Investigating the Response of an Operational Snowmelt Model to Unusual Snow Conditions and Melt Drivers

In the mountainous western United States, operational runoff forecasting centers typically use streamflow observations to calibrate coupled snow-hydrology models over multi-decadal periods.  When seasonal or annual snow conditions deviate from mean historic conditions, such as with extreme events (e.g., snow drought) or hydrologic disturbances (e.g., dust-on-snow), the accuracy of operational models can degrade.  Recently, streamflow forecasting errors in the Upper Colorado River Basin (UCRB) have been linked to interannual variations in dust radiative forcing, as retrieved from remote sensing.  Here we build off previous work in the UCRB to quantify spatial and temporal variations in optimal operational snow model parameters and evaluate linkages with interannual variations in snow accumulation (e.g., low vs. high snow years), spring snowstorm regime, spring temperature, and dust radiative forcing. Using 30 years of snow pillow data and corrected air temperature data at 110 stations in the Colorado-Utah-Wyoming domain, we derive optimal snow model parameters with independent calibrations at each site and year. Results show coherent spatial and temporal patterns in the model melt factors that are most strongly linked to peak snow accumulation and dust-on-snow.  These results have implications for operational forecasting, which uses fixed snow model parameters, irrespective of normality in snow conditions.