Climate Change Impact Assessment on Long Term Water Budget for ...

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175. Simula ted W ater Y ield. (mm). Observed Water Yield (mm). 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec.
2015 SWAT CONFERENCE, PURDUE

Climate Change Impact Assessment on Long Term Water Budget for Maitland Catchment in Southern Ontario By Vinod Chilkoti Aakash Bagchi Tirupati Bolisetti Ram Balachandar

Contents  Objectives  Methodology  Watershed description  SWAT model •

Data collection



Calibration and validation

 Climate change impact assessment study

2



Climate model



Inputs



Bias correction



SWAT model results

 Conclusions

Objective The major objective of the present research is

• To assess the climate change impact on long term water budget for Maitland catchment for 2071-2100 period using CanRCM4 climate model

3

Methodology • Hydrological modeling using Soil and Water Assessment Tool (SWAT) 2012 is done for Maitland River catchment

• Model is calibrated and validated using observed daily flows • Climate change impact assessment on catchment water budget is carried out • Canadian Regional Climate model (CanRCM4) nested in CanESM2 GCM for CORDEX NAM domain with 0.440 grid resolution is used

4

Methodology Climate change impact assessment process Input data

Climate model simulation

• Climate modeling • Role of climate scientist

Model output Reading model output Downscaling and Bias correction Hydrological model simulation 5

Impact Assessment

• Climate change impact assessment study

Study Area • Catchment located in south-western Ontario, Canada

N

N ONTARIO

6

Watershed Description • Catchment Area

:

2455 km2

• Elevation

:

235m to 525m

• River

:

Maitland, ~150km length

• Tributaries

:

Middle Maitland, Little Maitland, South Maitland

• Outfall

:

Drains into Lake Huron ~El. 185m (at Goderich ) W 81o 30’

W 82o 00’

W 81o 00’ N 44o 00’ N

Lake Huron

Maitland R

Goderich Benmiller

7

0

10 Km

N 43o 30’

Watershed Description • Annual average • rainfall • temperature • evapotransp.

: : :

~1100 mm ~ 2.6oC (min) to11.5oC (max.) ~ 550mm

: : :

81% 15% 3%

• Land cover

• Agriculture • Natural cover • Urban • Soils

• Harriston (silt loam) : • Huron (silt loam) : • Brookstone (clay) : 8

72% 10% 7%

Data Collection • Climate Data – Environment Canada • 5 climate station (1970-2015) • Flow data at Benmiller (1989 – 2013) Mt Forest N

Wroxeter Goderich Benmiller

Blyth

Newton 0

9

10 Km

Data Collection • Landuse – South Ontario Landuse Resource Information System (SOLRIS)

• Soil – National Soil Data Base, Soil landscapes of Canada (slc)

Land use

Agriculture Natural cover

10

Soil types

LISTOWEL BROOKSTON HARRISTON BURFORD DONNYBROOK HURON PIKE LAKE PERTH

Data Collection • Observed daily flow data at Environment Canada’s 02FE015 gauging station is available from 1989-2013

• 1989 – 2001 data is used 600

Calibration period (1989-1996)

Validation period (1997-2001)

Discharge (cumec)

500

400

300

200

100

0 Jan-89

11

Jan-90

Jan-91

Jan-92

Jan-93

Jan-94

Jan-95

Jan-96

Jan-97

Jan-98

Jan-99

Jan-00

Jan-01

Model Calibration • Model performance statistics • Nash-Sutcliffe Efficiency (NSE) NSE • Coefficient of determination, R2

where, Qm – measured or observed discharge Qs – simulated discharge 12

Sensitivity Analysis • Parameter sensitivity

• Most sensitive parameters GWQMN.gw, CN2.mgt, ESCO.hru, GW_REVAP.gw SMTMP.bsn 13

Model Calibration • Annual water yield calibration • 1989 - 1996 800

Obs

Water Yield (mm)

700

Sim

600

500 400 300 200 100

Simulated Water Yield (mm)

800

y = 0.92x + 55.98 R² = 0.85

700 600 500 400

300

0

300 1989

1990

1991

1992

1993

1994

1995

1996

Annual Water Yield

14

400

500

600

Observed Water Yield (mm)

700

800

Model Calibration • Monthly water yield calibration • 1989 - 1996 100

Obs

90

Sim

Water Yield (mm)

80

70 60 50 40 30 20 10

Simulated Water Yield (mm)

175

y = 0.64x + 18.95 R² = 0.68

150 125

100 75 50 25 0 0

0

Jan Feb Mar Apr May Jun

25

Jul Aug Sep Oct Nov Dec

Monthly Water Yield

15

50

75

100

125

Observed Water Yield (mm)

150

175

Model Calibration • Daily flow calibration • 1989 - 1996

Flow (cumec)

500

400

Sim Obs

Simulated Flow (cumec)

600

400 300 200 100 0 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96

y = 0.97x + 5.15 R² = 0.41 300

200

100

0

0

100

200 Observed Flow (cumec)

Daily stream flows

16

300

400

Model Validation • Annual and monthly water yield calibration • 1997 - 2001 175

y = 0.98x + 29.40 R² = 0.82

600

Simulated Water Yield (mm)

Simulated Water Yield (mm)

700

500 400 300

200

125 100 75 50 25

0 200

300

400

500

Observed Water Yield (mm)

Annual Water Yield

17

y = 0.73x + 11.56 R² = 0.77

150

600

700

0

25

50

75

100

125

Observed Water Yield (mm)

Monthly Water Yield

150

175

Model Validation

600

400

Obs

400 300 200

Simulated Flow (cumec)

500

Flow (cumec)

y = 1.09x + 2.60 R² = 0.57

Sim 300

200

100

100 0 Jan-97

0 Jan-98

Jan-99

Jan-00

Jan-01

0

Daily stream flows

18

100

200 Observed Flow (cumec)

300

400

Climate Change Impact Assessment Study • Weather input data for SWAT has been extracted from the climate model outputs

• Climate Model

: CanRCM4

• Parent GCM

: CanESM2

• Grid resolution

: 0.44 deg ~ 50 km

• CORDEX Domain

: NAM (North America)

• Modeling Agency : Canadian Center for Climate Modeling and Analysis (CCCma) • Experiments

: Historical r1i1p1(1971-2000) - Baseline : RCP 4.5 r1i1p1 (2071-2100) - Future

• Land use pattern is considered same for the future scenario 19

Climate model – Salient Features CORDEX NAM-44 Grid over Maitland Catchment

CORDEX (NAM)

Legend (11.00o, - 2.2o)

: (rlat, rlon)

[44.21oN, 81.57oW] : (Lat, Lon) : Grid point : Climate station

20

Climate Change Study - Inputs

Precipitation (mm)

• Comparison of observed and baseline period precipitation 140

140

120

120

100

100

80

80

60

60

40

40

20

20

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

Wroxeter

Newton

Mt.Forest

Observed data

Goderich

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

Wroxeter

Newton

MtForest

Goderich

Model baseline data

• Bias in the two precipitations is removed using Bias correction factor 21

Climate Change Study – Bias Correction • Bias correction factor for precipitation n

m 

P i 1 n

Obs ,i

 PRCM ,i

where, PRCM – model precipitation in base period Pobs – observed precipitation in base period

i 1

• Bias correction factor for minimum and maximum temperature 1 n 1 n  m   TObs ,i   TRCM ,i n i 1 n i 1

where, PRCM – model temperature in base period Pobs – observed temperature in base period n – no. of years in base period

• Monthly bias correction factor are computed 22

Climate Change Study - Bias Correction • Bias correction factor for precipitation 2.4 2.2 2.0 1.8

Bias Factor

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Jan

Feb

Mar

Goderich

23

Apr Blyth

May

Jun Wroxeter

Jul

Aug Newton

Sep

Oct MtForest

Nov

Dec

Climate Change Study - Inputs

Temperature (0c)

• Comparison of average monthly minimum temperature

25

25

20

20

15

15

10

10

5

5

0

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -5

-10

-10 Blyth

Wroxeter

Newton

Baseline

24

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

-5

MtForest

Goderich

Blyth

Wroxeter

Newton

MtForest

Future scenario RCP 4.5

Goderich

Climate Change Study - Inputs

Temperature (0C)

• Comparison of average monthly maximum temperature

40

40

35

35

30

30

25

25

20

20

15

15

10

10

5

5

0

0

-5

Jan Feb Mar Apr May Jun Blyth

Wroxeter

Newton

Jul Aug Sep Oct Nov Dec MtForest

Baseline

25

Goderich

-5

Jan Feb Mar Apr May Jun Blyth

Wroxeter

Jul Aug Sep Oct Nov Dec

Newton

MtForest

Future scenario RCP 4.5

Goderich

SWAT Model Results • Monthly precipitation 140

Spring

Winter

Summer

Precipitation (mm)

120 100 80 60

40 20 0 Oct

Nov

Dec

Jan

Feb

Mar

Baseline

26

Apr

May

Jun

Jul

Aug

Sep

RCP4.5

• Variation in precipitation in different periods: Winter (Oct-Feb) - increase by 17% Spring (Mar-May) – decrease by 3% Summer (Jun-Sep) – decrease by 10%

SWAT Model Results • Monthly Evapotranpiration (ET) 100

Evapotranspiration (mm)

90 80 70 60 50 40 30 20 10 0 Oct

Nov

Dec

Jan

Feb

Mar

Baseline

27

Apr

May

Jun

Jul

RCP4.5

• Variation in ET in different periods: Winter (Oct-Feb) - increase by 96% Spring (Mar-May) – increase by 86% Summer (Jun-Sep) – decrease by 3% • Peak ET shifts by 1-2 month

Aug

Sep

SWAT Model Results • Monthly total water yield 100

Spring

Winter

90

Summer

Water Yield (mm)

80 70 60 50 40 30 20 10 0 Oct

Nov

Dec

Jan

Feb

Mar

Baseline

28

Apr

May

Jun

Jul

Aug

Sep

RCP4.5

• Variation in total water yield in different periods: Winter (Oct-Feb) – increase by 28% Spring (Mar-May) – decrease by 28% Summer (Jun-Sep) – decrease by 50%

SWAT Model Results • Variation in water budget w.r.t Baseline period (% change)

• • • • 29

Surface Ground Water Water

Water Yield

Period

Precipitation

ET

All year Winter (Oct-Feb) Spring (Mar-Apr) Summer (May-Sep)

2.4

16.8

-33.6

15.0

-10.3

17.0

96.2

13.4

55.6

28.4

-2.9

86.9

-51.4

44.1

-28.3

-10.6

-2.6

-31.9

-70.5

-50.0

Overall water yield of the watershed reduces Winter period shows increase in water yield Spring period shows equal decrease in water yield Summer period has significant reduction in water yield

Conclusions and Future Work • SWAT hydrological model for Maitland catchment when forced with CanRCM4 climate model result, predicts:

- Severely strained summer months with ~50% reduced water availability w.r.t. baseline - Considerably reduced surface water yield during spring period - Peak evapotranspiration (ET) is advanced by a month period with increased peak • Change in hydrological regime has strong implications to agriculture Future Work • We proposed to perform SWAT simulation using ensemble of climate model outputs 30

Acknowledgements Partial funding support by the following is gratefully acknowledged • National Sciences and Engineering Research Council of Canada

• University of Windsor

31

THANK YOU

32