Doctor of Philosophy / Department of Biological & Agricultural Engineering / Kyle R. Douglas-Mankin / Water quality trading (WQT) is a market-based approach to improve water quality. It is an innovative, voluntary program that connects point source (PS) dischargers who need to reduce their pollutant loads with land managers who could offset those loads with nonpoint source (NPS) reductions to economically achieve water quality improvements in a watershed. The potential issues impeding WQT are its inability to address trading risks and quantify the uncertainty of potential load reduction in trades between PS and NPS. Recent research has also shown that trading information level and transaction costs cause problems in implementing WQT. Therefore, the goals of this study were to quantify the uncertainties of pollutant load reduction and delivery effect for potential trades, to estimate their spatiotemporal variations, and to provide information for stakeholders to reduce intangible costs of WQT. This study simulated agricultural cropland with more than 225 alternative land management practices to identify trends among these scenarios. Both total nitrogen and total phosphorus loads were modeled with SWAT and EUTROMOD for 36 years to analyze the potential load reduction, in-field uncertainty ratio, in-stream delivery ratio, and overall trading ratio (TR) in Lower Kansas watershed, Kansas. The analyses of site-specific effects in both geospatial and temporal aspects were also applied on subbasin level. The variant loading patterns and time distributions of each subbasin showed strong site-specific phenomena. The ANOVA of in-field nutrient load showed significant differences among the design criteria of scenarios. The results also showed a significant delivery and lake effects within the subbasins. The overall TR ranged from 1 to 2.2 or more in different scenarios. The advanced cluster analysis presented a potential method to eliminate the problems involved in fixed TRs while keeping the method simpler than finer-resolution floating TR system. Based on WQT geospatial data model, a three-tier GIS-based web interface Water Quality Trading Information Platform System (WQTIPS) was then developed for WQT information and assessment. A case study demonstrated WQTIPS can provide systematic, spatially information for stakeholders to assess the potential environmental benefit changes from the land management shifts using a simple interface. This study demonstrated that it is possible to automate water-quality trades, use watershed models to minimize trading risk and maximize water-quality benefits, and prioritize among possible trades both spatially and by BMP.

Le développement de technologies comme les systèmes de positionnement par satellites (GNSS), les communications sans fil, les systèmes de radio-identification (RFID) et des capteurs a augmenté la disponibilité de données spatio-temporelles décrivant des trajectoires d’objets mobiles. Des bases de données relationnelles peuvent être utilisées pour stocker et questionner les données capturées. Des applications récentes montrent l’intérêt d’une approche intégrant des trajectoires « sémantiques » pour intégrer des connaissances sur les comportements d’objets mobiles. Dans cette thèse, nous proposons une approche basée sur des ontologies. Nous présentons une ontologie pour les trajectoires. Nous appliquons notre approche à l’étude des trajectoires de mammifères marins. Pour permettre l’exploitation de nos connaissances sur les trajectoires, nous considérons l’objet mobile, des relations temporelles et spatiales dans notre ontologie. Nous avons évalué la complexité du mécanisme d’inférence et nous proposons des optimisations, comme l’utilisation d’un voisinage temporel et spatial. Nous proposons également une optimisation liée à notre application. Finalement, nous évaluons notre contribution et les résultats montrent l’impact positif de la réduction de la complexité du mécanisme d’inférence. Ces améliorations réduisent de moitié le temps de calcul et permettent de manipuler des données de plus grande dimension. / Spatio-temporal data describing trajectories of moving objects has increased as a consequence of the larger availability of such data due to current sensors techniques. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and sensors techniques. Although capturing technologies differ, the captured data has common spatial and temporal features. Thus, relational database management systems (RDBMS) can be used to store and query the captured data. RDBMS define spatial data types and spatial operations. Recent applications show that the solutions based on traditional data models are not sufficient to consider complex use cases that require advanced data models. A complex use case refers not only to data, but also to the domain expert knowledge and others. An inference mechanism enriches semantic trajectories with this knowledge. Temporal and spatial reasoning are fundamental for the inference mechanism on semantic trajectories. Several research fields are currently focusing on semantic trajectories to discover more information about mobile object behavior. In this thesis, we propose a modeling approach based on ontologies. We introduce a high-level trajectory ontology. The temporal and spatial parts form an implicit background of the trajectory model. So, we choose temporal and spatial models to be integrated with our trajectory model. We apply our modeling approach to a particular domain application : marine mammal trajectories. Therefore, we model this application and integrate it with our ontology. We implement our approach using RDF. Technically, we use Oracle Semantic Data Technologies. To accomplish reasoning over trajectories, we consider mobile objects, temporal and spatial knowledge in our ontology. Our approach demonstrates how temporal and spatial relationships that are common in natural language expressions (i.e., relations between time intervals like ”before”, ”after”, etc.) are represented in the ontology as user-defined rules. To annotate data with this kind of rules, we need an inference mechanism over trajectory ontology. Experiments over our model using the temporal and spatial reasoning address an inference computation complexity. This complexity is indicated in term of time computations and space storage. In order to reduce the inference complexity, we propose optimizations, such as domain constraints, temporal and spatial neighbor refinements. Moreover, controlling the repetition of the inference computation is also proposed. Even more, we define a refinement specifically for the application domain. Finally, we evaluate our contribution. Results show their positive impact on reducing the complexity of the inference mechanism. These refinements reduce half of the time computation and allow considering bigger size of the data.