Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Distribution, Abundance and Molecular Analysis of Genus Barbadocladius Cranston & Krosch (Diptera, Chironomidae) in Tropical, High Altitude Andean Streams and Rivers

The distribution of the genus Barbadocladius Cranston & Krosch (Diptera: Chironomidae), previously reported from Chile to Bolivia, has extended northwards. Larvae, pupae and pupal exuviae of this genus have been found in the high mountain tropical streams of Peru to 9°22′56″, but are restricted to very high altitude streams (altitudes over 3,278 m asl) compared to the lower altitude streams (below 1,100 m asl) in which the genus is reported in Chile and Argentina. Based on morphological studies, both described species in the genus, Barbadocladius andinus Cranston & Krosch and Barbadocladius limay Cranston & Krosch, have been found in Peru as pupae or pupal exuviae. Morphological analysis of the larvae and pupae revealed no differences between the two described species from Patagonia and Peru, which are of similar size and with a similar armament of hooklets and spines in pupal tergites and sternites. However, molecular analysis of larvae and pupae revealed that in Peru, there are at least two different evolutionary lines, one distributed widely and another restricted to one site. Phylogenetic analysis (using cox1 mitochondrial sequences) of all available sequences of Barbadocladius shows that the Chilean and Argentinean material differs from that of Peru. Therefore, a total of four molecular segregates are identified, although morphologically, neither larvae nor the pupae may be differentiated.

Riqueza y rareza de los insectos acuáticos de la laguna temporal de Espolla (Pla de l’Estany, Cataluña)

The richness and rarity of an insect community were analysed in a Mediterranean temporary pond located in NE of the Iberian Peninsula. The aquatic community was sampled weekly over 7 periods of flooding during y years (1996-1999). Distribution of rare species (with scarce presence at regional or peninsular levels, is detailed. The richness of the Espolla pond has been compared with that of other aquatic environments. The number of rare species of insects (8 taxa: 5 Corixidae, 1 Limnephilidae and 2 Chironomidae) and the insect richness (82 taxa) contrast with the traditional attribution of a low richness and rarity to the temporary aquatic environments

Spatiotemporal Aspects of Environmental Monitoring in the Complex Coastal Region of Southwest Finland

The management and conservation of coastal waters in the Baltic is challenged by a number of complex environmental problems, including eutrophication and habitat degradation. Demands for a more holistic, integrated and adaptive framework of ecosystem-based management emphasize the importance of appropriate information on the status and changes of the aquatic ecosystems. The thesis focuses on the spatiotemporal aspects of environmental monitoring in the extensive and geomorphologically complex coastal region of SW Finland, where the acquisition of spatially and temporally representative monitoring data is inherently challenging. Furthermore, the region is subject to multiple human interests and uses. A holistic geographical approach is emphasized, as it is ultimately the physical conditions that set the frame for any human activity. Characteristics of the coastal environment were examined using water quality data from the database of the Finnish environmental administration and Landsat TM/ETM+ images. A basic feature of the complex aquatic environment in the Archipelago Sea is its high spatial and temporal variability; this foregrounds the importance of geographical information as a basis of environmental assessments. While evidence of a consistent water turbidity pattern was observed, the coastal hydrodynamic realm is also characterized by high spatial and temporal variability. It is therefore also crucial to consider the spatial and temporal representativeness of field monitoring data. Remote sensing may facilitate evaluation of hydrodynamic conditions in the coastal region and the spatial extrapolation of in situ data despite their restrictions. Additionally, remotely sensed images can be used in the mapping of many of those coastal habitats that need to be considered in environmental management. With regard to surface water monitoring, only a small fraction of the currently available data stored in the Hertta-PIVET register can be used effectively in scientific studies and environmental assessments. Long-term consistent data collection from established sampling stations should be emphasized but research-type seasonal assessments producing abundant data should also be encouraged. Thus a more comprehensive coordination of field work efforts is called for. The integration of remote sensing and various field measurement techniques would be especially useful in the complex coastal waters. The integration and development of monitoring system in Finnish coastal areas also requires further scientific assesement of monitoring practices. A holistic approach to the gathering and management of environmental monitoring data could be a cost-effective way of serving a multitude of information needs, and would fit the holistic, ecosystem-based management regimes that are currently being strongly promoted in Europe.

Capillarity and fibre types in locomotory muscles of wild yellow-legged gulls (Larus cachinnans)

This study analyzes the capillarity and fibre-type distribution of six locomotory muscles of gulls. The morphological basis and the oxygen supply characteristics of the skeletal muscle of a species with a marked pattern of gliding flight are established, thus contributing to a better understanding of the physiology of a kind of flight with low energetic requirements. The four wing muscles studied (scapulotriceps, pectoralis, scapulohumeralis, and extensor metacarpi) exhibited higher percentages of fast oxidative glycolytic fibres (>70%) and lower percentages of slow oxidative fibres (<16%) than the muscles involved in nonflight locomotion (gastrocnemius and iliotibialis). Capillary densities ranged from 816 to 1,233 capillaries mm(-2), having the highest value in the pectoralis. In this muscle, the fast oxidative glycolytic fibres had moderate staining for succinate dehydrogenase and relatively large fibre sizes, as deduced from the low fibre densities (589-665 fibres mm(-2)). All these findings are seen as an adaptive response for gliding, when the wing is held outstretched by isometric contractions. The leg muscles studied included a considerable population of slow oxidative fibres (>14% in many regions), which suggests that they are adapted to postural activities. Regional variations in the relative distributions of fibre types in muscle gastrocnemius may reflect different functional demands placed on this muscle during terrestrial and aquatic locomotion. The predominance of oxidative fibres and capillary densities under 1,000 capillaries mm(-2) in leg muscles is probably a consequence of an adaptation for slow swimming and maintenance of the posture on land rather than for other locomotory capabilities, such as endurance or sprint activities.

Tissue distribution of retinoids in common dolphins (Delphinus delphis).

Exposure to organochlorines induces retinoid deficiency in mammals; hence, retinoids are potential biomarkers of the impact of these pollutants. Appropriate target tissues to monitor retinoids in cetaceans have not been properly identified because of a lack of information on the contribution of each tissue to total body retinoids. Therefore, we have addressed this issue by studying the contribution of the main body tissues to retinoids in 21 common dolphins obtained from incidental catches and in apparent good health and nutritive condition. Although concentrations in the liver were highest, those in blubber were also high and accounted for 43% of the total retinoid load of the compartments examined. As blubber can be obtained using non-invasive biopsy techniques, this tissue is proposed as a reliable indicator of retinoid status in cetaceans. However, blubber topographical variation in structure and composition requires standardization of sampling sites. Retinoid concentrations did not differ significantly between sexes or with body size for any of the tissues, but the lipid content of blubber strongly influenced these concentrations. Biopsies from healthy, free-ranging individuals are preferred to samples from stranded animals. Further research on the influence of factors (age, sex, reproductive condition, diet) that potentially affect retinoid levels is required to implement the use of retinoids as biomarkers of pollutant exposure in cetaceans.