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<p>This study integrates multifractal detrended fluctuation analysis (MFDFA) with climate-driven water quality dynamics to assess climate change impacts on Iran’s Helle River across multiple timescales. Unlike traditional linear models, this approach captures nonlinear, scale-dependent behaviors in water quality time series. Results show that climate change significantly alters water quality parameters: magnesium displays high sensitivity with notable variations, while acidity and sodium remain relatively stable. The study uniquely identifies scale-specific multifractal signatures, such as acidity (<i>Δα</i> = 1.27) and sulfate (<i>Δα</i> = 1.07), highlighting their variability under climate forcing. Nonlinear models sensitive to extreme fluctuations are recommended for accurate medium- and long-term forecasting, contrasting with linear models suited for short-term predictions. These findings link multifractal dynamics to climate-induced water quality changes, offering a framework for region-specific adaptation strategies. The outcomes provide actionable insights for sustainable water management in semi-arid regions facing climate extremes, advancing the understanding of complex climate-river interactions.</p>

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Evaluating climate change effects on water quality dynamics over multiple time scales: a case study of Iran’s Helle River basin

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Farhang Rahmani,
  • Mohammad Hadi Fattahi

摘要

This study integrates multifractal detrended fluctuation analysis (MFDFA) with climate-driven water quality dynamics to assess climate change impacts on Iran’s Helle River across multiple timescales. Unlike traditional linear models, this approach captures nonlinear, scale-dependent behaviors in water quality time series. Results show that climate change significantly alters water quality parameters: magnesium displays high sensitivity with notable variations, while acidity and sodium remain relatively stable. The study uniquely identifies scale-specific multifractal signatures, such as acidity (Δα = 1.27) and sulfate (Δα = 1.07), highlighting their variability under climate forcing. Nonlinear models sensitive to extreme fluctuations are recommended for accurate medium- and long-term forecasting, contrasting with linear models suited for short-term predictions. These findings link multifractal dynamics to climate-induced water quality changes, offering a framework for region-specific adaptation strategies. The outcomes provide actionable insights for sustainable water management in semi-arid regions facing climate extremes, advancing the understanding of complex climate-river interactions.