Soil Moisture Model Calibration and Validation: An ARS Watershed on the South Fork Iowa River

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increase...

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Bibliographic Details
Published in:Journal of hydrometeorology 2015-06, Vol.16 (3), p.1087-1101
Main Authors: Coopersmith, Evan J., Cosh, Michael H., Petersen, Walt A., Prueger, John, Niemeier, James J.
Format: Article
Language:English
Subjects:
Agriculture
Calibration
Capacity
Data assimilation
Data collection
Environmental monitoring
Estimate reliability
Estimates
Hydrologic models
Hydrological modeling
Hydrology
Hydrometeorology
Laboratories
Modeling
Parametric models
Physiographic features
Precipitation
Precipitation estimation
Remote sensing
Resolution
Rivers
Satellites
Sensors
Soil
Soil moisture
Soil surfaces
Soil water
Soils
Studies
Time series
Topography
Watersheds
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Summary:Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m³ m−3were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.
ISSN:1525-755X
1525-7541
DOI:10.1175/jhm-d-14-0145.1