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  • Publication
    Accès libre
    Hydrogeological modeling of the Roussillon coastal aquifer (France): stochastic inversion and analysis of future stresses
    (2023) ;
    Valentin Dall’Alba
    ;
    ;
    Sandra Lanini
    ;
    Yvan Caballero
    AbstractGlobal climate change-induced stresses on coastal water resources include water use restrictions, saline intrusions, and permanently modifying or damaging regional resources. Groundwater in coastal regions is often the only freshwater resource available, so an in-depth understanding of the aquifer, and the aquifer’s response to climate change, is essential for decision-makers. In this study, we focus on the coastal aquifer of Roussillon (southern France) by developing and investigating a steady-state groundwater flow model (MODFLOW 6) and calibrated with PEST++ on a Python interface (FloPy and PyEmu). Model input and boundary conditions are constrained by various scenarios of climate projections by 2080, with model results predicting the aquifer’s response (and associated uncertainty) to these external forcings. Using simple assumptions of intrusion estimates, model results highlight both strong climatic and anthropogenic impacts on the water table. These include aquifer drawdowns reaching several meters locally, and the seawater interface advancing locally several hundred meters inland and rising by several meters. Intrusions of this magnitude risk endangering exploited water wells and their sustainability. Our results demonstrate the critical importance of properly characterizing the geology and its heterogeneity for understanding aquifers at risk because poor predictions may lead to inappropriate decisions, putting critical resources at risk, particularly in coastal environments.
  • Publication
    Accès libre
    Automated Hierarchical 3D Modeling of Quaternary Aquifers: The ArchPy Approach
    When modeling groundwater systems in Quaternary formations, one of the first steps is to construct a geological and petrophysical model. This is often cumbersome because it requires multiple manual steps which include geophysical interpretation, construction of a structural model, and identification of geostatistical model parameters, facies, and property simulations. Those steps are often carried out using different software, which makes the automation intractable or very difficult. A non-automated approach is time-consuming and makes the model updating difficult when new data are available or when some geological interpretations are modified. Furthermore, conducting a cross-validation procedure to assess the overall quality of the models and quantifying the joint structural and parametric uncertainty are tedious. To address these issues, we propose a new approach and a Python module, ArchPy, to automatically generate realistic geological and parameter models. One of its main features is that the modeling operates in a hierarchical manner. The input data consist of a set of borehole data and a stratigraphic pile. The stratigraphic pile describes how the model should be constructed formally and in a compact manner. It contains the list of the different stratigraphic units and their order in the pile, their conformability (eroded or onlap), the surface interpolation method (e.g., kriging, sequential Gaussian simulation (SGS), and multiple-point statistics (MPS)), the filling method for the lithologies (e.g., MPS and sequential indicator simulation (SIS)), and the petrophysical properties (e.g., MPS and SGS). Then, the procedure is automatic. In a first step, the stratigraphic unit boundaries are simulated. Second, they are filled with lithologies, and finally, the petrophysical properties are simulated inside the lithologies. All these steps are straightforward and automated once the stratigraphic pile and its related parameters have been defined. Hence, this approach is extremely flexible. The automation provides a framework to generate end-to-end stochastic models and then the proposed method allows for uncertainty quantification at any level and may be used for full inversion. In this work, ArchPy is illustrated using data from an alpine Quaternary aquifer in the upper Aare plain (southeast of Bern, Switzerland).
  • Publication
    Accès libre
    Stochastic multi-fidelity joint hydrogeophysical inversion of consistent geological models
    In Quaternary deposits, the characterization of subsurface heterogeneity and its associated uncertainty is critical when dealing with groundwater resource management. The combination of different data types through joint inversion has proven to be an effective way to reduce final model uncertainty. Moreover, it allows the final model to be in agreement with a wider spectrum of data available on site. However, integrating them stochastically through an inversion is very time-consuming and resource expensive, due to the important number of physical simulations needed. The use of multi-fidelity models, by combining low-fidelity inexpensive and less accurate models with high-fidelity expensive and accurate models, allows one to reduce the time needed for inversion to converge. This multiscale logic can be applied for the generation of Quaternary models. Most Quaternary sedimentological models can be considered as geological units (large scale), populated with facies (medium scale), and finally completed by physical parameters (small scale). In this paper, both approaches are combined. A simple and fast time-domain EM 1D geophysical direct problem is used to first constrain a simplified stochastic geologically consistent model, where each stratigraphic unit is considered homogeneous in terms of facies and parameters. The ensemble smoother with multiple data assimilation (ES-MDA) algorithm allows generating an ensemble of plausible subsurface realizations. Fast identification of the large-scale structures is the main point of this step. Once plausible unit models are generated, high-fidelity transient groundwater flow models are incorporated. The low-fidelity models are populated stochastically with heterogeneous facies and their associated parameter distribution. ES-MDA is also used for this task by directly inferring the property values (hydraulic conductivity and resistivity) from the generated model. To preserve consistency, geophysical and hydrogeological data are inverted jointly. This workflow ensures that the models are geologically consistent and are therefore less subject to artifacts due to localized poor-quality data. It is able to robustly estimate the associated uncertainty with the final model. Finally, due to the simplification of both the direct problem and the geology during the low-fidelity part of the inversion, it greatly reduces the time required to converge to an ensemble of complex models while preserving consistency.