Wang et al. (2026) Decomposition, quantification and reduction of hydrological modelling uncertainty across multiple climate zones

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-10-18
• Authors: Yuhao Wang, Zhanling Li, Yingtao Ye, Xiaoyan Zhai, Zhanjie Li
• DOI: 10.1016/j.jhydrol.2026.136646

Research Groups

• Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences (Beijing)
• School of Water Resources and Environment, China University of Geosciences (Beijing)
• Ninghai County Water Conservancy Bureau
• Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research
• Beijing Key Laboratory of Urban Water Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University

Short Summary

This study developed a new framework to quantify and decompose hydrological modeling uncertainties from meteorological forcing, model structure, and parameters across 12 watersheds in three major climate zones in China, and proposed targeted strategies for uncertainty reduction.

Objective

• To investigate the decomposition, quantification, and reduction of hydrological modelling uncertainty across multiple climate zones, specifically focusing on contributions from meteorological forcing, model structure, and parameters.

Study Configuration

• Spatial Scale: 12 watersheds across three major climate zones in China: Northwestern Arid Zone (NAZ), Tibetan Plateau Zone (TPZ), and Eastern Monsoon Zone (EMZ).
• Temporal Scale: Not explicitly stated in the provided text.

Methodology and Data

• Models used: Lumped conceptual hydrological model WASMOD-E.
• Data sources: Meteorological products (as a source of uncertainty), and a new framework utilizing MANOVA for uncertainty decomposition.

Main Results

• The WASMOD-E model demonstrated good applicability across the study areas, performing better in the Northwestern Arid Zone (NAZ) and the Tibetan Plateau Zone (TPZ). However, NAZ exhibited the largest simulation uncertainty, while TPZ showed the smallest.
• Meteorological products were identified as the primary uncertainty contributor in the NAZ and TPZ. Model structures contributed most significantly in the Eastern Monsoon Zone (EMZ), while model parameters had only a marginal contribution across all study areas.
• The proposed dynamic quantile weighting and adaptive Bayesian model averaging strategies effectively enhanced simulation accuracy and reduced uncertainties stemming from meteorological products and model structural variations.

Contributions

• Developed a new framework to evaluate model applicability, quantify simulation uncertainty, and decompose the contributions of meteorological products, model structures, and model parameters using MANOVA.
• Proposed and validated effective targeted uncertainty reduction strategies (dynamic quantile weighting and adaptive Bayesian model averaging).
• Clarified the patterns governing hydrological uncertainties across different climate zones, providing new insights for improving hydrological models and enhancing forecasting capabilities.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Wang2026Decomposition,
  author = {Wang, Yuhao and Li, Zhanling and Li, Zhanling and Ye, Yingtao and Zhai, Xiaoyan and Li, Zhanjie and Li, Zhanjie},
  title = {Decomposition, quantification and reduction of hydrological modelling uncertainty across multiple climate zones},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136646},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136646}
}