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首页> 外文期刊>Journal of Hydrology >Evaluation of heterogeneity statistics as reasonable proxies of the error of precipitation quantile estimation in the Minneapolis-St. Paul region
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Evaluation of heterogeneity statistics as reasonable proxies of the error of precipitation quantile estimation in the Minneapolis-St. Paul region

机译:评估非均质性统计数据是明尼阿波利斯-圣米歇尔州降水分位数估计误差的合理代理。保罗地区

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Estimating precipitation frequency is important in engineering, agriculture, land use planning, and many other disciplines. The index flood method alleviates small sample size issues due to short record length by calculating normalized quantile estimates for averaged data from a "region" of gauges. For a perfectly homogeneous region this adds no error; heterogeneity statistics seek to quantify a real-world region's deviation from this assumption. Hosking and Wallis (1997) introduced a Monte Carlo heterogeneity statistic called here H_1 and used a simulation study to assess its utility while rejecting two similar statistics called here H_2 and H_3. A nearly linear relationship was found between H_1 and the percentage root mean square error (RMSE) increase due to heterogeneity, establishing H_1 as a "reasonable proxy" of quantile error. The H_1-percent RMSE added relationship found in the simulation experiment was used to find equivalent RMSEs for heterogeneity thresholds against which all three H statistics were tested. In this study the "reasonable proxy" relationship is evaluated across a highly skewed daily precipitation dataset in Minnesota for H_1, H_2 and H_3. Simulated regions used in quantile error estimation are generated using at-site L-moment ratios scaled toward the regional mean with a shrinkage multiplier. A linear relationship is found between Monte Carlo estimates of quantile RMSE and both H_1 and H_2 across all possible regionalizations of twelve gauges. H_2's relationship is less linear than H_1's as quantified by Pearson's r. A synthetic study is also undertaken using the same sample sizes, regional L-moment averages, and between-site variations as the Hosking and Wallis (1997) simulation. The H_2-percent RMSE added relationship is found to be nearly as linear as for H_1, complementing the enumeration study's findings. Because H_2's linear relationship with percent RMSE added has approximately one-fourth the slope of theH_1-RMSE relationship, heterogeneity thresholds calculated with reference to H_1 should not be applied to H_2. H_2 thresholds can be derived from the H_2-percent RMSE added relationship in analogous fashion to the method used in Hosking and Wallis (1997) for H_1. The resulting thresholds are one-fourth the magnitude of the H_1 thresholds.
机译:在工程,农业,土地利用规划和许多其他学科中,估计降水频率很重要。索引泛洪方法通过为量规“区域”中的平均数据计算归一化分位数估计,减轻了由于记录长度短而导致的小样本量问题。对于完全均匀的区域,这不会增加任何误差。异质性统计数据试图量化该假设与现实世界区域的偏差。 Hosking和Wallis(1997)引入了一个蒙特卡罗异质性统计量,这里称为H_1,并使用模拟研究来评估其效用,同时拒绝了两个相似的统计数据,这里称为H_2和H_3。由于异质性,在H_1和均方根误差百分比(RMSE)增大之间发现了几乎线性的关系,从而将H_1建立为分位数误差的“合理代理”。在模拟实验中找到的H_1-百分比RMSE添加关系用于为异质性阈值找到等效的RMSE,针对所有三个H统计数据都针对该异质性阈值进行了测试。在这项研究中,在明尼苏达州高度偏斜的H_1,H_2和H_3的日降水数据集中评估了“合理代理”关系。分位数误差估计中使用的模拟区域是使用收缩因子乘以朝向区域平均值缩放的现场L矩比生成的。在十二个标尺的所有可能区域中,在分位数RMSE的蒙特卡洛估计与H_1和H_2之间发现线性关系。 H_2的关系不像Pearson的r量化那样线性。还使用与Hosking和Wallis(1997)模拟相同的样本量,区域L矩平均值和站点间差异进行了综合研究。发现H_2与RMSE相加的关系与H_1几乎呈线性关系,补充了列举研究的结果。因为H_2与添加的RMSE的线性关系的斜率约为H_1-RMSE关系的四分之一,所以参考H_1计算的异质性阈值不应应用于H_2。 H_2阈值可以类似于Hosking和Wallis(1997)中H_1的方法,从H_2-百分比RMSE添加关系得出。所得阈值是H_1阈值的四分之一。

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