Characterization of Snowpack and Snowmelt Runoff in High-Elevation Remote Basins: Improving the Characterization of California’s Snowpack for Water and Energy Resource Management
May 25, 2023
Energy Research and Development (500)
Electric Program Investment Charge - EPIC
Steven A. Margulis
The Sierra Nevada in California provides not only most of the state’s water supply but also a significant portion of its energy supply via hydroelectric power. Existing hydropower systems are optimized for historical runoff patterns that are changing under long-term climate warming. Snow-dominated basins are particularly susceptible to changes in runoff regime (more rainfall versus less snowfall and earlier snowmelt). These effects have the potential to drastically change the hydrograph characteristics in river basins that supply hydropower. This project focused on developing an improved characterization of snow-dominated basins that contribute to water and hydropower supply. The primary objective was to understand the accumulation and melt of snow in these watersheds and how they contribute to runoff by developing a historical retrospective database (that is, snow reanalysis) over the Landsat remote sensing record (1985 – present). A “snow reanalysis” framework was used to characterize the climatology and variability of snow water resources over the study domain and the remote sensing record. The new database was then used as a mechanism to test new frameworks for predicting streamflow from these watersheds, assess climate models that are used for forecasting snowpack water resources, assess how runoff from these watersheds may evolve under climate change, and develop and test a new real-time algorithm for estimating snow accumulation and melt in these remote basins from newly available remote sensing products. The project results provide a new database for public use and indicate the potential for new tools to improve snow-derived streamflow forecasting from Sierra Nevada watersheds at a variety of lead times. Implementing such frameworks will have direct economic benefits by allowing for improvements in streamflow predictions and hydroelectric power forecasts and management.