Combining Snow Water Equivalent Data from Multiple Sources to Estimate Spatio-Temporal Trends and Compare Measurement Systems

Owing to the importance of snowfall to water supplies in the western United States, government agencies regularly collect data on snow water equivalent (the amount of water in snow) over this region. Several different measurement systems, of possibly different levels of accuracy and reliability, are in operation: snow courses, snow telemetry, aerial markers, and airborne gamma radiation. Data are available at more than 2,000 distinct sites, dating back a variable number of years (in a few cases to 1910). Historically, these data have been used primarily to generate flood forecasts and short-term (intra-annual) predictions of streamflow and water supply. However, they also have potential for addressing the possible effects of long-term climate change on snowpack accumulations and seasonal water supplies. We present a Bayesian spatio-temporal analysis of the combined snow water equivalent (SWE) data from all four systems that allows for systematic differences in accuracy and reliability. The primary objectives of our analysis are (1) to estimate the long-term temporal trend in SWE over the western U.S. and characterize how this trend varies spatially, with quantifiable estimates of variability, and (2) to investigate whether there are systematic differences in the accuracy and reliability of the four measurement systems. We find substantial evidence of a decreasing temporal trend in SWE in the Pacific Northwest and northern Rockies, but no evidence of a trend in the intermountain region and southern Rockies. Our analysis also indicates that some of the systems differ significantly with respect to their accuracy and reliability.