Discrete Applied Mathematics的目的是汇集算法和应用离散数学不同领域的研究论文,以及组合数学在信息学和科学技术各个领域的应用。发表在期刊上的文章可以是研究论文、简短笔记、调查报告,也可以是研究问题。“传播”部分将致力于尽可能快地出版最近的研究成果,这些成果由编辑委员会的一名成员检查和推荐出版。《华尔街日报》还将出版数量有限的图书公告和会议记录。这些程序将得到充分的裁决,并遵守《华尔街日报》的正常标准。官网链接:https://www.sciencedirect.com/journal/discrete-applied-mathematics/about/aims-and-scope

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Statistical flood frequency analysis coupled with hydrograph scaling is commonly used to generate design floods to assess dam safety assessment. The safety assessments can be highly sensitive to the choice of the statistical flood frequency model. Standard dam safety assessments are typically based on a single distribution model of flood frequency, often the Log Pearson Type III or Generalized Extreme Value distributions. Floods, however, may result from multiple physical processes such as rain on snow, snowmelt or rainstorms. This can result in a mixed distribution of annual peak flows, according to the cause of each flood. Engineering design choices based on a single distribution statistical model are vulnerable to the effects of this potential structural model error. To explore the practicality and potential value of implementing mixed distribution statistical models in engineering design, we compare the goodness of fit of several single- and mixed-distribution peak flow models, as well as the contingent dam safety assessment at Pueblo, Colorado as a didactic example. Summer snowmelt and intense summer rainstorms are both key drivers of annual peak flow at Pueblo. We analyze the potential implications for the annual probability of overtopping-induced failure of the Pueblo Dam as a didactic example. We address the temporal and physical cause separation problems by building on previous work with mixed distributions. We find a Mixed Generalized Extreme Value distribution model best fits peak flows observed in the gaged record, historical floods, and paleo floods at Pueblo. Finally, we show that accounting for mixed distributions in the safety assessment at Pueblo Dam increases the assessed risk of overtopping.

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