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Kerrou, Jaouhar

Nom
Kerrou, Jaouhar
Affiliation principale
Email
jaouhar.kerrou@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/889

Résultat de la recherche

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  • Publication
    Accès libre
    Conditioning Multi-Gaussian Groundwater Flow Parameters to Transient Hydraulic Head and Flowrate Data With Iterative Ensemble Smoothers: A Synthetic Case Study
    (2020-6)
    Lam, Dan-Thuy 
    ;
    Kerrou, Jaouhar 
    ;
    Renard, Philippe 
    ;
    Benabderrahmane, Hakim
    ;
    Perrochet, Pierre 
    Over the last decade, data assimilation methods based on the ensemble Kalman filter (EnKF) have been particularly explored in various geoscience fields to solve inverse problems. Although this type of ensemble methods can handle high-dimensional systems, they assume that the errors coming from whether the observations or the numerical model are multivariate Gaussian. To handle existing non-linearities between the observations and the variables to estimate, iterative methods have been proposed. In this paper, we investigate the feasibility of using the ensemble smoother and two iterative variants for the calibration of a synthetic 2D groundwater model inspired by a real nuclear storage problem in France. Using the same set of sparse and transient flow data, we compare the results of each method when employing them to condition an ensemble of multi-Gaussian groundwater flow parameter fields. In particular, we explore the benefit of transforming the state observations to improve the parameter identification performed by one of the two iterative algorithms tested. Despite the favorable case of a multi-Gaussian parameter distribution addressed, we show the importance of defining an ensemble size of at least 200 to obtain sufficiently accurate parameter and uncertainty estimates for the groundwater flow inverse problem considered.
  • Publication
    Accès libre
    Stochastic forecasts of seawater intrusion towards sustainable groundwater management: application to the Korba aquifer (Tunisia)
    Kerrou, Jaouhar 
    ;
    Renard, Philippe 
    ;
    Cornaton, Fabien 
    ;
    Perrochet, Pierre 
    A stochastic study of long-term forecasts of seawater intrusion with an application to the Korba aquifer (Tunisia) is presented. Firstly, a geostatistical model of the exploitation rates was constructed, based on a multi-linear regression model combining incomplete direct data and exhaustive secondary information. Then, a new method was designed and used to construct a geostatistical model of the hydraulic conductivity field by combining lithological information and data from hydraulic tests. Secondly, the effects of the uncertainties associated with the pumping rates and the hydraulic conductivity field on the 3D density-dependent transient model were analysed separately and then jointly. The forecasts of the impacts of two different management scenarios on seawater intrusion in the year 2048 were performed by means of Monte Carlo simulations, accounting for uncertainties in the input parameters as well as possible changes of the boundary conditions. Combining primary and secondary data allowed maps of pumping rates and the hydraulic conductivity field to be constructed, despite a lack of direct data. The results of the stochastic long-term forecasts showed that, most probably, the Korba aquifer will be subject to important losses in terms of regional groundwater resources.