatially very heterogeneous due to the distribution of precipitation, soil properties, water

use by plants or runoff processes. One of the earliest applications of remote-sensing relevant in hydrology was the

characterization of vegetation type, density and its status (e.g. Fensholt et al., 2006). This information is also of interest

as a proxy for evapotranspiration (e.g. Loukas et al., 2005). Vegetation may be an indicator for the presence of water

and the depth to groundwater level.

For flat terrain, the groundwater recharge potential over long time intervals is the long-term

average residual between precipitation (P) and evapotranspiration (ET). Both quantities can be estimated

from remote-sensing data. The precipitation can be estimated from cloud temperature data (e.g. Herman

et al., 1997) in combination with precipitation data from meteorological stations on the ground. Evapotranspiration can

be derived from multispectral satellite data via a surface energy balance. For example, a dry pixel will heat up to higher

temperatures than a pixel which has a large amount of water available for evaporative cooling. In this sense, radiation

data can be related to evapotranspiration. The fraction of net radiation energy consumed by evaporating water can be

estimated with different methods like, SEBAL (surface energy balance algorithm for land; Bastiaanssen et al., 1998a,b)

in which the energy fluxes in the surface energy balance are calculated explicitly, while in a simplified method described

by Roerink et al., (2000), this fraction is determined from a pixel-wise plot of surface temperature versus albedo. Other

methods use different dimensions of the feature space instead, e.g. the Normalized Difference Vegetation Index (NDVI),

which is a measure of the vigor of vegetation growth (Sandholt and Andersen 1993).

Though at present, both ET and P obtained from remote sensing may not be very accurate and the difference

in P–ET may lead to error propagation, especially when both quantities are of similar magnitude. This is often the case

in semiarid and arid areas. Still, the spatial patterns of P - ET may be of help in regionalization of traditional point

measurements of recharge, e.g. obtained with the chloride method (Brunner et al., 2004).

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3The spatial distribution of recharge may be very heterogeneous even if the distribution of

precipitation is homogeneous. If water collects and infiltrates in depressions, those may dominate

the total recharge of an area. This process has been documented in Niger (Leduc et al., 2001). Water

surfaces forming in the landscape and their temporal behavior can be identified by remote sensing,

e.g. via radar or multi-spectral characterization (e.g. McCarthy et al., 2003; Roshier and Rumbachs

2004). Their density and distribution are indicative of the spatial distribution of recharge.

In wetlands, the interaction between surface water and groundwater is crucial for the

understanding of the wetland behavior. The development of water surfaces and flooding patterns

over time is, in this case, a valuable data set for model calibration (Bauer et al., 2006a,b).

Groundwater recharge from rivers, streams and wetlands, under certain circumstances, can

also be inferred from remote sensing through anomalies in temperature or electrical conductivity. In

arid environments, evaporation is mostly through plants in the form of transpiration. This increases

salinity in groundwater and hence electrical conductivity. The freshly infiltrated water beneath a stream,

in contrast, has a low electrical conductivity. The varying electrical conductivity of the underground

can be detected by airborne electromagnetic methods (Paine and Collins 2003).

In arid and semi-arid areas, the discharge of groundwater via direct evaporation from the water

table and evapotranspiration by trees may account for most of the discharge of an aquifer. Discharge

via a draining stream, as in a humid zone, rarely occurs. The estimation of discharge via trees has been

the subject of remote-sensing studies looking both at ET derived from energy balance calculations as

well as single tree counts according to species and canopy size and combining this remote-sensing

information with information on the single tree, e.g. obtained from sap flow measurements (Lubczynski

and Gurwin 2005).

Salt crusts indicate high water tables with phreatic evaporation. They can be mapped by

multispectral satellite data and used as an indicator for phreatic fluxes and depth to groundwater

(Metternicht and Zinck 2003).

Soil-water balance calculations as a function of time require data in addition to average ET and

P to account for water storage in the soil. A soil-water balance model requires some information on

the field capacity of a soil which could be estimated on the basis of the soil type. Here, hyperspectral

satellite information can help (Chabrillat et al., 2002; Leone and Escadafal 2001; Shepherd and Walsh

2002; Ben-Dor et al., 2004) as well as gamma radiation counts from airborne platforms (Cook et al.,

1996) indicating clay content (Rainey et al., 2003). Soil moisture itself and its temporal variation may

in the future be accessible from passive and active microwave sensors. A mission, SMOS (Soil Moisture

and Ocean Salinity) has been launched in 2009 by France to observe soil moisture over the Earth’s land

mass with a repetitive coverage of 3 days. It is pertinent to emphasis that the moisture seen relates

only to the top centimeters and the use of this data type requires substantial modeling.

The vegetation vigor derived from multi-spectral satellite data can be used as an indicator for

irrigation and can, therefore, be employed as a relevant parameter in monitoring the irrigated areas

and for timing of irrigation (Droogers and Bastiaanssen 2002). 159

ConclusionThe potential of remote sensing for improving groundwater models is considerable and still underutilised. The range of

applications is substantial in model conceptualisation and model calibration. With all justified optimism, expectations

for the easy use of remote-sensing data in groundwater modeling should not be exaggerated. The defaults of any single

method can be counteracted by combining several methods. The remotely sensed data unfold the usefulness usually

in combination with a model in which even noisy or correlated data can be used for conditioning. Finally, it should be

remembered that the largest and most costly effort in applying remote sensing data to groundwater models lies in the

field work necessary to obtain a sufficient data base of ground truth.

ReferencesAlmeida, A.S., Frykman, P. (1994). Geostatistical modeling of Chalk properties in the Dan Field, Danish North Sea. In: JM

Yarus, RL Chambers (eds) Stochastic modeling and geostatistics: AAPG computer applications in geology, no. 3. AAPG,

Tulsa, OK.

Andersen, O.B., Hinderer, J. (2005). Global inter-annual gravity changes from GRACE: early results. Geophys Res Lett

32: L01402.

Andersen, O.B., Seneviratne, S.I., Hinderer, J., Viterbo, P. (2005). GRACE-derived terrestrial water storage depletion

associated with the 2003 European heat wave. Geophys Res Lett 32: L18405.

Bastiaanssen, W., Menenti, M., Feddes, R.A., Holtslag, A.A.M. (1998a). A remote sensing surface energy balance algorithm

for land (SEBAL). 1. Formulation. J Hydrol 213(1–4):198–212.

Bastiaanssen, W., Pelgrum, H.J., Wang Ma, Y., Moreno, J.F., Roerink, G.J., van der Wal, T. (1998b). A remote sensing

surface energy balance algorithm for land (SEBAL). 2. Validation. J Hydrol 213(1–4):213–229.

Bauer, P., Gumbricht, T., Kinzelbach, W. (2006a). A regional coupled surface water/ground water model of the Okavango

Delta, Botswana. Water Res Res 42:W04403.

Bauer, P., Held, R., Zimmermann, S., Linn, F., Kinzelbach, W. (2006b). Coupled flow and salinity transport modeling in

semi-arid environments: the Shashe River Valley, Botswana. J Hydrol 316(1–4):163–183.

Ben-Dor, E., Goldshleger, N., Braun. O., Kindel, B., Goetz, A.F.H., Bonfil, D., Margalit, N., Binaymini, Y., Karnieli, A.,

Agassi, M. (2004). Monitoring infiltration rates in semiarid soils using airborne hyperspectral technology. Int J Remote

Sens 25(13):2607–2624.

Berry PAM., Garlick, J.D., Freeman, J.A., Mathers, E.L. (2005). Global inland water monitoring from multi-mission altimetry.

Geophys Res Lett 32 (16):L16401.

Brunner, P., Bauer, P., Eugster, M., Kinzelbach, W. (2004). Using remote sensing to regionalize local precipitation recharge

rates obtained from the chloride method. J Hydrol 294(4):241–250.

Brunner, P., Hendricks Franssen, H.J., Kgotlhang, L., BauerGottwein, P. and Kinzelbach, W. (2007). How can remote

sensing contribute in groundwater modeling? Hydrogeology Journal, 15 (1): 5-18.

Bufton, J.L., Garvin, J.B., Cavanaugh, J.F., Ramosizquierro, L., Clem, T.D., Krabill, W.B. (1991). Airborne LIDAR for profiling

of surface topography. Opt Eng 30(1):72–78.

Carrera, J., Neuman, S.P. (1986). Estimation of aquifer parameters under transient and steady state conditions. 2.

Uniqueness, stability, and solution algorithms. Water Resour Res 22(2):211–227.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Chabrillat, S., Goetz, A.F.H., Krosley, L., Olsen, H.W. (2002). Use of hyperspectral images in the

identification and mapping of expansive clay soils and the role of spatial resolution. Remote Sens

Environ 82(2–3):431–445.

Chang, C.P., Chang, T.Y., Wang, C.T., Kuo, C.H., Chen, K.S. (2004). Land surface deformation

corresponding to seasonal ground-water fluctuation, determined by SAR interferometry in SW Taiwan.

Math Comput Simul 67(4–5):351–359.

Cook, S.E., Corner, R.J., Groves, P.R., Grealish, G.J. (1996). Use of airborne gamma radiometric data

for soil mapping. Aus J Soil Res 34(1):183–194.

European Space Agency (2005). River and Lake. http://www.earth.esa.int/riverandlake/.

Fensholt, R., Sandholt, I., Stisen, S., Tucker, C. (2006). Analysing NDVI for the African continent using

the geostationary meteosat second generation SEVIRI sensor. Remote Sens Environ 101(2):212–229

FEWS NET (2006) Famine Early Warning Systems Network, http://www.fews.net/.

Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press, Oxford.

Herman, A., Kumar, V.B., Arkin, P.A., Kousky, J.V. (1997). Objectively determined 10-day African rainfall

estimates created for famine early warning systems. Int J Remote Sens 18(10): 2147–2159.

Hoffmann, J., Zebker, H.A., Galloway, D.L., Amelung, F. (2001). Seasonal subsidence and rebound in

Las Vegas Valley, Nevada, observed by synthetic aperture radar interferometry. Water Resource Res

37(6):1551–1566.

Huyakorn, P.S., Pinder, G.F. (1983). Computational Methods in Subsurface Flow. Academic Press, New

York: 473 pp.

Jekeli, C., Dumrongchai, P. (2003). On monitoring a vertical datum with satellite altimetry and water-

level gauge data on large lakes. J Geod 77(7–8):447–453.

Leduc, C., Favreau, G., Schroeter, P. (2001). Long-term rise in a Sahelian water-table: the Continental

Terminal in South-West Niger. J Hydrol 243(1–2):43–54.

Leone, A.P., Escadafal, R. (2001). Statistical analysis of soil colour and spectroradiometric data for

hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem).

Int J Remote Sens 22(12):2311–2328.

Loukas, A., Vasiliades, L., Domenikiotis, C., Dalezios, N.R. (2005). Basin-wide actual evapotranspiration

estimation using NOAA/AVHRR satellite data. Phys Chem Earth 30(1–3):69–79.

Lubczynski, M.W., Gurwin, J. (2005). Integration of various data sources for transient groundwater

modeling with spatiotemporally variable fluxes: Sardon study case, Spain. J Hydrol 306(1–4):71–96.

McCarthy, J., Gumbricht, T., McCarthy, T.S., Frost, P.E., Wessels, K., Seidel, F. (2003). Flooding patterns

of the Okavango Wetland in Botswana between 1972 and 2000. Ambio 32(7):453–457.

Metternicht, G.I., Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote

Sens Environ 85(1):1–20.

Paine, J.G., Collins, E.W. (2003). Applying AEM induction in groundwater salinization and resource

studies, west Texas.SAGEEP, Denver, CO, pp 722–738161

Rainey, M.P., Tyler, A.N., Gilvear, D.J., Bryant, R.G., McDonald, P. (2003). Mapping intertidal estuarine

sediment grain size distr ibutions through airborne remote sensing. Remote Sens Environ 86(4):

480–490.

Rodriguez, E., Morris, C.S., Belz, J.E., Chapin, E.C., Martin, J.M., Daffer, W., Hensley, S. (2005). An assessment of the

SRTM topographic products. Technical Report JPL D-31639, Jet Propulsion Laboratory, Pasadena, CA.

Roshier, D.A., Rumbachs, R.M. (2004). Broad-scale mapping of temporary wetlands in arid Australia. J Arid Environ

56(2):249–263.

Sandholt, I., Andersen, H.S. (1993). Derivation of actual evapotranspiration in the Senegalese Sahel, using NOAA-AVHRR

data during the 1987 growing season. Remote Sens Environ 46(2):164–172.

Shepherd, K.D., Walsh, M.G. (2002). Development of reflectance spectral libraries for characterization of soil properties.

Soil Sci Soc Am J 66(3):988–998.

Slater, J.A., Garvey, G., Johnston, C., Haase, J., Heady, B., Kroenung, G., Little, J. (2006). The SRTM data “finishing”

process and products. Photogramm Eng Remote Sens 72(3):237–247.

Wang, J.F., Anderson, M.P. (1982). Introduction to Groundwater Modeling. Freeman, San Francisco, CA: 237 pp.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

ROLE OF EARTH OBSERVATION FOR GRASS ROOT LEVEL WATER RESOURCES PLANNING- TECHNOLOGY DEMONSTRATION FOR A CLUSTER OF VILLAGES IN SEMI-ARID REGION OF RAJASTHANRama Subramoniam S, Manoj Joseph, Bera AK and Sharma JR Regional Remote Sensing Centre (West), NRSC, Jodhpur, India Email: ramasubramoniam@gmail.com

IntroductionSustainable development aims at optimal use of natural resources, protection and

conservation of ecological systems, and improving economic efficiency. Food & Agriculture

Organization (FAO) defined sustainable development as the management and conservation

of natural resources base and the orientation of technological and institutional changes in

such a manner as to ensure the attainment and continued satisfaction of human needs for

present and future generations (FAO, 2013). Making sustainable development a living reality

requires integrated assessment of ecosystem and physical environment with the help of Earth

Observation data to know the potential and constraints of the area, socio economic situation

analysis for need assessment and long-term planning and development of area specific end-

user technologies, which are affordable and adoptable by the people. Approach towards

sustainable development should consider regional issues like environmental, physical factors

and social & cultural practices followed in the area.

The most important characteristics of semi-arid areas, which limit availability

of adequate soil moisture for plant growth, include high temperatures, low humidity,

intense sunlight and high winds. These factors encourage very high rates of potential

evapotranspiration to the extent that rainfall amounts exceed potential evapotranspiration

only in very few and scattered days. The region often witnesses dry spell which occurs during

the growing season. These dry spells occur with significant variation from season to season

in the same place and from place to place within the same season. In most cases therefore,

good amount of rain occurs at the wrong time and/or place. Because of large spatial and

temporal variability in rainfall distribution, rain-fed agriculture is very susceptible to water

shortage. In general total annual rainfall is sufficient for crop production but the highly variable

distribution in time and space frequently threatens crop production and contributes to food

insecurity (Jennie Barron, 2009). Also due to unproductive soil evaporation, the measured

rainfall is not available to the crops and results in low crop yields.

In the western semi-arid region of India, there is a rich cultural practice to collect

water in a village pond as runoff from the adjacent village common land/ grassland locally

known as gochar or oran land and use it for domestic requirements, which include drinking

water. With the increase in population and shrinkage in the runoff generating area, today the

water is not sufficient and also collected water is highly contaminated due to various reasons

and hence a large number of people are affected by water borne diseases in the area. On the

agricultural front, though farmers are fully dependent on monsoon rain for crops, there is no practice of collecting and

storing water individually or collectively for agricultural use/irrigation purpose (Sharma et al., 2011). Hence in-field rain

water harvesting can have an important impact and is a technique through which farmers can easily collect and store

about 8- 10% of the water in their own field for later use. The technology is low cost, highly decentralized empowering

individuals and communities to manage their water (Critchley and Siegert, 1991). It has been used to improve access to

water and sanitation at the local level. In agriculture, rainwater harvesting has demonstrated the potential of doubling

food production by 100% compared to the 10% increase from irrigation. Rainwater harvesting is the viable adaptation

strategy for people living with high rainfall variability, both for domestic supply and to enhance crop, livestock and other

forms of agriculture (Prinz et.al.1998 and WRC, 2008).

Advances in space based Earth Observation (EO) technology and its applications have great potential for water

resources planning. High resolution spatial and temporal satellite data from Indian satellites like RESOURCESAT and

CARTOSAT are available for creating various resource layers and GIS database on various resources on 1:10,000 scale.

This scale suffices the requirement of decentralized planning at grass root level and along with overlay of cadastral

boundaries will help in generating information in greater detail. Earth observations play an important role in understanding

the terrain and micro-hydrology of the region along with natural resource status. This technique provides accurate, up

to date and time series information that

can be used for site selection of suitable

Rain Water Harvesting (RWH) structures at

regional as well as micro scales for operational

decision making. The location suitability for

RWH depend on amount of rainfall, soil

characteristics (including texture, infiltration

and runoff rates) terrain features (including

topography and slope), current land use/

land cover, and hydrological features. Earth

observation is also useful for monitoring the

status of RWH implementation and regional

impacts of harvested water.

This paper is based on technology

demonstration for smart rainwater harvesting

with the help of EO data. A pilot study has

been carried out to identify the potential sites

for rain water harvesting and also to assess

the volume of harvestable rain water with the

help of remote sensing and GIS techniques.

The high resolution satellite data were used

for natural resource characterisation. The

Digital Elevation Model (DEM) is used to

analyse the topography, slope and also to

delineate potential drains. The integration of

above thematic layers results in identification

of potential rainwater harvesting sites at

village level and also in calculating the volume

of rain water than can be harvested from a Fig. 1: Location map of the study area

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3given field. The studies point to the importance of smart infield rain water harvesting in semi-arid regions

for food security, safe drinking water, introduction of agro horticulture, agro forestry practices and high

value crops for livelihood and achieving long term sustainability of local and regional populations.

Study Area

Study area covers an area of 584.58 km2 consist of 50 villages in Jodhpur and Barmer districts

of Rajasthan state (Figure1). It lies between 250 56’ 45”N to 260 12’ 41”N latitude and between

720 34’ 38”E to 720 53’ 10”E longitude. Land utilization pattern includes mixture of moderately

cultivated land (mostly rain fed agriculture), open scrub, dense scrub, waterbodies and settlement

areas. The mean annual rainfall in this region is 275 mm. About 86.14 % of annual rainfall is received

during south west monsoon and this is main sources of water for kharif crops. Soils are loamy sand

in texture followed by loam, sandy loam and fine sand. Out of the entire cadastral in 50 villages,

2.51% are of 0-1ha category and 22.96%, 18.97%, 47.1% are of area 1-3 ha, 3-5 ha and >5 ha

class respectively.

Data & MethodologyCartosat stereo data has been used to generate high resolution DEM for the study area.

Potential drains have been generated from Carto DEM. Land use/land cover map of the study area

has been generated from LISS-IV-Carto merged product. India Meteorological Department (IMD) daily

grid data (1901-2004) has been used to analyze the rainfall pattern of the study area. Soil texture map

from NRDB database has been used. Cadastral boundaries and other ancillary data has been collected

form department of land and settlement, Rajasthan.

Identification of the Potential Sites/fields for Rainwater Harvesting (RWH)

The major steps involved in the process of identifying RWH sites are as follows.

• Baselinesurveyandcreationofageo-spatialdatabaseusingGISandEarthObservationdatasets

at cadastral level for current land use, soil resources and rainfall for the cluster of villages.

• GenerationofhighresolutionDEMfromCartosatstereodata.

• GenerationofslopemapfromDEM

• DelineationofPotentialdrainsfromDEM

RWH sites identification analysis has been based on potential drains & its order and Multi-

criteria analysis using various parameters.

Multi-criteria Evaluation Multi-Criteria Evaluation (MCE) is used to identify the suitability of each grid cell for water

harvesting and storage. To generate multi-criteria based rankings, Weighted Overlay Process feature of

GIS is used. From the literature review and information obtained from field survey supported by expert

judgment, five criteria were selected for the identification of potential areas for in-field RWH viz., soil

texture (clay percentage), rainfall, slope, land use/cover and flow accumulation. Since all the criteria

are not equally important for the identification of potential RWH areas, different weights (Rainfall and

Flow accumulation (35%), LULC (15%), Texture (10%) and Slope (5%) were assigned to the criteria.

Also high suitability rank was given for areas with large rainfall surplus as it ensures the availability of

runoff to be harvested. High suitability rank has been given to low slope areas. Among the land use

category, higher rank has been given to open lands, agriculture lands and dense scrubs. In the case

of soil texture, ranks are assigned in such a way that areas with higher clay percentage are having 165

higher rating as the run-off is more for clay

sand. Block diagram showing the detailed

methodology is given in Figure 2.

Assessment of Volume of Harvestable Rainwater

To assess the volume of water

that can be harvested in a given field, a

methodology has been developed. The criteria

considered different factors like soil texture,

LU/LC, slope and rainfall of the given field.

Two methods have been proposed based on

annual rainfall and event rainfall.

Based on Annual RainfallIn this study annual rainfall of the

study area has been considered. During the

rainfall in the initial phase some of the water goes as soil moisture & seepage and after that runoff begins depending

upon the intensity of rain. This runoff depends on various factors like type & size of catchment, slope, land use, texture

of soil depth etc. Based on these factors run off coefficient is decided as per field observations in an area. This runoff

coefficient is a dimensionless figure and can be estimated from the individual field data (Murthy, 2003 and CGWB, 2007).

These coefficients have been adjusted for the Agricultural Land (no crop, early crop and late crop) and considering the

soil factors like compaction, moisture and Infiltration.

This coefficient is multiplied with the annual rainfall (mm) and the drainage area (sq.m), which gives the estimate

of the amount of water (L) that can be harvested in that particular area. It is assumed that around 20 % of water losses

due to various factors like evaporation, seepage etc. The runoff coefficient depends on different factors like type of

vegetation, slope and soil texture. Run off coefficients were derived based on two years’ experience at the pilot site, the

intensity of rain observed and last ten years daily rainfall data of the area (Table 1).

The formula for the calculation of total amount of rainwater available that can be harvested is:

Rainwater Harvested (L) = Annual rainfall (mm) x 0.8 x Area (sq.m) x Runoff coefficient

Based on Event RainfallThis concept takes the last 48 hrs rainfall and particular day intensity of rainfall. The following Table 2 describes

R factor of amount of rainfall occurrence.

The amount of water that can be harvested in a particular day is:

Rainwater Harvested (L) = Rainfall (mm) x R factor x Area (sq.m) x Runoff coefficient

Results and DiscussionNatural resource characterization of

the study area including rainfall, land use/land

cover and soil texture has been carried out.

Annual rainfall of the study area for the period

from 1901-2004 has been analysed. IMD daily

grid data of 10 m x 10 m has been used for

Fig. 2: Block diagram of multi-criteria evaluation for RWH site

Fig. 3: Annual rainfall and coefficient of variation of the study area

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3Table 1: Values of Runoff Coefficient Factor for different Soil Conditions

Table 2: Amount of Rainfall and R factor

Type of Vegetation

Slope Range (%)

Runoff Coefficient

Coarse (Sand,Loamy Sand, Sandy Loam)

Medium (Silty Loam, Loam, Silt, Sandy Clay Loam, Clay Loam, Silty Clay Loam)

Fine (Sandy Clay, Silty Clay, Clay)

Dense scrub 0-2 0.1 0.3 0.4

2-5 0.1 0.3 0.4

5-10 0.16 0.35 0.50

Open scrub/Open land

0-2 0.10 0.30 0.40

2-5 0.10 0.30 0.40

5-10 0.25 0.36 0.55

Agricultural Land (No crop)

0-2 0.28 0.47 0.58

2-5 0.30 0.50 0.60

5-10 0.40 0.60 0.70

Agricultural Land (early crop Stage)

0-2 0.17 0.31 0.33

2-5 0.20 0.33 0.37

5-10 0.30 0.39 0.40

Agricultural land (late crop stage)

0-2 0.24 0.38 0.40

2-5 0.25 0.42 0.45

5-10 0.35 0.48 0.50

Amount of rainfall (mm) R factor

No wetness (Last 48 hrs< 20mm rainfall)

Wetness (Last 48 hrs >20mm rainfall)

<20 0.10 0.20

20-30 0.40 0.60

>30 0.50 0.80

the analysis. The coefficient of variation of

weekly rainfall during crop growing season

over 100 years (Figure 3) shows high amount

of variation and imply a high uncertainty in

crop yields.

Land use and land cover map of the

study area is given in Figure 4. Land use pattern

is predominantly agriculture (91%), followed

by open scrub (5.6%), dense scrub (1.2%),

settlement (1.92%) and waterbody (0.28%).

The soil texture variation includes mostly

loamy sand soils followed by loam, sandy

loam and fine sand (Figure 5). Coarse texture

indicates low water accumulation and fine

texture indicates high water accumulation. Fig. 4: Land use/land cover map 167

Soils are low in organic matter and poor in

nutrients (available N is low, available P and

K status is medium). Micronutrients such as

Mn and Cu contents are adequate whereas Zn

and Fe are deficient. As soils have low clay and

silt content, nutrient adsorption and retention

by these soils are very low. Crop production

is limited due to low soil fertility.

Identification of RWH sitesPotential drains are derived using

Carto DEM. The existence of potential drains

on agriculture fields gives high weightage

for considering that field for RWH. Analysis

carried out by overlaying the potential drains

on cadastral fields shows that 1480 fields

(9.7%) having maximum potential to harvest

rain water (Figure 6). Existence of potential

drains during rainy season has been verified

in the test field.

Multi-criteria analysis has been

carried out based on layers viz. rainfall, land

use, soil texture, flow accumulation and

slope. Areas with good rainfall, higher slope

and flow accumulation are having high

suitability (Figure 7).

A rainwater harvesting calculator

has been developed to assess the volume

of harvestable rainwater based on annual

and event based rainfall (Figure 8). The tool

will help the farmer to identify his field and

calculate the volume of water than can be

harvested and also calculate the dimension

of the water harvesting structures.

ConclusionRole of EO data for identifying RWH sites

in semi-arid region has been investigated.

A technology demonstration has been carried

out for a cluster of villages. The results

obtained were validated in a test plot. The

study underlines the convergence of science

and technology with traditional wisdom.

The studies shows the importance of smart

Fig. 5: Soil Texture map

Fig. 6: Identified potential RWH sites

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

Fig. 7: RWH site suitability based on multi-criteria analysis

rain water harvesting in semi-arid regions

ensuring for food security, safe drinking

water, promoting agro horticulture, agro

forestry practices and high value crops for

achieving long term sustainable livelihood

for local populations.

ReferencesCentral Ground Water Board (CGWB),

(2007). Manual on Artificial Recharge of

Ground water.Ministry of Water Resources,

Government of India. Newdelhi.

Critchley, W. and Siegert, C. (1991).

Water Harvesting Manual.FAO Paper AGL/

MISC/17/91, FAO, Rome.

FAO. (2013). World inventory of fisheries.

Conditions for sustainable development.

Issues Fact Sheets. Ed. Rebecca Metzner

and Serge M. Garcia. In: FAO Fisheries and

Aquaculture Department. Rome.

Jennie Barron, (2009). Rainwater harvesting:

A lifeline for human well-being. United

Nations Environment Programme, Stockholm

Environment Institute, Sweden.

Murthy, V.V.N. (2003). Land and water

management engineering.Kalyani Publishers,

New Delhi.

Prinz, D., Oweis, T. and Oberle, A. (1998).

Rainwater Harvesting for Dry Land Agriculture

- Developing a Methodology Based on

Remote Sensing and GIS. In Proceedings of XIII International Congress Agricultural Engineering,

ANAFID, Rabat, Morocco. P. 2-6.

Sharma, J.R., Jay Pearlman and Chilka Sharma. (2011). Developing Earth Observation based End user

technology for making Sustainable development a living reality in semi-arid areas - Nurturing through

convergence of technologies at grass root level. IEEE Global Humanitarian Technology Conference,

DOI 10.1109/GHTC.2011.67

WRC, (2008). In-field Rainwater Harvesting (IRWH) adoption on small farm plots. www.wrc.org.za.

Fig. 8: In Field Smart Rainwater Harvesting Calculator

169

INDIA-WRIS WEBGIS DESIGN AND DEVELOPMENT OF WEB ENABLED WATER RESOURCES INFORMATION SYSTEM OF INDIA Sharma JR and Project TeamProject Director (India-WRIS) & Chief General Manager, RCs National Remote Sensing CentreISRO, Department of Space, Loknayak Bhawan, New Delhi, India Email: jrsharma50@gmail.com

IntroductionIn the emerging knowledge society and wide spread use to Information Technology, up-to-

date information on water resources is vital to support economic development, improve the quality of

life as well as to conserve the nature and the environment and hence; an operational water resources

information system at national level is essential for planning and development of the country. Looking

at this CWC and ISRO are jointly executing since January 2009; the project ‘Generation of database

and implementation of web enabled water resources information system of India short named as

‘India-WRIS WebGIS’.

India-WRIS WebGIS aims as a ‘Single Window solution’ for comprehensive, authoritative and

consistent data & information of India’s water resources in a standardized national GIS framework for

planning, development and management of water resources.

The current version 3.0 of the WebGIS portal is designed and developed keeping in view multi-

users from all sections of society, varied and multi-source data input, current map policy, requirement

of regular updates and near real time data accessibility, data security domains, scale of information

and level of access of the portal as well as download of different GIS maps, data and value added

products along with tool kit for further analysis and value addition.

The information system has four key elements besides other facilities namely:

1. Data input/entry/collection system

2. Data storage, analysis, and transformation into ‘user friendly’ information

3. Interactive system for geo-visualization and temporal analysis and

4. Information dissemination system in public domain as downloads and further

processing tools

Water Information in Public DomainDissemination of data in public domain constitute the most important aspect of the

water resources management being multi stakeholder’s involvement, people’s participation and

information sharing to increase transparency, public awareness, elevating the importance of water

information and enlighten public involvement in water management. The thrust has been towards

development of an open source user end web enabled information system. It provides adequate and

contemporary information on the state of water resources which are must for planning and water

resources management strategy. This, in turn, will ensure increase in public awareness about the crucial

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3issues related with water and attract their

participation in management, planning and

development of water resources of the nation

leading towards the holistic goal of water

security.

Water Wealth of IndiaOn an average, India receives annual

precipitation (including snowfall) of about

4000 km3. However, there exist considerable

spatial and temporal variations in the

distribution of rainfall and hence in availability

of water in time and space across the country.

It is estimated that out of the 4000 km3 water,

1869 km3 is average annual potential flow in

rivers available as water resource. Out of this total available water resource, only 1123 km3 is utilizable

(690 km3 from surface water resources and 433 km3 from ground water resources).

Water Resources – India at a Glance

Area of the country as % of World Area 2.4%

Population as % of World Population 17.1%

Water as % of World Water 4%

Rank in per capita availability 132

Rank in water quality 122

Average annual rainfall 1160 mm ( world average 1110 mm)

Range of distribution 150 -11690 mm

Range Rainy days 5-150, most rain 15 days in 100 hrs.

Range PET 1500-3500 mm

Per capita water availability (2010) 1588 m3

Scope of the Portal

Based on the requirements and data availability, comprehensive information have been collected,

thought fully categorized and arranged in GIS environment under 12 major and 30 sub information systems

besides base sub information system having large number of attributes data of last 5 – 100 years.

India-WRIS WebGIS Application Architecture (Technologies & Tools) The three components India-WRIS Web GIS Application are:

Database Design & Generation:

The database for India-WRIS is highly complex with numerous sources involved. Much of the data is

spatial in nature but the amount of associated data is very large and also having time series, and will

further increase exponentially with the passage of time. The creation and management of such data is a

colossal feat in itself and requires state of the art tools. The database standards and relationship have been

developed for all type of data. The database generation software used have the capabilities of creating

maps, viewing or exploring data, editing data, storing, conflation (integrating datasets from different

sources), transforming (into different coordinates systems, different representations, re-sampling,

resulting in new representation/format of the same data), querying, analyzing etc. 171

Water Resource at a glance Quantity - BCM Percentage

Annual precipitation (Including snowfall) 4000 100

Precipitation during monsoon 3000 75

Evaporation + Soil water 2131 53.3

Average annual potential flow in rivers 1869 46.7

Estimated utilizable water resources 1123 28.1

Source: Water Resources at a Glance 2011, CWC, New Delhi, (http://www.cwc.nic.in)

Basin Code

Basin Name Area (Sq. km)

1 Indus (Up to border) Basin 453931.87

2a Ganga Basin 808334.44

2b Brahmaputra Basin 186421.60

2c Barak and others Basin 45622.41

3 Godavari Basin 302063.93

4 Krishna Basin 254743.31

5 Cauvery Basin 85624.44

6 Subernarekha Basin 25792.16

7 Brahmani and Baitarni Basin 51893.68

8 Mahanadi Basin 139659.15

9 Pennar Basin 54243.43

10 Mahi Basin 38336.80

11 Sabarmati Basin 30678.59

12 Narmada Basin 92670.51

13 Tapi Basin 63922.91

14 West flowing rivers South of Tapi Basin 111643.87

15 East flowing rivers between Mahanadi and Godavari Basin 46243.06

16 East flowing rivers between Godavari and Krishna Basin 10345.16

17 East flowing rivers between Krishna and Pennar Basin 23335.82

18 East flowing rivers between Pennar and Cauvery Basin 63646.21

19 East flowing rivers South of Cauvery Basin 38646.11

20 West flowing rivers of Kutch and Saurashtra including Luni Basin 184441.06

21 Minor rivers draining into Bangladesh Basin 5453.23

22 Minor rivers draining into Myanmar Basin 24731.08

23 Area of North Ladakh not draining into Indus Basin 29238.78

24 Drainage Area of Andaman and Nicobar Islands Basin 6918.20

25 Drainage Area of Lakshadweep Islands Basin 462.59

Web Application & User Interface technology:

The major user requirement from the web portal is data dissemination; hence advance GIS data processing

systems at the back end augmented with the best database connectivity over the internet is used so that the user is able

to get intuitive and real time information.

User has the facility for data visualization, analysis on the client side and use further to create customized

reports. Adobe Flex is able to deliver Rich Internet Application (RIAs) across the enterprise and over the web efficiently.

Using the Flex Application Programme Interface (API), India-WRIS combines GIS based Web services from ArcGIS Server

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3with other Web content, which are displayed

in simple, dynamic mapping applications

over the Web. All the published map services

are compliant with OGC standards and the

services can be accessed using WMS, WFS,

WCS and KML standard formats. India-WRIS

system is using Oracle 11g, Relational Data

Base Management System (RDBMS) which

supports multi user system. ArcSDE as well

as oracle together used to handle geospatial

data and to createmultiuser geo-database.

Database Storage & Hosting:

In order to ensure reliable and

secure, 24 x 7 availability of the WebGIS,

a robust hosting architecture has been

designed. The same has been replicated at

three places namely, RRSC (West) - Jodhpur,

the data generation and s/w development

as lead centre; NRSC - Hyderabad for web

hosting and CWC - New Delhi for intranet

users and data validation & updation.

Public Outreach and Designing the Interface, Tools and Fac i l i t ies in India -WRIS WebGIS

Considering large number of factors

as; type and volume of data, large number

of varied users, ease of handling, varied nature of internet connectivity in the country, information

requirement by the users and available technologies. The user interface of the portal has been designed

carefully. The home page is divided into three sections:

Main Menu Toolbar

The main menu has six modules namely, WRIS Info Discovery, WRIS Explorer, WRIS Connect,

Input Data Builder, Share Success Story and Create Your WRIS. This is the heart of India-WRIS

information system, where all the major links to the various WebGIS modules are provided in a rich

Graphical User Interface (GUI) assisted format for easy access and use.

WRIS Info Discovery

This module provides the user in discovering information contained in India-WRIS of a particular

geographic area. The user can select area of interest based on the Administrative units, Hydrological

units and Constituency wise and is presented with a condensed list of all the information available in

India-WRIS for the area.173

Web Application Architecture

Tasks and Software/ Technologies Used for India –WRIS

Tasks Software / Technologies

WebGISFront end

2D Adobe Flex, HTML, PHP

3D ArcExplorer, .NET, ArcGlobe

Mobile .NET(Windows)/ Flex (Android) / JAVA (Symbian)

Meta Data Visual Basic

Data Generation (Digital Image Processing / GIS

Mapping)

ERDAS Imagine, ENVI, ArcMap, ArcCatalog, ArcSDE, AutoCAD 3D, IGIS, Map Window Library, GeodatabaseXML

Publishing / Web Geodata Services

ArcGIS Server

Geodatabase /Back end Oracle 11g, MySQL

Web hosting architecture

WRIS Explorer

This is the core module of India-

WRIS WebGIS, where all the data can be

explored and viewed using the various tools

available for the purpose.

Geo-Visualization - This section provides

basic facility to visualize all the layers together

in any combination by turning layers on and

off as per the requirements.

Sub-Information Systems - There are

12 major information systems namely, base

data, surface water, ground water, hydro-

met, water quality, snow cover/glacier, inland

navigation waterways, inter-basin transfer

links, hydro-met extremes, land resources,

water tourism and socio-economic. These

have been further divided into 35 sub-

information systems. Each sub-information

system is based on a particular theme. It

contains relevant layers and specially created

tools to make the best use of the data.

Temporal Analyst - A large amount of

water resources and related data regarding

hydrological, meteorological, pollution etc.

are temporal in nature. In order to represent

these datasets, a separate module has been

created, where facilities are provided to

represent the time series data using suitable charts, animations and to compare the data across stations or years.

Climate Trend Analysis - The aim of this section is to provide the users with a facility to analyze the changes in

climate over the course of past 104 years (1900-2004). This analysis can be performed on various units viz. River Basin,

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3State, District, and Village through various

tools such as tabular, statistical and graphical

analysis.

WRIS Connect

The sub-modules in WRIS Connect are:

Live Telemetry Data - This system

provides real time information such as

Water Level, Rainfall, Air Temperature, Wind

Speed, Wind Direction and Solar Radiation

measured at 466 telemetry stations across

the country. Water level data for the past

72 hours is displayed where as the other

parameters of the past hour are presented

on a dashboard.

Query Interface - User can have a

lot of queries answered directly through

WRIS Explorer and associated available

tools. To explore more details, user can

place queries through Query Interface that

contains set of fixed queries on various

hydrological parameters. The answers are

generated through different permutations

and combinations of these fixed queries. The

result of a query is displayed in spatial as well

as non-spatial formats.

Report Generation - This section has the

utility to automatically generate report of the

user defined area / region containing the all

data into tables and maps and allows ‘Save

As’ and ‘Download’ in .pdf format.

Data Download - Apart from viewing the

available data, the user may also wish to take

the data and perform analysis / add value. This

link allows the download of GIS layers and

associated attributes.

Automatic Map Generation - This

module provides the user with a highly

useful facility of generating high-quality,

multi layered, theme based maps in GeoPDF

format. The latest GeoPDF documents are

highly versatile PDF documents that have

Use friendly GUI - India-WRIS Version 3.0

2D-3D Linked View

Linked View with Bhuvan Poral

3D Viewing175

the features of a mini Geographic Information System complete with tools such as layer visibility toggling and attribute

data viewing.

Input Data Builder

This module aims at keeping the data content of the various layers of India-WRIS up to date by providing facilities

to the data providing sources to ingest the current attribute data directly into the relevant layers. The authorized users

can enter the respective spatial and non-spatial data in the specified format into the information system through this

facility. The three sub-modules of Input Data Builder are Spatial, Non-spatial and Metadata Input Builder.

Share Success Story

The objective of this module is to connect people for water resources planning and management by providing

platform to upload the success stories, so that, others can view, interact and practice.

Water Resources Planning & Management

Create Your WRIS - This module provides facilities to the user to have further analysis of the downloaded data, adding

new datasets using available general hydrology tools and generate report of the area.

2D-3D Linked View - Through this highly interactive feature, 3D fly-through simulations can be generated along

any linear feature like rivers, roads or user drawn features. The high resolution satellite imagery makes it a very realistic

flying experience.

Collaborative Planning – In the water resources sector, need is often felt to share and work collaboratively for

understanding and decision making. This tool provides the platform for this purpose wherein the users can share their

screen views, drawing & actions while communicating through text and voice messages.

General Information Toolbar

This toolbar is available in the lower section of the home page and provides links to general information about

India-WRIS as Visitor Number, Disclaimer, Sitemap, Links, Contact Us and Last Updated.

Conclusion The hydrological processes are continuous as well as complex and therefore, an updated comprehensive, reliable and

easily accessible Information System having time series data of the hydrological and meteorological observations is

pre-requisite for effective management of water resources.

AcknowledgementsWe are grateful to Dr. V.K. Dadhwal, Director, NRSC/ISRO for guidance, keen interest in the project and constant

encouragement. Project Team wishes to acknowledge the help and support received from CWC officials and Irrigation

Departments / Water Resources Departments of all the states of the country for making the large volume of time series

data available for the project. We gratefully thank all the individuals for fruitful discussions, sharing their expertise

and knowledge.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3

Universal Toolbar Universal toolbar is present at the top of the home page and has two sections.

First section The toolbar at the top of the page contains the links to popup window having information

required by the user at any point and toolbar is visible at all times. The links in this

toolbar are:

About WRIS This page contains a brief overview of India-WRIS project including its history, scope,

vision, goals, deliverables and time-frame for completion.

Accessibility This provides information for navigating through India-WRIS like screen resolution,

keyboard shortcuts for easy navigation etc.

Tools Numerous tools along with symbols for easy access are described in this part.

Metadata This link leads the user to the Metadata Explorer which provides comprehensive

information of the source of spatial and non-spatial data.

WRIS Wiki Comprehensive information for the water resources assets and projects of the country

is made available through WRIS Wiki application.

Help A comprehensive and universal help is documented in this section assisted with diagrams,

screenshots and short videos.

Search Consolidated search into the complete information system is provided.

Second section The advanced information toolbar is available right below the banner. It contains links

to pages containing detailed information that a user requires when visiting the home

page but may not require while exploring the other sections of the information system.

The links available in this toolbar are:

Home This link leads to the main page of India-WRIS Portal.

Publications Various documents generated for India-WRIS are made available and reports being

generated would be available to the users through links.

Gallery This section presents the user with an image gallery of events related to development

of India-WRIS project.

WRIS Mobile A precise version of India-WRIS is being developed for mobile and handheld devices.

FAQ This section contains answer to common questions and queries about the project and

outcome.

Feedback Provides interface to post user suggestions and feedback.

Sign In / Register This section provides provision for new user to register and get connected to India-WRIS

portal for updates. For downloads and providing data inputs, login is provided based

on user categories and with a password. 177

Six Categories of Tools

I. Navigation Tools Search by Proximity.

Zoom In: It zoom into a particular area on

map that is selected by the user.

IV. Personalization Tools

Zoom Out: Is zooms-out the map to

come out of the detailing.

Draw: Allows to draw a shape/line/point

on map.

Pan: It allows user to Pan around the

whole map.

Print: To print the current viewing area

in landscape or portrait mode.

Full Extent: It allows viewing the map at

the full extent.

Save as Image.

Previous Extent: It allows going to

previous extent when the map extent is

changed.

Bookmark: Allows bookmarking a

specific location on the map for future

reference.

Next Extent: It allows returning from the

previous extent.

Pin Mark: User can pin mark his location

of interest and type his comment on

same.

Map Overview: Provides location of

current view in context with larger map

area.

V. Advanced Tools

Go To: Zoom to an area based on

specified latitude and longitude.

Surface Profile: Generate the surface

terrain height graph of selected points

on map.

Select Area Zoom/Rubber Zoom: Smooth

Zooming into a selected area.

Network Analysis/Route Tool: It specifies

the defined route of road, rail and river

network.

II. Display Tools Tools to calculate parameters based on

location and user input.

Swipe: It swipe the selected layer in the

map to reveal underlying layers.

Linked View: Can View multiple view

of different information in a single

window.

Spotlight: It removes overlaid layer

from the selected portion for better

visualization.

VI. Sharing Tools

Magnifier: To view the zoom in layer

details of selected portion only.

Share a Link: Share the current view of

map with another user through mail.

Get Feature Info: On selecting a particular

feature it displays summary info.

iFrame: Sharing frame of India-WRIS in

other applications.

Identify: identify the details of all the

visible layers including the elevation

details of the point.

Links on twitter/Facebook/Google+.

III. Search and Query Tools

Query Builder: create user defined

queries.

NN

RM

S

BU

LL

ET

IN

-

DE

CE

MB

ER

2

01

3ReferencesBiswas, A. (1981). Integrated water management: Some international dimensions, J. of Hydrology,

51, 1-4, pp. 369-379.

Bouwer, H. (2000). Integrated water management: Emerging issues and challenges, Agricultural Water

Management, 45, 3, pp. 217-228.

Ganapathy, C. and Ernest, A.N.S. (2004). Environ. Inf. Arch., 2, pp. 938–945.

Kaden, S., Becker, A. and Gnauck, A. (1989). Decision-support systems for water management. IAHS

Publ. No. 180, pp. 11-21.

Kumar, R., Singh, R.D. and Sharma, K.D. (2005). Water resources of India, Current Science, Vol. 89,

No. 5, 10 SEPTEMBER 2005, pp. 794-811.

Lal, M. (2001).Climate change – Implications for India’s water resources. J. India Water Res. Soc., 21,

pp. 101–119.

Loucks, D. (1995). Developing and implementing decision support systems: A critique and a challenge,

Water Resource. Bulletin, 31, 4, pp.571-582.

Maidment, D. (1997). Opportunities for the development of a global water information system. In :

Land and Water Resources Information Systems - FAO Land and Water Bulletin 7 (Proceedings of a

Technical Consultation Rome, Italy, 15-17 December 1997), pp. 115-120.

O’Hagan, R.G., Robinson, B., Swan, G. and Finny, D. (2008). Web-based visualisation of water

information: an overview. CSIRO: Water for a Healthy Country,National Research Flagship.

Zalewski, M. (2002). Ecohydrology- the use of ecological and hydrological processes for sustainable

management of water resources, Hydrological Sciences Journal, 47, 5, pp. 823.

179