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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #302813

Title: Soil moisture and precipitation monitoring in the South Fork experimental watershed during the Iowa flood studies (IFloodS)

item Coopersmith, Evan
item Cosh, Michael
item PETERSEN, WALTER - National Aeronautics And Space Administration (NASA)
item KRAJEWSKI, WITOLD - University Of Iowa
item Prueger, John

Submitted to: American Society of Civil Engineers
Publication Type: Abstract Only
Publication Acceptance Date: 2/17/2014
Publication Date: 4/7/2014
Citation: Coopersmith, E.J., Cosh, M.H., Petersen, W., Krajewski, W., Prueger, J.H. 2014. Soil moisture and precipitation monitoring in the South Fork experimental watershed during the Iowa flood studies (IFloodS) [abstract]. American Society of Civil Engineers. p. 28.

Interpretive Summary:

Technical Abstract: Soil moisture estimates are valuable for hydrologic modeling and agricultural decision support. These estimates are typically produced via a combination of sparse in situ networks and remotely-sensed products or where sensory grids and quality satellite estimates are unavailable, through derived hydrologic and statistical models. For soil moisture estimates produced by modeling efforts, the accuracy of these estimates is determined by the precision and stability of the data products (more specifically the precipitation data) with which they are calibrated. Methods for watershed scale modeling of soil moisture will be investigated in a USDA-ARS watershed in the South Fork of the Iowa River (Iowa, USA). This represents an ideal location for investigating the performance of numerous precipitation data products available. Calibrating a simple, soil moisture model requiring only a precipitation time series and an in situ soil moisture time series, during the IFLOODS initiative in the spring of 2013 will provide insight into the reliability of each of these data products and their suitability for future decision support work. Within the South Fork watershed, each in situ soil moisture sensor is accompanied by a precipitation gauge. These precipitation gauges are then used to calibrate a simple-lumped soil moisture model at each sensor location. In addition, public, radar-driven precipitation estimates are available through NEXRAD at these locations. By comparing the efficacy of models calibrated with local precipitation gauges, models calibrated with public climate data (NEXRAD), and models calibrated with other precipitation data products developed and tested in the South Fork watershed, one can demonstrate the relative value of these precipitation data products for hydrologic modeling. Furthermore, as most data sources are imperfect, with occasional missing data points and/or values reported in error, multiple data products can serve as mechanisms of data-correction and/or cross-validation. When a single sensor reports an incorrect value or replacement when a data source fails to report altogether, an alternate source of precipitation data can be applied in its place. When a soil moisture sensor fails to report a value, parameters calibrated at similar locations within the watershed can be applied to the locally-available precipitation data. This work will explore calibration of soil moisture models with numerous sources of precipitation and ultimately, improve estimates by integrating these multiple inputs.