More than 130 lives were lost in the 2021 heavy precipitation and flood event in the Ahr Valley, Germany, where large parts of the valley were destroyed. Afterwards, public funding of about 15 billion Euros has been made available for reconstruction. However, with people and settlements being in highly exposed zones, the core question that is not sufficiently addressed is whether affected people want to rebuild in the same place, or rather opt to move out. The paper explores this question and assesses motivations and reasons for moving or staying in the Ahr Valley. For this purpose, a household survey was conducted focusing on 516 flood-affected households. The collected data was analyzed using descriptive and inferential statistics. The results revealed that the ownership of the house or flat significantly influenced the decision of whether to stay or to leave. In addition, an attachment to the place and the belief that such extreme events occur very rarely influenced the decision to stay and rebuild. Age, gender and household income barely influenced the decision to stay or to move to a new place. Interestingly, results demonstrated that many respondents view settlement retreat and the relocation of critical infrastructures as important options to reduce risk, however, many still rebuild in the same place. These insights enable local policy and practice to better address the needs of the population in terms of whether to stay or move after such an extreme disaster.

Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.

Adaptation strategies to climate change need information about present and future climatic conditions. However, next to scenarios about the future climate, scenarios about future vulnerability are essential, since also changing societal conditions fundamentally determine adaptation needs. At the international and national level, first initiatives for developing vulnerability scenarios and so-called shared socioeconomic pathways (SSPs) have been undertaken. Most of these scenarios, however, do not provide sufficient information for local scenarios and local climate risk management. There is an urgent need to develop scenarios for vulnerability at the local scale in order to complement climate change scenarios. Heat stress is seen as a key challenge in cities in the context of climate change and further urban growth. Based on the research project ZURES (ZURES 2020 website), the paper presents a new method for human vulnerability scenarios to heat stress at the very local scale for growing medium-sized cities. In contrast to global models that outline future scenarios mostly with a country-level resolution, we show a new method on how to develop spatially specific scenario information for different districts within cities, starting from the planned urban development and expansion. The method provides a new opportunity to explore how different urban development strategies and housing policies influence future human exposure and vulnerability. Opportunities and constraints of the approach are revealed. Finally, we discuss how these scenarios can inform future urban development and risk management strategies and how these could complement more global or national approaches.

Purpose: Enhancing the resilience of cities and strengthening risk-informed decision-making are defined as key within the Global Agenda 2030. Implementing risk-informed decision-making also requires the consideration of scenarios of exposure and vulnerability. Therefore, the paper presents selected scenario approaches and illustrates how such vulnerability scenarios can look like for specific indicators and how they can inform decision-making, particularly in the context of urban planning.Design/methodology/approach: The research study uses the example of heat stress in Ludwigsburg, Germany, and adopts participatory and quantitative forecasting methods to develop scenarios for human vulnerability and exposure to heat stress.Findings: The paper indicates that considering changes in future vulnerability of people is important to provide an appropriate information base for enhancing urban resilience through risk-informed urban planning. This can help cities to define priority areas for future urban development and to consider the socio-economic and demographic composition in their strategies.Originality/value: The value of the research study lies in implementing new qualitative and quantitative scenario approaches for human exposure and vulnerability to strengthen risk-informed decision-making.