Bayesian Calibration Points to Misconceptions in Three‐Dimensional Hydrodynamic Reservoir Modeling

Schwindt, Sebastian; Medrano, Sergio Callau; Mouris, Kilian; Beckers, Felix; Haun, Stefan; Nowak, Wolfgang; Wieprecht, Silke; Oladyshkin, Sergey

doi:10.1029/2022wr033660

Cited by 5 publications

(3 citation statements)

References 62 publications

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“…In fact, the proper assessment of any 3D lake model would require a comparison of simulated and observed velocity fields at several locations. However, even 3D models may produce unrealistic scenarios, especially when the calibration of their parameters was improperly or unsuccessfully conducted (Schwindt et al., 2023). Moreover, available measurements are often limited to water temperature profiles and velocity measurements are generally scarce or absent, thus limiting the application of 3D models.…”

Section: Classification Of Lake Temperature Modelsmentioning

confidence: 99%

“…It simulates two‐dimensional flow (in either the horizontal or vertical plane) and three‐dimensional flow (through the Delft3D‐FLOW module), as well as sediment transport, morphology, waves, water quality, and ecology through other specific modules of the modeling suite. This model has been successfully applied in several physical and ecological lake studies (e.g., Amadori et al., 2021; Guo et al., 2023b; Schwindt et al., 2023; Soulignac et al., 2019) and is at the basis of an online platform providing lake observations and three‐dimensional numerical simulations in near real‐time with short‐term forecasts and data assimilation (Baracchini, Wüest, & Bouffard, 2020). Another example is MITgcm (Massachusetts Institute of Technology General Circulation Model, Adcroft et al., 1997), whose hydrodynamic kernel is used to drive both atmospheric and oceanic models and that includes physical and biogeochemical parameterizations of key atmospheric and oceanic processes.…”

Section: Classification Of Lake Temperature Modelsmentioning

confidence: 99%

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Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Piccolroaz,

Zhu,

Ladwig

et al. 2024

Reviews of Geophysics

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Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

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Section: Classification Of Lake Temperature Modelsmentioning

confidence: 99%

Section: Classification Of Lake Temperature Modelsmentioning

confidence: 99%

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Piccolroaz,

Zhu,

Ladwig

et al. 2024

Reviews of Geophysics

View full text Add to dashboard Cite

show abstract

“…Bayesian inference uses Bayesian theory to combine new information with prior probabilities to obtain a new probability (Schwindt et al, 2023). This provides a method for correcting a prior distribution based on the sample information to obtain a posterior distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian‐based calibration for water quality model parameters

Bai,

Dong,

Peng

et al. 2023

Water Environment Research

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To improve the efficiency and accuracy of water quality model parameter calibration and avoid local optima and the phenomenon in which different parameters have the same effect, this paper proposed a novel Bayesian‐based water quality model parameter calibration method. Using Bayesian inference, the parameter calibration problem was converted into a posterior probability function sampling problem, which was sampled using the Markov Chain Monte Carlo algorithm. The convergence speed of the calibration was further improved by setting the optimized initial sampling value. The influences of the initial sampling value, Markov chain length, and proposal distribution form on the calibration effect were evaluated using four specific cases. The results indicate that (1) the mean relative error (MRE) of the parameter calibration results of this method is less than 10%, with the calibration MRE of Dx and Dy being 5.3% and 8.3%, respectively; (2) when the parameter sensitivity is low, the calibration effect of this method is relatively poor, with a calibration MRE of 46% for k; (3) the parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC, choosing a reasonable Markov chain length and a suitable proposal distribution form.Practitioner Points Bayesian‐based water quality model parameter calibration method is proposed and posterior probability distribution was sampled using the MCMC algorithm. Parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC. As a result, efficient and accurate parameter calibration of water quality models was achieved. This method is widely applicable to various models, and the calibration speed depends on the calculation speed of the model.

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Using automatic model calibration for 3D morphological simulations: a case study of the Bodendorf reservoir flushing

Shoarinezhad,

Olsen,

Wieprecht

et al. 2024

Environ Fluid Mech

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Reservoir sedimentation poses a significant challenge to water resource management. Improving the lifespan and productivity of reservoirs requires appropriate sediment management strategies, among which flushing operations have become more prevalent in practice. Numerical modeling offers a cost-effective approach to assessing the performance of different flushing operations. However, calibrating highly parametrized morphological models remains a complex task due to inherent uncertainties associated with sediment transport processes and model parameters. Traditional calibration methods require laborious manual adjustments and expert knowledge, hindering calibration accuracy and efficiency and becoming impractical when dealing with several uncertain parameters. A solution is to use optimization techniques that enable an objective evaluation of the model behavior by expediting the calibration procedure and reducing the issue of subjectivity. In this paper, we investigate bed level changes as a result of a flushing event in the Bodendorf reservoir in Austria by using a three-dimensional numerical model coupled with an optimization algorithm for automatic calibration. Three different sediment transport formulae (Meyer-Peter and Müller, van Rijn, and Wu) are employed and modified during the calibration, along with the roughness parameter, active layer thickness, volume fraction of sediments in bed, and the hiding-exposure parameter. The simulated bed levels compared to the measurements are assessed by several statistical metrics in different cross-sections. According to the goodness-of-fit indicators, the models using the formulae of van Rijn and Wu outperform the model calculated by the Meyer-Peter and Müller formula regarding bed patterns and the volume of flushed sediments.

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Bayesian Calibration Points to Misconceptions in Three‐Dimensional Hydrodynamic Reservoir Modeling

Cited by 5 publications

References 62 publications

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Bayesian‐based calibration for water quality model parameters

Using automatic model calibration for 3D morphological simulations: a case study of the Bodendorf reservoir flushing

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