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Coastal areas are urbanizing at unprecedented rates, particularly in low- and middle-income countries. Combinations of long-standing and emerging problems in these urban areas generate vulnerability for human well-being and ecosystems alike. This baseline study provides a spatially explicit global systematization of these problems into typical urban vulnerability profiles for the year 2000 using largely sub-national data. Using 11 indicator datasets for urban expansion, urban population growth, marginalization of poor populations, government effectiveness, exposures and damages to climate-related extreme events, low-lying settlement, and wetlands prevalence, a cluster analysis reveals a global typology of seven clearly distinguishable clusters, or urban profiles of vulnerability. Each profile is characterized by a specific data-value combination of indicators representing mechanisms that generate vulnerability. Using 21 studies for testing the plausibility, we identify seven key profile-based vulnerabilities for urban populations, which are relevant in the context of global urbanization, expansion, and climate change. We show which urban coasts are similar in this regard. Sensitivity and exposure to extreme climate-related events, and government effectiveness, are the most important factors for the huge asymmetries of vulnerability between profiles. Against the background of underlying global trends we propose entry points for profile-based vulnerability reduction. The study provides a baseline for further pattern analysis in the rapidly urbanizing coastal fringe as data availability increases. We propose to explore socio-ecologically similar coastal urban areas as a basis for sharing experience and vulnerability-reducing measures among them.

Abstract. The assessment of coastal flood risks in a particular region requires the estimation of typical damages caused by storm surges of certain characteristics and annualities. Although the damage depends on a multitude of factors, including flow velocity, duration of flood, precaution, etc., the relationship between flood events and the corresponding average damages is usually described by a stage-damage function, which considers the maximum water level as the only damage influencing factor. Starting with different (microscale) building damage functions we elaborate a macroscopic damage function for the entire case study area Kalundborg (Denmark) on the basis of multiple coarse-graining methods and assumptions of the hydrological connectivity. We find that for small events, the macroscopic damage function mostly depends on the properties of the elevation model, while for large events it strongly depends on the assumed building damage function. In general, the damage in the case study increases exponentially up to a certain level and then less steep.

A methodology to assess future development in patterns of vulnerability is presented which can support the assessment of global policies with regard to their impacts on specific vulnerabilities on the regional or local scale. Patterns of vulnerability, formalized by vulnerability profiles (e.g. for the livelihoods of dryland smallholder farmers) were investigated under different consistent indicator scenarios reflecting different global policies. After unfolding several principal possibilities to do such an analysis of temporal change in vulnerability patterns we could conclude that the concept of "Clusters of Change" (CoCs) is the most straight forward and promising approach. The main arguments are that each interpretation has necessarily to consider both, the starting situation and it's change over time ("poor and heavily improving", "rich and stagnating" etc.). This implies that we are looking for patterns which represent typical combinations of present states AND expected future changes. An application of the CoC-concept to the drylands vulnerability patterns considering the indicator set for the present situation and the same indicator set for 2050 under a baseline scenario was performed as a test. Comparison of the present vulnerability cluster partition with the spatial distribution of the CoCs revealed that most of these clusters are separated into an improving and a deteriorating part which shows where winners and losers of the baseline scenario are – an interesting result which illustrates the appropriateness of the CoC – method. To explore the potential of CoCs for the dryland vulnerability we applied the method to two different sets of scenarios until 2050: a baseline vs. Climate policy scenario (OECD, 2012) and a "policy first" scenario vs. "security first" scenario (UNEP, 2007). The first one serves as an example for a policy assessment while the second compares the vulnerability consequences of two scenarios based on different story-lines of further global development. The main conclusion to be drawn from these calculations is that the CoCs are rather insensitive with regard to the small differences between the scenarios. Regarding the first set of scenarios the relatively short time horizon of relevant influences of climate policies on climate change impacts and several indicators which are not influenced at all generate only a very small difference. The only significant change in the resulting vulnerability profiles was in the values of change in water scarcity: it was lower for all profiles in the climate policy case. The second set of scenarios is not directly related to policy decisions but to different global story-lines which deviate stronger. This resulted in an increasing cluster number from 4 (policy first) to 5 (security first) clusters, about 20% of the pixels changing cluster membership, 3 clusters showing the same spatial extent for both scenarios but the 4th cluster ("policy first") "losing" India which generates a separate cluster in the "security first" scenario. This allows for the interpretation that a further development according to the "security first"-storyline compared to the "policy first"-storyline would make a difference particularly for India. Closer inspection of the respective profile shows a qualitatively different situation indicating increased vulnerability compared to the "policy first" scenario where India shares one cluster with e.g., Northern Africa.

Die Veterinärmedizin und die praktischen Ausbildungen der Studierenden der Veterinärmedizin sind ein Arbeitsbereich mit vielfältigen Expositionen gegenüber diversen Säugetierallergenen, Milbenallergenen sowie mikrobiellen Bestandteilen von Bioaerosolen. Insbesondere die unvermeidbaren Allergenexpositionen stellen ein Risiko für eine Sensibilisierung und allergische Beschwerden dar. Allerdings liegen über die Höhe der Allergenbelastung keine ausreichenden Daten vor. Deshalb wurden Luftstaubmessungen an verschiedenen Orten der veterinärmedizinischen Fakultät und deren Ausbildungsbereichen in einem landwirtschaftlichen Forschungsbetrieb einschließlich einer Quantifizierung der Hauptallergene von Katze (Fel d 1), Hund (Can f 1), Pferd (Equ c 1), Rind (Bos d 2), Maus (Mus m 1) sowie Milbenallergenen verschiedener Spezies durchgeführt. Darüber hinaus wurden Endotoxine und (1-3)-β-Glucane als Marker für Schimmelpilz- bzw. Bakterienbelastung bestimmt. Die Tierallergene wurden vor allem dort detektiert, wo die entsprechenden Tiere behandelt (Untersuchungsräume) oder untergebracht wurden (Tierställe). Die Maximum-Werte betrugen 0,5 ng/m³ für Fel d 1, 11,4 ng/m³ für Can f 1, 729 ng/m³ für Equ c 1 und 1 439 ng/m³ für Bos d 2. Neben dem erwartungsgemäßen Nachweis von Tierallergenen in Bereichen mit Tierpräsenz konnte durch Verwendung von sensitiven Immunoassays der Transfer von Allergenen an Orte ohne Anwesenheit von Tieren detektiert werden (z. B. Fachschaft und Chemielabor). Milbenallergene verschiedener Spezies wurden hauptsächlich in Tierställen gefunden, die höchsten Konzentrationen traten im Hühnerstall auf. Endotoxin und (1-3)-β-Glucan wurden in allen Proben nachgewiesen und korrelierten signifikant miteinander. Die höchsten Konzentrationen beider Parameter wurden im Hühner- und Schweinestall gemessen.