Integrating seasonal forecasts into real-time drought management: Júcar River Basin case study

Abstract

In future years, and due to climate change, the frequency and intensity of extreme droughts will increase in some areas of the planet with water scarcity problems, affecting the reliability and vulnerability of water resource systems (WRS). Therefore, several approaches for real-time drought management were proposed in this study to improve the predictive capacity of currently used methodologies.This study was conducted in the Júcar River Basin, a highly regulated Mediterranean WRS whose experience in drought management is currently based on the combination of a stochastic model for future inflow series generation (using previous historical inflows) and a risk assessment model. Here, the possibility of improving and updating this approach was analysed by proposing three different models that integrate seasonal meteorological forecasts into the series generation process: i) an auto-regressive moving-average model with exogenous variables (ARMAX); ii) a hydrological model (HBV); and iii) an Artificial Neural Network (ANN) model. These models were also combined (individually) with a risk assessment model to assist in the decision-making process through a very intuitive drought risk indicator for several months in advance.The main results confirmed the potential for improving the predictive capacity of the current method using seasonal forecasts, especially with the ARMAX and ANN models under drought scenarios. Their results were more robust, with lower variabilities and uncertainty even after seven months, which represents a good opportunity to improve the decision-making process of this basin in a changing near future.