Abstract
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.
Highlights
Realistic soil moisture information is crucial in the simulation of floods and droughts in global climate change scenarios and global food security studies [1,2,3,4]
This study evaluated effective root zone soil moisture input data resolution effects on the consistency of physics-based, distributed hydrological model infiltration parameterization and simulation results
As the initial soil moisture was resampled to 4.5 km and to 9 km resolution, the hill-slope soil moisture variability was lost due to spatial averaging
Summary
Realistic soil moisture information is crucial in the simulation of floods and droughts in global climate change scenarios and global food security studies [1,2,3,4]. The correct initial soil moisture condition is significant as surface runoff generation, the partitioning of rainfall between infiltration and surface runoff, is controlled by catchment wetness conditions where effective rainfall and runoff increase with the corresponding increase in the wetness extent and conditions [9,10]. This runoff generation process takes place at a hill-slope scale. Initialization of soil moisture in a physics-based distributed hydrological model needs to capture the hillslope scale variability. Available satellite-based digital soil moisture data ranges from 9 km to 50 km in resolution
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