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CLIMATE CHANGE IMPACTS ON THE HYDROLOGY OF THE UPPER MISSISSIPPI RIVER BASIN AS DETERMINED BY AN ENSEMBLE OF GCMS Eugene S. Takle1, Manoj Jha,1 Christopher J. Anderson2, and Philip W. Gassman1 1Iowa State University, Ames, IA 2NOAA Earth System Research Laboratory Global Systems Division Forecast Research Branch, NOAA/ESRL/GSD/FRB, Boulder, CO [email protected] Research Question Previous research has shown An acceleration of the hydrological cycle Increased occurrence of extreme precipitation events in the US Midwest The Mississippi River is vital to the health and economy of the Midwest. How will streamflow and hydrologic components in the Upper Mississippi River Basin change in the future? Sub-Basins of the Upper Mississippi River Basin 119 sub-basins 474 hydrological response units Outflow measured at Grafton, IL Approximately one observing station 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 management on yields of water, sediment, and agricultural chemicals Physically based, including hydrology, soil temperature, plant growth, nutrients, pesticides and land management Simulation of 20th C Streamflow Period 1961-2000 (Streamflow observations are available) Use 9 GCMs from the IPCC AR4 Data Archive We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy Table 1. Global models used in the SWAT-UMRB sim ulations. Institution Model Name Lon x Lat Resolution W/m2 Cl. Sens NOAA Geophysical Fluid Dynamics La boratory (USA) GFDL-CM 2.0 2.5 o x 2.0 o 2.9 NOAA Geophysical Fluid Dynamics La boratory (USA) GFDL-CM 2.1 2.5 o x 2.0 o 2.0 Center f or Climate System Research (Japan) MIROC3.2(medres) 2.8 o x 2.8 o 1.3 Center f or 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 Canadian Centre for Climate Modeling & Analysis Canada) CGCM3.1(T47) 3.8 o x 3.8 o n/a UMR Streamflow Measured at Grafton (Gage) and Simulated with Observed Precipitation (Obs) and Precipitation Generated by GCMs 800 700 500 400 300 200 100 _m O C IR O C IR M RI M ed er s _h ire s L IP S IS S_ E_ R G IS S G 2. 1 G FD L 2. 0 FD G M G C C L 3. 0 BS O ag e 0 G Streamflow (mm) 600 UMR Streamflow Measured at Grafton (Gage) and Simulated with Observed Precipitation (Obs) and Precipitation Generated by GCMs 800 700 500 400 300 200 100 _m O C IR O C IR M RI M ed er s _h ire s L IP S IS S_ E_ R G IS S G 2. 1 G FD L 2. 0 FD G M G C C L 3. 0 BS O ag e 0 G Streamflow (mm) 600 UMR Streamflow Measured at Grafton (Gage) and Simulated with Observed Precipitation (Obs) and Precipitation Generated by GCMs 800 700 500 400 300 200 100 _m O C IR O C IR M RI M ed er s _h ire s L IP S IS S_ E_ R G IS S G 2. 1 G FD L 2. 0 FD G M G C C L 3. 0 BS O ag e 0 G Streamflow (mm) 600 UMR Streamflow Measured at Grafton (Gage) and Simulated with Observed Precipitation (Obs) and Precipitation Generated by GCMs 800 700 Model Mean 500 400 300 200 100 _m O C IR O C IR M RI M ed er s _h ire s L IP S IS S_ E_ R G IS S G 2. 1 G FD L 2. 0 FD G M G C C L 3. 0 BS O ag e 0 G Streamflow (mm) 600 Table 2. P-values of T-test of individua l GCM/SWAT s treamflow and poo led GCM/SWAT streamfl ow (labeled as GCM POOL) compared to OBS/SWAT. GCMs GFDL-CM 2.0 GFDL-CM 2.1 MIROC3.2(medres) MIROC3.2(hires) MRI GISS-AOM GISS-ER IPSL-CM4.0 CGCM3.1(T47) GCM POOL P-value 4.8303E-17 3.3774E-5 4.1050E-5 0.8312 0.3963E-8 0.0098 0.0124 0.0050 0.0229 0.5979 Table 2. P-values of T-test of individua l GCM/SWAT s treamflow and poo led GCM/SWAT streamfl ow (labeled as GCM POOL) compared to OBS/SWAT. GCMs GFDL-CM 2.0 GFDL-CM 2.1 MIROC3.2(medres) MIROC3.2(hires) MRI GISS-AOM GISS-ER IPSL-CM4.0 CGCM3.1(T47) GCM POOL P-value 4.8303E-17 3.3774E-5 4.1050E-5 0.8312 0.3963E-8 0.0098 0.0124 0.0050 0.0229 0.5979 Table 2. P-values of T-test of individua l GCM/SWAT s treamflow and poo led GCM/SWAT streamfl ow (labeled as GCM POOL) compared to OBS/SWAT. GCMs GFDL-CM 2.0 GFDL-CM 2.1 MIROC3.2(medres) MIROC3.2(hires) MRI GISS-AOM GISS-ER IPSL-CM4.0 CGCM3.1(T47) GCM POOL P-value 4.8303E-17 3.3774E-5 4.1050E-5 0.8312 0.3963E-8 0.0098 0.0124 0.0050 0.0229 0.5979 Results of Statistical Analysis all GCMs are serially uncorrelated at all lags and form unimodal distributions the data may be modeled as independent samples from an identical normal distribution rather than as time series all pair-wise comparisons except MIROC3.2(hires)/SWAT could be rejected at the 2% or higher level The T-test for the MIROC3.2(hires) had a p-value of 0.8312, p-value for MIROC3.2(medres) was 4.1x105 Conclusion: high resolution improves simulation of UMRB streamflow Ensemble of GCMs Individual time series of GCM/SWAT annual streamflow are uncorrelated to one another We may hypothesize that there is a population from which all GCM/SWAT results represent independent samples Test of the hypothesis of zero difference between mean annual streamflow of the pooled GCM/SWAT and OBS/SWAT results gives a p-value of 0.5979 Conclusion: use of GCM output to form an ensemble of streamflow results may provide a valid approach for assessing annual streamflow in the UMRB Hydrological Components Simulated by SWAT Table 3. Hyd rological componen ts sim ulated by SWAT. Hydrological components OBS (19681997) HadCM2/ Measured RegCM2 Data ~1990 GFDLCM 2.0 GFDLCM 2.1 MIROC MIROC 3.2 3.2 (hires) (medres) MRI GISSAOM GISS-ER IPSL-CM 4.0 CGCM 3.0 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 Notes: 1. Measured streamf low data is at Grafton, IL (USGS gag e # 05587450) . 2. All values are average annua l values (in mm) ave raged ove r 1963-2000 (unless otherwise specified); Years 1961 and 1962 are sim ulated as in iti ali zation period. 3. HadCM2/RegCM2 SWAT sim ulations are ave rage over 10-yea r period. Takle, E. S., M. Jha, and C. J. Anderson, 2005: Hydrological cycle in the Upper Mississippi River Basin: 20th century simulations by multiple GCMs. Geophys. Res. Lett., 32, L18407 10.1029/2005GL023630 (28 September) Hydrologic Components Simulated by SWAT Driven by Table 4. Results forGCMs the ense mble mean of SWAT dr ivenfor by GCMs and GCM/RCM 20Cand observed meteorological condi tions for the 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. Th e datasets used dif ferent ave raging pe riods as foll ows: OBS/SWAT: 1968-1997; GCM/SWAT: 19632000; MIROC3.2 (hir es)/SWAT: 1963-2000; and HadCM2/RegC M2/SWAT: 1990-1999. Jha, M., Z. Pan, E. S. Takle, R. Gu, 2004:. J. Geophys. Res. 109, D09105, doi:10.1029/2003JD003686 Results for 20C Simulations Use of a GCM drawn at random to drive SWAT could lead to sizable errors in streamflow and hydrological cycle components Use of the meteorological conditions from an ensemble of GCM/SWAT simulations, by contrast, performs quite well The lone high-resolution GCM does as well as the ensemble mean despite large errors in its lowerresolution sister model Global model results downscaled by a regional model (models chosen on the basis of availability) used to drive SWAT are inferior to those resulting from the GCM model mean and the high-resolution GCM Table 2. Model biases and climate change for each hydrological cycle component. Hydrologic Hydrologic Component/ Change Component/ Change Model Bias(%) (%) Model Bias(%) (%) Precipitation GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean Snowfall 22 -12 -12 -6 -3 -13 -16 -6 1 17 25 0 -4 -12 16 6 Snowmelt GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean 83 5 -19 73 -12 -6 13 20 -32 -20 5 -43 -79 -65 -17 -36 -32 -22 3 -43 -80 -65 -18 -37 GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean 155 -24 -39 73 -9 -30 -21 15 -30 -2 32 -31 -38 -63 -7 -20 -54 -42 -49 -34 -24 -29 -34 -38 45 5 5 46 37 32 14 27 154 16 27 33 29 0 -8 33 43 -17 -18 -40 Potential ET 176 50 76 22 63 27 11 61 4 43 45 -5 -12 -32 38 12 ET GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med 81 6 -19 71 -12 -7 13 19 Runoff Baseflow GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med MRI Mean Total Water -37 -26 -30 -25 -18 -20 16 7 12 12 6 3 GFDL 2.0 GISS AOM GISS ER IPSL MIROC-hi MIROC-med See Extended Abstract for summary of hydrologic component biases (20C) and changes for 21st C as simulated by SWAT Biases in Hydrologic Components GCMs underestimate annual precipitation by a modest amount, but overestimate streamflow Most models produce too much snow Models are inconsistent regarding the amount of runoff Baseflow is uniformly high PET and ET are uniformly low by 25 - 38% Total water yield is overestimated by all but one model Deficiency in ET forces a model to partition more soil water input to baseflow, which explains uniformly excessive baseflow and hence streamflow because baseflow is the dominant contributor to total water yield Streamflow is over-predicted in this basin by global models because of failure to resolve daily maximum temperatures in summer due to coarse resolution 21st Century Climate Simulations Results from 7 models were available at the time of analysis GFDL-CM 2.0 MIROC 3.2 (medres) MIROC 3.2 (hires) MRI GISS-AOM BIS_ER IPSL-CM 4.0 Period 2082-2099 Simulated Climate Change Although there is inconsistency among models, the mean precipitation created by the ensemble suggests an increase of 6% due to climate change. Changes in ET and PET are positive for all models, with more uniformity in ET. These changes likely result from temperature increases in the warm season. Substantial decreases in snowfall suggest that warming is strong in winter. Runoff decreases substantially for most models, possibly due to enhanced drying of soils (due to enhance ET) between rains, which then can hold more precipitation when the next event occurs. Total water yield shows wide variance among the models, with the ensemble mean showing almost no change from the contemporary climate. Conclusions Use of a single low-resolution GCM for assessing impact of climate change on hydrology of the UMRB carries the possibility of high bias High-resolution GCM might have substantially reduced biases (except for ET) Ensemble of GCMs reproduces observed 20C streamflow of UMRB quite well GCM/RCM has biases comparable to GCM Simulated climate change includes 6% increase in precipitation, increase in ET, decrease in snowfall, decrease in runoff and essentially no change in streamflow Acknowledgement We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy