PNNL

Abstract

Land surface models such as Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of climate and anthropogenic change impacts. However, to date, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have not been explored in detail. The common use of the suggested default hydrologic model parameters in CLM5 risks streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the currently employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). The baseline CLM5 default parameter performance is evaluated relative to a large (1,307) Latin Hypercube Sampling based diagnostic comparison of the quality of stremflow predictions using over 20 error measures. We contribute a global sensitivity analysis that clarifies that the parametric controls for CLM5 streamflow predictions vary significantly spatially across regions, temporal scales, and across error metrics of interest. Baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially in the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions but its impacts vary in complex ways across U.S. regions, timescales of focus, and flow regimes. Overall, CLM5’s surface runoff and soil water parameters have the largest effects on simulated high flows while canopy water and evaporation parameters have the most significant effects on capturing the water balance.