Preprints
https://doi.org/10.5194/nhess-2023-63
https://doi.org/10.5194/nhess-2023-63
11 May 2023
 | 11 May 2023
Status: this preprint is currently under review for the journal NHESS.

Review article: Towards Improved Drought Prediction in the Mediterranean Region – Modelling Approaches and Future Directions

Bouchra Zellou, Nabil El Moçayd, and EL Houcine Bergou

Abstract. There is a scientific consensus that the Mediterranean region (MEDR) is warming and as the temperature continues to rise, extreme events such as droughts and heat waves are becoming more frequent, severe, and widespread. Given the detrimental effects of droughts, it is crucial to accelerate the development of forecasting and early warning systems to minimize their negative impact. This paper examines the current state of knowledge in drought modeling and prediction using statistical, dynamical, and hybrid statistical-dynamical models, and suggests some research prospects to further improve drought prediction in this region. The review finds that while all methods have their strengths and shortcomings, hybrid statistical-dynamical methods can perform the most skillful prediction with a long lead time. However, the application of these methods is still challenging due to the lack of high-quality observational data and the limited computational resources. Finally, the paper concludes by discussing the importance of using a combination of sophisticated methods such as data assimilation techniques, machine learning models, and copula models and integrating data from different sources (e.g., remote sensing data, in-situ measurements, and reanalysis) to improve the accuracy and efficiency of drought forecasting.

Bouchra Zellou et al.

Status: open (until 24 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Bouchra Zellou et al.

Bouchra Zellou et al.

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Short summary
In this study, we underscore the critical importance of strengthening drought prediction capabilities in the Mediterranean region. We present an in-depth evaluation of current drought forecasting approaches, encompassing statistical, dynamical, and hybrid statistical-dynamical models, and highlight unexplored research opportunities. Additionally, we suggest forth viable directions to enhance drought prediction and early warning systems within the area.
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