Kim et al. (2026) Advancing understanding of parameterization effects in global hydrologic models through multi-model, multi-variable evaluation

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-27
• Authors: Junho Kim, Jonghun Kam, Daeryong Park, Kuk-Hyun Ahn
• DOI: 10.1016/j.jhydrol.2026.135208

Research Groups

• Department of Civil and Environmental Engineering, Kongju National University, Cheon-an, South Korea
• Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea
• Department of Civil and Environmental Engineering, Konkuk University, Seoul, South Korea

Short Summary

This study investigates how parameter choices in four Global Hydrological Models (GHMs) affect hydrological simulations by optimizing them with multi-variable data across 228 global watersheds, revealing that optimized parameters generally outperform default settings and highlighting risks of high-flow overestimation with default parameters.

Objective

• To examine how parameter choices affect simulation outcomes in four Global Hydrological Models (GHMs) by optimizing them using different hydrological variables across 228 watersheds worldwide.

Study Configuration

• Spatial Scale: 228 watersheds worldwide
• Temporal Scale: Historical baseline period

Methodology and Data

• Models used: CWatM, PCR-GLOBWB, H08, HydroPy
• Data sources: Hydrological variables (discharge, actual evapotranspiration, soil wetness, total water storage) used for parameter optimization and evaluation.

Main Results

• Global Hydrological Models (GHMs) with optimized parameters generally outperform those using default values during the historical baseline period.
• Default parameters occasionally lead to poor predictions, particularly suboptimal discharge simulations in cold regions.
• Default settings sometimes overestimate high flows, which could lead to inaccurate flood projections under climate change.
• While multi-model ensembles can enhance simulation accuracy, the use of default parameters within these ensembles may still introduce the risk of overestimating high-flow predictions.

Contributions

• Provides a comprehensive multi-model, multi-variable evaluation of parameterization effects in global hydrologic models, addressing a previous lack of such investigations.
• Quantifies the impact of default parameter choices on various hydrological variables and extremes (high and low flows) across a diverse set of global watersheds.
• Underscores the critical importance of robust parameterization strategies for improving the reliability of climate change projections derived from GHMs.

Funding

• Not specified in the provided text.

Citation

@article{Kim2026Advancing,
  author = {Kim, Junho and Kam, Jonghun and Park, Daeryong and Ahn, Kuk-Hyun},
  title = {Advancing understanding of parameterization effects in global hydrologic models through multi-model, multi-variable evaluation},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135208},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135208}
}