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<p>Accurate hydrological modeling in high-mountainous snow-dominated basins is essential for effective water resource management, particularly in climate change-sensitive regions. To better understand the processes that govern hydrological responses, model calibration against multiple variables offers a valuable approach for reducing parameter uncertainty and model equifinality. In data-scarce environments, simple lumped-parameter hydrological models that account for snow accumulation and melting processes are particularly useful. In this study, we used the Témez lumped hydrological model enhanced by the integration of a new semi-distributed snow module to simulate key snow-related processes. We performed a novel sensitivity analysis of the efficiency of the models depending on the adopted multi-objective functions within an automatic procedure to calibrate and validate the models. We evaluated three calibration approaches by varying the weight of the snow cover objective <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{w}_{S}\)</EquationSource> </InlineEquation>. The first procedure consisted of a single-objective calibration against streamflow alone. The other procedures applied multi-objective calibration against streamflow and snow cover, which differed in the performance metric used for the snow component: Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The results demonstrated that incorporating snow cover data into the calibration process improved snow cover simulation without significantly compromising streamflow efficiency, except when the streamflow weight <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{w}_{Q}\)</EquationSource> </InlineEquation> was reduced to zero. Notably, the KGE-based approach yielded a better-defined Pareto front with a more robust snow cover efficiency and reduced bias. Our findings also revealed that snow-related parameters were highly sensitive to the inclusion of snow cover data. Key parameters exhibited substantial changes, with a reduction in variability of approximately 30%.</p>

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Pareto-Based Multi-Objective Calibration of a Hydrological Model Integrating Streamflow and Snow Cover Area

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Jose-David Hidalgo-Hidalgo,
  • David Pulido-Velazquez,
  • Antonio-Juan Collados-Lara,
  • A. Arda Şorman,
  • A. Şensoy

摘要

Accurate hydrological modeling in high-mountainous snow-dominated basins is essential for effective water resource management, particularly in climate change-sensitive regions. To better understand the processes that govern hydrological responses, model calibration against multiple variables offers a valuable approach for reducing parameter uncertainty and model equifinality. In data-scarce environments, simple lumped-parameter hydrological models that account for snow accumulation and melting processes are particularly useful. In this study, we used the Témez lumped hydrological model enhanced by the integration of a new semi-distributed snow module to simulate key snow-related processes. We performed a novel sensitivity analysis of the efficiency of the models depending on the adopted multi-objective functions within an automatic procedure to calibrate and validate the models. We evaluated three calibration approaches by varying the weight of the snow cover objective \(\:{w}_{S}\) . The first procedure consisted of a single-objective calibration against streamflow alone. The other procedures applied multi-objective calibration against streamflow and snow cover, which differed in the performance metric used for the snow component: Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The results demonstrated that incorporating snow cover data into the calibration process improved snow cover simulation without significantly compromising streamflow efficiency, except when the streamflow weight \(\:{w}_{Q}\) was reduced to zero. Notably, the KGE-based approach yielded a better-defined Pareto front with a more robust snow cover efficiency and reduced bias. Our findings also revealed that snow-related parameters were highly sensitive to the inclusion of snow cover data. Key parameters exhibited substantial changes, with a reduction in variability of approximately 30%.