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.

中文翻译：

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基于卫星影像的SERVES土壤水分，用于基于初始分布流域水文模拟的土壤水分初始输入规模分析

数据采集​​和有效的水文模型初始化处理方法，例如土壤湿度和参数值识别，对于基于物理的洪水和与洪水有关的灾害（如泥沙和泥石流）的分布式分水岭建模至关重要。现场测量可以提供对土壤水分的准确估算，但是由于有效覆盖大面积区域所需的物理传感器的数量，此类技术受到了限制。可用的基于卫星的数字土壤湿度数据的分辨率范围从9 km到20 km，这掩盖了山坡规模的土壤湿度细节。现有的基于卫星的分布式土壤水分数据的分辨率限制已经影响了基于物理的分布式模拟结果对土壤水分分辨率方差的严格分析。此外，可用的基于卫星的数字土壤湿度数据仅代表顶部土壤柱的几厘米，而对于有效的根区湿度知之甚少。最近开发的一种土壤水分估算方法称为SERVES（通过基于植被指数的蒸散量和土壤特性估算根区的土壤水分）通过估算30 m分辨率的分布式有效根区土壤水分来克服基于卫星的土壤水分数据的这一局限性。在这项研究中，使用GSSHA（网格地表次表层水文分析）模型开发了雷诺兹河实验集水区子汇水区的分布式分水岭水文模型。在流域水文参数值的校准和识别过程中，以30 m分辨率估算的SERVES土壤水分被部署。将30 m分辨率的SERVES土壤湿度数据重新采样到4500 m和9000 m分辨率，并分别用于校准的水文模型，以确定土壤湿度分辨率对模型模拟输出和模型参数值的影响。结果发现，随着初始土壤水分分辨率的粗化，模拟流量被低估，渗透率/渗透量被高估，较高的土壤水分状态分布被滤除。为了补偿模拟结果中的这种差异，相对于降低的分辨率，土壤饱和导水率值降低了。将30 m分辨率的SERVES土壤湿度数据重新采样到4500 m和9000 m分辨率，并分别用于校准的水文模型，以确定土壤湿度分辨率对模型模拟输出和模型参数值的影响。结果发现，随着初始土壤水分分辨率的粗化，模拟流量被低估，渗透率/体积被高估，较高的土壤水分状态分布被滤除。为了补偿模拟结果中的这种差异，相对于降低的分辨率，土壤饱和导水率值降低了。将30 m分辨率的SERVES土壤湿度数据重新采样到4500 m和9000 m分辨率，并分别用于校准的水文模型，以确定土壤湿度分辨率对模型模拟输出和模型参数值的影响。结果发现，随着初始土壤水分分辨率的粗化，模拟流量被低估，渗透率/渗透量被高估，较高的土壤水分状态分布被滤除。为了补偿模拟结果中的这种差异，相对于降低的分辨率，土壤饱和导水率值降低了。结果发现，随着初始土壤水分分辨率的粗化，模拟流量被低估，渗透率/渗透量被高估，较高的土壤水分状态分布被滤除。为了补偿模拟结果中的这种差异，相对于降低的分辨率，土壤饱和导水率值降低了。结果发现，随着初始土壤水分分辨率的粗化，模拟流量被低估，渗透率/渗透量被高估，较高的土壤水分状态分布被滤除。为了补偿模拟结果中的这种差异，相对于降低的分辨率，土壤饱和导水率值降低了。