Flow prediction is regarded as a major computational process in strategic water resources planning. Prediction’s lead time has an inverse relationship with results’ accuracy and certainty. This research studies the impact of climate-atmospheric indices on surface runoff predictions with a long lead time. To this end, the correlation of 36 long-distance climate indices with runoff was examined at 10 key nodes of the Great Karun multi-reservoir system in Iran, and indices with higher correlation are divided into 4 different groups. Then, using Artificial Neural Network (ANN) and Ensemble Learning to combine the input variables, flow is predicted in 6-month horizons, and results are compared with observed values. To assess the impact of extending the prediction lead time, results from the proposed model are compared with those of a monthly prediction model. The performed comparison shows that using an ensemble approach improves the final results significantly. Moreover, Tropical Pacific SST EOF, Caribbean SST, and Nino1 + 2 indices are found to be influential parameters to the basin’s inflow. It is observed that the performance of the prediction process varies in different hydrological conditions and the best results are obtained for dry seasons.
Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash—Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.
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