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Medium to Long-Term Probabilistic Forecasting for Efficient Water Resource Management

Author(s): Ashish Sharma

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Keywords: Probabilistic forecasting; Water resources management

Abstract: This paper is part of a study to develop a framework for conditional prediction of rainfall and streamflow using available hydro-climatic information. The hydrologic prediction problem is addressed using a two step approach-identification of appropriate predictors of rainfall or streamflow for seasonal to interannual conditional prediction, and, using the identified predictors in a rainfall or streamflow prediction model to assist in risk-based reservoir operation applications. The warragamba dam, a large water supply reservoir near sydney, australia, is used to illustrate the applicability of the proposed methods. A nonparametric partial mutual information criterion is used in a stepwise manner as the basis for identifying predictors. The predictors are chosen amongst several variables that are thought to influence the warragamba rainfall and streamflow, amongst them being prior values of the variable being predicted, prior values of warragamba rainfall for prediction of warragamba streamflow, and commonly used indices describing the strength of an enso anomaly. In addition, predictors are also selected from amongst sea surface temperature anomalies averaged over 5°×5°latitude-longitude blocks covering most of the world's ocean surface. A nonparametric conditional prediction model is formulated using the identified predictors, the utility of the selected predictors being evaluated by predicting each year on record in a leave-one-cross-validation mode. It is shown that these predictions have the potential for an extra 15-35% water to be used for water resource management with no added risk being taken.


Year: 2001

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