Browsing by Author "Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)"

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Open Access
Application of copulas as a new geostatistical tool
(2010) Li, Jing; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
In most geostatistical analysis, the spatial variability is solely described with a variogram or a covariance function. These measures have three major problems. First of all, they imply a Gaussianity assumption for the spatial dependence structure. This is, however, often violated in reality as manifested by the dataset investigations in this study. Second, they are sensitive to outliers and thus can be easily polluted by measurement anomalies. Third, they describe the spatial dependence as an integral over the whole marginal distribution of the parameter values. The change of the dependence strength with different quantiles of the parameter is not reflected by these measures, hence not considered in the subsequent interpolation or simulation procedures. But this aspect can be of vital importance for some prediction and design purposes. For example, to predict the flow or transport behavior in a heterogeneous subsurface, where cracks or connected paths, whose spatial continuity deviates largely from the mean trend, exist. Or to extend a monitoring network for noncompliance with environmental standards, where analysis of estimation uncertainty plays a central role and the difference in the uncertainties for different parameter values cannot be overlooked. To overcome the above addressed problems, the concept of copulas is borrowed in this study as an alternative to the traditional geostatistical tools for spatial description and modeling. As a counterpart of the shortcomings of variogram/covariance function, the main advantages of using copula are also threefold. First of all, it captures the {\it pure} dependence among the random variables separately from their univariate distributions and thus the influence of measurement outliers or very skewed distributions vanishes. Second, since it describes the dependence as a full distribution instead of the mean behavior, the variation of dependence strength for different quantiles is revealed, which considerably improves the estimation and prediction quality. Last but not the least, non-Gaussian theoretical copulas which are suitable for spatial modeling can be developed so that the Gaussianity assumption is no more a must and the real dependence structure can be mimicked better. In this study, methodology of using copulas for spatial modeling is established, including making the basic hypothesis, defining empirical copulas as a substitute for variogram/covariance, adopting and devising scale invariant measures for quantification of spatial dependence. Theoretical non-Gaussian copulas which are suitable for spatial modeling are derived and the model inference approach which is a combination of maximum likelihood and multiple-point statistics is proposed as well. The application of the methodology breaks down into three parts. The first part focuses on spatial interoplation, where the procedure of interpolation based on conditional copula is developed. An example of spatial interpolation of groundwater parameters in Baden-Württemberg (Germany) shows that the copula based approach gives better cross validation results than the ordinary and indicator kriging methods. Validation of the confidence intervals estimated from conditional copulas indicates that they are more realistic than the estimation variances obtained from ordinary and indicator kriging. The second part deals with the topic of spatial simulations. In this part, simulation algorithms of generating realizations of multivariate non-Gaussian dependence are developed for both unconditional and conditional cases. Spatial analysis of the hydraulic conductivity measurements of Las Cruces Trench Site shows that the spatial dependence of this dataset exhibit clear anisotropic and non-Gaussian behavior. Statistical tests of the realizations from three copula models parameterized on this dataset, i.e., the Gaussian copula, the v-transformed normal copula and the maximum normal copula, also indicate that the Gaussian copula is most likely to be rejected, while the maximum normal copula is proved to be the most suitable one. The significance of multivariate dependence structure described by copulas to the flow and transport behavior is studied indrectly by investigating the topological/connectivity characteristics of the realizations from different copula models. In the third part, the approach of spatial modeling based on copulas is applied to facilitate observation network design where the estimated conditional copulas describing the probabilistic structure of the values at the unsampled locations are embedded into the utility function which acts as the objective function to be maximized in order to select the optimal location for new measurement. The application to expand the observation network of groundwater quality parameters in a sub-region of Baden-Wüttemberg demonstrates the potentialities of the methodology.
Open Access
Climate sensitivity of a large lake
(2013) Eder, Maria Magdalena; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Lakes are complex ecosystems that are on the one hand more or less enclosed by defined borders, but are on the other hand connected to their environment, especially to their catchment and the atmosphere. This study is examinig the climate sensitivity of large lakes using Lake Constance as an example. The lake is situated in Central Europe at the northern edge of the Alps, at the boundary of Austria, Germany and Switzerland. The maximum depth is 235 m, the total surface area is 535 km³ and the total volume 48.45 km². The numerical simulations in this study have been performed with the lake model system ELCOM-CAEDYM. The model system was validated using three different data sets: Observations of a turbid underflow after a flood flow in the main tributary, a lake-wide field campaign of temperature and phytoplankton, and long term monitoring data of temperature and oxygen in the hypolimion. The model system proved to be able to reproduce the effects of a flood flow in the largest tributary,. A huge turbid underflow was observed flowing into the main basin after an intense rain event in the Alps in August 2005. A numerical experiment showed the influence of the earth’s rotation on the flow path of the riverine water within the lake. The model also reproduced the temperature evolution and distribution and to some extent the phytoplankton patchiness measured in spring 2007 during an intensive field campaign. The model reproduced the measured time series of temperature and oxygen in the deep hypolimnion measured in the years 1980-2000. This indicates, that the vertical mixing and the lake’s cycle of mixing and stratification was reproduced correctly. Based on the model set-up validated with long term monitoring data, climate scenario simulations were run. The main focus was on temperature and oxygen concentrations in the hypolimnion, the cycle of stratification and mixing, and the heat budget of the lake. The meteorological boundary conditions for the climate scenario simulations were generated using a weather generator instead of downscaling climate projections from Global Climate Models. This approach gives the possibility to change different characteristics of the climate independently. The resulting lake model simulations are ”what-if”-scenarios rather than predictions, helping to obtain a deeper understanding of the processes in the lake. The main results can be summarized as follows: An increase in air temperature leads to an increase in water temperature, especially in the upper layers. The deep water temperature increases as well, but not to the same extent as the temperature of the epilimnion. This results in an increased vertical temperature difference. Due to the non-linear shape of the temperature-density curve, the difference in density grows even stronger than the temperature difference. This results in enhanced stratification stability, and consequently in less mixing. Complete mixing of the lake becomes more seldom in a warmer climate, but even in the scenario simulations with air temperature increased by 5 °C, full circulation took place every 3-4 years. Less complete mixing events lead to less oxygen in the hypolimnion. Additionally, as many biogeochemical processes are temperature dependant, the oxygen consumption rate is larger in warmer water. In the context of this study, climate variability is defined as episodes with daily average air temperatures deviating from the long-term average for this day of year. The episodes can be described by their duration in days and their amplitude in °C. Changes in climate variability can have very different effects, depending on the average air and water temperatures. The effects are stronger in lakes with higher water temperatures: For the hypolimnetic conditions, the seasonality in warming is important: Increasing winter air temperatures have a much stronger effect on the water temperatures in the lake than increasing summer temperatures. The combined effects of a warmer climate and higher nutrient concentrations enhances oxygen depletion in the hypolimnion. Finally, it is discussed, to what extent the results of this study are transferrable to other lakes. The reactions of Lake Constance to climate change are determined by the physical, geographical and ecological characteristics of the lake. Hydrodynamic reactions are defined by the mixing type, water temperatures and the residence time of the water in the lake. Furthermore it is important that the lake is almost never completely ice-covered, and that there are only minor salinity differences. The reactions of the ecosystem are determined also by the oligotrophic state of the lake. Results of this study thus can be transferred to other deep, monomictic, oligotrophic fresh water lakes, as for example the other large perialpine lakes of glacial origin.
Open Access
Clustering simultaneous occurrences of extreme floods in the Neckar catchment
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Modiri, Ehsan; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Open Access
Data processing and model choice for flood prediction
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Herma, Felix; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
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.
Open Access
Distributed conceptual hydrological modelling - simulation of climate, land use change impact and uncertainty analysis
(2007) Götzinger, Jens; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
This thesis deals with the application of distributed conceptual hydrological models for the simulation of climate and land use change impact, and the quantification of prediction uncertainty. The following four main questions are addressed: - Can we model the impact of global change on water resources, and what kind of models are necessary to predict the effect of land use change on the water balance in a catchment? - How can we use these models within current policy approaches such as integrated water resources management? Is it possible to integrate regional-scale models to simulate and evaluate interdisciplinary water management scenarios? - What will be the impact of a changing climate and land use on the water resources in a catchment? - In general, how can we quantify the uncertainties associated with such simulations in a universally-valid framework? Most of the results which are presented here have been achieved within the framework of the EU-funded project RIVERTWIN, an acronym for “A Regional Model for Integrated Water Management in Twinned River Basins''. In light of the EU Water Framework Directive and the EU-Water Initiative, this project has dealt with adjusting, testing and implementing an integrated regional model for the strategic planning of water resources management in twinned river basins under contrasting ecological, social and economic conditions. The regional model allows for the impact assessment of demographic and economic development and the effects of global climate and land use changes on the availability and quality of water bodies in humid-temperate, subhumid tropical as well as semiarid regions. The existing integration framework was first tested in the European Neckar basin, which has high data availability and adequate data density. The transferability of the model to other regions with different economic levels, ecological standards and with low data availability was tested using the Ouémé basin in Benin (West Africa). To reach these goals, the semi-distributed, conceptual HBV model was transformed into a raster-based version. Four regionalisation methods for parameter estimation were developed and compared. Model integration with the groundwater flow model was achieved by exchanging simulated groundwater recharge and baseflow. To assess the impact of global change, four climate and four land use scenarios in the Neckar basin and two climate and four land use scenarios in the Ouémé basin were simulated. In the Neckar, the results were compared to the conceptual model LARSIM, which is operational at the State Institute for Environmental Protection Baden-Württemberg. Finally, a new uncertainty analysis methodology was developed. It is based on the separation of model error into input and process-based uncertainty sources. The model error due to uncertain meteorological data can be quantified by stochastic methods. The process uncertainty can be derived from the model sensitivity with respect to the parameter groups describing the considered process. Both parts together can be used to define a consistent error model that improves the calibration and uncertainty estimation of the environmental models. The thesis is structured in the following way: In Chapter 2, after a short review of hydrological modelling, the basics of regionalisation, integrated water resources management, the LARSIM and HBV models, and the basics of uncertainty analysis are introduced. Chapter 3 provides some details on the study sites chosen for this thesis, the Neckar basin and the Ouémé basin, and the available data for these sites. Chapter 4 forms the core of this thesis, by providing the description of the distributed HBV model, the regionalisation methods, the integration concept and the climate scenarios. The results for both of the basins are presented in Chapter 5. A new uncertainty analysis method is introduced and demonstrated by a case study in Chapter 6. Chapter 7 closes the thesis with a summary, some general conclusions and an outlook on future work.
Open Access
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.
Open Access
Generation of a realistic temporal structure of synthetic precipitation time series for sewer applications
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Open Access
Geostatistical methods for the identification of flow and transport parameters in the subsurface
(2005) Nowak, Wolfgang; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Per definition, log-conductivity fields estimated by geostatistical inversing do not resolve the full variability of heterogeneous aquifers. Therefore, in transport simulations, the dispersion of solute clouds is under-predicted. Macrotransport theory defines dispersion coefficients that parameterize the total magnitude of variability. Using these dispersion coefficients together with estimated conductivity fields would over-predict dispersion, since estimated conductivity fields already resolve some of the variability. Up to presence, only a few methods exist that allow to use estimated conductivity fields for transport simulations. A review of these methods reveals that they are either associated with excessive computational costs, only cover special cases, or are merely approximate. Their predictions hold only in a stochastic sense and cannot take into account measurements of transport-related quantities in an explicit manner. In this dissertation, I successfully develop, implement and apply a new method for geostatistical identification of flow and transport parameters in the subsurface. The parameters featured here are the log-conductivity and a scalar log-dispersion coefficient. The extension to other parameters like retardation coefficients or reaction rates is straightforward. Geostatistical identification of flow parameters is well-known. However, simultaneous identification together with transport parameters is new. In order to implement the new method, I develop a modified Levenberg-Marquardt algorithm for the Quasi-Linear Geostatistical Approach and extend the latter to the generalized case of uncertain prior knowledge. I derive the sensitivities of the state variables of interest with respect to the newly introduced scalar log-dispersion coefficient. Further, I summarize and extend the list of spectral methods that help to drastically speed up the expensive matrix operations involved in geostatistical inverse modeling. If the quality and quantity of input data is sufficient, the new method accurately simulates the dispersive mechanisms of spreading, dilution and the irregular movement of the center of mass of a plume. Therefore, it adequately predicts mixing of solute clouds and effective reaction rates in heterogeneous media. I perform extensive series of test cases in order to discuss and prove certain properties of the new method and the new dispersion coefficient. The character and magnitude of the identified dispersion coefficient depends strongly on the quality and quantity of input data and their potential to resolve variability in the conductivity field. Because inverse models of transport are coupled to inverse models of flow, the information in the input data has to sufficiently characterize the flow field. Otherwise, transport-related input data cannot be interpreted. Application to an experimental data set from a large-scale sandbox experiment and comparison to results from existing approaches in macrotransport theory show good agreement.
Open Access
High order interactions among environmental variables : diagnostics and initial steps towards modeling
(2013) Rodríguez Fernández, Jhan Ignacio; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
In the field of geostatistics and spatial statistics, variogram based models have proved a very flexible and useful tool. However, such spatial models take into account only interdependencies between pairs of variables, mostly in the form of covariances. In the present work, we point out to the necessity to extend the interdependence models beyond covariance modeling; we summarize some of the difficulties arising when attempting such extensions; and propose an approach to address these difficulties.
Open Access
Hydrological consequences of land use/land cover and climatic changes in mesoscale catchments
(2003) Samaniego-Eguiguren, Luis Eduardo; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Eine Vielzahl von hydrologischen Untersuchungen weist nach, dass die beobachteten Veränderungen verschiedener Merkmale des Wasserkreislaufs von der geographischen Lage und dem Maßstab, in dem die Untersuchungen durchgeführt wurden, abhängen. Im allgemeinen wird der Wasserkreislauf in einem Wassereinzugsgebiet durch klimatische Änderungen und/oder Änderungen der Bodenbedeckung bzw. Flächennutzung beeinflusst. Die Ermittlung der genauen Gründe der beobachteten Schwankungen des Wasserkreislaufs einer mittleren räumlichen Maßstabsebene ist eine besondere Herausforderung, weil zum einen ein Mangel an Informationen über die räumliche Verteilung der relevanten erklärenden Variablen besteht, und zum anderen die räumliche Heterogenität von Parametern unbekannt ist. Das Ziel der vorliegenden Untersuchung ist es, ein allgemein anwendbares Verfahren zu entwickeln, das es erlaubt, die beobachteten zeitlichen Schwankungen der Abflussmerkmale eines Wassereinzugsgebietes in zwei unabhängige Komponenten aufzuspalten. Die eine Komponente wird ausschließlich durch klimatische Schwankungen erklärt, während die zweite Komponente lediglich durch Änderungen der Flächennutzung bzw. Bodenbedeckung beeinflusst wird. Um diese Aufspaltung zu erreichen, wird folgender Algorithmus vorgeschlagen: Von einem Satz von Variablen ausgehend werden zunächst für jedes der gegebenen Abflussmerkmale so viele nicht-linearen Modelle kalibriert und auf ihre Anpassungsgüte hin bewertet, wie es Kombinationen dieser Variablen gibt. Anschließend wird die Robustheit jedes Modells durch ein Kreuz-Validierungsverfahren geschätzt, und schließlich wird die statistische Signifikanz jeder erklärenden Variablen mit Hilfe eines Permutations-Tests ermittelt. Die Optimierung der Parameter jedes Modells wird mit Hilfe eines Generalized Reduced Gradient Algorithmus durchgeführt. Abschließend wird mit Hilfe eines Algorithmus das robusteste Modell ausgewählt, welches die folgenden drei Bedingungen simultan am besten erfüllt: Erstens soll es die geringst mögliche Zahl von Variablen verwenden, aber den größtmöglichen Erklärungsbeitrag für die Schwankungen der Stichprobe liefern. Zweitens soll es von Ausreißern möglichst wenig beeinflusst werden und drittens sollen alle verwendeten Variablen auf einem 5%igen Niveau statistisch signifikant sein. Anschließend werden die so kalibrierten Modelle mit einem stochastischen Flächennutzungsmodell verknüpft, um die hydrologischen Wirkungen von Änderungen der Flächennutzung bzw. Bodenbedeckung und der klimatischen Änderungen in einem Einzugsgebiet mittlerer Größe zu simulieren. Die Größenordndung dieser Wirkungen wird mit einem probabilistischen Verfahren mit Hilfe einer sequentiellen Monte-Carlo-Simulation geschätzt. Die Simulation geht von vier unterschiedlichen Szenarien aus, die die wahrscheinlichen Entwicklungen der makro-klimatischen und der sozio-ökonomischen Bedingungen im Untersuchungsraum darstellen. Die vorgestellte Methodik wurde in einem rund 4000 km2 großen Einzugsgebiet des Oberen Neckars entwickelt und getestet. Eine Übertragung auf andere Einzugsgebiete ist möglich.
Open Access
Investigation of changes in hydro-meteorological time series using a depth-based approach
(2015) Yulizar; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
The climate is a complex interactive system between the atmosphere, the land surface, the oceans and others. A change in climate is not an issue of one or two days, but it takes place over a long period of time. Hydrology is one of the fields that is affected due to climate change. It describes the process of the movement of water on Earth, also known as the water cycle system. In this field, temperature and precipitation are the two main parameters that need to be analyzed in order to know about the water cycle system's behavior. Temperature increases throughout the globe and changes in precipitation distribution are two examples where change has already occurred on Earth. These phenomena that occur on Earth give us information about changes in the meteorological variables which have no boundaries and which affect the process of the water cycle system. This means that changes in the hydro-meteorological series might not only affect the means, variances, and extremes at individual locations, but they might also have an affect on the spatial and temporal dynamics. These changes in the multivariate scale would lead to the occurrence of unusual events. An example of an unusual event could be, one area being very warm but at the same time another area being very cold. Situations that have never occurred before might appear and others might disappear. Within the framework of this research, the frequencies and magnitudes of unusual events on temporal and spatial scales are investigated. Here a statistical tool that is called a depth function is used. It is based on the outlyingness function. Many types of depth function have been developed nowadays, and in this study the half-space depth function is used due to its robustness for defining the occurrence of unusual events. The general idea of a depth function is to measure the centrality of a point with respect to a dataset. Here, points that have a low depth that are located on, near, and outside a boundary are classified as unusual events. In another word, unusual events are defined based on their geometrical position in a multivariate set of observations using outlyingness function. Under this definition, all extremes are unusual events, but other combinations might also be considered as unusual. In this study, a low depth value with a threshold of 5 is used for the analysis. The main methodology is based on a cross depth calculation. It enables the identification of newly appearing and disappearing situations. Three possibilities might be obtained from the analysis; growing, shrinking, and translation. The daily data from temperature and precipitation series across Europe and the United States are used in this study to illustrate the methodology. In addition, the daily discharge series from the River Rhine and the River Neckar in Germany are also used to define the occurrence of unusual events. The investigation was carried out based on spatial and temporal scales that consist of discrete and over-time analysis, respectively. In the discrete approach, two equally long periods were analyzed with a cross depth calculation, so that we will define on how many days there are appearances and disappearances. In another way, the over-time approach or moving windows analysis with different aggregation levels was used so that we can observe the oscillation of unusual events. From the analysis, it shows any individual events may not be considered as an extreme at one time or one location, but due to their joint or simultaneous occurrence, it might lead to extreme events on a multivariate scale. These events, furthermore are called as unusual events. The results show that all the hydro-meteorological events show an oscillation in the occurrence unusual events. This means that unusual events not only occur at one time, but they change dynamically at different time periods on the spatial and temporal scales. In the temperature series, we can clearly observe that unusual events change dynamically from time to time. A similar situation also can be found in the precipitation series, where the unusual events show an oscillation in their occurrence. In the precipitation analysis, zero values are taken into consideration during the investigation. The discharge series also shows a similar condition with temperature and precipitation, where they have an oscillation in the occurrence of unusual events. With regard to magnitude, the number of unusual days for temperature is higher than for precipitation. This result leads to a situation, for instance, where droughts occur for a longer time than floods, that take place on a short time scale. Another result also shows that the occurrence of unusual precipitation does not showing a coherent situation with regard to the occurrence of unusual discharge events. This situation might be influenced by a time lag in the rainfall going between the surface into the river, catchment characteristics, river training, and others.
Open Access
Modellierung von Bodenerosion und Sedimentaustrag bei Hochwasserereignissen am Beispiel des Einzugsgsgebiets der Rems
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Schönau, Steffen; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Die vorliegende Dissertation untersucht Bodenerosion und Sedimentaustrag bei Hochwasserereignissen und Starkniederschlägen im Einzugsgebiet der Rems (Flussgebiet Neckar, Stromgebiet Rhein). Es werden die Grundlagen des Zusammenspiels von (Stark-) Niederschlag, Hochwasser und Sturzfluten, Bodenerosion und Sedimentaustrag sowie deren messtechnische und modellbasierte Erfassung dargestellt. Die Anwendung empirischer Modellansätze im Untersuchungsgebiet beinhaltet Modellparametrisierung, -kalibrierung und -validierung sowie Regionalisierung für die Übertragbarkeit auf unbeobachtete Gebiete. Es erfolgt eine Untersuchung des räumlichen Zusammenhangs der flächenhaften Eingangsdaten und Modellergebnisse sowie die Beurteilung der Wirkung von konservierender Bodenbearbeitung auf die Bodenabtrags- und Sedimentaustragsschätzungen. Es werden sowohl langandauernde advektive, zu Flusshochwasser führende Niederschlagsereignisse betrachtet als auch kurzzeitige konvektive Sommerereignisse, die nur zu wenig Abfluss oder aber auch zu Sturzfluten führen. Mit der entwickelten Methodik können saisonale und gebietsspezifische Eigenheiten wie Niederschlagscharakteristika, Landnutzung und Landbedeckung sowie Anfangsbodenfeuchte berücksichtigt werden. Ein Ergebnis ist die Bereitstellung von Eingangsdaten für die Optimierung der Steuerung von Hochwasserrückhaltebecken und Speichern zur gezielten Retention stofflicher Belastungen. Teile der Untersuchungen für diese Dissertation haben ihren Ursprung im RIMAX-Verbundvorhaben "Entwicklung eines integrativen Bewirtschaftungskonzepts für Trockenbecken und Polder zur Hochwasserrückhaltung".
Open Access
Modernization criteria assessment for water resources planning; Klamath Irrigation Project, U.S.
(2008) Freeman, Beau J.; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Agricultural irrigation is the largest consumer of diverted surface water and groundwater resources in the world, with major regions becoming critically water deficit. Agriculture in the western United States (US) and elsewhere has reached the point where the demands from irrigators, domestic users, and various commercial interests for allocated quantities and qualities are beyond acceptable levels for environmental needs in many river basins. Despite decades of investment in irrigation projects by governments, foreign lending agencies, and development banks in numerous countries, irrigation performance remains unsatisfactorily low and in many places progress is being reversed due to water logging, salinization, over-drafting of aquifers, environmental degradation, and infrastructure deterioration. Maintaining current irrigation practices will lead to worsening environmental and economic consequences. To restore healthy ecosystems and sustain irrigated agriculture, irrigation modernization should be promoted as a key component of basin-level water management to effectively balance competing water needs. Improvements in the technical and economic efficiency of irrigation water use through modernization increase the quantity and quality of freshwater available in a river basin. Significant public and private investments in modernization will be required to facilitate the precise control and monitoring of reallocated flows at different levels of irrigation systems, especially on a real-time basis, and thus provide excellent water delivery service to water districts, end-users, and other commercial and environmental stakeholders. This doctoral study investigates a specific problem that many irrigation professionals and water resources planners will face in the future: how to effectively analyze and make an assessment of irrigation modernization project-alternatives. Selecting the best modernization strategy to pursue from potential project-alternatives in water resources planning is a complex decision-making process. Irrigation modernization alternatives and their impacts involve a variety of diverse stakeholders in the selection of preferred engineering solutions based on subjectively defined criteria (quantitative and qualitative). As a consequence, technical feasibility, environmental, social/community, institutional, political, and economic factors have to be properly assessed as part of water resources planning. This research introduces a strategic decision analysis methodology for the definition, evaluation, ranking, and selection of appropriate modernization strategies in an engineering case study of the Klamath Irrigation Project (89,000 ha). In 2001 a combination of events occurred there that led to one of the most prominent conflicts over water supplies in the U.S. Due to stricter flow requirements put in place to protect fish species and a critical drought, irrigation water was unexpectedly withheld from the majority of farms in the Project, resulting in major economic losses, calling the basis for environmental restrictions into question, and generating intense political controversy. The composite programming approach is applied to develop a project ranking index based on standardized indicators – effective for analyzing the trade-offs associated with balancing technical and water conservation considerations with eco-system health, economics, and risk. This modernization criteria assessment requires defining the management objectives according to the nature of the internal processes and agro-hydrological features of the system, selection of alternative engineering solutions, selection of appropriate decision criteria relevant to the specific water-related problems, and the assignment of desirable and critical threshold values pertinent to each criterion. Input data consist of hydrologic, agronomic, engineering, economic, and political/policy information.
Open Access
New concepts for regionalizing temporal distributions of precipitation and for its application in spatial rainfall simulation
(Stuttgart: Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Mosthaf, Tobias; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Open Access
Non-multi-Gaussian spatial structures: process-driven natural genesis, manifestation, modeling approaches, and influences on dependent processes
(2013) Guthke, Philipp; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
In this thesis, an asymmetry function is identified as important additional measure to describe characteristic non-Gaussian features of spatially distributed variables more realistically. It is demonstrated, how asymmetry functions measure the difference in the strength of dependence between high and low quantiles and can be related to different characteristic length scales of spatial processes.
Open Access
Nonlinear estimation of short time precipitation using weather radar and surface observations
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Yan, Jieru; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Rain gauges are the foundation in hydrology to collect rainfall data, however, gauge observations alone are limited at representing the complete rainfall distribution. On the other hand, weather radar can provide complete rainfall distribution at high temporal and spatial resolution, yet concerns about the biases in radar rainfall estimates hamper the direct use of radar data in hydrological applications. Thus, merging radar measurements and rain gauge observations for surface precipitation estimation, by exploiting the strength and minimizing the weaknesses of each method, is in an area of active research. Among all the sources of errors of radar rainfall estimates, the uncertainty in the relationship between radar reflectivity Z and rainfall rate R, namely the Z-R relationship, is regarded as a massive source of uncertainty. There is a whole branch of studies on delivering an accurate Z-R relationship based on different drop size distributions, rainfall regimes and geographical locations. The focus of this study is not to derive an accurate Z-R relationship, but to correct the radar rainfall estimates by the available surface observations nonlinearly. Specifically, radar data are used in the relative magnitudes, as a quantile map to indicate the spatial pattern of precipitation. A marginal distribution function is generated based on surface observations and the collocated radar quantiles, whereby the quantile map can be transformed to a precipitation map. It is a common practice to construct radar-gauge pairs by assuming vertical and instant falling of the hydrometeors onto the ground. Obviously, the assumption is invalid on many occasions, as it ignores a significant fact that it takes time for the hydrometeors to reach the ground and during the descending, the hydrometeors are very likely to be drifted by the wind, especially with a large measurement height and with the existence of snow. The effect of wind drift can result in great discrepancy of radar and gauge data if the vertical collocation is assumed, especially for domains of small size and for events with convective behavior. To tackle this, a method to quantify the wind effect is proposed and the result of the quantification is integrated in surface precipitation estimation. The spatial pattern of precipitation changes along the vertical distance. The change in the spatial pattern can be induced by many factors, such as uniform movement of the field, further development of precipitation below the radar measurement height, evaporation, nonuniform movement of the field, etc. The quantification scheme for the wind effect proposed in this study considers an overall migration of the field. It is assumed that the entire field moves uniformly with a single vector. The other factors causing the vertical variation of the spatial pattern cannot be captured by the scheme. To remediate the situation, random changes in the spatial pattern are allowed. Two conditional simulation methods, random mixing and phase annealing, are employed to generate realizations of surface precipitation.
Open Access
Parameter optimization of theoretically consistent Intensity Duration Frequency (IDF) curves and their uncertainty quantification
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2024) Amin, Bushra; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Open Access
Physically based spatially distributed rainfall runoff modelling for soil erosion estimation
(2010) Thapa, Pawan Kumar; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Addressing different environmental and geomorphologic issues needs prediction of erosion patterns and source areas within the catchment. Several modeling alternatives exist, all with certain potential and limitations. Physically-based distributed erosion models are very much data-hungry making them of limited use in data-poor countries where erosion problem is se-verer. In addition, owing to problems like, large spatial and temporal variability of soil ero-sion phenomena and uncertainty associated with input parameter it is clear that accurate erosion prediction is still difficult and problem will not be solved by constructing even more complex models. USLE is simple but still most widely used erosion model. Its adequate ca-pability for predicting gross erosion has been proved in innumerable cases. However, the pre-diction capability has, so far, been assessed based on their ability to correctly predict lumped results at watershed outlet. The first objective of work is to investigate reliability of predicting spatial patterns of catch-ment erosion using the simple USLE-based erosion model when fed with better hydrology us-ing a physically-based spatially-distributed rainfall-runoff model (WaSiM-ETH). A small agricultural catchment (Ganspoel), located in central Belgium is chosen for investigation. The runoff and sediment yield at catchment outlet and the spatially distributed erosion within the catchment for different events have been simulated. Several results, mainly from, SCS-CN and WaSiM-ETH for erosivity computation and different algorithms for topographical factors and sediment delivery ratio (SDR) computation have been compared. Besides the predictions at outlet, the simulated spatially distributed erosion patterns and source areas have agreed rea-sonably well with the observed ones and also with the results from another physically-based more complex and data-intensive erosion model (MEFIDIS). This improved capability of simple erosion model for predicting spatial patterns of catchment erosion is extended further to devise an approach for determining spatially and temporally varying erosion risk in a big-ger Rems catchment in southern Germany. Runoff distributions are estimated from long-term simulation with WaSiM-ETH, crop cover distribution is obtained from series of MODIS-NDVI. The soil and topographical features, obtained from soil map and DEM, are considered to be temporally constant. The spatial and temporal variability hence captured through the in-tersection of Hydrologically Sensitive Areas, HSAs (from runoff simulations) and Erosion Susceptible Areas, ESAs (from geomorphic factors) yields dynamics of the erosion risk areas categorized as Critical Source Areas (CSAs). Hence, in this research work, it is shown that the dynamic behavior in hydrological sensitivity and erosion risk, estimated in such a simple ap-proach, potentially lessens landuse restrictions on landowners as the arable and agricultural fields could be prioritized for management practices by their degree of hydrological and ero-sive sensitivity. On the other hand, this research work also reveals some unreasonable consequences that have been encountered while calibrating the distributed rainfall-runoff model. From the calibration of the events in Ganspoel catchment, using Gauss-Marcquardt-Levenberg algorithm, very nice results are obtained with closely matching hydrographs and quite high NS efficiency. But a very much unrealistic patterns are observed with almost all the runoff is coming from a small isolated patch in the catchment. In Rems catchment, the model is calibrated using more accepted Shuffled-Complex-Evolution (SCE-UA) algorithm where also it is seen that the very good model performance are not accompanied by reasonable runoff patterns. A new concept, based on a statistical depth function, has been investigated further which yields not a single best parameter set but several sets of good parameter. The model performs quite well and runoff patterns within the catchment are also reasonable. But the amount of surface runoff from the different good parameter sets, when separated by using a digital filter, are found to vary highly, thus giving unacceptably different results when they are used further. The high values of spatial correlation and the rank correlation among the surface runoff from different good parameter sets prove that the patterns are uniform and reasonable but high variation in the amount raise the question mark in their quantitative reliability. These results, thus, show the very good predictions by the rainfall-runoff model but for all wrong reasons. This indi-cates that simply the better hydrograph prediction by a physically-based distributed rainfall-runoff model does not guarantee better hydrology representation by it thus making its distrib-uted results in doubt to be accepted.
Open Access
Process-oriented modeling of spatial random fields using copulas
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2016) Hörning, Sebastian; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)