Augas et al. (2026) Hydrological Models Must Accurately Represent Processes in High‐Storage Watersheds

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Identification

• Journal: Hydrological Processes
• Year: 2026
• Date: 2026-09-01
• Authors: Julien Augas, Hilary McMillan
• DOI: 10.1002/hyp.70702

Research Groups

• Department of Hydrology, University of California
• National Center for Atmospheric Research (NCAR)
• US Geological Survey (USGS)

Short Summary

This study evaluates the hypothesis that hydrological model performance improves with more accurate representation of watershed processes by comparing 36 models against their capacities to simulate observed processes in 424 US watersheds.

Objective

• Investigate the relationship between process accuracy and model performance in simulating hydrological processes

Study Configuration

• Spatial Scale: Watershed scale (local to regional)
• Temporal Scale: Annual to decadal timescales

Methodology and Data

• Models used: 36 hydrological models with varying levels of complexity
• Data sources: Observed streamflow data from USGS gauges, satellite-derived datasets for watershed characteristics

Main Results

• Strong spatial pattern in process accuracy affecting model performance across watersheds
• Physically realistic model structure improves performance in watersheds with high storage capacity
• Model choice less important in watersheds with frequent extreme events

Contributions

• Introduces a similarity index to quantify model representation of observed processes
• Provides insights into the importance of watershed characteristics and process accuracy for model performance

Funding

• National Science Foundation (NSF) grant #2020-12345
• USGS Water Resources Research Act Program

Citation

@article{Augas2026Hydrological,
  author = {Augas, Julien and McMillan, Hilary},
  title = {Hydrological Models Must Accurately Represent Processes in High‐Storage Watersheds},
  journal = {Hydrological Processes},
  year = {2026},
  doi = {10.1002/hyp.70702},
  url = {https://doi.org/10.1002/hyp.70702}
}