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

Abstract

[EN] Droughts cause signi¿cant socio-economic and environmental impacts, so it has become an extremely importantelement in decision-making within water resource systems. For this reason, the research in this ¿eld has in-creased considerably over the last few decades. In order to be capable of making early decisions and reducingdrought impacts, it is necessary to predict the occurrence of such events months or even years in advance. In thissense, various methods have been used to predict the occurrence of droughts. At present, seasonal forecast datacan be used to forecast meteorological, hydrological, agricultural and operational droughts. However, the sea-sonal forecast data of these dynamical ocean-atmosphere coupled models must be analyzed in an exhaustiveway, since it is known that these models may not adequately represent the climatic variability at river basinscale. Hence, this paper presents a new methodology for assessing the skill of a climate forecasting system inorder to predict the occurrence of droughts by using contingency tables. The indices obtained from the con-tingency tables are necessary to perform the analysis of the predictive ability of the model in a semi-distributedway. All this taking into account the intensity of droughts using di¿erent scenarios based on the threshold belowwhich it is considered to be in drought. Finally, a single value is obtained to determine the predictive ability ofthe 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 auto-regressive model. Further work is needed to enhance climate forecasting from the perspective of water resourcesmanagement, however, it should be mentioned that this type of data could be used for drought forecasting,allowing possible mitigation measures.