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<p>Urbanization and climate change have substantially altered rainfall–runoff processes, intensifying seasonal hydrological imbalances in mountainous basins. While widely used models such as hydrological analogy, the SCS-CN method, and reciprocal formulations are individually established, their systematic integration for mechanism-oriented runoff analysis remains limited. In this study, a structured three-stage coupling framework was developed in the Feng gao River basin (Southwest China) to quantify spatiotemporal variability of runoff coefficients (<i>RC</i>s) and to diagnose rainfall–runoff mechanisms across land-use types. First, regionalized runoff scaling based on hydrological analogy provided basin-level runoff constraints under ungauged conditions. Second, these constraints were used to calibrate SCS–CN parameters, reducing seasonal bias in conventional applications. Third, a reciprocal diagnostic model was introduced to characterize nonlinear rainfall–RC relationships and to extract physically interpretable parameters representing structural runoff ceilings (1/a) and rainfall sensitivity (b). Results show strong consistency between the calibrated SCS-CN outputs and regionalized runoff estimates (<i>r</i> = 0.9732), while revealing systematic seasonal contrasts between dry and wet periods. Land-use heterogeneity exerted pronounced control on runoff generation: water bodies (84.37%) and transportation land (67.69%) exhibited high and stable RCs, cropland (34.40%) and built-up land (32.50%) showed transitional responses, whereas forest (22.39%), grassland (25.53%), and shrubland (16.63%) functioned as hydrological buffers. The reciprocal model successfully captured nonlinear rainfall–runoff dynamics across all land-use categories (R² &gt; 0.85), enabling explicit linkage between land-use structure and runoff sensitivity. By bridging empirical regional scaling with process-based calibration and parameter-diagnostic modeling, this study advances a transferable framework for runoff estimation in data-scarce mountainous basins. The integration strategy enhances both predictive reliability and mechanistic interpretability, providing practical support for watershed management under accelerating urbanization and climate variability.</p>

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Spatiotemporal Variability of Runoff Coefficients and Rainfall–Runoff Responses in a Mountainous Basin Using Integrated Hydrological Analogy and SCS–CN Models

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Zhang Jing,
  • Yang Lu Ping,
  • Chen Zi-jing,
  • Peng Shi-tao,
  • Xing Bing

摘要

Urbanization and climate change have substantially altered rainfall–runoff processes, intensifying seasonal hydrological imbalances in mountainous basins. While widely used models such as hydrological analogy, the SCS-CN method, and reciprocal formulations are individually established, their systematic integration for mechanism-oriented runoff analysis remains limited. In this study, a structured three-stage coupling framework was developed in the Feng gao River basin (Southwest China) to quantify spatiotemporal variability of runoff coefficients (RCs) and to diagnose rainfall–runoff mechanisms across land-use types. First, regionalized runoff scaling based on hydrological analogy provided basin-level runoff constraints under ungauged conditions. Second, these constraints were used to calibrate SCS–CN parameters, reducing seasonal bias in conventional applications. Third, a reciprocal diagnostic model was introduced to characterize nonlinear rainfall–RC relationships and to extract physically interpretable parameters representing structural runoff ceilings (1/a) and rainfall sensitivity (b). Results show strong consistency between the calibrated SCS-CN outputs and regionalized runoff estimates (r = 0.9732), while revealing systematic seasonal contrasts between dry and wet periods. Land-use heterogeneity exerted pronounced control on runoff generation: water bodies (84.37%) and transportation land (67.69%) exhibited high and stable RCs, cropland (34.40%) and built-up land (32.50%) showed transitional responses, whereas forest (22.39%), grassland (25.53%), and shrubland (16.63%) functioned as hydrological buffers. The reciprocal model successfully captured nonlinear rainfall–runoff dynamics across all land-use categories (R² > 0.85), enabling explicit linkage between land-use structure and runoff sensitivity. By bridging empirical regional scaling with process-based calibration and parameter-diagnostic modeling, this study advances a transferable framework for runoff estimation in data-scarce mountainous basins. The integration strategy enhances both predictive reliability and mechanistic interpretability, providing practical support for watershed management under accelerating urbanization and climate variability.