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Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

机译:在埃塞俄比亚山区流域水文模型中,通过水流模拟评估高分辨率卫星降雨产物

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This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.258 and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model treamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.
机译:这项研究的重点是评估四种广泛使用的全球高分辨率卫星降雨产品[气候预测中心的变质技术(CMORPH)产品,热带降雨测量任务(TRMM)多卫星降水分析(TMPA)近实时产品(3B42RT) ,TMPA方法后实时研究版本产品(3B42)和使用人工神经网络(PERSIANN)产品进行的遥感信息的降水估计],其空间流模拟为0.258,时间分辨率为3 h,埃塞俄比亚299平方公里山区流域的土壤和水评估工具(SWAT)水文模型。结果表明卫星降雨量估计值存在明显偏差。 3B42RT和CMORPH产品的性能优于3B42和PERSIANN。使用以两种不同方法校准的SWAT模型检查了每种卫星降雨的预测能力:以雨量计降雨作为输入,并以每种卫星降雨产物作为输入。当使用特定于输入的降雨数据而不是使用雨量计数据校准模型时,可以大大改善模型的水流模拟。使用卫星降雨量估算值校准SWAT所得到的曲线数值远远高于标准列表值,因此,将标准列表参数值与卫星降雨量输入配合使用时必须格外小心。该研究还表明,卫星降雨量估计值的偏差校正可显着改善模型仿真。经过偏差校正和模型重新校准后,可获得基于卫星降雨输入的最佳模型仿真。

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