Tangible use of Airborne Snow Observatory SWE Products for enhanced runoff forecasting

Streamflow across the western US is predominately snowmelt driven and as such model SWE representation is a major source of uncertainty in current runoff forecasts. Sparse point measurement networks with daily measurements have been recently supplemented with spatially complete snow depth measurements and SWE estimates from the Airborne Snow Observatory (ASO). The ASO data are currently the state-of-the-art for instantaneous SWE mapping in mountain terrain and are designed specifically for improving runoff estimates. In this study, we evaluated areal mean SWE estimates from the River Forecast Center snow model (SNOW-17) with instantaneous SWE estimates from ASO. The evaluation spanned a 7-year period from 2013 - 2019 and included data from both Colorado and California to elicit regional differences. The evaluation produced relationships between model-derived and observations-derived SWE estimates that can be leveraged to improve the state of snow in the SNOW-17 model in an operational setting. These results demonstrate the value of ASO observations for operational runoff estimations and also provide a broader pathway toward improved runoff forecasts through technological advancements with a greater degree of confidence. In practice, these results provide a tangible opportunity for immediate incorporation of ASO spatial distributions into current River Forecast Center operations.