GIS BASED HYDROLOGICAL MODELLING FOR

CLIMATE CHANGE IMPACT ASSESSMENT

Dr. Amardeep Singh, MoWR Prof . A. K. Gosain, IIT Delhi

Model Description • SWAT (Soil and Water Assessment Tool) • Conceptual, distributed, continuous time

model • Developed to predict the impact of land

management practices on water, sediment and agricultural chemical yields in large, watersheds

• Version used SWAT 2000 with an Arc View (GIS) interface.

Description of the study area (Cauvery River Basin)

• Area of the basin = 81,155 sq km • Length of the river = 802 km • Riparian States = Karnataka, Tamil Nadu,

Kerala, Pondicherry • Earlier Agreements = 1892 and 1924 • Cauvery Water Dispute Tribunal = 1990 • Interim Order = 1991 • Cauvery River Authority = 1998 • Final Award= 2007

Data Used for Hydrological Modelling

• Survey of India Contour Maps on a scale of 1:250,000

• Land Use Maps on a scale of 1:250,000 • Soil Series Maps on a scale of 1:250,000 • Weather data pertaining to the

precipitation, temperature, solar radiation, wind speed, relative humidity

• Reservoir and irrigation data

Steps involved in Modelling • Digitisation of contours • DEM generation • Watershed delineation • Hydrologic Response Units (HRU) generation

through overlay of land use and soil maps • Input of climatic data • Input of reservoir and irrigation data • SWAT run for the simulation period (1970 to

1989)

Simulation of Cauvery basin

• Area modelled = 67,000 sq km • Number of: Subbasins= 42 HRU’s =231 Soil classes=6 Land use classes=8 (Threshold 5/5%)

$

$

$ $

$

$

$

$

$

Kabini

Mettur

Harangi

Bhavani

Amravathy

Suvarnavathy

Marconahalli

Krishnaraj Sagar

Kabini

Mettur

Harangi

Bhavani

Amravathy

Suvarnavathy

Marconahalli

Krishnaraj Sagar

HemavathyHemavathy

WatershedStreams

$ Reservoirs

N

EW

S

Cauvery river basin

$

$ $

$

$ $$

$

$

9

85

4

38

26

2

29

21

13

33

20

23

35

42

1936

37

1410

22

32

34

18

16

31

2527

6 7

28

11

1715

41

30

12

3

40

24

139

WatershedSubbasins

$ Reservoirs

100 0 100 200 Miles

N

EW

S

Watershed

9

85

4

38

26

2

29

21

13

33

20

23

35

42

1936

37

1410

22

32

34

18

16

31

2527

6 7

28

11

1715

41

30

12

3

40

24

139

Dem0 - 288.754288.754 - 577.509577.509 - 866.263866.263 - 1155.0171155.017 - 1443.7721443.772 - 1732.5261732.526 - 2021.2812021.281 - 2310.0352310.035 - 2598.789

Subbasins

100 0 100 200 Miles

N

EW

S

Watershed

SwatLandUseClassAGRCFRSDFRSEFRSTORCDPASTUIDUURHDWATRWETLWETNWPAS

N

EW

S

Cauvery river basin

SoilClassCOIMBTOREGUTTAPALPALATHURAISampleTYAYAMAGNDALUVIJYAPURA

100 0 100 200 Miles

N

EW

S

Watershed

Model Calibration

Land Use Scenarios

Variation of Average Flows

Variation of Average Flows

Variation of 75% Dependable Flows

Variation of 75% Dependable Flows

• Data generated by Hadley Centre UK • HadRM2 Series • Daily values of precipitation, max temp,

min temp, wind speed,relative humidity and solar radiation taken

• Period taken – 2041 to 2060 • Precipitation taken as the variable of

interest and its mean monthly as well as annual values compared over a 20 year period.

Future Climate Change Scenarios

Comparison between present and future climate change flows

Some Inferences…

• Even though the above table suggests that there is a very significant increase in flows (for example for S11 case, the increase in flow is more than 50 percent for the futuristic climate change scenario), the same may not be taken directly. This is because, as shown earlier, there was a significant difference in the RM2 control data and the present actual data. For example, consider the values of annual precipitation. If we compare RM2 control data with the RM2 futuristic data, increase in precipitation is less than 2%. This agrees with the average increase in precipitation between the RM2 control and futuristic scenario, calculated as a part of NATCOM Project (India’s National Communication to the UNFCCC) for the Cauvery basin (2.7%). (Gosain and Rao, 2003). However if we now compare the present actual value (1000 mm) and RM2 control values (1253.5), the difference is of the order of 27%.

Hence, it can be stated that the results obtained using the simulated climatic series like RM2 should be used only after verifying them for the area under consideration, which can be done by comparing the control values with the actually observed data.

Some Inferences…

-15

-10

-5

0

5

10

15

20

25

30

μ μ+σ μ-σ μ+2σ μ-2σ Precipitation

Variability of January precipitations for Present data

-40

-20

0

20

40

60

80

100

μ μ+σ μ-σ μ+2σ μ-2σ Precipitation

Variability of January precipitations for RM2 Future data

Month Present RM2 Future

µ ± σ µ ± 2σ µ ± σ µ ± 2σ

January 80 90 80 90

February 90 90 80 95

March 90 95 75 90

April 65 100 74 100

May 55 95 90 100

June 70 95 80 95

July 64 100 68 95

August 68 100 69 95

September 58 100 63 100

October 80 95 80 95

November 75 90 85 95

December 75 95 95 95

Variability of monthly precipitations of present and futuristic climate change scenarios

Though RM2 future monthly precipitation series shows more variance than present series, there is no significant difference in the variance of two series as determined using F two sample test for variance. However, on an annual scale, RM2 future annual precipitation series shows more variance than present series and the difference is found to be significant using F two sample test for variances. In other words, intensification of the hydrological cycle can be seen in the future climate change scenario, and it appears to be significant on an annual basis.

•Simulation modelling can play a very significant role in conflict resolution by generating a series of scenarios or options for the stakeholders, so as to enable them to take sound rational decisions.

•Also, implications of climate change on the availability of water in the shared watercourse and consequently, share of each riparian state, can be analysed using modelling techniques.

Conclusions

Thank You