The effects of downscaling method on the variability of simulated watershed response to climate change in five U.S. basins.

Abstract

Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output to develop finer-resolution information for use in water resources impacts assessments. Downscaling, however, can also introduce its own uncertainties into water resources impacts assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). We also assess the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and non-downscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impacts assessments for water - the selection of downscaled climate change information.