Integration of Snow Data from Remote Sensing into Operational Streamflow Forecasting in the Western United States

Thursday, 18 December 2014: 11:05 AM
Stacie Bender1, Thomas H Painter2, William P Miller1 and Konstantinos Andreadis2, (1)NOAA Colorado Basin River Forecast Center, Salt Lake City, UT, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Managers of water resources depend on snowmelt-driven runoff for multiple purposes including water supply, irrigation, attainment of environmental goals, and power generation. Emergency managers track flood potential, particularly in years with above-normal snow conditions. The Colorado Basin River Forecast Center (CBRFC) of the National Weather Service issues operational streamflow forecasts in the western United States. Runoff during the critical April through July period is predominantly driven by snowmelt; therefore, the CBRFC and users of its forecasts consider snow observations to be highly valuable.

In CBRFC’s area of responsibility, the density of stations within gauge-based observation networks is not ideal. Snowpack estimates from satellite-borne instruments may aid in filling data gaps where information from point networks is unavailable. CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate remotely-sensed snow data from NASA’s MODIS instrument into CBRFC forecasts. The partnership will enter its third year in 2015 and demonstrates an invaluable collaboration between operational and research scientists.

Research indicates that streamflow prediction errors could be reduced through use of remotely-sensed snow data. In the first two years of collaboration, CBRFC and JPL increased forecaster awareness of snow conditions via the MODIS datasets, which subsequently increased forecaster confidence in manual modifications to snowpack simulations. Indication of the presence or lack of snow by MODIS assisted CBRFC forecasters in determining the cause of divergence between modeled and gauged streamflow. Indication of albedo conditions at the snow surface provided supporting information about the potential for accelerated snowmelt rates. CBRFC and JPL also continued retrospective analysis of relationships between the remotely-sensed snow data and streamflow patterns.

Utilization of remotely-sensed snow data is an important piece of the snowmelt-driven streamflow prediction challenge. The CBRFC-JPL collaboration is expected to continue over the next several years as CBRFC and JPL work together to improve estimates of snowpack conditions used in operational forecasting of snowmelt-driven streamflow.