Examining the Relationship between Snow Telemetry and Interactive Multisensor Snow to Predict Spring Streamflow in the Columbia River Basin

In the Northwestern United States, meltwater from snow accumulated on mountains serves as the dominant water supply for many communities. The efficient distribution and use of this renewable, yet temporally and spatially variable resource, relies on the accurate forecasting of Spring streamflow. Here, we examine the utility of adding a specifically defined Snow Telemetry (SNOTEL) variable to already existing satellite-derived snow cover models to predict Spring discharge in the Columbia River Basin. We examined six subbasins of interest in the Columbia River Basin: the Yakima, the Deschutes, the John Day, the Clearwater, the Pend Oreille, and the Kootenai. Within these six subbasins, we propose 144 models; the majority of which contain statistically significant predictive value in forecasting Spring streamflow in the Columbia River Basin. We also discuss remediation for multicollinearity by Principal Component Analysis for the models in which satellite-derived data and snow telemetry data are highly correlated.