Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System - Sean W. Fleming; Karl Rittger; Catalina M. Oaida Taglialatela; Indrani Graczyk (Water Resources Research)
Abstract Seasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA‐enabled accuracy gains are most consistent and explainable for short‐lead, late‐season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring‐summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late‐season snowpack. fSCA also improved accuracy for long‐lead, early‐season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring‐summer snowmelt. The AI‐based hydrologic model generally outperformed the statistical model and, in some cases, better‐capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data‐driven hydrologic models using genetic algorithm‐based predictor selection could help guide new SNOTEL site selection.