Articles | Volume 28, issue 1
https://doi.org/10.5194/hess-28-303-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-303-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty
Cécile Coulon
CORRESPONDING AUTHOR
Département de géologie et de génie géologique, Université Laval, Québec (Quebec), G1V 0A6, Canada
Centre québécois de recherche sur l'eau, Québec (Quebec), G1V 0A6, Canada
Centre d'études nordiques, Université Laval, Québec (Quebec), G1V 0A6, Canada
current address: INTERA SAS, Limonest, France
Jeremy T. White
INTERA Geosciences Pty Ltd, Perth, Australia
Alexandre Pryet
Département de géologie et de génie géologique, Université Laval, Québec (Quebec), G1V 0A6, Canada
EPOC (UMR 5805), CNRS, Univ. Bordeaux & Bordeaux INP, France
Laura Gatel
Département de géologie et de génie géologique, Université Laval, Québec (Quebec), G1V 0A6, Canada
Centre québécois de recherche sur l'eau, Québec (Quebec), G1V 0A6, Canada
Jean-Michel Lemieux
Département de géologie et de génie géologique, Université Laval, Québec (Quebec), G1V 0A6, Canada
Centre québécois de recherche sur l'eau, Québec (Quebec), G1V 0A6, Canada
Centre d'études nordiques, Université Laval, Québec (Quebec), G1V 0A6, Canada
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Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
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We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
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Short summary
In coastal areas, groundwater managers require information on the risk of well salinization associated with various pumping scenarios. We developed a modeling approach to identify the optimal tradeoff between groundwater pumping and probability of salinization, considering model parameter and historical observation uncertainty as well as uncertainty in sea level and recharge projections. The workflow can be implemented in a wide range of coastal settings.
In coastal areas, groundwater managers require information on the risk of well salinization...