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Climate Change Impacts on the Hydrology of the Upper Mississippi River Basin Eugene S. Takle with significant assistance from Manoj Jha, Chris Anderson, Phil Gassman, and Mahesh Sahu Atmospheric Science Seminar, ISU, 13 September 2005 If we had perfect predictability of low-resolution global climate fields, how well can we downscale this predictability to stream flow at one point? Outline Upper Mississippi River Basin Soil and Water Assessment Tool (SWAT) Climate information Observations Contemporary climate Future climate Flow simulations Water quality Summary Sub-Basins of the Upper Mississippi River Basin 119 sub-basins Outflow measured at Grafton, IL Approximately one observing station per sub-basin Approximately one model grid point per sub-basin Soil Water Assessment Tool (SWAT) Long-term, continuous watershed simulation model (Arnold et al,1998) Daily time steps Assesses impacts of climate and land management on yields of water, sediment, and agricultural chemicals Physically based, including hydrology, soil temperature, plant growth, nutrients, pesticides and land management Calibration of SWAT: Annual Stream Flow at Grafton, IL Calibration of SWAT: Monthly Stream Flow at Grafton, IL Validation of SWAT: Annual Stream Flow at Grafton, IL Validation of SWAT: Monthly Stream Flow at Grafton, IL Downscaling Methods Dynamical downscaling (use GCM to provide b.c. for RCM) Statistical or empirical transfer functions to relate local climate to GCM output Climate analog procedures Combinations of statistical and dynamical methods Downscaling For applications of global climate model results to hydrology, there is a significant mismatch between the spatial scales of the model resolution and features of drainage basins. Approximate locations of points for a 2.5o x 2.5o global model grid RegCM2 Simulation Domain Red = global model grid point Green/blue = regional model grid points SWAT Output with Various Sources of Climate Input Annual Stream Flow Simulated by SWAT Driven by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions Seasonal Stream Flow Simulated by SWAT Driven by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions Mean Monthly Precipitation Simulated by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions 120 (a) RegCM2 Snowfall (mm) 100 SWAT 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 60 (b) RegCM2 SWAT Runoff (mm) 50 40 30 20 10 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Aver. 120 (c) RegCM2 Evapotranspiration (mm) 100 SWAT 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 120 (d) RegCM2 Snowmelt (mm) 100 SWAT 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hydrological component comparison between RegCM2 and SWAT RegCM2 SWAT Evapotranspiration 588 528 Surface runoff 151 166 Snowmelt 256 240 Note: All values are in mm per year averaged for 1980-1988 in NNR run. Ten-Year Mean Precipitation Generated by the RegCM2 Regional Climate Model Driven with HadCM2 Global Model Results for the Contemporary and Future Scenario (2040s) Climate Ten-Year Mean Monthly Stream Flow Generated by the RegCM2 Regional Climate Model Driven with HadCM2 Global Model Results for the Contemporary and Future Scenario (2040s) Climate Hydrologic Budget Components Simulated by SWAT under Different Climates Hydrologic budget components Calibration (19891997) Validation (19801988) NNR (19801988) CTL (around 1990s) SNR (around 2040s) % Change (SNR-CTL) Precipitation 856 846 831 898 1082 21 Snowfall 169 103 237 249 294 18 Snowmelt 168 99 230 245 291 19 Surface runoff 151 128 151 178 268 51 GW recharge 154 160 134 179 255 43 Total water yield 273 257 253 321 481 50 Potential ET 947 977 799 787 778 -1 Actual ET 547 541 528 539 566 5 All units are mm Yield is sum of surface runoff, lateral flow, and groundwater flow Relation of Runoff to Precipitation for Various Climates Summary of RCM Studies RCM provides meteorological detail needed by SWAT to resolve sub-basin variability of importance to streamflow There is strong suggestion that climate change introduces changes of magnitudes larger than variation introduced by the modeling process Relationship of streamflow to precipitation might change in future scenario climates Alternative to Dynamical Downscaling Global Model Results “…a de facto minimum standard of any useful downscaling method for hydrologic applications: the historic (observed) conditions must be reproducible.” Wood, et al., 2004: Climatic Change 62:189 Linear interpolation of GCM results Spatial disaggregation Bias corrected spatial disaggregation Note: These methods could be applied to downscaled (RCM) results as well. Hypothesis: Simple linear interpolation of global climate model results as input to SWAT is incapable of reproducing historical (observed) hydrological conditions in the Upper Mississippi River Basin Global models used in the SWAT-UMRB simulations (20C) Institution Model Name Lon x Lat Resolution W/m2 Cl. Sens NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.0 2.5 o x 2.0 o 2.9 NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.1 2.5 o x 2.0 o 2.0 Center for Climate System Research (Japan) MIROC3.2(medres) 2.8 o x 2.8 o 1.3 Center for Climate System Research (Japan) MIROC3.2(hires) 1.125 o x 1.125o 1.4 Meteorological Research Institute (Japan) MRI 2.8 o x 2.8 o 0.86 NASA Goddard Institute for Space Studies (USA) GISS-AOM 4 o x 3o 2.6 NASA Goddard Institute for Space Studies (USA) GISS-ER 5 o x 4o 2.7 Institut Pierre Simon Laplace (France) IPSL-CM4.0 3.75 o x 2.5 o 1.25 Canadian Centre for Climate Modeling & Analysis Canada) CGCM3.1(T47) 3.8 o x 3.8 o n/a IR FD bl e M RI _m ed er s s L IP S _h ire ns em O C 2. 1 _E R IS S O C G L 2. 0 3. 1 _A O M IS S IR ul tim od el E M M G G L M FD G C G C BS ag e O G Streamflow (mm) Simulation of Streamflow by 9 Global Models and Model Ensemble 800 700 600 500 400 300 200 100 0 P-values of T-test of individual GCM/SWAT streamflow and pooled GCM/SWAT streamflow (labeled as GCM POOL) compared to OBS/SWAT GCMs P-value GFDL-CM 2.0 4.8303E-17 GFDL-CM 2.1 3.3774E-5 MIROC3.2(medres) 4.1050E-5 MIROC3.2(hires) MRI 0.8312 0.3963E-8 GISS-AOM 0.0098 GISS-ER 0.0124 IPSL-CM4.0 0.0050 CGCM3.1(T47) 0.0229 GCM POOL 0.5979 Hypothesis: Simple linear interpolation of global climate model results as input to SWAT is incapable of reproducing historical (observed) hydrological conditions in the Upper Mississippi River Basin Results: Hypothesis is true for individual models (except MIROC-hires) Hypothesis is false for MIROC-hires Hypothesis is false for the ensemble of GCMs Hydrological components simulated by SWAT. Hydrological components OBS/ SWAT (19681997) HadCM2/ Measured RegCM2 Data ~1990 GFDLCM 2.0 GFDLCM 2.1 MIROC MIROC 3.2 3.2 (hires) (medres) MRI GISS_AO GISS-ER M IPSL-CM 4.0 CGCM 3.1 Precipitation 846 846 900 1032 910 736 821 707 746 746 793 859 Snowfall 118 - 244 213 196 110 104 134 125 95 202 140 Snowme lt 116 - 241 211 193 107 100 130 120 94 200 138 Surface runoff 100 - 148 215 140 55 75 58 63 51 147 80 Baseflow 181 - 213 330 223 145 213 109 170 182 196 161 Potential ET 967 - 788 759 854 1054 984 1011 744 729 692 970 Evapotranspiration (ET) 557 - 533 484 540 531 527 532 505 506 445 611 Total water yield 275 253 350 531 353 194 279 162 227 227 336 232 113 - 101 110 78 78 88 66 63 56 87 86 84 81 95 108 55 58 69 50 52 53 90 57 Standard Precipitation Deviation of annual Streamf low values Notes: 1. Measured streamf low data is at Grafton, IL (USGS gage # 05587450). 2. All values are average annua l values (in mm) ave raged ove r 1963-2000 (unless otherw is e specifi ed); Years 1961 and 1962 are sim ulated as in iti ali zation period. 3. HadCM2/RegCM2 SWAT sim ulations (Jha et al., 2004) are ave rage ove r 10-yea r period. Table 4.for Results for the multi-model ensemble mean SWAT driv Results the multi-model ensemble mean of of SWAT driven by GCMs and observed meteorological conditions Table 4. Results for the multi-m odel ensemble me an o f SWAT driven by G CMs and fortions sub-periods ofthethe observed meteorological condi for sub -periods of 20C. 20C Hydrological components OBS/ SWAT Measured data Precipitation 846 Snowfall GCM/SWAT MIROC 3.2 (hires) HadCM2/RegCM2 /SWAT Amount % Diff. Mean % diff. Amount % diff. 846 817 -03 821 -03 900 + 06 118 - 147 +25 104 -12 244 +206 Snowme lt 116 - 144 +24 100 -13 241 +208 Surface runoff 100 - 98 -02 75 -25 148 + 48 Baseflow 181 - 192 +06 213 +18 213 + 18 Potential ET 967 - 866 -10 984 +02 788 - 15 ET 557 - 520 -07 527 -05 533 - 04 Total water yield 275 253 282 +11 279 +10 350 + 38 Note: Percent dif ferenc es are calculated from measured data when ava il able and otherwise from result s of SWAT driven by observed meteorology. Dif ferent ave raging periods were used as fo ll ows: OBS/SWAT: 1968-1997; GCM/SWAT: 1963-2000; MIROC3.2 (hires)/SWAT: 1963-2000; and HadCM2/RegCM2/SWAT: 1990-1999. Result s in the last two columns are from Jha et al. (2004). Global models used in the SWAT-UMRB simulations (2082-2099) Institution Model Name Lon x Lat Resolution W/m2 Cl. Sens NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.0 2.5 o x 2.0 o 2.9 Center for Climate System Research (Japan) MIROC3.2(medr es) 2.8 o x 2.8 o 1.3 Center for Climate System Research (Japan) MIROC3.2(hires) 1.125 o x 1.125o 1.4 Meteorological Research Institute (Japan) MRI 2.8 o x 2.8 o 0.86 NASA Goddard Institute for Space Studies (USA) GISS-AOM 4o x 3o 2.6 NASA Goddard Institute for Space Studies (USA) GISS-ER 5o x 4o 2.7 Institut Pierre Simon Laplace (France) IPSL-CM4.0 3.75 o x 2.5 o 1.25 Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%) Change (%) Precipitation GFDL 2.0 Hydrologic Component/ Model Change (%) Snowfall 22 1 GISS AOM -12 17 GISS AOM GISS ER -12 25 GISS ER IPSL -6 0 MIROC-hi -3 -4 MIROC-med -13 -12 MRI -16 16 -6 6 Mean Bias(%) GFDL 2.0 81 -32 6 -22 -19 3 71 -43 -12 -80 MIROC-med -7 -65 MRI 13 -18 Mean 19 -37 IPSL MIROC-hi Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%) Change (%) Hydrologic Component/ Model Snowmelt GFDL 2.0 Bias(%) Change (%) Runoff 83 -32 GFDL 2.0 155 -30 5 -20 GISS AOM -24 -2 -19 5 GISS ER -39 32 73 -43 IPSL 73 -31 -12 -79 MIROC-hi -9 -38 MIROC-med -6 -65 MIROC-med -30 -63 MRI 13 -17 MRI -21 -7 Mean 20 -36 Mean 15 -20 GISS AOM GISS ER IPSL MIROC-hi Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%) Change (%) Baseflow GFDL 2.0 Hydrologic Component/ Model Bias(%) Change (%) Potential ET 176 4 GFDL 2.0 -54 45 GISS AOM 50 43 GISS AOM -42 5 GISS ER 76 45 GISS ER -49 5 IPSL 22 -5 IPSL -34 46 MIROC-hi 63 -12 MIROC-hi -24 37 MIROC-med 27 -32 MIROC-med -29 32 MRI 11 38 MRI -34 14 Mean 61 12 Mean -38 27 Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%) Change (%) ET Hydrologic Component/ Model Bias(%) Change (%) Total Water GFDL 2.0 -37 16 GISS AOM -26 7 GISS ER -30 IPSL GFDL 2.0 154 -8 GISS AOM 16 33 12 GISS ER 27 43 -25 12 IPSL 33 -17 MIROC-hi -18 6 MIROC-hi 29 -18 MIROC-med -20 3 MIROC-med 0 -40 MRI -22 12 MRI -7 25 Mean -25 10 Mean 36 3 Preliminary Interpretation Models consistently under-estimate ET and PET (likely due to coarse resolution) Low ET forces more water to baseflow High baseflow increases total water yield Hence I assert that low-resolution models over-predict streamflow because they are incapable of resolving high daily max temps that have a disproportionate influence on ET Current Work Look at more global models Look at ensembles of individual models Look at the low, medium, and highresolution results for MIROC Extend SWAT to better simulate subsurface effects of riparian buffer strips (Mahesh Sahu) Application of SWAT model to simulate riparian buffer zone Mahesh Sahu, Graduate Research Assistant CCEE Present Scheme in Swat for riparian buffer simulation Crop Crop Buffer Strip Buffer Strip River River Hill Slope scheme Conventional SWAT: Present Mahesh Sahu, Graduate Research Assistant CCEE Flows simulated in SWAT Existing Hill slope option & its limitations Q surface Q lateral Q GW The existing hill slope option has the capability to incorporate the surface flow from the crop area through the buffer zone area. Lateral and groundwater flow links are NOT present in the existing hill slope scheme. Mahesh Sahu, Graduate Research Assistant CCEE Future Directions Couple GCM, RCM, SWAT, Crop Model and Economic Model Evaluate policy alternatives: Impact of introducing conservation practices Impact of introducing incentives Hypothesis: It is possible to balance profitability with sustainability in an intensively managed agricultural area under changing climate through development of robust policy GCM OBS NNR RCM Climate Over UMRB Crop Model Crop Yield Soil Drainage Land-use SWAT Management Choices Economic Model OBS Stream flow Soil Carbon Crop Production Water Quality Evaluate Sustainability and Profitability Incentives Public Policy Summary Changes to the hydrological cycle associated with climate change are of high societal importance Dynamical downscaling of global model results by a regional model gives 20% increase in precipitation in the basin and 50% increase in streamflow Summary Linear interpolations of individual low-resolution GCMs are incapable of simulating historical streamflows in the UMRB Linear interpolation of a high-resolution global model is capable of simulating historical streamflows in the UMRB An ensemble of linear interpolations of individual low-resolution GCMs is capable of simulating historical streamflows in the UMRB