Introduction: The 2021 European floods in Germany, Belgium, and the Netherlands significantly impacted healthcare. With climate change increasing flood risks, healthcare preparedness is essential. Floods affect healthcare directly and indirectly by disrupting patient access, damaging infrastructure and impeding care continuity. Our interdisciplinary research in the Netherlands systematically assesses flood impacts on healthcare, optimises disaster preparedness, patient logistics, and continuity and explores crisis governance, incorporating lessons from coronavirus disease-2019 (COVID-19). Methods: Our multi-sited, interdisciplinary project titled “Pandemic lessons for flood disaster preparedness” includes literature reviews on: (i) the (in) direct impacts of floods on healthcare, (ii) disaster decision-making strategies and (iii) patient logistics during crises. Empirically, ethnographic methods (interviews, focus groups, document analyses, and observations) will: (a) assess hospital flood preparedness, (b) explore decision-making and crisis management strategies and (c) analyse the dynamics of health system governance during floods. Data from these sources and flood scenarios will inform models on healthcare impacts and decision-making, culminating in a simulation game for research and training. Discussion: This study offers a comprehensive, interdisciplinary approach to understanding and improving healthcare system preparedness for floods. By integrating diverse fields such as healthcare governance, disaster risk management, logistics and hydraulic engineering, we provide a unique lens on resilience. A key strength is the incorporation of lessons from the COVID-19 pandemic, allowing us to draw parallels between pandemic response and flood preparedness. In addition, our simulation game serves as a robust tool for translating knowledge into practice. However, the study’s reliance on collaboration with busy healthcare and disaster response professionals may limit engagement. Moreover, the absence of direct public and patient involvement in the research design, though partially mitigated by engaging representative organizations, presents a potential limitation. Lastly, the challenge of obtaining real-time data from flood events could introduce recall bias, but triangulation of various data sources aims to address this issue. Despite these challenges, the study’s integration of long-term data from recent floods and focus on healthcare-specific crisis governance provides valuable insights for improving disaster preparedness.

High Impact Low Probability events (HILPs), often referred to as outliers, are becoming more important in disaster management because they are linked to complex risks and tipping points in interconnected systems. Recent events, such as the cascading effects of the coronavirus pandemic, rising uncertainties from global geopolitical instability, and successive and concurrent extremes driven by climate change, underscore the limitations of relying solely on severe but plausible scenarios for risk practitioners and policymakers. Despite the critical need to integrate HILPs into risk assessment models and emergency preparedness, the field is fragmented, with inconsistent definitions and methodologies. We present a perspective developed under the HORIZON AGILE project (AGnostic risk management for high Impact Low probability Events), which introduces two comprehensive definitions of HILPs and a taxonomy designed to enhance risk assessment, resilience analysis, and crisis management. We provide a validated scientific definition for the academic community and an operational definition tailored for practitioners and stakeholders. Additionally, our taxonomy offers a structured framework to address outlier events that often fall below traditional risk thresholds, ensuring that low-probability, high-impact scenarios with cascading and concurrent dynamics are effectively integrated into risk registers, legislation, and standards development. This study shows how this approach improves methods like stress testing and scenario modelling, especially for the loss of critical services. This empowers policymakers, practitioners, and stakeholders to include more scenarios in their strategies, enhancing resilience and preparedness.

Increasingly, our cities are confronted with crises. Fuelled by climate change and a loss of biodiversity, increasing inequalities and fragmentation, challenges range from social unrest and outbursts of violence to heatwaves, torrential rainfall, or epidemics. As crises require rapid interventions that overwhelm human decision-making capacity, AI has been portrayed as a potential avenue to support or even automate decision-making. In this paper, I analyse the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems. These super wicked problems are characterised by a coincidence of great complexity and urgency. I will argue that from this combination, specific challenges arise that are only partially covered in the current guidelines and standards around trustworthy or human-centered AI. By following a decision-centric perspective, I argue that to solve urgent crisis problems, the context, capacities, and networks need to be addressed. AI for crisis response needs to follow dedicated design principles that ensure (i) human control in complex social networks, where many humans interact with AI; (ii) principled design that considers core principles of crisis response such as solidarity and humanity; (iii) designing for the most vulnerable. As such this paper is meant to inspire researchers, AI developers and practitioners in the space of AI for (urban) crisis response – and other urgent and complex problems that urban planners are confronted with.

Urban vulnerability is affected by changing patterns of hazards due to climate change, increasing inequalities, rapid urban growth and inadequate infrastructure. While we have a relatively good understanding of how urban vulnerability changes in space, we know relatively little about the temporal dynamics of urban vulnerability. This paper presents a framework to assess urban vulnerability over time and space to address this gap. We apply the framework to Amsterdam, Rotterdam, and The Hague, the Netherlands. Using high-resolution, anonymised ambulance calls and socio-economic, built environment, and proximity data, we identify three temporal patterns: ’Midday Peaks’, ’Early Birds’, and ’All-Day All-Night’. Each pattern represents a unique rhythm of risk arising from the interaction of people with diverse demographic and socio-economic backgrounds and the temporal flow of their daily activities within various urban environments. Our findings also highlight the polycentric nature of modern Dutch cities, where similar rhythms emerge in areas with varying population densities. Through these case studies, we demonstrate that our framework uncovers the spatio-temporal dynamics of urban vulnerability. These insights suggest that a more nuanced approach is necessary for assessing urban vulnerability and enhancing preparedness efforts.

Urban areas are dynamic systems, in which different infrastructural, social and economic subsystems continuously co-evolve. As such, disruptions in one system can propagate to another. However, open challenges remain in (i) assessing the long-term implications of change for resilience and (ii) understanding how resilience propagates throughout urban systems over time. Despite the increasing reliance on data in smart cities, few studies empirically investigate long-term urban co-evolution using data-driven methods, leading to a gap in urban resilience assessments. This paper presents an approach that combines Getis-ord Gi* statistical and correlation analyses to investigate how cities recover from crises and adapt by analysing how the spatial patterns of urban characteristics and their relationships changed over time. We illustrate our approach through a study on Helsinki’s road infrastructure, socioeconomic system and built-up area from 1991 to 2016, a period marked by a major socioeconomic crisis. By analysing this case study, we provide insights into the co-evolution over more than two decades, thereby addressing the lack of longitudinal studies on urban resilience.

Increasing frequency of climate-related disruptions requires transformational responses over the lifecycles of interconnected urban systems with short- and long-term change dynamics. However, the aftermath of disruptions is often characterised by short-sighted decision-making, neglecting long-term urban shifts. In this study, we present a first attempt to develop the theoretical foundation for temporal dynamics for increasingly disrupted yet ”connecting and moving” cities that can be used in planning for urban resilience. Using the lens of climate urbanism, we conceptualise the interplay of temporal dynamics to empirically examine how planning practice perceives and addresses temporality in two regions - Amsterdam, the Netherlands, and Mumbai, India. Our findings reinforce that disruptions do not inform long-term planning. Endogenous and exogenous dynamics of change are not viewed together nor used to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches that can leverage temporal dynamics, we propose two options for temporally flexible urban planning processes.

Covid-19 has dramatically changed life in cities across the globe. What remains uncertain is how national policies and appeals to comply with suggested rules translate to changes in the behaviour of citizens in urban areas. This lack of local knowledge leaves urban policy makers and planners with few clues as to the determinants of social resilience in cities during protracted crises like a pandemic. Methods are required to measure the capacity of people to conduct routine activities without risking exposure to a prevalent disease, particularly for those most vulnerable during a health crisis. By spanning the fields of urban resilience, human geography, mobility studies and the behavioural sciences, this study explores how to measure social resilience in cities during a protracted crisis. Using a public participation GIS online platform, we observe changes in citizen behaviour within urban spaces during the Covid-19 pandemic. Inhabitants from three districts of a Dutch city mapped their activity routines during the lockdown period and during the year before the pandemic. Spatio-temporal analysis reveals changes in the clustering of activities into what we describe as ‘activity bubbles'. We reflect on the influence of the urban space on these changes and assess the contribution of this exploratory research methodology for gaining insights into behavioural change. Implications for urban planning and resilience theory are discussed.

BACKGROUND: Prompted by recent shocks and stresses to health systems globally, various studies have emerged on health system resilience. Our aim is to describe how health system resilience is operationalised within empirical studies and previous reviews. We compare these to the core conceptualisations and characteristics of resilience in a broader set of domains (specifically, engineering, socio-ecological, organisational and community resilience concepts), and trace the different schools, concepts and applications of resilience across the health literature. METHODS: We searched the Pubmed database for concepts related to 'resilience' and 'health systems'. Two separate analyses were conducted for included studies: a total of n = 87 empirical studies on health system resilience were characterised according to part of health systems covered, type of threat, resilience phase, resilience paradigm, and approaches to building resilience; and a total of n = 30 reviews received full-text review and characterised according to type of review, resilience concepts identified in the review, and theoretical framework or underlying resilience conceptualisation. RESULTS: The intersection of health and resilience clearly has gained importance in the academic discourse with most papers published since 2018 in a variety of journals and in response to external threats, or in reference to more frequent hospital crisis management. Most studies focus on either resilience of health systems generally (and thereby responding to an external shock or stress), or on resilience within hospitals (and thereby to regular shocks and operations). Less attention has been given to community-based and primary care, whether formal or informal. While most publications do not make the research paradigm explicit, 'resilience engineering' is the most prominent one, followed by 'community resilience' and 'organisational resilience'. The social-ecological systems roots of resilience find the least application, confirming our findings of the limited application of the concept of transformation in the health resilience literature. CONCLUSIONS: Our review shows that the field is fragmented, especially in the use of resilience paradigms and approaches from non-health resilience domains, and the health system settings in which these are used. This fragmentation and siloed approach can be problematic given the connections within and between the complex and adaptive health systems, ranging from community actors to local, regional, or national public health organisations to secondary care. Without a comprehensive definition and framework that captures these interdependencies, operationalising, measuring and improving resilience remains challenging.

Involuntary displacement from conflict and other causes leads to clustering of refugees and internally displaced people, often in long-term settlements. Within refugee-hosting countries, refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Yet, the exposure of refugee settlements to climatic events is underresearched. In this article, we study the exposure of the 20 largest refugee settlements worldwide to extreme variations in climatic conditions. The analysis integrates exposure of camp locations compared to the national trends for both slow- and rapid-onset events and includes descriptive statistics, signal-to-noise analyses, and trend analyses. Our findings show that most refugee settlements included face relatively high exposure to slow-onset events, including high temperatures (for settlements in Kenya, Ethiopia, Rwanda, Sudan, and Uganda), low temperatures (in the case of Jordan and Pakistan), and low levels of rainfall (in Ethiopia, Rwanda, Kenya, and Uganda) compared to national averages. Our findings for rapid-onset events-heatwaves, coldwaves, and extreme rainfall-are less conclusive compared to country trends, although we find relatively high exposure to extreme rainfall in Cox's Bazar, Bangladesh. Our analyses confirm that refugee populations are exposed to extreme weather conditions postdisplacement, which, in combination with their sociopolitical exclusion, poses a threat to well-being and increased marginalization. Our findings call for an inclusive and integrated approach, including refugees and their host communities, in designing climate adaptation and sustainable development policies, in order to promote equitable sustainable development pathways in refugee-hosting countries.