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Open AccessArticle

Satellite Imagery-Based SERVES Soil Moisture for the Analysis of Soil Moisture Initialization Input Scale Effects on Physics-Based Distributed Watershed Hydrologic Modelling

1
Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180-6199, USA
2
U.S. Army Corps of Engineers, Albuquerque District, Albuquerque, NM 87109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2108; https://doi.org/10.3390/rs12132108
Received: 19 May 2020 / Revised: 28 June 2020 / Accepted: 29 June 2020 / Published: 1 July 2020
Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide accurate estimates of soil moisture, but such techniques are limited due to the number of physical sensors required to cover a large area effectively. Available satellite-based digital soil moisture data ranges from 9 km to 20 km in resolution which obscures the soil moisture details of a hill slope scale. This resolution limitation of available satellite-based distributed soil moisture data has impacted critical analysis of soil moisture resolution variance on physics-based distributed simulation results. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column and that would inform little about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed effective root zone soil moisture at 30 m resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with the GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 m resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 m resolution SERVES soil moisture data was resampled to 4500 m and 9000 m resolutions and was separately employed in the calibrated hydrological model to determine the soil moisture resolution effect on the model simulated outputs and the model parameter values. It was found that the simulated discharge is underestimated, infiltration rate/volume is overestimated and higher soil moisture state distribution is filtered out as the initial soil moisture resolution was coarsened. To compensate for this disparity in the simulated results, the soil saturated hydraulic conductivity value decreased with respect to the decreased resolutions. View Full-Text
Keywords: soil moisture; initial condition; input grid resolution; scale effects; SERVES; hydrological modelling soil moisture; initial condition; input grid resolution; scale effects; SERVES; hydrological modelling
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MDPI and ACS Style

Pradhan, N.R.; Floyd, I.; Brown, S. Satellite Imagery-Based SERVES Soil Moisture for the Analysis of Soil Moisture Initialization Input Scale Effects on Physics-Based Distributed Watershed Hydrologic Modelling. Remote Sens. 2020, 12, 2108.

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