Migrating geoprocessing routines to web services for water resource management applications

Current geo web services and spatial data infrastructures support effectively the most common requirements and needs of spatial information users, such as discovering, accessing and visualization datasets. However, specialized users like water scientists carry out time-consuming tasks for capturing, analyzing and processing huge volumes of hydrological data by using commonly traditional GIS tools. To help water scientists in approaching to the spatial data infrastructure to use more effectively and efficiently their data and models, we describes in this paper an approach to model and develop distributed geoprocessing services for water resource management applications built on top of Spatial Data Infrastructures (SDI). We illustrate our approach in a scenario for predicting runoff in the Alpine basins which may serve to easily extrapolate our experiences to other specialized application fields like environmental and climate changes applications, making the SDI vision available to a broader audience.