Influence of the Predictive Rainfall/Runoff Model Accuracy on an Optimal Water Resource Management Strategy

This work regards the improvement of water asset management and modelling strategies proposed recently for hydrographical networks in a context of global warming which exacerbates extreme events. Indeed, hydrographical systems, and more specifically those including inland waterways, are large scale systems that involve mass energy transport phenomena consisting in the amount of water in excess induced by extreme rainy events. The water excess has to be dispatched over the complete network in order to anticipate the effects of potential floods and rejected to the sea either by gravity flow heeding the tides or by the utilization of pumps. The latter solution leads to big operation costs and has to be minimized. These goals are fulfilled by means of an integrated model within a flow-based network and a quadratic optimization based on constraints. The recent improvements in the field of predictive rainfall/runoff modelling approaches helps to enhance the operational goals of this water management strategy. A particular example of these approaches are those related to the identification of black-box ARX models in its linear and nonlinear structures. A performance comparison between the recursive least square and the recursive instrumental variable estimation of an ARX model and the estimation of a Linear Parameter Varying ARX-based model is carried out on a river located in the north of France with a prediction horizon of 24 h. Further, the performance of the resource management methodology is tested by means of a realistic case study based on a portion of the real inland waterways located in the north of France.