Evaluating Hydroclimatic Change Signals from Statistically and Dynamically Downscaled GCMs and Hydrologic Models

Abstract

his study analyzed potential hydroclimatic change in the Peace River basin in the province of British Columbia, Canada, based on two structurally different approaches: (i) statistically downscaled global climate models (GCMs) using the bias-corrected spatial disaggregation (BCSD) and (ii) dynamically downscaled GCM with the Canadian Regional Climate Model (CRCM). Additionally, simulated hydrologic changes from the GCM?BCSD-driven Variable Infiltration Capacity (VIC) model were compared to the CRCM integrated Canadian Land Surface Scheme (CLASS) output. The results show good agreements of the GCM?BCSD?VIC simulated precipitation, temperature, and runoff with observations, while the CRCM-simulated results differ substantially from observations. Nevertheless, differences (between the 2050s and 1970s) obtained from the two approaches are qualitatively similar for precipitation and temperature, although they are substantially different for snow water equivalent and runoff. The results obtained from the five Coupled Global Climate Model, version 3, (CGCM3)-driven CRCM runs are similar, suggesting that the multidecadal internal variability is not a large source of uncertainty for the Peace River basin. Overall, the GCM?BCSD?VIC approach, for now, remains the preferred approach for projecting basin-scale future hydrologic changes, provided that it explicitly accounts for the biases and includes plausible snow and runoff parameterizations. However, even with the GCM?BCSD?VIC approach, projections differ considerably depending on which of an ensemble of eight GCMs is used. Such differences reemphasize the uncertain nature of future hydroclimatic projections.