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Improving Water Resources Management in the Western U.S. through use of Remote Sensing Data and Seasonal Climate Forecasts
Investigators: Dennis Lettenmaier (PI, U. Washington), S. Soorooshian (U. Cal. Irvine), A. Wood (U. Washington), A. Steinmann (U. Washington), B. Imam (U. Cal. Irvine)
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Abstract
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The purpose of this project is to assess the potential for MODIS and AMSR-E satellite data
with NASA GMAO forecasts to improve the performance of USDA NRCS and Bureau of Reclamation
RiverWare water supply forecast decision support tools to provide improved predictions
of snowmelt runoff processes for reservoir and other water management decisions.
This study uses NASA remote sensing data and hydrologic and climate prediction modeling
in a partnership with three operational water management agencies:
The USDA Natural Resources Conservation Service, which provides seasonal streamflow forecasts over most of the west,
the U.S. Bureau of Reclamation, which has decision authority within the Klamath River basin
(where there have been ongoing and highly publicized conflicts over water allocation),
and the California Department of Water Resources, which has decision authority for much of the Sacramento River basin.
NASA research results of two types will be used in this research.
The first are EOS remote sensing data products of several types, including
MODIS snow cover extent, evapotranspiration (from which crop water use will be estimated),
and reservoir surface temperature (a key variable for estimation of reservoir evaporation),
and AMSR-E snow water equivalent.
A second category will be NASA experimental seasonal climate forecasts produced by the GMAO model,
which are already utilized in the UW west wide seasonal hydrologic forecast system.
The primary outcome will be improved predictions of snowmelt runoff for reservoir
and other water management decisions.
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NASA Products
Terra - MODIS, Aqua - MODIS, AMSR-E
Project Partners
Bureau of Reclamation, California Dept. of Water Resources, NRCS
Decision Support Tools
The University of Washington experimental west-wide seasonal hydrologic forecast system produces 6-12
month lead time hydrologic forecasts at approximately 100 forecast points in five major river basins within the
western U.S. The system is an outgrowth of the North American LDAS (N-LDAS) project, and uses the NLDAS
1/8 degree spatial grid, as well as N-LDAS vegetation, soils, and other data. The system is based on the
University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology
model, driven by climate ensembles downscaled from the NCEP Seasonal Forecast Model (SFM), the NASA
NSIPP1 global model, and an ensemble version of the CPC official seasonal outlooks (12 month lead time). UW
also produces parallel forecasts via the Extended Streamflow Prediction (ESP) method, and a further
conditioning of the ESP ensembles by ENSO and PDO state. The primary forecast products are: 1) monthly
streamflow distributions and runoff volume statistics at the specified forecast points; and 2) west-wide spatial
maps of monthly forecast ensemble averages for runoff, soil moisture, and snow water equivalent (SWE). UW
reports results of initial real-time testing of the system with bi-monthly updates for the Pacific Northwest, and for
a larger expanded domain (most of the U.S. west of the Rocky Mountains).
Reports
