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<p>Understanding the temporal evolution of hydro meteorological trends is critical for climate adaptation and water resource management, yet conventional trend analysis methods provide static assessments that obscure important regime shifts and non-stationary behavior. This study introduces a Rolling Window Innovative Trend Analysis (RW-ITA) methodology that captures the dynamic evolution of climate trends through sequential analysis of overlapping time windows. The method was applied to long-term temperature, precipitation, evapotranspiration, and streamflow data using 30-year and 20-year rolling windows with 1-year overlap, complemented by monthly-scale analysis. The RW-ITA approach successfully identified complex temporal dynamics that conventional whole-series trend analysis would mask. Temperature analysis revealed three distinct phases: initial non-significant warming (1929–1950s), mid-20th-century cooling (1950–1970s), and sustained warming from the mid-1970s onward, with accelerated trends exceeding 99% confidence intervals from the 1980s. Precipitation exhibited oscillatory behavior with alternating periods of significant increases and decreases. Evapotranspiration demonstrated a three-phase evolution with the 20-year analysis revealing a recent sharp reversal to decreasing trends (1996–2020). Most critically, streamflow analysis identified a fundamental hydrological regime shift in the mid-1970s, transitioning from a weak, non-significant upward tendency to a sustained and significant decline persisting through 2018. The comparative analysis of 30-year and 20-year windows demonstrated scale-dependent trend detection capabilities, with shorter windows providing enhanced sensitivity to rapid transitions while longer windows offered greater stability. Monthly RW-ITA analysis revealed distinct seasonal signatures in environmental change, with streamflow demonstrating remarkably consistent seasonal behavior in the mid-1970s transition across all months. The RW-ITA methodology extends innovative trend analysis into a window-resolved framework with a calibrated significance test, providing essential insights for understanding environmental system dynamics under changing climatic conditions. The identification of regime shifts and scale-dependent behaviors has profound implications for water resource planning and climate adaptation strategies, emphasizing the need for adaptive management approaches that account for the non-stationary nature of hydro meteorological systems.</p>

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Introducing Rolling Window Innovative Trend Analysis (RW-ITA): A New Method for Identifying Hidden Trends in Hydro-Meteorological Variables

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Musa Esit,
  • Ibrahim Halil Deger,
  • Mehmet Ishak Yuce,
  • Islam Yasa,
  • Ergun Akbas

摘要

Understanding the temporal evolution of hydro meteorological trends is critical for climate adaptation and water resource management, yet conventional trend analysis methods provide static assessments that obscure important regime shifts and non-stationary behavior. This study introduces a Rolling Window Innovative Trend Analysis (RW-ITA) methodology that captures the dynamic evolution of climate trends through sequential analysis of overlapping time windows. The method was applied to long-term temperature, precipitation, evapotranspiration, and streamflow data using 30-year and 20-year rolling windows with 1-year overlap, complemented by monthly-scale analysis. The RW-ITA approach successfully identified complex temporal dynamics that conventional whole-series trend analysis would mask. Temperature analysis revealed three distinct phases: initial non-significant warming (1929–1950s), mid-20th-century cooling (1950–1970s), and sustained warming from the mid-1970s onward, with accelerated trends exceeding 99% confidence intervals from the 1980s. Precipitation exhibited oscillatory behavior with alternating periods of significant increases and decreases. Evapotranspiration demonstrated a three-phase evolution with the 20-year analysis revealing a recent sharp reversal to decreasing trends (1996–2020). Most critically, streamflow analysis identified a fundamental hydrological regime shift in the mid-1970s, transitioning from a weak, non-significant upward tendency to a sustained and significant decline persisting through 2018. The comparative analysis of 30-year and 20-year windows demonstrated scale-dependent trend detection capabilities, with shorter windows providing enhanced sensitivity to rapid transitions while longer windows offered greater stability. Monthly RW-ITA analysis revealed distinct seasonal signatures in environmental change, with streamflow demonstrating remarkably consistent seasonal behavior in the mid-1970s transition across all months. The RW-ITA methodology extends innovative trend analysis into a window-resolved framework with a calibrated significance test, providing essential insights for understanding environmental system dynamics under changing climatic conditions. The identification of regime shifts and scale-dependent behaviors has profound implications for water resource planning and climate adaptation strategies, emphasizing the need for adaptive management approaches that account for the non-stationary nature of hydro meteorological systems.