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Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach

机译：连续降雨模拟：2.区域化每日降雨产生方法

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This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from "nearby" gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the "target" location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison,,and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state-based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest.
机译：本文是本期两本中的第二本，本白皮书提供了一个框架，可用于在标距和非标距位置模拟连续（不间断）降雨序列。本文的最终目的是提出一种在任何位置随机生成连续的次日降雨序列的方法，从而保留一定范围内的聚合尺度的统计数据。在本文中，我们通过采用从统计上相似的降雨序列的“附近”雨量器中采样每日降雨记录的原理，通过在任何位置生成每日序列的原理来完善区域化算法。该方法包括两个不同的步骤：首先，确定一组每日降雨序列在统计上与所关注位置相似的位置，其次是开发从这些位置采样每日降雨的算法。第一步，考虑了澳大利亚2708条每日降雨量记录的所有双变量组合之间的相似性，并建立了逻辑回归模型来预测站点之间的相似性是许多生理协变量的函数。基于模型结果，可以为任何未使用的感兴趣位置（“目标”位置）标识许多具有足够每日降雨量记录的附近位置，然后将其用作随机生成每日降雨量序列的基础。连续模拟算法在可比较历史悠久的每日降雨量记录的五个位置进行了测试，发现在代表观测到的每日记录的分布和依赖性方面表现良好。然后，使用第一篇论文中描述的基于降雨状态的分解方法，将这些日常序列分解为次日时间步长，发现这些序列可以很好地表示目标位置的连续降雨序列。

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  • 来源
    《Water resources research》 |2012年第1期|p.W01536.1-W01536.16|共16页
  • 作者

    Rajeshwar Mehrotra; Seth Westra; Ashish Sharma; Ratnasingham Srikanthan;

  • 作者单位

    School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia;

    School of Civil, Environmental and Mining Engineering,University of Adelaide, SA 5005, Australia;

    School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia;

    Water Division, Australian Bureau of Meterology,G.P.O. Box 1289, Melbourne, Victoria 3001, Australia;

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