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BTU
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.
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.
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.
Am Osterwochenende 2013 kam die Restwasserdotierung im
Spöl, einem Gebirgsbach im Schweizerischen Nationalpark,
unerwartet zum Erliegen. Die daraufhin eingeleitete Öffnung
des Grundablasses der Stauanlage Punt dal Gall der Engadiner
Kraftwerken AG, (EKW), führte zwar zu einer Wiederbenetzung
des Bachbettes, zeitgleich wurden aber auch enorme
Mengen an Feinsedimenten in den Spöl eingetragen. Diese
beiden Vorfälle im Bereich der Stauanlage führten zu einer
massiven Beeinträchtigung der aquatischen Lebensgemeinschaft
im Spöl.
Als unmittelbare Massnahmen wurden der Turbinenbetrieb in
Ova Spin eingestellt, der ungewöhnlich tiefe Wasserstand des
Stausees Livigno angehoben und der Spöl mit erhöhten und
variierenden Dotierwassermengen beschickt. Zudem wurde
eine Task-Force für die weitere Lagebeurteilung und unmittelbare
Zustandserhebung einberufen.
Eine erste Begutachtung des Spöls zeigte, dass der Umweltunfall
zu einem starken Eintrag von Feinsedimenten in das
Bachbett geführt hatte. Insbesondere im oberen Drittel des
betroffenen Gewässerabschnitts wurden die Benthosbesiedlung
und der Fischbestand erheblich geschädigt. Ein anfänglich
kommunizierter «ökologischer Gau» bzw. Totalausfall beim
Fischbestand konnte allerdings nicht festgestellt werden.
Die allmähliche Erholung der Gewässerökologie konnte in den
Folgeuntersuchungen vom Herbst 2013 und Frühjahr 2014 bestätigt
werden. Die untersuchten Fische waren bei sehr guter
Kondition, und hatten wieder die gesamte Länge des oberen
Spöl besiedelt. Die Präsenz von natürlich aufkommenden
Jungfischen lässt die Hoffnung zu, dass sich der Fischbestand,
der derzeit bei rund 50% des Ausgangszustands liegt, in den
nächsten Jahren gänzlich erholen wird.
Die EPFL in Lausanne hat die möglichen Ursachen ergründet,
welche zum Umweltereignis geführt haben. Die Studie ist zum
Schluss gekommen, dass der ausserordentlich tiefe Seestand,
der jedoch noch 18 m über dem konzessionierten Absenkziel
lag, zur Freilegung grosser Flächen an abgelagerten Sedimenten
führte. Durch sogenannte Trübeströme gelangten, auch
auf Grund des normalen Weiterbetriebs der Anlage, grosse
Sedimentfrachten entlang des Seegrunds innerhalb kürzester
Zeit bis zum Grundablass und zur Dotierfassung. Dieser
Vorgang führte zu ersten, grösseren Einträgen von Feinsedimenten
über die Dotiervorrichtung in den Spöl und zur zeitweisen
Verstopfung derselben, ohne dass dies die installierte
Abflussmessung registrierte.
Folge war die Trockenlegung des Spöls unterhalb der Staumauer.
Die Feststellung dieser unzulässigen Situation führte
zum Entscheid, den Grundablass zu öffnen. Aus betriebstechnischer
Sicht war dieser Entscheid korrekt und notwendig,
um die Sicherheit der Anlage zu garantieren, sowie die Dotiereinrichtung
vom blockierenden Material wieder zu befreien.
Der zuvor erwähnte weitere Austritt von Feinsedimenten in schliessden
Spöl war jedoch die Konsequenz. Folgende Massnahmen
wurden definiert, um zukünftig ähnliche Ereignisse verhindern
zu können:
• Verzicht auf ein Absenken des Wasserstands im Stausee
Livigno unter 1735 m ü.M.
• Weiterführung der bisherigen künstlichen Hochwasser
• Installation eines redundanten Abflussmesssystems in der
Dotiereinrichtung mit kontinuierlicher Trübemessung
• Höhersetzung des Einlaufs zum Dotiersystem
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.
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.
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.
To answer the impacts under specific what-if scenarios together with simulation tools has been demanding in different environmental problems. In this contribution, a general software framework for time series scenario composition is proposed to deal with this issue. It is done through providing an interface to process available raw time series data and to compose scenarios of interest. These composed scenarios can be further converted to a set of time series data, e.g. boundary conditions, for simulation tasks in order to investigate the impacts. This software framework contains four modules: data pre-processing, event identification, process identification, and scenario composition. These modules mainly involve Time Series Knowledge Ming (TSKM), fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the raw time series data and then interconnect them. These extracted features together with other statistical information form the most basic elements, MetaEvents, for the semi-automatic scenario composition. Besides, a software prototype with two application examples containing measured hydrological and hydrodynamic data are used to demonstrate the benefit of the concept. The results present the capability of reproducing similar time series patterns from specific scenarios comparing to the original ones as well as the capability of generating new artificial time series data from composed scenarios based on the interest of users for simulation tasks. Overall, the framework provides an approach to fill the gap between raw data and simulation tools in engineering suitable manner.