Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)
Abstract
Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate 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 generating 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 commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling-based diagnostic comparison of the quality of streamflow predictions using over 20 error measures. We contribute a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially 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 the water balance. We also share the datasets which can guide parameter uncertainty characterization (UC) of CLM5 and aid in model calibration for a wide range of hydrologic applications including droughts and floods.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGC42M0871Y