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Water & Energy Cycles

An accurate product of snow water equivalent (SWE) at a regional scale is critical for agriculture, water resources management, hydrologic forecasting, weather forecasting, and seasonal climate prediction. Unfortunately, such product does not exist. AMSR-E derived SWE is available but it may have significant errors in certain environments, such as boreal forest regions, where snowpack is deep or wet, or when the grain size changes significantly. We will develop a new SWE product from remote sensing (GRACE and MODIS) in conjunction with land surface modeling and data assimilation. We will then compare such product with in situ data from various sources. Our SWE retrieval algorithm will consist of two steps. The first step is primarily involved with GRACE measurements of terrestrial storage changes (the monthly product and the Level 1B data) and the simulations using the Community Land Model (CLM). In the second step we will perform multi-sensors data assimilation with ensemble Kalman filtering and smoothing, which optimally combine GRACE, MODIS and CLM simulations according to their uncertainties. The products from the first and second steps will be compared and evaluated in terms of their relative accuracy. The proposed research activities are directly relevant to the NASA Terrestrial Hydrology Program. Our work will lead to improved understanding and modeling of SWE, snow cover, and river discharges in the cold regions. The proposed integration of GRACE, MODIS, GLDAS, and in-situ data with land-surface modeling will be of great benefit to NASA and the broad scientific community.

Project PI: Zong-Liang Yang/University of Texas at Austin

The University of Texas at Austin Department of Geological Sciences 1 University Station C1100 Austin, TX 78712

Phone: (512) 471-3824

Fax: (512) 471-9425

Email: liang@mail.utexas.edu

http://jackson-d-1.geo.utexas.edu/researcher.php?researcher_id=1039

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Started: Aug 10, 2010

Last Activity: Dec 16, 2010

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