Information theory-based multi-objective design of rainfall network for streamflow simulation

Abstract

Rainfall data are needed for water resource management and decision making and are obtained from rainfall networks. These data are especially important for streamflow simulations and forecasting the occurrence of intense rainfall during the flood season. Therefore, rainfall networks should be carefully designed and evaluated. Several methods are used for rainfall network design, and information theory-based methods have recently received significant attention. This study focuses on the design of a rainfall network, especially for streamflow simulation. A multi-objective design method is proposed and applied to the Wei River basin in China. We use the total correlation as an indicator of information redundancy and multivariate transinformation as an indicator of information transfer. Information redundancy refers to the overlap of information between rainfall stations, and information transfer refers to the rainfall-runoff relationship. The outlet streamflow station (Huaxian station in the Wei River basin) is used as the target station for the streamflow simulation. A non-dominated sorting genetic algorithm (NSGA‐II) was used for the multi-objective optimization of the rainfall network design. We compared the proposed multi-objective design with two other methods using an artificial neural network (ANN) model. The optimized rainfall network from the proposed method led to reasonable outlet streamflow forecasts with a balance between network efficiency and streamflow simulation. Our results indicate that the multi-objective strategy provides an effective design by which the rainfall network can consider the rainfall-runoff process and benefit streamflow prediction on a catchment scale.