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Article

Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models

by
S. M. Seyedpour
1,2,*,
C. Henning
1,2,
P. Kirmizakis
3,
S. Herbrandt
4,
K. Ickstadt
4,
R. Doherty
5 and
T. Ricken
1,2
1
Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569 Stuttgart, Germany
2
Porous Media Laboratory, Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569 Stuttgart, Germany
3
Department of Geosciences, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
4
Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany
5
School of Natural and Built Environment, Queen’s University Belfast, Belfast BT9 5AG, UK
*
Author to whom correspondence should be addressed.
Water 2023, 15(1), 159; https://doi.org/10.3390/w15010159
Submission received: 22 November 2022 / Revised: 19 December 2022 / Accepted: 23 December 2022 / Published: 31 December 2022
(This article belongs to the Section Soil and Water)
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Abstract

To maximize the usefulness of groundwater flow models for the protection of aquifers and abstraction wells, it is necessary to identify and decrease the uncertainty associated with the major parameters such as permeability. To do this, there is a need to develop set of estimates representing subsurface heterogeneity or representative soil permeability estimates. Here, we use a coupled Random Field and extended Theory of Porous Media (eTPM) simulation to develop a robust model with a good predictive ability that reduces uncertainty. The coupled model is then validated with a physical sandbox experiment. Uncertainty is reduced by using 500 realisations of the permeability parameter using the eTPM approach. A multi-layer contaminant transport scenario with varying permeabilities, similar to what could be expected with shallow alluvial sediments, is simulated. The results show that the contaminant arrival time could be strongly affected by random field realizations of permeability compared with a modelled homogenous permeability parameter. The breakthrough time for heterogeneous permeabilities is shorter than the homogeneous condition. Using the 75% confidence interval (CI), the average contaminant concentration shows 4.4% variation from the average values of the considered area and 8.9% variation in the case of a 95% confidence interval.
Keywords: groundwater contamination; contaminant transport; extended Theory of Porous Media; random fields; heterogeneous soil; physical sandbox experiment groundwater contamination; contaminant transport; extended Theory of Porous Media; random fields; heterogeneous soil; physical sandbox experiment

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MDPI and ACS Style

Seyedpour, S.M.; Henning, C.; Kirmizakis, P.; Herbrandt, S.; Ickstadt, K.; Doherty, R.; Ricken, T. Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models. Water 2023, 15, 159. https://doi.org/10.3390/w15010159

AMA Style

Seyedpour SM, Henning C, Kirmizakis P, Herbrandt S, Ickstadt K, Doherty R, Ricken T. Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models. Water. 2023; 15(1):159. https://doi.org/10.3390/w15010159

Chicago/Turabian Style

Seyedpour, S. M., C. Henning, P. Kirmizakis, S. Herbrandt, K. Ickstadt, R. Doherty, and T. Ricken. 2023. "Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models" Water 15, no. 1: 159. https://doi.org/10.3390/w15010159

APA Style

Seyedpour, S. M., Henning, C., Kirmizakis, P., Herbrandt, S., Ickstadt, K., Doherty, R., & Ricken, T. (2023). Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models. Water, 15(1), 159. https://doi.org/10.3390/w15010159

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