Onen et al. (2026) Strategic Deep‐Water Observations Enhance Probabilistic Parameter Estimation of Lake Hydrodynamic Models

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-09-01
• Authors: Mustafa Onur Onen, Robert Ladwig, Isabel Douterelo, Geoff J. C. Darch, Charles Rougé
• DOI: 10.1029/2025wr042103

Research Groups

• Department of Civil and Environmental Engineering, University of Wisconsin-Madison
• Center for Limnology, University of Wisconsin-Madison

Short Summary

This study investigates the impact of observation depth on lake water temperature prediction accuracy and parameter equifinality using a physics-based model. The results show that single-depth deep-water observations can outperform shallower depths and full-profile observations in achieving accurate predictions.

Objective

• Investigate how the number and location of observation depths affect temperature prediction accuracy and parameter equifinality in lake hydrodynamic models.

Study Configuration

• Spatial Scale: Lake Mendota, WI, USA (a mid-latitude lake in North America)
• Temporal Scale: 7 years of hourly water temperature data

Methodology and Data

• Models used: General Lake Model (GLM) with probabilistic parameter estimation within a Generalized Likelihood Uncertainty Estimation framework
• Data sources: Hourly water temperature observations from Lake Mendota

Main Results

• Single-depth deep-water observations consistently outperform shallower depths and sometimes even full-profile observations in achieving accurate match throughout the water column.
• Dual-depth scenarios show that single-depth surface mixed layer observations can be improved by adding a second observation depth.

Contributions

• This study provides practical examples for optimizing observational strategies and advancing lake hydrodynamic modeling, focusing on critical depths relevant to drinking water reservoir management.

Funding

• This research was supported by the National Science Foundation (grant number: NSF-EAR-1554950) and the University of Wisconsin-Madison's Center for Limnology.

Citation

@article{Onen2026Strategic,
  author = {Onen, Mustafa Onur and Ladwig, Robert and Douterelo, Isabel and Darch, Geoff J. C. and Rougé, Charles},
  title = {Strategic Deep‐Water Observations Enhance Probabilistic Parameter Estimation of Lake Hydrodynamic Models},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2025wr042103},
  url = {https://doi.org/10.1029/2025wr042103}
}