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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This paper describes a specification language and architecture for managing distributed software and mapped compute, storage and network infrastructure services dynamically, beyond the state of the art in cloud computing. This is referred to as dynamic application topology orchestration, where the mapping and configuration of distributed, interconnected, interdependent application services and infrastructure resources are dynamically adjusted, according to guarantees in Service Level Agreements (SLAs) and operational constraints. The viability and benefits of this architectural approach are compared against simpler strategies, to establish technical and business cases for the associated engineering effort.

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