Regional regression models for hydro‐climate change impact assessment |
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| Authors: | Rabi Gyawali Veronica W Griffis David W Watkins Neil M Fennessey |
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| Institution: | 1. Wisconsin Department of Natural Resources (WI‐DNR), Madison, WI, USA;2. Civil and Environmental Engineering, Michigan Technological University, Houghton, MI, USA;3. Civil and Environmental Engineering, University of Massachusetts–Dartmouth, Dartmouth, MA, USA |
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| Abstract: | Hydro‐climatic impacts in water resources systems are typically assessed by forcing a hydrologic model with outputs from general circulation models (GCMs) or regional climate models. The challenges of this approach include maintaining a consistent energy budget between climate and hydrologic models and also properly calibrating and verifying the hydrologic models. Subjective choices of loss, flow routing, snowmelt and evapotranspiration computation methods also increase watershed modelling uncertainty and thus complicate impact assessment. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to predict selected streamflow quantiles directly from meteorological variable output from climate models using regional regression models that also include physical basin characteristics. In this study, regional regression models are developed for the western Great Lakes states using ordinary least squares and weighted least squares techniques applied to selected Great Lakes watersheds. Model inputs include readily available downscaled GCM outputs from the Coupled Model Intercomparison Project Phase 3. The model results provide insights to potential model weaknesses, including comparatively low runoff predictions from continuous simulation models that estimate potential evapotranspiration using temperature proxy information and comparatively high runoff projections from regression models that do not include temperature as an explanatory variable. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | climate change regional regression models hydrologic models hydro‐climatic framework Great Lakes basin PET |
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