Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification
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DOI：
10.1016/s0022-1694(00)00346-2
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发表时间：
2000-12
影响因子：
6.4
通讯作者：
Ashish Sharma
中科院分区：
文献类型：
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作者：
Ashish Sharma
有效利用现有水资源是进入21世纪的世界面临的一个严重问题。引起水资源管理人员关切的一个重要问题是，发生严重和持续的干旱，使水库蓄水量减少到危险的程度。此类干旱通常与低频气候波动有关，例如厄尔尼诺南方涛动（ENSO）。本文是一项研究的一部分，开发一个框架，利用现有的水文气候信息的降雨概率预报。本文是在这个问题上发表的三个系列中的第一个，并提出了一种方法，用于确定最佳的预测，可用于制定一个强大的和有效的概率预测模型。这里提出的预测器识别方法使用互信息准则的非参数实现作为变量之间的依赖性的度量。该标准是基于联合概率分布的特征，而不是偏离最佳拟合曲线。一个“部分”的互信息标准的基础上，确定一个以上的预测逐步的方式。该方法使用非参数核方法来描述所涉及变量的联合概率分布。该方法进行了测试的范围内的综合生成的数据集，其依赖属性是已知的。从应用部分互信息标准，以确定预测因子的季度降雨量使用一系列水文气候系统变量的结果，在这三篇论文系列的第二篇论文。
Effective use of available water resources is a serious problem facing the world as it enters the 21st century. An important source of concern to water resources managers is the occurrence of severe and sustained droughts that deplete reservoir storage to dangerous levels. Such droughts are often associated with low frequency climatic fluctuations, such as the El Niño Southern Oscillation (ENSO). This paper is part of a study to develop a framework for rainfall probabilistic forecasting using available hydro-climatic information. This paper is the first in a series of three published in this issue, and presents an approach for identifying optimal predictors that can be used to formulate a robust and efficient probabilistic forecast model. The predictor identification approach presented here uses a nonparametric implementation of the mutual information criterion as a measure of dependence between variables. The criterion is based on a characterisation of the joint probability distribution, instead of deviations off a curve of best fit. A “partial” mutual information criterion is presented as the basis for identifying more than one predictor in a stepwise manner. The method uses nonparametric kernel methods to characterise the joint probability distribution of the variables involved. The method is tested on a range of synthetically generated datasets whose dependence attributes are known beforehand. Results from the application of the partial mutual information criterion to identify predictors of quarterly rainfall using a range of hydro-climatic system variables, are presented in the second paper of this three-paper series.