For large populations across the western U.S., water supply prediction relies centrally on knowledge of spring snow conditions, where anomalous snowpack can provide critical early warning of drought. Yet, a warmer future portends for reduced snowpacks, presenting a major challenge to the current paradigm of snow-based forecasting. The water management landscape across the west is variable, with some smaller systems relying exclusively on snow information to make local statistical forecasts, while others use operational forecast information from more sophisticated statistical or dynamic forecasts that also leverage snow information. To overcome potential shortcomings from current methods, there is a need to evaluate possible alternatives to snow-based predictions to better inform management and planning for key parts of the western U.S., like the Intermountain West (IMW) and Pacific Northwest (PNW) Drought Early Warning System (DEWS) regions.
This project will develop new techniques for drought prediction that do not rely purely on snow-based methods, harnessing alternative techniques to improve scientists’ ability to predict and respond to drought. A key innovation will be the use of machine learning tools to find ways to improve current and future drought prediction.