Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting

Abstract

Streamflow forecasting is a crucial task for hydropower operations, flood forecasting and water resource use optimization. Ensemble Streamflow Prediction (ESP) is commonly used in the case of long-term lead times (a few months or seasons ahead). ESP covers the use of historical meteorological scenarios in driving a hydrological model to generate an ensemble of possible future streamflow. Many studies have evaluated methods for selecting optimal subsets of scenarios to improve forecasting skill, and indeed, this is still an ongoing area of research. In this study, we propose a procedure that calculates the maximum potential skill of a classic ESP forecast. A genetic algorithm (GA) is used to determine the best possible set of climatological scenarios given any ensemble size. Along with providing a direct estimate of the ESP forecasting potential in hindcast experiments, the method can be used as a reference for comparing other methods to ESP. The procedure is also used to compare classical ESP, a well-established forecasting method, with two new methods, namely, the Analogue method and the Contingency Table (CT) approach. A discriminant analysis is finally implemented to attempt to identify key features of ESP members that performed well as compared to their counterparts using historic climatology and climate indices. It is shown while there exists a potential for improvement, a lot of research must still be realized to exploit this potential. The procedure was tested over two basins in Canada. In general, results showed that for any forecast date, decreasing the ensemble size led to a higher potential for better forecasting skills. However, the method does not yet allow identifying the subset of the entire climatology to be used to maximize the ESP forecast performance.