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ORIGINAL RESEARCH article

Front. Water
Sec. Water and Hydrocomplexity
Volume 6 - 2024 | doi: 10.3389/frwa.2024.1523898
This article is part of the Research Topic Climate, Water and Land in Africa: Research Trends and Challenges View all 7 articles

Seasonal Prediction of Rainfall Variability in the West African Sudan-Sahel

Provisionally accepted
Manuel Rauch Manuel Rauch 1,2Jan Bliefernicht Jan Bliefernicht 1Windmanagda Sawadogo Windmanagda Sawadogo 1Souleymane SY Souleymane SY 1Moussa Waongo Moussa Waongo 3Harald Kunstmann Harald Kunstmann 2*
  • 1 University of Augsburg, Augsburg, Germany
  • 2 Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
  • 3 Agrhymet Regional Centre, Niamey, Niger

The final, formatted version of the article will be published soon.

    The Sudan-Sahel region in West Africa is highly vulnerable to rainfall variability, which poses significant challenges to agriculture and water resource management. This study provides an assessment of seasonal rainfall prediction models in the region, focusing on the West African Regional Climate Outlook Forum (WARCOF, 1998(WARCOF, -2023)), the latest generation of the seasonal forecasting system from the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5, 1981-2023), and a novel atmospheric circulation-pattern-based logistic regression model . The circulation-pattern-based model, which integrates key atmospheric dynamics like near-surface wind anomalies, outperforms both WAR-COF and SEAS5 in predicting interannual rainfall variability. While WARCOF and SEAS5 demonstrate some predictive skill, both models exhibit biases: WARCOF has a dry bias, and SEAS5 displays both dry and wet biases. The circulation-pattern-based model, despite a slight wet bias, delivers more accurate categorical predictions and offers greater reliability. An economic value analysis reveals that the circulation-pattern-based model provides a broader range of positive economic outcomes, making it more suitable for decision-making across various cost-loss scenarios. By introducing this novel model and evaluating traditional forecasting techniques, this study lays the groundwork for more accurate and reliable seasonal rainfall predictions.

    Keywords: Seasonal rainfall prediction, rainfall variability, Sudan-Sahel region, West Africa, Kmeans, Logistic regresion

    Received: 15 Nov 2024; Accepted: 24 Dec 2024.

    Copyright: © 2024 Rauch, Bliefernicht, Sawadogo, SY, Waongo and Kunstmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Harald Kunstmann, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76344, Baden-Württemberg, Germany

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