Climate Change Impact Assessment on Long Term Water Budget for Maitland Catchment in Southern Ontario

Transcription

1 215 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

2 Contents Objectives Methodology Watershed description SWAT model Data collection Calibration and validation Climate change impact assessment study Climate model Inputs Bias correction SWAT model results 2 Conclusions

3 Objective The major objective of the present research is To assess the climate change impact on long term water budget for Maitland catchment for period using CanRCM4 climate model 3

4 Methodology Hydrological modeling using Soil and Water Assessment Tool (SWAT) 212 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.44 grid resolution is used 4

5 Methodology Climate change impact assessment process Input data Climate model simulation Model output Climate modeling Role of climate scientist Reading model output Downscaling and Bias correction Hydrological model simulation Climate change impact assessment study 5 Impact Assessment

6 Study Area Catchment located in south-western Ontario, Canada N ONTARIO N 6

7 Watershed Description Catchment Area : 2455 km 2 Elevation : 235m to 525m River : Maitland, ~15km length Tributaries : Middle Maitland, Little Maitland, South Maitland Outfall : Drains into Lake Huron ~El. 185m (at Goderich ) W 82 o Lake Huron W 81 o 3 Maitland R W 81 o N N 44 o Goderich Benmiller 7 1 Km N 43 o 3

8 Watershed Description Annual average rainfall : ~11 mm temperature : ~ 2.6 o C (min) to11.5 o C (max.) evapotransp. : ~ 55mm Land cover Agriculture : 81% Natural cover : 15% Urban : 3% Soils Harriston (silt loam) : 72% Huron (silt loam) : 1% Brookstone (clay) : 7% 8

9 Data Collection Climate Data Environment Canada 5 climate station ( ) Flow data at Benmiller ( ) Mt Forest N Wroxeter Goderich Benmiller Blyth Newton 1 Km 9

10 Data Collection Landuse South Ontario Landuse Resource Information System (SOLRIS) Soil National Soil Data Base, Soil landscapes of Canada (slc) 1 Land use Agriculture Natural cover Soil types LISTOWEL BROOKSTON HARRISTON BURFORD DONNYBROOK HURON PIKE LAKE PERTH

11 Discharge (cumec) Data Collection Observed daily flow data at Environment Canada s 2FE15 gauging station is available from data is used 6 5 Calibration period Validation period ( ) ( ) Jan-89 Jan-9 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan- Jan-1 11

12 Model Calibration Model performance statistics Nash-Sutcliffe Efficiency (NSE) NSE Coefficient of determination, R 2 where, Q m measured or observed discharge Q s simulated discharge 12

13 Sensitivity Analysis Parameter sensitivity Most sensitive parameters 13 GWQMN.gw, CN2.mgt, ESCO.hru, GW_REVAP.gw SMTMP.bsn

14 Water Yield (mm) Simulated Water Yield (mm) Model Calibration Annual water yield calibration Obs Sim 8 7 y =.92x R² = Observed Water Yield (mm) Annual Water Yield 14

15 Water Yield (mm) Simulated Water Yield (mm) Model Calibration Monthly water yield calibration Obs Sim y =.64x R² = Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Observed Water Yield (mm) Monthly Water Yield 15

16 Flow (cumec) Simulated Flow (cumec) Model Calibration Daily flow calibration Sim Obs 4 3 y =.97x R² = Jan-89 Jan-9 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan Observed Flow (cumec) Daily stream flows 16

17 Simulated Water Yield (mm) Simulated Water Yield (mm) Model Validation Annual and monthly water yield calibration y =.98x R² = y =.73x R² = Observed Water Yield (mm) Observed Water Yield (mm) Annual Water Yield Monthly Water Yield 17

18 Flow (cumec) Simulated Flow (cumec) Model Validation 6 5 Sim Obs 4 3 y = 1.9x R² = Jan-97 Jan-98 Jan-99 Jan- Jan Observed Flow (cumec) Daily stream flows 18

19 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 :.44 deg ~ 5 km CORDEX Domain : NAM (North America) Modeling Agency : Canadian Center for Climate Modeling and Analysis (CCCma) Experiments : Historical r1i1p1(1971-2) - Baseline : RCP 4.5 r1i1p1 (271-21) - Future Land use pattern is considered same for the future scenario 19

20 Climate model Salient Features CORDEX NAM-44 Grid over Maitland Catchment CORDEX (NAM) 2 Legend (11. o, o ) : (rlat, rlon) [44.21 o N, o W] : (Lat, Lon) : Grid point : Climate station

21 Precipitation (mm) Climate Change Study - Inputs Comparison of observed and baseline period precipitation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Blyth Wroxeter Newton Mt.Forest Goderich Observed data Blyth Wroxeter Newton MtForest Goderich Model baseline data Bias in the two precipitations is removed using Bias correction factor 21

22 Climate Change Study Bias Correction Bias correction factor for precipitation m n i 1 n i 1 P P Obs, i RCM, i where, P RCM model precipitation in base period P obs observed precipitation in base period Bias correction factor for minimum and maximum temperature m 1 n T 1 Obs, i n i 1 n i 1 n T RCM, i where, P RCM model temperature in base period P obs observed temperature in base period n no. of years in base period Monthly bias correction factor are computed 22

23 Bias Factor Climate Change Study - Bias Correction Bias correction factor for precipitation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Goderich Blyth Wroxeter Newton MtForest 23

24 Temperature ( c) Climate Change Study - Inputs Comparison of average monthly minimum temperature Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -1-1 Blyth Wroxeter Newton MtForest Goderich Blyth Wroxeter Newton MtForest Goderich Baseline Future scenario RCP

25 Temperature ( C) Climate Change Study - Inputs Comparison of average monthly maximum temperature Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Blyth Wroxeter Newton MtForest Goderich Blyth Wroxeter Newton MtForest Goderich Baseline Future scenario RCP

26 Precipitation (mm) SWAT Model Results Monthly precipitation Winter Spring Summer Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Baseline RCP Variation in precipitation in different periods: Winter (Oct-Feb) - increase by 17% Spring (Mar-May) decrease by 3% Summer (Jun-Sep) decrease by 1%

27 Evapotranspiration (mm) SWAT Model Results Monthly Evapotranpiration (ET) Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Baseline RCP4.5 Variation in ET in different periods: Winter (Oct-Feb) - increase by 96% Spring (Mar-May) increase by 86% 27 Summer (Jun-Sep) decrease by 3% Peak ET shifts by 1-2 month

28 Water Yield (mm) SWAT Model Results Monthly total water yield 1 9 Winter Spring Summer Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Baseline RCP 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 5%

29 SWAT Model Results Variation in water budget w.r.t Baseline period (% change) Period Precipitation ET Surface Water Ground Water Water Yield All year Winter (Oct-Feb) Spring (Mar-Apr) Summer (May-Sep) 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 29

30 Conclusions and Future Work SWAT hydrological model for Maitland catchment when forced with CanRCM4 climate model result, predicts: - Severely strained summer months with ~5% 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 3

31 Acknowledgements Partial funding support by the following is gratefully acknowledged National Sciences and Engineering Research Council of Canada University of Windsor 31

32 32 THANK YOU