MODELING CLIMATE CHANGE IMPACTS ON WATER BALANCE COMPONENTS OF THE MACKINAW RIVER WATERSHED, CENTRAL ILLINOIS

Understanding the response of water cycle dynamics to climate change and human activity is essential for best management of water resources. This study used the USDA Soil-Water Assessment Tool (SWAT) to measure and predict major water balance variables including stream discharge, potential aquifer recharge, and surface storage in a small-scale watershed (~2,930 km²) in Central Illinois. The watershed is predominantly tile-drained agricultural land, which controls the nutrient dynamics and hydrology. Two reservoirs, Evergreen Lake and Lake Bloomington, and the Mahomet Aquifer in the watershed are used for public water supply. The subject watershed has been very sensitive to recent droughts, such that an interim water supply plan has been developed for water management. To assess how the watershed dynamics are affected by future climate change, this study used high-resolution climate projection data (~12 km) in a calibrated and validated SWAT hydrologic model. Using an ensemble of General Circulation Models (GCMs), as well as the GFDL ESM2M and CCSM4.0 individually, four (4) representative concentration pathways (RCPs) developed by the IPCC Coupled Model Intercomparison Project Fifth Assessment Report (CMIP5) were used for prediction of precipitation and temperature for the watershed. Precipitation and temperature are predicted to increase by mid-century for all scenarios. Ensemble, GFDL ESM2M, and CCSM4.0 GCM simulations arrive at similar conclusions for each RCP, and predict an amplification of current watershed dynamics. Periods of drought and flooding are predicted by the models. Results indicate continued nutrient loading of the surficial reservoirs that are used for public water supply and recreation. Nutrient management measures will need to remain in place and be enhanced. This study involving a small-scale watershed can be used to further project behavior of larger watersheds, such as the Illinois River and ultimately the Mississippi River, using similar methods and high-resolution data.