Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping

Abstract In the Western United States, water supply forecasting has traditionally relied on snow water equivalent measurements at ground-based stations due to their strong correlations with streamflow volume during spring and summer. However, stations are sparse and sample a small area, prompting interest in spatially complete – but costly – basin-wide mapping from airborne surveys or future satellite missions. Here we show that adding strategic measurements at snow hotspots – localized areas with untapped information for predicting streamflow – consistently outperforms spatially complete surveys that provide basin-average snowpack, both in basins with and without existing stations. While both improve forecast skill, hotspot monitoring increases correlations with streamflow volume by 11-14% (median) across 390 basins, compared to 4% from basin-wide surveys. These findings hold across snowpack datasets, skill metrics, and statistical models. The greatest gains in water supply prediction come from leveraging existing stations and expanding snow measurements to the right places, rather than everywhere.