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Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources

Peter Kropf, Wolfgang Kurtz, Andrei Lapin, Oliver Schilling, Qi Tang, Eryk Schiller, Torsten Braun, Daniel Hunkeler, Harry Vereecken, Edward Sudicky & Harrie-Jan Hendricks Franssen

Résumé Online 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.
   
Mots-clés Cloud computingIntegrated hydrological modellingData assimilationWater resources managementHydroGeoSphereWireless sensor networks
   
Citation P. Kropf, et al., "Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources," Environmental Modelling and Software, vol. 93, p. 418-435, July 2017.
   
Type Article de périodique (Anglais)
Date de publication 1-7-2017
Nom du périodique Environmental Modelling and Software
Volume 93
Pages 418-435
URL https://doi.org/10.1016/j.envsoft.2017.03.011