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<p>Surface runoff is a key hydrological process that controls water availability, flood generation, soil erosion, and ecosystem stability, particularly in high-Andean basins that are highly vulnerable to climate change. This study developed an integrated spatial modeling framework based on the Soil Conservation Service Curve Number (SCS-CN) method, Geographic Information System (GIS), multitemporal land cover dynamics, and CMIP6 climate projections to assess the impacts of climate change on runoff generation in the Ilave River Basin, Peru. Land cover maps from 1985 to 2022 were combined with hydrological soil group types, slope classes, and precipitation records from 18 meteorological stations through overlay. Future runoff conditions were simulated using the SSP1-2.6, SSP2-4.5, and SSP5-8.5 climate scenarios, derived from an ensemble of ACCES-CM2, HadGEM3-GC31-LL, and MPI-ESM1-2-HR models, for the period 2021–2040. Results revealed a progressive decline in areas with low runoff coefficient (&lt; 0.4) and a significant expansion of medium- and high-runoff classes (&gt; 0.45), associated with agricultural expansion, vegetation degradation, and glacier retreat. Areas characterized by runoff between 300 and 450&#xa0;mm increased from 2571.9 km<sup>2</sup> in 1985 to 2723.5 km<sup>2</sup> in 2022. Under SSP-8.5, high-runoff areas are projected to increase by more than 45% compared with historical conditions. Glacier coverage decreased from 6.3 km<sup>2</sup> in 1985 to 0.1 km<sup>2</sup> in 2022 and remains critically reduced under all future scenarios. The proposed spatial modeling framework provides an effective approach for evaluating hydrological responses to combined land-cover and climate-change drivers. It supports climate adaptation and watershed management in vulnerable high-mountain ecosystems.</p>

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GIS-based SCS-CN modeling of surface runoff under CMIP6 climate change scenarios and land cover dynamics in the high-Andean Ilave River Basin, Peru

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Fredy Calizaya,
  • Osmar Cuentas,
  • Abel Mejía,
  • Elmer Calizaya,
  • Maryluz Cuentas,
  • Melvin Pozo,
  • Cirilo Caira,
  • Walquer Huacani

摘要

Surface runoff is a key hydrological process that controls water availability, flood generation, soil erosion, and ecosystem stability, particularly in high-Andean basins that are highly vulnerable to climate change. This study developed an integrated spatial modeling framework based on the Soil Conservation Service Curve Number (SCS-CN) method, Geographic Information System (GIS), multitemporal land cover dynamics, and CMIP6 climate projections to assess the impacts of climate change on runoff generation in the Ilave River Basin, Peru. Land cover maps from 1985 to 2022 were combined with hydrological soil group types, slope classes, and precipitation records from 18 meteorological stations through overlay. Future runoff conditions were simulated using the SSP1-2.6, SSP2-4.5, and SSP5-8.5 climate scenarios, derived from an ensemble of ACCES-CM2, HadGEM3-GC31-LL, and MPI-ESM1-2-HR models, for the period 2021–2040. Results revealed a progressive decline in areas with low runoff coefficient (< 0.4) and a significant expansion of medium- and high-runoff classes (> 0.45), associated with agricultural expansion, vegetation degradation, and glacier retreat. Areas characterized by runoff between 300 and 450 mm increased from 2571.9 km2 in 1985 to 2723.5 km2 in 2022. Under SSP-8.5, high-runoff areas are projected to increase by more than 45% compared with historical conditions. Glacier coverage decreased from 6.3 km2 in 1985 to 0.1 km2 in 2022 and remains critically reduced under all future scenarios. The proposed spatial modeling framework provides an effective approach for evaluating hydrological responses to combined land-cover and climate-change drivers. It supports climate adaptation and watershed management in vulnerable high-mountain ecosystems.