Estimating Snow Water Equivalent from the Montana Mesonet

As the severity and frequency of drought increases, it has become increasingly important to monitor factors that drive watersheds. In the Rocky Mountain West, snowpack is an important hydrologic input that feeds water supply and agriculture downstream. A greater understanding of snowpack distribution throughout headwater states, such as Montana, allows informed management of irrigation, drought, and flooding. The Montana Mesonet, which is currently comprised of 178 stations across the State of Montana, is one tool that allows the effective monitoring of meteorological and hydrological variables across the State. Current Montana Mesonet stations are only equipped with snow depth sensors. This limits the understanding of snowpack because freeze-thaw cycles allow for a relatively consistent snow water equivalent (SWE), while snow depth decreases through compression. Using data from SNOTEL Sites across Montana, we developed multiple generalized linear models (GLMs) to predict snow water equivalent based on hydrologic variables. We tested combinations of snow depth, cumulative precipitation, temperature, and soil moisture as independent variables in the models. All independent variables used are also variables recorded by Montana Mesonet sites. We then underwent a process of model selection. With the selected model, we found that it had a very strong predictive ability by using just snow depth as the independent variable. We then applied the selected model to Montana Mesonet sites, calculating the snow water equivalent at each.