Abstract
Numerous applications require fine-resolution (10–30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9–60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil () model downscales soil moisture using fine-resolution topography, vegetation, and soil data, but it requires specification of 16 parameters. In previous applications, the parameters have been calibrated using detailed in situ soil moisture data, but very few regions have such data. This study aimed to evaluate model performance when the parameters are estimated from global data sets instead of site-specific calibration. Methods were developed to estimate key parameters from the data sets, and the global model (without site-specific calibration) was applied to six study sites. The global model results were compared with the results of locally calibrated models and to in situ soil moisture observations. The use of global data sets decreases downscaling performance and reduces the spatial variability in the fine-resolution soil moisture patterns. Overall, however, the global model provides more reliable soil moisture estimates than simply using the coarse-resolution moisture.
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Acknowledgments
This work was supported by the Army Research Lab (ARL) Small Business Innovative Research (SBIR) program. It was also supported in part by the USDA National Institute of Food and Agriculture, Hatch Project 1009616. We thank three anonymous reviewers for their comments and suggestions to improve this paper.
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Journal of Hydrologic Engineering
Volume 23 • Issue 11 • November 2018
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©2018 American Society of Civil Engineers.
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Received: Nov 13, 2017
Accepted: May 16, 2018
Published online: Sep 10, 2018
Published in print: Nov 1, 2018
Discussion open until: Feb 10, 2019
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