Universität Stuttgart
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Item Open Access Investigations on functional relationships between cohesive sediment erosion and sediment characteristics(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Beckers, Felix; Wieprecht, Silke (Prof. Dr.-Ing.)Item Open Access Bayesian inversion and model selection of heterogeneities in geostatistical subsurface modeling(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Reuschen, Sebastian; Nowak, Wolfgang (Prof. Dr.-Ing.)Item Open Access Coupling free flow and flow in porous media in biological and technical applications : from a simple to a complex interface description(2014) Baber, Katherina; Helmig, Rainer (Prof. Dr.-Ing.)The objective of this work is the development of model concepts and methods for the coupling of free flow and flow in porous media. Coupling concepts of varying complexity ranging from a simple to a pore-scale to a complex interface approach are derived. The main focus is the development and testing of an REV-scale coupling concept that accounts for drop dynamics at the interface. The developed coupling concepts are based on the assumption of thermodynamic equilibrium and on flux balances. The formulation of mechanical equilibrium in the pore-scale and complex interface concept is challenging due to the scale-dependent definition of pressure. The combination of microscopic and macroscopic pressure formulations causes pressure jumps at the interface and non-physical pressure gradients. Hence, an extensive discussion of the pressure conditions is given. The coupled model is implemented in the C++ simulator DuMux (Flemisch et al., 2011) using the mortar method. The applicability of the developed concepts is assessed on the basis of two applications: transvascular exchange and drop dynamics in PEM fuel cells. In Mosthaf et al. (2011) and Baber et al. (2012), we develop a simple interface concept for coupling non-isothermal compositional two-phase flow in the porous-medium with a non-isothermal compositional single-phase system in the free-flow region. The concept is based on the two-domain approach with a simple interface devoid of thermodynamic properties. In this work, the simple interface concept is applied to model transvascular exchange. The simulations reproduce filtration and reabsorption and reveal the influence of wall and tissue parameters on the final distribution of therapeutic agents. However, the complex structure of the micro-vascular wall and the influence of the different pathways cannot be resolved by the presented approach. In some applications, the complex structure of the interface and the processes happening therein cannot be described by a simple interface devoid of thermodynamic properties. In such cases, it might be beneficial to resolve the interface layer or interface region on the pore-scale. We present a first step towards a resulting coupled pore-/REV-scale model where the interface is described by a bundle-of-tubes approach. The coupling concept between the one-phase free-flow, the pore-scale and the two-phase porous-medium model is based on flux continuity and the assumption that pore-and REV-scale pressure are equal. We develop an REV-scale interface concept - the complex interface concept - that describes drop formation, growth and detachment on hydrophobic interface between free and porous-medium flow. The interface stores the mass and energy of the drops without resolving them. The direct exchange between free-flow and porous-medium region next to the drop is also part of the coupling concept since it preserves the exchange processes described by the simple interface concept. The fraction of the interface which is covered by drops is used to obtain an area-weighted average of the coupling conditions with and without drop so that coupling conditions for the whole interface are obtained. The complex interface concept captures drop formation, growth and detachment. These processes are influenced by the conditions of both the free-flow and porous-medium region. The temporal evolution of the drop volume is an outcome of the model. The number of drops that can form on the interface is defined a priori by choosing the size of a drop REV. Neither the influence of the drops on the free-flow conditions nor film flow or sliding and merging of drops is included since the focus is on the interface description. The model is applied to simulate drop formation in the cathode of PEM fuel cells. In fuel cells, water is generated by the electro-chemical reaction in the catalyst layer and flows through the hydrophobic porous fibre structure of the GDL. Reaching the GC, water forms drops on the hydrophobic interface between GC and GDL. The drops significantly influence the water management in fuel cells which must be optimised to achieve good performance and durability. The numerical results show that it is possible to include drop dynamics in the REV-scale coupling conditions between free and porous-medium flow. Drop formation, growth and detachment are represented correctly, if the evaporation from the drop surface is neglected. The interface-coverage ratio, which is an indicator for the quality of the water management, can be predicted. The simulations for a higher number of drops suggest that the interface conditions dominate the system. A parameter study shows that interface wettability and free-flow velocity have a significant influence on the drop growth and detachment. In summary, this work reveals the potential of the developed coupling concepts to deal with realistic problems and exposes the need for further improvement and development.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 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 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 Development and parameter estimation of conceptual snow-melt models using MODIS snow-cover distribution(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) Gyawali, Dhiraj Raj; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)Due to a high spatio-temporal variability observed in the inherent snow-related processes in snow-dominated regimes, reliable representation of spatial distribution of seasonal snow has remained a critical challenge for effective monitoring of seasonal evolution of snow and subsequently hydrological estimations, in mountainous regions around the world. This issue, coupled with the crucial relevance to climate change, is further exacerbated by data scarcity in these regions. To address this issue, this thesis presents a novel standalone calibration technique employing the pixel-wise binary (’snow’, ’no snow’) information from MODIS snow-cover images to calibrate independent conceptual snow-melt models, thereby estimating model parameters from individual or sets of MODIS images. This methodology exploits the pertinent information of snow-cover distribution from the freely available remote sensing images, to reliably simulate snow-processes in data scarce regions. Switzerland and Baden-Württemberg were selected as study snow regimes, with the former representing partly longer duration snow and the latter associated with a shorter duration. Different extensions of parsimonious conceptual snow-melt models were developed and used to simulate the snow-cover distribution, with all models showcasing an adept and robust simulation. The selection of binary snow-cover information as calibration variable permits relatively complex snow-melt modules to be calibrated with more robustness because of reduced uncertainty associated with the calibration data. This work further identifies and recommends different simulation thresholds for defining the calibration data (NDSI thresholds), selecting the images for calibration (cloud cover thresholds), and reclassifying the snow water equivalent (SWE) outputs to snow-cover information (SWE thresholds). Furthermore, validation of the MODIS based snow-melt model calibration and the simulated melt outputs was carried out using a modified hydrological model (modified HBV variant) without the snow-routine. This hydrological performance was contrasted with the standard HBV model calibrated solely on discharge. The melt output provided as standalone inputs to the modified HBV was observed to impart an enhanced discharge prediction. As compared with the discharge calibrated standard HBV, a reduction in uncertainty in terms of model performance was observed along with reduced parameter compensation. The increase in model performance is deemed for ‘the right reason’ as the snow processes are adeptly represented by process-informed parameters. The estimation of the parameters solely from MODIS information not only eliminates the reliance on a single calibration variable ’discharge’ which is already an availability constraint in the higher altitudes but also preserves the spatial heterogeneity at a more regional level. This methodology holds a crucial relevance for discharge simulation in areas with episodic days of snow, where the snow processes can be calibrated quickly on images without having to calibrate the entire hydrological model. The study approach shows that the addition of freely available snow-cover information in estimating the parameters of snow-melt models utilizing the snow/no-snow information and a modest and globally available input data demand, facilitates a simple, spatially flexible approach to calibrate snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data, especially in data scarce regions.Item Open Access Development of efficient multiscale multiphysics models accounting for reversible flow at various subsurface energy storage sites(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Becker, Beatrix; Helmig, Rainer (Prof. Dr.-Ing.)Energy storage is an essential component of future energy systems with a large share of renewable energy. Apart from pumped hydro storage, large scale energy storage is mainly provided by underground energy storage systems. In this thesis we focus on chemical subsurface storage, i.e., the storage of synthetic hydrogen or synthetic natural gas in porous formations. To improve understanding of the complex and coupled processes in the underground and enable planning and risk assessment of subsurface energy storage, efficient, consistent and adequate numerical models for multiphase flow and transport are required. Simulating underground energy storage requires large domains, including local features such as fault zones and a representation of the transient saline front, and simulation times spanning the whole time of plant operation and beyond. In addition, often a large number of simulation runs need to be conducted to quantify parameter uncertainty, and efficient models are needed for data assimilation as well. Therefore, a reduction of model complexity and thus computing effort is required. Numerous simplified models that require less computational resources have been developed. In this thesis we focus on a group of multiscale models which use vertically integrated equations and implicitly include fine-scale information along the vertical direction that is reconstructed assuming vertical equilibrium (VE). Classical VE models are restricted to situations where vertical equilibrium is valid in the whole domain during most of the simulated time. This may not be the case for underground energy storage, where simulated times may be too short and locally a high degree of accuracy and complexity may be required, e.g., around the area where gas is extracted for the purpose of energy production. The three core chapters of this thesis present solutions to adapt VE models for the simulation of underground energy storage, with increasing complexity.