Stefan Hochrainer-Stigler

Contemporary risk landscapes are shaped by systemic phenomena—compound disasters, cascading failures, non‐stationarity, and cross‐domain crises—that challenge conventional frameworks. While the classical risk equation (Risk = Hazard × Vulnerability × Exposure) remains a useful heuristic, it relies on a separability assumption that breaks down in feedback‐rich, co‐evolving systems. This failure reflects a familiar diagnosis: risk can be mistaken for a complicated problem, decomposable and optimizable, when it is a complex one, characterized by emergence and adaptation. Our contribution herein is not this distinction but its analytical consequence. Drawing on an interdisciplinary workshop on systemic risk and resilience, we propose inseparability as a foundational premise for disaster risk science. Hazard, exposure, and vulnerability might be better understood as dynamically coupled processes rather than separable variables. We derive implications across four axes: reframing vulnerability as a non‐ergodic system property; addressing cross‐scale dynamics; shifting from risk control to adaptive management; and treating knowledge co‐production as a scientific necessity.

Natural disasters can cause economic losses that extend beyond the directly damaged assets and locations through production, demand, credit, and trade linkages. Yet disaster-economic assessments often remain focused on single events, single countries, or direct-damage estimates, providing less evidence on how multi-hazard shocks propagate through interconnected economies and sectors. This paper applies a multi-country, multi-sector macroeconomic agent-based model, covering twenty-six EU Member States and sixty-two NACE industries, to analyse a stakeholderinformed sequential flood-drought-earthquake stress-test scenario in the Danube basin. The scenario is compared with a no-disaster baseline using matched-seed Monte Carlo simulations. The results show that cumulative output losses concentrate in directly affected economies, while economies without direct hazard exposure show positive deviations relative to baseline in several output, labour-market, and fiscal indicators; these deviations are interpreted as modelled reallocation of economic activity rather than welfare gains. Sectoral disruptions are concentrated in a limited set of country-sector pairs, particularly in agriculture, forestry, fishing, trade, tourism, and utilities, while bilateral trade flows show rerouting and reconstruction-related adjustments rather than a uniform collapse. The findings indicate that disaster-risk assessment in interconnected economies should complement direct-damage measures with diagnostics of transboundary propagation, sectoral persistence, and fiscal and labour-market spillovers.

This study develops a dynamic flow allocation and performance assessment framework for Urban Multimodal Transportation Networks (UMTNs), integrating metro, tram, and bus modes with explicit representation of inter-layer transfers and capacity constraints. Using Amsterdam as a case study, the framework combines a bidirectional, geographically detailed network model with a dynamic minimum-cost maximum-flow assignment approach to capture hourly passenger flow redistribution under varying demand scenarios and disruption conditions. Unlike topology-based models, the proposed method incorporates network structure, origin–destination demand variability, capacity-constrained routing and iterative flow allocation to simulate cascading effects for the UMTNs. Moreover, the model evaluates system performance using a system serviceability index, measuring the proportion of satisfied passenger demand under targeted disruptions relative to normal conditions. Results show that capacity utilization patterns differ substantially across transportation modes. In addition, strong directional asymmetries are observed in passenger flows, indicating that link importance is shaped not only by physical characteristics but also by temporal and directional travel demand. Third, multimodal integration enhances system performance by enabling rerouting across modes, reducing the impact of critical link failures, particularly under failures in bus and metro modes. Finally, the identification of critical links is highly sensitive to the composition of the OD matrix.Overall, the findings demonstrate that network criticality emerges from the interaction between topology, capacity constraints, and demand-driven flow dynamics. The proposed framework provides a robust tool for evaluating system performance and supports more resilient planning of urban transportation systems under disruptions.

There is a crucial interdependence between managing disasters driven by extreme weather events and adapting to climate change. Integrating and coordinating strategies to manage extreme weather with climate adaptation plans is critical for the long-term resilience of local communities. This article provides a set of indicators to assess the integration of existing disaster risk management (DRM) and climate change adaptation (CCA) processes and strategies. Developed and refined through application in four Real World Labs within the DIRECTED project, these indicators were designed as a transferable tool to measure and analyze how well a given region’s DRM and CCA processes are connected and integrated, and to identify potential leverage points to improve this integration. We refer to integration in terms of degrees to which disciplinary and institutional structures are aware, informed by, and processually related, or disconnected and siloed off. Indicators are divided into three separate governance dimensions: the governance and institutional context, risk management and risk knowledge, and knowledge co-production. The governance and institutional context dimension focuses on a region’s governance structure, and the division of responsibilities and accountability that come with that structure. The risk management and risk knowledge dimension highlights the relationship between DRM processes and underlying CCA agendas. Finally, the knowledge co-production dimension explores the level of collaborative arrangements that exist within and between institutions, as well as the goals and needs that a given region and its institutions might have. These indicators are applied to various European regions, showing how they can be used to assess and develop DRM and CCA integration.

Community flood resilience is defined as a multi-dimensional capacity, shaped by the dynamic interplay of social, human, financial, physical and natural capitals. Yet, previous studies and existing adaptation strategies have predominantly addressed these dimensions in isolation, neglecting the analysis of their interactions. This study contributes to the development of integrated, systems-based adaptation strategies by examining how interactions among capitals shape resilience outcomes. Drawing on Flood Resilience Measurement for Communities (FRMC) data from 293 flood-prone communities from 19 developing countries globally, this study adopts a mixed-methods design combining network analysis of covariation between capitals with a qualitative thematic analysis of stakeholder-perceived causal mechanisms linking those capitals. Our findings reveal that resilience is not simply the sum of independent capitals, but an emergent property of their interactions. Both quantitative and qualitative analysis show dense connections among capitals, with social capital found to play a central role by enabling coordination, resource mobilization, and knowledge exchange. Financial and human capitals were also found to act as foundational enablers, particularly in supporting the development and effective use of physical and natural assets. Feedback loops among capitals highlight the importance of mutually reinforcing dynamics in building adaptive capacity. These findings underscore the need for disaster risk reduction and climate adaptation strategies to move beyond siloed interventions. We advocate for integrated funding and policy frameworks that target coupled capitals, such as pairing infrastructure development with social & financial capacity-building, to leverage synergies across sectors.

Research about natural hazards has expanded from single-hazard to multi-hazard ones. While single hazards have been extensively studied in the past and many quantitative statements about intensities and severities are available, empirical studies about multi-hazard events and corresponding dependencies are still rare. This paper introduces statistical models for estimating the dependencies between different hazard types based on Poisson-type event processes. Moreover, the models are applied to data for several natural hazard events from the Danube Region within Europe. We found several multi-hazard interactions between extreme temperature, wildfire, drought, and flood hazards. The analysis should help to bridge the gap between the more conceptual contributions to this discussion by providing empirical evidence on interactions on a large-scale region as well as providing statistical models how to estimate them.

Despite the growing literature on climate change adaptation across diverse academic fields, a significant research gap persists in the documented synthesis of concrete adaptation actions implemented on the ground. The current lack of synthesized documentation hinders effective learning across diverse contexts and limits the development of robust adaptation strategies, especially due to existing monitoring and evaluation frameworks that often rely on high-level, broad categorizations that obscure the true mechanisms of change. This paper directly addresses this under-explored area by analyzing community-level flood resilience interventions undertaken by the Zurich Flood Resilience Alliance (ZFRA) in 19 countries between 2019 and 2024. Employing a mixed-methods approach, we identify seven emergent intervention categories, encompassing infrastructure and physical improvements, community engagement and knowledge building, community flood resilience planning, community action groups, nature-based solutions, asset protection and diversification, and early warning systems. We examine these intervention categories in relation to the Sustainable Livelihoods Framework’s five capitals and the crucial resilience capacities of absorptive, adaptive, and transformative abilities. Our analysis demonstrates that interventions within the same broad category can have vastly different functionalities, impacting diverse capitals and contributing to varying resilience stocks (capital) and process (capacities) – suggesting a shift away from monitoring and evaluating interventions through broad, aggregated categorizations. This study underscores the importance of considering the interplay between different intervention types and the various dimensions of community resilience, offering valuable insights for practitioners and policymakers seeking to enhance the effectiveness of adaptation efforts and bridge the persistent gap between adaptation planning and tangible action on the ground.

Effective management of future regional climate risks in interconnected multi‐sectoral systems is complicated by uncertainties in risk drivers within both human and natural systems. Comprehensive yet comprehensible targeted climate risk information exploring these uncertainties is essential for the strategic allocation of limited resources to vulnerable areas and sectors in the region. Yet conventional approaches struggle to provide it. This study addresses this gap by introducing an interdisciplinary framework incorporating meteorological, hydrological, and socio‐economic perspectives. A “plausibilistic” flood risk assessment approach is presented which combines both climate and socio‐economic storylines. Plausible climate scenario storylines are sampled based on their relevance for local impacts, allowing the assessment of conditional changes in high‐impact probabilistic discharges. Plausible socio‐economic storylines are integrated to asses future urban area and economic sectoral development. This information allows the projection of the impact potential in the region and its cascading socio‐economic effects. An example application to the flood‐prone, transboundary Lielupe basin shared by Latvia and Lithuania highlights sub‐catchments and sectors consistently vulnerable across diverse, relevant, and credible set of future storylines. The framework thus equips regional risk managers with targeted and robust risk information, providing a strong knowledge base for prioritizing adaptation planning.

Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being. A key gap in achieving this is limited standardized and validated disaster resilience measurement frameworks that operate at local levels and are universally applicable. The Flood Resilience Measurement for Communities (FRMC) is a foremost tool for community flood resilience assessment. It follows a structured approach to comprehensively assess community flood resilience across five classes of capacities (capitals) to support strategic investment in resilience strengthening initiatives. The FRMC is a further development of an earlier version (the FRMT, the Flood Resilience Measurement Tool). The FRMT has been developed and applied between 2015 and 2017 in 118 flood prone communities across nine countries. It has been validated in terms of content and face validity as well as in terms of reliability. To reduce redundancy and survey effort, the FRMC holds a lesser number of indicators (44 versus 88) and has now been applied in over 320 communities across 20 countries. We examine the validation for the revised resilience construct and the new community applications and present a comprehensive overview of the statistical and user validation process and outcomes in both practical and scientific terms. The results confirm the validity, reliability as well as usefulness of the FRMC framework and tool. Furthermore, our approach and results provide insights for other resilience measurement approaches and their validation efforts. We also present a comprehensive discussion about the dynamic aspects of flood resilience at community level, and the many validation aspects that need to be incorporated both in terms of quantification efforts as well as usability on the ground.

The concept of systemic resilience, as it is understood in the context of climate change adaptation addressing systemic risks and polycrisis, is an inherently normative notion that carries ethical weight. To account for these implications, systemic resilience needs to be supplemented with ethical reflections on a system’s function, why it should be made resilient, and who the resilience serves. Crucially, considerations surrounding various forms of justice, such as participatory, procedural, distributive, and historical, need to be accounted for when making decisions about a community’s resilience in the face of increasing climate hazards. Resilience in the context of systemic risks and climate adaptation currently does not account for its ethical implications. This investigation builds on complexity science research and specifically the expanded concept of systemic resilience. In this article, the concept of systemic resilience is applied to the local level, highlighting its ethical underpinnings in the process. Specifically, a case-study explores the application of the ethically informed version of systemic climate resilience, exploring how the Rhine-Erft catchment in Germany could be assessed on this basis.