An integrated model of water–sediment-energy simulation and its application in the Xiaolangdi reservoir

Abstract

The Xiaolangdi (XLD) Reservoir stands as a pivotal reservoir for the water–sediment regulation and power generation in the Yellow River Basin. The coexistent process of water–sediment-energy during the pre-flood period causes the competition among multi-objectives: power generation, water supply, deposition reduction in the reservoir area and the downstream channel. To balance the trade-off between reservoir sustainability and utility value, a model framework is proposed in this study, following the route of “scheme design-model simulation-scheme evaluation”. The framework integrates a reservoir operation module, a hydrodynamic-based water–sediment-energy simulation module, and a benefit evaluation method based on the Fuzzy Neural Network (FNN). Firstly, the water–sediment-energy simulation module was validated against the 2013 and 2014 water–sediment regulation events, showing good agreement with the field measurements. Subsequently, by altering two key scheduling parameters of CWL (connecting water level) and RWL (refilled water level) based on the 2014 water–sediment regulation event, different operation schemes were evaluated based on the comprehensive performance of VS (vented sediment amount from reservoir), TV (total volume of discharge greater than 2600 m³/s), PG (power generation amount), and SC (final storage capacity) with the trained FNN. The proposed optimal values of CWL and RWL were 217 m and 211 m, with the relative membership degrees of 0.750 and 0.754, respectively. This finding suggests that maintaining a continuous low pool level in the XLD Reservoir is optimal for securing higher comprehensive benefits in case of insufficient water inflow, with the effect of deposition reduction being a notable advantage.