Assessment of drought by precipitation variability in Mediterranean coastal countries using entropy-based analysis


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Barbaros F., De Ketelaere D., Vargün F., Günaçtı M. C., Çetinkaya C. P.

The 13th World Congress of the European Water Resources Association (EWRA) on Water Resources and Environment, "New challenges in understanding and managing water-related risks in a changing environment", Palermo, İtalya, 24 - 28 Haziran 2025, cilt.1, ss.109-110, (Özet Bildiri)

  • Yayın Türü: Bildiri / Özet Bildiri
  • Cilt numarası: 1
  • Basıldığı Şehir: Palermo
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.109-110
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The Mediterranean region is susceptible to climate change as intensive as worldwide, experiencing

increasing drought frequencies and intensities (Singh 2013). The analysis of long-term hydro-meteorological

data is crucial for water resources management and studies on the impacts of climate change.

Understanding the probabilistic nature of drought occurrences is also essential for sustainable water

resources management. Utilizing historical rainfall data of multiple locations, the research applied entropy

methods to quantify uncertainty and detect trends in precipitation patterns.

This study employs entropy-based statistical methods to analyze rainfall data of coastal Mediterranean

countries (Shannon 1948), aiming to assess drought by precipitation variability and forecast potential trends,

evaluating percent changes in rainfall by assessing long-term historical data to manage future basin

management strategies. Maps were generated to visualize the spatial distribution of rainfall uncertainty across

the Mediterranean basin, including information on percentage changes in rainfall from 1960 to 2020. These

maps serve as a critical tool for understanding drought-prone areas and their future potential development with

the information they include. Furthermore, entropy maps visually represent rainfall uncertainty, enhancing the

interpretability of results and identifying critical drought-prone regions (Singh 2024).