panchthar climate change sector profiles. m-ta 7984_climate change sect… · 2 citation: moste....

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Panchthar Climate Change

Sector Profiles

Change Sector Profiles

Pilot Program for Climate Resilience-PPCR 3 MAINSTREAMING CLIMATE CHANGE IN DEVELOPMENT

TA-7984 NEP: MAINSTREAMING CLIMATE CHANGE

RISK MANAGEMENT IN DEVELOPMENT 1 Main Consultancy Package (44768-012)

Pilot Program for Climate Resilience-PPCR 3

MAINSTREAMING CLIMATE CHANGE IN DEVELOPMENT

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Citation: MoSTE. 2014. TA 7984 Climate Change District Profiles for Panchthar. Prepared by the

International Centre for Environmental Management (ICEM) for the Nepal Ministry of Science,

Technology and Environment (MoSTE) and the Asian Development Bank (ADB), as part of the

Pilot Program for Climate Resilience - PPCR3, Mainstreaming Climate Change in Development.

Kathmandu, Nepal.

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TABLE OF CONTENTS

BACKGROUND ............................................................................................................................. 5

1 Overview of the Climate Change sector Profiles ..................................................................... 8

1.1 Profile structure .......................................................................................................................... 8

1.2 Summary of the Panchthar climate change impacts .................................................................. 8

2 Modelling methodology and assumptions ............................................................................ 10

2.1 Overview of the vulnerability and adaptation assessment approach ...................................... 10

2.2 Requirements for climate data ................................................................................................. 11

2.3 Baseline hydrometeorological data .......................................................................................... 11

2.4 Future climate projections ........................................................................................................ 12

2.5 Climate indicator and Impact modelling methodology ............................................................ 15

2.6 Model accuracy and confidence levels ..................................................................................... 17

2.7 Sector Profile updates ............................................................................................................... 19

3 Panchthar model Overview ................................................................................................. 20

3.1 Model set-up ............................................................................................................................. 20

3.2 Hydrological model accuracy .................................................................................................... 23

4 Maps for hotspot identification and impact overview .......................................................... 25

5 Site and sector specific information ..................................................................................... 30

Annex I: List of available climate change indicator and impact maps ........................................... 62

Annex II: List of available climate change indicator and impact time series and graphs ................ 65

1 . B A S I C T I M E S E R I E S A N D G R A P H S .............................................................................. 65

2 . G R A P H P R O D U C T S F O R S P E C I F I C N E P A L G O V E R N M E N T P L A N N I N G

P R O C E S S E S ....................................................................................................................................... 68

Annex III: Example sector climate profile identification matrix .................................................... 71

BIBLIOGRAPHY ........................................................................................................................... 76

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L IST OF F IGURES

Figure 1. Key phases in TA 7984 and data inputs from TA 7173: green diagram illustrates the main phases in TA7173, with the red diagram showing the main phases in TA 7984. The blue box indicates the outputs of TA 7173 to be used in TA 7984 vulnerability assessment process. .................................. 10

Figure 2. Gap filled precipitation time series in Lomanghtan. .................................................................. 12

Figure 3. Existing downscaling procedure for Nepal ................................................................................. 13

Figure 4. Improved downscaling process .................................................................................................. 14

Figure 5. Schematic representation of the IWRM model. ........................................................................ 17

Figure 6. IWRM model construction. ........................................................................................................ 17

Figure 7. Panchthar watershed model area. ............................................................................................. 20

Figure 8. Model grid elevations for the Panchthar district. ...................................................................... 21

Figure 9. Model grid land use classes for the Panchthar district. ............................................................. 22

Figure 10. Model meteorological stations. “N2…”-stations are temperatures from re-analysis data, other stations are Nepal national precipitation monitoring stations. ...................................................... 23

Figure 11. Comparison between computed (black line) and measured (red line) daily discharges in Mul Ghat for the year 1984. ............................................................................................................................. 24

Figure 12. Comparison between computed (black line) and measured (red line) daily discharges in Mul Ghat for the year 1995. ............................................................................................................................. 24

Figure 13. Panchthar model output locations. Sites where profiles are output are indicated with red points. ........................................................................................................................................................ 30

Figure 14. Upper catchment areas for the Memeng Jagat, Phidim and Ts4 output locations. ................ 30

Figure 15. Change in flow duration/ dependability. ................................................................................. 68

Figure 16. Flood return periods. ............................................................................................................... 69

Figure 17. Intensity-Duration-Frequency (IDF) curve. .............................................................................. 69

Figure 18. Flash flood travel times. ........................................................................................................... 70

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BACKG ROUND This report was developed as part of the TA – 7984 NEP: Mainstreaming Climate Change Risk Management in Development Project supported by ADB with funding from the Climate Investment Fund (CIF), and implemented by the Ministry of Science, Technology and Environment (MOSTE) in partnership with ICEM – International Centre for Environmental Management. The project involves line departments working together with MOSTE in eight districts to develop and test a vulnerability assessment and adaptation planning approach tailored for their needs. The aim is to distil the lessons of the district experience into reforms at national level for planning and managing more resilient infrastructure. The national agencies are those concerned with infrastructure development throughout Nepal such as irrigation, roads and bridges, water induced disasters, urban planning and water supply and sanitation systems (Figure 1). Figure 1: TA – 7984 NEP infrastructure sector department partners

A core group of technical staff from each of the departments participated in working sessions and missions to the eight districts of Kathmandu, Dolakha, Achham, Banke, Myagdi, Chitwan, Panchthar and Mustang (Figure 2) where vulnerability assessments and adaptation planning exercises were conducted for existing strategic infrastructure assets. The target districts were identified by core group members to reflect the diverse ecological zones of the country and varying environmental and social conditions in which infrastructure is built. The district experience and sector analysis is documented in district reports, sector synthesis reports and linked guides for use on a systematic basis in each department.

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The core group comprised of some 30 members from 9 government agencies with each agency having a wider range of staff involved in the process of setting and implementing reform priorities with support from the project team (Figure 3). Figure 2: Target districts for developing an approach to infrastructure vulnerability assessment and adaptation planning

Sector focal points on the core group have a key role in promoting the climate change mainstreaming in their departments so that the design and management of existing and planned infrastructure progressively adjusts to become more resilient to the most significant projected changes and their associated potential impacts.

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Figure 3: Infrastructure sector department climate change core group

INTRODUCTION TO THIS REPORT

As an early step in the vulnerability assessment of infrastructure in each of the eight districts, it was necessary to prepare a climate change threat profile for that locality. The district profiles built on understanding past regular and extreme climate and then projecting trends to better appreciate future climatic and hydrological conditions which infrastructure agencies need to plan for.

This Climate Change Threat Profile District Report documents the climate change threats and opportunities facing Panchthar District. This threat overview relies on projections of future climate change in Panchthar District for the period 2040-2060 compared to a baseline of 1980-2000. Statistical downscaling for 20 temperature and precipitation stations was used to develop these projections using IPCC scenario A1B and four GCMs including PRECIS – Providing Regional Climate scenarios for Impact Studies; RegCM4 -- Regional Climate Model version 4; ARPEGE; and WRF- Weather Research and Forecasting model version 3.2. The downscaled datasets were prepared under ADB TA 7173 Strengthening Capacity for Managing Climate Change and the Environment and improved under this project.

The datasets were obtained from Department of Hydrology and Meteorology and the Asian Disaster Preparedness Centre, Thailand. The results of the downscaling were incorporated into a basin-wide hydrological model which computed changes in precipitation, evapotranspiration, PET, soil moisture, river discharge and runoff for every 120 x 120m grid cell in the district. Additional parameters computed include river water levels, flooding, erosion, sediment concentration, slope stability/land slide risk and irrigation demand. The full range of climate change threats has been summarized into key threats likely to impact on infrastructure development sectors in the district.

Vulnerability assessments and adaptation planning for the selected sector infrastructure assets in the district were made based on the extreme climate and hydrological events derived from this District CC Threat Profile report. The methodology and results adopted for the CC threats computation was presented and discussed in a stakeholder workshop held on 11

th May,2014.

Core Group

MoSTENPD & NPMs Counterpart

and focal point of

DOLIDAR

Counterpart and focal point of

DOR

Counterpart and focal point of DWSS

Counterpart and focal point of

DHM

Counterpart and focal point of MoFALD

Counterpart and focal

point of DOI

Counterpart and focal point of DUDBC

Counterpart and focal point of DWIDP

MOSTE | Ma i ns tr eam i ng C l ima te C ha ng e R is k Ma nag e me nt i n D ev e lopm e nt Panchthar C l ima te Change Sec t or Pr of i le

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1 OV ERVIEW OF THE CL IM ATE CH ANG E SECTOR P ROFILES

1 . 1 P R O F I L E S T R U C T U R E

The profile is divided into two parts: (i) climate impact maps and (ii) site and sector specific time series and

graphs. The use of the maps includes:

Overview of baseline and future hydrological conditions including rainfall, temperature, snow

cover, glacier melting, evaporation and water surface and soil water availability

Overview of climate threats on district level including intense rainfall, high temperatures, droughts,

flooding, flash floods, landslides, erosion etc.

Hotspot identification

Overlay with population, agriculture and infrastructure data for damage quantification and socio-

economic analysis

Obtaining local data in case of missing more detailed time series and graph data.

Use of maps focuses on strategic and decision making level but can contribute to local planning and

management also. The maps show not only average conditions but also extreme event magnitude and

frequency.

The use of the time series and graphs includes:

Water supply and demand analysis

Irrigation planning

Infrastructure design for roads, bridges, hydropower and other water storage dams, urban

drainage, irrigation structures, water intakes, water supply, waste water treatment, sanitation etc.

Hydropower planning (economics, operations)

Disaster contingency planning.

1 . 2 S U M M A R Y O F T H E P A N C H T H A R C L I M A T E C H A N G E I M P A C T S

Panchthar district total catchment area is 5’504 km2 of which the district area covers 1’235 km2 (Figure 7).

The future projection window is 2040 – 2060. In other words downscaled climate projections are extracted

for this period and the analysed changes used to project historical observation data into 2050.

According to the PRECIS climate projections the mean daily maximum temperature is expected to rise 1.7 -

2 °C depending on the area. Similar rise is expected for the annual maximum temperature. Minimum

temperatures are also expected to rise about 2 - 3 °C on the average. Consequently there is slight expected

rise in potential evapotranspiration 0.2 – 0.3 mm/d especially during the dry season.

Wet season average precipitation is expected to increase about 30%. Extreme event rainfall is expected to

increase even more. Dry season is expected to become dryer especially in the already dryer Western part of

the district.

Maximum pluvial (rainfed) flooding is expected to increase especially in the North-Eastern part of the

district.


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Erosion is excepted to double in large part of the district.

River flow is expected to increase significantly especially in July. Depending on the locale increase is about

30 – 80%.


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2 MOD ELLING METH OD OLOG Y AND ASSUMP TIO N S 2 . 1 O V E R V I E W O F T H E V U L N E R A B I L I T Y A N D A D A P T A T I O N A S S E S S M E N T

A P P R O A C H

TA 7984 utilises a version of the ICEM CAM vulnerability and adaptation methodology revised specifically

for use in Nepal. CAM is a simple, process based approach to assessing the vulnerability of sector assets to

climate change which characterises the threat, exposure and sensitivity of each asset to each specific

change in hydroclimate parameter to give a detailed description of the impact. Impact is than coupled with

an assessment of adaptive capacity to characterise the vulnerability of the asset to climate change, which

are then used to scope and prioritise options for adaptation response (Figure 1).

Figure 1. Key phases in TA 7984 and data inputs from TA 7173: green diagram illustrates the main phases in

TA7173, with the red diagram showing the main phases in TA 7984. The blue box indicates the outputs of

TA 7173 to be used in TA 7984 vulnerability assessment process.

The first step in this process is a characterisation of the climate change threat which requires the development of hydrological models capable of replicating the main hydrological and catchment processes of the water cycle. The input data for the set-up and calibration of the catchment models requires time series data for temperature and rainfall at a daily or hourly time step. TA 7984 was designed to inherit the


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rainfall and temperature data under baseline and future climate conditions from the previous TA 7173 project (Figure 1).

2 . 2 R E Q U I R E M E N T S F O R C L I M A T E D A T A

The TA 7984 requirements for the baseline and projected time series data of temperature and rainfall input

data include:

(i) High temporal resolution meteorological data: Average hydrological and meteorological

conditions are not sufficient for in-depth sector analysis due to the high vulnerability to extreme

events of Nepal infrastructure development sectors. Input data sets should ideally be at a sub-

daily time step (3 hourly, 6 hourly), or at a minimum a daily time step so that the climate change

threat analysis can capture short duration events (flash floods, landslides, GLOF etc.) which are a

central element of Nepal’s climate vulnerability.

(ii) High spatial resolution meteorological data: The highly variable elevation of Nepal’s watersheds

reduces the ability of utilising meteorological data from a given station in the surrounding areas of

the catchment. Input meteorological data needs to be of a high spatial resolution with small grid

cell sizes for gridded data or a large number and even distribution of point-data for stations.

(iii) Accurate representation of the baseline climate and climate variability: Nepal’s historic climate is

highly variable between seasons and between years. Climate simulations for temperature and

rainfall need to accurately represent the past variability in order to build confidence in future

projections.

(iv) Detailed information on the GCM and downscaling process and assumptions: GCMs and dynamic

downscaling techniques differ significantly in how they resolve atmospheric processes. These

assumptions can have a significant impact on the results produced by a model and need to be

described in detail for downstream model applications. A simple example is the provision of data

on the assumed downscaling model ground elevations that is required for temperature and

precipitation elevation correction in the hydrological modelling.

2 . 3 B A S E L I N E H Y D R O M E T E O R O L O G I C A L D A T A

Historical observation data preparation for modelling proceeds in three steps: (i) data combination and

formatting, (ii) gap filling and (iii) quality control.

(i) The objective for data combination and formatting is to combine separate parameter, year and

station files into continuous time series that can be used in modelling. For example in

Kathmandu district 1,300 rainfall files have been combined to continuous station files. In

addition 260 temperature files have been combined.

(ii) Modelling requires continuous time series whereas historical monitoring time series have in

most cases gaps. The methodology to fill in the gaps is to correlate data in two or more stations


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and to use nearby stations and the correlation function to fill in missing data. Example of gap

filling is presented in Figure 2.Figure 2. Gap filled precipitation time series in Lomanghtan.

(iii) Quality control has three main tasks: check for 0-values that should be converted to missing

values, check for unrealistic (too large or too small) values, in case of discharge measurements

monitoring quality can be assessed through comparison with model results.

Figure 2. Gap filled precipitation time series in Lomanghtan.

2 . 4 F U T U R E C L I M A T E P R O J E C T I O N S

The TA 7984 modelling team has received bias corrected downscaled climate datasets that are based on

three global General Circulation Models (GCMs):

PRECIS – Providing Regional Climate scenarios for Impact Studies – is based on HadRM3 version

developed at Hadley Centre and UK Met Office

RegCM4 -- Regional Climate Model version 4 – is developed at International Centre for Theoretical

Physics, Italy and Physics of Weather Climate Centre at National Centre for Atmospheric Research

(NCAR), USA

ARPEGE has been developed for operational numerical weather forecast by Météo-France in

collaboration with ECMWF (Reading, U.K.). It is used to derive medium resolution data before

applying WRF- Weather Research and Forecasting model version 3.2 developed by NCAR for high

resolution downscaling.

Because the GCMs have poor resolution, of the order of 100 km, they need to be downscaled to local scale.

Figure 2 summarises the downscaling process from the three GCMs:


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Figure 3. Existing downscaling procedure for Nepal

Downscaling of these GCM data sets was undertaken by three institutes: (a) TERI – The Energy & Resources

Institute (India), (b) ADPC – Asian Disaster Preparedness Centre (Thailand), and (c) BCCR – Bjerkness Centre

for Climate Research (Norway). This gridded downscaled data was used in turn to obtain station data

corresponding to the location of the existing hydro-met monitoring station network. Bias correction of the

downscaled datasets was required to ensure a fit to historical observed data. A comparison of the observed

and downscaled baseline at the national level and monthly time step revealed that the downscaled data

over-estimates rainfall in the high altitude northern parts of Nepal and slightly under-estimates rainfall in

the lowland areas of the Terai. In response to this discrepancies the model outputs were corrected for bias

using a method of scaling outputs based on a standard deviation ratio at a monthly time step (Chang et al,

2007):

( )

The gridded data with bias correction was provided to the TA 7984 team by the Department of Hydrology

and Meteorology (DHM). The station data with bias correction was supplied to the TA 7984 team by ADPC.

It should be noted that although the GCM, RMC and bias correction sources for the station and gridded

downscaling are the same the end results are quite different. The TA 7984 team could not locate

documentation on how the gridded data was converted to station data and is therefore not able to explain

the discrepancies between the two datasets. However, some of the delivered station data – at least the

DYNAMIC DOWNSCALING using Regional Climate Models (RCM)

GLOBAL CIRCULATION MODELS (GCMs)

ECHAM05 ECHAM05, GFDL2.0, CCSM and HadCM

PRECIS REGCM4 ARPEGE

WRF

BIAS CORRECTED GRIDDED DATA SET Gridded & bias corrected

ADPC 2 20 x 20 km

TERI 20 x 20km

BCCR 12 x 12 km

BIAS CORRECTED POINT DATA SET gridded & bias

ADPC 1 TERI BCCR

SRES SCENARIO

A1B A2

ECHAM05

ECHAM04

HadCM3Q0

ADPC 1 25 x 25 km

ADPC 2

PRECIS

Gridded baseline and

projection climate datasets

Station baseline and

projection climate datasets


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RegCM data – may not actually be bias corrected because of the large discrepancy between observations

and the model data.

The TA modelling team has identified number of issues with the downscaled datasets and reported them to

the ADB (Technical Note, Review of historical and future projection meteorological data for TA 7984 CC

threat assessments, February 2012). The proposed methodology to create improved climate (projection)

datasets is:

Figure 4. Improved downscaling process

In the TA 7984 the proposed methodology is applied to the target districts using only one future climate

projection (PRECIS downscaling). The PRECIS downscaling is considered to be most reliable in representing

baseline climate but the TA team can see potential large utility using the other ones also to assess possible

climate change ranges. Also for instance the WRF downscaling could potentially provide required very high

resolution data when possible errors in spatial references would be corrected.

Projection of extreme events can’t be fully scientific as in most cases there are only few extreme events in

any given observation time series. The procedure to estimate climate change caused changes in extreme

events is:

initial projection is calculated using changes in monthly mean value, variance and number of dry

days

11 max and min values for temperature, 11 max values for precipitation, are selected for the (i)

observation baseline, (ii) computed projection, and climate model (iii) baseline and (iv)

projection; this usually covers well extreme values

minimum and maximum daily temperatures are processed separately


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projection is changed in a way which gives same change in median extreme values as in the

climate model results

projection is changed in a way that gives same variability change around the median values as for

the climate projections

results are automatically checked for possible outliers (Huber + winsoring)

based on the observation extremes and extreme changes between the climate model baseline

and projection, user evaluates whether any modification is needed using either software

suggested new value or user provided value.

2 . 5 C L I M A T E I N D I C A T O R A N D I M P A C T M O D E L L I N G M E T H O D O L O G Y

There exists multitude of impact models. They can be grouped into hydrological watershed models (e.g.

SWAT, HEC-HMS, MIKE-NAM), crop models (e.g. DSSAT, FAO AquaCrop, crop suitability model LUSET),

water resources management models (e.g. IQQM, SOURCE, WEAP) and hydraulic/ hydrodynamic/ flood

models (e.g. MIKE11, HEC-RAS, ISIS). Integrated modelling studies need to set up, connect and synthetise

these classes of models (see Table 1). Setting up and managing different models is rather laborious and

error prone and results require extra effort for synthesis.

Table 1. Integrated climate threat modelling projects

As an alternative for using separate models in used in the TA 7984. It is based on one integrated model

combining functionalities of the required separate modelling tools. This IWRM model has been developed

and used in the following ADB projects: ADB RETA 6420 (O Mon gas fired power plant in the Mekong Delta);

ADB TA-6420 (Mekong Delta Bridges Rapid Climate Change Threat and Vulnerability Assessment); ADB

7779-VIE (Support for the National Target Program on Climate Change with a Focus on Energy and

Transport); ADB TA-8267 (Strengthening Integrated Water and Flood Management Implementation in


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Thailand); ADB TA 4669-CAM (Technical Assistance to the Kingdom of Cambodia for the Study of the

Influence of Built Structures on the Fisheries of the Tonle Sap). In addition the IWRM model has been

applied since 2001 in many Mekong River Commission, World Bank and private sector consultancy projects

in South-East Asia.

The IWRM model together with its modelling platform has following advantages:

Is a rapid assessment tool requiring days or weeks instead of months or years for application

Has strong governmental and institutional framework in Asia since 2001 for both development

and application

Fully integrates hydrological, flood, crop/ farming system and water resources management

modelling

Is based on state-of-the art sub-models such as distributed hydrology, FAO AquaCrop physiological

crop growth, slope stability, erosion and reservoir sedimentation

Supports large number of global and national databases

Includes climate (change) downscaling

Has one-to-one correspondence to watershed properties described by GIS layers (topography,

land use, soil); this enables easy model set-up and transport of results to GIS

Is easy to operate through intuitive graphical user interface

Includes numerous statistical and other indicator outputs, both GIS and time series, for different

sector needs

Includes numerous pre- and post-processing tools

Links to monitoring and other model data; for instance can use for flooding internal flood

modelling, monitoring data or any other flood model outputs

Is license free

Works under same platform software as more detailed models for specific studies such as the 3D

flood, sediment, water quality and fish production model.

IWRM model is based on distributed modelling approach shown in Figure 5. . In distributed modelling

watershed is divided into small grid cells (in GIS pixels). Hydrological and other processes are computed in

each grid cell and the grid cells are connected through mass transport above soil (rivers and overland flow)

and in the soil. Model grid is constructed by combining soil, land use, topography and river network

together (igure 6). Observed and projected meteorological data as well as water utilisation and

infrastructure are added to the model together with the grid.


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Figure 5. Schematic representation of the IWRM model.

igure 6. IWRM model construction.

2 . 6 M O D E L A C C U R A C Y A N D C O N F I D E N C E L E V E L S

After model has been set-up its results are compared to measurements and model is calibrated. Some

model parameters remain more or less constant between different applications as they are well established

Model structure

Surface/unsaturated layer

component for each grid

box

Ground water component

River component


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from literature or from previous applications, they have minor impact on model results or they have similar

impact to other parameters that are primary parameters. Other parameters are related to more or less

unknown watershed parameters and need to be calibrated. The latter typically include soil properties,

especially soil thickness and water conductivity. Different measures exists for determining goodness of

model fit but for the IWRM model usually coefficient of determination based on the square of difference

between the modelled and measured value (R2) is used. When R2 is 1 the fit is perfect. In case of good

meteorological data coverage of a model area the discharge R2 is typically quite high, between 0.9 and 0.98.

In the Nepal case R2 is below this range, 0.3 – 0.8. The main factors affecting results in Nepal are:

the highest rainfall areas on the mountain slopes or other difficult to access locations are lacking

data; this not only leads to under-representation of these areas but also over-representation of

other areas; for instance local rainfall event can have way too large impact for a basin because its

extent is not known

snowfall and snow depth information is seriously lacking

highest mountain humidity condensation affects clearly flows and should be included in

modelling

discharge measurements are based on rating curves which relate water levels to flow; the rating

curves need to be updated on a regular basis because of morphological changes in river channel

and consequent changes in water levels; in Nepal very large river channel changes can happen

suddenly; the quality of flow values is not clear but it can be assumed based on model and

discharge comparison that quality varies depending measurement period – clear examples are

provided for each district in the profiles.

Despite the limitations in data coverage and quality and time available for model calibration the model

represent quite well hydrological characteristics of the target districts. Modelling results can be also

deemed reliable in representing changes caused by climate change scenarios.

Model is capable of computing large number of indicators (see Annexes I and II). It has been impossible to

calibrate these parameters such as slope stability, snow melt, ground water depth, flooding, flash flooding,

flow velocity/channel erosion, watershed erosion, irrigation demand, pluvial flooding etc. because of

resource constraints. However, some of these parameters are presented as they have been calibrated for

other countries and can provide useful information for climate change impact magnitudes. One of the main

indicators missing for the most part except Kathmandu Valley are river water levels and flooding

information. They would be easy to include given enough time for data collection, model calibration and

flood modelling validation.

A much larger issue than the hydrological modelling accuracy is the confidence in the climate change

projections. Temperature projections are considered rather reliable and the projections can be also verified

to some extent by historical data. However, precipitation projections contain large uncertainties especially

for monsoon areas as document by the IPCC 5. Under the large uncertainty the TA 7984 modelling team

has taken following approach:

focus on current climate, its variability and extreme event – this provides wealth of information

about climate vulnerability and adaptation measures

add projected changes to the baseline – this reduces the possible biases and errors in the climate

models


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use different climate models and scenarios to explore ranges of possible outcomes

(unfortunately this has not been possible during the TA as the original climate projection data

provided requires lot of further checking and processing).

2 . 7 S E C T O R P R O F I L E U P D A T E S

Climate (change) science is evolving, models are developed further and more monitoring based information

is available each year. At the same time climate information user based is expected to expand and new user

requirements emerge. Also the eight districts described in the sector profiles represent only a small area of

Nepal. Because of these reasons it is important that the profiles are expanded and updated regularly. For

this end DHM is receiving training in order to be able to apply modelling to new districts and update the

existing ones.


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3 P ANCH TH AR MOD EL OV ERV IEW 3 . 1 M O D E L S E T - U P

Dolakha model grid cell (“pixel”) size is 300 m. Panchthar model area corresponding to the Panchthar

district watershed is shown in Figure 7.

Figure 7. Panchthar watershed model area.


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Panchthar district grid elevations reach from 400 m to 4’600 m:

Figure 8. Model grid elevations for the Panchthar district.

Elevation [m]


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The land use is dominated by evergreen forest and agriculture:

Figure 9. Model grid land use classes for the Panchthar district.

Practically all Panchthar soil is counted under lithosol soil class.

Panchthar model meteorological stations are presented in Figure 10. Because temperature monitoring

time series were not available re-analysis data was used instead for temperature.


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Figure 10. Model meteorological stations. “N2…”-stations are temperatures from re-analysis data, other

stations are Nepal national precipitation monitoring stations.

3 . 2 H Y D R O L O G I C A L M O D E L A C C U R A C Y

Mul Ghat station downstream of Panchthar watershed has been used for model calibration. Figure 11 and

Figure 12 show modelled and measured discharge for the years 1974 and 1987 respectively. It is clear from

the figures that calibration for specific years results in bad fit for number of other years and that the quality

of the discharge data varies from year to year. In any case the measure for goodness of fit (R2) is relatively

high, 0.67. Computed average flow is 383 m3/s and measured 320 m3/s.


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Figure 11. Comparison between computed (black line) and measured (red line) daily discharges in Mul

Ghat for the year 1984.

Figure 12. Comparison between computed (black line) and measured (red line) daily discharges in Mul

Ghat for the year 1995.


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4 MAPS FOR H OTSP OT ID ENTIF ICATI ON AND IMP ACT OVERV IEW

Wet season mean daily maximum temperature [°C] and change in 2050.

Wet season mean annual maximum temperature [°C] and change in 2050.


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Wet season mean monthly precipitation [mm/m] and change in 2050.

Wet season mean annual maximum precipitation [mm/d] and change in 2050.


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50 year precipitation event [mm/d] and change in 2050.

Dry season mean monthly precipitation [mm/m] and change in 2050.


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Maximum pluvial flooding [mm] and change in 2050.

Dry season potential evapotranspiration (PET) [mm/d] and change in 2050.


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Average annual erosion rate [kg/m2/a] and change in 2050.


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5 S ITE AND SECTOR S P ECIF IC INFORMATION

Figure 13 presents model output locations for time series. Three locations, Memeng Jaga, Phidim and Ts4,

are selected for further processing and presentation in this document. The elevations of the stations are

1’758, 540 and 447 m. The corresponding upper catchment areas are indicated in

Figure 14. The catchment areas are 22, 360 and 5’504 km2.

Figure 13. Panchthar model output locations. Sites where profiles are output are indicated with red

points.

Figure 14. Upper catchment areas for the Memeng Jagat, Phidim and

Ts4 output locations.


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The indicators shown in this document and the main sectors using them are presented in Table 2.

Table 2. List of output indicators and main sectors utilising them.

Main indicator Characteristics Abbreviation Unit Sector

minimum daily temperature Tmin ◦C agriculture, infra

monthly average

exceedange probability

return periods

maximum daily temperature Tmax ◦C agriculture, infra

monthly average


return periods

daily precipitation Prec mm/d all


return periods

monthly precipitation Prec mm/m all

monthly average

return periods

number of dry days in a month

rainfall 10 min annual max intensity Intensity mm/10 min agriculture, infra

return periods

rainfall 60 min annual max intensity Intensity mm/60 min agriculture, infra

return periods

river discharge discharge m3/s all

monthly average


monthly 80% dependable flow

return periods

groundwater depth groundwater

depth

m agriculture, water supply

monthly average


return periods

pluvial flooding pluvial flood mm all


return periods

potential evapotranspiration PET mm/d agriculture

monthly average


return periods

TSS (total suspended solids) TSS mg/l land management, fisheries,

irrigation, water supply

monthly average


return periods


32

Tmin_C in Memeng_Jagat

Baseline

month n avg std min max 1 1864 6.28 1.78 2.15 11.72 2 1696 7.22 1.92 1.59 12.82 3 1860 9.84 1.83 3.97 14.25 4 1800 13.09 1.47 8.57 16.78 5 1860 16.37 1.54 11.22 20.32 6 1800 19 0.94 15.96 22.25 7 1860 19.79 0.95 16.93 24.05 8 1860 19.39 1.08 15.88 24.12 9 1800 17.27 1.43 12.88 22.98

10 1860 13.28 1.85 8.47 18.51 11 1800 9.58 1.53 5.66 13.54 12 1860 6.89 1.84 0.65 12.25

2050

month n avg std min max 1 1864 8.52 1.71 4.12 14.49 2 1696 10.74 1.8 4.53 16.05 3 1860 13.06 1.73 7.43 17.12 4 1800 15.42 1.11 11.98 18.23 5 1860 17.52 1.42 12.87 21.69 6 1800 20.82 0.97 17.66 24.31 7 1860 21.77 0.96 18.88 25.95 8 1860 21.25 1.1 17.71 26.09 9 1800 19.15 1.41 14.84 24.76

10 1860 15.87 1.21 11.87 19.29 11 1800 12.85 1.08 8.96 15.49 12 1860 10.12 1.69 4.39 15.02

02

03

04

05

06

07

08

09

10

11

12

month

8

10

12

14

16

18

20

22

Tm

in_C

Memeng_Jagat monthly averages

BL

2050

5 10 15 20 25

Tmin_C

0

20

40

60

80

100

%

Memeng_Jagat exceedance probability

BL

2050

10 20 30 40 50 60 70 80 90

year

2

3

4

5

Tm

in_C

Memeng_Jagat return periods

BL

2050


33

Tmin_C in Phidim

Baseline


10 1860 18.27 1.83 13.52 23.36 11 1800 14.53 1.5 10.6 18.66 12 1860 11.88 1.77 5.47 17.06

2050


10 1860 20.78 1.28 16.85 24.26 11 1800 17.73 1.05 13.99 20.45 12 1860 15.04 1.67 9.17 19.98

02

03

04

05

06

07

08

09

10

11

12

month

12

14

16

18

20

22

24

26T

min

_C

Phidim monthly averages

BL

2050

5 10 15 20 25 30

Tmin_C

0

20

40

60

80

100

%

Phidim exceedance probability

BL

2050

10 20 30 40 50 60 70 80 90

year

7

8

9

10

Tm

in_C

Phidim return periods

BL

2050


34

Tmin_C in Ts4

Baseline month n avg std min max

1 1864 8.72 1.47 4.58 14.1 2 1696 10.13 1.64 5.9 15.41 3 1860 13.04 1.59 8.28 17.25 4 1800 16.03 1.24 12.58 19.11 5 1860 18.99 1.41 15.65 23.22 6 1800 21.47 0.9 18.36 25.64 7 1860 22.13 0.95 19.59 26.59 8 1860 21.75 1.04 16.75 26.36 9 1800 19.72 1.42 15.64 25.17

10 1860 15.68 1.73 11.12 20.43 11 1800 11.89 1.4 4.49 16.3 12 1860 9.28 1.56 1.9 14.28

2050 month n avg std min max

1 1864 10.9 1.78 5.39 17.32 2 1696 13.57 1.71 9.09 18.94 3 1860 16.37 1.28 12.25 19.76 4 1800 17.96 0.45 16.5 20.19 5 1860 20.36 1.47 16.89 24.79 6 1800 22.98 1.05 19.35 28.48 7 1860 23.77 0.98 21.17 29.04 8 1860 23.46 1.08 18.35 28.81 9 1800 21.49 1.45 17.37 27.11

10 1860 17.93 1.53 13.92 22.1 11 1800 14.87 0.76 11.12 17.79 12 1860 12.56 1.64 5.67 17.79

02

03

04

05

06

07

08

09

10

11

12

month

10

12

14

16

18

20

22

24T

min

_C

Ts4 monthly averages

BL

2050

5 10 15 20 25 30

Tmin_C

0

20

40

60

80

100

%

Ts4 exceedance probability

BL

2050

10 20 30 40 50 60 70 80 90

year

4.5

5

5.5

6

6.5

7

7.5

Tm

in_C

Ts4 return periods

BL

2050


35

Tmax_C in Memeng_Jagat

Baseline

month n avg Std min max 1 1864 14.86 1.97 8.88 20.29 2 1696 16.55 2.38 8.56 22.96 3 1860 19.96 2.24 12.68 25.67 4 1800 22.68 1.67 16.87 26.96 5 1860 25.05 1.55 19.9 29.44 6 1800 27.14 1.11 21.92 30.92 7 1860 27.3 1.34 22.38 33.07 8 1860 26.96 1.28 23.47 31.4 9 1800 25.06 1.54 20.33 31.52

10 1860 21.45 1.78 16.34 26.41 11 1800 18.01 1.62 12.75 22.44 12 1860 15.15 1.84 8.79 19.99

2050


10 1860 23.28 1.85 17.92 28.42 11 1800 20.54 1.43 15.96 24.43 12 1860 17.9 1.04 13.36 22.39

02

03

04

05

06

07

08

09

10

11

12

month

16

18

20

22

24

26

28T

max_C


BL

2050

10 15 20 25 30 35

Tmax_C

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

30

31

32

33

34

35

Tm

ax_C


BL

2050


36

Tmax_C in Phidim

Baseline

month n avg Std min max 1 1864 20.07 1.9 14.05 25.24 2 1696 21.89 2.35 13.81 28.25 3 1860 25.39 2.25 18.01 31.16 4 1800 28.11 1.68 22.16 32.38 5 1860 30.41 1.55 25.24 34.91 6 1800 32.37 1.14 27 36.17 7 1860 32.43 1.42 26.93 38.48 8 1860 32.14 1.34 27.98 36.66 9 1800 30.26 1.57 25.54 36.82

10 1860 26.67 1.77 21.53 31.78 11 1800 23.2 1.63 17.88 27.77 12 1860 20.31 1.77 14.26 24.96

2050


10 1860 28.44 1.84 23.09 33.69 11 1800 25.65 1.45 20.94 29.73 12 1860 23.03 0.97 18.43 27.01

02

03

04

05

06

07

08

09

10

11

12

month

22

24

26

28

30

32

34

Tm

ax_C


BL

2050

15 20 25 30 35 40

Tmax_C

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

35

36

37

38

39

40

Tm

ax_C


BL

2050


37

Tmax_C in Ts4

Baseline


10 1860 24.72 1.77 19.12 30.71 11 1800 21.2 1.72 15.08 26.52 12 1860 18.26 1.6 12.83 23.56

2050

month n avg Std min max 1 1864 20.38 1.97 15.48 25.99 2 1696 23.95 2.76 15.72 32.11 3 1860 27.38 2.23 20.1 33.15 4 1800 27.25 0.79 24.71 31.81 5 1860 30.87 1.9 25.36 36.54 6 1800 32.19 1.86 22.71 37.1 7 1860 31.53 1.89 23.35 40.09 8 1860 31.65 1.64 24 36.72 9 1800 29.99 1.89 23.03 36.99

10 1860 26.36 1.72 20.89 32.22 11 1800 23.43 1.64 17.58 28.5 12 1860 20.7 1.35 15.51 25.17

02

03

04

05

06

07

08

09

10

11

12

month

20

22

24

26

28

30

32

Tm

ax_C


BL

2050

15 20 25 30 35 40

Tmax_C

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

34

35

36

37

38

39

40

Tm

ax_C

Ts4 return periods

BL

2050


38

Prec_mm_d in Memeng_Jagat

0 50 100 150 200 250

Prec_mm_d

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

100

150

200

250

Pre

c_m

m_d


BL

2050


39

Prec_mm_d in Phidim

0 20 40 60 80 100 120 140 160 180

Prec_mm_d

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

60

80

100

120

140

160

180

200

Pre

c_m

m_d


BL

2050


40

Prec_mm_d in Ts4

0 20 40 60 80 100 120 140 160

Prec_mm_d

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

80

100

120

140

160

180

Pre

c_m

m_d

Ts4 return periods

BL

2050


41

Prec_mm_month in Memeng_Jagat

02

03

04

05

06

07

08

09

10

11

12

month

100

200

300

400

500

600

700P

rec_m

m_m

onth


BL

2050

10 20 30 40 50 60 70 80 90

year

600

800

1000

1200

1400

Pre

c_m

m_m

onth


BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

5

10

15

20

25

30

days

Memeng_Jagat number of dry days

BL

2050


42

Prec_mm_month in Phidim

02

03

04

05

06

07

08

09

10

11

12

month

100

200

300

400P

rec_m

m_m

onth


BL

2050

10 20 30 40 50 60 70 80 90

year

300

400

500

600

700

800

900

Pre

c_m

m_m

onth


BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

10

15

20

25

30

days

Phidim number of dry days

BL

2050


43

Prec_mm_month in Ts4

02

03

04

05

06

07

08

09

10

11

12

month

50

100

150

200

250

300

350P

rec_m

m_m

onth


BL

2050

10 20 30 40 50 60 70 80 90

year

300

400

500

600

700

Pre

c_m

m_m

onth

Ts4 return periods

BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

5

10

15

20

25

30

days

Ts4 number of dry days

BL

2050


44

Intensity_mm_10min in Memeng_Jagat

Intensity_mm_10min in Phidim

10 20 30 40 50 60 70 80 90

year

10

15

20

25

30

35

Inte

nsity

_m

m_10m

in


BL

2050

10 20 30 40 50 60 70 80 90

year

10

15

20

25

Inte

nsity

_m

m_10m

in


BL

2050


45

Intensity_mm_10min in Ts4

Intensity_mm_60min in Memeng_Jagat

10 20 30 40 50 60 70 80 90

year

8

10

12

14

16

18

20

22

Inte

nsity

_m

m_10m

inTs4 return periods

BL

2050

10 20 30 40 50 60 70 80 90

year

30

40

50

60

70

80

Inte

nsity

_m

m_60m

in


BL

2050


46

Intensity_mm_60min in Phidim

Intensity_mm_60min in Ts4

10 20 30 40 50 60 70 80 90

year

20

30

40

50

60

Inte

nsity

_m

m_60m

in


BL

2050

10 20 30 40 50 60 70 80 90

year

20

25

30

35

40

45

50

Inte

nsity

_m

m_60m

in

Ts4 return periods

BL

2050


47

Discharge_m3_s in Memeng_Jagat

Baseline

month n avg std min max 1 1864 0.42 0.15 0.07 3.28 2 1696 0.29 0.14 0.04 3.24 3 1860 0.22 0.18 0.02 3.64 4 1800 0.31 0.5 0.01 5.02 5 1860 0.66 1 0.05 12.75 6 1800 1.22 1.46 0.08 15.69 7 1860 2.5 2.4 0.32 16.74 8 1860 2.51 1.84 0.63 13.73 9 1800 2.27 1.42 0.73 11.76

10 1860 1.41 0.82 0.67 12.68 11 1800 0.83 0.27 0.5 5.62 12 1860 0.61 0.37 0.36 7.43

2050


10 1860 1.83 1.76 0.69 23.16 11 1800 0.86 0.16 0.53 2.26 12 1860 0.59 0.08 0.39 0.83

02

03

04

05

06

07

08

09

10

11

12

month

1

2

3

4

Dis

charg

e_m

3_s


BL

2050

0 5 10 15 20 25 30

Discharge_m3_s

0

20

40

60

80

100

%


BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Dis

charg

e_m

3_s

Memeng_Jagat 80% dependable flow

BL

2050

10 20 30 40 50 60 70 80 90

year

10

15

20

25

30

Dis

charg

e_m

3_s


BL

2050


48

Discharge_m3_s in Phidim

Baseline

month n avg std min max 1 1864 4.37 1.84 0.67 40.21 2 1696 3.07 1.09 0.71 20.81 3 1860 2.42 1.88 0.39 39.45 4 1800 3.2 3.81 0.38 36.04 5 1860 6.97 9.47 0.64 138.82 6 1800 12.96 15.22 1.3 152 7 1860 27.48 25.5 4.05 197.23 8 1860 27.29 20.58 6.7 190.97 9 1800 24.42 14.57 7.11 111.33

10 1860 15.22 9.8 6.58 176.54 11 1800 9.18 4.01 5 91.11 12 1860 6.46 3.57 3.61 63.44

2050


10 1860 21.04 20.7 7.26 308.19 11 1800 10.42 2.04 5.4 24.91 12 1860 7.07 1.21 3.82 10.75

02

03

04

05

06

07

08

09

10

11

12

month

10

20

30

40

50D

ischarg

e_m

3_s


BL

2050

0 50 100 150 200 250 300 350

Discharge_m3_s

0

20

40

60

80

100

%


BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

0

2

4

6

8

10

12

14

16

Dis

charg

e_m

3_s

Phidim 80% dependable flow

BL

2050

10 20 30 40 50 60 70 80 90

year

100

150

200

250

300

350

Dis

charg

e_m

3_s


BL

2050


49

Discharge_m3_s in Ts4

Baseline


10 1860 294.48 196.53 93.15 1482.23 11 1800 106.44 76.71 59.85 1755.02 12 1860 66.75 24.25 41.69 503.02

2050

month n avg std min max 1 1864 49.02 10.66 9.14 149.15 2 1696 33.92 7.63 6.98 93.4 3 1860 68.98 86.1 6.14 1194.95 4 1800 168.56 129.92 17.49 885.38 5 1860 169.06 81.65 43.93 655.32 6 1800 952.61 575.31 110.91 3274.71 7 1860 1672.63 558.24 474.18 4224.99 8 1860 1312.47 386 422.92 3088.19 9 1800 1092.59 493.14 294.36 2882.21

10 1860 510.09 400.35 118.38 2727.45 11 1800 124.12 40.8 68.74 846.2 12 1860 72.12 11.84 46.43 112

02

03

04

05

06

07

08

09

10

11

12

month

200

400

600

800

1000

1200

1400

1600D

ischarg

e_m

3_s


BL

2050

02

03

04

05

06

07

08

09

10

11

12

month

0

200

400

600

800

1000

1200

Dis

charg

e_m

3_s

Ts4 80% dependable flow

BL

2050

10 20 30 40 50 60 70 80 90

year

2000

2500

3000

3500

4000

Dis

charg

e_m

3_s

Ts4 return periods

BL

2050


50

Groundwater_Depth_m in Memeng_Jagat

Baseline

month n avg std min max 1 1864 -2.75 0.25 -3.93 -2.43 2 1696 -2.92 0.23 -4 0 3 1860 -3.03 0.24 -4.06 0 4 1800 -3.07 0.27 -4.06 0 5 1860 -2.83 0.31 -3.7 0 6 1800 -2.5 0.37 -3.38 0 7 1860 -2 0.41 -3.02 0 8 1860 -1.63 0.34 -2.84 0 9 1800 -1.52 0.32 -2.62 0

10 1860 -1.77 0.26 -2.67 0 11 1800 -2.18 0.2 -2.88 0 12 1860 -2.49 0.16 -3.07 0

Baseline

month n avg std min max 1 1864 -2.6 0.28 -3.94 -2.24 2 1696 -2.79 0.25 -4.01 0 3 1860 -2.93 0.26 -4.07 0 4 1800 -2.94 0.3 -4.04 0 5 1860 -2.92 0.32 -3.78 0 6 1800 -2.77 0.39 -3.78 0 7 1860 -2.06 0.46 -3.07 0 8 1860 -1.66 0.37 -2.93 0 9 1800 -1.54 0.35 -2.73 0

10 1860 -1.66 0.28 -2.71 0 11 1800 -2 0.22 -2.7 0 12 1860 -2.31 0.17 -2.88 0

02

03

04

05

06

07

08

09

10

11

12

month

-3

-2.8

-2.6

-2.4

-2.2

-2

-1.8

-1.6

Gro

undw

ate

r_D

epth

_m


BL

2050

-4 -3 -2 -1 0

Groundwater_Depth_m

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

-3.6

-3.5

-3.4

-3.3

-3.2

Gro

undw

ate

r_D

epth

_m


BL

2050


51

Groundwater_Depth_m in Phidim

Baseline


10 1860 -4.02 0.62 -4.66 0 11 1800 -4.14 0.53 -4.66 0 12 1860 -4.25 0.44 -4.66 0

2050


10 1860 -3.31 0.74 -4.66 0 11 1800 -3.49 0.68 -4.66 0 12 1860 -3.71 0.61 -4.66 0

02

03

04

05

06

07

08

09

10

11

12

month

-4.6

-4.4

-4.2

-4

-3.8

-3.6

-3.4

Gro

undw

ate

r_D

epth

_m


BL

2050

-5 -4 -3 -2 -1

Groundwater_Depth_m

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

-4.8

-4.75

-4.7

-4.65

-4.6

Gro

undw

ate

r_D

epth

_m


BL

2050


52

Groundwater_Depth_m in Ts4

Baseline


10 1860 -4.66 0.02 -4.66 0 11 1800 -4.66 0.02 -4.66 0 12 1860 -4.66 0.02 -4.66 0

2050


10 1860 -4.5 0.24 -4.66 0 11 1800 -4.56 0.18 -4.66 0 12 1860 -4.61 0.12 -4.66 0

02

03

04

05

06

07

08

09

10

11

12

month

-4.64

-4.62

-4.6

-4.58

-4.56

-4.54

-4.52

-4.5

Gro

undw

ate

r_D

epth

_m


BL

2050

-5 -4.5 -4 -3.5 -3 -2.5

Groundwater_Depth_m

0

20

40

60

80

100

%


BL

2050


53

PluvialFlood_mm in Memeng_Jagat

0 50 100 150 200

PluvialFlood_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

60

80

100

120

140

160

180

200

220

Plu

via

lFlo

od_m

m


BL

2050


54

PluvialFlood_mm in Phidim

0 20 40 60 80 100 120 140 160 180

PluvialFlood_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

60

80

100

120

140

160

180

200

Plu

via

lFlo

od_m

m


BL

2050


55

PluvialFlood_mm in Ts4

0 20 40 60 80 100 120 140

PluvialFlood_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

60

80

100

120

140

160

180

Plu

via

lFlo

od_m

m

Ts4 return periods

BL

2050


56

PET_mm in Memeng_Jagat

Baseline


10 1860 4.39 0.37 1.96 6.05 11 1800 3.98 0.37 2.3 5.06 12 1860 3.49 0.37 2.07 4.67

Baseline

month n avg std min max 1 1864 4 0.61 1.39 5.67 2 1696 4.93 0.76 2.01 6.92 3 1860 5.39 0.51 2.97 6.69 4 1800 4.76 0.53 2.22 6.23 5 1860 5.18 0.5 3.43 6.57 6 1800 4.67 0.57 1.69 6.68 7 1860 4.22 0.49 2.45 6.07 8 1860 4.69 0.43 3.05 6.14 9 1800 4.89 0.49 2.12 6.73

10 1860 4.3 0.58 1.94 6.04 11 1800 4.01 0.4 2.29 5.23 12 1860 3.64 0.26 2.68 4.94

02

03

04

05

06

07

08

09

10

11

12

month

3.5

4

4.5

5

PE

T_m

m


BL

2050

1 2 3 4 5 6 7 8

PET_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

6

6.2

6.4

6.6

6.8

7

7.2

7.4

PE

T_m

m


BL

2050


57

PET_mm in Phidim

Baseline


10 1860 5.13 0.43 2.17 6.66 11 1800 4.7 0.43 2.54 6.01 12 1860 4.16 0.41 2.68 5.66

Baseline


10 1860 4.99 0.61 2.1 6.73 11 1800 4.68 0.48 2.5 6.18 12 1860 4.29 0.34 3.11 5.9

02

03

04

05

06

07

08

09

10

11

12

month

4.5

5

5.5

6

PE

T_m

m


BL

2050

2 3 4 5 6 7 8 9

PET_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

7

7.2

7.4

7.6

7.8

8

8.2

8.4

PE

T_m

m


BL

2050


58

PET_mm in Ts4

Baseline


10 1860 5.14 0.49 2.3 6.72 11 1800 4.66 0.52 1.75 6.19 12 1860 4.07 0.43 2.34 5.48

2050

month n avg std min max 1 1864 4.52 0.6 1.3 6.23 2 1696 5.44 0.89 1.67 7.84 3 1860 6.26 0.74 2.96 8.16 4 1800 5.53 0.32 4.33 6.87 5 1860 6.12 0.69 2.4 7.83 6 1800 5.54 0.91 1.68 7.76 7 1860 4.85 0.8 0.6 7.59 8 1860 5.32 0.7 1.57 7.33 9 1800 5.52 0.74 1.66 8

10 1860 5.1 0.54 2.22 6.87 11 1800 4.68 0.66 1.6 6.54 12 1860 4.09 0.43 2.4 5.47

02

03

04

05

06

07

08

09

10

11

12

month

4.5

5

5.5

6P

ET

_m

m


BL

2050

1 2 3 4 5 6 7 8 9

PET_mm

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

7

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

PE

T_m

m

Ts4 return periods

BL

2050


59

TSS_mg_l in Memeng_Jagat

Baseline

month n avg std min max 1 1864 10.83 108.36 0 2162.79 2 1696 12.97 126.88 0 2516.84 3 1860 31.03 194.98 0 2745.6 4 1800 131.21 404.05 0 3184.38 5 1860 208.9 439.22 0 2639.99 6 1800 270.37 428.29 0 2544.16 7 1860 328.72 396.89 0 2269.7 8 1860 248.2 334.09 0 1648.59 9 1800 171.48 292.2 0 1509.17

10 1860 45.57 183.45 0 1688.55 11 1800 7.8 83.17 0 1635.45 12 1860 9.49 92.52 0 1654.94

2050


10 1860 93.39 251.27 0 1668.64 11 1800 0.18 6.16 0 251.86 12 1860 0 0 0 0

02

03

04

05

06

07

08

09

10

11

12

month

0

100

200

300

400T

SS

_m

g_l


BL

2050

0 500 1000 1500 2000 2500 3000 3500

TSS_mg_l

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

2400

2600

2800

3000

3200

3400

3600

TS

S_m

g_l


BL

2050


60

TSS_mg_l in Phidim

Baseline


10 1860 50.66 191.38 0 1988.76 11 1800 13.02 108.95 0 1705.19 12 1860 12.37 109.87 0 1980.95

2050


10 1860 116.22 275.15 0 1957.69 11 1800 0.42 10.85 0 415 12 1860 0 0 0 0.06

02

03

04

05

06

07

08

09

10

11

12

month

0

100

200

300

400

500

TS

S_m

g_l


BL

2050

0 500 1000 1500 2000 2500

TSS_mg_l

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

2200

2400

2600

2800

3000

3200

TS

S_m

g_l


BL

2050


61

TSS_mg_l in Ts4

Baseline

month n avg std min max 1 1864 11.12 85.44 0 1624.74 2 1696 17.71 92.63 0 1608.22 3 1860 44.31 163.52 0 1778.4 4 1800 109.96 215.1 0 1390.69 5 1860 124.89 177.02 0.38 1166.51 6 1800 119.32 129.15 0.11 776.33 7 1860 135.62 102.49 0.09 583.94 8 1860 103.43 87.25 0.06 584.08 9 1800 82.39 97.15 0.04 628.09

10 1860 30.79 96.77 0 1246.8 11 1800 9.99 70.51 0 1363.66 12 1860 11.21 87.94 0 1568.97

2050

month n avg std min max 1 1864 5.5 63.5 0 1229.74 2 1696 3.78 32.28 0 702.03 3 1860 113.21 257.47 0 2038.15 4 1800 147.63 240.8 0 1608.5 5 1860 9.65 38.59 0.01 852.45 6 1800 172.26 152.75 0.05 923.47 7 1860 171.32 114.43 0.13 623.68 8 1860 78.7 73.29 0.05 511.32 9 1800 101.54 108.66 0.02 747.46

10 1860 54.72 112.31 0 1091.51 11 1800 0.48 9.06 0 306.94 12 1860 0 0 0 0.01

02

03

04

05

06

07

08

09

10

11

12

month

0

20

40

60

80

100

120

140

160

180T

SS

_m

g_l


BL

2050

0 500 1000 1500 2000

TSS_mg_l

0

20

40

60

80

100

%


BL

2050

10 20 30 40 50 60 70 80 90

year

1200

1400

1600

1800

2000

2200

TS

S_m

g_l

Ts4 return periods

BL

2050


62

ANNEX I : L IST OF AVAILABLE CL IMATE CH ANGE IND ICATOR AND IMP ACT MAPS

For infrastructure following exposure criteria have been identified (ICEM “Vulnerability assessment of

strategic infrastructure”):

Duration (e.g. hours or days of flooding)

Location (e.g. distance from flood)

Intensity (e.g. strength of rainfall, speed of flood)

Volume or Flow (e.g. size of event)

Aspect (orientation to the threat).

Maps for these criteria are already produced during each model run but they can’t cover all specific

requirements. It is usually possible to modify software to produce maps corresponding to any specific

requirement. Riverine flood maps can be produced only for limited locations due to the fact that flood

modelling requires more data, model calibration and time than is currently available. However, ADB is

supporting a flood risk mapping project that will provide necessary data for flood mapping. Also, pluvial

flooding maps are routinely produced during each model run.

Currently available basic indicator maps include:

dry season average discharge (m3/s)

wet season average discharge (m3/s)

average daily evapotranspiration dry season (mm/d)

average daily evapotranspiration wet season (mm/d)

erosion (kg/m2)

flood average depth (m); calculated only for flooded time

flood average yearly duration (d); calculated only for flooded time

flood maximum depth (m); calculated for the whole simulation period

flooding probability for any given year (0 - 1)

max discharge (m3/s)

slope stability index (smaller number less stable)

max rainfed water on ground (mm)

average daily PET dry season (mm/d)

average daily PET wet season (mm/d)

average dry season maximum precipitation (mm/d)

average wet season maximum precipitation (mm/d)

average daily max temp dry season (C)

average daily max temp wet season (C)

average dry season maximum daily max temp (C)

average wet season maximum daily max temp (C)

average daily min temp dry season (C)

average daily min temp wet season (C)

surface soil layer av. available water dry season (mm)


63

surface soil layer av. available water wet season (mm)

deep soil layer av. available water dry season (mm)

deep soil layer av. available water wet season (mm)

av. available surface water dry season (mm)

av. available surface water wet season (mm)

Currently available frequency maps include:

maximum discharge [m3/s], 5 year return period




exceedance probability for 500 m3/s flow

exceedance probability for 1000 m3/s flow

maximum river water depth [m], 5 year return period




exceedance probability for 1 m river water depth

exceedance probability for 5 m river water depth

maximum river flow velocity [m/s], 5 year return period




exceedance probability for 1 m/s river flow velocity

exceedance probability for 3 m/s river flow velocity

maximum flood depth [m], 5 year return period




exceedance probability for 1 m flood depth

exceedance probability for 2 m flood depth

5 year flood depth, baseline




5 year flood depth, 2050 projection




number of days discharge is below 1 m3/s, 5 year return period





64

exceedance probability for number of days is > 10 for flow < 1 m3/s








maximum precipitation [mm/d], 5 year return period




exceedance probability for 100 mm/d precipitation

exceedance probability for 150 mm/d precipitation

Irrigation and crops related maps include:

climate suitability for a crop (0 - 100)

temp suitability change for a crop (%)

water suitability change for a crop (%)

number of drought months

crop water use (m3/ha)

irrigation demand (m3/ha)

actual irrigation use (m3/ha)

irrigation deficit (m3/ha)

year day when crop fails because of drought

crop water logging year day

crop yield (tn/ha)


65

ANNEX I I : L IST OF AV AILABLE CL IMATE CH AN GE IND ICATOR AND IMPACT T IME SERIES AND G RAP HS

1 . B A S I C T I M E S E R I E S A N D G R A P H S

Number of time series products have been identified for sector use in Nepal and are produced

automatically through macro calls. The automated products include graphs, reports (rtf-format) and

numerical Excel sheets (csv-format). The output list is shown below. It is possible to add new parameters if

required to the list.

Monthly statistics

o avg std min

Weekly statistics

o avg std min

Monthly range

Frequency

Exceedange probability

Return periods

Typical monthly extreme.

The reports are produced for following variables:

Daily minimum temperature [C]

Daily maximum temperature [C]

Precipitation [mm/d]

10 min rainfall intensity [mm]

60 min rainfall intensity [mm]

Pluvial flood depth [mm]

Number of dry days

Length of dry periods [d]

Discharge [m3/s]

Potential evapotranspiration (PET) [mm/d]

Evapotranspiration [mm/d]

Groundwater depth [m]

River flow velocity [m/s]

Surface soil layer water amount [mm]

Deeper soil layer water amount [mm]

Total suspended solids TSS [mg/l]

Water elevation (river stage) [m].

In addition to the automated time series report generation the model software includes also number of

separate time series processing tools including:

Grouping statistics


66

Cumulative series

Histogram

Moving average

Gaussian average

Same time average

Month and week averages (averaging over many years)

Flowspider

Windrose

Self differentiation

Autocorrelation

Fast Fourier transform

Lag plot

R2

Basic statistics

Correlation

Linear regression

Statistical (down) scaling.

The above time series products are based on primary model time series outputs which are in turn based on

primary model simulation parameters. The primary time series outputs are:

Name Unit Description

prec mm precipitation

pwater mm precipitation as water

psnow mm precipitation as snow

tavg C air temperature

tmin C min air temperature

tmax C max air temperature

tlr C/m temperature lapse rate

swin Kj/m2/s incoming shortwave radiation

cloud cloudiness

rhum % relative humidity

wind m/s wind speed

pet mm potential evaporation

etr mm evapotranspiration

epan mm pan evaporation

atmp mb atmospheric pressure

lai Leaf Area Index

pms plant maturity index

tind dC temperature index

tindn dC negative temperature index

alb albedo

snowe mm snow water equivalent

snowc mm snow water content

hsnow m snow depth

intercp mm interception

surfw mm/d surface water increase

s0 mm surface depression storage


67

s1 mm soil level 1 storage

s2 mm soil level 2 storage

q0 mm/d surface runoff

q1 mm/d soil level 1 runoff

q2 mm/d soil level 2 runoff

qriver m3/s river discharge

rvel m/s river velocity

q4 mm/d drainage discharge

r0 m river/lake level

r1 m2/m3 river area/lake volume

t0 C soil surface temperature

t1 C soil level 1 temperature




ice1 soil level 1 ice content

ice2 soil level 2 ice content

twater water temperature

dpo4 ug/l soluble phosphorus concentration

c0 ug/l soluble phosphorus concentration

ppar ug/l particulate phosphorus concentration

c1 ug/l particulate phosphorus concentration

ss0 mg/l clay concentration

ss1 mg/l silt concentration

ss2 mg/l sand concentration

c2 mg/l clay concentration

c3 mg/l silt concentration

c4 mg/l sand concentration

ptot ug/l total phosphorus concentration

tss mg/l TSS concentration

c7 mg/l wq7 concentration



mdpo4 kg/d soluble phosphorus load, c*q

mppar kg/d particulate phosphorus load, c*q

mss0 kg/d clay load, c*q

mss1 kg/d silt load, c*q

mss2 kg/d sand load, c*q

mptot kg/d total phosphorus load, c*q

mtss kg/d TSS load, c*q

m7 kg/d wq7 load, c*q



mptot kg/d total phospohrous load

mntot kg/d total nitrogen load

mtss mg/l total suspended sediments load

gwd m groundwater depth

fld m flood depth

riu m3/s river water usage

gwu m3/s groundwater water usage

iru m3/d irrigation water usage

crua m3/d/ha av crop water use


68

cdema m3/d/ha av irri demand

ciusa m3/d/ha av irri use

cdefa m3/d/ha av irri deficit

cru m3/d/ha crop water use

cdem m3/d/ha irri demand

cius m3/d/ha irri use

cdefic m3/d/ha irri deficit

red m3/s reservoir discharge

rev Ml reservoir water volume

rewl m reservoir water level

fldepth m flood depth

rootz m crop rooting depth

canopy canopy cover

transpiration mm/d crop transpiration

biomass t/ha crop biomass

yield t/ha crop yield

avyield t/ha average crop yield

diveruse m3/d diversion use

2 . G R A P H P R O D U C T S F O R S P E C I F I C N E P A L G O V E R N M E N T P L A N N I N G P R O C E S S E S

Nepal Government has established design procedures and criteria for different infrastructure categories.

Examples of these include rainfall IDF (Intensity-Duration-Frequency) curves for drainage design, flood

return periods for bridge design and meteorological CROPWAT inputs for estimating required irrigation

capacity. Model outputs feed into these procedures improving baseline assessment and providing future

climate adjusted values for design.

Examples of some graph products are shown below.

Figure 15. Change in flow duration/ dependability.


69

Figure 16. Flood return periods.

Figure 17. Intensity-Duration-Frequency (IDF) curve.


70

Figure 18. Flash flood travel times.


71

ANNEX I I I : EX AMP LE SECTOR CL IMATE P ROFI LE IDENTIF ICATION MATRI X

For the time being no established sector climate profiles exists for Nepal. In developing the sector profiles one could take into account:

Earlier general district climate profiles

Examples provided in this document about possible climate indicators

Development of sector climate profile identification matrix (example of WATSON matrix is presented in ANNEX III)

Nepal Government established sector procedures.

One should distinguish at least between two types of needs for climate profiles: (i) strategic national planning where overall understanding about climate

change as well as identification of priority threats and hotspots are important and (ii) more localised infrastructure design, contingency planning and socio-

economic evaluation.

Coming up with definitive sector profiles during will be practically impossible. Because of this the sector profiles need to be flexible and need updates as

improved information will be available and as user capacity and needs develop. It can be envisaged that in the future DHM will host a web site where user

can access sector hotspot maps, click on area of interests, select list of climate impact information, examine the information in an accessible form and

further analyse the information. In essence the web site would be exploratory tool for interactively and iteratively retrieve relevant data for decision

making, planning and design. DHM would regularly update the web site information with monitoring and new climate change modelling data.

WATSAN Components

Required Parameters under each CC Threat

Increased Temperature and Reduced Rainfall (Drought)

Increased Rainfall

Increased Flow in River Landslides Flash Floods


72

Source and Catchment

Map showing average increase in temperature across the district. This would help the engineers to identify where the assets are located and how they are impacted by the drought and its extent. This would also help to understand the catchment area analysis.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This exercise ensures the water security and future storage planning.

No impact on source and catchment.

Map showing the critical zones for landslides. This would help the authorities to protect the source and catchment.

Map showing intensity and frequency of such events would be useful to protect the source and catchment.

Intake Point Map showing average increase in temperature across the district.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall.

Map showing the extent of inundation to see if there are any existing intake points are affected.

Map showing the critical zones for landslides. This would help the authorities to protect the intake point.

Map showing intensity and frequency of such events would be useful to protect the intake point from submergence.

Pipelines

Map showing average increase in temperature across the district. This helps to see which zones are affected and mapping will help to plan protective measures to prevent any cracks in the pipes along such critical areas.

Increases in rainfall from base case to future case will be useful. Increased rainfall brings more flows that might erode the soil around the pipelines and expose them to the open environment that attracts frequent damages.

WL increases in the river will be useful in areas where the pipeline crossings are common.

Map showing the critical zones for landslides. This would help the authorities to protect the transmission pipelines that are running along the historic landslide areas.

Map showing intensity and frequency of such events would be useful to protect the pipelines washed away from such events.


73

Reservoirs

No impact because no water arrives at reservoir during drought conditions.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This exercise ensures planning for O&M works for more sediments arriving at reservoir that might reduce the capacity.

No impact because reservoirs are not affected by the increased flow in River.

Map showing the critical zones for landslides. This would help the authorities to identify the existing reservoirs location in relation to the projected landslide zone and plan strengthening works. Similarly, avoid constructing any future reservoirs in Historic/projected landslide areas.

Map showing the intensity and frequency of such events would be useful to protect the reservoir from over-toppling scenarios.

Water Treatment Plant (WTP)

No impact because no water arrives at WTP during drought conditions.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This exercise ensures planning for O&M works for more sediments arriving at WTP and load on the treatment process.

No impact because WTP are not affected by the increased flow in River.

Map showing the critical zones for landslides. This would help the authorities to identify the existing WTP location in relation to the projected landslide zone and plan strengthening works. Similarly, avoid constructing any future WTP in Historic/projected landslide areas.

Map showing the intensity and frequency of such events would be useful to protect the WTP from submergence.

Sewage and Water Pumping Stations

No impact.

Increase in rainfall from the base case to the projected scenario that help to calculate the

Map showing the extent of inundation to see if there are any PS’s affected.

Map showing the critical zones for landslides. This would help the authorities to

Map showing the intensity and frequency of such events would be useful to protect the


74

amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This exercise ensures the operating philosophy of PS.

identify the existing PS location in relation to the projected landslide zone and plan strengthening works. Similarly, avoid constructing any future PS in Historic/projected landslide areas.

PS from submergence.

Septic Tanks

Map showing average increase in temperature across the district. This would help the engineers to identify where the assets are located and protect them from increased temperature scenarios to avoid public health issues.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This exercise ensures to prevent the frequent overflows from septic tanks.

Map showing the extent of inundation to protect the septic tanks along the banks of the river.

Map showing the critical zones for landslides to protect the existing septic tanks collapse.

Map showing the intensity and frequency of such events would be useful to protect the septic tanks from submergence and mix of sewage with the rainfall run-off.

Sewage Treatment Plant (STP)

Map showing average increase in temperature across the district. Increased temperature has an impact on the biological process which eventually kills the useful bacteria that helps to disintegrate the organic matter.

Increase in rainfall from the base case to the projected scenario that help to calculate the amount of discharge/flow that can be expected due to the increased intensity and duration of rainfall. This would help to plan for emergency bypass

Map showing the extent of inundation to see if there are any STP’s.

Map showing the critical zones for landslides. This would help the authorities to identify the existing STP location in relation to the projected landslide zone and plan strengthening works. Similarly, avoid constructing any future

Map showing the intensity and frequency of such events would be useful to protect the STP from submergence.


75

alternatives to protect the STP from shock loading.

STP in Historic/projected landslide areas.


76

BIBL IOG RAPH Y

DHM/ADPC (2011): Nepal Climate Data Portal-User Manual (v0.6)

Intergovernmental Panel on Climate Change (IPCC), Ed. (2001). Climate Change 2001: Synthesis Report. A Contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change: Cambridge and New York, NY, USA, Cambridge University Press

IPCC (2013). Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change, Fifth Assessment Report

IPCC (2007). Climate change 2007: Impacts, adaptation and vulnerability - Summary for policymakers. A report of the Working Group II of the Intergovernmental Panel on Climate Change, Fourth Assessment Report

Practical Action Nepal (2009): Temporal and spatial variability of climate change over Nepal (1976 - 2005), Practical Action Nepal Office, 2009 ISBN: 978-9937-8135-2-5

SDMC. (2008): Feasibility study for preparation of Digital Vulnerability Atlas of SAARC Countries, Research Report SAARC, New Delhi.

Shakya B. (2002): A new approach within hydrometeorological technique for estimation of PMP in Nepal. Flood Defence Science Press, New York, ISBN 1-880132-54-0

Shakya B. (2004): Practical Hydrology and Meteorology for Environmental Studies, A text book, BS publication

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