Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent

Abstract Effective water resources management in California relies substantially on real-time information of snow water equivalent (SWE) at basin and mountain range scales as mountain snowpacks provide the primary water supply for the State. However, SWE estimation based solely on remote sensing, modeling, or ground observations alone does not meet contemporary operational requirements. This study develops a statistically-based data-fusion framework to estimate SWE in real-time, which combines multi-source datasets including satellite-observed daily mean fractional snow-covered area (DMFSCA), snow pillow SWE measurements, physiographic data, and historical SWE patterns into a linear regression model (LRM). We test two LRMs: a baseline regression model (LRM-baseline) that uses physiographic data and historical SWE patterns as independent variables, and an FSCA-informed regression model (LRM-FSCA) that includes the DMFSCA from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery as an additional independent variable. The performance of the model is comprehensively evaluated and compared with two operational models – the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). By incorporating the satellite-observed DMFSCA, LRM-FSCA outperforms LRM-baseline with increased median R2 from 0.54 to 0.60, and reduced median PBIAS of basin average SWE from 2.6% to 2.2% in the snow pillow cross-validation. LRM-FSCA explains 87% of the variance in the snow course SWE measurements with 0.1% PBIAS, while LRM-baseline explains a lower 81% variance with 1.4% PBIAS, both of which show higher accuracy than SNODAS (73% and -2.4%, respectively) and NWM-SWE (75% and -15.9%, respectively). Additionally, LRM-FSCA explains 85% of the median variance in the Airborne Snow Observatory SWE with -9.2% PBIAS, which is comparable to the LRM-baseline (86% and -11.3%, respectively) and substantially better than SNODAS (64% and 28.2%, respectively) and NWM-SWE (33% and -30.1% respectively). This study shows a substantial model improvement by constraining the geographical and seasonal variation on snow-cover via satellite observation and highlights the values of using multi-source observations in real-time SWE estimation. The developed SWE estimation framework has crucial implications for effective water supply forecasting and management in California, where climate extremes (e.g., droughts and floods) require particularly skillful monitoring practices.