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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Validating Large Scale Networks Using Temporary Local Scale Networks

Authors
item Cosh, Michael
item Jackson, Thomas
item Schaefer, G - USDA-NRCS-NWCC

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: January 16, 2009
Publication Date: March 3, 2009
Citation: Cosh, M.H., Jackson, T.J., Schaefer, G.L. 2009. Validating large scale networks using temporary local scale networks [abstract]. Workshop on Soil Moisture and Soil Temperature Monitoring in the U.S. Climate Reference Network. 2009 CDROM.

Technical Abstract: The USDA NRCS Soil Climate Analysis Network and NOAA Climate Reference Networks are nationwide meteorological and land surface data networks with soil moisture measurements in the top layers of soil. There is considerable interest in scaling these point measurements to larger scales for validating remote sensing and modeling, but work must be done to both judge the accuracy and representativeness of these network stations before the data can be used for these purposes. Using a combination of permanent watershed scale networks, ground sampling campaigns, and local temporary networks, a methodology for validating individual climate stations for soil moisture scaling is proposed. Using similar in situ installations to those found on the nationwide networks, watershed scale soil moisture sites can be deployed for several months during hydrologically active periods. After establishing temporal stability for the network, scaling equations can be developed for the permanent installation which will allow that single measurement to represent an entire watershed. As an example of this methodology, in situ networks at five ARS watersheds were equipped with dense soil moisture networks along with a SCAN station. Each network demonstrated some degree of temporal stability and representative stations were identified and lessons on station siting were learned.

Last Modified: 7/25/2014
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