Ning et al. (2025) Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-25
• Authors: Zhongrui Ning, Jianyun Zhang, Nan Wu, Hossein Hashemi, Fernando Jaramillo, Amir Naghibi, Kang Xie, Yuli Ruan, Cuishan Liu, Guoqing Wang, Jerker Jarsjö
• DOI: 10.1016/j.jhydrol.2025.134852

Research Groups

• The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
• Yangtze Institute for Conservation and Development, Nanjing 210098, China
• Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China
• Nanjing Hydraulic Research Institute, Nanjing 210029, China
• Division of Water Resources Engineering, LTH, Lund University, Lund 22100, Sweden
• Department of Physical Geography, Stockholm University, Stockholm 10691, Sweden

Short Summary

This study evaluates the performance of 14 monthly water balance models across over 2,000 global catchments, revealing that while most models perform reasonably well, their accuracy significantly declines in high-latitude or snow-dominated regions and improves with the inclusion of non-linear snow modules and in more humid conditions.

Objective

• To evaluate the performance and structural sensitivity of 14 monthly water balance models across more than 2,000 global catchments, comparing their capacities to reproduce water-cycle processes and identify thresholds where performance varies significantly.

Study Configuration

• Spatial Scale: More than 2,000 global catchments.
• Temporal Scale: Monthly water balance simulations.

Methodology and Data

• Models used: 14 different monthly water balance models (MWBMs).
• Data sources: Not explicitly stated in the provided text, but implies standard hydrological inputs (e.g., precipitation, temperature, runoff observations for calibration/validation).

Main Results

• Most evaluated models produce reasonable runoff simulations across a wide range of settings.
• Model performance notably declines in catchments at latitudes above 60°N or when the ratio of snowfall to total precipitation exceeds 20 %.
• The inclusion of a non-linear snow module significantly improved model performance in snow-affected basins, attributed to a better representation of physical processes like freeze–thaw cycles, rather than just an increased number of fitting parameters.
• Models generally performed slightly better in relatively humid regions than in arid ones.
• Model performance was not sensitive to model structure in sufficiently humid catchments, with approximate thresholds of annual precipitation exceeding 1000 mm and annual runoff exceeding 300 mm.
• Simulation results for larger catchments were frequently more stable than those for smaller catchments.

Contributions

• Provides a comprehensive multi-model evaluation of 14 monthly water balance models across a large global sample of over 2,000 catchments.
• Identifies specific geographical and climatic thresholds (e.g., latitude > 60°N, snowfall/precipitation ratio > 20 %, annual precipitation > 1000 mm, annual runoff > 300 mm) that significantly influence MWBM performance and structural sensitivity.
• Demonstrates the critical importance and benefits of incorporating non-linear snow modules for accurate simulations in snow-affected regions, linking improvements to better physical process representation.
• Offers insights to help assess, identify, and address potential biases in regional and global water balance modeling studies, aiding in adaptation to hydroclimatic change.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Ning2025Multimodel,
  author = {Ning, Zhongrui and Zhang, Jianyun and Wu, Nan and Hashemi, Hossein and Jaramillo, Fernando and Naghibi, Amir and Xie, Kang and Ruan, Yuli and Liu, Cuishan and Wang, Guoqing and Jarsjö, Jerker},
  title = {Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134852},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134852}
}