02 Fakultät Bau- und Umweltingenieurwissenschaften

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3

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Author: search.filters.author.Bárdossy, AndrásSubject: search.filters.subject.620

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    Hochwasser – Staatsfeind Nr. 1
    (2002) Ehret, Uwe; Bárdossy, András
    In diesem Beitrag wird zusammen mit einer Erläuterung der Entstehung und der verschiedenen Arten von Hochwasser ein kurzer Überblick über die Arten des Hochwasserschutzes und der Hochwasservorhersage gegeben. Während sich die staatlichen Vorhersageinstitutionen momentan hauptsächlich auf große Flüsse wie Donau, Rhein und Neckar konzentrieren, wurde im Rahmen eines Forschungsprojekts am Institut für Wasserbau (IWS) ein Vorhersage- und Warnsystem für ein kleines Flusseinzugsgebiet, den Goldersbach bei Tübingen, entwickelt.
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    Grundlagenbericht Niederschlags-Simulator (NiedSim3)
    (Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Mosthaf, Tobias; Gunzenhauser, Sarah; Seidel, Jochen; Bárdossy, András
    Das Programmsystem NiedSim3 (Niederschlags-Simulation) ist ein stochastischer Generator, mit dem für einen beliebigen, frei wählbaren Punkt in einer Modellregion Niederschlagszeitreihen erzeugt werden können, deren statistische Eigenschaften denen des natürlichen Niederschlags an diesem Ort entsprechen.
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    Regionalizing nonparametric models of precipitation amounts on different temporal scales
    (2017) Mosthaf, Tobias; Bárdossy, András
    Parametric distribution functions are commonly used to model precipitation amounts at gauged and ungauged locations. Nonparametric distributions offer a more flexible way to model precipitation amounts. However, the nonparametric models do not exhibit parameters that can be easily regionalized for application at ungauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate the usage of daily gauges for sub-daily resolutions.
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    A methodology to estimate flow duration curves at partially ungauged basins
    (2020) Ridolfi, Elena; Kumar, Hemendra; Bárdossy, András
    The flow duration curve (FDC) of streamflow at a specific site has a key role in the knowledge on the distribution and characteristics of streamflow at that site. The FDC gives information on the water regime, providing information to optimally manage the water resources of the river. In spite of its importance, because of the lack of streamflow gauging stations, the FDC construction can be a not straightforward task. In partially gauged basins, FDCs are usually built using regionalization among the other methods. In this paper we show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Different weather conditions lead to different FDCs for the same catchment. The differences can often be significant. Similarly, the FDC built at a site for a specific period cannot be used to retrieve the FDC at a different site for the same time window. In this paper, we propose a new methodology to estimate FDCs at partially gauged basins (i.e., target sites) using precipitation data gauged at another basin (i.e., donor site). The main idea is that it is possible to retrieve the FDC of a target period of time using the data gauged during a given donor time period for which data are available at both target and donor sites. To test the methodology, several donor and target time periods are analyzed and results are shown for different sites in the USA. The comparison between estimated and actually observed FDCs shows the reasonability of the approach, especially for intermediate percentiles.
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    Changing correlations : a flexible definition of non-Gaussian multivariate dependence
    (2023) Bárdossy, András
    Dependencies between variables are often very complex, and may for high values, be different from that of the low values. As the normal distribution and the corresponding copula behave symmetrically for low and high values the frequent application of the normal copula for the description of the dependence may be inappropriate. In this contribution a new way of defining high dimensional multivariate distributions with changing correlations is presented. The method can also be used for a flexible definition of tail dependence. Examples of copulas with linear changing correlations illustrate the methodology. Parameter estimation methods and simulation procedures are discussed. A five dimensional example using groundwater quality data and another four dimensional one using air pollution data, are used to illustrate the methodology.