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<p>This study focused on the upstream (Gannan), midstream (Dingxi), and downstream (Linxia) sections of the Tao River in northwest China. It proposed a river health assessment method suitable for regions exhibiting pronounced “mountain-oasis-desert” spatial heterogeneity, providing an important case for understanding the individualized responses of rivers in arid regions within a common overarching pattern. We applied a genetic algorithm enhanced Analytic Hierarchy Process (GA-AHP) to analyze the weight data from 300 cases through iteration to reduce the empirical bias common in traditional methods. We applied a coupled evaluation model that integrates the matter-element model with Gaussian membership functions to comprehensively assess river health, and used Pearson correlation analysis to identify key ecological response indicators for each section of the river. The research results indicated a stepwise decline in river health along the flow, exhibiting pronounced spatial heterogeneity. In the upstream, 92% of indicators were rated above “Fair”, compared with 67% in both the midstream and downstream. 41.2% of indicators showed significant longitudinal declines, and another 33.3% exhibited noticeable downward trends along the river course. Correlation analysis highlighted distinct spatial heterogeneity in ecological response indicators across the basin, underscoring the need for zoned management strategies adapted to local conditions.</p>

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Spatial heterogeneity of river health in the Taohe River basin as a Northwest inland river of China

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Zhi Qu,
  • Yan Zhang,
  • Ying Zhang,
  • Yun Wei,
  • Chenjia An

摘要

This study focused on the upstream (Gannan), midstream (Dingxi), and downstream (Linxia) sections of the Tao River in northwest China. It proposed a river health assessment method suitable for regions exhibiting pronounced “mountain-oasis-desert” spatial heterogeneity, providing an important case for understanding the individualized responses of rivers in arid regions within a common overarching pattern. We applied a genetic algorithm enhanced Analytic Hierarchy Process (GA-AHP) to analyze the weight data from 300 cases through iteration to reduce the empirical bias common in traditional methods. We applied a coupled evaluation model that integrates the matter-element model with Gaussian membership functions to comprehensively assess river health, and used Pearson correlation analysis to identify key ecological response indicators for each section of the river. The research results indicated a stepwise decline in river health along the flow, exhibiting pronounced spatial heterogeneity. In the upstream, 92% of indicators were rated above “Fair”, compared with 67% in both the midstream and downstream. 41.2% of indicators showed significant longitudinal declines, and another 33.3% exhibited noticeable downward trends along the river course. Correlation analysis highlighted distinct spatial heterogeneity in ecological response indicators across the basin, underscoring the need for zoned management strategies adapted to local conditions.