Three-Phase-Based Approach to Develop a River Health Prediction and Early Warning System to Guide River Management

Abstract

To effectively manage a river system, systematic tracking and diagnosing the change and risks of a river system are essentially required to efficiently conserve or restore its conditions. Hence, this study focuses on how to integrate current status assessment, trend prediction, and cause diagnosis in river health to guide early warning decision-making in river protection and management. This study has presented a three-phase approach by coupling spatial with nonspatial information in a highly systematic and reliable way, and an early warning system has been designed. In phase I, the current health status is assessed and nowcasted by using the order degree of each indicator. In phase II, health predictors, including the single perspective-based health index (HI) (e.g., water quality index (WQI) and index of biotic integrity (IBI)) and multi-perspective-based health index, have been forecasted under normal conditions or emerging conditions using predictive models. In phase III, key causal factors threatening the river health have been identified to enable early notification and to address unexpected events before occurrence. Although different modeling methods can be used in each phase to demonstrate this concept, we tested the model of partial least square regression (PLSR) associated with time series. Additionally, the three-phase approach has been integrated with geographic information system (GIS) and a decision support system (DSS) to develop a river health prediction and early warning system (RHP-EWS), an automatic prediction and decision-making tool. This tool was implemented to deal with the landing of typhoon “Maria” in 2018 into the Shanxi River watershed in China. Because of the timely responses and decisions, the drinking water supply was not influenced. However, the models should be extended to other river systems for testing and improvement at different temporal or spatial scales.