Climate vulnerability assessments are an important prerequisite for establishing successful climate adaptation strategies. Despite a growing number of assessments on the national or global scale, there is still a need for regionalized studies with a high resolution to identify meso-scale vulnerability patterns. In this paper, we present an indicator-based assessment that was carried out in the Trinational Metropolitan Region Upper Rhine within the Interreg-V project Clim’Ability. The analyzed region is characterized by strong cross-border and transnational linkages, similar ecological features and climatic stressors but differing political, administrative, cultural and legal conditions. In this rather complex setting, we operationalized a state-of-the art vulnerability framework using 18 quantified indicators and aggregating them into a vulnerability index. We show that it is possible to downscale the methods used in recent assessments to a regional context with a challenging data situation and discuss strengths and uncertainties. The results are mapped for stakeholder communication purposes. They provide an evidence-base to the identification of the trinational vulnerability pattern and may enable stakeholders and decision-makers to enhance their own climate adaptation planning.

Background: Tickborne-encephalitis (TBE) is a potentially life-threating neurological disease that is mainly transmitted by ticks. The goal of the present study is to analyze the potential uniform environmental patterns of the identified TBEV microfoci in Germany. The results are used to calculate probabilities for the present distribution of TBEV microfoci in Germany based on a geostatistical model. Methods: We aim to consider the specification of environmental characteristics of locations of TBEV microfoci detected in Germany using open access epidemiological, geographical and climatological data sources. We use a two-step geostatistical approach, where in a first step, the characteristics of a broad set of environmental variables between the 56 TBEV microfoci and a control or comparator set of 3575 sampling points covering Germany are compared using Fisher’s Exact Test. In the second step, we select the most important variables, which are then used in a MaxEnt distribution model to calculate a high resolution (400 × 400 m) probability map for the presence of TBEV covering the entire area of Germany. Results: The findings from the MaxEnt prediction model indicate that multi annual actual evapotranspiration (27.0%) and multi annual hot days (22.5%) have the highest contribution to our model. These two variables are followed by four additional variables with a lower, but still important, explanatory influence: Land cover classes (19.6%), multi annual minimum air temperature (14.9%), multi annual sunshine duration (9.0%), and distance to coniferous and mixed forest border (7.0%). Conclusions: Our findings are based on defined TBEV microfoci with known histories of infection and the repeated confirmation of the virus in the last years, resulting in an in-depth high-resolution model/map of TBEV microfoci in Germany. Multi annual actual evapotranspiration (27%) and multi annual hot days (22.5%) have the most explanatory power in our model. The results may be used to tailor specific regional preventive measures and investigations.

Information on individual hazard perception while cycling and the associated feeling of safety are key aspects to foster sustainable urban cycling mobility. Although cyclist’s perceptions must also be critically reviewed, such crowdsourced Volunteered Geographic Information (VGI) provides wide-ranging insights on diverse hazard categories in cycling. In this case study in the city of Freiburg, Germany, hazard perceptions, information about lane types, and the underlying routes were crowdsourced via an open source smartphone application by a small group with the aim of providing cyclists with effective solutions. By dealing with levels of reliability, we show that even a small group of laypersons can generate an extensive and valuable set of VGI consisting of comprehensive hazard categories. We demonstrate that (1) certain hazards are interlinked to specific lane types, and (2) the individual hazard perceptions and objective parameters, i.e., accident data, are often congruent spatially; consequently, (3) dangerous hot spots can be derived. By considering cyclists’ needs, this approach outlines how a people-based perspective can supplement regional planning on the local scale.