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Renard, Philippe
Nom
Renard, Philippe
Affiliation principale
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Directeur de Recherche
Email
Philippe.Renard@unine.ch
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Voici les éléments 1 - 4 sur 4
- PublicationAccès libreStochastic simulation of rainfall and climate variables using the direct sampling technique(2015)
; An accurate statistical representation of hydrological processes is of paramount importance to evaluate the uncertainty of the present scenario and make reliable predictions in a changing climate. A wealth of historic data has been made available in the last decades, including a consistent amount of remote sensing imagery describing the spatio-temporal nature of climatic and hydrological processes. The statistics based on such data are quite robust and reliable. However, to explore their variability, most stochastic simulation methods are based on low-order statistics that can only represent the heterogeneity up to a certain degree of complexity.
In the recent years, the stochastic hydrogeology group of the University of Neuchâtel has developed a multiple-point simulation method called Direct Sampling (DS). DS is a resampling technique that allows the preservation of the complex data structure by simply generating data patterns similar to the ones found in the historical data set. Contrarily to the other multiple-point methods, DS can simulate either categorical or continuous variables, or a combination of both in a multivariate framework.
In this thesis, the DS algorithm is adapted to the simulation of rainfall and climate variables in both time and space. The developed stochastic weather or climate generators include the simulation of the target variable with a series of auxiliary variables describing some aspects of the complex statistical structure characterizing the simulated process. These methods are tested on real application cases including the simulation of rainfall time-series from different climates, the variability exploration of future climate change scenarios, the missing data simulation within flow rate time-series and the simulation of spatial rainfall fields at different scales. If a representative training data set is used, the proposed methodologies can generate realistic simulations, preserving fairly well the statistical properties of the heterogeneity. Moreover, these techniques result to be practical simulation tools, since they are adaptive to different data sets with minimal effort from the user perspective. Although leaving large room for improvement, the proposed simulation approaches show a good potential to explore the variability of complex hydrological processes without the need of a complex statistical model. - PublicationAccès libreStochastic heterogeneity modeling of braided river aquifers: a methodology based on multiple point statistics and analog data(2015)
; ; In this thesis a new pseudo-genetic method to model the heterogeneity of sandy gravel braided-river aquifers is proposed. It is tested and compared with other modeling approaches on a case study of contaminant transport. Indeed, in Switzerland or in mountainous regions, braided-river aquifers represent an important water resource that need to be preserved. In order to manage this resource, a good understanding of groundwater flow and transport in braided-river aquifers is necessary. As the complex heterogeneity of such sedimentary deposits strongly influences the groundwater flow and transport, groundwater behavior predictions need to rely on a wide spectrum of geological model realizations.
To achieve realistic sedimentary deposits modeling of braided river aquifers, the proposed pseudo-genetic algorithm combines the use of analogue data with Multiple-Point Statistics and process-imitating methods. The integration of analogue data is a key feature to provide additional, complementary and necessary information in the modeling process. Assuredly, hydrogeologist are often subject to field data scarcity because of budget, time and field constraints. Multiple-Points Statistics recent algorithms, on one hand, allow to produce realistic stochastic realizations from training set with complex structures and at the same time allow to honor easily conditioning data. On the other hand, process-imitating methods allow to generate realistic patterns by mimicking physical processes.
The proposed pseudo-genetic algorithm consists of two main steps. The first step is to build main geological units by stacking successive topography realizations one above the other. So, it mimics the successive large flood events contributing to the formation of the sedimentary deposits. The successive topographies are Multiple-Point Statistics realizations from a training set composed of Digital Elevation Models of an analogue braided-river at different time steps. Each topography is generated conditionally to the previous one. The second step is to generate fine scale heterogeneity within the main geological units. This is performed for each geological unit by iterative deformations of the unit bottom surface, imitating so the process of scour filling. With three main parameters, the aggradation rate, the number of successive iterations and the intensity of the deformations, the algorithm allows to produce a wide range of realistic cross-stratified sedimentary deposits.
The method is tested in a contaminant transport example, using as reference Tritium tracer experiment concentration data from MADE site, Columbus, Mississippi, USA. In this test case, an assumption of data scarcity is made. Analogue data are integrated in the geological modeling process to determine the input parameters required -- characteristic dimensions and conductivity statistical properties -- for two variants of the proposed pseudo-genetic algorithm as well as for multi-gaussian simulation and object based methods. For each conceptual model, flow and transport simulations are run over 200 geological model realizations to cover a part of the uncertainty due to the input parameters. A comparison of the plume behavior prediction is performed between the different conceptual models.
The results show that geological structures strongly influence the plume behavior, therefore the choice or the restriction to specific conceptual models will impact the prediction uncertainty. Though little information are available for the modeler, it is possible to achieve reasonable predictions by using analogue data. Of course, with limited information, it is impossible to make an accurate prediction to match the reference, and none of each conceptual model produces better predictions but all are useful to cover the uncertainty range. The results also underline the need to consider a wide exploration of the input parameters for the various conceptual models in order to recover the uncertainty. - PublicationAccès libre3D stochastic modeling of karst aquifers using a pseudo-genetic methodology(2013)
; Le but de cette thèse est le développement d'une méthodologie de modélisation des aquifères karstiques. Premièrement, la géométrie des conduits karstiques est simulée. La géométrie de ces conduits est contrôlée à large échelle par la géologie (modèle géologique), et à plus petite échelle par la fracturation (modèle stochastique de fracturation).
Deuxièmement, ces modèles géométriques (formations 3D et conduits ensemble) sont utilisés comme base pour la simulation d'écoulement et de transport.
En dernière partie, le simulateur de conduits SKS ("Stochastic Karst Simulator") est couplé avec la simulation d'écoulement et transport pour investiguer une approche inverse qui permette d'utiliser cette méthodologie dans une étude d'incertitude.
La méthodologie de simulation des conduits karstiques développée ici se base sur une approche dite "pseudo-génétique", càd qui mime les résultats des processus de spéléogénèses, sans pour autant simuler toute la dynamique complexe de ces processus, comme la dissolution et le transport réactif de calcite, etc. Dans cette approche pseudo-génétique les conduits karstiques sont simulés par une physique approchée, qui se base sur le principe de minimisation de l'énérgie. L'eau se déplace dans un milieu en cherchant le chemin de moindre résistance. Ce principe est utilisé ici, par l'utilisation d'un algorithme de Fast Marching, qui permet de calculer le chemin de moindre effort., The focus of this thesis is the development of a methodology to model karst aquifers. First the geometry of the karst conduits is simulated. Their geometry is controlled by the geology at large scale (geological model) and by the fracturation at smaller scale (stochastic model of fractures).
Secondly, these geometrical models (3D geological formation together with conduits) are used as base for flow and transport simulation.
Finally, the karst conduit generator called SKS ("Stochastic Karst Simulator") is coupled with the physical simulation (flow and transport) to investigate an inverse approach which allows to use this methodology in an uncertainty analysis.
The karst conduits simulation methodology is called pseudo genetic because it mimic the results of the speleogenetic processes, without simulating all the complex kinetic of karst systems genesis, like reactive transport, calcite dissolution and precipitation. In this approach, the karst conduits are simulated by approaching the physical principle of minimization of energy using a Fast Marching Algorithm. This algorithm allow to compute the minimum effort path, which is assumed to be the one used by water, and consequently the preferential dissolution., Das Ziel dieser Dissertation besteht in der Entwicklung einer Methode zur Modellierung von Karstaquiferen. In einem ersten Schritt wird die Geometrie des Karstsystems simuliert. Im grossen Massstab wird die Geometrie des Karstsystems durch die Geologie bestimmt (geologisches Modell), in kleinerem Massstab durch Klüfte (stochastisches Modell zur Kluftgenese). In einem zweiten Schritt wird dieses Modell als Grundlage für die Modellierung von Grundwasserströmung und Stoffransport im Karstsystem verwendet.
Schliesslich wird die Simulation des Karstsystems mittels des Karst-Simulator (SKS, "Stochastic Karst Simulator") mit Simulationen von Grundwasserströmung und Stoffransport gekoppelt, um einen inversen Ansatz zu untersuchen, der es erlauben würde diese Methode im Rahmen einer Unsicherheitsanalyse zu verwenden.
Die hier entwickelte Methodik zur Simulierung von Karstaquiferen basiert auf einem sogenannt "pseudogenetischen" Ansatz, weil sie speleogenetische Prozesse nachbildet, ohne die komplexe Dynamik dieser Prozesse, wie Auflösung und reaktiver Transport von Kalzit etc., im Detail zu simulieren. Die Karstgenese wird in dieser Methodik vielmehr durch einen Ansatz angenähert, der auf dem Prinzip der Energieminimierung beruht. Dabei wird ein Fast Marching Algorithmus verwendet, um zwischen den Stellen mit Wasserzuflüssen ins Karstsystem und den Karstquellen den Weg des geringsten Widerstandes zu berechnen., Lo scopo di questa tesi, è lo svipuppo di una metodologia di modellizzazione realistica degli acquiferi carsici. Nella prima parte viene modellizzata la geometria dei condotti carsici. La geometria di questi ultimi, è controllata a larga scala dalla geologia (modello geologico) e a piu piccola scala dalla fratturazione (modello di fratturazione stocastico).
In seguito questi modelli geometrici vengono usati come base per la simulazione di fluidi e trasporto di contaminanti. E infine, il simulatore di condotti carsici SKS ("Stochastic Karst Simulator") è usato insieme alla simulazione di fluidi e trasporto per valutare l'applicabilità di un approcio inverso che permetta di utilizzare questa metodologia di simulazione.
La metodologia di simulazione dei condotti carsici è detta "pseudo genetica", perchè tenta di approssimare la fisica complessa della speleogenesi (come il trasporto reattive, la dissoluzione/precipitazione della calcite) senza dover risolverla numericamente. In effetti si basa sul principio fisico della minimizzazione dell'energia, usando un algoritmo di Fast Marching per calcolare il cammino di minor resistenza (cioè quello che dovrebbe seguire l'acqua). In pratica questa metodologia simula dei sistemi carsici maturi, direttamente nel loro stato finale. - PublicationAccès libreGeological stochastic imaging for aquifer characterization(2009)