Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response
Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response
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DOI：
10.1029/2005wr004398
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发表时间：
2004-12
影响因子：
5.4
通讯作者：
Y. Hong;K. Hsu;H. Moradkhani;S. Sorooshian
中科院分区：
文献类型：
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作者：
Y. Hong;K. Hsu;H. Moradkhani;S. Sorooshian
本文的目的是促进开发一个端到端的不确定性分析框架，可以量化基于卫星的降水估计误差特性，并评估误差传播到水文模拟的影响。首先，与基于卫星的降水估计相关的误差被假定为降雨时空积分尺度、雨强和采样频率的非线性函数。该函数的参数是通过使用美国西南部高分辨率的基于卫星的降水估计和雨量计校正的雷达降水数据来确定的。对16个选定的5° × 5°经纬度网格进行的参数敏感性分析显示，每个参数相对于其平均值的方差约为12-16%。最后，利用蒙特卡罗模拟方法进一步分析了降水估计误差对水文响应不确定性的影响。通过这种方法，产生100个集合成员的降水数据，作为强迫输入到一个概念性的降雨径流水文模型，并量化由此产生的径流预测的不确定性。案例研究证明了在密西西比的叶河流域。与传统方法相比，降水估计误差作为降雨率的固定比率，所提出的框架提供了更现实的量化降水估计误差，并提供了改进的不确定性评估的误差传播到水文模拟。进一步的研究表明，雷达降雨产生的径流序列一致包含在卫星降雨产生的径流的不确定性界在95%的置信区间。
The aim of this paper is to foster the development of an end‐to‐end uncertainty analysis framework that can quantify satellite‐based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite‐based precipitation estimates is assumed as a nonlinear function of rainfall space‐time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high‐resolution satellite‐based precipitation estimates and gauge‐corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5° × 5° latitude‐longitude grids shows about 12–16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall‐runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall‐generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval.