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

Wolfgang Kurtz, Andrei Lapin, Oliver S. Schilling, Qi Tang, Eryk Schiller, Torsten Braun, Daniel Hunkeler, Harry Vereecken, Edward Sudicky, Peter Kropf, Harrie-Jan Hendricks Franssen, Philip Brunner
2017 Environmental Modelling & Software  
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,
more » ... 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. (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. The usefulness of these scientific and technological advances for water resources management have been documented in the literature but their joint application is still limited. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimization framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the integrated hydrological model HydroGeoSphere, which was adapted for the use in a cloud computing environment. The changes in the model codes for the utilization of the cloud infrastructure were minimal and mainly concerned the forward propagation of the model ensemble. A synthetic data assimilation experiment based on the widely used tilted V-catchment problem served as a benchmark for testing the usefulness of the proposed modelling platform and for evaluating the computational efficiency of the cloud-based implementation. Results 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.
doi:10.1016/j.envsoft.2017.03.011 fatcat:cfhhhxzso5bgxavb5jaeclxjnm