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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
Statistical downscaling of extremes of precipitation in mesoscale catchments from different RCMs and their effects on local hydrology
(2011) Alam, Muhammad Mahboob; Bardossy, Andras (Prof. Dr. rer. nat. Dr. -Ing.)
Global climate models are the only available comprehensive tools for studying the affects of climate change on our earth in terms of changes in different meteorological and hydrological variables in future. Precipitation and temperature are two of the most important meteorological variables with regards to their affects on other meteorological (e.g. humidity, evaporation etc.) and hydrological (e.g. river runoff) variables and on human life (e.g. food fibre production, economy etc.). Among other important local and large scale phenomenon that affects the occurrence and amount of precipitation (and severity of temperature), geographical and topographical conditions perhaps play most important role in the behaviour of these variables in certain area. This makes the two variables more or less local phenomenons that need to be specifically studied for each area of interest individually. Unfortunately the scale at which global climate models (GCMs) operate is too large for any meaningful study to be performed related to future patterns of these two variables on local scale. Different methodologies have thus been developed to downscale (i.e. to increase the resolution of) GCM data to the local scale. The two broad categories of downscaling methodologies are statistical and dynamical downscaling. In statistical downscaling methodology, an attempt is made to develop a relationship between large scale GCM modelled variable (called predictor) and local scale observed/measured variable (called predictant). Assuming that in future this relationship will hold, the relationship is used to predict local scale predictand for future simulated scenarios of predictor. In dynamical downscaling (the so called regional climate models (RCMs)) on the other hand, an attempt is made to embed a complete physical model of more or less the same complexity as GCM, in a GCM and upon receiving values from GCM at its boundaries, recalculate all possible physical formulations at a much finer scale. The local conditions are thus taken in to account and the results are believed to be more suitable for local scale studies. Both downscaling methodologies have been extensively applied in climate change and impact studies around the world with varying degree of success and new techniques are consistently being developed to improve upon them. Both methodologies have associated advantages and disadvantages. While statistical downscaling is computationally much cheaper than RCMs, statistical downscaling is based on basic assumption of stationarity which is sometimes hard to justify. RCMs on the other hand although attempt to solve physical equations at local scale, does also inherit bias from the parent GCM. This thesis presents statistical downscaling methodology which attempts to correct for the biases that are inherited by different RCMs. Three different RCMs are considered for German part of Rhine basin and using bias correction methodology based on correction of quantiles of precipitation (and temperature for some studies), new scenarios of precipitation are developed. Further, a distributed version of conceptual hydrological model HBV is calibrated and validated for German part of Rhine basin and raw and downscaled RCM scenarios of precipitation are fed into the model to ascertain the future hydrological regime in face of climate change for this important river. The downscaling procedure briefly discussed above was applied in two ways. In the first case the statistical downscaling methodology was performed on RCM data without considering any constraint during quantile-quantile exchange between RCM control and scenario runs. In the second case, the quantile-quantile exchange was conditioned on occurrence of certain circulation pattern. It was briefly discussed above how precipitation (occurrence and amount) is conditioned by certain phenomenon. In addition to geographical and topographical location, precipitation also depends upon large scale circulation patterns. Thus it was assumed that conditioning the downscaling methodology also on circulation patterns would bring about better results. To realize above concept, classification of circulation patterns is performed. Fuzzy rule based classification methodology is used to classify circulation patterns. Two new methodologies of classification of circulation patterns are presented in this thesis. One is based on low flow conditions in rivers in the study area and the other is based on clustering of precipitation stations. The new classification methodology is believed to provide better classification of circulation patterns in that the difference between the individual classes is enhanced and similarity among the same class intensified. A classification analysis measure called wetness index was developed and used to identify critical circulation patterns among the classified circulation patterns. Critical circulation patterns were identified for extreme wet and dry conditions and it was shown that all extreme cases of floods and droughts are caused by identified critical CPs. This thesis also presents and applies another statistical downscaling methodology based on multivariate autoregressive model of order 1 (one). The methodology makes use of the classification of circulation patterns described above. The parameters of the autoregressive model depend upon the circulation patterns. The methodology is used for number of head catchments in southern and eastern Germany. Head catchments by definition have very quick response time to any significant precipitation event. They contribute quickly to the surface runoff and if they are head catchments of larger rivers, may also result in bigger flood events. Downscaling of precipitation was performed for these catchments by using mean sea level pressure (MSLP) as predictor and local station precipitation as predictant. The model was developed such that ensemble of daily precipitation could be produced. Thereby enabling one to estimate associated uncertainty. Finally drought analysis are performed for German part of Rhine basin using Palmer drought severity index. A FORTRAN routine is developed which can calculate different kind of drought indices such as Palmer drought severity index, Palmer hydrological drought index, and monthly moisture anomaly index for certain catchment. The program developed is also capable of simultaneously mapping the results. The mapping of results makes it possible to ascertain the severity of drought over the larger area. The analysis of drought is performed for observational gridded data set and for control and A1B scenarios of three different RCMs.
Open Access
Large-scale high head pico hydropower potential assessment
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Schröder, Hans Christoph; Wieprecht, Silke (Prof. Dr.-Ing.)
Due to a lack of site-related information, Pico hydropower (PHP) has hardly been a projectable resource so far. This is particularly true for large area PHP potential information that could open a perspective to increase the size of development projects by aggregating individual PHP installations. The present work is extending the capabilities of GIS based hydropower potential assessment into the PHP domain through a GIS based PHP potential assessment procedure that facilitates the discrimination of areas without high head PHP potential against areas with PHP potential and against areas with so called “favorable PHP potential”. The basic unit of the spatial output is determined by the underlying PHP potential definition of this work: a standardized PHP installation and the required hydraulic source, together called standard unit, are located on an area of one square kilometer. The gradation of the output is a consequence of the verification techniques. Several large area PHP potential field assessment methods, based on contemplative analysis techniques, are developed in this work. Field assessments were conducted in Yunnan Province/China, Costa Rica, Ecuador and Sri Lanka. The aim for all field assessments is to get a comprehensive view on the PHP potential distribution of the entire country/province. Application of the GIS based PHP potential assessment procedure is aimed at the global tropical and subtropical regions.
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
Pressure management via brine extraction in geological CO2 storage : adaptive optimization strategies under poorly characterized reservoir conditions
(2019) González-Nicolás, Ana; Cihan, Abdullah; Petrusak, Robin; Zhou, Quanlin; Trautz, Robert; Godec, Michael; Birkholzer, Jens T.
Industrial-scale injection of CO2 into the subsurface increases the fluid pressure in the reservoir, which if not properly controlled can potentially lead to geomechanical damage (i.e., fracturing of the caprock or reactivation of faults) and subsequent CO2 leakage. Brine extraction is one approach for managing formation pressure, effective stress, and plume movement in response to CO2 injection. The management of the extracted brine can be expensive (i.e., due to transportation, treatment, disposal, or re-injection), with added cost to the carbon capture and sequestration (CCS); thus, minimizing the volume of extraction brine is of great importance to ensure that the economics of CCS are favorable. The main objective of this study is to demonstrate the use of adaptive optimization methods in the planning of brine extraction and to investigate how the quality of initial site characterization data and the use of newly acquired monitoring data (e.g. pressure at observation wells) impact the optimization performance. We apply an adaptive management approach that integrates monitoring, calibration, and optimization of brine extraction rates to achieve pre-defined pressure constraints. Our results show that reservoir pressure management can be extremely benefited by early and high frequency pressure monitoring during early injection times, especially for poor initial reservoir characterization. Low frequencies of model calibration and optimization with monitoring data may lead to optimization problems, because either pressure buildup constraints are violated or excessively high extraction rates are proposed. The adaptive pressure management approach may constitute an effective tool to manage pressure buildup under uncertain reservoir conditions by minimizing the volumes of extracted brine while controlling pressure buildup.
Open Access
Wasserinfiltration in die ungesättigte Zone eines makroporösen Hanges und deren Einfluss auf die Hangstabilität
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2016) Germer, Kai; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Hangrutschungen stellen in besiedelten Regionen eine große Gefahr dar, weil nicht selten direkt bewohnte Bereiche betroffen sind. Aber auch die Rutschungsauswirkungen auf die Infrastruktur wie Verkehrswege und Versorgungseinrichtungen können der Gesellschaft Schaden zufügen. Zum einen ergibt sich im Zusammenhang mit Hangrutschungen und allgemein Massenbewegungen das Betätigungsfeld der direkten Stabilisierung und Verhinderung von Rutschungen durch beispielsweise Tiefdrainagen und aufwendige ingenieursbauliche Maßnahmen. Zum anderen, und das ist Gegenstand dieser Arbeit, ergibt sich das Tätigkeitsfeld der grundlagenorientierten Forschung, um die Prozesse, die zu Hangrutschungen führen, besser verstehen zu können. Mit dem Heumöser Hang in Österreich (Vorarlberg), einem sich sehr langsam bewegenden Großhang (Kriechhang), liegt ein Untersuchungsobjekt vor, an dem vielfältige hydrologische Prozesse stattfinden, die schon über mehrere Jahre hinweg untersucht wurden. Die Untersuchungen resultierten in der Hypothese, dass sich am Heumöser Hang entwickelnder Makroporenfluss zu schnellen hydraulischen Veränderungen im Innern des Hangkörpers führe. Die hydraulischen Veränderungen zeigen sich insbesondere in starken Porenwasserdruckanstiegen (unter anderem in einem gespannten Grundwasserleiter), die teilweise in der Tiefe des Hanges zu Auftriebskräften führen, die den Hang destabilisieren, so dass dieser sich schubweise insgesamt etwa ein bis zwei Dezimeter im Jahr talwärts bewegt. Zum Erarbeiten des Prozessverständnisses bezüglich des Zusammenhanges zwischen Infiltration und Hangstabilität wurden große Bodenproben vom Hang im Labor untersucht und Experimente an zwei technischen Modellen durchgeführt. Mit der vorgestellten Vorgehensweise und der Separierung der Untersuchungen und Experimente konnten für den Heumöser Hang relevante hydrologische und mechanische Teilaspekte erarbeitet werden, die verknüpft die Hangbewegungshypothese in weiten Teilen bestätigen können. Insbesondere bestätigten die Messungen an den originären Bodenproben vom Heumöser Hang, dass der Makroporenfluss so dominant sein kann, dass potentiell schneller Porenwasserdruckanstieg in der Tiefe erzeugt werden kann. Dennoch kann ein Makroporenfluss generell insbesondere bei trockenen Matrixbedingungen vermindert werden. Die Verminderung des Makroporenflusses wurde anhand von Experimenten mit Sand gezeigt. Der Prozess des Wassertransfers von Makropore zu umgebender Matrix ist im Sand sehr deutlich zu sehen. Darüber hinaus konnten bei Bodensäulen- und Bodenprobenexperimenten viele methodische Herangehensweisen getestet und verglichen werden. Weil im Vorfeld der Untersuchungen schon abgeschätzt wurde, dass nur mit größeren Proben eine für den Standort bessere Repräsentativität der Ergebnisse erhalten werden kann, wurde besonders viel Wert auf die Methodik der Großprobennahme gelegt. So ist in der vorgestellten Arbeit ein neuartiger Ansatz zur Großprobennahme entwickelt worden, bei dem die Proben ungestört frei gelegt wurden und mit Haushaltsfolie, Montageschaum und Außenmodulen eingehüllt wurden. Nur die Großprobennahme garantierte ein Mindestmaß an Erfassung von Heterogenitäten und Bodenstrukturen im Dezimeterbereich, wie z.B. Makroporen und Risse. Auch die laboratorischen Versuchsaufbauten zur Anwendung der Multi-Step-Outflow- und Evaporationsmethode an den Großproben wurden einmalig größenangepasst entwickelt.
Open Access
Integrated fuzzy-GIS approach for assessing regional soil erosion risks
(2011) Bakimchandra, Oinam; Wieprecht, Silke (Dr.-Ing.)
Modelling a dynamic and physical process, such as soil erosion, is prone to errors and problems. The availability of the right kind of data source, quality of data used, scale issues in modelling, measurement errors etc. and the complexity of the model in itself are some of the issues that are explicitly addressed and reported in soil erosion research studies. Existing soil erosion models based on physical processes are very data demanding in both their amount of variables and their temporal and spatial resolution requirements. Hence, data scarcity and lack of reliable data tend to pose a problem for successful application of physical based erosion models. On the other hand, less data demanding empirical based models are developed for a certain environmental set up using erosion plot studies and thus their applicability is restricted to regions where they were developed. Another significant aspect that is overlooked in many past soil erosion risk assessment studies is the nature of the various environmental control parameters involved in modelling, which are fuzzy in reality. When mapping erosion risk, the introduction of fuzzy sets instead of crisp sets to define classes (i.e. degree of hazard or risk) will help to incorporate a degree of fuzziness within each class of the governing parameters. It is found that various existing soil erosion risk models consider each feature and spatial units present on the landscape or catchment as having distinct boundaries. In reality, the existing natural boundaries are much more complex. To cope with such problems of class boundaries and to incorporate the expert knowledge that can represent the processes under investigation, there is a need of fuzzy logic based modelling approach. In this PhD research, a simple and efficient fuzzy logic-based soil erosion risk model for monitoring the soil erosion risk distribution over a regional landscape is developed. The developed model is known as Fuzzy-Water Erosion Risk Classification and Assessment Model (F-WERCAM). As the name indicates, this model is intended for water based soil erosion risk classification and their assessment using a fuzzy logic modelling concept in a GIS platform. The model is designed or set up in such a way that it has minimum input data requirements for model execution, provided the considered input parameters are the main primary governing factors that influence the soil erosion risk of a region. One of the salient features in the F-WERCAM is the multi-stage modelling approach. It consist of 3 stages namely, Stage 1- mapping of the Soil Protection Index (SPI), Stage 2- mapping of the Potential Erosion Risk Index (PERI) and Stage 3- mapping of the Actual Erosion Risk Index (AERI).This set up allows for the simplification of the fuzzy rule bases by reducing the number of input parameters at each stage of the modelling. In addition, this approach allows for a step-by-step evaluation of the intermediate results. For instance, Stage 1 of the modelling approach allows for the evaluation of the SPI of a region, before integrating with the PERI of Stage 2, to obtain the final AERI of a region (Stage 3). The final soil erosion risk map provides qualitative based information on the distribution pattern of the soil erosion risk classes over a region. Apart from the qualitative based spatial information on soil erosion risk obtained from this model, the possibility of transferring the output erosion risk index into quantitative soil loss values (in t/ha/yr) is explored and discussed in this study. The model is successfully tested in Upper Awash Basin in Ethiopia and further used to produce a soil erosion risk map of Italy. The ability of fuzzy logic to describe and transform the knowledge in a descriptive human like manner in the form of simple rules using linguistic variables has provides a new direction and opening to explore and develop a simple and well structured framework for soil erosion risk assessment. Overall, the integration of fuzzy logic within GIS using remotely sensed data in this research tries to address the problems of data scarcity, uncertainty in the input model parameters and handling of large spatial data effectively. From the various assessments and evaluations presented in this research, it is found that such an expert based fuzzy logic model has the potential to be used as a practical tool for assessment of regional soil erosion risk by policy makers and scientists.
Open Access
Simulation, identification and characterization of contaminant source architectures in the subsurface
(2014) Koch, Jonas; Nowak, Wolfgang (Jun.-Prof. Dr.-Ing.)
Improper storage and disposal of non-aqueous-phase liquids (NAPLs) has resulted in widespread subsurface contamination, threatening the quality of groundwater as freshwater resource. Contaminants with low immiscibility and solubility in the aqueous phase, remain as a separate phase. They dissolve into the groundwater and spread within the aquifer over long periods of time, before the contaminants are fully depleted. Due to their typically high toxicity, even low concentrations in groundwater may pose high risks on ecosystems and human health. The spatial distribution of contaminants in the subsurface (i.e., the contaminant source architecture, CSA for short) is highly irregular and not precisley predictable. Yet, the complex and uncertain morphology of CSAs and its interactions with uncertain aquifer parameters and groundwater flow have to be accounted for and need to be resolved at the relevant scale to maintain adequate prediction accuracy. The abundance of contaminated sites and difficulties of remediation efforts demand decisions to be based on a sound risk assessment. To this end, screening or investigation methods are applied. These methods assess which sites pose large risks, which ones can be left to natural attenuation, which ones need expensive remediation, and what remediation approach would be most promising. For this, it is important to determine relevant characteristics or impact metrics, such as geometric characteristics of the unknown CSA , total mass, potential mass removal by remediation, emanating dissolved mass fluxes and total mass discharge in past and future, predicted source depletion times, and the possible impact on drinking water wells, and thus on human health. The same characteristics are also important for designing monitoring or remediation schemes. Due to sparse data and natural heterogeneity, this risk assessment needs to be supported by adequate predictive models with quantified uncertainty. These models require an accurate source zone description, i.e., the distribution of mass of all partitioning phases in all possible states, mass-transfer algorithms, and the simulation of transport processes in the groundwater. Due to limited knowledge and computer resources, a selective choice of the relevant processes for the relevant states and decisions on the relevant scale is both sensitive and indispensable. Thus, it is an important research question what is a meaningful level of model complexity and how to obtain a physically and statistically consistent model framework. Almost every estimate of the desired impact metrics will be uncertain due to the typical uncertainty that is inherent in any process description in a heterogeneous subsurface environment, and due to the complex and non-linear interdependencies between aquifer parameters, CSA, groundwater velocities, and mass transfer. Thus, stochastic methods are indispensable because they can provide reasonable error bars and allow the involved stakeholders to take decisions in proportion to the posed risks of contaminated sites. In order to restrict this huge uncertainty, field data need to be assimilated by inverse models. To this end, concentration observations possess promising information on CSA geometries, transport processes, and aquifer parameters. Revealing these valuable information, however, requires an efficient inverse model that is again physically and stochastically consistent. In particular, the identification of CSAs has to cope with non-unique problems, non-linear interdependencies, and enhanced mixing and plume deformation in a heterogeneous environment. The overall goal of this thesis is to provide a sound basis for rational decisions that arise in the assessment of contaminated sites. Therefore, three theses are postulated in the following, for which their significance and validity is demonstrated throughout this work. 1.) The model framework must at least account for the heterogeneity of aquifers, the irregularity of flow fields, realistic and thus complex-shaped CSAs, the three-dimensionality of natural systems, adequate physical interlinkages of the key parameters at the adequate spatial and temporal scales, and it must at least treat the uncertainty of aquifer parameters and of the CSA. 2.) Joint identification of CSAs and aquifer parameters based on concentration observations can be achieved via non-linear and non-unique Bayesian inversion. An accurate and efficient inverse method for this task can be by obtained by applying the method of adjoint states and utilizing the linearity of the transport equation. 3.) The enhanced mixing of dissolved DNAPL and the solute plume deformation in heterogeneous aquifers significantly influences the inference quality of CSAs from downstream concentration observations. Knowledge on the driving processes of enhanced mixing allows to chose adequate measurement designs.
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
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.

Universität Stuttgart

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