Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources. (July 2017)

• Record Type: Journal Article
• Authors: Kurtz, Wolfgang; Lapin, Andrei; Schilling, Oliver S.; Tang, Qi; Schiller, Eryk; Braun, Torsten; Hunkeler, Daniel; Vereecken, Harry; Sudicky, Edward; Kropf, Peter; Hendricks Franssen, Harrie-Jan; Brunner, Philip
• Is Part Of: Environmental modelling & software. Volume 93(2017)
• Page Start: 418
• Page End: 435
• Publication Date: 2017-07
• Subjects: Cloud computing -- Integrated hydrological modelling -- Data assimilation -- Water resources management -- HydroGeoSphere -- Wireless sensor networks; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.03.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A cloud-based real-time modelling and data assimilation framework is established. Can be connected to a cloud-based data acquisition and monitoring module. HydroGeoSphere is used as the hydrological forward model. The tiltedV-catchment problem is used as a benchmark for the system.