The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rainfall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km²) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations.

中文翻译：

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捷克共和国吉钦卡河小流域遥感和水文模型土壤水分的时空分析

流域内土壤水分的时空动态知识对于洪水预报中的降雨径流模型非常重要。在这项研究中，对小而富裕的吉钦卡河流域（75.9 km ²）在捷克共和国。由于卫星数据的空间分辨率相对较粗糙，因此使用Qu等人开发的方法缩小了卫星土壤湿度数据的比例。（2015）。根据网格单元的平均土壤湿度和已知的水力土壤特性，估算土壤水分的亚网格变异性。SHETRAN模型已经过校准，并根据集水口的观测水流水文图进行了验证。根据大多数应用标准，获得了两种不同的土壤水分信息之间的良好相关性。