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Benchmarking NASA Snow Research Results in NWS Hydrological Decision Support
Investigators: Jagadish Shukla (PI, 1), P. Houser (2), D. Cline (3), L. Rundquist (3)
1- George Mason University (GMU)
2- GMU, Center for Research on Environment and Water (CREW)
3- NOAA's National Operational Hydrologic Remote Sensing Center
4- NOAA's NWS Alaska-Pacific River Forecast Center
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Abstract
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Terrestrial snow processes are important for energy management, water management, and
disaster management applications of national priority because of the timing of snowmelt
and the subsequent fate of melted water play an extremely important role in the
hydrological response of catchments.
In the global water cycle, terrestrial snow is a dynamic fresh-water reservoir that
stores precipitation and delays runoff. On average, over 60% of the northern hemisphere
land surface has snow cover in midwinter, and over 30% of Earth's total land surface
has seasonal snow (Robinson et al., 1993). NOAA has operational decision support tools (DST)
in place to provide snow information to a wide variety of end-users and applications.
NASA has conducted considerable research and developed advanced measurement and modeling
tools to improve snow information. These tools are sufficiently mature to transfer them
to NOAA's primary snow decision support framework, SNODAS.
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NASA Products
MODIS, AMSR-E, Gravity Recovery and Climate Experiment (GRACE),
Model - NASA's Land Information System (LIS)
Project Partners
NOAA
Decision Support Tools
NOAA's operational Snow Data Assimilation System
( SNODAS ) is a major part of the
National Operational Hydrologic Remote Sensing Center .
SNODAS is a critically important Decision Support Tool (DST) which is widely used
to make operational decisions on agricultural production, water resource management,
flood, drought, weather and climate prediction, hazard mitigation and mobility assessment.
This crosscutting Integrated Systems Solutions (ISS) project will therefore
transfer, demonstrate and enable the use of snow-related NASA observational and modeling
research results in NOAA's SNODAS to improve
water management, disaster management and agricultural efficiency decision support.
Reports
