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Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia

机译:多卫星降水产品对水流模拟的评估:以东亚朝鲜半岛汉江流域为例

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The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R 2 ), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R 2 , NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R 2 = 0.54, NSE = 0.49, PBIAS = [?52.70–28.30%]) and CMADS (R 2 = 0.44, NSE = 0.42, PBIAS = [?29.30–41.80%]) data perform better than those utilizing PERSIANN (R 2 = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R 2 = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics.
机译:降水数据的准确性和充分性在环境研究和水文模型中起着关键作用。它们对水文模型的模拟结果有重大影响;因此,在数据稀缺地区进行可靠的水文模拟是一项艰巨的任务。可以利用先进的技术来提高源自卫星的降雨数据的准确性,这可以用来克服数据短缺的问题。我们的研究旨在(1)评估不同卫星降水产品的准确性,例如热带雨量测量任务(TRMM 3B42 V7),使用人工神经网络(PERSIANN)从遥感信息中进行降水估算,PERSIANN-气候数据记录(PERSIANN-CDR) ),以及将SWAT模型(CMADS)的中国气象同化驾驶数据集与测得的降雨数据进行比较; (2)使用SWAT模型将其应用于韩国汉江流域的径流模拟。基于统计度量,即正确比例(PC),检测概率(POD),频率偏差指数(FBI),一致性指数(IOA),均方根误差(RMSE) ),TRMM和CMADS的平均绝对误差(MAE),确定系数(R 2)和偏差,降雨数据显示,与雨量计相比,其精度优于PERSIANN和PERSIANN-CDR。 TRMM和CMADS数据还可以捕获山区的空间降雨模式。由SWAT模型使用地面降雨数据模拟的水流与观测到的水流非常吻合,平均Nash-Sutcliffe效率(NSE)为0.68。在SWAT模型中将这四个卫星降雨产品用作流模拟的输入,并对结果进行了比较。平均R 2,NSE和偏差百分比(PBIAS)表明,使用TRMM(R 2 = 0.54,NSE = 0.49,PBIAS = [?52.70–28.30%])和CMADS(R 2 = 0.44,NSE = 0.42)的水文模型,PBIAS = [?29.30–41.80%])数据的性能要优于使用PERSIANN(R 2 = 0.29,NSE = 0.13,PBIAS = [38.10–83.20%])和PERSIANN-CDR(R 2 = 0.25,NSE = 0.16)的数据,PBIAS = [12.70–71.20%])数据。总体而言,鉴于从TRMM和CMADS获得的降雨数据可用于以可接受的精度模拟汉江流域的流量,因此本研究的结果令人满意。基于这些结果,TRMM和CMADS降雨数据在汉江流域以及其他具有相似气候和地形特征的地区的水文模拟和水资源管理中发挥着重要作用。

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