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


Kurtz, Wolfgang; Lapin, Andrei; Schilling, Oliver S.; Tang, Qi; Schiller, Eryk Jerzy; Braun, Torsten; Hunkeler, Daniel; Vereecken, Harry; Sudicky, Edward; Kropf, Peter; Hendricks Franssen, Harrie-Jan; Brunner, Philip (2017). Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources. Environmental Modelling & Software, 93:418-435.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:27 April 2017
Deposited On:04 Oct 2019 10:19
Last Modified:06 Oct 2019 06:02
Publisher:Elsevier
ISSN:1364-8152
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.envsoft.2017.03.011
Other Identification Number:merlin-id:18077

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