Seasonal meteorological drought projections over Iran using the NMME data

Accurate and well-planned forecasts provide critical information for preparedness and mitigation strategies as well as the sustainable practice of water resources conservation. In this paper, an experimental seasonal drought forecasting system has been developed based on meteorological hindcasts, generated by the North American Multi-Model Ensemble (NMME) models. The proposed toolbox comprises (1) NMME data as well as observations, (2) post-processing methods, namely GrandNMME and bias correction methods to statistically post-process precipitation predictions, (3) evaluation metrics of a multi-criteria decision-making method (namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)) to choose the best post-processed improved data, and (4) the Standardized Precipitation Index (SPI) calculator as the central engine, where distribution maps of seasonal drought forecasts are generated. The toolbox has been utilized for the case of Iran. The country is located in semiarid and arid regions of the world, facing considerable water crisis including droughts. Results indicated that the proposed NMME-based drought forecasting toolbox has a significant skill in forecasting droughts over the study area and provides critical information for early warnings, medium-term response planning and taking preventive measures.