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
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.
• 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
