@article{Caspari2023, author = {Caspari, Catharina}, title = {Situative Vulnerabilit{\"a}t als Ausdruck der Menschenrechtssprache?}, series = {MenschenRechtsMagazin}, volume = {28}, journal = {MenschenRechtsMagazin}, number = {1}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2941-1149}, doi = {10.25932/publishup-58774}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-587743}, pages = {5 -- 15}, year = {2023}, abstract = {In Vorbereitung der Allgemeinen Bemerkung Nr. 36 zum Recht auf Leben vollzieht der Menschenrechtsausschuss der Vereinten Nationen eine begriffliche Wendung: Fortan wird der Ausschuss nicht mehr von "vulnerable persons", sondern von "persons in situations of vulnerability" sprechen. Zugleich scheint in der Wendung ein ge{\"a}ndertes Verst{\"a}ndnis von Vulnerabilit{\"a}t zu liegen, welches strukturelle Ungleichheiten und {\"a}ußere Umst{\"a}nde, die Verwundbarkeit erzeugen, begrifflich erfasst. Das neue Verst{\"a}ndnis scheint damit auch die Problematik der Zuschreibung von Verwundbarkeit zu entsch{\"a}rfen, die ihrerseits zu Marginalisierung betroffener Individuen f{\"u}hren kann. Der Beitrag vollzieht die Debatte um das neue Verst{\"a}ndnis von Vulnerabilit{\"a}t im Menschenrechtsausschuss nach, und kontextualisiert diese innerhalb der aktuellen Spruchpraxis des Ausschusses. Besondere Aufmerksamkeit gilt dabei den Klimaf{\"a}llen, welche, so wird argumentiert, in besonderer Weise {\"a}ußere, vulnerabilit{\"a}tsproduzierende Umst{\"a}nde adressieren. Schließlich werden die potenziellen St{\"a}rken und Schw{\"a}chen der begrifflichen Wendung reflektiert.}, language = {de} } @phdthesis{Mester2023, author = {Mester, Benedikt}, title = {Modeling flood-induced human displacement risk under global change}, doi = {10.25932/publishup-60929}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-609293}, school = {Universit{\"a}t Potsdam}, pages = {XVI, 143}, year = {2023}, abstract = {Extreme flooding displaces an average of 12 million people every year. Marginalized populations in low-income countries are in particular at high risk, but also industrialized countries are susceptible to displacement and its inherent societal impacts. The risk of being displaced results from a complex interaction of flood hazard, population exposed in the floodplains, and socio-economic vulnerability. Ongoing global warming changes the intensity, frequency, and duration of flood hazards, undermining existing protection measures. Meanwhile, settlements in attractive yet hazardous flood-prone areas have led to a higher degree of population exposure. Finally, the vulnerability to displacement is altered by demographic and social change, shifting economic power, urbanization, and technological development. These risk components have been investigated intensively in the context of loss of life and economic damage, however, only little is known about the risk of displacement under global change. This thesis aims to improve our understanding of flood-induced displacement risk under global climate change and socio-economic change. This objective is tackled by addressing the following three research questions. First, by focusing on the choice of input data, how well can a global flood modeling chain reproduce flood hazards of historic events that lead to displacement? Second, what are the socio-economic characteristics that shape the vulnerability to displacement? Finally, to what degree has climate change potentially contributed to recent flood-induced displacement events? To answer the first question, a global flood modeling chain is evaluated by comparing simulated flood extent with satellite-derived inundation information for eight major flood events. A focus is set on the sensitivity to different combinations of the underlying climate reanalysis datasets and global hydrological models which serve as an input for the global hydraulic model. An evaluation scheme of performance scores shows that simulated flood extent is mostly overestimated without the consideration of flood protection and only for a few events dependent on the choice of global hydrological models. Results are more sensitive to the underlying climate forcing, with two datasets differing substantially from a third one. In contrast, the incorporation of flood protection standards results in an underestimation of flood extent, pointing to potential deficiencies in the protection level estimates or the flood frequency distribution within the modeling chain. Following the analysis of a physical flood hazard model, the socio-economic drivers of vulnerability to displacement are investigated in the next step. For this purpose, a satellite- based, global collection of flood footprints is linked with two disaster inventories to match societal impacts with the corresponding flood hazard. For each event the number of affected population, assets, and critical infrastructure, as well as socio-economic indicators are computed. The resulting datasets are made publicly available and contain 335 displacement events and 695 mortality/damage events. Based on this new data product, event-specific displacement vulnerabilities are determined and multiple (national) dependencies with the socio-economic predictors are derived. The results suggest that economic prosperity only partially shapes vulnerability to displacement; urbanization, infant mortality rate, the share of elderly, population density and critical infrastructure exhibit a stronger functional relationship, suggesting that higher levels of development are generally associated with lower vulnerability. Besides examining the contextual drivers of vulnerability, the role of climate change in the context of human displacement is also being explored. An impact attribution approach is applied on the example of Cyclone Idai and associated extreme coastal flooding in Mozambique. A combination of coastal flood modeling and satellite imagery is used to construct factual and counterfactual flood events. This storyline-type attribution method allows investigating the isolated or combined effects of sea level rise and the intensification of cyclone wind speeds on coastal flooding. The results suggest that displacement risk has increased by 3.1 to 3.5\% due to the total effects of climate change on coastal flooding, with the effects of increasing wind speed being the dominant factor. In conclusion, this thesis highlights the potentials and challenges of modeling flood- induced displacement risk. While this work explores the sensitivity of global flood modeling to the choice of input data, new questions arise on how to effectively improve the reproduction of flood return periods and the representation of protection levels. It is also demonstrated that disentangling displacement vulnerabilities is feasible, with the results providing useful information for risk assessments, effective humanitarian aid, and disaster relief. The impact attribution study is a first step in assessing the effects of global warming on displacement risk, leading to new research challenges, e.g., coupling fluvial and coastal flood models or the attribution of other hazard types and displacement events. This thesis is one of the first to address flood-induced displacement risk from a global perspective. The findings motivate for further development of the global flood modeling chain to improve our understanding of displacement vulnerability and the effects of global warming.}, language = {en} } @phdthesis{Schoppa2023, author = {Schoppa, Lukas}, title = {Dynamics in the flood vulnerability of companies}, doi = {10.25932/publishup-59242}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-592424}, school = {Universit{\"a}t Potsdam}, pages = {X, 165}, year = {2023}, abstract = {River flooding is a constant peril for societies, causing direct economic losses in the order of \$100 billion worldwide each year. Under global change, the prolonged concentration of people and assets in floodplains is accompanied by an emerging intensification of flood extremes due to anthropogenic global warming, ultimately exacerbating flood risk in many regions of the world. Flood adaptation plays a key role in the mitigation of impacts, but poor understanding of vulnerability and its dynamics limits the validity of predominant risk assessment methods and impedes effective adaptation strategies. Therefore, this thesis investigates new methods for flood risk assessment that embrace the complexity of flood vulnerability, using the understudied commercial sector as an application example. Despite its importance for accurate risk evaluation, flood loss modeling has been based on univariable and deterministic stage-damage functions for a long time. However, such simplistic methods only insufficiently describe the large variation in damage processes, which initiated the development of multivariable and probabilistic loss estimation techniques. The first study of this thesis developed flood loss models for companies that are based on emerging statistical and machine learning approaches (i.e., random forest, Bayesian network, Bayesian regression). In a benchmarking experiment on basis of object-level loss survey data, the study showed that all proposed models reproduced the heterogeneity in damage processes and outperformed conventional stage-damage functions with respect to predictive accuracy. Another advantage of the novel methods is that they convey probabilistic information in predictions, which communicates the large remaining uncertainties transparently and, hence, supports well-informed risk assessment. Flood risk assessment combines vulnerability assessment (e.g., loss estimation) with hazard and exposure analyses. Although all of the three risk drivers interact and change over time, such dependencies and dynamics are usually not explicitly included in flood risk models. Recently, systemic risk assessment that dissolves the isolated consideration of risk drivers has gained traction, but the move to holistic risk assessment comes with limited thoroughness in terms of loss estimation and data limitations. In the second study, I augmented a socio-hydrological system dynamics model for companies in Dresden, Germany, with the multivariable Bayesian regression loss model from the first study. The additional process-detail and calibration data improved the loss estimation in the systemic risk assessment framework and contributed to more accurate and reliable simulations. The model uses Bayesian inference to quantify uncertainty and learn the model parameters from a combination of prior knowledge and diverse data. The third study demonstrates the potential of the socio-hydrological flood risk model for continuous, long-term risk assessment and management. Using hydroclimatic ad socioeconomic forcing data, I projected a wide range of possible risk trajectories until the end of the century, taking into account the adaptive behavior of companies. The study results underline the necessity of increased adaptation efforts to counteract the expected intensification of flood risk due to climate change. A sensitivity analysis of the effectiveness of different adaptation measures and strategies revealed that optimized adaptation has the potential to mitigate flood risk by up to 60\%, particularly when combining structural and non-structural measures. Additionally, the application shows that systemic risk assessment is capable of capturing adverse long-term feedbacks in the human-flood system such as the levee effect. Overall, this thesis advances the representation of vulnerability in flood risk modeling by offering modeling solutions that embrace the complexity of human-flood interactions and quantify uncertainties consistently using probabilistic modeling. The studies show how scarce information in data and previous experiments can be integrated in the inference process to provide model predictions and simulations that are reliable and rich in information. Finally, the focus on the flood vulnerability of companies provides new insights into the heterogeneous damage processes and distinct flood coping of this sector.}, language = {en} } @phdthesis{MetinUsta2021, author = {Metin Usta, Ay{\c{s}}e Duha}, title = {The role of risk components and spatial dependence in flood risk estimations}, doi = {10.25932/publishup-49255}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-492554}, school = {Universit{\"a}t Potsdam}, pages = {XIX, 97}, year = {2021}, abstract = {Flooding is a vast problem in many parts of the world, including Europe. It occurs mainly due to extreme weather conditions (e.g. heavy rainfall and snowmelt) and the consequences of flood events can be devastating. Flood risk is mainly defined as a combination of the probability of an event and its potential adverse impacts. Therefore, it covers three major dynamic components: hazard (physical characteristics of a flood event), exposure (people and their physical environment that being exposed to flood), and vulnerability (the elements at risk). Floods are natural phenomena and cannot be fully prevented. However, their risk can be managed and mitigated. For a sound flood risk management and mitigation, a proper risk assessment is needed. First of all, this is attained by a clear understanding of the flood risk dynamics. For instance, human activity may contribute to an increase in flood risk. Anthropogenic climate change causes higher intensity of rainfall and sea level rise and therefore an increase in scale and frequency of the flood events. On the other hand, inappropriate management of risk and structural protection measures may not be very effective for risk reduction. Additionally, due to the growth of number of assets and people within the flood-prone areas, risk increases. To address these issues, the first objective of this thesis is to perform a sensitivity analysis to understand the impacts of changes in each flood risk component on overall risk and further their mutual interactions. A multitude of changes along the risk chain are simulated by regional flood model (RFM) where all processes from atmosphere through catchment and river system to damage mechanisms are taken into consideration. The impacts of changes in risk components are explored by plausible change scenarios for the mesoscale Mulde catchment (sub-basin of the Elbe) in Germany. A proper risk assessment is ensured by the reasonable representation of the real-world flood event. Traditionally, flood risk is assessed by assuming homogeneous return periods of flood peaks throughout the considered catchment. However, in reality, flood events are spatially heterogeneous and therefore traditional assumption misestimates flood risk especially for large regions. In this thesis, two different studies investigate the importance of spatial dependence in large scale flood risk assessment for different spatial scales. In the first one, the "real" spatial dependence of return period of flood damages is represented by continuous risk modelling approach where spatially coherent patterns of hydrological and meteorological controls (i.e. soil moisture and weather patterns) are included. Further the risk estimations under this modelled dependence assumption are compared with two other assumptions on the spatial dependence of return periods of flood damages: complete dependence (homogeneous return periods) and independence (randomly generated heterogeneous return periods) for the Elbe catchment in Germany. The second study represents the "real" spatial dependence by multivariate dependence models. Similar to the first study, the three different assumptions on the spatial dependence of return periods of flood damages are compared, but at national (United Kingdom and Germany) and continental (Europe) scales. Furthermore, the impacts of the different models, tail dependence, and the structural flood protection level on the flood risk under different spatial dependence assumptions are investigated. The outcomes of the sensitivity analysis framework suggest that flood risk can vary dramatically as a result of possible change scenarios. The risk components that have not received much attention (e.g. changes in dike systems and in vulnerability) may mask the influence of climate change that is often investigated component. The results of the spatial dependence research in this thesis further show that the damage under the false assumption of complete dependence is 100 \% larger than the damage under the modelled dependence assumption, for the events with return periods greater than approximately 200 years in the Elbe catchment. The complete dependence assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139 \%, 188 \% and 246 \% for the UK, Germany and Europe, respectively. The misestimation of risk under different assumptions can vary from upstream to downstream of the catchment. Besides, tail dependence in the model and flood protection level in the catchments can affect the risk estimation and the differences between different spatial dependence assumptions. In conclusion, the broader consideration of the risk components, which possibly affect the flood risk in a comprehensive way, and the consideration of the spatial dependence of flood return periods are strongly recommended for a better understanding of flood risk and consequently for a sound flood risk management and mitigation.}, language = {en} } @phdthesis{Weisshuhn2020, author = {Weißhuhn, Peter}, title = {Assessing biotope vulnerability to landscape changes}, doi = {10.25932/publishup-44277}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-442777}, school = {Universit{\"a}t Potsdam}, pages = {v, 134}, year = {2020}, abstract = {Largescale patterns of global land use change are very frequently accompanied by natural habitat loss. To assess the consequences of habitat loss for the remaining natural and semi-natural biotopes, inclusion of cumulative effects at the landscape level is required. The interdisciplinary concept of vulnerability constitutes an appropriate assessment framework at the landscape level, though with few examples of its application for ecological assessments. A comprehensive biotope vulnerability analysis allows identification of areas most affected by landscape change and at the same time with the lowest chances of regeneration. To this end, a series of ecological indicators were reviewed and developed. They measured spatial attributes of individual biotopes as well as some ecological and conservation characteristics of the respective resident species community. The final vulnerability index combined seven largely independent indicators, which covered exposure, sensitivity and adaptive capacity of biotopes to landscape changes. Results for biotope vulnerability were provided at the regional level. This seems to be an appropriate extent with relevance for spatial planning and designing the distribution of nature reserves. Using the vulnerability scores calculated for the German federal state of Brandenburg, hot spots and clusters within and across the distinguished types of biotopes were analysed. Biotope types with high dependence on water availability, as well as biotopes of the open landscape containing woody plants (e.g., orchard meadows) are particularly vulnerable to landscape changes. In contrast, the majority of forest biotopes appear to be less vulnerable. Despite the appeal of such generalised statements for some biotope types, the distribution of values suggests that conservation measures for the majority of biotopes should be designed specifically for individual sites. Taken together, size, shape and spatial context of individual biotopes often had a dominant influence on the vulnerability score. The implementation of biotope vulnerability analysis at the regional level indicated that large biotope datasets can be evaluated with high level of detail using geoinformatics. Drawing on previous work in landscape spatial analysis, the reproducible approach relies on transparent calculations of quantitative and qualitative indicators. At the same time, it provides a synoptic overview and information on the individual biotopes. It is expected to be most useful for nature conservation in combination with an understanding of population, species, and community attributes known for specific sites. The biotope vulnerability analysis facilitates a foresighted assessment of different land uses, aiding in identifying options to slow habitat loss to sustainable levels. It can also be incorporated into planning of restoration measures, guiding efforts to remedy ecological damage. Restoration of any specific site could yield synergies with the conservation objectives of other sites, through enhancing the habitat network or buffering against future landscape change. Biotope vulnerability analysis could be developed in line with other important ecological concepts, such as resilience and adaptability, further extending the broad thematic scope of the vulnerability concept. Vulnerability can increasingly serve as a common framework for the interdisciplinary research necessary to solve major societal challenges.}, language = {en} } @misc{KuhlickeSeebauerHudsonetal.2020, author = {Kuhlicke, Christian and Seebauer, Sebastian and Hudson, Paul and Begg, Chloe and Bubeck, Philip and Dittmer, Cordula and Grothmann, Torsten and Heidenreich, Anna and Kreibich, Heidi and Lorenz, Daniel F. and Masson, Torsten and Reiter, Jessica and Thaler, Thomas and Thieken, Annegret and Bamberg, Sebastian}, title = {The behavioral turn in flood risk management, its assumptions and potential implications}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {3}, issn = {1866-8372}, doi = {10.25932/publishup-51769}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517696}, pages = {24}, year = {2020}, abstract = {Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood-related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household-level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events (Assumption 3). We believe that an investigation of the behavioral turn is important because if the outlined assumptions do not hold, there is a risk of creating and strengthening inequalities in FRM. Therefore, we outline the current intellectual and empirical knowledge as well as future research needs. Generally, we argue that more collaboration across intellectual catchments is needed, that future research should be more theoretically grounded and become methodologically more rigorous and at the same time focus more explicitly on the normative underpinnings of the behavioral turn.}, language = {en} } @misc{HudsonPhamHagedoornetal.2020, author = {Hudson, Paul and Pham, My and Hagedoorn, Liselotte and Thieken, Annegret and Lasage, Ralph and Bubeck, Philip}, title = {Self-stated recovery from flooding}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1140}, issn = {1866-8372}, doi = {10.25932/publishup-50348}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-503488}, pages = {17}, year = {2020}, abstract = {Social inequalities lead to flood resilience inequalities across social groups, a topic that requires improved documentation and understanding. The objective of this paper is to attend to these differences by investigating self-stated flood recovery across genders in Vietnam as a conceptual replication of earlier results from Germany. This study employs a regression-based analysis of 1,010 respondents divided between a rural coastal and an urban community in Thua Thien-Hue province. The results highlight an important set of recovery process-related variables. The set of relevant variables is similar across genders in terms of inclusion and influence, and includes age, social capital, internal and external support after a flood, perceived severity of previous flood impacts, and the perception of stress-resilience. However, women were affected more heavily by flooding in terms of longer recovery times, which should be accounted for in risk management. Overall, the studied variables perform similarly in Vietnam and Germany. This study, therefore, conceptually replicates previous results suggesting that women display slightly slower recovery levels as well as that psychological variables influence recovery rates more than adverse flood impacts. This provides an indication of the results' potentially robust nature due to the different socio-environmental contexts in Germany and Vietnam.}, language = {en} } @phdthesis{Schroeter2020, author = {Schr{\"o}ter, Kai}, title = {Improved flood risk assessment}, doi = {10.25932/publishup-48024}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-480240}, school = {Universit{\"a}t Potsdam}, pages = {408}, year = {2020}, abstract = {Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties. This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models. Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance.}, language = {en} } @phdthesis{Sterzel2019, author = {Sterzel, Till}, title = {Analyzing global typologies of socio-ecological vulnerability}, doi = {10.25932/publishup-42883}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-428837}, school = {Universit{\"a}t Potsdam}, pages = {137}, year = {2019}, abstract = {On a planetary scale human populations need to adapt to both socio-economic and environmental problems amidst rapid global change. This holds true for coupled human-environment (socio-ecological) systems in rural and urban settings alike. Two examples are drylands and urban coasts. Such socio-ecological systems have a global distribution. Therefore, advancing the knowledge base for identifying socio-ecological adaptation needs with local vulnerability assessments alone is infeasible: The systems cover vast areas, while funding, time, and human resources for local assessments are limited. They are lacking in low an middle-income countries (LICs and MICs) in particular. But places in a specific socio-ecological system are not only unique and complex - they also exhibit similarities. A global patchwork of local rural drylands vulnerability assessments of human populations to socio-ecological and environmental problems has already been reduced to a limited number of problem structures, which typically cause vulnerability. However, the question arises whether this is also possible in urban socio-ecological systems. The question also arises whether these typologies provide added value in research beyond global change. Finally, the methodology employed for drylands needs refining and standardizing to increase its uptake in the scientific community. In this dissertation, I set out to fill these three gaps in research. The geographical focus in my dissertation is on LICs and MICs, which generally have lower capacities to adapt, and greater adaptation needs, regarding rapid global change. Using a spatially explicit indicator-based methodology, I combine geospatial and clustering methods to identify typical configurations of key factors in case studies causing vulnerability to human populations in two specific socio-ecological systems. Then I use statistical and analytical methods to interpret and appraise both the typical configurations and the global typologies they constitute. First, I improve the indicator-based methodology and then reanalyze typical global problem structures of socio-ecological drylands vulnerability with seven indicator datasets. The reanalysis confirms the key tenets and produces a more realistic and nuanced typology of eight spatially explicit problem structures, or vulnerability profiles: Two new profiles with typically high natural resource endowment emerge, in which overpopulation has led to medium or high soil erosion. Second, I determine whether the new drylands typology and its socio-ecological vulnerability concept advance a thematically linked scientific debate in human security studies: what drives violent conflict in drylands? The typology is a much better predictor for conflict distribution and incidence in drylands than regression models typically used in peace research. Third, I analyze global problem structures typically causing vulnerability in an urban socio-ecological system - the rapidly urbanizing coastal fringe (RUCF) - with eleven indicator datasets. The RUCF also shows a robust typology, and its seven profiles show huge asymmetries in vulnerability and adaptive capacity. The fastest population increase, lowest income, most ineffective governments, most prevalent poverty, and lowest adaptive capacity are all typically stacked in two profiles in LICs. This shows that beyond local case studies tropical cyclones and/or coastal flooding are neither stalling rapid population growth, nor urban expansion, in the RUCF. I propose entry points for scaling up successful vulnerability reduction strategies in coastal cities within the same vulnerability profile. This dissertation shows that patchworks of local vulnerability assessments can be generalized to structure global socio-ecological vulnerabilities in both rural and urban socio-ecological systems according to typical problems. In terms of climate-related extreme events in the RUCF, conflicting problem structures and means to deal with them are threatening to widen the development gap between LICs and high-income countries unless successful vulnerability reduction measures are comprehensively scaled up. The explanatory power for human security in drylands warrants further applications of the methodology beyond global environmental change research in the future. Thus, analyzing spatially explicit global typologies of socio-ecological vulnerability is a useful complement to local assessments: The typologies provide entry points for where to consider which generic measures to reduce typical problem structures - including the countless places without local assessments. This can save limited time and financial resources for adaptation under rapid global change.}, language = {en} } @phdthesis{Gawron2019, author = {Gawron, Marian}, title = {Towards automated advanced vulnerability analysis}, doi = {10.25932/publishup-42635}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426352}, school = {Universit{\"a}t Potsdam}, pages = {149}, year = {2019}, abstract = {The identification of vulnerabilities in IT infrastructures is a crucial problem in enhancing the security, because many incidents resulted from already known vulnerabilities, which could have been resolved. Thus, the initial identification of vulnerabilities has to be used to directly resolve the related weaknesses and mitigate attack possibilities. The nature of vulnerability information requires a collection and normalization of the information prior to any utilization, because the information is widely distributed in different sources with their unique formats. Therefore, the comprehensive vulnerability model was defined and different sources have been integrated into one database. Furthermore, different analytic approaches have been designed and implemented into the HPI-VDB, which directly benefit from the comprehensive vulnerability model and especially from the logical preconditions and postconditions. Firstly, different approaches to detect vulnerabilities in both IT systems of average users and corporate networks of large companies are presented. Therefore, the approaches mainly focus on the identification of all installed applications, since it is a fundamental step in the detection. This detection is realized differently depending on the target use-case. Thus, the experience of the user, as well as the layout and possibilities of the target infrastructure are considered. Furthermore, a passive lightweight detection approach was invented that utilizes existing information on corporate networks to identify applications. In addition, two different approaches to represent the results using attack graphs are illustrated in the comparison between traditional attack graphs and a simplistic graph version, which was integrated into the database as well. The implementation of those use-cases for vulnerability information especially considers the usability. Beside the analytic approaches, the high data quality of the vulnerability information had to be achieved and guaranteed. The different problems of receiving incomplete or unreliable information for the vulnerabilities are addressed with different correction mechanisms. The corrections can be carried out with correlation or lookup mechanisms in reliable sources or identifier dictionaries. Furthermore, a machine learning based verification procedure was presented that allows an automatic derivation of important characteristics from the textual description of the vulnerabilities.}, language = {en} }