TY - CHAP A1 - He, Qianwen A1 - Molkenthin, Frank A1 - Wendland, Frank A1 - Herrmann, Frank T1 - Evaluation of different interpolation schemes for precipitation and reference evapotranspiration and the impact on simulated large-scale water balance in Slovenia T2 - European Geosciences Union, General Assembly 2016, Vienna, Austria N2 - Precipitation and reference evapotranspiration (ET0) are two main climate input components for hydrological models, which are often recorded or calculated based on measuring stations. Interpolation schemes are implemented to regionalize data from measuring stations for distributed hydrological models. This study had been conducted for 5 months, with the aim of: (1) evaluating three interpolation schemes for precipitation and reference evapotranspiration (ET0); (2) assessing the impact of the interpolation schemes on actual evapotranspiration and total runoff simulated by a distributed large-scale water balance model - mGROWA. The study case was the Republic of Slovenia, including a high variability in topography and climatic conditions, with daily meteorological data measured in 20 stations for a period of 44 years. ET0 were computed by both FAO Penman-Monteith equation and Hargreaves equation. The former equation is recommended as the standard equation, while the ET0 calculated by the latter one for Slovenia had a certain deviation (+150 mm/a) from it. Ordinary Kriging, Regression Kriging and Linear Regression were selected to regionalize precipitation and ET0. Reliability of the three interpolation schemes had been assessed based on the residual obtained from cross-validation. Monthly regionalized precipitation and ET0 were subsequently used as climate input for mGROWA model simulation. Evaluation of the interpolation schemes showed that the application of Regression Kriging and Linear Regression led to an acceptable interpolation result for reference evapotranspiration, especially in case the FAO Penman-Monteith equation was used. On the other hand, Regression Kriging also provided a more convincing interpolated result for precipitation. Meanwhile, mGROWA simulation results were affected by climate input data sets generated by applying difference interpolation schemes. Therefore, it is essential to select an appropriate interpolation scheme, in order to generate a convincing climate input. Y1 - 2016 UR - http://meetingorganizer.copernicus.org/EGU2016/EGU2016-8570.pdf N1 - EGU2016-8570 PB - European Geophysical Society CY - Katlenburg-Lindau ER - TY - CHAP A1 - Altenkirch, Nora A1 - Mutz, Michael A1 - Molkenthin, Frank A1 - Zlatanović, Sanja A1 - Trauth, Nico T1 - Untangling hyporheic residence time distributions and whole stream metabolisms using a hydrological process model T2 - European Geosciences Union, General Assembly 2016, Vienna, Austria N2 - The interaction of the water residence time in hyporheic sediments with the sediment metabolic rates is believed to be a key factor controlling whole stream metabolism. However, due to the methodological difficulties, there is little data that investigates this fundamental theory of aquatic ecology. Here, we report on progress made to combine numerical modeling with a series of manipulation to laboratory flumes overcoming methodological difficulties. In these flumes, hydraulic conditions were assessed using non-reactive tracer and heat pulse sensor. Metabolic activity was measured as the consumption and production of oxygen and the turnover of reactive tracers. Residence time and metabolic processes were modeled using a multicomponent reactive transport code called Min3P and calibrated with regard to the hydraulic conditions using the results obtained from the flume experiments. The metabolic activity was implemented in the model via Monod type expressions e.g. for aerobic respiration rates. A number of sediment structures differing in residence time distributions were introduced in both, the model and the flumes, specifically to model the biogeochemical performance and to validate the model results. furthermore, the DOC supply and surface water flow velocity were altered to test the whole stream metabolic response. Using the results of the hydrological process model, a sensitivity analysis of the impact of residence time distributions on the metabolic activity could yield supporting proof of an existing link between the two. Y1 - 2016 UR - http://meetingorganizer.copernicus.org/EGU2016/EGU2016-14435.pdf N1 - EGU2016-14435 PB - European Geophysical Society CY - Katlenburg-Lindau ER - TY - THES A1 - Notay, Kunwar Vikramjeet T1 - Model coupling in hydroinformatics systems through the use of autonomous tensor objects N2 - 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. KW - Hydroinformatics Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-36182 UR - https://opus4.kobv.de/opus4-btu/frontdoor/index/index/docId/3618 ER - TY - CHAP A1 - Notay, Kunwar Vikramjeet A1 - Mendoza-Lera, Clara A1 - Federlein, Laura L. A1 - Molkenthin, Frank T1 - A coupled subsurface-flow and metabolism model to study the effects of solute fluxes in the hyporheic zone T2 - Proceedings of the 28th EnviroInfo 2014 Conference, Oldenburg, Germany September 10-12, 2014 N2 - The hyporheic zone and the streambed host a great part of the energy and material fluxes through river ecosystems. However, the role of heterogeneities in the hyporheic zone in metabolism is not clearly understood. This paper proposes a new way to approach the question by using a coupled subsurface-flow and metabolism model for investigating the role of heterogeneities in the hyporheic metabolism. Our results show that (i) our coupled model is feasible for investigating solute fluxes in the hyporheic zone under heterogeneous set-ups, and (ii) the incorporation of heterogeneities seems be of relevance for hyporheic metabolism estimations. Y1 - 2014 UR - http://enviroinfo.eu/sites/default/files/pdfs/vol8514/0293.pdf SN - 978-3-8142-2317-9 PB - BIS-Verlag CY - Oldenburg ER - TY - CHAP A1 - Wiegleb, Gerhard A1 - Bröring, Udo A1 - Gnauck, Albrecht A1 - Brux, Holger A1 - Luther, B. ED - Thinh, Nguyen Xuan T1 - Long-Term Prediction of Macrophyte Community Development in Lowland Streams Using Artificial Neural Networks T2 - Modellierung und Simulation von Ökosystemen, Workshop Kölpinsee 2013 Y1 - 2014 SN - 978-3-944101-98-9 SP - 217 EP - 238 PB - Rhombos-Verlag CY - Berlin ER - TY - CHAP A1 - Notay, Kunwar Vikramjeet A1 - Li, Chi-Yu A1 - Simons, Franz T1 - Model Coupling by the Use of Autonomous Tensor Objects T2 - Forum Bauinformatik 2010 Y1 - 2010 SN - 978-3-8322-9456-4 SP - 161 EP - 168 PB - Shaker CY - Aachen ER - TY - CHAP A1 - Li, Chi-Yu A1 - Notay, Kunwar Vikramjeet T1 - Information Mining Framework Concept for Time-Series Data in Hydroinformatics Systems T2 - Forum Bauinformatik 2010 Y1 - 2010 SP - 161 EP - 168 PB - Shaker CY - Aachen ER - TY - GEN A1 - Nuswantoro, Riyandi A1 - Diermanse, F. A1 - Molkenthin, Frank T1 - Probabilistic flood hazard maps for Jakarta derived from a stochastic rain-storm generator T2 - Journal of Flood Risk Management N2 - Generally, the methods to derive design events in a flood-modelling framework do not take into account the full range of extreme storm events and therefore do not take into account all aleatory uncertainties originating from rainfall intensity and spatial variability. The design event method uses a single simulation in order to represent an extreme event. The study presents a probabilistic method to derive flood inundation maps in an area where rainfall is the predominant cause of flooding. The case study area is the Jakarta Basin, Indonesia. It typically experiences high-intensity and short-duration storms with high spatial variability. The flood hazard estimation framework is a combination of a Monte Carlo (MC)-based simulation and a simplified stochastic storm generator. Several thousands of generated extreme events are run in the Sobek rainfall–runoff and 1D-2D model. A frequency analysis is then conducted at each location in the flood plain in order to derive flood maps. The result shows that in general, design events overestimate the flood maps in comparison with the proposed MC approach. The MC approach takes into account spatial variability of the rainfall. However, this means that there is a need to have a high number of MC-generated events in order to better estimate the extreme quantiles. As a consequence, the MC approach needs much more computational resources and it is time-consuming if a full hydrodynamic model is used. Hence, a simplified flood model may be required to reduce the simulation time. KW - Flood inundation KW - Jakarta Basin KW - Monte Carlo KW - spatial variability KW - uncertainty Y1 - 2016 UR - http://onlinelibrary.wiley.com/doi/10.1111/jfr3.12114/full U6 - https://doi.org/10.1111/jfr3.12114 SN - 1753-318X VL - 9 IS - 2 SP - 105 EP - 124 ER - TY - GEN A1 - Altenkirch, Nora A1 - Zlatanović, Sanja A1 - Woodward, K. Benjamin A1 - Trauth, Nico A1 - Mutz, Michael A1 - Molkenthin, Frank T1 - Untangling hyporheic residence time distributions and whole stream" "metabolism using a hydrological process model T2 - Procedia Engineering N2 - The interaction of the water residence time (RT) in hyporheic sediments with the sediment metabolic rates is believed to be a key factor controlling whole stream metabolism. However, due to the methodological difficulties, there is little data that investigates this fundamental theory of aquatic ecology. Here, we report on progress made to combine numerical modelling with a series of modification to laboratory flumes overcoming methodological difficulties e.g. by creating steady flow paths for assessment of metabolic rates. To model the biogeochemical performance and to validate the model results, sediment structures were introduced in both, the model and the flumes, leading to differing RT distributions. Furthermore, the DOC supply in the flumes was manipulated to test the whole stream metabolic response with regard to RT distributions. In the flumes, hydraulic conditions were assessed using conservative tracer and heat as tracer. Metabolic activity was assessed using oxygen dynamics as a proxy of community respiration (CR). Residence time and metabolic processes were modelled using a multicomponent reactive transport code called MIN3P and calibrated with regard to the hydraulic conditions using the results obtained from the flume experiments. Monod type expressions were used to implement metabolic activity terms in the model. Using the results of the hydrological process model, a sensitivity analysis of the impact of RT distributions on the metabolic activity could yield supporting proof of an existing link between the two. KW - residence times KW - metabolism KW - MIN3P Y1 - 2016 UR - http://www.sciencedirect.com/science/article/pii/S1877705816319877 U6 - https://doi.org/10.1016/j.proeng.2016.07.598 SN - 1877-7058 N1 - 12th International Conference on Hydroinformatics, HIC 2016, Incheon, Korea VL - 154 SP - 1071 EP - 1078 ER - TY - THES A1 - Li, Chi-Yu T1 - Time series scenario composition framework in Hydroinformatics Systems T1 - Ein Software-Framework für die Erstellung von Zeitreihenszenarien in Hydroinformatik-Systemen N2 - 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. N2 - Seit der erste automatische, programmierbare und betriebsfähige Computer, Z3, im Jahr 1941 entwickelt wurde, sind Computer ein unverzichtbares Werkzeug für die vielfältigen Aufgaben in der ingenieurwissenschaftlichen Forschung und Praxis geworden. Auf dem Gebiet der Hydroinformatik gibt es eine Reihe von Werkzeugen, die den Fokus u. a. auf Datenerfassung und -management, Datenanalyse, numerische Simulationen, Modellkoppelung sowie Ergebnisauswertung in unterschiedlichen Raum- und Zeitskalen legen. Ein wesentlicher Arbeitsschritt wird jedoch nur unzureichend unterstützt: die Aufbereitung von Rohdaten zur Spezifikation von Szenarien als Eingabegrößen für Simulationswerkzeuge. In dieser Forschungsarbeit wird ein generelles Konzept für die ingenieurgerechte Erstellung von Zeitreihen zur Szenarienspezifikation vorgeschlagen. Das Ziel des Konzepts ist die Bereitstellung von Zeitreihen als Eingangsdatensätze, z. B. Randbedingungen, für Simulationsaufgaben zur Analyse von benutzerspezifizierten Was-Wäre-Wenn-Szenarien. Die Szenarien werden aus verfügbaren Rohdaten unterschiedlicher Quellen, z. B. Feld- und Labormessungen und Simulationsergebnissen, erstellt. Das Konzept protokolliert zudem den Arbeitsablauf durch zugehörige Metadaten, um die Nachvollziehbarkeit der Arbeitsschritte sicherzustellen. Das Konzept ist datengesteuert und halbautomatisch. Es enthält vier wesentliche Module: Datenvorbereitung, Eventidentifizierung, Prozessidentifizierung und Szenariokomposition. Diese Module verwenden als theoretische Grundlagen vor allem Time Series Knowledge Mining (TSKM), Fuzzylogik und Multivariate Adaptive Regression Splines (MARS), um Merkmale verschiedener Zustandsgrößen aus den gesammelten Daten zu extrahieren und miteinander zu verbinden. Die gesammelten Merkmale samt anderen statistischen Daten gestalten die grundsätzlichsten Komponenten, sog. MetaEvents, für die Szenariokomposition und die weitere Generierung der resultierenden Zeitreihen für die Simulation der Szenarien. Die MetaEvents werden halbautomatisch mit Hilfe von Aspects, Primitive Patterns, Successions und Events gebildet. Zusätzlich werden durch Prozessidentifizierung funktionale Beziehungen zwischen den verschiedenen Zustandsvariablen abgeleitet. Die MetaEvents stellen komplementäre Merkmale dar und berücksichtigen die identifizierten physikalischen Beziehungen zwischen den verschiedenen Zustandsvariablen aus den verfügbaren Zeitreihendaten anstatt der traditionellen getrennten Verarbeitung. Die mit den MetaEvents zusammengestellten/komponierten Szenarien ermöglichen die Generierung von resultierenden Zeitreihen von Randbedingungen für numerische Simulationen. Ein Software-Prototyp dieses Konzepts wurde auf Basis von Java- und R-Software-Technologien entworfen und implementiert. Der Prototyp zusammen mit vier Prototyp-Anwendungsbeispielen – ein mathematisch-analytischer Datensatz, ein künstlicher hydrologischer Datensatz, ein real gemessener hydrologischer Datensatz und ein hydrodynamischer Datensatz – werden benutzt, um die Funktionsfähigkeit des Prototyps und die Eigenschaften des Konzepts zu demonstrieren und nachzuweisen. Die Anwendungsbeispiele weisen nach, das Zeitreihenmuster aus spezifischen Originalszenarien reproduziert werden können und zeigen die Fähigkeit auf, für den Anwender, z. B. numerische Modellierer, Zeitreihen für relevante, interessante Szenarien zu generieren. In dieser Hinsicht demonstriert es die Fähigkeit, die Auswirkungen von Was-Wäre-Wenn-Szenarien mit Simulationswerkzeugen effizient vorzubereiten. Das halbautomatische Konzept des Prototyps verhindert auch eine Black-Box Anwendung und berücksichtigt Kenntnisse und Erfahrungen der Anwender als Fachexperten. Damit stellt das Konzept einen wertvollen, innovativen Schritt zu ganzheitlichen Hydroinformationssystemen dar, um eine ingenieurgerechte, effiziente Datenaufbereitung von Zeitreihen aus Rohdaten als Eingabedatensätze für Simulationswerkzeuge bereitzustellen. KW - Ereigniserkennung KW - Hydroinformatik KW - Szenariokomposition KW - TSKM KW - Zeitreihenanalyse KW - Event identification KW - Hydroinformatics KW - Scenario composition KW - TSKM KW - Time series analysis Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-33650 UR - http://opus4.kobv.de/opus4-btu/frontdoor/index/index/docId/3365 ER -