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- PublicationAccès libreReal-time Environmental Monitoring for Cloud-based Hydrogeological Modeling with HydroGeoSphereThis paper describes an architecture for real-time environmental modeling. It consists of a wireless mesh network equipped with sensors and a cloud-based infrastructure to perform real-time environmental sim- ulations using a physics-based model combined with an Ensemble Kalman Filter. The purpose of the system is to optimize groundwater abstraction close to a river. These initial studies demonstrate that the cloud infrastructure can simultaneously compute a large number of simula- tions, thus allowing for the implementation of Ensemble Kalman Filters in real-time.
- PublicationAccès libreWireless Mesh Networks and Cloud Computing for Real Time Environmental SimulationsPredicting the influence of drinking water pumping on stream and groundwater levels is essential for sustainable water management. Given the highly dynamic nature of such systems any quantitative analysis must be based on robust and reliable modeling and simulation approaches. The paper presents a wireless mesh-network framework for environmental real time monitoring integrated with a cloud computing environment to execute the hydrogeological simulation model. The simulation results can then be used to sustainably control the pumping stations. The use case of the Emmental catchment and pumping location illustrates the feasibility and effectiveness of our approach even in harsh environmental conditions.
- PublicationAccès libreIntegrating hydrological modelling, data assimilation and cloud computing for real-time management of water resourcesOnline data acquisition, data assimilation and integrated hydrological modelling have become more and more important in hydrological science. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimisation framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the fully-coupled, physically-based hydrological model HydroGeoSphere, which is able to run in a cloud computing environment. A synthetic data assimilation experiment based on the widely used tilted V-catchment problem showed that the computational overhead for the application of the data assimilation platform in a cloud computing environment is minimal, which makes it well-suited for practical water management problems. Advantages of the cloud-based implementation comprise the independence from computational infrastructure and the straightforward integration of cloud-based observation databases with the modelling and data assimilation platform.