Evaluation of Noah-MP snow simulation across site conditions in the Western US

Quantifying spatio-temporal variability in snow water resources is a challenge especially relevant in regions that rely on snowmelt for water supply. Model accuracy is often limited by uncertainties in meteorological forcings and/or suboptimal physics representation. In this study, we evaluate the performance and sensitivity of Noah-MP snow simulations from ten model configurations across 199 sites in the Western US. Nine experiments are constrained by observed meteorology to test snow-related physics options, and the tenth tests an alternative source of meteorological forcings. We find that the base case, which aligns with the National Water Model configuration and uses observations-based forcings, overestimates observed accumulated SWE at 90% of stations by a median of 9.6%. The model performs better in the accumulation season at colder, drier sites and in the melt season at wetter, warmer sites. Accumulation metrics are sensitive to model configuration in two experiments, and melt metrics in six. Alterations to model physics cause changes to median accumulation metrics from −13% to 2.3% with the greatest change due to precipitation partitioning; and to melt metrics from −10% to 3% with the greatest change due to surface resistance configuration. The experiment with alternative forcings causes even greater and wider-ranging changes (medians ranging −29% to 6%). Not all stations share the same best-performing model configuration. At most stations, the base case is outperformed by four alternative physics options which also significantly impact snow simulation. This research provides insights into the performance and sensitivity of snow predictions across site conditions and model configurations.