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<p>Understanding the dynamics of reference evapotranspiration (ET₀) is vital for managing water resources and adapting agricultural practices to climate variability. This study explores the dynamic behavior of monthly ET₀ across 14 agro-climatic zones in India over the period 2000–2020 using high-resolution ERA5 reanalysis data. ET₀ was calculated via the FAO-56 Penman-Monteith method, and its temporal complexity was analyzed using Recurrence Quantification Analysis (RQA) and Cross Recurrence Quantification Analysis (CRQA). The results reveal distinct spatial patterns viz. coastal zones exhibit stable and periodic ET₀ behavior, while arid and mountainous regions display chaotic and less predictable dynamics. Meteorological drivers such as temperature and solar radiation show strong, consistent influence in dry regions, whereas humidity and pressure are more significant in humid and high-altitude zones. The use of Monte Carlo permutation testing confirmed the statistically significance of these relationships, emphasizing the spatial heterogeneity of ET₀-climate coupling. These insights highlight the limitations of uniform hydrological models and advocate for region-specific strategies in irrigation planning and climate resilience.</p>

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Analysing the Hydro-Meteorological synchronization of reference evapotranspiration across Indian Mainland using cross recurrence approach

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • S Adarsh,
  • M Athira,
  • Ali Najah Ahmed,
  • Ahmed El-Shafie,
  • N Salman

摘要

Understanding the dynamics of reference evapotranspiration (ET₀) is vital for managing water resources and adapting agricultural practices to climate variability. This study explores the dynamic behavior of monthly ET₀ across 14 agro-climatic zones in India over the period 2000–2020 using high-resolution ERA5 reanalysis data. ET₀ was calculated via the FAO-56 Penman-Monteith method, and its temporal complexity was analyzed using Recurrence Quantification Analysis (RQA) and Cross Recurrence Quantification Analysis (CRQA). The results reveal distinct spatial patterns viz. coastal zones exhibit stable and periodic ET₀ behavior, while arid and mountainous regions display chaotic and less predictable dynamics. Meteorological drivers such as temperature and solar radiation show strong, consistent influence in dry regions, whereas humidity and pressure are more significant in humid and high-altitude zones. The use of Monte Carlo permutation testing confirmed the statistically significance of these relationships, emphasizing the spatial heterogeneity of ET₀-climate coupling. These insights highlight the limitations of uniform hydrological models and advocate for region-specific strategies in irrigation planning and climate resilience.