Extension at oceanic spreading centers ranges from ultra-slow (dominantly tectonic) to ultra-fast spreading (dominantly magmatic). This variation is reflected in the morphology of the spreading ridge segments and the magmatic productivity observed on the seafloor. These relationships are well understood at Mid-Ocean Ridges (MOR), but less is known about spreading centers above subduction zones. This study is part of a larger initiative to create the first 1:1,000,000 scale geological maps of different subduction zones at the Indo-Australian margin. This is a region of some of the fastest-growing crust on Earth and exhibits prolific magmatic-hydrothermal activity in back-arc basins. Previous work has shown that crustal growth associated with westward subduction of the Pacific Plate is characterized by highly distributed extension in back-arc basins, with numerous and simultaneously active spreading centers. In the NW Lau Basin, two of the spreading centers are punctuated by large-scale magmatic centers that coincide with anomalous mantle input (as documented by large-scale mantle helium anomalies) − features that are not well known in other basins. Detailed mapping at 1:200,000 scale shows that these spreading centers are related to near-field transcurrent faulting that developed in the early stages of the Lau back-arc basin. Translation across two oppositely moving fault zones induced rotation of the intervening crust and two anomalous spreading centers (Rochambeau Rifts and the Northwest Lau Spreading Center) opened obliquely to these structures. Both show inflated axial volcanic ridges that may be a product of an anomalous melt supply relative to the spreading rate. The marked variation in the morphology and magmatic output are thought to be controlled by input of melt from adjacent sources (Samoan plume) or the channeling of melt into a zone of thicker pre-existing crust, or both. These findings have important implications for understanding the origins of large-scale magmatic input in back-arc basins, where many fossil ore deposits have formed, thus providing important guides for resource exploration in ancient volcanic terranes on land.

The implementation and application of Geographic Information Systems (GIS) and spatial analyses have become standard practice in many archaeological projects. In this study, we demonstrate how GIS can play a crucial role in the study of taphonomy, i.e., understanding the processes that underpinned the creation of archaeological deposits, in this case the distribution of artifacts across an archeological site. The Stelida Naxos Archeological Project (SNAP) is focused on the exploration of a Paleolithic-Mesolithic stone tool quarry site located on the island of Naxos, Greece. An extensive pedestrian survey was conducted during the 2013 and 2014 archeological field seasons. An abundance of lithic material was collected across the surface, with some diagnostic pieces dating to more than 250 Kya. Spatial statistical analysis (Empirical Bayesian Kriging) was conducted on the survey data to generate predictive distribution maps for the site. This study then determined the contextual integrity of the surface artifact distributions through a study of geomorphic processes. A digital surface model (DSM) of the site was produced using Unmanned Aerial Vehicle (UAV) aerial photography and Structure from Motion (SfM) terrain modeling. The DSM employed to develop a Revised Universal Soil Loss Equation (RUSLE) model and hydrological flow models. The model results provide important insights into the site geomorphological processes and allow categorization of the diagnostic surface material locations based on their contextual integrity. The GIS analysis demonstrates that the surface artifact distribution has been significantly altered by post-depositional geomorphic processes, resulting in an overall low contextual integrity of surface artifacts. Conversely, the study identified a few areas with high contextual integrity, loci that represent prime locations for excavation. The results from this study will not only be used to inform and guide further development of the archeological project (as well as representing significant new data in its own right), but also contributes to current debates in survey archaeology, and in mapping and prospection more generally. This project demonstrates the benefit of using spatial analysis as a tool for planning of pedestrian surveys and for predictive mapping of artifact distributions prior to archaeological excavations. / Thesis / Master of Science (MSc)

L’objectif général de cette thèse est de caractériser la dynamique des transferts des bactéries fécales à l’aide d’une modélisation spatio-temporelle, à l’échelle du bassin versant (BV) dans une région agricole et à l’échelle événementielle. Ce projet vise à mieux comprendre l'influence des processus hydrologiques, les facteurs environnementaux et temporels impliqués dans l’explication des épisodes de contamination microbienne des eaux récréatives. Premièrement, un modèle bayésien hiérarchique a été développé pour quantifier et cartographier les niveaux de probabilité des eaux à être contaminées par des effluents agricoles, sur la base des données spectrales et des variables géomorphologiques. Par cette méthode, nous avons pu calculer les relations pondérées entre les concentrations d’Escherichia coli et la distribution de l’ensemble des paramètres agro-pédo-climatiques qui régissent sa propagation. Les résultats ont montré que le modèle bayésien développé peut être utilisé en mode prédictif de la contamination microbienne des eaux récréatives. Ce modèle avec un taux de succès de 71 % a mis en évidence le rôle significatif joué par la pluie qui est la cause principale du transport des polluants. Deuxièmement, le modèle bayésien a fait l’objet d'une analyse de sensibilité liée aux paramètres spatiaux, en utilisant les indices de Sobol. Cette démarche a permis (i) la quantification des incertitudes sur les variables pédologiques, d’occupation du sol et de la distance et (2) la propagation de ces incertitudes dans le modèle probabiliste c'est-à-dire le calcul de l’erreur induite dans la sortie par les incertitudes des entrées spatiales. Enfin, une analyse de sensibilité des simulations aux différentes sources d’incertitude a été effectuée pour évaluer la contribution de chaque facteur sur l’incertitude globale en prenant en compte leurs interactions. Il apparaît que sur l’ensemble des scénarios, l’incertitude de la contamination microbienne dépend directement de la variabilité des sols argileux. Les indices de premier ordre de l’analyse de Sobol ont montré que parmi les facteurs les plus susceptibles d’influer la contamination microbienne, la superficie des zones agricoles est le premier facteur important dans l'évaluation du taux de coliformes. C’est donc sur ce paramètre que l’attention devra se porter dans le contexte de prévision d'une contamination microbienne. Ensuite, la deuxième variable la plus importante est la zone urbaine avec des parts de sensibilité d’environ 30 %. Par ailleurs, les estimations des indices totaux sont meilleures que celles des indices de premier ordre, ce qui signifie que l’impact des interactions paramétriques est nettement significatif pour la modélisation de la contamination microbienne Enfin, troisièmement, nous proposons de mettre en œuvre une modélisation de la variabilité temporelle de la contamination microbiologique du bassin versant du lac Massawippi, à partir du modèle AVSWAT. Il s'agit d'une modélisation couplant les composantes temporelles et spatiales qui caractérisent la dynamique des coliformes. La synthèse des principaux résultats démontrent que les concentrations de coliformes dans différents sous-bassins versants se révèlent influencées par l’intensité de pluie. La recherche a également permis de conclure que les meilleures performances en calage sont obtenues au niveau de l'optimisation multi-objective. Les résultats de ces travaux ouvrent des perspectives encourageantes sur le plan opérationnel en fournissant une compréhension globale de la dynamique de la contamination microbienne des eaux de surface. / Abstract : The aim of this study was to predict water faecal contamination from a bayesian probabilistic model, on a watershed scale in a farming area and on a factual scale. This project aims to better understand the influence of hydrological, environmental and temporal factors involved in the explanation of microbial contamination episodes of recreational waters. First, a bayesian probabilistic model: Weight of Evidence was developed to identify and map the probability of water levels to be contaminated by agricultural effluents, on the basis of spectrals data and geomorphologic variables. By this method, we were able to calculate weighted relationships between concentrations of Escherichia coli and distribution of key agronomic, pedologic and climatic parameters that influence the spread of these microorganisms. The results showed that the Bayesian model that was developed can be used as a prediction of microbial contamination of recreational waters. This model, with a success rate of 71%, highlighted the significant role played by the rain, which is the main cause of pollution transport. Secondly, the Bayesian probabilistic model has been the subject of a sensitivity analysis related to spatial parameters, using Sobol indications. This allowed (1) quantification of uncertainties on soil variables, land use and distance and (2) the spread of these uncertainties in the probabilistic model that is to say, the calculation of induced error in the output by the uncertainties of spatial inputs. Lastly, simulation sensitivity analysis to the various sources of uncertainty was performed to assess the contribution of each factor on the overall uncertainty taking into account their interactions. It appears that of all the scenarios, the uncertainty of the microbial contamination is directly dependent on the variability of clay soils. Sobol prime indications analysis showed that among the most likely to influence the microbial factors, the area of farmland is the first important factor in assessing the coliforms. Importance must be given on this parameter in the context of preparation for microbial contamination. Then, the second most important variable is the urban area with sensitivity shares of approximately 30%. Furthermore, estimates of the total indications are better than those of the first order, which means that the impact of parametric interaction is clearly significant for the modeling of microbial contamination. Thirdly, we propose to implement a temporal variability model of microbiological contamination on the watershed of Lake Massawippi, based on the AVSWAT model. This is a model that couples the temporal and spatial components that characterize the dynamics of coliforms. The synthesis of the main results shows that concentrations of Escherichia coli in different sub-watersheds are influenced by rain intensity. Research also concluded that best performance is obtained by multi-objective optimization. The results of these studies show the prospective of operationally providing a comprehensive understanding of the dynamics of microbial contamination of surface water.

De nombreuses recherches se focalisent sur l'étude des aires de distribution des espèces qui se décalent vers des conditions plus adaptées à leurs besoins physiologiques sous l'effet du changement climatique. Le choix des indices utilisés pour caractériser l'écologie des espèces et définir leur vulnérabilité au réchauffement en cours est souvent conditionné par leur disponibilité, alors qu'il devrait être basé sur les connaissances en écophysiologie qui les concernent. D'autre part, la résolution spatiale parfois grossière utilisée n'est pas toujours pertinente au regard de l'échelle à laquelle les processus biologiques se déroulent. Dans ce cadre, l'objectif de ce travail est de cartographier à fine résolution spatiale les bilans en eau des sols et leurs différentes composantes à l'échelle des forêts de France, et d'évaluer leur intérêt pour modéliser la distribution ou la productivité des espèces au regard des indices traditionnellement utilisés. Dans un premier temps, nous avons modélisé et cartographié les différentes composantes du bilan en eau des sols, et tout particulièrement le rayonnement solaire et la réserve utile maximale en eau (RUM) des sols forestiers à partir des relevés de l'Inventaire Forestier National (IFN). Ces données ont été combinées avec des températures et des précipitations pour spatialiser le bilan en eau des sols forestiers de France. Les principaux résultats montrent l'importance de la nébulosité dans la prise en compte du calcul du rayonnement solaire, et l'inefficacité des indices dérivés de l'exposition pour en simuler les valeurs à l'échelle de la France. Nous avons également déterminé qu'il est possible de réaliser avec des informations simples à collecter une carte des RUM des sols forestiers de France. Elle permet de prédire la croissance des essences avec une efficacité comparable aux valeurs relevées sur des placettes et d'améliorer la modélisation de la distribution de certaines essences. Enfin, nous démontrons que les calculs de bilans en eau qui prennent en compte la réserve en eau des sols sont plus efficaces que les bilans hydriques climatiques ou les pluies, particulièrement pour ce qui concerne les espèces hygrophiles ou xérophiles. Ces résultats laissent penser que l'importance de l'eau a été sous-estimée dans l'analyse de la distribution des espèces et l'étude des conséquences du changement climatique sur les plantes. Les données produites permettent de progresser dans la connaissance de l'écologie des espèces et de mieux caractériser la vulnérabilité des espèces, ouvrant la porte à la création d'outils plus fonctionnels pour aider les gestionnaires à évaluer les impacts du changement de climat et à s'y adapter. / Numerous researches focus on species distribution shifts toward ecological conditions most suited to plants under climate change. Ecological indices used to characterize species ecology and to define their vulnerability over broad areas are often at coarse resolution and are determined by data availability. The aim of this work was to map soil water balance and its different components at a fine spatial resolution, and to evaluate their interest to model plant distribution and growth over the whole French forests. We firstly modeled and mapped the solar radiation and the soil water holding capacity of forest soils. These data were combined with temperatures and precipitation to map the soil water balance. For solar radiation, the main results showed that this parameter is only accurately predicted at the French scale when cloudiness is taken into account. We also showed that soil water holding capacity can be mapped at the French scale using the basic information collected on numerous plots from the French national forest inventory. Values extracted from the soil water holding capacity map allowed predicting tree species growth with efficiency similar to values estimated on plots. We also demonstrated soil water balance is more efficient than climatic water balance or precipitation to model species distribution, mainly for hygrophilous and xerophilous species. These results suggest importance of available water could be underestimated when determining the ecological niche of species. These maps allow to improve species ecology knowledge and to help in the determination of their vulnerability area to climate change.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Social demands on soil information have grown dramatically, meanwhile the soil surveys are seldom carried out in the country. Digital soil mapping techniques can be applied to infer the spatial distribution of soil from existing soil maps or from reference areas, extrapolating this information to areas not mapped. The purpose of this study was to apply in a Geographic Information System the Multiple Logistic Regressions (MLR) using Principal Components (PC) as explanatory variables to predict soil classes spatial distribution. The study area was the region of municipality São Pedro do Sul / RS. For the development of predictive models a set of nine terrain attributes were used. Model training was executed on an existing soil map and with a survey carried out in a reference area, both in a 1:50.000 scale. The first three retained PC explained 65.57% of the data variability. The predictive models which used PC had lower values of kappa index. The most accurate predicted map reached a kappa value of 63.20% and was generated by using the nine attributes of land as predictive covariates. The mapping accuracy is sensitive to similarities between the mapped classes, and mapping in a more homogeneous categorical level reduces the accuracy of the predicted maps. Soil classes relatively not representative in the training maps are not properly spatialized. The use of MLR allows spatializing of soil classes to areas not mapped, although the use of PC needs to be tested with a larger number of covariates. / As demandas da sociedade pela informação solo têm crescido, porém levantamentos pedológicos praticamente não ocorrem mais no país. Técnicas de Mapeamento Digital do Solo podem ser empregadas para inferir a distribuição espacial de classes de solos a partir de mapas existentes e áreas de referência, extrapolando esta informação para áreas não mapeadas. O objetivo deste estudo foi empregar em um Sistema de Informações Geográficas as Regressões Logísticas Múltiplas (RLM) utilizando-se de Componentes Principais (CP) como variáveis explicativas para a predição espacial de classes de solos. A área de estudo foi na região do município de São Pedro do Sul / RS. Para o desenvolvimento dos modelos preditivos foram utilizados um conjunto de nove atributos do terreno. O treinamento dos modelos foi executado em um mapa de solos existente, e em um levantamento realizado em áreas de referência, ambos na escala 1:50.000. As três primeiras CP retidas explicaram 65,57% da variabilidade dos dados. Os modelos preditivos que empregaram CP obtiveram menores valores do índice kappa. O mapa predito mais acurado empregou os nove atributos do terreno e alcançou um valor de kappa de 63,20%. A acurácia do mapeamento é sensível a semelhança entre as classes mapeadas, e o mapeamento em níveis categóricos mais homogêneos reduz a precisão dos mapas preditos. Classes de solos relativamente pouco representativas não são corretamente espacializadas. O emprego de RLM permite espacializar classes de solos para áreas não mapeadas, embora o emprego de CP necessite ser testado com um maior número de covariáveis.