The UN Sustainable Development Goals (SDGs) present a global agenda addressing social, economic, and environmental challenges in a holistic approach. Universities can contribute to the implementation of the SDGs by providing know-how and best-practice examples to support implementation and by integrating issues of sustainability into their operations, research, education, and science-society interactions. In most of the signatory countries of the Agenda 2030, an overview of the extent to which universities have already addressed the SDGs in research is not available. Using the example of universities in Austria, this study presents a tool to map research that addresses sustainability topics as defined by the SDGs. The results of an analysis of scientific projects and publications show current focus areas of SDG related research. Research on SDG 3 (Good Health and Well-Being) and SDG 4 (Quality Education) is well represented by universities in Austria, while other SDGs, such as SDG 1 (No Poverty) or SDG 14 (Life Below Water), are under-represented research fields. We anticipate the results will support universities in identifying the thematic orientation of their research in the framework of the SDGs. This information can facilitate inter-university cooperation to address the challenge of implementing the SDGs.

The western honey bee ( Apis mellifera ) is one of the most important insects kept by humans, but high colony losses are reported around the world. While the effects of general climatic conditions on colony winter mortality were already demonstrated, no study has investigated specific weather conditions linked to biophysical processes governing colony vitality. Here, we quantify the comparative relevance of four such processes that co-determine the colonies' fitness for wintering during the annual hive management cycle, using a 10-year dataset of winter colony mortality in Austria that includes 266 378 bee colonies. We formulate four process-based hypotheses for wintering success and operationalize them with weather indicators. The empirical data is used to fit simple and multiple linear regression models on different geographical scales. The results show that approximately 20% of winter mortality variability can be explained by the analysed weather conditions, and that it is most sensitive to the duration of extreme cold spells in mid and late winter. Our approach shows the potential of developing weather indicators based on biophysical processes and discusses the way forward for applying them in climate change studies.

To design effective adaptation measures to a heating climate, decision-makers need a state-of-the-art, regional and sector-specific knowledge about future climate impacts. Tailoring this information to the needs of policymakers requires collaboration between scientists and stakeholders. A lot of literature on design principles and comprehension of scientific visualisations exists. However, the links between objective comprehension, perceived usefulness for communication and aesthetics of climate change impact maps have rarely been analysed in empirical studies. In a co-design effort together with stakeholders in adaptation planning and climate change communication experts, regional climate change impact maps were developed and published as open-access dataset. The comprehension, aesthetics and perceived usefulness of different map design features were qualitatively and quantitatively evaluated in a two-step survey. Designs with less information density were understood best, found most aesthetical and useful for communication practice. Uncertainties were deemed necessary by participants, but not understood well when combined with other variables on the same map sheet. Map understanding varied significantly with the cognitive difficulty of a task. This difference was robust over user groups. Co-designing maps at the science-policy interface have the potential to create more useful and comprehensible communication materials and thus supports adaptation planning with the best available information on future climate impacts.

Rainfall is the main trigger for debris flows in torrent catchments. A systematic analysis of the critical rainfall for debris-flow initiation in Austria is lacking. This study quantifies and compares rainfall events that historically triggered debris flows and fluvial floods in Austria and assesses the expected impact of climate change on the frequency and spatial extent of such events. We derive critical rainfall conditions using hourly rainfall estimates based on radar-rain gauge data for more than 3600 torrent events between 2003 and 2022. Past and future return periods are estimated based on an ensemble of regional climate projections covering three emission scenarios until 2100. We find that the regional variations of the critical rainfall patterns are greater than the differences between the types of processes. A general dependence of triggering rainfall on the antecedent rainfall cannot be confirmed. In a warming climate, both the probability of occurrence and the affected area in Austria are projected to increase for both process types and across all emission scenarios. However, these changes are unevenly distributed throughout Austria. The results indicate that regional-scale predictions can improve mountain risk management in a changing climate, while risk management for individual torrents will benefit from process-based understanding.

Global warming levels (GWLs) are increasingly becoming a central concept in climate change studies. In recent years, their integrative quality for climate change impact analysis has been demonstrated, and methodological advancements have helped to compensate for some inherent shortfalls of the concept. However, their applicability at the regional and local level is debatable, and no study to date has examined the possibility of linking local climate scenarios to GWLs. For the case of Austria, we evaluate the relation between global and regional warming patterns, and whether version changes of global climate models could be incorporated into local climate scenarios by means of the GWLs concept, without updating the actual data. We apply the time sampling approach, where GWLs are determined as periods when global mean temperature anomalies cross a certain threshold. GWL periods are sampled both from the global models in the background of the local climate scenarios (CMIP5), and from an equivalent ensemble of newer-generation climate models (CMIP6). Uncertainties resulting from sampling GWLs from different global climate model ensembles are examined, and prospects for local climate change impact assessments are discussed. Accounting for updated global climate model versions might be useful when the changes at certain GWLs are related to fixed reference periods, but temperature increments between GWLs remain relatively constant across model versions, even on the regional level. The study bridges a significant gap to link local climate projections to GWLs. Climate change impacts assessments that build on those datasets can benefit from the integrative character of GWLs, making studies comparable across multiple disciplines and model versions, and thus fostering a way to communicate local climate change impacts more comprehensible.

Climate warming has been observed as the main cause of changes in diversity, community composition, and spatial distribution of different plant and invertebrate species. Due to even stronger warming compared to the global mean, bumblebees in alpine ecosystems are particularly exposed to these changes. To investigate the effects of climate warming, we sampled bumblebees along an elevational gradient, compared the records with data from 1935 and 1936, and related our results to climate models. We found that bumblebee community composition differed significantly between sampling periods and that increasing temperatures in spring were the most plausible factor explaining these range shifts. In addition, species diversity estimates were significantly lower compared to historical records. The number of socio-parasitic species was significantly higher in the historical communities, while recent communities showed increases in climate generalists and forest species at lower elevations. Nevertheless, no significant changes in community-weighted means of a species temperature index (STI) or the number of cold-adapted species were detected, likely due to the historical data resolution. We conclude that the composition and functionality of bumblebee communities in the study area have been significantly affected by climate warming, with changes in land use and vegetation cover likely playing an additional important role.