Influence of inflows modeling on management simulation of water resources system

This paper investigates the influence of different hydrological input data on the management simulation of a water resources system (WRS). Three complete simulations were carried out using synthetic inflow series generated with several stochastic models: an autoregressive moving average (ARMA) model, the Lane condensed temporal disaggregation model, and a nonlinear model based on a multilayer perceptron artificial neural network (MLP-ANN) with a random component embedded. The validation of the stochastic models was performed using comparisons of relevant drought statistics from synthetic series with those from the historical records. Since the analyzed WRS includes five inflow sites, multivariate models were applied. The MLP-ANN model showed the best performance. The management simulations of the WRS were executed with the decision support system AQUATOOL under a probabilistic approach. This approach gives probabilities of demand failures of the WRS, which were used to evaluate the influence of the three applied stochastic models on the simulation results. Significant differences were found.