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Item Open Access Porosity and permeability alterations in processes of biomineralization in porous media - microfluidic investigations and their interpretation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Weinhardt, Felix; Class, Holger (apl. Prof. Dr.-Ing)Motivation: Biomineralization refers to microbially induced processes resulting in mineral formations. In addition to complex biomineral structures frequently formed by marine organisms, like corals or mussels, microbial activities may also indirectly induce mineralization. A famous example is the formation of stromatolites, which result from biofilm activities that locally alter the chemical and physical properties of the environment in favor of carbonate precipitation. Recently, biomineralization gained attention as an engineering application. Especially with the background of global warming and the objective to reduce CO2 emissions, biomineralization offers an innovative and sustainable alternative to the usage of conventional Portland cement, whose production currently contributes significantly to global CO2 emissions. The most widely used method of biomineralization in engineering applications, is ureolytic calcium carbonate precipitation, which relies on the hydrolysis of urea and the subsequent precipitation of calcium carbonate. The hydrolysis of urea at moderate temperatures is relatively slow and therefore needs to be catalyzed by the enzyme urease to be practical for applications. Urease can be extracted from plants, for example from ground jack beans, and the process is consequently referred to as enzyme-induced calcium carbonate precipitation (ECIP). Another method is microbially induced calcium carbonate precipitation (MICP), which uses ureolytic bacteria that produce the enzyme in situ. EICP and MICP applications allow for producing various construction materials, stabilizing soils, or creating hydraulic barriers in the subsurface. The latter can be used, for example, to remediate leakages at the top layer of gas storage reservoirs, or to contain contaminant plumes in aquifers. Especially when remediating leakages in the subsurface, the most crucial parameter to be controlled is its intrinsic permeability. A valuable tool for predicting and planning field applications is the use of numerical simulation at the scale of representative elementary volumes (REV). For that, the considered domain is subdivided into several REV’s, which do not resolve the pore space in detail, but represent it by averaged parameters, such as the porosity and permeability. The porosity describes the ratio of the pore space to the considered bulk volume, and the permeability quantifies the ease of fluid flow through a porous medium. A change in porosity generally also affects permeability. Therefore, for REV-scale simulations, constitutive relationships are utilized to describe permeability as a function of porosity. There are several porosity-permeability relationships in the literature, such as the Kozeny-Carman relationship, Verma-Pruess, or simple power-law relationships. These constitutive relationships can describe individual states but usually do not include the underlying processes. Different boundary conditions during biomineralization may influence the course of porosity-permeability relationships. However, these relationships have not yet been adequately addressed. Pore-scale simulations are, in principle, very well suited to investigate pore space changes and their effects on permeability systematically. However, these simulations also rely on simplifications and assumptions. Therefore, it is essential to conduct experimental studies to investigate the complex processes during calcium carbonate precipitation in detail at the pore scale. Recent studies have shown that microfluidic methods are particularly suitable for this purpose. However, previous microfluidic studies have not explicitly addressed the impact of biomineralization on hydraulic effects. Therefore, this work aims to identify relevant phenomena at the pore scale to conclude on the REV-scale parameters, porosity and permeability, and their relationship. Contributions: This work comprises three publications. First, a suitable microfluidic setup and workflow were developed in Weinhardt et al. [2021a] to study pore space changes and the associated hydraulic effects reliably. This paper illustrated the benefits and insights of combining optical microscopy and micro X-ray computed tomography (micro XRCT) with hydraulic measurements in microfluidic chips. The elaborated workflow allowed for quantitative analysis of the evolution of calcium carbonate precipitates in terms of their size, shape, and spatial distribution. At the same time, their influence on differential pressure could be observed as a measure of flow resistance. Consequently, porosity and permeability changes could be determined. Along with this paper, we published two data sets [Weinhardt et al., 2021b, Vahid Dastjerdi et al., 2021] and set the basis for two other publications. In the second publication [von Wolff et al., 2021], the simulation results of a pore-scale numerical model, developed by Lars von Wolff, were compared to the experimental data of the first paper [Weinhardt et al., 2021b]. We observed a good agreement between the experimental data and the model results. The numerical studies complemented the experimental observations in allowing for accurate analysis of crystal growth as a function of local velocity profiles. In particular, we observed that crystal aggregates tend to grow toward the upstream side, where the supply of reaction products is higher than on the downstream side. Crystal growth during biomineralization under continuous inflow is thus strongly dependent on the locally varying velocities in a porous medium. In the third publication [Weinhardt et al., 2022a], we conducted further microfluidic experiments based on the experimental setup and workflow of the first contribution and published another data set [Weinhardt et al., 2022b]. We used microfluidic cells with a different, more realistic pore structure and investigated the influence of different injection strategies. We found that the development of preferential flow paths during EICP application may depend on the given boundary conditions. Constant inflow rates can lead to the development of preferential flow paths and keep them open. Gradually reduced inflow rates can mitigate this effect. In addition, we concluded that the coexistence of multiple calcium carbonate polymorphs and their transformations could influence the temporal evolution of porosity-permeability relationships.Item Open Access High-resolution spatio-temporal measurements of the colmation phenomenon under laboratory conditions(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Mayar, Mohammad Assem; Wieprecht, Silke (Prof. Dr.-Ing.)The fine sediment infiltration and accumulation into the gravel bed of rivers, the so-called colmation phenomenon, is a pernicious process exacerbated by anthropogenic activities. Owing to the importance and complexity of this phenomenon, it has been widely studied over the last decades. Various devices and methods have been developed to assess this phenomenon, where most of them are destructive and sample-based, resulting in an alteration of the natural conditions. Therefore, non-intrusive techniques, which provide spatial and temporal details with a high-resolution, are required to discretize the mechanisms involved in the colmation process. To address these issues, investigations under laboratory conditions may simplify the complexity of nature and enable individual and exactly defined boundary conditions to be investigated. Therefore, this thesis aims at (i) developing a non-intrusive and undisturbed measurement method for the high-resolution spatio-temporal measurements of the sediment infiltration processes and the development of sediment accumulation in an artificial river bed under laboratory conditions, (ii) applying this method to certain experiments for the assessment of the effects of different boundary conditions on sediment infiltration, and (iii) investigating the colmation phenomenon (also known as clogging) of gravel beds. For this purpose, the gamma-ray attenuation method is used together with an artificial gravel bed arranged from the spheres with various diameters and placed in a laboratory flume. This new method works based on the gamma radiation that passes through the infiltrated sediments, water, and bed spheres, in which the gamma-ray attenuation is linked to the variations of the infiltrated sediments’ quantity. The main simplification of this approach is that gravel beds are represented by the combinations of different-sized spheres. This gives the opportunity to fully distinguish infiltrating sediments from the bed material, reduce the complexity of the natural environment, and allows for repetitive measurements of the same position with different boundary conditions. From the results of this study, first, the gamma-ray attenuation measurement method was optimized to resolve the inconsistencies in the measurements. Subsequently, the concept of the non-intrusive and undisturbed measurement is proved through box experiments. Additional reproducibility experiments in the laboratory flume, for a similar bed structure, showed only small deviations between two experiments with the same setup. Consequently, the established technique was used in a series of experiments to evaluate the effects of different supply rates, total supply masses, and sediment particle size boundary conditions on the sediment infiltration and colmation processes. Vertical profiles of the infiltrated sediment were quantified through high spatial resolution measurements. Furthermore, to evaluate the infiltrating sediment accumulation development, and the temporal variations of the infiltrated sediments, the vertical profile measurements were first repeated after a specific time-period to track interval-averaged variations in all positions of the vertical axis. Next, a specific position of the vertical axis was measured continuously during the entire experiment in a high temporal resolution. The measured vertical profiles illustrate the vertical distribution, colmation, and unimpeded percolation of the infiltrated sediments. The dynamic one-point measurement precisely identifies the three phases (the start of the pore-filling, the required time to fill the pore, and the final amount of infiltrated sediments including natural fluctuation during the ongoing experiments) of the sediment infiltration or the possible clogging. As a limitation, the gamma-ray attenuation system’s current configuration only works in artificial gravel beds because of the given density difference between infiltrated sediments and the artificial bed structure. Intense radiations that pass through the natural bed's thickness are capable of detecting a significant amount of infiltrated sediments. However, small amounts of infiltrated sediments will create only a minimal shift in attenuation, which might be confused with the statistical error. In addition, the legal restriction against using radioactive material in the natural environment is another reason for not applying it in the field. Furthermore, the gamma-ray attenuation method cannot resolve the sediment distribution in the measurement horizon and provides an integrative result for each measurement position. In addition, if a mixture of silt, clay, and sand is supplied to the experiment, the gamma-ray attenuation system will produce a bulk result of all the infiltrated materials. To conclude, despite the limitations mentioned above, the gamma-ray attenuation method offers a unique opportunity for the non-intrusive and undisturbed measurements of the sediment infiltration or the special case of colmation, with a high spatio-temporal resolution. This method has the potential to quantify the investigated processes on a millimetric spatial scale, if the measurement time is not a constraint, or vice versa, in a high temporal resolution (seconds) for a specific position, if spatial scale is not important. Moreover, the gamma-ray attenuation approach can simultaneously measure the longitudinal distribution of the sedimentological processes, if multiple instruments or a single device with several radiation-emitting-holes is in operation. Last, but not least, rather than the spheres, artificial gravel beds could be made of any substance with a composition significantly different from the infiltrating sediments, and the boundary conditions of the experiments can be improved in order to attain conditions close to nature. Finally, the gamma-ray attenuation method can be integrated with advanced flow measurement instruments such as Particle Image Velocimetry (PIV) and other high-resolution endoscopic devices to track the behavior of fine sediment infiltration and its clogging process in the porous gravel beds as it occurs in nature.Item Open Access Large-scale high head pico hydropower potential assessment(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Schröder, Hans Christoph; Wieprecht, Silke (Prof. Dr.-Ing.)Due to a lack of site-related information, Pico hydropower (PHP) has hardly been a projectable resource so far. This is particularly true for large area PHP potential information that could open a perspective to increase the size of development projects by aggregating individual PHP installations. The present work is extending the capabilities of GIS based hydropower potential assessment into the PHP domain through a GIS based PHP potential assessment procedure that facilitates the discrimination of areas without high head PHP potential against areas with PHP potential and against areas with so called “favorable PHP potential”. The basic unit of the spatial output is determined by the underlying PHP potential definition of this work: a standardized PHP installation and the required hydraulic source, together called standard unit, are located on an area of one square kilometer. The gradation of the output is a consequence of the verification techniques. Several large area PHP potential field assessment methods, based on contemplative analysis techniques, are developed in this work. Field assessments were conducted in Yunnan Province/China, Costa Rica, Ecuador and Sri Lanka. The aim for all field assessments is to get a comprehensive view on the PHP potential distribution of the entire country/province. Application of the GIS based PHP potential assessment procedure is aimed at the global tropical and subtropical regions.Item Open Access A modeling approach for alpine rivers impacted by hydropeaking including the second law inequality(2012) Tuhtan, Jeffrey Andrew; Wieprecht, Silke (Prof. Dr.-Ing.)An outcome of daily electrical energy consumption is that storage hydropower releases must match the changes in daily demand. These local, high intensity fluctuations are commonly called hydropeaking. Due to their large departure from natural flow rates, the river ecosystems downstream of hydro operations are forced to react. This causes a large shift in the dissipative regime of the river, affecting the entire food web from Sparganium emersum to Salmo trutta. Although the cause of hydropeaking in alpine rivers is obvious, assessing its ecological effects is not an easy task. The study of hydropeaking impacts on river ecology demands a great deal of new theoretical and phenomenological investigation. Complicating such studies is the fact that river ecosystems themselves are not stable systems but are evolving over time, even under steady flow conditions. Although ecological models of aquatic ecosystems have been present for several decades, it is currently not possible to model a fish’s response to the short-term fluctuations in the flow field caused by hydropeaking with the same degree of accuracy which has been achieved under steady flow conditions. The use of numerical models to assess the impacts of hydropeaking on aquatic ecosystems is still in its infancy. The challenge of this dissertation is to construct a theoretical framework that can be used to study abiotic-biotic interactions under highly unsteady conditions. The model is constructed through the lens of thermodynamics, by looking at system interactions in terms of the contributions of the relative equilibrium states: mechanical, chemical, and thermal. The objectives of this dissertation are: 1. Incorporate thermodynamic principles into an aquatic habitat model which can be effectively applied for highly unsteady flow regimes. 2. Evaluate the model in terms of performance, ease of application and theory. This work proposes a new kind of fish habitat model using thermodynamic concepts for use in European alpine rivers affected by hydropeaking. Ecosystem states may be found which allow for optimal systems in which animate components such as fish are able to participate. Furthermore, we show that a ‘first law’ approach which invokes only the conservation of energy is not sufficient to understand the energetics of the alpine river ecosystem. It is necessary to view the ecosystem in terms of its free energy and its entropy as well. This ‘second law’ methodology provides powerful insight and results in a more objective modeling approach to assess hydropeaking impacts on fish considering real-world conditions.Item Open Access Efficient concepts for optimal experimental design in nonlinear environmental systems(2014) Geiges, Andreas; Nowak, Wolfgang (Jun.-Prof. Dr.-Ing.)In modern scientific and practical applications, complex simulation tools are increasingly used to support various decision processes. Growing computer power in the recent decades allowed these simulation tools to grow in their complexity in order to model the relevant physical processes more accurately. The number of required model parameter that need to be calibrated is strongly connected to this complexity and hence is growing as well. In environmental systems, in contrast to technical systems, the relevant data and information for adequate calibration of these model parameters are usually sparse or unavailable. This hinders an exact determination of model parameters, initial and boundary conditions or even the correct formulation of a model concept. In such cases, stochastic simulation approaches allow to proceed with uncertain or unknown parameters and to transfer this input uncertainty to estimate the prediction uncertainty of the model. Thus, the predictive quality of an uncertain model can be assessed and thus represents the current state of knowledge about the model prediction. In the case that the prediction quality is judged to be insufficient, new measurement data or information about the real system is required to improve the model. For maximizing the benefits of such campaigns, it is necessary to assess the expected data impact of measurements that are collected according to a proposed campaign design. This allows to identify the so called 'optimal design' that promises the highest expected data impact with respect to the particular model purpose. This thesis addresses data impact analysis of measurements within nonlinear systems or nonlinear parameter estimation problems. In contrast to linear systems, data impact in nonlinear systems depends on the actual future measurement values, which are unknown at the stage of campaign planing. For this reason, only an expected value of data impact can be estimated, by averaging over many potential sets of future measurement values. This nonlinear analysis repeatedly employs nonlinear inference methods and is therefore much more computationally cumbersome than linear estimates. Therefore, the overall purpose of this thesis is to develop new and more efficient methods for nonlinear data impact analysis, which allow tackling complex and realistic applications for which in the past only linear(ized) methods were applicable. This thesis separated efficiency of data impact estimation into three different facets: Accuracy: The first goal of this thesis is the development of a nonlinear and fully flexible reference framework for the accurate estimation of data impact. The core of the developed method is the bootstrap filter, which was identified as the most efficient method for fast and accurate simulation of repeated of nonlinear Bayesian inference for many potential future measurement values. The method is implemented in a strict and rigorous Monte-Carlo framework based on a pre-computed ensemble of model evaluations. The non-intrusive nature of the framework allows its application for arbitrary physical systems and the consideration of any type of uncertainty. Computational speed: The second part of this thesis investigates the theoretical background of data impact analysis in order to identify potentials to speed up this analysis. The key idea followed in this part originates from the well-known symmetry of Bayes Theorem and of a related information measure called Mutual Information. Both allow considering a reversal of the direction of information analysis, in which the roles of potential measurement data and the relevant model prediction are exchanged. Since the space of potential measurements is usually much larger than the space of model prediction values and since both have fundamentally different properties, the reversal of the information assessment offers a high potential for increasing the evaluation speed. Robustness: The last basic facet of an efficient data impact estimation considers the robustness of such estimates with regard to the uncertainty of the underlying model. Basically, model-based data impact estimates are subject to the same uncertainty as any other model output. Thus, the data impact estimate can be regarded as just another uncertain model prediction. Therefore, the high uncertainty of the model (which is the reason for the search for new calibration data) also affects the process of evaluating the most useful new data. In summary, the developed methods and theoretic principles allow for more efficient evaluation of nonlinear data impact. The use of nonlinear measures for data impact lead to an essential improvement of the resulting data acquisition design with respect to a relevant prediction quality. These methods are flexibly applicable for any physical model system and allow the consideration of any degree of statistical dependency. Especially the interactive approach that counters the high initial uncertainties of the model does lead to huge improvement in the design of data acquisition. All achieved conceptual and practical improvements in the evaluation of nonlinear data impact assessment allow using such powerful nonlinear methods also for complex and realistic problems.Item Open Access Uncertainty studies and risk assessment for CO2 storage in geological formations(2013) Walter, Lena Sophie; Class, Holger (apl. Prof. Dr.-Ing.)Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO2 emissions into the atmosphere. The assessment of the risks related to CO2 storage is an important task. Events such as CO2 leakage and brine displacement could result in hazards for human health and the environment. In this thesis, a systematic and comprehensive risk assessment concept is presented to investigate various levels of uncertainties and to assess risks using numerical simulations. Depending on the risk and the processes, which should be assessed, very complex models, large model domains, large time scales, and many simulations runs for estimating probabilities are required. To reduce the resulting high computational costs, a model reduction technique (the arbitrary polynomial chaos expansion) and a method for model coupling in space are applied. The different levels of uncertainties are: statistical uncertainty in parameter distributions, scenario uncertainty, e.g. different geological features, and recognized ignorance due to assumptions in the conceptual model set-up. Recognized ignorance and scenario uncertainty are investigated by simulating well defined model set-ups and scenarios. According to damage values, which are defined as a model output, the set-ups and scenarios can be compared and ranked. For statistical uncertainty probabilities can be determined by running Monte Carlo simulations with the reduced model. The results are presented in various ways: e.g., mean damage, probability density function, cumulative distribution function, or an overall risk value by multiplying the damage with the probability. If the model output (damage) cannot be compared to provided criteria (e.g. water quality criteria), analytical approximations are presented to translate the damage into comparable values. The overall concept is applied for the risks related to brine displacement and infiltration into drinking water aquifers. The uncertainties on all three levels are investigated in three approaches with different focus. The concept can also be applied to CO2 leakage or hazards related to other technologies in the subsurface such as methane storage or atomic waste disposal. In the second part of this thesis, uncertainty studies for two realistic storage formations (the pilot site Ketzin (Germany) and a realistic storage formation in the North German Basin) are performed to investigate the related uncertainties and to reduce them as much as possible. For the Ketzin site, history matching of the measurement data, is an important task for dynamic modeling and essential for future risk assessment. A systematic approach to fit the data set using inverse modeling is presented in this work. For future risk assessment for realistic sites, e.g. for the Ketzin site, the uncertainty studies and the history matching approach provide important information. Finally, CCS is discussed in the context of risk perception and the possible input of the risk assessment concept presented in this work is discussed. This work is a first attempt to connect the technical risk assessment for CO2 storage to the social science approach for risk assessment. It is bridging the gap between engineering and social sciences by integrating the technical quantification of risk into the wider context of a comprehensive risk governance model.Item Open Access Entwicklung eines ökologisch-ökonomischen Vernetzungsmodells für Wasserkraftanlagen und Mehrzweckspeicher(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Fenrich, Eva Katrin; Wieprecht, Silke (Prof. Dr.-Ing.)Die Bereitstellung von Frischwasser für die Bewässerung, Trink- und Brauchwasser sowie umweltfreundlich produzierter elektrischer Energie ist eine der wichtigsten Grundlagen für die Entwicklung einer Region oder eines Landes. Viele unterschiedliche Nutzungsansprüche auf begrenzte Ressourcen sind zu beachten und abzuwägen. Die vernetzten Versorgungsrisiken im Nexus „Wasser, Energie, Nahrung“ sind gleichermaßen eine große Herausforderung für Politik und Ingenieure. Wasserkraft stellt eine saubere, CO2-neutrale, regenerative Energiequelle dar. Jedoch sind aufgrund der Veränderung des Abflussregimes und der Querverbauung der Gewässer große Auswirkungen auf die lokale Ökologie zu erwarten. Diese Auswirkungen auf die lokale oder auch globale Flussökologie bedingen, dass bei der Planung von Wasserkraftanlagen und Mehrzweckspeichern auf ein komplexes System an Einflüssen eingegangen werden muss. Die Wechselwirkungen zwischen den unterschiedlichen Nutzungsarten einerseits und der Fluss- und Auenökologie andererseits müssen in ihrer Gesamtheit erfasst werden. Aufgrund der langen Lebensdauer der Anlagen ist es notwendig sehr eingehend die Auswirkungen eines Projekts in allen Bau- und Betriebsphasen zu untersuchen, da es sich hierbei nicht um kurzfristige Eingriffe, von denen sich das natürliche Gewässer wieder erholen kann, handelt. Ebenso ist es bei Wasserkraftanlagen und Mehrzweckspeichern, wie bei allen großen Infrastrukturmaßnahmen wichtig, dass Entscheidungsträger die Möglichkeit bekommen, übersichtlich Einblicke in die Wirkungszusammenhänge zu gewinnen und Projektvarianten zu vergleichen. Dies ist insbesondere auch dann relevant, wenn verschiedene Interessengruppen oder Projektpartner eine Einigung über die Weiterverfolgung bestimmter Projektvarianten erzielen sollen. Ausgehend von der vorgestellten Problematik wird eine ganzheitliche qualitative und quantitative Bewertung von Wasserkraftanlagen und Mehrzweckspeichern sowohl für die Planung als auch für den Betrieb vorgestellt. Hierzu wurde ein Bilanzierungsmodell entwickelt, das auf Grundlage Leontief'scher Input-Output-Analyse als Entscheidungsunterstützung für Projektentscheidungen beim Neubau und der Erneuerung von Anlagen dienen kann. Die Input-Output-Analyse, ein Verfahren der empirischen Wirtschaftsforschung, das für volkswirtschaftliche Analysen eingesetzt wird, ist ein geeignetes Werkzeug, um Verflechtungen zwischen verschiedenen Aspekten eines Systems zu beschreiben. Durch die Möglichkeit, Stoff- und Wirtschaftsströme in unterschiedlichen Einheiten miteinander zu verknüpfen, eignet sich die Input-Output-Analyse sehr gut zur Modellierung komplexer vernetzter Strukturen. Zunächst wurden qualitative Modelle für die jeweiligen Anlagentypen aufgestellt und anschließend an die Bedingungen des betrachteten Projekts angepasst. Hierzu wurden die Systemgrenzen festgelegt und bestimmt, welche Nutzungsarten zum aktuellen Betrachtungszeitraum relevant sind. Mit Hilfe von Input-Output-Graphen werden die Gesamtsysteme anschaulich dargestellt. Traditionell stehen die Knoten des Graphen für die Sektoren einer Volkswirtschaft und die Kanten stellen die jeweiligen Verflechtungen dar. Produkte eines Sektors einer Volkswirtschaft werden zur Produktion von Gütern und Dienstleitungen anderer Sektoren benötigt. Die Richtung der jeweiligen Kante des Graphen stellt eine Lieferbeziehung dar. Bei der Bewertung von Wasserkraftanlagen und Mehrzweckspeichern werden an Stelle von Sektoren einzelne Aspekte innerhalb des Projektes, wie beispielsweise die Trinkwassergewinnung oder die Erzeugung elektrischer Energie, sowie als Primärinputs natürliche Ressourcen betrachtet. Die Input-Output-Graphen können anschließend teilweise mit Hilfe graphentheoretischer Überlegungen vereinfacht werden. Beispielsweise können Teilgraphen zusammengefasst oder zirkuläre Abhängigkeiten aufgedeckt werden. Von besonderem Interesse sind häufig die indirekten Lieferbeziehungen zwischen Sektoren, die zunächst nicht direkt ersichtlich sind, im Input-Output-Modell aufgrund der Darstellung als Systemgraph jedoch deutlich erkennbar werden. Ein wichtiger Grund, qualitative Modelle zu erstellen, kann unter anderem auch sein, verschiedene Projekte oder Projektvarianten zunächst aufgrund ihrer Struktur zu vergleichen, oder um schon vorhandene Projekte unterschiedlicher Größe als Grundlage für die Datenbeschaffung neu geplanter Projekte zu nutzen. Dieses qualitative Modell wird jeweils für eine bestimmte Anlagengröße und Nutzungsart quantifiziert und anschließend werden iterativ Nutzungs-Szenarien evaluiert. Bei Bedarf kann als abschließende Untersuchung das so entwickelte Input-Output-Modell als Grundlage einer linearen Optimierung verwendet werden. Quantitative Gesamtmodelle und lineare Optimierungsmodelle sind jeweils stark abhängig von den betrachteten Projektvarianten. Durch eine vernetzte Formulierung ökonomischer und ökologischer Fragestellungen wird eine quantitative Bewertung der gegenseitigen Beeinflussung ermittelt. Anhand von Fallstudien wurde die Anwendbarkeit der zuvor erarbeiteten Methodik auf verschiedene Anlagentypen und -größen verifiziert und das Modell weiterentwickelt. Um die grundsätzliche Anwendbarkeit der Input-Output-Analyse auf Wasserkraftanlagen und Mehrzweckspeicher zu untersuchen, wurde zunächst ein sehr einfaches schwach vernetztes System eines Ausleitungskraftwerks an der Drau in Österreich untersucht. Hierbei wurde vor allem auf die Vernetzung von Wasserdargebot, energetischer Nutzung und Flussökologie eingegangen. Die Integration von Bewässerung und Landnutzungsparametern in einem Input-Output-Modell wurde anhand eines Bewässerungssystems in Venezuela untersucht. Hierbei werden vor allem auch sozioökonomische Aspekte mit integriert. In einer weiteren Fallstudie wurde ein Ausleitungskraftwerk an der unteren Argen mit gleichzeitiger Wasserentnahme zur Bewässerung untersucht. Als sehr stark vernetztes System wird das Kandadji-Projekt am Niger, ein typisches Mehrzweckspeicher-Projekt mit Bewässerung, Wasserkraft und Trinkwassergewinnung, betrachtet. Schließlich wird, um die Bandbreite der Anwendbarkeit des entwickelten Modells darzustellen, eine Fallstudie für ein Gezeitenkraftwerk zusammen mit einer Landnutzungs-Wassergütemodellierung im Küstenbereich erstellt. Die verschiedenen Fallstudien geben einen Überblick über die Bandbreite der Anwendungsbereiche des hier entwickelten Modells. Deutlich zu erkennen ist, dass qualitative Modelle und auch quantifizierte Teilmodelle jeweils übertragbar auf andere Projekte und Projektvarianten sein können. Damit wurde Ingenieuren und Entscheidungsträgern ein wertvolles Werkzeug in die Hand gegeben, um die Auswirkungen von Wasserkraftanlagen und Mehrzweckspeichern in allen Planungs- und Betriebsphasen zu bewerten.Item Open Access Methods for physically-based model reduction in time : analysis, comparison of methods and application(2013) Leube, Philipp Christoph; Nowak, Wolfgang (Jun.-Prof. Dr.-Ing.)Model reduction techniques are essential tools to control the overburdening costs of complex models. One branch of such techniques is the reduction of the time dimension. Major contributions to solve this task have been based on integral transformation. They have the elegant property that by choosing suitable base functions, e.g., the monomials that lead to the so-called temporal moments (TM), the dynamic model can be simulated via steady-state equations. TM allow to maintain the required accuracy of hydro(geo)logical applications (e.g., forward predictions, model calibration or parameter estimation) at a reasonably high level whilst controlling the computational demand, or, alternatively, to admit more conceptual complexity, finer resolutions or larger domains at the same computational costs, or to make brute force optimization tasks more feasible. In comparison to classical approaches of model reduction that involve orthogonal base functions, however, the base functions that lead to TM are non-orthogonal. Also, most applications involving TM used only lower-degree TM without providing reasons for their choice. This led to a number of open research questions: - Does non-orthogonality impair the quality and efficiency of TM? - Can other temporal base functions more efficiently reduce dynamic systems than the monomials that lead to TM? - How can compression efficiency associated with temporal model reduction methods be quantified and how efficiently can information be compressed? - What is the value of temporal model reduction in competition with the computational demand of other discretized or reduced model dimensions, e.g., repetitive model runs through Monte-Carlo (MC) simulations? In this work, I successfully developed tools to analyze and assess existing techniques that reduce hydro(geo)logical models in time, and answered the questions posed above. As an overall conclusion, I found that there is no way of temporal model reduction for dynamic systems based on arbitrary integral transforms with (non-)polynomial base functions that is better than the monomials leading to TM. However, the order of TM as opposed to other model dimensions (e.g., number of MC realizations) should be carefully determined prior the model evaluation. TM can help to improve highly complex systems through upscaling. Based on my findings, I hope to encourage more studies to work with the concept of TM. Especially because the number of studies found in the literature that employ TM with real data is small, more improved tests on existing data sets should be performed as proof of concept for practical applications in real world scenarios. Also, I hope to encourage those who limited their TM applications to only lower-order TM to consider a longer moment sequence. My study results specifically provide valuable advice for hydraulic tomography studies under transient conditions to use TM up to the fourth order. This might potentially alleviate the loss of accuracy used as argument against TM by certain authors.Item Open Access A holistic approach to assess the impact of global change on reservoir sedimentation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2024) Mouris, Kilian; Wieprecht, Silke (Prof. Dr.-Ing.)Item Unknown Sediment transport computation using a data-driven adaptive neuro-fuzzy modelling approach(2012) Habtamu Gezahegn Tolossa; Wieprecht, Silke (Prof. Dr.-Ing.)Reliable approaches for the computation of sediment transport rates in natural rivers are vital for sustainable utilization and management of water resources. They are especially essential for prediction of erosion, sedimentation and related river morphological changes, design of flood control structures. During the last decades, many researchers have focused on analysing the process of sediment transport and put forward a plethora of sediment transport equations to choose from. Estimation of amount and mode of sediment under transport is challenging and is usually accomplished by utilizing empirical or semi-empirical equations. Prediction errors of the existing equations are usually very high for practical applications, and show significant discrepancy from observed transport rates. There is a very high degree of uncertainty and fuzziness associated with the results from different equations. Recently, the utilization of data-driven fuzzy modelling, which is especially attractive for modelling complex processes about which the physics governing them is too complex to be represented in mathematical equations, has become more popular. Sediment transport modelling is one of the focus areas in this regard. This research focuses on assessing applicability of data-driven adaptive neuro-fuzzy based modelling approach for estimating sediment transport rates. A general fuzzy model has four components: fuzzifier, fuzzy rule base, fuzzy inference, and defuzzifier. Four dominant parameters affecting sediment transport capacity are selected and used for constructing the data-driven fuzzy model, using comprehensive sets of laboratory and field data. These parameters are depth, flow velocity, bed or energy slope and median size of sediment particles. The laboratory data is categorized into sand and gravel, and the river data includes measured input parameters and bed load and total bed-material load transport rates for the Rhine and Elbe Rivers. The laboratory and river datasets are treated separately because the ranges of the input and output parameters are significantly different. A methodology for selecting training and test datasets is recommended. Two third of the available datasets are used for training and one third for testing. The Takagi-Sugeno fuzzy inference system is selected because it is the most suitable method for generating fuzzy rules from measured data, the output is crisp and adaptive techniques can be used for model optimization. The initial fuzzy model is obtained by grid partitioning of the input variables, and fuzzy clustering of input and output variables. The optimisation of the model is performed by data-driven tuning of the fuzzy model parameters using the adaptive neuro-fuzzy inference system (ANFIS) so that the model output is able to reproduce the measured total bed-material and bed load transport rates. The antecedent and premise parameters of the fuzzy model are adjusted during model training with ANFIS. A sensitivity analysis for the combination of input parameters, and number and type of membership functions for each input parameter is also performed to determine the significance of the parameters on the accuracy of the developed model. The results of the sensitivity analysis are implemented to optimize the structure of the final rule base, which results in a compact and optimum rule system. Further refining of the developed fuzzy models is performed for the Rhine and Elbe by dividing the rivers into sections based on river morphology and relevant input parameters. A detailed comparison of the results of the fuzzy rule based model with the results of other commonly utilized sediment transport functions is also performed. Selection of the sediment transport equations is done based on the ranges of the input parameters and recommended applicability of the equations. Validation of the model developed for the Rhine river is done by implementing the ANFIS model for estimating erosion and deposition rates, and computing average annual bed level change for a section of the Upper Rhine. The results of the investigation show that the data-driven adaptive neuro-fuzzy modelling approach can be a powerful alternative technique for correctly estimating both bed and total bed-material transport rates. Three generalized-bell shaped membership functions for each of the input parameters are found to be the most efficient with respect to accuracy and model complexity. The fuzzy model performed better than the selected explicit transport equations both for the laboratory and field datasets. The analysis of the model outputs shows that large computation errors are associated with low sediment transport rates which are not significant for river morphology changes. In the case of laboratory data, acceptable accuracy is obtained by using three input variables (velocity, slope, and median particle size) in the fuzzy model. However, water depth should be included for rivers like the Rhine and Elbe. Total load is estimated with better accuracy than bed load, this is also the case for most of sediment transport equations. Overtraining may occur during model optimization and care should be taken to avoid overtraining by checking the performance of the fuzzy model on the test data. Collection of sufficient and good quality training data which represent the properties of the process to be modelled is a necessary precondition for a successful application of data-driven modelling. The limitation of the approach is that its accuracy purely depends on the quality and quantity of input data. The generalization capacity and transferability of the developed models to other reaches with comparable hydraulic and morphologic characteristics should be assessed carefully.