Towards an effective application of parameter estimation and uncertainty analysis to mathematical groundwater models

Herrera, Paulo; Marazuela, Miguel Ángel; Formentin, Giovanni; Hofmann, Thilo

doi:10.1007/s42452-022-05086-w

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…The parameter estimation software PEST has been used for decades in the field of groundwater modeling for a variety of applications including: parameter estimation, uncertainty analysis, predictive simulation, and data value assessment (Doherty 2013;Doherty 2015;Lotti and Formentin 2020;Herrera et al 2022). These analyses typically feed already collected site measurements to PEST, so that PEST can estimate the underlying site properties that, when input to a site model, would generate those outputs (i.e., match with or calibrate to the measurements).…”

Section: Methodsmentioning

confidence: 99%

PEST and AEM Modeling for Data Acquisition Planning

McLane¹

2023

Groundwater

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A well‐planned field data collection program should be designed to (1) collect a sufficient set of data of the right types at the right locations, and (2) collect a parsimonious set of data to avoid unnecessary costs. Combining PEST and a simple analytic element method (AEM) groundwater flow model for the site of interest provides a relatively simple, low‐cost method of developing such a program. AEM models are well suited to this approach because they are quick to develop yet hydraulically accurate, reducing impacts on project budgets at early data collection planning stages; and quick to run, solving rapidly for the many iterations that PEST requires to generate good parameter estimates. This article shows two examples of this method: one for a steady state watershed model, and one for a transient pumping test project to demonstrate that PEST coupled with a simple AEM model that sketches out the key features of a site conceptual model can be an efficient tool in planning key parts of a hydrogeologic site investigation.

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Section: Methodsmentioning

confidence: 99%

PEST and AEM Modeling for Data Acquisition Planning

McLane¹

2023

Groundwater

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“…The PFLOTRAN computer code [603,686] solves the mass and energy conservation equations for a range of aqueous phases, supercritical carbon dioxide, black oil, and a gaseous phase in variably saturated porous media in one, two, or three dimensions. It couples two modules, PFLOW and PTRAN, which represent flow and reactive transport.…”

Section: Pflotranmentioning

confidence: 99%

“…A promising alternative to the existing optimisation tools included in the modelling software are algorithms written in modern scientific languages such as R and Python, e.g., FloPy [596], ogs5py [597], RedModRPhree [598], toughie [599], and r2ogs5 [600]. It should be emphasised that model calibration can be even more challenging than model construction, as it requires an understanding of phenomena beyond hydrogeology and relies on disciplines such as algebra, geostatistics, geophysics and programming [472,[601][602][603]. In addition to the choice of method, another important aspect is the data used in the calibration.…”

mentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.

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“…Environmental tracers whose concentrations exhibit large oscillations in their amplitude and duration have a broad range of applications (Herrera et al, 2022b(Herrera et al, , 2022aMassoudieh, 2013;Thiros et al, 2021). As daily or weekly oscillations of a tracer are damped after a few meters of traveling through an aquifer (Brünjes et al, 2016), tracers with seasonal oscillations are more suitable for large-scale tracing (Molina-Giraldo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Seasonal biodegradation of the artificial sweetener acesulfame enhances its use as a transient wastewater tracer

Marazuela¹,

Formentin²,

Erlmeier³

et al. 2022

Preprint

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The persistence of the artificial sweetener acesulfame potassium (ACE) in wastewater treatment and subsequently in the aquatic environment has made it a widely used marker of wastewater in both surface water and groundwater. However, the recently observed biodegradation of ACE during wastewater treatment has questioned the validity of this application. In this study, we assessed the use of ACE not only as a marker of wastewater, but also as a transient wastewater tracer that allows both the calculation of mixing ratios and travel times through the aquifer as well as the calibration of transient groundwater flow and mass transport models. Our analysis was based on the data obtained in a nearly 8-year river water and groundwater sampling campaign along a confirmed wastewater-receiving riverbank filtration site located close to a drinking water supply system. We confirm that temperature controls ACE concentrations and thus their seasonal oscillation. River water data showed that ACE loads decreased from 1,500-4,000 μg·s−1 from December to June (cold season; T<10°C) to 0-500 μg·s−1 from July to November (warm season; T>10°C). This seasonal oscillation was transferred to the aquifer and preserved >3 km through the aquifer, with ACE concentrations oscillating between values below the detection limit in the warm season to 0.15 μg·L−1 in the cold season. The seasonal variation in ACE degradation during wastewater treatment enables the sweetener’s use as a transient tracer of wastewater inflows and riverbank filtration. In addition, the arrival time of the ACE concentration peak can be used to estimate groundwater flow velocity and mixing ratios, thereby demonstrating its potential in the calibration of groundwater numerical models.

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Towards an effective application of parameter estimation and uncertainty analysis to mathematical groundwater models

Cited by 5 publications

References 26 publications

PEST and AEM Modeling for Data Acquisition Planning

PEST and AEM Modeling for Data Acquisition Planning

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Seasonal biodegradation of the artificial sweetener acesulfame enhances its use as a transient wastewater tracer

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