Can better representation of low-elevation snowpack improve operational forecasts?
To what extent do low-elevation snowpack contribute to streamflow forecast errors is current forecast models? What improvement in forecast skill can be gained by changing the spatial configuration of forecast models including improvements to their representation of low-elevation snow and to reservoir inflows? What improvement in forecast skill can be gained by incorporating in remotely-sensed and/or ground-based snow products into forecast models?
Need and Benefit
The Great Plains Region includes a range of reservoirs that receive at least some inflow from low-elevation snowpack.
The inability to accurately forecast inflows has been attributed to a lack of accurate representation of low-elevation
snowpack and snowmelt in current forecast models, and present challenges to water management. The lack of
forecast skill can result in flooding events, including the Spring 2018 flooding on the Milk River in northern Montana.
Improved forecasts would provide operators with the ability to mitigate these events and improve water management.
This work will directly aid water management in the Great Plains Region, consistent with the Regional Director's
identified need. Data, tools, and approaches could be used to address similar challenges identified in other
Reclamation Regions (e.g. MP and PN) as well as address needs identified by Science and Technology Program's
Water Operations and Planning Research Area, including incorporating remotely sensed snow (Need #1) developing
tools (ASSET) to decrease the latency between data availability and use (Need #2) and generally improving forecast
Contact the Principal Investigator for information about partners.
Bureau of Reclamation Review
The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.
Better Representation of Low Elevation Snowpack to Improve Operational Forecasts (final, PDF, 3.2MB)
By Daniel P. Broman, Andrew W. Wood
Report completed on April 29, 2022