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Review Article

Flood Modelling: Recent Indian Contributions

R CHANDRA RUPA1 and PP MUJUMDAR1,2,3,*

1Department of Civil Engineering, Indian Institute of Science, Bangalore, Karnataka 560 012, India2Interdisciplinary Centre for Water Research (ICWaR), Indian Institute of Science, Bangalore, India3Divecha Center for Climate Change, Indian Institute of Science, Bangalore, India

(Received on 03 April 2018; Revised on 03 January 2019; Accepted on 30 April 2019)

Flood modelling is an important first step towards understanding and managing floods, at all spatial scales, from a large

catchment of a river to highly developed urban areas and to individual property and infrastructure. Recent advances in

modelling techniques along with sophisticated computational tools and data products have facilitated a rapid progress in

flood modelling. Despite this progress, an accurate assessment and forecasting of floods with the associated risk is still

elusive due to uncertainties at each stage of the modelling. This paper presents a review of flood modelling with specific

focus on India. In recent years, significant contributions have been made by Indian researchers to the hydrologic science in

general, and to flood modelling in particular. Indian contributions to the areas of hydrologic modelling of floods, flood

forecasting, flash flood modelling, urban floods, and risk assessment and mitigation are reviewed in the paper. Since the

emphasis is on flood modelling, the related topics of flood frequency analysis are excluded, although some representative

studies on extreme rainfall frequencies are briefly mentioned. The paper concludes with some perspectives on future

directions in flood modelling and actions needed for sustainable flood management practices, in the country.

Keywords: Flood Modelling; Flood Forecasting; Uncertainties; Risk Assessment; Flash Floods; Urban Floods

*Author for Correspondence: E-mail: [email protected]

Proc Indian Natn Sci Acad 85 No. 4 December 2019 pp. 705-722

Printed in India. DOI: 10.16943/ptinsa/2019/49648

Introduction

Floods are the most common and third most damaging

natural hazards globally after storms and earthquakes

(Wilby and Keenan, 2012). Flooding is a major concern

in India, as indeed it is in the other parts of the world.

Consequently, a great effort has been put by Indian

research community in developing models and

methodologies to understand floods at various spatial

scales. Significant contributions have been made by

the community in recent years, in the areas of flood

estimation, multivariate hydrologic modelling,

uncertainty quantification, climate change impacts,

flood hazard and risk assessment. Large river basins

such as the Ganga, Mahanadi, Godavari and Krishna

basins have been studied by several researchers to

understand the nature of river flooding. Several studies

also focus on modelling urban floods. A main objective

of the brief review presented in this paper is to compile

information on the Indian research on flood modelling

and to provide perspectives on implementable actions

to mitigate the impacts of floods. Modelling aspects

of the floods, including hydrologic issues, data

requirements and uncertainty quantification are first

discussed, followed by modelling for climate change

impact assessment. Flood forecasting, with an

emphasis on forecasting of flash floods is discussed

next. Urban flooding, which is known to be increasing

in frequency and spatial extent in the country, is

reviewed next. The concluding remarks of the paper

provide some thoughts on future directions for flood

modelling and implementable policies.

Flood Modelling

Flood modelling and creating flood inundation maps

are essential to understand the possible impacts of

floods of a given magnitude and to initiate actions on

the ground to minimize the damages. Hydrodynamic

modelling plays an important role in obtaining the flood

characteristics, i.e., the magnitude, duration and the

spatial distribution of flooding. Progress in

hydrodynamic modelling during the last decade has

led to considerable improvements in ability to simulate

706 R Chandra Rupa and PP Mujumdar

flooding scenarios. Models may be classified

depending on how the catchment processes are

represented (deterministic or stochastic) or on how

the catchment is discretized spatially (lumped or

distributed). Routing models that estimate the flood

wave propagation along a river channel, have been

developed with the continuity, momentum and data

driven approaches (Perumal and Price, 2013;

Gopakumar and Mujumdar, 2008; Perumal and Sahoo,

2007; Mujumdar, 2001). Studies on flood frequency

analysis, based on statistical models, are in general

used for understanding the long-term changes in flood

magnitudes and frequencies (Guru and Jha, 2015;

Kamal et al., 2017; Kumar et al., 2003; Basu and

Srinivas, 2014; Santhosh and Srinivas, 2013).

Mujumdar and Nagesh Kumar (2012) discussed

various aspects of floods including the hydrological

modelling for floods, climate change impacts

assessment, remote sensing and GIS for modelling

floods, along with case studies.

Different hydrological modelling software such

as HEC-HMS, SWAT, MIKE, VIC and SWMM are

used for modelling flows in the river basins. It is

instructive to note that most hydrologic models

reproduce the average conditions very well but fail to

simulate the extreme conditions. The models also

perform badly in catchments with significant structural

interventions such as dams, unless the flows are

normalised. For example, Nandi and Reddy (2017)

used a VIC model for simulating the hydrological

variables over Krishna River Basin and concluded

that the VIC model can handle large-scale variability

but overestimates the stream flow in the downstream

portion as the effect of storage structures are not

considered in their model. Chawla and Mujumdar

(2015) used VIC model to simulate the Upper Ganga

Basin (UGB) and analysed the stream flow patterns

and magnitude of runoff. Fig. 1 shows the calibration

and validation results from VIC model. They have

also presented optimum set of parameters for the two

regions (upstream and midstream regions of UGB)

along with their performance measures during

calibration. On the other hand, hydrodynamic models

specifically tailored for flood flow simulations would

serve better for the purpose. Patro et al. (2009) used

a one-dimensional hydrodynamic model to simulate

the discharges in the Mahanadi River basin for the

monsoon period (June-September) of the year 2004.

They developed the model by integrating information

from various sources i.e., refining the cross-sections

derived from SRTM DEM along with the measured

river cross-sections, available river discharge as well

as water-level data at different gauging sites.

Efforts are currently being put to improve the

use of hydrologic models for simulating floods at river

basin scales by driving them with state of the art

numerical weather prediction (NWP) models and/or

with improved rainfall data products (e.g., Chawla

and Mujumdar, 2019). Shah and Mishra (2016)

evaluated the performance of VIC model with several

rainfall data sets to simulate the daily discharges in

the Mahanadi river basin and concluded that bias in

real-time precipitation products affects the initial

condition and precipitation forcing, which in turn

affects flood peak timing and magnitudes.

Hydrologic models, meant primarily for stream

flow simulation, are applied commonly in flood

Fig. 1: Calibration and validation results of (a) upstream

and (b) midstream regions (Source: Chawla and

Mujumdar, 2015). The figure shows that the average

conditions are in general simulated well by the model,

but the extreme flood flows are poorly simulated

Flood Modelling: Recent Indian Contributions 707

modelling studies as well, and from the applications it

is in general concluded that the models are able to

reproduce the flood flows with ‘acceptable accuracy’.

However, acceptable accuracy involves subjectivity

and to overcome this limitation, the statistical goodness-

of-fit measures are used to estimate the model

efficiency. Even the use of such measures does not

necessarily allow an objective judgment of model

performance, which depends on the specific objectives

of the model application for given a catchment and

observed data. Also, each group working on a basin

develops its own model, resulting in an excess of such

tools for each major basin. For example, the Mahanadi

basin is modelled by many researchers (Mondal and

Mujumdar, 2012; Ghosh et al., 2010; Pattanayak et

al., 2017; Asokan and Dutta 2008; Gosain et al., 2006;

Jena et al., 2014; Patro et al., 2009). Johnston and

Smakhtin (2014) discussed on how much modelling is

enough for a river basin. Their study reviewed

hydrological modelling in four large basins -Nile,

Mekong, Ganges and Indus –and suggested four areas

for action to improve effectiveness and reduce

duplication in hydrological modelling. The input data,

model details and the results, should be published on

a common platform, to allow more coordinated

approaches and benefit from past modelling

experiences. For each major basin, an appropriate

agency should be identified to take responsibility for

coordination and data sharing, to reduce redundancy

of effort and promote collaboration among

researchers. Initiatives are required to improve the

quality and quantity of data for the models (for

calibration and validation purpose) and novel

approaches are essential for data collection (remote

sensing, crowd-sourcing and community-based

observations) and data assimilation from different

sources.

Data Requirements

The efficiency and usefulness of any model depends

on the quantity and quality of the data, it is presented

with during the calibration and validation stages. The

data requirements of flood modelling for river basins

fall into three distinct categories, (i) topographic data

of the basin and the channel and/or the river, (ii)

hydrometeorological data including precipitation,

temperature, solar radiation, land use-land cover and

sub-surface hydraulic properties, and (iii) time series

of flow rates and stage data to provide model input

and output boundary conditions for model calibration

and validation. Acquisition of part of this data has

been made possible by developments in the field of

remote sensing. Remote sensing, from both satellites

and aircraft, allows the collection of spatially distributed

data over large areas and reduces the need for costly

ground survey. However, the ground measurements

complement the data from the satellites, and are useful

for validation of the satellite products. Due to the

advances in satellite remote sensing in the last few

decades, more and more geo-spatial datasets related

to hydrology - such as topography, soil and land use-

have become available through several open sources.

Durga Rao et al. (2011) developed a flood forecast

model for the Godavari Basin considering a distributed

modelling approach with space inputs. They have

computed the topographic and hydraulic parameters

using the land use/land cover grid that is derived from

the Indian Remote Sensing Satellite (IRS-P6) AWiFS

sensor data (56 m resolution), Shuttled Radar

Topographic Mission (SRTM) Digital Elevation Model

(DEM), and the soil textural grid. The model is

calibrated and validated using the field

hydrometeorological data. The model was tested

during the 2010 floods with real-time 3-hour interval

hydrometeorological and daily evapotranspiration data

and found that the accuracy in estimating the peak

flood discharge and lag time was good. Mondal et al.

(2016) have reviewed the progress in hydrological

modelling achieved in India during the last about five

years and discussed the data requirements of the

hydrological modelling. Despite the availability of

various open sources for useful data, there is still a

lack of good quality and quantity of data at required

spatial and temporal, specifically when extremes are

modelled, leading to uncertainties in the model results.

Due to resource constraints and limited ground

measurements, often the modeller needs to extrapolate

information from the available measurements in space

and time. In addition, uncertainties in the climate

models, which are used to assess the likely

hydrological impact of future system response, (for

example to climate and land management change),

further compounds the uncertainties in the projections.

It is important to quantify and communicate such

uncertainties for use in policy making.

Uncertainty Quantification

Uncertainties are inherent in any modelling process


and originate from a wide range of sources, from model

formulation to the quality and quantity of data.

Uncertainties cannot be eliminated, but their amplitude

should be estimated andtheir sources should be

identified to understand the impact on modelling

(Deletic et al., 2012). Beven (2006) stated that there

are many sources of uncertainty that interact non-

linearly in the modelling process. However, not all

uncertainty sources can be quantified with acceptable

levels of accuracy, and the proportion of uncertainty

sources being ignored may be high in environmental

(including hydrological) modelling investigations

(Deletic et al., 2012, Harremoës, 2003; Doherty and

Welter, 2010). The uncertainties should be quantified

wherever possible and propagate them to the decisions

of risk. Specifically, in the precipitation data,

quantifying uncertainties is crucial in obtaining the risks

and resilience of the hydrologic system.

Precipitation is one of the hydrologic variables

with a potential to impart the highest amount of

uncertainty due to high variability in both space and

time. Mondal et al. (2016) reported that although,

Indian Meteorological Department (IMD) has a wide

network of rain gauges there are still data sparse

regions. Afew studies have explored using rainfall data

from satellite data such as Tropical Rainfall

Measurement Mission (TRMM) to overcome this gap

(Indu and Nagesh Kumar, 2014; Mondal et al., 2018).

Attempts have also been made to integrate the satellite

data with forecasting systems to improve the

performance of the models in predicting floods

(Sharma et al., 2017; Mitra et al., 2013). However,

there are high uncertainties associated with

precipitation, and still gaps exist in precisely quantifying

the uncertainties in literature. Quantification of

uncertainties especially in the extreme rainfall that

causes flooding can be quantified using statistical

methods (assimilating the data from various sources)

and the Bayesian techniques (e.g., Chandra Rupa et

al., 2015).

In addition to the uncertainty in precipitation,

there are several uncertainties in the other inputs to a

hydrologic model. The upstream discharge and the

roughness relationship for the main channel, for

example, havea major influence on the uncertainty in

the modelled water levels. Floodplain bathymetry, weir

formulation and discretization of floodplain topography

add most to the uncertainties in model outcomes

(Warmink et al., 2011). Yaduvanshi et al. (2018)

analysed the runoff response during extreme rain

events over the Basin of Subarnarekha River in India

using soil and water assessment tool (SWAT).They

analysed the sensitivity of basin parameters to improve

the flood runoff simulation efficiency of the model.

Climate Change Impacts

Changing climate increases the risks associated with

floods. The magnitude and the frequency and of flood

discharges are affected due to climate change, and

there is a clear indication that the changes in the

magnitude and frequency will continue in the future

due to continuous increase in the concentration of

greenhouse gasses (GHGs) in the atmosphere (IPCC,

2012). The streamflow variation is region or basin

specific and is not uniform across the world. Mujumdar

and Ghosh (2008) presented an overview of the

methodologies developed for assessing hydrologic

impacts of climate change with an emphasis on

statistical techniques for regional impact assessment

and modelling of uncertainty, resulting from the use

of multiple climate models. They have demonstrated

the methodologies with the case study of Orissa

meteorological subdivision and Mahanadi river basin,

which shows a possible decreasing trend in rainfall

and monsoon streamflow of the region in future.

Gosain et al. (2006) quantified the climate change

impacts on the water resources of Indian River basins.

Their study included an assessment of impact on

floods. They have considered more than 12 river

basins of the country, simulated 40 years (20 years

present and 20 years future) of weather data, and

concluded that under the Green House Gas (GHG)

scenario, severity of droughts and intensity of floods

in various parts of the country may get deteriorated.

Hirabayashi et al. (2013) used a state-of-the-art global

river routing model with an inundation scheme to

compute river discharges and flooded areas. An

ensemble of projections under a new high-

concentration scenario shows a large increase in flood

frequency in Southeast Asia, Peninsular India, eastern

Africa and the northern half of the Andes, with small

uncertainty in the direction of change.

Asokan and Dutta (2008) analysed the water

resources in the Mahanadi river basin under the

current and projected climate conditions and

concluded that the basin is expected to experience


progressively increasing intensities of flood in

September and drought in April in future. Also, they

analysed the water demand estimation considering

sectors of domestic, irrigation and industry and

concluded that the future water demand shows an

increasing trend until 2050, beyond which the demand

will decrease owing to the assumed regulation of

population explosion.

Precipitation is a basic and important input to

the hydrological models for flood estimations. There

are studies showing the increases in the extreme

precipitation due to climate change (e.g., Guhathakurta

et al., 2011). Mondal and Mujumdar (2015) analysed

changes in extreme rainfall characteristics over India

using a high-resolution gridded dataset. Intensity,

duration and frequency of precipitation over a

threshold in the summer monsoon season are modelled

by non-stationary distributions whose parameters vary

with physical covariates like the El-Nino Southern

Oscillation index (ENSO-index - indicator of large-

scale natural variability), global average temperature

(indicator of human-induced global warming) and local

mean temperatures (indicator of localized changes).

Fig. 2 shows the grid-wise best statistical models for

intensity, duration and frequency of extreme rainfall.

They concluded that the intensity, duration and

frequency exhibit non-stationarity due to different

drivers and no spatially uniform pattern is observed in

the changes in them across the country.

However, an extreme precipitation event may

not be responsible for an adverse flood situation. There

are several other factors including the antecedent

moisture conditions and basin conditions. Jena et al.

(2014) analysed floods in Mahanadi basin in eastern

India and examined if the increase in flooding in the

basin is only due to increase in extreme rainfall. They

Fig. 2: Grid-wise best statistical models for (i) extreme rainfall intensity (ii) extreme rainfall duration and (iii) extreme

rainfall frequency: (a) spatial patterns (b) percentage of locations falling under each category of models (Source:

Mondal and Mujumdar, 2015)


carried out a region based analysis of extreme

precipitation and concluded that the frequencies of

high floods in Mahanadi basin is due to an increase in

extreme precipitation in the middle reaches of the

basin.

In the context of climate change, even if the

observed past is stationary, there can be

nonstationarities in future hydrologic extremes. In such

cases, possible realizations of future can be obtained

from the climate model driven physically based

hydrologicmodel streamflow projections. For

nonstationary future realizations, the research problem

is to investigate how long the stationary historical

return levels of floods will remain valid, considering

uncertainties in the estimation of observed and

projected return levels (Mondal and Mujumdar, 2016).

In the transient case, the effective return levels (Katz

et al. 2002) are considered by holding the probability

of exceedance fixed at eachyear, because the one-

to-one relation between return period and probability

of exceedance is no longer valid as the probability of

exceedance changes from year to year. Mondal and

Mujumdar (2016) detected the changes in the flood

return levels and concluded that coherent change in

flood return level across the projections is not detected

in the Columbia River using streamflow projections.

Though there are studies reported for detection and

attribution of hydrological extremes for the Indian

region (Jain and Kumar, 2012; Sonali and Nagesh

Kumar, 2016), no explicit research is carried out for

studying the changes in the flood return levels. Met

Office Hadley Center, UK, complied robust

information on the physical impacts of climate change

for more than 20 countries, including India (http://

eprints.nottingham.ac.uk/2040/12/India.pdf, assessed

on 27.03.2018). The Center has studied the climate

change impacts in the terms of varying return periods

for various aspects including water stress, drought,

precipitation, pluvial and fluvial flooding, cyclones and

crop yields. A research report prepared by Joint Global

Change Research Institute and Battelle Memorial

Institute, Pacific Northwest Division, US, notified that

climate projections indicate several changes in India’s

future climate, given inherent uncertainties (NIC,

2009). Their report indicated that a warming of 0.5oC

is likely over all India by the year 2030, which is

approximately equal to the warming over the 20th

century. And a warming of 2-4oC by the end of this

century is expected, with the maximum increase over

northern India. Also, increase in extreme precipitation

events, both in magnitude and frequency, is likely

leading to significant flooding. These changes will

have implications on increases in flood frequencies

and intensities.

Several uncertainties abound, in addition to the

data uncertainties, when one is interested in projecting

the flood discharges using a hydrological model.

Atmosphere-Ocean Global Climate Models

(AOGCMs) are credible and reliable tools for global

scale climate analyses. Downscaling methods are used

for transferring coarse-scale climate information to

regional scale. Projections of hydro-climatic variables

using downscaling comprises several sources of

uncertainties. Uncertainties may arise from (i) the

selection of the climate model, (ii) the choice of carbon

emission scenarios, (iii) the choice of downscaling

methods, (iv) the selection of hydrological model and

model parameters and (v) the internal variability of

the climate system. Prudhomme and Davies (2009)

reported that selection of climate models createsa

higher uncertainty in the downscaling process

compared to the choice of emission scenarios or model

parameterization. However, it is also concluded that

significant source of uncertainty in hydrologic

projections is due to downscaling methods, compared

to the choice of climate models and emission

scenarios. (Bürger et al., 2012; Mandal et al., 2016).

Significant progress has been made by researchers

in developing methods for quantifying uncertainties

with respect to the Indian context (Mujumdar and

Ghosh, 2008; Ghosh et al., 2010; Raje and Mujumdar,

2010a, Raje and Mujumdar, 2010b; Mondal and

Mujumdar, 2012).

The structural difference in the uncertainties in

parameter estimation and the hydrological models can

have a significant effect on the spatial and temporal

distribution of runoff. Limited literature is available in

India, which investigates all sources of uncertainty

(including (i) input uncertainty, e.g., sampling and

measurement errors in rainfall observations; (ii) output

uncertainty, e.g., rating curve errors affecting runoff

estimates; (iii) structural uncertainty, arising from

formulation of hydrologic models; and (iv) parametric

uncertainty, reflecting the incompetence to specify

exact values of model parameters due to uncertainties

in the calibration data, model approximations, etc.) in

streamflow projections under climate change. Chawla


and Mujumdar (2018) segregated the uncertainties in

streamflow projections arising from (i) General

Circulation Models (GCMs), (ii) emission scenarios,

(iii) land use scenarios, (iv) stationarity assumption of

the hydrologic model, and (v) internal variability of

the processes, using analysis of variance (ANOVA)

approach. They have used Variable Infiltration

Capacity (VIC) model over the Upper Ganga Basin

(UGB) and concluded that model parameters vary

with time, annulling the often-used assumption of

model stationarity. They found that the streamflow

reduces in future in the UGB under the nonstationary

model condition, and the model stationarity assumption

and GCMs along with their interactions with emission

scenarios, act as dominant sources of uncertainty.

Several researchers have studied quantifying the

uncertainties in different river basins of India (e.g.,

Yaduvanshi et al., 2018; Uniyal et al., 2015; Singh et

al., 2014; Srivastav et al. 2007). However, studies

are required to understand the sources of uncertainties

and methods to segregate them in modelling the

streamflow.

Flood Forecasting

Flood forecasting systems can be developed over a

wide range of temporal scales ranging from hours to

days, or even months, depending on the spatial scale

of interest and/or the availability of reliable weather

forecasts. The ability of weather forecasts has

improved considerably over the last 3 decades.

Improvements in weather forecasts and advances in

monitoring, remote sensing, data collection and models

have led to simultaneous improvements in the flood

forecasting ability. Depending on the availability of

the data (both hydrological and meteorological), basin

characteristics, computational facilities, lead time of

forecasts, and the purpose for which the forecast is

used, different flood-forecasting techniques are being

used in India. Some of the usually employed

techniques include: (i) simple relations based on stage-

discharge relationships, (ii) co-axial correlation

diagrams developed utilizing the stage, discharge and

rainfall data, etc., (iii) event based hydrological system

models for small to moderate-sized catchments, (iv)

network models consisting of the sub-basins and sub-

reaches for the large-sized catchments, and (v)

deterministic hydrologic models (at selected places).

In addition to the above, stochastic models have

also been applied for real-time flood forecasting. In

India, the statistical approach is most widely used to

formulate real-time flood forecasts, in addition to the

computing techniques such as ANN and fuzzy logic

(Lohani et al., 2014; Mukerji et al., 2009; Lohani et

al., 2006, Thirumalaiah and Deo, 2000). Event-based

network models are applied along with the multi-

parameter hydrological models (Rahman et al., 2012)

to some pilot projects. Singh (2008) consolidated the

Indian experiences in real-time flood forecasting

highlighting the flood problems, data requirements,

methods employed for issuing flood forecasts and

research developments in the area of real-time flood

forecasting. He reported that the flash floods are the

most severe and there is no effective system

implemented for flash-flood forecasting for arid and

semi-arid regions. Some flood-warning arrangements

exist in the country, but these are largely aimed at

transmitting limited information on flood levels from

upstream points to the areas lower down. Such

warnings have limited utility in as much as they do

not indicate the likely levels and the time of arrival of

floods at the vulnerable places. Further, they do not

often give adequate advance notice. To improve the

flood forecasting, an integrated approach combining

the real-time observations as an input to the

hydrological model and an effective communication

systems is required. Several if-then scenarios and

algorithms should be analysed and an efficient warning

system based on the analysis should be setup.

Communication plays an important role in sending the

warnings to the concerned authorities and therefore

a good communication system is required in

disseminating the flood information.

Flash Flood Forecasting

Flash floods are characterized by rapid occurrence,

with very limited opportunity for issuing warnings.

They are often accompanied by other natural hazards

(such as landslides and mud flows), causing damage

to buildings and businesses, collapse of hydraulic

infrastructure and in extreme situations, loss of life.

Recent floods in Kedarnath, Uttarakhand (during 2013

and 2015) are a classic example of flash floods in the

Mandakini River that killed thousands of people and

livestock, devastating the country. Though the duration

of the events was small compared to other flood

disasters in the country, they resulted in severe

damage to property and life because they were


accompanied by simultaneous landslides and debris

flow. Post-disaster satellite images depict that the river

banks were eroded completely along the Kedarnath

valley due to the flash floods. Therefore, it is necessary

to identify the likely places of flash flooding and

vulnerable areas.

Durga Rao et al. (2014) concluded that an

extreme erosion took place in the upstream portion of

Kedarnath, besides the breach of Chorabari Lake and

deposition of debris/sediments in the valley. They have

carried out hydrological and hydraulic simulation study

of the Mandakini River using space-based inputs to

quantify the causes of the flash floods and their

impact. Flood inundation simulations were done using

CARTO DEM of 10 m posting in which the combined

effect of lake breach and high-intensity rainfall flood

was examined. As the slopes are very steep in the

upstream catchment area, lag-time of the peak flood

was found to be less and the Kedarnath valley was

washed away without any alert. The study reveals

quantitative parameters of the disaster which was due

to an integrated effect of high rainfall intensity, sudden

breach of Chorabari Lake and very steep topography.

Mandal and Chakrabarty (2016) developed a

simulation model of surface runoff in upper Teesta

basin using HEC-RAS and HEC-HMS by integrating

meteorological and morphological data in the

geospatial environment. Most recently, Chawla et al.

(2018) implemented a WRF model to investigate the

impact of different processes on extreme rainfall

simulation, by considering a representative event that

occurred during 15–18 June 2013 over the Ganga

Basin in India. They have improved the model

performance through incorporation of detailed land

surface models (LSMs) and analysed the effects of

model grid spacing with two sets of downscaling

ratios. They concluded that higher downscaling ratio

causes higher variability and consequently large errors

in the simulations.

Over the recent years, there has been an

increase in attention to improve flash flood warnings

in India. GCM predictions of climate change show an

increase in extreme precipitation events, which may

lead to more severe flash flooding. In addition, more

areas will be prone to flash flooding as a result of

increased urbanization (Jha et al., 2012). To address

hazards due to flash floods, there is a need for flash

flood forecasting with high spatial resolution and

adequate lead-time. Advances in flash flood

forecasting have been achieved through a range of

improvements in observing capabilities, modelling

techniques, and decision support systems

(Hapuarachchi et al., 2011). The most notable

improvements are satellite and radar observations and

associated techniques for use of these data. However,

India needs larger attention in the area of radar

hydrology, which is lacking at present. Advanced

techniques have been developed for deriving very high

resolution (spatial and temporal) real-time rainfall

estimates from weather radar data merged with

gauged rainfall. Also, a number of satellite-based

precipitation products with high temporal and spatial

resolution (near real-time) have recently been

developed. New techniques have been developed for

merging multiple sources of information to produce

rainfall forecasts with extended lead-times. These

techniques combine satellite-based rainfall, radar

rainfall, gauged rainfall and Numerical Weather

Prediction (NWP) model outputs to produce rainfall

forecasts (Xie and Arkin, 1996; Huffman et al., 1997;

Alberoni et al., 2000; Sinclair and Pegram, 2005;Dutta

et al., 2017; Hayden and Liu, 2018). Applications of

these techniques to Indian situations are still to be

developed and demonstrated.

Challenges in Flash Flood Forecasting

Developing a fully functional end-to-end forecasting

system ranging from data observations to public

evacuation involves many challenges. Lead time of

forecast is the most critical factor in such a system

as the time-lag between rainfall event and flash flood

is short. The forecasts should be made with adequate

lead-time to provide effective warnings, and therefore,

obtaining reliable forecasts of precipitation with

adequate lead-time are quite important. With advances

in science and computational facilities, precipitation

forecasting methods need to be improved through

better understanding of meteorological and

climatological processes and through better

observation networks.

Complex phenomena are involved for triggering

flash floods and to understand the underlying complex

natural hydrological processes, high quality observed

data are required including land use, soil properties,

soil moisture, morphology, and upstream conditions.

With the availability of reliable data, hydrologic/


hydraulic models can be used for flash flood

forecasting and the uncertainty estimates of forecasts

improve the credibility of a forecast system. However,

the development of methodologies incorporating

uncertainties into the decision-making process is a

major challenge in flash flood forecasting and warning.

Further research is required for developing

dynamically varying probabilistic risks, which can be

used in the decision-making process.

For using a reliable forecast, even if the warnings

are accurate and on time, another major challenge is

to disseminate the alerts to general public to take proper

action. Though, with the advancements in

communication technologies, dissemination of flood

warnings to the public is possible, a knowledge gap is

often observed between the risk understood by the

public and the risk communicated by the authorities.

The improvement of knowledge and public awareness

of flash floods is therefore a very important aspect.

The research community must therefore work

together with the stakeholders including government

and the public to ensure that the science and

technology developed by them is put to right use for

societal benefit.

Urban Flooding

Recent increase in magnitude and frequency floods

in Indian cities of Mumbai, Chennai, Bengaluru,

Hyderabad, Vadodhara, Delhi and other cities have

posed a great challenge to the hydrologic modelling

community. Urban flooding is, in a hydrologic sense,

a case of flash flooding with high discharges

accumulating within a very short duration due to

increased impervious areas. The expansion of urban

areas due to population growth combined with climate

change increases the risk of frequent and severe urban

flash floods. Unfortunately, at the present stage, there

is no integrated model or system capable of dealing

with the complex hydrological behaviour of urban flash

floods. New modelling techniques, along with

extensive instrumentation and communication

infrastructure are needed for forecasting and

management of urban flash flood (WMO, 2012; WMO

2008).

The devastating floods that hit Chennai city and

other parts of Tamil Nadu during November-

December 2015 claimed more than 400 lives and

caused enormous economic damages. Such a

mammoth loss to life and property posed a challenge

to the scientific community in developing a

comprehensive understanding of the event. Answers

to a number of pressing questions related to the

conditions prevailing during and immediately preceding

the flooding period are necessary towards developing

such an understanding. These include, among others:

(a) what were the atmospheric conditions that caused

the high intensity rainfall, (b) how was the rainfall

distributed spatially and temporally, (c) how much flow

occurred in the three rivers passing through the city–

the Kosasthalaiyar River, the Cooum River and the

Adyar River - and the Buckingham canal, (d) how

were the two reservoirs upstream of the city, viz., the

Chembarambakkam reservoir and the Poondi

reservoir, operated, (e) how much overland flow was

generated in the city due to rainfall over the city alone,

(f) how did the storm water drainage system respond,

(g) which areas in the city were inundated and for

how long, (h) how did the waters recede after the

rains ceased, (i) what were the health implications of

the event, (j) did the land use change in the city over

the years exacerbate the flooding, and, most

importantly, (k) what actions need to be taken so that

for similar rainfall patterns repeating in future, the

city would not face such a devastating deluge?. A

rapid assessment report of the event, prepared by a

voluntary team of researchers (Narasimhan et al.,

2016), addresses these issues in a great detail.

In case of urban flood modelling, extreme

precipitation analysis takes an important place.

Quantification of extreme precipitation and associated

uncertainties at short duration is important. Chandra

Rupa et al. (2015) concluded that Bayesian analysis

quantifies uncertainties in the parameters of an

extreme value distribution fitted to the data in a better

way, when compared to standard parameter estimation

methods like Maximum Likelihood Estimation (MLE).

Bayesian analysis enables reliable estimates of short

duration extreme precipitation events.Fig.3a shows

the posterior distribution of return levels for 10-year

return period and for durations of 15-min, 30-min, -

hour, 3-hour, 6-hour, 12-hour and 24-hour, obtained

from the Bayesian approach and using MLE

approach. Fig. 3b shows the posterior distribution of

return levels for 15-min duration for different return

periods. The return levels obtained from the estimates

of parameters using MLE method are marked (black

circles) in Fig. 3. From the figure, it is seen that the


return levels obtained from the MLE method shifts

from the mode of the posterior distribution of return

levels obtained from Bayesian analysis as the duration

and the return period reduces. Typically, in urban

areas, the infrastructure is built for short duration

precipitation events and for return periods of order 2-

year and 5-year. At these short durations and low

return periods, the standard statistical methods like

MLE underestimates the return levels.

In addition to the precipitation, other factors such

as changes in land use, climate change has impact on

the urban flooding (Praskievicz and Chang, 2009).

Zope et al. (2016) investigated the impact of land

use–land cover (LULC) change and urbanization on

floods for an urban catchment of the Oshiwara River

in Mumbai using HEC-GeoHMS and HEC-HMS

models. They have concluded that the flood

inundation area is increased by 5.61% for the 100-

year return period and 6.04% for the 10-year return

period. They showed that the lower return periods

led to a maximum change in peak discharge/volume

of runoff compared to higher return periods for change

in land use conditions. Need for an integrated

approach to flood management, considering the land

use change into the hydrological model is emphasised

by Suriyaand Mudgal (2012).Considering several

factors affecting the extreme precipitation, Agilan and

Umamahesh (2015) analysed changes in daily and

sub-daily (4-h) extreme rainfall using various climate

change detection indices for Hyderabad city. They

concluded that non-stationarity in daily extreme rainfall

is associated with global processes (ENSO cycle and

global warming) and non-stationarity in sub-daily (4-

h) extreme rainfall is associated with local processes

(urbanization and local temperature changes).These

findings have a great implication in urban flood

modelling. Other cities suchas Delhi (Kovats and

Akhtar, 2008), Chennai and Kolkata (Sen, 2013) are

also studied to understand the impacts on urban floods

due to changes in precipitation, land-use and climate.

In addition, improper maintenance and management

of hydraulic infrastructure often causes flooding in

urban areas (Gupta, 2007; Narasimhan et al., 2016).

Risk Assessment and Mitigation Measures

Around the world, acceleration in population growth

and changes in land-use patterns and climate have

increased human vulnerability to floods. Harmful

impacts of floods include mortality, morbidity and

widespread damage of crops, infrastructure and

property (Doocy et al., 2013; IPCC, 2007). However,

if the floods are modelled accurately and if risk

assessment studies are conducted, the impacts can

be minimised by pre-emptive actions. A general

(A) (B)

Fig. 3. (A) Probability density function (pdf) of return levels (mm/hr) for 10-year return period for various durations in

comparison with return levels from Maximum Likelihood Estimation (MLE) method. Black circle is the return level

obtained from MLE method at the corresponding duration. (B) pdf of return levelsfor15-min duration at different

return periods in comparison with return levels obtained from MLE method (Black circles). As the duration and the

return period reduces the return levels obtained from the MLE method shifts away from the mode of the posterior

distribution of return levels obtained from Bayesian approach


approach to define flood risk is by considering the

product of the flood hazard (i.e., the physical and

statistical aspects of flooding, like the magnitude,

extent and hours of flooding, return period of the event

etc.) and the vulnerability (i.e., the exposure of floods

to people and assets, and the liability of elements at

risk to suffer from flood damage etc.). Flood risk

management involves not only managing the prevailing

flood risk situation, but also planning for a system that

aims to minimize flood risk (WMO 2009a). This

process involves risk analysis on a regular basis and

evaluation of hazard based on the latest information,

hydrometeorological data, technical developments and

altered conditions due to urbanization and land use

change. In this context, web based GIS tools have

proven to be useful for flood mapping as well as risk

mapping (Sanyal and Lu, 2006; Kulkarni et al., 2014,

Shivaprasad Sharma et al., 2018; Hazarika et al.,

2018). Artificial intelligence and computational

intelligence methods based on big data analysis are

also applied for early flood detection (Fotovatikhah et

al., 2018).

Bajracharya et al. (2007) assessed glacial lake

outburst flood hazard in the Sagarmatha region using

dam break and hydrodynamic modelling. They have

prepared a glacial lake outburst flood vulnerability

rating map to identify vulnerable settlements. Sindhu

and Durga Rao (2016) modelled the Brahmani-

Baitarani River Basin for flood damage mitigation

assessment. Abbas et al. (2015) assessed the policy

and planning processes and flood-related scientific

research in India, Pakistan and Bangladesh. A

comparison of the existing flood management systems

of the three countries is undertaken based on a

systematic review, and a framework for sustainable

flood management in the region is suggested. Their

results of the literature analysis reveal poor support

from scientific research focusing on flooding issues

in the case of Pakistan, while Bangladesh and India

seem to have benefited from research support in

formulating their flood management strategies. In

India, the Natural Disaster Monitoring Agency

(NDMA) has prepared guidelines for flood

management and suggested structural and non-

structural measures (NDMA, 2008).

Flood risk reduction and management strategies

in urban context with example of the Chennai city

are discussed by Gupta and Nair (2010). The Chennai

rapid assessment report, prepared by Narasimhan et

al. (2016), is a voluntary work by researchers from

different institutes in the country to provide an

understanding of various factors that influenced the

devastating floods in Chennai during Nov. and Dec.

2015. Kumar et al. (2017) analysed the existing flood

control measures. Based on their analysis, they

suggested several implementable structural and non-

structural measures for alleviating the problem of

riverine as well as urban flooding in the national capital

territory of Delhi. In case of Bangalore, an Integrated

Urban Flood Management (Mujumdar et al., 2017)

project is carried out by different research

organizations and risk maps showing the areas of

flooding for a pilot study area in Bengaluru city have

been developed (Fig. 4).

Concluding Remarks

The brief review provided in the paper highlights the

contributions of the Indian research community to the

area of flood modelling. While the scientific

contributions have been significant, the corresponding

transfer of the knowledge to policy and implementation

has been poor. This section brings out perspectives

on directions to be pursued both by the research

community and the policy makers to ensure that the

results of research are useful in better understanding

the science of floods and mitigating the impacts of

floods.

Changing rainfall patterns, due to both natural

and anthropogenic causes, have exacerbated the

flooding problem in India. The frequencies, magnitudes

and the spatial extent of the floods are known to be

increasing in the country. Unfortunately, infrastructure

development has lagged behind the economic and

population growth, resulting in increasing losses and

damages due to floods. Poorly conceived drainage

infrastructure, coupled with other problems like

encroachments, have significantly contributed to the

flood deluge year after year, in the face of increasing

rainfall intensities, making flash floods a common

occurrence. Capacity to deal with rapid changes -

such as increase in in extreme rainfall events and

rapid urbanisation and the ability to anticipate and

adapt to slow changes and trends (population increase,

climate change) is very minimal in the country, which

poses new challenges for flood management. To

overcome thesechallenges and to aim towards a flood


resilient society the following specific actions are

proposed that integrate the scientific knowledge with

technologies and administrative policies:

l Data Issues: There are several sources of data

such as ground observations, satellite imagery,

radar data and histories of past flood situations,

which provides a valuable information and helps

in modelling the floods precisely. Integrated

approaches, merging all the relevant data, is

required to overcome data crunch and avoid

uncertainties arise due to insufficient quality and

quality of data. It is also the responsibility of the

custodians of the data (typically, the government

agencies that collect and archive the data) to

make the data available for research studies.

Events such as the Kedarnath floods of 2013

and 2015 could lend a great deal of insight if

models are developed to reconstruct those

events. While the scientific capability to do this

exists in the country, the efforts are greatly

handicapped by a lack of useful data – both

because of an absence of data and because of

inaccessibility to the available data.

l Integrated modelling: In general, flood

forecasting and risk management is quite

challenging for many reasons, including those

due to uncertaintiesarising out of lack of data,

calibration of modelparameters etc. Many

researchers have worked on quantifying

uncertainties in extreme precipitation, modelling

of drainage system using hydrologic models,

quantification of uncertainties in using different

hydrologic models, risk and reliability analysis,

creation of hazard and vulnerability maps and

measures for sustainable development.

However, an integrated approach is lacking, and

it is an important aim to pursue by the

researchers as high uncertainties are associated

in each step of modelling. It is also necessary

that the large number of tools and models

developed for a given river basin (e.g., the

Mahanadi river basin) are compared for their

effectiveness and the results synthesised to

make them useful for policy makers. It is the

responsibility of the research community to

communicate the uncertainty in the results in a

way that can be understood and used by the

policy makers.

l Flood inundation maps: Generation of what-if

scenarios based on probability of flooding and

the resilience of the infrastructure system and

development of new methodologies for planning

under uncertainty for flood management is

required. Probabilistic flood inundationmaps –

similar to the seismic maps developed for the

country - should be developed for each basin,

Fig. 4: Flood inundation maps of the Hulimavu-Madivala catchment in Bengaluru (A) under normal conditions; (B) with

different land-use characteristics (increased urbanization) and (C) with 30% reduction in runoff due to rainwater

harvesting

(C)(B)(A)


and must be revisited frequently to account for

the rapidly changing landuse, climate,

demography etc.

l Collaboration between the Research Community

and Policy Makers: A significant amount of

internationally recognised research is

conductedin the country today. Several advances

have been made in developing reliable models,

improving flood forecasts and quantifying and

reducing uncertainties. However, this knowledge

is not transferred to the policy and administrative

decision making. The government bodies and

the policy makers should involve researchers

while developing contingency planning for

disasters. Also, advanced forecasting and

warning systems with sufficient lead times

should be developed with the help of researchers

to disseminate flood warnings to the local

communities under threat. The National Disaster

Management Agency (NDMA) has played an

important role in bringing the two – apparently

disparate communities – together when

guidelines were developed by the Agency. Such

efforts should be pursued with vigour at all

administrative levels. It is also incumbent on the

research community to respond positively to such

initiatives.

l Flood risk management as part of IWRM: The

failure of localised attempts to deal with fluvial

flooding has stressed the need to take a strategic

approach to catchment management. Also, there

are compelling arguments to deal with other

catchment functions at a river basin scale,

including water supply and hydro-ecology.

Therefore, flood risk management needs to

become an integral aspect of a multi-functional

approach to river basins as it is addressed in

IWRM (Integrated Water Resources

Management). Adaptive management

approaches including the interdisciplinary,

system-oriented and trans-disciplinary research

are required to cope up with the increasing

uncertainties due to global and climate changes

and the fast changing socio-economic conditions.

In essence, IWRM approaches combining the

hydrological models, flood risk management and

socio-economic conditions while preserving the

ecosystems are much commanded (Grabs et al.,

2007, WMO 2009b, WMO 2011; WMO 2017).

l Flood as a resource: With severe water stresses

occurring year after year in several parts of the

country – and simultaneously, the floods causing

a huge damage every year – it is necessary to

evolve a new paradigm of floods as a resource

and not as a disaster. Newer ways and methods

of storing and using the flood waters need to be

developed. This may require identifying

groundwater potential zones to recharge the

groundwater, analysing quality of flood water

and assessing the possibility of implementation

of green infrastructure and rainwater harvesting

units to harvest/reuse the flood water. Flood

modelling helpsin identifying the potential areas

of flooding, flood magnitudes and the associated

time of flooding. With this information, the flood

volumes can be utilized to either percolate into

the ground depending on the ground water

conditions or for storing by linking the storage

units in particular catchments and/or designing

the storage units depending on the amount of

flood.

l Training and Capacity Development: As in many

other fields, there is a very high need of trained

manpower in the country to provide a scientific

support to policy makers on large number of

issues related to floods. Besides the

contemporary approaches to mitigating flood

effects, there is a vast repository of traditional/

indigenous knowledge in the country useful in

local adaptation practices. Such conventional

practices are to be integrated with advanced

scientific knowledge to provide adaptive

responses to the flooding problem at all scales.

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