ABSTRACT:Ten pairs of snow sensors were analyzed to investigate the feasibility of predicting snow water equivalent at high‐elevation, telemetered snow sensor sites from lower‐elevation sensors. The need for this analysis stems from an agreement between the California Department of Water Resources and the USDA Forest Service to temporarily allow snow sensors in California's wilderness areas so that a predictive relationship can be developed. After 10 or 15 years, the agreement calls for the sensors to be removed. Initial efforts to aprioriselect sensor pairs were based on proximity, colocation within a basin, and annual precipitation amount, but regression yielded poor fits (R2<0.65) and high standard errors in eight of the ten cases. Analysis of the results suggested that eleva‐tional similarity was the most important selection criteria, and that all available sensors near the target site should be analyzed via a regression screening. Using elevation for selection and the regression screening, five sensors that initially had poor fits were reanalyzed. Each of the five sensors was paired with between two and five new sensors, and R2values improved between 27 and 46 percent. Various data smoothing and editing algorithms were evaluated, but they rarely resulted in improved