Dynamic precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US

While the importance of dynamic precipitation phase partitioning to get accurate estimates of rain versus snow amounts has been established, hydrology models rely on simplistic static temperature-based partitioning. We evaluate changes in model bias for a suite of snow and streamflow metrics between static and dynamic partitioning. We used the VIC-CropSyst coupled crop hydrology model and performed a comprehensive evaluation using 164 snow telemetry observations across the Pacific Northwest (1997-2015).  We found that transition to the dynamic method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (~50% mean bias reduction), (b) daily SWE in winter months (reduction of relative bias from -30% to -4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for a majority of the observational snow telemetry stations considered (depending on the metric, 75% to 88% of stations showed improvements). We also find improvements in estimates of basin-level streamflow and peak SWE over streamflow. However, there was a degradation in bias for snow-off dates, likely because errors in modeled snowmelt dynamics—which cannot be resolved by changing the precipitation partitioning—become important at the end of the cold season.  These results emphasize that the hydrological modeling community should transition to incorporating dynamic precipitation partitioning so we can better understand model behavior, improve model accuracies, better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.