Presented by Dr . Debasri - PowerPoint Presentation

Slide1

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

University

Kolkata,India

Slide2

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.

Slide3

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.

Slide4

Location of the Study Area

Slide5

Subarnarekha river basin

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

The river length is 450km.

It originates in Jharkhand highlands (23˚18’ N, 85˚11’E , elevation 740m).

It drains a sizable portions of the three States of Jharkhand, Orissa and West Bengal and finally debouches into the Bay of Bengal.

Average annual rainfall 1350 mm.

Annual yield of water constitutes about 0.4% of the country’s total surface water resources.

Annual

utilisable

water resources have been estimated to be 9.66 MCM

Slide6

Parameter19992009JanuaryOctoberJanuaryOctober PerviousAgriculture (%)20.8321.3226.7430.49Forest (%)49.1351.7245.545.5Grassland (%)10.7711.417.599.39Water body (%)10.128.667.118.66Pervious (%)90.86 93.13 87.26 94.2 Impervious (%)9.14 6.87 12.74 5.8

Land use

Slide7

Work

The 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.

 

Slide8

Typical HEC-HMS representation of watershed runoff.

Slide9

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.

Slide10

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.

Slide11

Model Evaluation

The criteria for model evaluation adopted involves the following:Sensitivity 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. Percentage error in simulated volume (PEV) Percentage error in simulated peak (PEP), and Net difference of observed and simulated time to peak (NDTP) Nash–Sutcliffe model efficiency (EFF)

Vol

o

= observed

runoff volume (m

3

)

Vol

c

= computed

runoff volume (m

3

)

Slide12

Q

oi

=

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)

Q

po

= observed peak discharge (m

3

/s)

Qpc = computed peak discharge (m3/s)

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

Slide13

Calibration Analysis

Stream flow hydrograph Non-monsoon 1999

Slide14

Stream flow hydrograph Monsoon 1999

Calibration Analysis

Slide15

Performance measures table of the model for calibration years

Season

Perform

a

nce

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

Slide16

Validation Analysis

Stream flow hydrograph

Non-monsoon 2004

Slide17

Stream flow hydrograph

Monsoon 2004

Slide18

SeasonPerformance Measures20042007MonsoonPEV (%)-26.14-2.46PEP (%)-5-0.95NDTP0 day0 dayEFF0.780.91Non-MonsoonPEV (%)- 11.5- 14.9PEP (%)- 49+ 11.15NDTP0 day0 dayEFF0.740.81

Performance measures table of the model for validation years

Slide19

Impacts of climate change

Slide20

Annual Rainfall Analysis

Slide21

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%)

Slide22

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

Slide23

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%) .

Slide24

Q0

simulation

Slide25

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.

 

Slide26

Q1 simulation

Slide27

Q1 simulation

The 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.

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

A 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.

Slide28

Q14

simulation

Slide29

Q14 simulation

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

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

Slide30

Q0 Simulation

Slide31

Annual 24 hr Maximum Rainfall

Slide32

Annual 24-h maximum rainfall

Projected to be lower than the historical

highest

for the future years

excepting for five years.

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

%

Projected

to be lower than the historical highest for

Q1 and Q14 simulations.

is

highest for Q14 (excepting 2030)

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

Slide33

Potential

Evapotranspiration

Slide34

Monthly variation of Potential Evapotranspiration

Q0 simulation

Slide35

Q1 simulation

Slide36

Q14 simulation

Slide37

 

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 .

 

Slide38

Q0 simulation

Q1 simulation

Q14 simulation

Streamflow

Hydrographs

Slide39

Flow pattern

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

As per

Q0 simulation,(4

) of the years showed annual peak in May

and

(8) in October

and (1

) of the years in November

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

Slide40

Annual Stream Flow Volume Analysis

Slide41

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

Slide42

T

he

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

.

Slide43

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

Slide44

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 in month of May)

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

From 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).

Slide45

Slide46

Annual Peak Flow

Analysis

Slide47

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 2

nd

and 3

rd

highest peak flow.

Annual peak flows (1

st

, 2

nd

and 3

rd

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.

Slide48

Flow Duration Curve Analysis

Flow-duration Curve for historical observed data

Slide49

Slide50

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

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).

Slide51

conclusion

Slide52

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

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

Non-monsoonal monthly rainfall in future periods is expected to increase

Annual

24-hr maximum rainfall

projected

to be lower than the historical

highest for

the future years excepting

for five years.

The

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

%

The annual distribution of projected monthly PET values is found to follow the pattern of historical average PET values

.

Monthly

deviation in PET values from the historical average is small for the future years(-

13%

to +

13%

--- the non monsoonal deviation is higher

).

Slide53

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

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

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 )

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 2

nd

and 3

rd

highest peak flow.

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

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

).

Slide54

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

Slide55

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

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

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.

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.

Annual peak flows (1

st

, 2

nd

and 3

rd

highest) in the years 2030, 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.

Slide56

Impact of Climate change on Water Arena

Water availability in the basin is expected to be normal or above normal.

Seasonal shift in stream flow pattern is expected and it may have some effects on aquatic ecosystem.

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

Increased peak flow (~9200cumec) is expected on one

occassion

and this may lead to disastrous situation.

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.

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)

Ensemble of scenario should be considered.

Q0 and Q14----similar outcome.

Q1 simulation outcome is different:

No change in

streamflow

pattern ;

Reduced water availability (

upto

70 % less flow);Peak flow less, higher PET;

Slide57

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.

Slide58

THANK YOU

Slide59

Slide60

Slide61

Slide62

Slide63

Slide64

Deviation of Projected Flow for Q0 Simulation

 

Historical

14-20

dv

21-30

dv

31-40

dv

JAN

737.528

463.8571429

-37.1065

106.9961

-85.4926

246.2793

-66.6075

FEB

544.4403

353.4714286

-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

345.0158

1276.942857

270.1115

138.2952

-59.9163

194.4142

-43.6506

MAY

441.514

701.4714286

58.87864

9347.455

2017.137

7050.926

1496.988

JUN

6305.441

3198.342857

-49.2765

7301.846

15.80231

10399.69

64.93203

JUL

14914.27

10794.81429

-27.6209

19008.95

27.45471

21730.83

45.70491

AUG

21659.49

19252.87143

-11.1112

27193.92

25.55198

25544.1

17.93489

SEP

18112.98

27643.51429

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

2585.268

6865.471429

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

Slide65

May20224675May202362187May203217451May203329361May20349222May20366143

 

Peak Rain

Month

PET

2021

216.49

Sep

decrease

2029

216.49

Sep

decrease

2033

231.27

Oct

increase(2%)

2035

228.02

July

decrease

Slide66

SMA parameterSeasonMonsoonNon- MonsoonCanopy Storage (mm)4.674.67Surface Storage (mm)50.850.8Max Rate of Infiltration (mm/hr)33Impervious (%)11.0716.88Soil Storage (mm)316.16320.75Tension Storage (mm)106.4117.35Soil Percolation (mm/hr)0.290.29Groundwater 1 Storage (mm)1112Groundwater 1 Percolation (mm/hr)0.290.29Groundwater 1 Coefficient (hr)7842Groundwater 2 Storage (mm)1819Groundwater 2 Percolation (mm/hr)0.210.21Groundwater 2 Coefficient (hr)670610

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

Slide67

Landuse

/

Landcover

Map of Subarnarekha River Basin October 2009

Slide68

Landuse

/

Landcover

Map of Subarnarekha River Basin January 2009

Slide69

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.

Slide70

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.

Slide71

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

.

Slide72

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.

Slide73

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.

Slide74

Unit Hydrograph Transform Method

Snyder Unit Hydrograph ParameterStandard Lag (Tp, hr)Peaking Co-efficient (Cp)Upper Sub-basin500.6

Muskingum Routing Method

Muskingum Routing Parameter

K (hr)

X

Upper Sub-basin

49

0.3

Slide75

references

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.

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.

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.

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.

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

.

Divya

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

.

Slide76

Fleming, M. and V. Neary, 2004. Continuous Hydrologic Modeling Study with the Hydrologic Modeling System. Journal of Hydrologic Engineering, 9(3): 175-183. 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.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. 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.Subramanya, K., 2002. Engineering Hydrology. Second Edition, Tata McGraw-Hill Publishers, New Delhi.

Continued

Slide77

Q1 simulation

Slide78

Q14 simulation