Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification A. Sharma * School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia Received 15 November 1999; revised 23 June 2000; accepted 29 June 2000 Abstract 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 Nin ˜ o 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.  2000 Elsevier Science B.V. All rights reserved. Keywords: Prediction; Nonparametric; Statistical methods; Probability 1. Introduction Effective use of available water resources is a serious problem facing the world as it enters the 21st century. An increasing demand for water and an ever-uncertain supply in the form of precipitation has always been a cause of concern to water resource managers. Another concern in the Australasian and South East Asian region is the occurrence of severe and sustained droughts that deplete reservoir storage to dangerous levels, forcing operators to enforce water supply restrictions. Such droughts are often associated with long-term or low frequency climatic fluctuations, such as the El Nin ˜o Southern Oscillation (ENSO). Reservoir operation for regions not affected by such long-term climatic fluctuations is relatively simple as compared to the case when effects of interannual Journal of Hydrology 239 (2000) 232–239 www.elsevier.com/locate/jhydrol 0022-1694/00/$ - see front matter  2000 Elsevier Science B.V. All rights reserved. PII: S0022-1694(00)00346-2 * Fax: +61-2-9385-6139. E-mail address: a.sharma@unsw.edu.au (A. Sharma).