Presented by

Dr. Debasri RoyAssociate Professor

School of Water Resources Engineering Jadavpur University

CLIMATE CHANGE IMPACTSON

WATER ARENA OF A RIVER BASIN IN INDIA

D. Roy ,S. Begam, S. Jana and S. Sinha School of Water Resources Engineering

Jadavpur UniversityKolkata,India

BACKGROUND Climate change is currently an issue of great concern . Flood is expected to occur more frequently in certain regions. Drought related and competing water issues is expected to intensify in other regions. Rainfall distribution pattern is also expected to change. These changes could imply some changes in water resources in different parts of the

world. South Asia in general and India in particular, are considered particularly vulnerable to

climate change and its adverse socio-economic effects. Reasons: low adaptive capacities to withstand the adverse impacts of climate change

due to the high dependence of the majority of the population on climate-sensitive sectors like agriculture and forestry and lack of financial resources.

Vast regional variabilities exist in India that affect the adaptive capacity of the country to climate change.

Therefore, there is a need to evaluate the impact of climate on water resources in India at regional and local level.

o In this scenario, attempt has been made to assess the impacts of climate induced changes on the water scenario in the upper portion upto Ghatshila gauging site (area 14472 sq. km. , river length 175 km. )and lying between the latitudes 22018’ N and 22037’N and longitudes 86038’E and 870E)of the interstate basin of the Subarnarekha river (co-basin riparian states are Jharkhand , Orissa and West Bengal ) of eastern part of India.

LOCATION OF THE STUDY AREA

SUBARNAREKHA RIVER BASIN

o The smallest (0.6% of geographical area of the country) of the fourteen major river basins of India(19,296 sq.km).

o The river length is 450km.o It originates in Jharkhand highlands (23˚18’ N, 85˚11’E , elevation 740m).o It drains a sizable portions of the three States of Jharkhand, Orissa and West Bengal

and finally debouches into the Bay of Bengal. o Average annual rainfall 1350 mm. o Annual yield of water constitutes about 0.4% of the country’s total surface water

resources. o Annual utilisable water resources have been estimated to be 9.66 MCM

Parameter1999 2009

January October January October

 

Pervious

Agriculture (%) 20.83 21.32 26.74 30.49

Forest (%) 49.13 51.72 45.5 45.5Grassland (%) 10.77 11.41 7.59 9.39Water body (%) 10.12 8.66 7.11 8.66

Pervious (%) 90.86 93.13 87.26 94.2

Impervious (%) 9.14 6.87 12.74 5.8

Land use

WORKThe work comprises:

Development of hydrologic model of the basin with the help of the catchment simulation model viz. Hydrologic Modeling System (HEC-HMS 3.5) developed by the Hydrologic Engineering Center, USA using historical data .

Running of the model for future period under Q0, Q1 and Q14 simulations of A1B scenario—generated using regional climate model (RCM) PRECIS (Providing Regional Climates for Impacts Studies) developed by the Hadley Centre, UK and run at the Indian Institute of Tropical Meteorology (IITM), Pune, India at 50 km × 50 km horizontal resolution over the South Asian domain for A1B scenario (Special Report on Emissions Scenarios (SRES) prepared under the Intergovernmental Panel on Climate Change (IPCC) coordination.

Analyzing precipitation, potential evapotranspiration, streamflow under changed climate scenario and those under historical scenario to ascertain impact of climate change on water resources in the basin.

 

Typical HEC-HMS representation of watershed runoff.

CLIMATE CHANGE SCENARIO The IPCC SRES scenario set comprises four scenario

families: A1, A2, B1 and B2. The A1 family includes three groups reflecting a consistent variation of the scenario (A1T, A1FI and A1B). Hence, the SRES emissions scenarios consist of six distinct scenario groups, all of which are plausible and together capture the range of uncertainties associated with driving forces.

Scenario A1: The A1 scenario family describes a future world of

very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies.

A1FI scenario : fossil intensive A1T scenario : non-fossil energy sources A1B scenario: balance across all sources where

balance is defined as not relying too heavily on one particular energy source

Boundary conditions from three simulations from a 17-member Perturbed Physics Ensemble generated using Hadley Center Coupled Model (HadCM3) for the Quantifying Uncertainty in Model Predictions (QUMP) project have been used to drive PRECIS at IITM, Pune, India for the period 1961–2098 in order to generate an ensemble of future climate change scenarios (Q0, Q1 and Q14 ) for the Indian region at 50 km × 50 km horizontal resolution for A1B scenario.

MODEL EVALUATIONThe criteria for model evaluation adopted involves the following:

oSensitivity Analysis --- The sensitivity analysis of the model was performed to

determine the important parameters which needed to be precisely estimated to make

accurate prediction of basin yield.

oPercentage error in simulated volume (PEV)

o Percentage error in simulated peak (PEP), and

o Net difference of observed and simulated time to peak (NDTP)

o Nash–Sutcliffe model efficiency (EFF)

100)(

o

co

VolVolVol

PEV Volo = observed runoff volume (m3)Volc = computed runoff volume (m3)

n

iooi

n

i

n

icioiooi

QQ

QQQQEFF

1

2

1 1

22

)(

)()(

Qoi = ith ordinate of the observed discharge (m3/s)

= mean of the ordinates of observed discharge (m3/s)

Qci = ith ordinate of the computed discharge (m3/s)

)( pcpo TTNDTP

100)(

po

pcpo

QQQ

PEPQpo = observed peak discharge (m3/s)Qpc = computed peak discharge (m3/s)

Tpo = time to peak of observed discharged(h)Tpc = time to peak of computed discharge (h)

oQ

Calibration Analysis

Stream flow hydrograph Non-monsoon 1999

1 15 29 43 57 71 85 99 1131271411551690

2000

4000

6000

8000

10000

12000

simulated flowObserved flow

Stream flow hydrograph Monsoon 1999

Calibration Analysis

Performance measures table of the model for calibration years

Season Performance Measures

1999 2001

Monsoon

PEV (%) 22.66 68.91

PEP (%) 22.45 4.59

NDTP 1 day 1 day

EFF 0.70 0.37

Non-Monsoon

PEV (%) 32.85 10.18

PEP (%) 9.13 41.2

NDTP 0 day 0 day

EFF 0.50 0.66

Validation Analysis

Stream flow hydrograph Non-monsoon 2004

Stream flow hydrograph Monsoon 2004

Season Performance Measures 2004 2007

Monsoon

PEV (%) -26.14 -2.46

PEP (%) -5 -0.95

NDTP 0 day0 day

EFF 0.78 0.91

Non-Monsoon

PEV (%) - 11.5 - 14.9

PEP (%) - 49 + 11.15

NDTP 0 day 0 day

EFF 0.74 0.81

Performance measures table of the model for validation years

Impacts of Climate Change

Annual Rainfall Analysis

Annual rainfall in all the projected years are found to be normal or above normal (1.5 – 35)% except for 5 years.

the highest value is 1860.2 mm the lowest one 925.6 mm lower (by 32%)

Annual rainfall for historical and future years under Q0, Q1 and Q14 simulations

The annual rainfall for Q0 simulation in the projected years (except 2040 and 2050) is found to be higher than the other two simulations----close to Q14 simulation. Rainfall for Q14 in 2040 and 2050 is higher than historical average(22% and 28%).

annual rainfall lowest for Q1 simulation and also lower than historical average(21 to 51%) .

Janua

ry

Februa

ryMarc

hApri

lMay Jun

eJul

y

August

Septem

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Octobe

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Novem

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2014-20202020's2030's2050

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Q0 simulation

Monsoonal rainfall ( July, August and September) and March rainfall in all the projected years do not show significant deviation (compared to non-monsoonal rainfall)from historical values.

Non-monsoonal rainfall in future periods show marked deviation (increase) from historical ones.

The highest increase (1331.6 %) is found for the month of May in decade of 2020 and the second highest increase (774.4 %) in the month of Nov. in decade of 2030 and the third highest increase in the month of Dec. and Jan. in 2050.

 

JANMAR

MAY JUL SEPNOV

-100

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fall

Q1 simulation

Q1 simulation

oThe highest increase (586%) in rainfall is found for the month of Nov in 2050 following the second highest increase (470%) in the month of December 2050,October 2050 and February 2050.

oA noticeable increase has also been found for February 2030 March 2020,September 2030 only.

oA decrease in monthly rainfall values (-36% to-95%) from corresponding historical values has been observed for almost all the months with a maximum decrease (95%) in month of June, 2040.

JAN MAR MAY JUL SEP NOV-500

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Q14 simulation

o A noticeable increase in monthly rainfall has been found in April2020, November and December of 2030 and 2050 with maximum increase (1766%) in December 2050

o Monthly rainfall deviation for Q0 ,Q14 almost similar for 2030 and 2050.

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Annual 24 hr Maximum RainfallR

ainf

all i

n m

m

Q0 Simulation

Historical

20202030

20402050

050

100150200250300350

HighestlowestQ0Q1Q14Ra

infa

ll (m

m)

Annual 24 hr Maximum Rainfall

Annual 24-h maximum rainfall

o Projected to be lower than the historical highest for the future years excepting for five years.

o The quantum of decrease in the value ranges from 20 % to 80%

o Projected to be lower than the historical highest for Q1 and Q14 simulations.

o is highest for Q14 (excepting 2030)

o Rainfall is higher for Q1 than for Q0 excepting 2030 and 2040

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

-15

-10

-5

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5

10

15

2014-20202020s2030s2050

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T

POTENTIAL EVAPOTRANSPIRATION

Monthly variation of Potential Evapotranspiration

Q0 simulation

Q1 simulation

Q14 simulation

  The annual distribution of projected monthly PET values is

found to follow the pattern of historical average PET values. For Q0 simulation monthly deviation is small(-13to +13%). Monthly PET values lie close to the historical one for the year

of 2014 – 2020. The monthly PET values for FEB to APR for the decade of

2020 is found to be higher than the historical one. The monthly PET values for APR to JUN and SEP, OCT for

the decade of 2030 is found to be higher than the historical one. As per Q1 and Q14 simulations, monthly PET values in

projected years are higher than corresponding historical values (excepting for the year 2020)---larger increase has been found in quantum of monthly PET during the months of March, April, May(for Q1~19%) and June .

 

Q0 simulation Q1 simulation

Q14 simulation

Streamflow Hydrographs

Flow pattern

o No change in pattern of stream flow over that of historical flow is observed in the projected years for Q1 simulation

o As per Q0 simulation,(4) of the years showed annual peak in May and (8) in October and (1) of the years in November

o As per Q14 simulation , annual peak flow is observed in May and in October (rather than in monsoon)in 2040 and 2050 respectively.

Annual Stream Flow Volume Analysis

Deviation in annual stream-flow volume (MCM) from historical stream-flow volume for projected years under Q0, Q1 and Q14 simulations

The stream-flow volumes for projected years (excluding year 2014 and 2020) are higher than the corresponding historical one. The highest increase(166%) during 2031-40,followed by 2021-2030 (147%) and 2014-2020

For Q1 simulation annual stream-flow volumes for all the projected years have been found to be lower (range 32% to 70%) than the average historical value and for 2020 and 2030 for Q14.

The annual stream-flow volumes have been found to lie very close to the historically observed flow volume for 2040 under Q0 & Q14 simulations and also for 2050 under Q14 simulation only.

Janua

ry

Februa

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2031-2040

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Deviation of monthly flow volume (future decadal average) from historical flow for Q0 simulation

oStream-flow volumes during monsoon in the projected years show smaller deviation (10 to 50%) from historical values compared to those in non-monsoon.(35% to 270%----even higher in month of May)

oStream-flow volumes for projected months from January to April (excluding year 2014- 2020) are lower than the corresponding historical one.

oFrom October to December, stream-flow volumes are higher than the corresponding historical one showing maximum variation in the month May for two future periods (2021-2030 & 2031-2040).

0

2000

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12000Annual Peak Flow(2014-40)

Time Period

Dis

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ge(c

umec

)

Annual Peak Flow Analysis

Peak flows for Q0 simulation have been found to be lower than the historically observed annual highest peak --- the peak flow approaches historical value for three years only – and on one occasion peak flow is higher than historically observed 2nd and 3rd highest peak flow.Annual peak flows (1st, 2nd and 3rd highest) in the years 2020,2030, 2040, 2050 have been found to be much lower than hist. av. in Q0, Q1 and Q14 simulation.Peak flow is the lowest for Q1 simulation among the three.

FLOW DURATION CURVE ANALYSIS

Flow-duration Curve for historical observed data

0 10 20 30 40 50 60 70 80 90 1000

1000

2000

3000

4000

5000

6000

2014-2020

Pp=Percentage time indicated discharge is equalled or exceeded

Dis

char

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umec

)

0 10 20 30 40 50 60 70 80 90 1000

1000200030004000500060007000

2021-2030

Pp=Percentage time indicated discharge is equalled or exceeded

Dis

char

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umec

)

0 10 20 30 40 50 60 70 80 90 1000

100020003000400050006000700080009000

10000

2031-2040

Pp=Percentage time indicated discharge is equalled or exceeded

Dis

char

ge (c

umec

)

Flow characteristic of the stream during historical and future years was found to be similar. Non-perennial flow condition was found to exist in both historical and projected years80% of time the discharge of the stream was found to equal or exceed 80 cumec ,117 cumec and 107 cumec in 2014-20,2021-30 and in 2031-40, (against historical flow of 20 cumec) and 90% dependable flow for those period was found to exceed 22.3,57.8 and 36.2 cumec (against historical flow of 8.3 cumec).

conclusion

o Annual rainfall in all the projected years are found to be normal or above

normal except for five years (1.5 – 35.13 % )---the highest value is 1860.2 mm in 2015 --the lowest one 925.6 mm (by 32%) for 2035 o Monsoonal rainfall ( July, August and September) and March rainfall in all the projected

years do not show significant deviation from historical values.o Non-monsoonal monthly rainfall in future periods is expected to

increase o Annual 24-hr maximum rainfall projected to be lower than the historical highest for the

future years excepting for five years.o The quantum of decrease in the value ranges from 20 % to 80%o The annual distribution of projected monthly PET values is found to follow the pattern of

historical average PET values. o Monthly deviation in PET values from the historical average is small for

the future years(- 13% to +13% --- the non monsoonal deviation is higher).

o Change in pattern of stream flow over that of historical flow is observed in the projected years ----18%(4) of the years showed annual peak in May and 30% (8) in October(8) and 3%(1) of the years in November

o The stream-flow volumes for projected years (excluding two years) are higher than the corresponding historical one(by 6 % to 166% ).

o Stream-flow volumes during monsoon in the projected years show smaller deviation (10 to 50%) from historical values compared to those in non-monsoon.(35% to 270% ---even higher for May )o Peak flows for Q0 simulation have been found to be lower than the historically

observed annual highest peak --- the peak flow approaches historical value for three years only---– and on one occasion peak flow is higher than historically observed 2nd and 3rd highest peak flow.

o Flow characteristic of the stream during historical and future years was found to be similar. Non-perennial flow condition was found to exist in both historical and projected years

o 80% of time the discharge of the stream was found to equal or exceed 80 cumec ,117 cumec and 107 cumec in 2014-20,2021-30 and in 2031-40 (against historical flow of 20 cumec) and 90% dependable flow for those period was found to exceed 22.3,57.8 and 36.2 cumec (against historical flow of 8.3 cumec).

o Annual rainfall for Q0 simulation in the projected years (except 2040 and 2050) is found to be higher than the other two simulations----close to Q14 simulation.o Annual rainfall is the lowest for Q1 simulationo Monthly rainfall deviation is almost similar Q0 ,Q14 for 2030 and 2050 o A decrease in monthly rainfall values from corresponding historical values has been observed for almost all the months for Q1 simulation.o Annual 24-h maximum rainfall lie close to each other (within 30%) for three

simulations---excepting for 2030.o As per Q1 and Q14 simulations, monthly PET values in projected years are higher

than corresponding historical values (excepting for the year 2020)---larger increase has been found in quantum of monthly PET during the months of March, April, May(for Q1 simulation ~19%) and June.

 

Inter comparison of simulations

o No change in pattern of stream flow over that of historical flow is observed in the projected years for Q1 simulation

o Pattern of flow Q0 and Q14 similar –non monsoonal flow higher than monsoonal

o Annual stream-flow volumes for all the projected years have been found to be lower (range 32% to 70%) than the average historical value for Q1 simulation and for 2020 and 2030 for Q0 and Q14 simulations.

o The annual stream-flow volumes have been found to lie very close to the historically observed flow volume for 2040 under Q0 & Q14 simulations and also for 2050 under Q14 simulation only.

o Annual peak flows (1st, 2nd and 3rd highest) in the years 2030, 2030, 2040, 2050 have been found to be much lower than hist. av. in Q0, Q1 and Q14 simulation .

o Peak flow is the lowest for Q1 simulation among the three.

Impact of Climate change on Water Arena

o Water availability in the basin is expected to be normal or above normal.o Seasonal shift in stream flow pattern is expected and it may have some effects on

aquatic ecosystem.o Low flow characteristic of the river is expected to be better than historical and it

may be good for aquatic ecosystem.o Increased peak flow (~9200cumec) is expected on one occassion and this may lead

to disastrous situation.o Decreased peak flow (~1000 cumec)is expected on one occassion and this may

hinder natural flushing of the channel—leading to loss of its carrying capacity.o Higher PET values during non monsoon (March to June and October to Dec.))is

projected and non-monsoonal monthly rainfall in future periods is expected to increase (by large amount) ----this may affect crop production (Rabi and Boro crops)

o Ensemble of scenario should be considered.o Q0 and Q14----similar outcome.o Q1 simulation outcome is different:o No change in streamflow pattern ;o Reduced water availability (upto 70 % less flow);Peak flow less, higher PET;

ACKNOWLEDGEMENT Sincere thanks are being acknowledged for kind assistance rendered in the

form of data and related matter by officials and personnel of the India Meteorological Department GoI, Central Water Commission GoI, National Remote Sensing Center, GoI, National Bureau of Soil Survey and Land Use Planning (NBSS and LUP), GoI and Irrigation and Waterways, GOWB. Thanks are also acknowledged to the Ministry of Water Resources, Government of India for providing financial assistance for the work.

THANK YOU

Deviation of Projected Flow for Q0 Simulation

  Historical 14-20 dv 21-30 dv 31-40 dv

JAN 737.528463.857142

9 -37.1065 106.9961 -85.4926 246.2793 -66.6075

FEB 544.4403353.471428

6 -35.0762 16.52649 -96.9645 153.9528 -71.7227

MAR 405.1881 275.4 -32.0316 108.0219 -73.3403 58.20821 -85.6343

APR 345.01581276.94285

7 270.1115 138.2952 -59.9163 194.4142 -43.6506

MAY 441.514701.471428

6 58.87864 9347.455 2017.137 7050.926 1496.988

JUN 6305.4413198.34285

7 -49.2765 7301.846 15.80231 10399.69 64.93203

JUL 14914.2710794.8142

9 -27.6209 19008.95 27.45471 21730.83 45.70491

AUG 21659.4919252.8714

3 -11.1112 27193.92 25.55198 25544.1 17.93489

SEP 18112.9827643.5142

9 52.61712 27207.49 50.20989 28167.52 55.51013

OCT 7595.29 20422.1 168.8785 13244.55 74.37853 21315.68 180.6434

NOV 2585.2686865.47142

9 165.5614 5737.941 121.9477 7367.176 184.9676

DEC 1167.928 3800.114286 225.3722 2959.877 153.4296 3267.858 179.7995

May 2022 4675May 2023 62187May 2032 17451May 2033 29361May 2034 9222May 2036 6143

  Peak Rain Month PET2021 216.49 Sep decrease2029 216.49 Sep decrease2033 231.27 Oct increase(2%)2035 228.02 July decrease

SMA parameter SeasonMonsoon Non- Monsoon

Canopy Storage (mm) 4.67 4.67Surface Storage (mm) 50.8 50.8

Max Rate of Infiltration (mm/hr) 3 3Impervious (%) 11.07 16.88

Soil Storage (mm) 316.16 320.75Tension Storage (mm) 106.4 117.35

Soil Percolation (mm/hr) 0.29 0.29Groundwater 1 Storage (mm) 11 12

Groundwater 1 Percolation (mm/hr) 0.29 0.29Groundwater 1 Coefficient (hr) 78 42Groundwater 2 Storage (mm) 18 19

Groundwater 2 Percolation (mm/hr) 0.21 0.21Groundwater 2 Coefficient (hr) 670 610

Table for Input SMA parameters (calibrated) used in the model

Landuse/ Landcover Map of Subarnarekha River Basin October 2009

Landuse/ Landcover Map of Subarnarekha River Basin January 2009

HEC-HMS MODEL Designed to simulate the precipitation–runoff processes of dendritic watershed systems ,with

soil moisture accounting (SMA)algorithm , it accounts for a watershed’s soil moisture balance over a long-term period and is suitable for simulating daily, monthly, and seasonal streamflow. The SMA algorithm takes explicit account of all runoff components including direct runoff surface flow) and indirect runoff (interflow and groundwater flow)(Ponce (1989) .The model requires inputs of daily rainfall, soil condition and other hydro meteorological data.

The HMS SMA algorithm represents the watershed with five storage layers viz., canopy – interception, surface-depression ,soil profile ,groundwater storages (1 and 2) as shown in the Fig.2 involving twelve parameters viz., canopy interception storage, surface depression storage, maximum infiltration rate, soil storage, tension zone storage and soil zone percolation rate and groundwater 1 and 2 storage depths ,storage coefficients and percolation rates.

Rates of inflow to, outflow from and capacities of the layers control the volume of water lost from or gained by each of these storage layers. Current storage contents are calculated during the simulation and vary continuously both during and between storms. Besides precipitation the only other input to the SMA algorithm is a potential evapotranspiration rate (HEC 2000).

For the present study:- Runoff depth was computed using SMA method. Clark unit hydrograph technique with the peak and time to peak computed by Snyder’s unit

hydrograph technique method was adopted to compute streamflow hydrograph. Linear reservoir method was used to model base flow . Muskingum method of channel routing was used to generate discharge hydrograph at

downstream point in channel.

The soil moisture accounting loss method uses five layers to represent the dynamics of water movement above and in the soil. Layers include canopy interception, surface depression storage, soil, upper groundwater, and lower groundwater. The soil layer is subdivided into tension storage and gravity storage. Groundwater layers are not designed to represent aquifer processes; they are intended to be used for representing shallow interflow processes.

METHODOLOGY Delineation

of catchment boundary and stream network of the sub-basin in Google Earth 6.1.0.5001 with the help of topo-sheets; and find the basin characteristics (sub-basin area, main stream length and slope etc.)

Processing of all input data for use in HEC-HMS (version 3.4) model which include the following steps:-

Computation of Average rainfall of sub-basin by Theissen polygon method for historical and projected years.

Two of the 12 parameters needed for the SMA algorithm (canopy interception storage and imperviousness) were estimated by the processing of land use land cover (LULC) Satellite Imagery. The land use data is created with the help of Geomatica Freeview 10.3.

Four of the 12 parameters needed for the SMA algorithm (maximum infiltration rate, maximum soil storage, tension zone storage and soil percolation rate) were estimated from the information on soil of the study area.

Other parameters (GW1 and GW2 storage and coefficient) needed for the SMA algorithm and parameters needed for routing method (value of Muskingum K and X) were estimated from the daily discharge data at gauging station Jamshedpur and Ghatsila. The parameter GW1 and GW2 percolation rate were estimated through calibration.

Computation of monthly Evapotranspiration rate by Penman’s method.

Creating the basin network in HEC-HMS model and setting of all input parameters properly for the model.

Calibration of the model for all the input parameter related to the basin.

Validation of the model for the sub-basin.

Running the model for projected years under changed climate.

MATERIALS Software Packages: HEC-HMS 3.4 HEC-DSSVue 2.0.1 Google Earth (Version 6.1.0.5001) Geomatica Freeview 10.3 GrADS (Version 2.0.a9.oga.1) Fast Stone Capture 7.4

Toposheets: The Survey of India at Kolkata, W.B.

Rainfall and Temperature data (daily): India Meteorological Department, GoI Pune and Indian Institute of Tropical Meteorology, GoI, Pune. Other daily meteorological data such as relative humidity, wind speed and actual sunshine hours were collected from India Meteorological Department, GoI, Kolkata.

Hydrological Discharge data (daily): Central Water Commission, GoI, Bhubaneswar, Odisha .

Satellite imagery data: National Remote Sensing Center, GoI, Hyderabad.

Soil data: National Bureau of Soil Survey & Land Use Planning (NBSS & LUP), GoI, Salt lake, Kolkata.

Unit Hydrograph Transform Method

Snyder Unit Hydrograph Parameter

Standard Lag (Tp, hr) Peaking Co-efficient (Cp)

Upper Sub-basin 50 0.6

Muskingum Routing MethodMuskingum Routing

ParameterK (hr) X

Upper Sub-basin 49 0.3

REFERENCESo Acharya, A., K. Lamb, and T.C. Piechota, 2013. Impacts of Climate Change on Extreme

Precipitation Events Over Flamingo Tropicana Watershed. Journal of the American Water Resources Association.

o Ayka, A., 2008. Hydrological Models Comparison for Estimation of Floods in the Abaya-Chamo Sub-Basin. A thesis presented to the school of Graduate studies CIVIL Engineering Department of the Addis Ababa University.

o Bae, Beg-Hyo, Il-Won Jung, and D.P. Lettenmaier, 2011. Hydrologic Uncertainties in Climate Change from IPCC AR4 GCM simulations of the Chungju Basin, Korea. Journal of Hydrology, Vol. 401(1): 90-105.

o Bingner, R.L., C.E. Murphee, and C.K. Mutchler, 1989. Comparison of sediment yield models on various watershed in Mississippi. Trans. ASAE. 32(2): 529-534.

o Das, S. and S.P. Simonovic, 2012. Assessment of Uncertainty in Flood Flows under Climate Change Impacts in the Upper Thames River Basin, Canada. British Journal of Environment & Climate Change, 2(4): 318-338.

o Divya & S. K Jain, 1993. Sensitivity of catchment response to climatic change scenarios. IAMAP/IAHS Workshop, 11-23 July, Yokohama, Japan.

o Fleming, M. and V. Neary, 2004. Continuous Hydrologic Modeling Study with the Hydrologic Modeling System. Journal of Hydrologic Engineering, 9(3): 175-183.

o Hydrologic Modeling System HEC-HMS. Technical Refrence Manual, 2000. US Army Corps of Engineers, Hydrologic Engineering Center, 609 Second Street, Davis, CA 95616-4687 USA.

o IPCC (2007) Climate Change, 2007. Synthesis Report. Contribution of Working Group I, II and III to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge. United Kingdom and New York.

o Kumar, K.K., S.K. Patwardhan, A. Kulkarni, K. Kamala, K.K. Rao and R. Jones, 2011. Simulated Projections for Summer Monsoon Climate over India by a high-resolution regional Climate Model (PRECIS). Current Science, Vol. 101, No. 3.

o Subramanya, K., 2002. Engineering Hydrology. Second Edition, Tata McGraw-Hill Publishers, New Delhi.

Continued

Q1 simulation

Q14 simulation