Paper
2 February 2004 Terrestrial water and energy systems for water resource applications
David L. Toll, Kristi Arsenault, Paul Houser, Jared Entin, Brian Cosgrove, Christa Peters-Lidard, Matthew Rodell
Author Affiliations +
Abstract
NASA/GSFC has developed with other groups a Land Data Assimilation System (LDAS) to output water and energy budgets for the primary purpose of improving weather and climate prediction. However, LDAS water and energy cycle outputs also may be coupled with other information to help with a wide range of water resources applications. For example, LDAS results may be used for water availability and quality, agricultural management and forecasting, assessment and prediction of snowmelt runoff, and flood and drought impact and prediction. Specifically, LDAS uses various satellites and ground based observations within a land surface modeling and data assimilation framework to produce optimal output fields of terrestrial energy, water and carbon fluxes. Current land surface outputs are gridded at 1/4° resolution globally and 1/8° for North America with work in progress to convert to a 1-km global grid. Integrated modeling, observations and data assimilation at various spatial and temporal scales helps LDAS to quantify terrestrial water, energy and biogeochemical processes. LDAS applications described in this paper are aimed at improving weather and seasonal forecasts. In addition, we also summarize the use of LDAS data to assist critical needs specified by the U.S. Bureau of Reclamation water resources management for selected basins in the western U.S.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David L. Toll, Kristi Arsenault, Paul Houser, Jared Entin, Brian Cosgrove, Christa Peters-Lidard, and Matthew Rodell "Terrestrial water and energy systems for water resource applications", Proc. SPIE 5234, Sensors, Systems, and Next-Generation Satellites VII, (2 February 2004); https://doi.org/10.1117/12.514325
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KEYWORDS
Data modeling

Soil science

Systems modeling

Atmospheric modeling

Climatology

Solar radiation models

Satellites

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