Skill assessment of a seasonal forecast model to predict drought events for water resource systems

Droughts cause significant socio-economic and environmental impacts, so it has become an extremely important element in decision-making within water resource systems. For this reason, the research in this field has increased considerably over the last few decades. In order to be capable of making early decisions and reducing drought impacts, it is necessary to predict the occurrence of such events months or even years in advance. In this sense, various methods have been used to predict the occurrence of droughts. At present, seasonal forecast data can be used to forecast meteorological, hydrological, agricultural and operational droughts. However, the seasonal forecast data of these dynamical ocean-atmosphere coupled models must be analyzed in an exhaustive way, since it is known that these models may not adequately represent the climatic variability at river basin scale. Hence, this paper presents a new methodology for assessing the skill of a climate forecasting system in order to predict the occurrence of droughts by using contingency tables. The indices obtained from the contingency tables are necessary to perform the analysis of the predictive ability of the model in a semi-distributed way. All this taking into account the intensity of droughts using different scenarios based on the threshold below which it is considered to be in drought. Finally, a single value is obtained to determine the predictive ability of the forecasting model for the entire basin. The proposed methodology is applied to the Júcar river basin in Spain. It has been found that the analyzed forecast model shows better results than those obtained using an autoregressive model. Further work is needed to enhance climate forecasting from the perspective of water resources management, however, it should be mentioned that this type of data could be used for drought forecasting, allowing possible mitigation measures.