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Assessing the impact of climate change forMahanadi basin using SWAT model

P. C. Nayak1, B. Venkatesh, T. Thomas and Y. R. Satyaji Rao

1Deltaic Regional Centre,National Institute of Hydrology,

Kakinada-533 003, AP

Objectives:

•Assessment of change in hydrometerological andhydrological data by employing statisticalsignificance test.•To downscale the GCMs output•To downscale the GCMs output•Hydrological assessment using SWAT model

Study area map

Data collected

S. No. Gauaging site

Period Years Catchment area (km2)

1 Salebhatta 1971-2009 39 4650

2 Sundergarh 1978-2009 32 58702 Sundergarh 1978-2009 32 5870

3 Kesinga 1978-2009 32 11,960

4 Kantamal 1971-2009 39 19,600

IMD gridded rainfall data (10 X 10) from 1901 to 2004

GCM DataEnvironment Canada, Canadian Centre forClimate Modelling and Analysis, CanESM2,R1, R2, R3, R4, R5 (256 X 192)

Historical data (1951 – 2005)Historical data (1951 – 2005)

Future projection : 2006 - 2100 (eg., RCP2.6, 4.5, 8.5 Scenario)

MK Sign Test Statistics for Discharge data

S. No.Gauaging

site H alpha Z Inference

1Salebhatta 1 0.05 -3.989Decreasing trend in the Pre-whitened series1Salebhatta 1 0.05 -3.989whitened series

2Sundergarh 0 0.05 -0.2377NO trend in the Pre-whitened series

3Kesinga 1 0.05 35.79Increasing trend in the Pre-whitened series

4Kantamal 1 0.05 30.11Increasing trend in the Pre-whitened series

Monthly Total Precipitation [1961-2004]

400

500

600

Mon

thly

Pre

cipi

tatio

n (m

m)

Kantamal Kesinga Salebhata Sundergarh

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

0

100

200

300

Mon

thly

Pre

cipi

tatio

n (m

m)

Months

Plot showing number of rainy days for different

sub-basin in Mahanadi river

1960 1970 1980 1990 2000 2010

105

120

135

150

165

180

135

150

165

180

195

210

225

Num

ber

of r

ainy

day

s

Kantamal Rainfall Trend line

Num

ber

of

rain

y d

ays Kesinga Rainfall

Trend line

1960 1970 1980 1990 2000 2010

1960 1970 1980 1990 2000 2010

105

120

135

150

165

180

1960 1970 1980 1990 2000 2010

90

105

120

135

150

165

180

195

Num

ber

of

rain

y d

ays

Salebhata Rainfall Trend Line

Nu

mbe

r o

f ra

iny

day

s

Years [1961-2004]

Sundergarh Rainfall Trend line

Plot showingnumber of rainydays for 1 daymaximumrainfall forKantamal 2

4

Rainfall more than 75mm Trend line

Num

ber

of r

ainy

da

ys

0

2

4 Rainfall more than 100mm Trend line

Num

ber

of r

ainy

da

ys

Kantamalsubbasin inMahanadi river

1960 1970 1980 1990 2000 2010

0

2

4

6

8


Year from 1961-2004

Num

ber

of

rain

y da

ys

0

Num

ber

of r

ainy

da

ys

Plot showingnumber of rainydays for 1 daymaximumrainfall for 0

2


Nu

mb

er o

f ra

iny

day

s

0.0

0.5

1.0 Rainfall more than 100mm Trend line

Num

ber

of r

ainy

day

s

rainfall forKesinga sub –basin inMahanadi river

1960 1970 1980 1990 2000 2010

0

2

4

6


Year from 1961-2004

Num

ber

of r

ainy

da

ys

0

Nu

mb

er o

f ra

iny

day

s

Plotshowingnumber ofrainy daysfor 1 daymaximumrainfall for

0

2


Nu

mb

er o

f ra

iny

days

0

2


Nu

mbe

r of

rai

ny d

ays

rainfall forSalebhatasub basin inMahanadiriver 1960 1970 1980 1990 2000 2010

0

2

4

6


Year from 1961-2004

Num

ber

of

rain

y da

ysN

um

ber

of

rain

y da

ys

Plot showingnumber of rainydays 1 daymaximum rainfallfor Ib sub-basin in

2

4

6


Nu

mb

er o

f ra

iny

days

0

2


Nu

mb

er o

f ra

iny

days

for Ib sub-basin inMahanadi river

1960 1970 1980 1990 2000 2010

0

2


Year from 1961-2004

Num

ber

of

rain

y d

ays

0

2

Nu

mb

er o

f ra

iny

days

Regime Shift Detection TestWavelet analysis is becoming a common toolfor analyzing localized variations of powerwithin a time series. By decomposing a timeseries into time–frequency space, one is ableseries into time–frequency space, one is ableto determine both the dominant modes ofvariability and how those modes vary intime.

Kantamal Sub-basin

1965 1970 1975 1980 1985 1990 1995 2000-2

0

2

4

Time (year)

Rai

nfal

l (m

m)

a) Rainfall in mm (seasonal)

b) Rainfall Wavelet Power Spectrum

0.5

1

c) Global Wavelet Spectrum

Time (year)

Per

iod

(yea

rs)

1965 1970 1975 1980 1985 1990 1995 2000

1

2

4

8

16

320 5 10 15

x 104Power (mm2)

1965 1970 1975 1980 1985 1990 1995 20000

5

10

15x 10

4

Time (year)

Avg

var

ianc

e (m

m2 )

d) 2-8 yr Scale-average Time Series

Kesinga Sub-basin

1965 1970 1975 1980 1985 1990 1995 2000-2

0

2

4

Time (year)

Rai

nfal

l (m

m)


b) Rainfall Wavelet Power Spectrum c) Global Wavelet Spectrum

Time (year)

Per

iod

(yea

rs)

1965 1970 1975 1980 1985 1990 1995 2000

0.5

1

2

4

8

16

320 2 4 6 8 10 12

x 104Power (mm2)

1965 1970 1975 1980 1985 1990 1995 20000

5

10

15x 10

4

Time (year)

Avg

var

ianc

e (m

m2 )


Salebhata Sub-basin

1965 1970 1975 1980 1985 1990 1995 2000-2

0

2

4

Time (year)

Rai

nfal

l (m

m)



0.5


Time (year)

Per

iod

(yea

rs)

1965 1970 1975 1980 1985 1990 1995 2000

0.5

1

2

4

8

16

320 5 10 15

x 104Power (mm2)

1965 1970 1975 1980 1985 1990 1995 20000

5

10

15x 10

4

Time (year)

Avg

var

ianc

e (m

m2 )


Ib Sub-basin

1965 1970 1975 1980 1985 1990 1995 2000-2

0

2

4

Time (year)

Rai

nfal

l (m

m)



0.5


Time (year)

Per

iod

(yea

rs)

1965 1970 1975 1980 1985 1990 1995 2000

0.5

1

2

4

8

16

320 2 4 6 8 10

x 104Power (mm2)

1965 1970 1975 1980 1985 1990 1995 20000

0.5

1

1.5

2x 10

5

Time (year)

Avg

var

ianc

e (m

m2 )


Downscaling PrecipitationThe area of typical GCM grid cells range between 10,000km2 and 90,0002 km, but watershed area is less than GCMGrid area, there is difference/ mismatch in spatial scales.

To overcome mismatched spatial scale:

– (1) based on analogies with different climatic zones – (1) based on analogies with different climatic zones

– (2) Downscaling methodologies (statistical/dynamic).

There are three types of statistical downscaling, namely weather classification methods, weather generators, and transfer functions methods.

Change FactorMethodology

Anandhi, A., A. Frei, D. C. Pierson, E. M. Schneiderman, M. S. Zion, D. Lounsbury, and A. H. Matonse (2011), Examination of change factor methodologies for climate change impact assessment, Water Resour. Res., 47, W03501, doi:10.1029/2010WR009104.

Downscaling Precipitation • CanESM2_1960_2005_R1 & CanESM2_2006_2100_R1_RCP26 • CanESM2_1960_2005_R1 & CanESM2_2006_2100_R1_RCP45• CanESM2_1960_2005_R1 & CanESM2_2006_2100_R1_RCP85• CanESM2_1960_2005_R1 & CanESM2_2006_2100_R2_RCP26 • CanESM2_1960_2005_R1 & CanESM2_2006_2100_R2_RCP45• CanESM2_1960_2005_R1 & CanESM2_2006_2100_R2_RCP85• CanESM2_1960_2005_R1 & CanESM2_2006_2100_R2_RCP85• CanESM2_1960_2005_R1 & CanESM2_2006_2100_R3_RCP26• .• .• .• .

• Total 75 projections are generated using CFM for each sub-basin)

Uncertainty analysis

• To establish confidence in the projected precipitation downscaled from GCMscenario outputs, it was important that the downscaled outputs represented thescenario outputs, it was important that the downscaled outputs represented thecurrent state of the precipitation regimes reasonably well. In fact, the confidenceon the reliability of the climate change anomalies computed from the scenariosrun relied on the downscaled outputs’ ability to represent the baseline Climate.

Non-parametric Uncertainty Test• The Wilcoxon Signed Rank test (Wilcoxon,

1945) is one of the best nonparametricmethods for conducting hypothesis tests(Conover, 1980) and widely used in(Conover, 1980) and widely used inuncertainty analysis of downscaled climateparameters provided with predictor scenariosof the GCMs (Dibike et al., 2008; Khan et al.,2006a,b).

Uncertainty Test• Analysis carried out using daily, monthly,

seasonal and annual GCM downscaledprecipitation data. If more than 20% data isoutside the 95% confidence level, is rejected.outside the 95% confidence level, is rejected.

• Monthwise (Jan, Feb, Mar, …time series)uncertainty analysis is carried out to check thevariation in projected future rainfall.

Plot showing data band

Decadal Rainfall variation

1300.0

1400.0

1500.0

1600.0

Kantamal

Kesinga

Salebhatta

Sungergarh

1000.0

1100.0

1200.0

0 1 2 3 4 5 6

Linear (Kantamal)

Linear (Kesinga)

Linear (Salebhatta)

Linear (Sungergarh)

CanESM2_1960_2005_R1 & CanESM2_2006_2100_R1_RCP26


400.0

450.0

500.0

550.0

Kantamal

Kesinga

Salebhatta

Sungergarh

Linear (Kantamal)

300.0

350.0

400.0

0 1 2 3 4 5 6

Linear (Kantamal)

Linear (Kesinga)

Linear (Salebhatta)

Linear (Sungergarh)



400.0

450.0

500.0

550.0

Kantamal

Kesinga

Salebhatta

Sungergarh

Linear (Kantamal)

300.0

350.0

400.0

0 1 2 3 4 5 6

Linear (Kantamal)

Linear (Kesinga)

Linear (Salebhatta)

Linear (Sungergarh)


Rainfall-runoff model development using SWAT Model

• 5 years data for warming upCalibration of Data

– Kantamal 1978 -2000– Kantamal 1978 -2000– Kesinga 1983-1990 – Slaebhata 1978-1985 – Sundergarh 1983-1992

• Rest of the data used for Validation

SWAT model development

Topographic information were derived usingDigital Elevation Model (DEM) data (the SRTMDEM have been used).

The soil data available in NBSS&LUP, whichcontains soil maps at a 1:250,000 scale.

Calibration of the model was done by adoptingthe manual calibration procedure.

Kantamal basin map

Sub-Basin 133HRU 154

Calibrated Model ParametersParameter Kantamal Kesinga Sundargarh Salebhata

Calibrated Rank Calibrated Rank Calibrated

Rank Calibrated

values

Rank

values values values Alpha_Bf 0.95 6 6.84E-01 6 0.18 5 0.03 7

Cn2 76.40 1 81.70 1 76.10 2 85.10 1Epco 0.42 9 0.82 9 0.61 1 0.00 10Esco 0.04 4 0.77 4 0.65 4 0.12 4Esco 0.04 4 0.77 4 0.65 4 0.12 4Gw_Delay 3.00 10 7.38 10 5.33 6 0.00 9

Gwqmn 402.00 3 820.00 3 745.00 7 0.27 3

Rchrg_Dp 0.76 2 0.16 2 0.70 10 0.66 2

Sol_Awc 12.00 5 21.50 5 20.50 3 0.10 5

Sol_K 16.60 7 19.90 7 19.80 9 0.04 6Surlag 0.92 8 0.00 8 0.25 8 0.02 8

NSE 0.78 0.82 0.76 0.63

Observed and Computed plots during calibration period

7000

14000

7000

14000 Observed

Computed)

Dis

char

ge [

m3 /s

]

Kesinga Observed

Computed)

Kantamal

1/1/1983 1/1/1985 1/1/1987 1/1/1989 1/1/1991

0

6/19/1980 6/19/1985 6/19/1990 6/19/1995 6/19/2000

0

6/17/1976 6/17/1978 6/17/1980 6/17/1982 6/17/1984

0

2000

4000

6000

6/16/1981 6/16/1984 6/16/1987 6/16/1990

0

2000

4000 Observed

Computed)

Dis

char

ge [

m3

/s]

Period [Daily]

Salebhata

Observed

Computed)

Period [Daily]

Sundergarh

FUTURE WATERRESOURCESSCENARIO –DEPENDABLE

FLOWS AT KESINGA

0 20 40 60 80 100

0

200

400

600

800

1000

1200

1400

RCP 2.6RCP 4.5 RCP 8.5

De

pend

able

flo

ws

(cum

ecs)

0 20 40 60 80 100

0

200

400

600

800

1000

1200

1400


400

600

800

1000

1200 RCP 2.6RCP 4.5 RCP 8.5

De

pend

able

flo

ws

(cum

ecs)

400

600

800

1000

1200

1400


0 20 40 60 80 100

0

200

400

De

pend

able

flo

ws

(cum

ecs)

I0 20 40 60 80 100

0

200

400

M

0 20 40 60 80 100

0

200

400

600

800

1000

1200

1400


De

pend

able

flo

ws

(cum

ecs)

Probability of exceedance (%)

0 20 40 60 80 100

0

100

200

300

400

500

600

700

800

900


Observed

FUTURE WATERRESOURCESSCENARIO –DEPENDABLE

FLOWS AT

0 20 40 60 80 100

0

500

1000

1500

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flo

ws

(cum

ecs)

0 20 40 60 80 100

0

500

1000

1500

RCP 2.6 RCP 4.5 RCP 8.5

1000

1500

RCP 2.6 RCP 4.5 RCP 8.5

De

pend

abl

e flo

ws

(cu

me

cs)

1000

1500

RCP 2.6 RCP 4.5 RCP 8.5F

KANTAMAL

0 20 40 60 80 100

0

500

De

pend

abl

e flo

ws

(cu

me

cs)

I

0 20 40 60 80 100

0

500

M

0 20 40 60 80 100

0

500

1000

1500

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flo

ws

(cum

ecs)


0 20 40 60 80 100

0

500

1000

1500


Observed

FUTURE WATERRESOURCES SCENARIO

– DEPENDABLEFLOWS AT SALEBHATA 0 20 40 60 80 100

0

50

100

150

200

250

300

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)

0 20 40 60 80 100

0

50

100

150

200

250

300

RCP 2.6 RCP 4.5 RCP 8.5

150

200

250

300

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)

150

200

250

300

RCP 2.6 RCP 4.5 RCP 8.5

0 20 40 60 80 100

0

50

100

Dep

enda

ble

flow

s (c

umec

s)

I

0 20 40 60 80 100

0

50

100

M

0 20 40 60 80 100

0

50

100

150

200

250

300

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)


0 20 40 60 80 100

0

50

100

150

200

250

300


Observed

FUTURE WATERRESOURCES SCENARIO –DEPENDABLE FLOWS AT

SUNDERGARH0 20 40 60 80 100

0

200

400

600

800

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)

0 20 40 60 80 100

0

200

400

600

800

RCP 2.6 RCP 4.5 RCP 8.5

400

600

800

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)

400

600

800

RCP 2.6 RCP 4.5 RCP 8.5

0 20 40 60 80 100

0

200

Dep

enda

ble

flow

s (c

umec

s)

I

0 20 40 60 80 100

0

200

M

0 20 40 60 80 100

0

200

400

600

800

RCP 2.6 RCP 4.5 RCP 8.5

Dep

enda

ble

flow

s (c

umec

s)


0 20 40 60 80 100

0

100

200

300

400

500


Observed

Conclusions• Sign test indicates that discharge is increasing except Ong

tributary of Mahanadi River.• Number of rainy days is decreasing for all sub-basins.• Severe rainfall events are increasing for most of the basin

except Ib tributary.• Wavelet analysis indicated that there is change in rainfall• Wavelet analysis indicated that there is change in rainfall

pattern after 1980• Decadal analysis of projected rainfall shows that there is

decrease in rainfall for some scenarios 10 to 35 %.• All the RCP scenarios show that there is an increase in flood

in the future. It is shown that the RCP-2.6 and RCP-4.5 showmoderate increase in the runoff, whereas RCP-8.5 showssignificant increase.


Documents

3. P C Nayak · p g \u 6fdoh dyhudjh 7lph 6hulhv } Á v o ] v pw ] ] ] } v 7khduhdriw\slfdo*&0julgfhoovudqjhehwzhhq np dqg np exwzdwhuvkhgduhdlvohvvwkdq*&0 *ulgduhd wkhuhlvgliihuhqfh

3. P C Nayak · p g \u 6fdoh dyhudjh 7lph 6hulhv } Á v o ] v pw ] ] ] } v 7khduhdriw\slfdo&0julgfhoovudqjhehwzhhq np dqg np exwzdwhuvkhgduhdlvohvvwkdq&0 *ulgduhd wkhuhlvgliihuhqfh