My thesis is part of the development of Geographic Information Systems (GIS) and their ability to respond to environmental challenges that are expressed in a global and transversal way. We consider a context in which geographical information is growing, in addition the amount of data available continues to grow. Therefore, the need a tool for decision support has never been stronger. This study aim to solve problems related to water and the environment when the data become too large for sequential computing. The main objective of the thesis proposes a platform for distributed computing on a cluster of computers that parallelizes the watershed computing of major rivers and the determination of the flow accumulation. The idea is based on the construction of a minimal spanning tree, via a hierarchy of graphs, modeling the water route on the DEM toward the ocean. The technique begins from computing catchment basins that are set of pixels for which a drop of water will end the same local minimum. After that, a hierarchy of basins is computed in order to give the catchment basins of the rivers in the DEM. The study continues with a description of a parallel algorithm for computing the global flow accumulation for automatic drainage network extraction in large digital elevation models. Finally, the thesis presents a version ≪out-of-core≫ of our parallel algorithms to extend the scope of our work in clusters of size small that cannot load into memory the entire treated DEM.
