North et al. (2026) Hydrologic model parameter estimation in snow-dominated headwater catchments using multiple observation datasets

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-08-17
• Authors: Lauren H. North, Adrienne M. Marshall, Glen A. Tootle, Lisa Davis, Andy W. Wood, Eric J. Anderson
• DOI: 10.5194/hess-30-5195-2026

Research Groups

• Hydrologic Science and Engineering, Colorado School of Mines
• Civil and Environmental Engineering, University of Alabama
• Department of Geography, University of Alabama
• National Center for Atmospheric Research
• Cooperative Institute for Research in Environmental Sciences

Short Summary

This study investigates the impact of alternative observations on hydrologic model parameter estimation in snow-dominated headwater catchments. The authors use a multi-observational sensitivity and uncertainty analysis to evaluate the effect of different observation datasets on model performance.

Objective

• Investigate the role of multiple observation datasets in identifying informative parameters for hydrologic model calibration.
• Assess the impact of alternative observations on streamflow prediction performance.

Study Configuration

• Spatial Scale: Four headwater catchments in the Upper Colorado River Basin, with a range of climatologic and geographic attributes.
• Temporal Scale: Water years 1982-2022 (41 years) for sensitivity analysis, and 2013-2022 (10 years) for Monte Carlo filtering.

Methodology and Data

• Models used: US Geologic Survey’s pywatershed, a distributed parameter hydrologic model.
• Data sources:
  • In situ measurements of discharge, snow water equivalent, and soil moisture from the NRCS SNOTEL network.
  • Remotely sensed observations of snow-covered area (MODIS), actual evapotranspiration (OpenET), and soil moisture (SMAP).

Main Results

• The number and type of parameters identified as informative are influenced by the target observation dataset.
• Discharge observations consistently elicit the largest number of sensitive parameters, with between 10-14 parameters identified among the four catchments.
• Alternative observations can constrain equifinality and improve model performance.

Contributions

• This study provides new insights into the variable value of alternative observation data for streamflow prediction in snow-dominated headwater catchments.
• The results highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.

Funding

• This research was supported by [list projects, programs, and reference codes that funded this research].

Citation

@article{North2026Hydrologic,
  author = {North, Lauren H. and Marshall, Adrienne M. and Tootle, Glen A. and Davis, Lisa and Wood, Andy W. and Anderson, Eric J.},
  title = {Hydrologic model parameter estimation in snow-dominated headwater catchments using multiple observation datasets},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-5195-2026},
  url = {https://doi.org/10.5194/hess-30-5195-2026}
}