Location: Hydrology and Remote Sensing LaboratoryTitle: Multi-scale soil moisture model calibration and validation: An ARS Watershed on the South Fork of the Iowa River
|PETERSEN, WALTER - National Aeronautics And Space Administration (NASA)|
|NIEMEIER, JAMES - University Of Iowa|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2015
Publication Date: 6/1/2015
Citation: Coopersmith, E.J., Cosh, M.H., Petersen, W., Prueger, J.H., Niemeier, J. 2015. Multi-scale soil moisture model calibration and validation: An ARS Watershed on the South Fork of the Iowa River. Journal of Hydrometeorology. 16(3):1087-1101. DOI:10.1175/JHM-D-14-0145.1
Interpretive Summary: One limitation of soil moisture networks is the expense of equipment and the ability to maintain a large network. By using simple modeling techniques and incorporating precipitation estimates, soil moisture networks can be transformed to high resolution moisture maps with accuracies which are comparable to remote sensing products of soil moisture. A test site in central Iowa was selected for its simplicity in topography and soil type which are both influential on soil moisture dynamics. The results of this study are useful for land managers and operators who are making large scale water management decisions, as well as, the remote sensing community, which needs high resolution data for calibration/validation of their products.
Technical Abstract: 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 resolution 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 4 km precipitation estimates from 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 Study (IFLOODS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using NLDAS precipitation and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average root mean square error values of 0.031 m3/m3 and 0.045 m3/m3 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.