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<p>This study applies the Modified Crossing Empirical Trend Analysis (MCETA) to assess temperature and rainfall changes over time in Tunisia’s largest watershed, Medjerda High Valley. It analyzes the annual climatic datasets of Beja, Jendouba, and Kef, from 1985 to 2023 on different levels of variability (5%, 50%, and 95%). To assess the climate pattern complexity detection ability of MCETA, it was benchmarked against classical methods like Sen’s Slope estimator and Crossing Empirical Trend Analysis (CETA). The risk thresholds were calculated using CDF analysis, while the data normality was confirmed with the Kolmogorov-Smirnov test. The results obtained show that this region has exhibited a strong warming trend, with over 100% of temperature records demonstrating rises. Regarding precipitation patterns, the findings reveal that 52.4% of the rainfall series displayed increasing trends, while 47.6% exhibited decreasing trends. MCETA particularly well performed the distinction between median and extreme rainfall behavior. Droughts were more pronounced at central station (Kef), while northwestern station (Jendouba and Beja) maintained a persistent flooding risk. This study demonstrates the potential of MCETA in climate risk management using resilient water system strategies and offers a benchmark for the analysis of climatic extremes in Mediterranean and semi-arid regions.</p>

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Risk-sensitive climate trend detection in the Medjerda High Valley, Tunisia: application of the modified crossing empirical trend analysis (MCETA)

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Sahar Abidi,
  • Ahmet Iyad Ceyhunlu,
  • Hamza Pi̇r,
  • Gokmen Ceribasi,
  • Mahnaz Gümrükçüoğlu Yi̇ği̇t

摘要

This study applies the Modified Crossing Empirical Trend Analysis (MCETA) to assess temperature and rainfall changes over time in Tunisia’s largest watershed, Medjerda High Valley. It analyzes the annual climatic datasets of Beja, Jendouba, and Kef, from 1985 to 2023 on different levels of variability (5%, 50%, and 95%). To assess the climate pattern complexity detection ability of MCETA, it was benchmarked against classical methods like Sen’s Slope estimator and Crossing Empirical Trend Analysis (CETA). The risk thresholds were calculated using CDF analysis, while the data normality was confirmed with the Kolmogorov-Smirnov test. The results obtained show that this region has exhibited a strong warming trend, with over 100% of temperature records demonstrating rises. Regarding precipitation patterns, the findings reveal that 52.4% of the rainfall series displayed increasing trends, while 47.6% exhibited decreasing trends. MCETA particularly well performed the distinction between median and extreme rainfall behavior. Droughts were more pronounced at central station (Kef), while northwestern station (Jendouba and Beja) maintained a persistent flooding risk. This study demonstrates the potential of MCETA in climate risk management using resilient water system strategies and offers a benchmark for the analysis of climatic extremes in Mediterranean and semi-arid regions.