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Research Project: Response of Ecosystem Services in Agricultural Watersheds to Changes in Water Availability, Land Use, Management, and Climate

Location: Water Management and Systems Research

Title: Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources

item GAO, J - Texas A&M University
item SHESHUKOV, A - Kansas State University
item YEN, H - Texas A&M University
item Mankin, Kyle
item White, Michael
item Arnold, Jeffrey

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2018
Publication Date: 10/23/2018
Citation: Gao, J., Sheshukov, A.Y., Yen, H., Douglas-Mankin, K.R., White, M.J., Arnold, J.G. 2018. Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. Journal of Hydrology. 568:551-561.

Interpretive Summary: Decision makers need to understand how future climate will affect streamflow to help guide water management decisions in the future. Watershed modelers can simulate future streamflow, but to do so, they need good estimates of future climate. Estimates of future rainfall and temperature from global-scale climate models (called general circulation models, GCMs) are too coarse to be directly useful for watershed models. Methods to take coarse GCM data from monthly to daily timeseries and from large grid areas to smaller areas can “downscale” future climate data to a useable, local scale. But these methods add uncertainty to the estimates of future climate and streamflow. This study quantified how much uncertainty was added to streamflow estimates by three types of historical data commonly used to correct and downscale GCM data to a local scale: raingage data (NCDC), fine-gridded radar data (NEXRAD), and fine-gridded modeled data (PRISM). This uncertainty was compared to uncertainty from other sources: from three GCMs and from three future carbon emissions scenarios. We used the SWAT watershed model in a west-central Kansas watershed for this study. We found that the type of data used to downscale the coarse GCM climate data added as much uncertainty to the estimates of streamflow as the differences between GCMs and more uncertainty than the differences between emissions scenarios. Usually modelers use several GCMs and several emissions scenarios but just one downscaling data source. That approach was shown to misrepresent future streamflow. Because we don’t know which GCM or emissions scenario or downscaling dataset gives the “right” future climate, we encourage modelers to use several different downscaling datasets to ensure the model estimates “bracket” the range of possible future streamflow and help decision makers plan for the full range of streamflow possible in the future.

Technical Abstract: Uncertainty in simulating hydrologic response to future climate is generally assumed to result from the combined uncertainties of the General Circulation Model (GCM), representative concentration pathway (RCP), downscaling method, and hydrologic model used. However, another source of uncertainty, the observed climate data source used to statistically downscale and bias-correct GCM projections, has largely been overlooked. This study assessed the shifts, variability, and uncertainty in streamflow simulation from three downscaling data sources (NCDC land-based weather stations, NEXRAD spatial grid, and PRISM spatial grid) relative to those introduced by six GCMs and three RCPs in west-central Kansas, U.S. Streamflow simulated by the Soil and Water Assessment Tool (SWAT) hydrologic model was found to be more sensitive to future precipitation than to maximum and minimum temperatures. The greatest uncertainty in simulated streamflow was associated with selection of the GCM. Uncertainty in simulated streamflow associated with the observed bias-correction data source (NCDC, PRISM, NEXRAD) was greater than with RCPs and was primarily related to uncertainty in precipitation. This study highlighted the importance of recognizing uncertainty from bias-correction data sources in representing future climate scenarios in hydrologic simulations.