LS Environmental Informatics
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There is an increased focus on interdisciplinary research in hydroinformatic related projects for applications such as integrated water resources management, climate change modelling, etc. The solution of common problems in interdisciplinary projects requires the integration of hydroinformatic models into hydroinformatic systems by coupling of models, enabling them to efficiently share and exchange information amongst themselves.
Coupling of models is a complex task and involves various challenges. Such challenges arise due to factors such as models required to be coupled together lacking coupling capabilities, different models having different internal data formats, lack of a coupling mechanism, etc. From the perspective of physics, different models may use different discretisations in space and time, operate on different scales in space and time, etc.
A model coupling concept using a coupling broker, that is independent from the coupled models, has been developed in this work and been implemented as a prototype for a software framework for coupling hydroinformatic models. It is based on the approach of tensor objects and the ideas of the OpenMI standard for model coupling. Tensor objects are a complete representation of physical state variables including dimensions, units, values, coordinate systems, geometry, topology and metadata. They are autonomous entities that can adapt themselves to the requirements of coupled models through operations such as scaling, mapping, interpolation in space and time, etc. The central entity in coupling is the Tensor Exchange Server, which acts as the coupling broker. It is responsible for defining the coupling mechanism, brokering the communication between the models and adapting the information to the requirements of the coupled models by taking advantage of the functionality provided by tensor objects. By fulfilling these roles in coupling, the coupling broker concept goes one step further than tools such as the OpenMI standard and facilitates the task of coupling models since each coupled model doesn't individually need to be adapted to be able to perform these tasks on its own.
The usefulness of the coupling broker concept for coupling models is demonstrated with the help of three application examples: firstly, a subsurface-flow model coupled with a model simulating metabolism in the hyporheic zone, secondly, a subsurface-flow model coupled with a surface-flow model and finally, an information management system presenting the results of a hydrodynamic simulation of a section of the river Rhine. These examples demonstrate the extensibility and flexibility of the presented coupling concept, which can be used to couple multiple hydroinformatic models in hydroinformatic systems.
Since Z3, the first automatic, programmable and operational computer, emerged in 1941, computers have become an unshakable tool in varieties of engineering researches, studies and applications. In the field of hydroinformatics, there exist a number of tools focusing on data collection and management, data analysis, numerical simulations, model coupling, post-processing, etc. in different time and space scales. However, one crucial process is still missing — filling the gap between available mass raw data and simulation tools.
In this research work, a general software framework for time series scenario composition is proposed to improve this issue. The design of this framework is aimed at facilitating simulation tasks by providing input data sets, e.g. Boundary Conditions (BCs), generated for user-specified what-if scenarios. These scenarios are based on the available raw data of different sources, such as field and laboratory measurements and simulation results. In addition, the framework also monitors the workflow by keeping track of the related metadata to ensure its traceability.
This framework is data-driven and semi-automatic. It contains four basic modules: data pre-processing, event identification, process identification, and scenario composition. These modules mainly involve Time Series Knowledge Mining (TSKM), fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the collected data and interconnect themselves. The extracted features together with other statistical information form the most fundamental elements, MetaEvents, for scenario composition and further time series generation. The MetaEvents are extracted through semi-automatic steps forming Aspects, Primitive Patterns, Successions, and Events from a set of time series raw data. Furthermore, different state variables are interconnected by the physical relationships derived from process identification. These MetaEvents represent the complementary features and consider identified physical relationships among different state variables from the available time series data of different sources rather than the isolated ones. The composed scenarios can be further converted into a set of time series data as, for example, BCs, to facilitate numerical simulations.
A software prototype of this framework was designed and implemented on top of the Java and R software technologies. The prototype together with four prototype application examples containing mathematical function-generated data, artificial model-synthetic hydrological data, and measured hydrological and hydrodynamic data, are used to demonstrate the concept. The results from the application examples present the capability of reproducing similar time series patterns from specific scenarios compared to the original ones as well as the capability of generating artificial time series data from composed scenarios based on the interest of users, such as numerical modelers. In this respect, it demonstrates the concept’s capability of answering the impacts from what-if scenarios together with simulation tools. The semi-automatic concept of the prototype also prevents from inappropriate black-box applications and allows the consideration of the knowledge and experiences of domain experts. Overall, the framework is a valuable and progressive step towards holistic hydroinformatics systems in reducing the gap between raw data and simulation tools in an engineering suitable manner.