1. 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

2. If we had perfect predictability of low-resolution global climate fields, how well can we downscale this predictability to stream flow at one point?

3. Outline  Upper Mississippi River Basin  Soil and Water Assessment Tool (SWAT)  Climate information Observations Observations Contemporary climate Contemporary climate Future climate Future climate  Flow simulations  Water quality  Summary

4. 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

5. 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

6. Calibration of SWAT: Annual Stream Flow at Grafton, IL

7. Calibration of SWAT: Monthly Stream Flow at Grafton, IL

8. Validation of SWAT: Annual Stream Flow at Grafton, IL

9. Validation of SWAT: Monthly Stream Flow at Grafton, IL

10. Downscaling Methods Dynamical downscaling (use GCM to provide b.c. for RCM) Dynamical downscaling (use GCM to provide b.c. for RCM) Statistical or empirical transfer functions to relate local climate to GCM output Statistical or empirical transfer functions to relate local climate to GCM output Climate analog procedures Climate analog procedures Combinations of statistical and dynamical methods Combinations of statistical and dynamical methods

11. 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 2.5 o x 2.5 o global model grid

12. RegCM2 Simulation Domain Red = global model grid point Green/blue = regional model grid points

13. SWAT Output with Various Sources of Climate Input

14. Annual Stream Flow Simulated by SWAT Driven by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions

15. Seasonal Stream Flow Simulated by SWAT Driven by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions

16. Mean Monthly Precipitation Simulated by the RegCM2 Regional Climate Model with NNR Lateral Boundary Conditions

21. RegCM2SWAT Evapotranspiration588528 Surface runoff 151166 Snowmelt256240 Note: All values are in mm per year averaged for 1980-1988 in NNR run. Hydrological component comparison between RegCM2 and SWAT

22. 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

23. 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

24. Hydrologic budget components Calibration (1989- 1997) Validation (1980- 1988) NNR CTL (around 1990s) SNR (around 2040s) % Change (SNR-CTL) Precipitation856846831898108221 Snowfall16910323724929418 Snowmelt1689923024529119 Surface runoff 15112815117826851 GW recharge 15416013417925543 Total water yield 27325725332148150 Potential ET 947977799787778 Actual ET 547541528539566 5 Hydrologic Budget Components Simulated by SWAT under Different Climates All units are mm Yield is sum of surface runoff, lateral flow, and groundwater flow

25. Relation of Runoff to Precipitation for Various Climates

26. Summary of RCM Studies RCM provides meteorological detail needed by SWAT to resolve sub-basin variability of importance to streamflow 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 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 Relationship of streamflow to precipitation might change in future scenario climates

27. Alternative to Dynamical Downscaling Global Model Results Linear interpolation of GCM results Linear interpolation of GCM results Spatial disaggregation Spatial disaggregation Bias corrected spatial disaggregation Bias corrected spatial disaggregation “…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 Note: These methods could be applied to downscaled (RCM) results as well.

28. 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

29. Global models used in the SWAT-UMRB simulations (20C) InstitutionModel NameLon x Lat Resolution W/m2 Cl. Sens NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.02.5 o x 2.0 o 2.9 NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.12.5 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 1.125 o 1.4 Meteorological Research Institute (Japan)MRI2.8 o x 2.8 o 0.86 NASA Goddard Institute for Space Studies (USA) GISS-AOM4 o x 3 o 2.6 NASA Goddard Institute for Space Studies (USA) GISS-ER5 o x 4 o 2.7 Institut Pierre Simon Laplace (France)IPSL-CM4.03.75 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

30. Simulation of Streamflow by 9 Global Models and Model Ensemble

31. P-values of T-test of individual GCM/SWAT streamflow and pooled GCM/SWAT streamflow (labeled as GCM POOL) compared to OBS/SWAT GCMsP-value GFDL-CM 2.04.8303E-17 GFDL-CM 2.13.3774E-5 MIROC3.2(medres)4.1050E-5 MIROC3.2(hires)0.8312 MRI0.3963E-8 GISS-AOM0.0098 GISS-ER0.0124 IPSL-CM4.00.0050 CGCM3.1(T47)0.0229 GCM POOL0.5979

32. 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

33. Hydrological components simulated by SWAT.

34. Table 4. Results for the multi-model ensemble mean of SWAT driven by GCMs and observed meteorological conditions for sub-periods of the 20C. Note: Percent differences are calculated from measured data when available and otherwise from results of SWAT driven by observed meteorology. Different averaging periods were used as follows: OBS/SWAT: 1968-1997; GCM/SWAT: 1963-2000; MIROC3.2 (hires)/SWAT: 1963-2000; and HadCM2/RegCM2/SWAT: 1990-1999. Results in the last two columns are from Jha et al. (2004). Results for the multi-model ensemble mean of SWAT driven by GCMs and observed meteorological conditions for sub-periods of the 20C

35. Global models used in the SWAT-UMRB simulations (2082-2099) InstitutionModel NameLon x Lat Resolution W/m2 Cl. Sens NOAA Geophysical Fluid Dynamics Laboratory (USA) GFDL-CM 2.02.5 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 1.125 o 1.4 Meteorological Research Institute (Japan) MRI2.8 o x 2.8 o 0.86 NASA Goddard Institute for Space Studies (USA) GISS-AOM4 o x 3 o 2.6 NASA Goddard Institute for Space Studies (USA) GISS-ER5 o x 4 o 2.7 Institut Pierre Simon Laplace (France)IPSL-CM4.03.75 o x 2.5 o 1.25

36. Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%)Change (%)Hydrologic Component/ Model Bias(%)Change (%) PrecipitationSnowfall GFDL 2.0221GFDL 2.081-32 GISS AOM-1217GISS AOM6-22 GISS ER-1225GISS ER-193 IPSL-60IPSL71-43 MIROC-hi-3-4MIROC-hi-12-80 MIROC-med-13-12MIROC-med-7-65 MRI-1616MRI13-18 Mean-66Mean19-37

37. Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%)Change (%) Hydrologic Component/ Model Bias(%)Change (%) SnowmeltRunoff GFDL 2.083-32GFDL 2.0155-30 GISS AOM5-20GISS AOM-24-2 GISS ER-195GISS ER-3932 IPSL73-43IPSL73-31 MIROC-hi-12-79MIROC-hi-9-38 MIROC-med-6-65MIROC-med-30-63 MRI13-17MRI-21-7 Mean20-36Mean15-20

38. Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%)Change (%)Hydrologic Component/ Model Bias(%)Change (%) BaseflowPotential ET GFDL 2.01764GFDL 2.0-5445 GISS AOM5043GISS AOM-425 GISS ER7645GISS ER-495 IPSL22-5IPSL-3446 MIROC-hi63-12MIROC-hi-2437 MIROC-med27-32MIROC-med-2932 MRI1138MRI-3414 Mean6112Mean-3827

39. Model biases and climate change for each hydrological cycle component (2082-2099) Hydrologic Component/ Model Bias(%)Change (%)Hydrologic Component/ Model Bias(%)Change (%) ETTotal Water GFDL 2.0-3716GFDL 2.0154-8 GISS AOM-267GISS AOM1633 GISS ER-3012GISS ER2743 IPSL-2512IPSL33-17 MIROC-hi-186MIROC-hi29-18 MIROC-med-203MIROC-med0-40 MRI-2212MRI-725 Mean-2510Mean363

40. Preliminary Interpretation Models consistently under-estimate ET and PET (likely due to coarse resolution) Models consistently under-estimate ET and PET (likely due to coarse resolution) Low ET forces more water to baseflow Low ET forces more water to baseflow High baseflow increases total water yield 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 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

41. Current Work Look at more global models Look at more global models Look at ensembles of individual models Look at ensembles of individual models Look at the low, medium, and high- resolution results for MIROC Look at the low, medium, and high- resolution results for MIROC Extend SWAT to better simulate sub- surface effects of riparian buffer strips (Mahesh Sahu) Extend SWAT to better simulate sub- surface effects of riparian buffer strips (Mahesh Sahu)

42. Application of SWAT model to simulate riparian buffer zone Mahesh Sahu, Graduate Research Assistant CCEE

43. Present Scheme in Swat for riparian buffer simulation CropBuffer Strip River Conventional SWAT: Present Crop Buffer Strip River Hill Slope scheme Mahesh Sahu, Graduate Research Assistant CCEE

44. Flows simulated in SWAT Existing Hill slope option & its limitations Q surface Q lateral Q GW Switch grass Corn 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

45. Future Directions Couple GCM, RCM, SWAT, Crop Model and Economic Model Couple GCM, RCM, SWAT, Crop Model and Economic Model Evaluate policy alternatives: Evaluate policy alternatives: Impact of introducing conservation practices Impact of introducing conservation practices Impact of introducing incentives Impact of introducing incentives Hypothesis: Hypothesis: It is possible to balance profitability with sustainability in an intensively managed agricultural area under changing climate through development of robust policy

46. Water Quality Public Policy Incentives Climate Over UMRB Stream flow Soil Drainage Management Choices Crop Yield Land-use NNR OBS Soil Carbon Evaluate Sustainability and Profitability SWAT RCM Economic Model Crop Model GCM OBS Crop Production

47. Summary Changes to the hydrological cycle associated with climate change are of high societal importance 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 Dynamical downscaling of global model results by a regional model gives 20% increase in precipitation in the basin and 50% increase in streamflow

48. Summary Linear interpolations of individual low-resolution GCMs are incapable of simulating historical streamflows in the UMRB 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 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 An ensemble of linear interpolations of individual low-resolution GCMs is capable of simulating historical streamflows in the UMRB