TY - JOUR
T1 - Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources
AU - Kurtz, Wolfgang
AU - Lapin, Andrei
AU - Schilling, O. S.
AU - Tang, Qi
AU - Schiller, Eryk
AU - Braun, Torsten Ingo
AU - Hunkeler, Daniel
AU - Vereecken, Harry
AU - Sudicky, Edward A.
AU - Kropf, Peter G.
AU - Hendricks Franssen, Harrie Jan
AU - Brunner, Philip Andreas
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
U2 - 10.1016/j.envsoft.2017.03.011
DO - 10.1016/j.envsoft.2017.03.011
M3 - Article
VL - 93
SP - 418
EP - 435
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
SN - 1364-8152
ER -