The impact of using different probability representations in application of equidistant quantile matching for bias adjustment of daily precipitation over the Daqing River Basin, North China,International Journal of Climatology

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The impact of using different probability representations in application of equidistant quantile matching for bias adjustment of daily precipitation over the Daqing River Basin, North China
International Journal of Climatology ( IF 3.5 ) Pub Date : 2021-06-27 , DOI: 10.1002/joc.7272
Lv Mingcong ₁ , Gao Xueping ₁ , Liu Yinzhu ₁ , Ju Wenhui ₂ , Sun Bowen ₁ , Li Wenjun ₃ , Zhou Xushen ₃

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This study focuses on how the representation of daily precipitation probability distributions may affect the application of equidistant quantile matching (EDCDFm). Five representations, that is, two parametric distributions and three nonparametric approaches, are selected. The parametric distributions include Gamma and Weibull while the nonparametric approaches include empirical cumulative distribution function (ECDF) and kernel density estimation method (KDE) as well as an improved KDE named diffusion-based kernel density estimation (DKDE). All five methods were applied to correct the daily precipitation of 11 stations over the Daqing River Basin, North China in 1981–2015. The results demonstrated that DKDE is closer to ECDF than the other three methods in the goodness-of-fit evaluation. Furthermore, nonparametric methods present advantages over parametric methods; especially, DKDE and ECDF are skilful equally and both of them display impressive comprehensive performance than other methods by multi-index. These findings suggest that representing the precipitation probability distributions accurately is beneficial for the bias correction and selecting one or more suitable indexes is of great significance to the verification of EDCDFm. This study can provide guidance for future water resources management of the Daqing River Basin.

中文翻译：

使用不同概率表示法对华北大庆河流域日降水量偏差调整应用等距分位数的影响

本研究的重点是每日降水概率分布的表示如何影响等距分位数匹配 (EDCDFm) 的应用。选择了五种表示，即两种参数分布和三种非参数方法。参数分布包括 Gamma 和 Weibull，而非参数方法包括经验累积分布函数 (ECDF) 和核密度估计方法 (KDE) 以及改进的 KDE，称为基于扩散的核密度估计 (DKDE)。1981-2015年华北大庆河流域11个站点日降水量均采用五种方法进行校正。结果表明，在拟合优度评估中，DKDE 比其他三种方法更接近 ECDF。此外，非参数方法比参数方法具有优势；尤其是 DKDE 和 ECDF 技术同样娴熟，并且通过多指标，它们都显示出比其他方法令人印象深刻的综合性能。这些研究结果表明，准确地表示降水概率分布有利于偏差校正，选择一个或多个合适的指标对 EDCDFm 的验证具有重要意义。本研究可为未来大庆河流域水资源管理提供指导。这些研究结果表明，准确地表示降水概率分布有利于偏差校正，选择一个或多个合适的指标对 EDCDFm 的验证具有重要意义。本研究可为未来大庆河流域水资源管理提供指导。这些研究结果表明，准确地表示降水概率分布有利于偏差校正，选择一个或多个合适的指标对 EDCDFm 的验证具有重要意义。本研究可为未来大庆河流域水资源管理提供指导。

更新日期：2021-06-27

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