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A quantitative model of groundwater flows contributing to the Goblenz state water scheme at the north-western fringe of the Kalahari was developed within this study. The investigated area corresponds to the Upper Omatako basin and encompasses an outer mountainous rim and sediments of the Kalahari sand desert in the centre. This study revealed the eminent importance of the mountainous rim for the water balance of the Kalahari, both in terms of surface and ground water. A hydrochemical subdivision of groundwater types in the mountain rim around the Kalahari was derived from cluster analysis of hydrochemical groundwater data. The western and south-western secondary aquifers within rocks of the Damara Sequence, the Otavi Mountain karst aquifers of the Tsumeb and Abenab subgroups as well as the Waterberg Etjo sandstone aquifer represent the major hydrochemical groups. Ca/Mg and Sr/Ca ratios allowed to trace the groundwater flow from the Otavi Mountains towards the Kalahari near Goblenz. The Otavi Mountains and the Waterberg were identified as the main recharge areas showing almost no or only little isotopic enrichment by evaporation. Soil water balance modelling confirmed that direct groundwater recharge in hard-rock environments tends to be much higher than in areas covered with thick Kalahari sediments. According to the water balance model average recharge rates in hard-rock exposures with only thin sand cover are between 0.1 and 2.5 % of mean annual rainfall. Within the Kalahari itself very limited recharge was predicted (< 1 % of mean annual rainfall). In the Upper Omatako basin the highest recharge probability was found in February in the late rainfall season. The water balance model also indicated that surface runoff is produced sporadically, triggering indirect recharge events. Several sinkholes were discovered in the Otavi Foreland to the north of Goblenz forming short-cuts to the groundwater table and preferential recharge zones. Their relevance for the generation of indirect recharge could be demonstrated by stable isotope variations resulting from observed flood events. Within the Kalahari basin several troughs were identified in the pre-Kalahari surface by GIS-based analyses. A map of saturated thickness of Kalahari sediments revealed that these major troughs are partly saturated with groundwater. The main trough, extending from south-west to north-east, is probably connected to the Goblenz state water scheme and represents a major zone of groundwater confluence, receiving groundwater inflows from several recharge areas in the Upper Omatako basin. As a result of the dominance of mountain front recharge the groundwater of the Kalahari carries an isotopic composition of recharge at higher altitudes. The respective percentages of inflow into the Kalahari from different source areas were determined by a mixing-cell approach. According to the mixing model Goblenz receives most of its inflow (70 to 80 %) from a shallow Kalahari aquifer in the Otavi Foreland which is connected to the Otavi Mountains. Another 15 to 10 % of groundwater inflow to the Kalahari at Goblenz derive from the Etjo sandstone aquifer to the south and from inflow of a mixed component. In conclusion, groundwater abstraction at Goblenz will be affected by measures that heavily influence groundwater inflow from the Otavi Mountains, the Waterberg, and the fractured aquifer north of the Waterberg.
The study investigates the water resources and aquifer dynamics of the igneous fractured aquifer-system of the Troodos Mountains in Cyprus, using a coupled, finite differences water balance and groundwater modelling approach. The numerical water balance modelling forms the quantitative framework by assessing groundwater recharge and evapotranspiration, which form input parameters for the groundwater flow models. High recharge areas are identified within the heavily fractured Gabbro and Sheeted Dyke formations in the upper Troodos Mountains, while the impervious Pillow Lava promontories - with low precipitation and high evapotranspiration - show unfavourable recharge conditions. Within the water balance studies, evapotranspiration is split into actual evapotranspiration and the so called secondary evapotranspiration, representing the water demand for open waters, moist and irrigated areas. By separating the evapotranspiration of open waters and moist areas from the one of irrigated areas, groundwater abstraction needs are quantified, allowing the simulation of single well abstraction rates in the groundwater flow models. Two sets of balanced groundwater models simulate the aquifer dynamics in the presented study: First, the basic groundwater percolation system is investigated using two-dimensional vertical flow models along geological cross-sections, depicting the entire Troodos Mountains up to a depth of several thousands of metres. The deeply percolating groundwater system starts in the high recharge areas of the upper Troodos, shows quasi stratiform flow in the Gabbro and Sheeted Dyke formations, and rises to the surface in the vicinity of the impervious Pillow Lava promontories. The residence times mostly yield less than 25 years, the ones of the deepest fluxes several hundreds of years. Moreover, inter basin flow and indirect recharge of the Circum Troodos Sedimentary Succession are identified. In a second step, the upper and most productive part of the fractured igneous aquifer-system is investigated in a regional, horizontal groundwater model, including management scenarios and inter catchment flow studies. In a natural scenario without groundwater abstractions, the recovery potential of the aquifer is tested. Predicted future water demand is simulated in an increased abstraction scenario. The results show a high sensitivity to well abstraction rate changes in the Pillow Lava and Basal Group promontories. The changes in groundwater heads range from a few tens of metres up to more than one hundred metres. The sensitivity in the more productive parts of the aquifer-system is lower. Inter-catchment flow studies indicate that - besides the dominant effluent conditions in the Troodos Mountains - single reaches show influent conditions and are sub-flown by groundwater. These fluxes influence the local water balance and generate inter catchment flow. The balanced groundwater models form thus a comprehensive modelling system, supplying future detail models with information concerning boundary conditions and inter-catchment flow, and allowing the simulation of impacts of landuse or climate change scenarios on the dynamics and water resources of the Troodos aquifer-system.
The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.
In the future Internet, the people-centric communication paradigm will be complemented by a ubiquitous communication among people and devices, or even a communication between devices. This comes along with the need for a more flexible, cheap, widely available Internet access. Two types of wireless networks are considered most appropriate for attaining those goals. While wireless sensor networks (WSNs) enhance the Internet’s reach by providing data about the properties of the environment, wireless mesh networks (WMNs) extend the Internet access possibilities beyond the wired backbone. This monograph contains four chapters which present modeling and optimization methods for WSNs and WMNs. Minimizing energy consumptions is the most important goal of WSN optimization and the literature consequently provides countless energy consumption models. The first part of the monograph studies to what extent the used energy consumption model influences the outcome of analytical WSN optimizations. These considerations enable the second contribution, namely overcoming the problems on the way to a standardized energy-efficient WSN communication stack based on IEEE 802.15.4 and ZigBee. For WMNs both problems are of minor interest whereas the network performance has a higher weight. The third part of the work, therefore, presents algorithms for calculating the max-min fair network throughput in WMNs with multiple link rates and Internet gateway. The last contribution of the monograph investigates the impact of the LRA concept which proposes to systematically assign more robust link rates than actually necessary, thereby allowing to exploit the trade-off between spatial reuse and per-link throughput. A systematical study shows that a network-wide slightly more conservative LRA than necessary increases the throughput of a WMN where max-min fairness is guaranteed. It moreover turns out that LRA is suitable for increasing the performance of a contention-based WMN and is a valuable optimization tool.
Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called ‘biodiversity crisis’, which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called ‘megadiverse’ nations cover less than 10% of the earth’s land surface but support nearly 70% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico’s large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.
Internet applications are becoming more and more flexible to support diverge user demands and network conditions. This is reflected by technical concepts, which provide new adaptation mechanisms to allow fine grained adjustment of the application quality and the corresponding bandwidth requirements. For the case of video streaming, the scalable video codec H.264/SVC allows the flexible adaptation of frame rate, video resolution and image quality with respect to the available network resources. In order to guarantee a good user-perceived quality (Quality of Experience, QoE) it is necessary to adjust and optimize the video quality accurately. But not only have the applications of the current Internet changed. Within network and transport, new technologies evolved during the last years providing a more flexible and efficient usage of data transport and network resources. One of the most promising technologies is Network Virtualization (NV) which is seen as an enabler to overcome the ossification of the Internet stack. It provides means to simultaneously operate multiple logical networks which allow for example application-specific addressing, naming and routing, or their individual resource management. New transport mechanisms like multipath transmission on the network and transport layer aim at an efficient usage of available transport resources. However, the simultaneous transmission of data via heterogeneous transport paths and communication technologies inevitably introduces packet reordering. Additional mechanisms and buffers are required to restore the correct packet order and thus to prevent a disturbance of the data transport. A proper buffer dimensioning as well as the classification of the impact of varying path characteristics like bandwidth and delay require appropriate evaluation methods. Additionally, for a path selection mechanism real time evaluation mechanisms are needed. A better application-network interaction and the corresponding exchange of information enable an efficient adaptation of the application to the network conditions and vice versa. This PhD thesis analyzes a video streaming architecture utilizing multipath transmission and scalable video coding and develops the following optimization possibilities and results: Analysis and dimensioning methods for multipath transmission, quantification of the adaptation possibilities to the current network conditions with respect to the QoE for H.264/SVC, and evaluation and optimization of a future video streaming architecture, which allows a better interaction of application and network.
The Mediterranean area reveals a strong vulnerability to future climate change due to a high exposure to projected impacts and a low capacity for adaptation highlighting the need for robust regional or local climate change projections, especially for extreme events strongly affecting the Mediterranean environment. The prevailing study investigates two major topics of the Mediterranean climate variability: the analysis of dynamical downscaling of present-day and future temperature and precipitation means and extremes from global to regional scale and the comprehensive investigation of temperature and rainfall extremes including the estimation of uncertainties and the comparison of different statistical methods for precipitation extremes. For these investigations, several observational datasets of CRU, E-OBS and original stations are used as well as ensemble simulations of the regional climate model REMO driven by the coupled global general circulation model ECHAM5/MPI-OM and applying future greenhouse gas (GHG) emission and land degradation scenarios.
Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 %. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 % for cotton and 30.4 % for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 % to 23.8 % for cotton and from -0.4 % to -19.4 % for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.
Dynamic interactions and their changes are at the forefront of current research in bioinformatics and systems biology. This thesis focusses on two particular dynamic aspects of cellular adaptation: miRNA and metabolites.
miRNAs have an established role in hematopoiesis and megakaryocytopoiesis, and platelet miRNAs have potential as tools for understanding basic mechanisms of platelet function. The thesis highlights the possible role of miRNAs in regulating protein translation in platelet lifespan with relevance to platelet apoptosis and identifying involved pathways and potential key regulatory molecules. Furthermore, corresponding miRNA/target mRNAs in murine platelets are identified. Moreover, key miRNAs involved in aortic aneurysm are predicted by similar techniques. The clinical relevance of miRNAs as biomarkers, targets, resulting later translational therapeutics, and tissue specific restrictors of genes expression in cardiovascular diseases is also discussed.
In a second part of thesis we highlight the importance of scientific software solution development in metabolic modelling and how it can be helpful in bioinformatics tool development along with software feature analysis such as performed on metabolic flux analysis applications. We proposed the “Butterfly” approach to implement efficiently scientific software programming. Using this approach, software applications were developed for quantitative Metabolic Flux Analysis and efficient Mass Isotopomer Distribution Analysis (MIDA) in metabolic modelling as well as for data management. “LS-MIDA” allows easy and efficient MIDA analysis and, with a more powerful algorithm and database, the software “Isotopo” allows efficient analysis of metabolic flows, for instance in pathogenic bacteria (Salmonella, Listeria). All three approaches have been published (see Appendices).
Computer systems have replaced human work-force in many parts of everyday life, but there still exists a large number of tasks that cannot be automated, yet. This also includes tasks, which we consider to be rather simple like the categorization of image content or subjective ratings. Traditionally, these tasks have been completed by designated employees or outsourced to specialized companies. However, recently the crowdsourcing paradigm is more and more applied to complete such human-labor intensive tasks. Crowdsourcing aims at leveraging the huge number of Internet users all around the globe, which form a potentially highly available, low-cost, and easy accessible work-force.
To enable the distribution of work on a global scale, new web-based services emerged, so called crowdsourcing platforms, that act as mediator between employers posting tasks and workers completing tasks. However, the crowdsourcing approach, especially the large anonymous worker crowd, results in two types of challenges. On the one hand, there are technical challenges like the dimensioning of crowdsourcing platform infrastructure or the interconnection of crowdsourcing platforms and machine clouds to build hybrid services. On the other hand, there are conceptual challenges like identifying reliable workers or migrating traditional off-line work to the crowdsourcing environment. To tackle these challenges, this monograph analyzes and models current crowdsourcing systems to optimize crowdsourcing workflows and the underlying infrastructure. First, a categorization of crowdsourcing tasks and platforms is developed to derive generalizable properties. Based on this categorization and an exemplary analysis of a commercial crowdsourcing platform, models for different aspects of crowdsourcing platforms and crowdsourcing mechanisms are developed. A special focus is put on quality assurance mechanisms for crowdsourcing tasks, where the models are used to assess the suitability and costs of existing approaches for different types of tasks. Further, a novel quality assurance mechanism solely based on user-interactions is proposed and its feasibility is shown. The findings from the analysis of existing platforms, the derived models, and the developed quality assurance mechanisms are finally used to derive best practices for two crowdsourcing use-cases, crowdsourcing-based network measurements and crowdsourcing-based subjective user studies. These two exemplary use-cases cover aspects typical for a large range of crowdsourcing tasks and illustrated the potential benefits, but also resulting challenges when using crowdsourcing.
With the ongoing digitalization and globalization of the labor markets, the crowdsourcing paradigm is expected to gain even more importance in the next years. This is already evident in the currently new emerging fields of crowdsourcing, like enterprise crowdsourcing or mobile crowdsourcing. The models developed in the monograph enable platform providers to optimize their current systems and employers to optimize their workflows to increase their commercial success. Moreover, the results help to improve the general understanding of crowdsourcing systems, a key for identifying necessary adaptions and future improvements.