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Joint-probability Methods for Precipitation and Flood Frequencies Analysis

机译:降水和洪水频率分析的关节概率方法

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Flooding in irrigation district is often caused by the encounter of flood from the upstream and the precipitation jacking. Archimedean copulas were applied to evaluate the encounter probability of precipitation and flood. The problem of obtaining the joint distribution was reduced to determine the appropriate copula. Four different Archimedean copulas were applied to simulate the joint probability of annual maximum precipitation flood level on two neighboring hydrological stations located at the Pearl River delta, China. Goodness-of-fit tests were introduced to determine the best copula by RMSE and AIC criterion. The joint distribution and condition distribution were obtained based on the best copula. Results show that the joint probability curves were significantly influenced by the marginal distributions. The bivariate precipitation and flood frequency distributions were determined using the copula method without assuming the same form of the marginal distributions. Comparison of the copula-based distributions with bivariate probability distribution showed that the copula-based distribution fit the observed precipitation and flood data better.
机译:灌溉区的洪水往往是由洪水从上游和降水超出引起的。阿基米德共计款适用于评估降水和洪水的遭遇概率。降低了获得关节分布的问题以确定合适的拷贝。应用了四种不同的Archimedean Copulas来模拟位于中国珠江三角洲的两位邻近水文站的年度最大降水洪水水平的联合概率。引入了健康的测试,以通过RMSE和AIC标准来确定最佳谱系。基于最佳拷贝获得联合分布和条件分布。结果表明,联合概率曲线受边缘分布的显着影响。使用Copula方法测定双变化的沉淀和泛频分布,而不假设相同形式的边际分布。基于Copua的分布与双变量概率分布的比较表明,基于Copula的分布更好地拟合了观察到的降水和洪水数据。

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