Key role of deep-water observations for Probabilistic Parameter Estimation in Lake Hydrodynamic Models
Year: 2025
Type: preprint
Abstract: Physics-based hydrodynamic models simulating lake water temperature often face challenges due to sparse observational data and equifinality, where many parameter combinations yield similar goodness-of-fit to observations, complicating calibration. This study presents a framework to investigate how the number and location of observation depths used in calibration influence temperature prediction accuracy and parameter equifinality. We leverage a computationally efficient one-dimensional physics-based model (GLM; General Lake Model), and apply probabilistic parameter estimation within a Generalized Likelihood Uncertainty Estimation (GLUE) framework. Using seven years of hourly water temperature data from Lake Mendota (WI, USA), we evaluate parameter equifinality with the Gini index and assess predictive performance using multiple metrics across single-depth, dual-depth, and full-profile observation scenarios. Results show that single-depth deep-water observations consistently outperform shallower ones – and sometimes even full-profile observations, significantly reducing equifinality and achieving robust accuracy throughout the water column. Contrary to expectations, full-profile data do not yield superior predictive performance throughout the water column. Instead, it dilutes the influence of deep-water observations on parameter selection. Dual-depth scenarios show that single-depth surface mixed layer observations can be vastly improved by adding a second observation depth. These findings provide practical guidance for optimizing observational strategies and advancing uncertainty-aware lake modeling. In particular, this work supports the management of drinking water reservoirs by informing the strategic selection of fewer, well-placed deep-water observations. In doing so, it encourages model development where these are available – e.g., drinking water reservoirs commonly rely on deep-layer intakes, with routine measurements carried out at water treatment plants.
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415107018
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