Investigating the Role of Snow Water Equivalent on Streamflow Predictability During Drought

This NIDIS-funded study, led by researchers at the University of Colorado Boulder/Cooperative Institute for Research in Environmental Sciences (CIRES), identified challenges in predicting seasonal water supply during drought years in snow-dominated basins of the western United States due to climate warming.

The researchers proposed novel approaches to improve forecast accuracy through adaptive and dynamic training of the models. They found that training water-supply prediction models on below-median snow years or dynamically selected years based on snow water equivalent (SWE) conditions significantly reduces forecast errors up to 20% during drought years. These methods offer practical advancements for water resource management, to support the reliability of snow-based forecasting approaches and address critical challenges in water supply planning under climate variability.

Read the research article in the Journal of Hydrometeorology, or learn more about this research.