Abbas et al. (2026) Disentangling precipitation forcing and parameter uncertainty in a multi-objective surface–subsurface hydrologic modeling framework

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-07-30
• Authors: Salam A. Abbas, Jeremy T. White, Ryan T. Bailey, Michael James White, Natalja Čerkasova, Jeffrey G. Arnold
• DOI: 10.1016/j.jhydrol.2026.136136

Research Groups

• Department of Civil and Environmental Engineering, Colorado State University
• INTERA Incorporated
• Grassland Soil and Water Research Laboratory, USDA–ARS
• Texas A&M AgriLife Research, Blackland Research and Extension Center
• Klaipeda University Marine Research Institute

Short Summary

This study investigates the impact of precipitation forcing uncertainty on parameter estimation and predictive performance in coupled surface–subsurface hydrologic models. The authors propose a multi-objective framework to represent precipitation uncertainty during calibration.

Objective

• Investigate the effect of precipitation forcing uncertainty on hydrologic parameters and predictive outcomes in coupled surface–subsurface hydrologic models

Study Configuration

• Spatial Scale: Two humid-region watersheds, Winnebago River basin (Iowa, USA) and Monocacy River basin (Maryland, USA)
• Temporal Scale: Monthly mean streamflow and remotely sensed evapotranspiration (ET)

Methodology and Data

• Models used: SWAT+ gwflow
• Data sources: Remotely sensed evapotranspiration (ET), monthly mean streamflow

Main Results

• Precipitation parameterization significantly improved streamflow simulation during simultaneous streamflow–ET calibration in the Winnebago River basin.
• The benefits of precipitation parameterization were watershed-dependent rather than universal, as both PV and PF experiments showed strong streamflow performance in the Monocacy River basin.

Contributions

• This study extends precipitation-uncertainty analysis to coupled surface–subsurface hydrologic modeling, providing insight into how precipitation forcing uncertainty interacts with parameter estimation, sensitivity patterns, and water-balance partitioning across contrasting watershed settings.

Funding

• No funding information is provided in the paper.

Citation

@article{Abbas2026Disentangling,
  author = {Abbas, Salam A. and White, Jeremy T. and Bailey, Ryan T. and White, Michael James and Čerkasova, Natalja and Arnold, Jeffrey G.},
  title = {Disentangling precipitation forcing and parameter uncertainty in a multi-objective surface–subsurface hydrologic modeling framework},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136136},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136136}
}