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United States Department of Agriculture

Agricultural Research Service


item Crow, Wade
item Bindlish, R.
item Jackson, Thomas

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2005
Publication Date: 9/15/2005
Citation: Crow, W.T., Bindlish, R., Jackson, T.J. 2005. The added value of spaceborne passive microwave retrievals for runoff ratio forecasting. Geophysical Research Letters. (32)L18401, doi: 10.1029/2005GL023543.

Interpretive Summary: Land surface hydrologists will soon have operational access to estimates of global soil moisture derived from sensors aboard earth-orbiting satellites. It is important to carefully assess have valuable such observations are for key hydrologic applications like streamflow forecasting. This long term study (5 years over 26 different hydrologic basins) uses an existing satellite soil moisture data set to examine the marginal improvement in land surface runoff forecasting (above and beyond that is possible using currently available observations) associated with the integration of remotely-sensed soil moisture estimates into a simple hydrologic model. Results demonstrate that satellite-based soil moisture observations will improve forecasters ability to predict land surface response to precipitation.

Technical Abstract: Using existing data sets of passive microwave spaceborne soil moisture retrievals, streamflow, and precipitation for 26 basins in the United States Southern Great Plains, a 5-year analysis is performed to quantify the value of soil moisture retrievals derived from the Tropical Rainfall Mission (TRMM) Microwave Imager (TMI) X-band(10.7 GHz) radiometer for forecasting storm event-scale runoff ratios. The predictive ability of spaceborne soil moisture estimates is objectively compared to that obtainable using only available rainfall observations and the antecedent precipitation index ($API$). The assimilation of spaceborne observations into an $API$ soil moisture proxy is demonstrated to add marginal value to the forecasting of land surface response to precipitation.

Last Modified: 05/27/2017
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