Universität Stuttgart

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Publication Type: search.filters.UBSTyp.DissertationInstitute: search.filters.UBSInstitut.Institut für Wasser- und UmweltsystemmodellierungStart date: 2021End date: 2021Subject: search.filters.subject.550

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Now showing 1 - 5 of 5
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    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.)
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    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.)
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    Developing and calibrating a numerical model for microbially enhanced coal-bed methane production
    (Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Emmert, Simon; Class, Holger (apl. Prof. Dr.-Ing.)
    Experimental investigations demonstrate the potential of microbially enhanced coal-bed methane (MECBM) production on the lab scale. However, no in-depth mathematical and conceptual model including all sub-processes is reported in literature so far. With this study, we develop and present a conceptual food-web, included into a numerical model, that is calibrated and validated using batch experiments. The model is extended to model flow and transport features, test hypotheses, and compare against column experiments. Additionally, a sensitivity analysis of the model parameters as well as a preliminary study regarding operator-splitting techniques for the MECBM model are presented.
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    Spectral induced polarization of calcite precipitation in porous media
    (2021) Izumoto, Satoshi; Huisman, Johan Alexander (Prof. Dr.)
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    Generating weather for climate impact assessment on lakes
    (Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Schlabing, Dirk; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
    Lakes are driven in part by weather and they are affected by climate. The complex physical and biological processes that govern their behaviour makes it impossible to estimate their reactions to changed climatic conditions in a trivial manner. This work presents two weather generators (WGs), which are stochastic abstractions of observed meteorological time series, as tools for climate impact assessment on lakes. They enable to define "what if"-scenarios based on prescribed temperature changes, that are propagated to the rest of the generated variables, thus producing statistically balanced time series. For propagating the changes, linear and non-linear models of dependence were explored. The linear model consists of a Vector-Autoregressive process and the non-linear of a pair-wise copula construction method. Both methods are enhanced by phase randomization, a technique that uses the Fourier transform to generate "surrogate data" and helps to maintain longer-term dependencies in this context. The thesis also proposes a novel way to generate precipitation which works by estimating dryness probability from the state of non-precipitation variables and transforming the result to construct a time series without dry gaps. An upside to this treatment of precipitation is that it does not require a precipitation occurrence model and no different parameterizations for wet and dry states for the non-precipitation variables. The WGs were tested for their ability to extrapolate from colder towards warmer weather. While the more complex, copula-based method performed better than the simpler, linear method, it is shown that only relying on statistical relationships can mis-project the changes in dependent variables that accompany temperature increases. The two WGs are contrasted with a non-parametric K-Nearest Neighbors resampler, highlighting differences between those approaches and underlining specific weaknesses. The parametric WGs overestimate spread, while the resampler lacks variability resulting from a tendency to choose central values in both a uni- and multivariate sense.