Hidalgo et al. (2026) Pareto-Based Multi-Objective Calibration of a Hydrological Model Integrating Streamflow and Snow Cover Area

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-01-01
• Authors: Jose David Hidalgo Hidalgo, David Pulido‐Velazquez, Antonio-Juan Collados-Lara, A. Arda Şorman, A. Şensoy
• DOI: 10.1007/s11269-025-04369-2

Research Groups

• Spanish Geological Survey (IGME-CSIC), Water and Global Change Research, Granada, Spain
• Department of Civil Engineering, University of Granada, Granada, Spain
• Department of Civil Engineering, Eskişehir Technical University, Eskişehir, Turkey

Short Summary

This study developed and calibrated an enhanced Témez hydrological model with a semi-distributed snow module using multi-objective optimization against streamflow and snow cover area (SCA) data. It found that incorporating SCA data significantly improved snow simulation and parameter identifiability, with the Kling-Gupta Efficiency (KGE) metric for SCA yielding more robust results.

Objective

• To extend the Témez hydrological model by integrating a semi-distributed degree-day snow module to simulate streamflow, snowmelt, snow water equivalent, and snow cover area (SCA).
• To assess how accurately the integrated snow module reproduces observed SCA.
• To evaluate how varying the weight assigned to the snow component affects overall model performance and SCA representation.
• To investigate how different snow performance metrics (Nash-Sutcliffe Efficiency and Kling-Gupta Efficiency) influence SCA simulation and the robustness of calibrated parameters within a multi-objective calibration framework.

Study Configuration

• Spatial Scale: Canales basin, Sierra Nevada, southern Spain (176.3 km²; elevation range 812 to 3474 m.a.s.l.). The snow module operates at a 500 m x 500 m spatial resolution.
• Temporal Scale: Daily time step for all simulations and data. Calibration and validation periods were two equal 10-year intervals, starting from October 1, 2000.

Methodology and Data

• Models used:
  • Snow-Témez model: A lumped conceptual Témez hydrological model enhanced with a semi-distributed degree-day snow module.
  • SCE-UA algorithm (Shuffled Complex Evolution - University of Arizona) for automatic multi-objective optimization.
  • Distributed Cellular Automata (CA) model: Used to generate daily SCA series for the Sierra Nevada mountain range, serving as observed SCA data.
• Data sources:
  • Daily precipitation and temperature: AEMET 5 km x 5 km high-resolution gridded climate dataset, downscaled to 500 m x 500 m.
  • Streamflow measurements: Daily data from a gauge station at the inlet of the Canales reservoir (2000–2020), provided by the Guadalquivir River Basin Authority.
  • Daily Snow Cover Area (SCA): Simulated by a CA model, which was calibrated using MODIS Terra Snow Cover Daily Global 500 m Grid (MOD10A1) products.

Main Results

• The Cellular Automata (CA) model accurately reproduced the temporal dynamics of SCA, showing a Nash-Sutcliffe Efficiency (NSE) of 0.78 for both calibration and validation periods, with low dispersion and high correlation with MODIS observations.
• Multi-objective calibration, integrating streamflow and SCA data, significantly improved SCA simulation performance without substantially compromising streamflow efficiency, particularly when the streamflow weight (wQ) was greater than 0.
• The Kling-Gupta Efficiency (KGE)-based multi-objective approach yielded a more distinct and better-defined Pareto front, systematically reducing bias in SCA simulation, compared to the Nash-Sutcliffe Efficiency (NSE)-based approach.
• The inclusion of SCA data in the calibration process led to a substantial reduction in the variability of key snow-related parameters (e.g., degree-day factor during snowmelt phase (DDFs), melt temperature threshold (Tm), and snowfall temperature threshold (Tc)), with a reduction of approximately 30% for melt-related parameters.
• Streamflow-generation parameters were largely unaffected by the inclusion of SCA data, except for the excess runoff coefficient (C), which showed more constrained values in the single-objective calibration.

Contributions

• This study is the first to evaluate the benefits of multi-objective calibration for the Témez model coupled with a snow module, explicitly generating Snow Cover Area (SCA) as an output.
• It demonstrates that integrating remote sensing-derived SCA data into the calibration process of a lumped conceptual hydrological model significantly enhances the representation of snow processes and improves parameter identifiability, addressing model equifinality.
• It highlights the superior performance of Kling-Gupta Efficiency (KGE) over Nash-Sutcliffe Efficiency (NSE) as an objective function for SCA in multi-objective calibration, leading to more robust and physically interpretable outcomes with reduced bias.
• The research quantifies the reduction in parameter variability for snow-related parameters (approximately 30%) when SCA data is included, reinforcing the value of multivariable calibration for constraining different parts of the hydrological system.

Funding

• STAGES-IPCC (TED2021-130744B-C21/AEI/https://doi.org/10.13039/501100011033/Unión Europea NextGenerationEU/PRTR)
• SIGLO-PRO (PID2021-128021OB-I00/AEI/https://doi.org/10.13039/501100011033/FEDER, UE)
• SER-PM (2908/22; Organismo Autónomo Parques Nacionales) from the National Park Research Program
• SIERRA-CC (PID2022-137623OA-I00) funded by MICIU/AEI/https://doi.org/10.13039/501100011033 and by ERDF, UE

Citation

@article{Hidalgo2026ParetoBased,
  author = {Hidalgo, Jose David Hidalgo and Pulido‐Velazquez, David and Collados-Lara, Antonio-Juan and Şorman, A. Arda and Şensoy, A.},
  title = {Pareto-Based Multi-Objective Calibration of a Hydrological Model Integrating Streamflow and Snow Cover Area},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-025-04369-2},
  url = {https://doi.org/10.1007/s11269-025-04369-2}
}