Societal determinants of flood-induced displacement.

What explains human consequences of weather-related disaster? Here, we explore how core socioeconomic, political, and security conditions shape flood-induced displacement worldwide since 2000. In-sample regression analysis shows that extreme displacement levels are more likely in contexts marked by low national income levels, nondemocratic political systems, high local economic activity, and prevalence of armed conflict. The analysis also reveals large residual differences across continents, where flood-induced displacement in the Global South often is much more widespread than direct human exposure measures would suggest. However, these factors have limited influence on our ability to accurately predict flood displacement on new data, pointing to important, hard-to-operationalize heterogeneity in flood impacts across contexts and critical data limitations. Although results are consistent with an interpretation that the sustainable development agenda is beneficial for disaster risk reduction, better data on societal consequences of natural hazards are critically needed to support evidence-based decision-making.

Residential natural hazard risk and mental health effects.

Mental health effects are frequently reported following natural disasters. However, little is known about effects of living in a hazard-prone region on mental health. We analyzed data from 9,312 Gulf Long-term Follow-up Study participants who completed standardized mental health questionnaires including the Patient Health Questionnaire-9 (depression=score≥10), Generalized Anxiety Disorder Questionnaire-7 (anxiety=score≥10), and Primary Care PTSD Screen (PTSD=score≥3). Geocoded residential addresses were linked to census-tract level natural hazard risk scores estimated using the National Risk Index (NRI). We considered an overall risk score representing 18 natural hazards, and individual scores for hurricanes, heatwaves, coastal flooding and riverine flooding. Log binomial regression estimated prevalence ratios (PR) and 95% confidence intervals (CI) for associations between risk scores (quartiles) and mental health outcomes. Increasing hurricane and coastal flooding scores were associated with all mental health outcomes in a suggestive exposure-response manner. Associations were strongest for PTSD, with PRs for the highest vs. lowest quartile of hurricane and coastal flooding risks of 2.29(1.74-3.01) and 1.59(1.23-2.05), respectively. High heatwave risk was associated with anxiety (PR=1.25(1.12-1.38)) and depression (PR=1.19(1.04-1.36)) and suggestively with PTSD (PR=1.20(0.94-1.52)). Results suggest that living in areas prone to natural disasters is one factor associated with poor mental health status.

To burn or not to burn: governance of wildfires in Australia.

Globally, wildfires are increasing in extent, frequency, and severity. Although global climate change is a major driver and large-scale governance interventions are essential, focusing on governance at smaller scales is of great importance for fostering resilience to wildfires. Inherent tensions in managing wildfire risk are evident at such scales, as objectives and mandates may conflict, and trade-offs and impacts vary across ecosystems and communities. Our study feeds into debates about how to manage wildfire risk to life and property in a way that does not undermine biodiversity and amenity values in social-ecological systems. Here, we describe a case study where features of adaptive governance emerged organically from a dedicated planning process for wildfire governance in Australia. We found that a governance process that is context specific, allows for dialogue about risk, benefits, and trade-offs, and allows for responsibility and risk to be distributed amongst many different actors, can provide the conditions needed to break down rigidity traps that constrain adaptation. The process enabled actors to question whether the default risk management option (in this case, prescribed burning) is aligned with place-based risks and values so they could make an informed choice, built from their participation in the governance process. Ultimately, the community supported a move away from prescribed burning in favor of other wildfire risk management strategies. We found that the emergent governance system has many features of adaptive governance, even though higher level governance has remained resistant to change. Our study offers positive insights for other governments around the world interested in pursuing alternative strategies to confronting wildfire risk.

Supercell tornadoes are much stronger and wider than damage-based ratings indicate.

Tornadoes cause damage, injury, and death when intense winds impact structures. Quantifying the strength and extent of such winds is critical to characterizing tornado hazards. Ratings of intensity and size are based nearly entirely on postevent damage surveys [R. Edwards et al., Bull. Am. Meteorol. Soc. 94, 641-653 (2013)]. It has long been suspected that these suffer low bias [C. A. Doswell, D. W. Burgess, Mon. Weather Rev. 116, 495-501 (1988)]. Here, using mapping of low-level tornado winds in 120 tornadoes, we prove that supercell tornadoes are typically much stronger and wider than damage surveys indicate. Our results permit an accurate assessment of the distribution of tornado intensities and sizes and tornado wind hazards, based on actual wind-speed observations, and meaningful comparisons of the distribution of tornado intensities and sizes with theoretical predictions. We analyze data from Doppler On Wheels (DOW) radar measurements of 120 tornadoes at the time of peak measured intensity. In striking contrast to conventional damage-based climatologies, median tornado peak wind speeds are ∼60 m⋅s-1, capable of causing significant, Enhanced Fujita Scale (EF)-2 to -3, damage, and 20% are capable of the most intense EF-4/EF-5 damage. National Weather Service (NWS) EF/wind speed ratings are 1.2 to 1.5 categories (∼20 m⋅s-1) lower than DOW observations for tornadoes documented by both the NWS and DOWs. Median tornado diameter is 250 to 500 m, with 10 to 15% >1 km. Wind engineering tornado-hazard-model predictions and building wind resistance standards may require upward adjustment due to the increased wind-damage risk documented here.

Upslope migration of snow avalanches in a warming climate.

Snow is highly sensitive to atmospheric warming. However, because of the lack of sufficiently long snow avalanche time series and statistical techniques capable of accounting for the numerous biases inherent to sparse and incomplete avalanche records, the evolution of process activity in a warming climate remains little known. Filling this gap requires innovative approaches that put avalanche activity into a long-term context. Here, we combine extensive historical records and Bayesian techniques to construct a 240-y chronicle of snow avalanching in the Vosges Mountains (France). We show evidence that the transition from the late Little Ice Age to the early twentieth century (i.e., 1850 to 1920 CE) was not only characterized by local winter warming in the order of +1.35 °C but that this warming also resulted in a more than sevenfold reduction in yearly avalanche numbers, a severe shrinkage of avalanche size, and shorter avalanche seasons as well as in a reduction of the extent of avalanche-prone terrain. Using a substantial corpus of snow and climate proxy sources, we explain this abrupt shift with increasingly scarcer snow conditions with the low-to-medium elevations of the Vosges Mountains (600 to 1,200 m above sea level [a.s.l.]). As a result, avalanches migrated upslope, with only a relict activity persisting at the highest elevations (release areas >1,200 m a.s.l.). This abrupt, unambiguous response of snow avalanche activity to warming provides valuable information to anticipate likely changes in avalanche behavior in higher mountain environments under ongoing and future warming.

Humanitarian need drives multilateral disaster aid.

As the climate changes, human livelihoods will increasingly be threatened by extreme weather events. To provide adequate disaster relief, states extensively rely on multilateral institutions, in particular the United Nations (UN). However, the determinants of this multilateral disaster aid channeled through the UN are poorly understood. To fill this gap, we examine the determinants of UN disaster aid using a dataset on UN aid covering almost 2,000 climate-related disasters occurring between 2006 and 2017. We make two principal contributions. First, we add to research on disaster impacts by linking existing disaster data from the Emergency Events Database (EM-DAT) to a meteorological reanalysis. We generate a uniquely global hazard severity measure that is comparable across different climate-related disaster types, and assess and bolster measurement validity of EM-DAT climate-related disasters. Second, by combining these data with social data on aid and its correlates, we contribute to the literature on aid disbursements. We show that UN disaster aid is primarily shaped by humanitarian considerations, rather than by strategic donor interests. These results are supported by a series of regression and out-of-sample prediction analyses and appear consistent with the view that multilateral institutions are able to shield aid allocation decisions from particular state interests to ensure that aid is motivated by need.

Using mobile phone data to evaluate access to essential services following natural hazards.

Natural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor-intensive and of limited geographic scope. In this article, we develop an approach to understanding facility-level inaccessibility across a broad geographic area based on location-based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017.

Weighing structural damage and social susceptibility: A decision-making tool to perform longitudinal studies of geographically large hazard events.

Geographically large climatic hazard events are occurring more frequently, and with this increase, more research emphasis is being placed on their impact. However, a metrology for selecting which communities to survey following an event is not frequently discussed and as a result does not effectively incorporate all relevant disciplines in disaster research. This article provides a method for selecting communities for inclusion in field studies not only based on anticipated damage but also community-level social factors that show predictive power in long-term analyses. Due to the perishable nature of disaster data, this social metric and the field study decision tool were developed with the intent of being as rapidly implementable as possible while still providing insight regarding long-term post-event community trends. The community-level social metric was developed using a hindcasting technique for hazard events in the years 2011, 2012, and 2013. Thresholds for stability and decline were established for both the predicted and the actual community outcomes. Of the communities that the model predicted would recover, 73% recovered using the definition provided. Meanwhile 74% of the communities predicted to decline did indeed decline, again using the definition provided. To enhance operability of this social metric, a decision-making tool for community selection was also formalized and implemented in a field study conducted following the December 2021 quad-state tornado outbreak in the United States. The lessons learned from this first implementation were used to inform a refined version of the decision-making tool that is presented herein.

Individual heat adaptation: Analyzing risk communication, warnings, heat risk perception, and protective behavior in three German cities.

Extreme heat poses severe health threats, as the increased numbers of hospitalizations and fatalities during heat waves show, though little is known about adaptive behavior toward heat. We conducted a household survey on individual perceptions of heat stress and individual heat protection in the summer and autumn of 2019. In total, 1417 people from three medium-sized German cities participated via telephone or online. Based on the Protective Action Decision Model (PADM), which we adapted to heat stress, we analyzed links between risk perception, environmental and demographic factors, perceptions of stakeholders, different heat warning messages, as well as actual and intended adaptive behavior. Overall, the PADM constructs explained around 16% of the variance in protection motivation, 19% in protective response, and 23% in emotion-focused coping. Context factors (i.e., temperature, risk communication, gender, age, and homeownership) were significant predictors of the addressed outcome variables as were psychological factors (i.e., perceived personal vulnerability, response efficacy, response costs, preparedness, and perceived external responsibility). We further explored the effect of different warning messages on situational knowledge and intended behavioral adaptation in an experimental setting. Results showed that respondents felt significantly better informed after receiving a warning with action recommendations and reported more intended specific behaviors. Our research gives insights into individual protective action decision-making processes. Based on our findings, we recommend tailoring risk communication strategies and combining heat warnings with action recommendations whenever possible to increase understanding and individual adaptation.

Mapping the natural disturbance risk to protective forests across the European Alps.

Mountain forests play an essential role in protecting people and infrastructure from natural hazards. However, forests are currently experiencing an increasing rate of natural disturbances (including windthrows, bark beetle outbreaks and forest fires) that may jeopardize their capacity to provide this ecosystem service in the future. Here, we mapped the risk to forests' protective service across the European Alps by integrating the risk components of hazard (in this case, the probability of a disturbance occurring), exposure (the proportion of forests that protect people or infrastructure), and vulnerability (the probability that the forests lose their protective structure after a disturbance). We combined satellite-based data on forest disturbances from 1986 to 2020 with data on key forest structural characteristics (cover and height) from spaceborne lidar (GEDI), and used ensemble models to predict disturbance probabilities and post-disturbance forest structure based on topographic and climatic predictors. Wind and bark beetles are dominant natural disturbance agents in the Alps, with a mean annual probability of occurrence of 0.05%, while forest fires were less likely (mean annual probability <0.01%), except in the south-western Alps. After a disturbance, over 40% of forests maintained their protective structure, highlighting the important role of residual living or dead trees. Within 30 years after wind and bark beetle disturbance, 61% of forests were likely to either maintain or recover their protective structure. Vulnerability to fires was higher, with 51% of forest still lacking sufficient protective structure 30 years after fire. Fire vulnerability was especially pronounced at dry sites, which also had a high fire hazard. Combining hazard and vulnerability with the exposure of protective forests we identified 186 Alpine municipalities with a high risk to protective forests due to wind and bark beetles, and 117 with a high fire risk. Mapping the disturbance risk to ecosystem services can help identify priority areas for increasing preparedness and managing forests towards lower susceptibility under an intensifying disturbance regime.

[Heat and heavy rain prevention measures in daycare centres and care facilities: an evaluation of risk perception, communication and information materials]. / Maßnahmen zur Hitze- und Starkregenvorsorge in Kitas und Pflegeeinrichtungen: Eine Evaluation von Risikowahrnehmung, Kommunikation und Informationsmaterialien.

BACKGROUND AND AIM: Heat and heavy rain can have negative health impacts for people in Germany. Vulnerable groups in particular, such as children and the elderly, are at increased risk and require special precautions. This paper examines how employers of the municipal administration and facilitating organisations perceive the risk of heat and heavy rain for daycare centres and care facilities, and to what extent an exchange takes place between the municipal level and the facilities. In addition, specially developed information materials with recommendations for action for adapting to heat and heavy rain that are aimed at such facilities were evaluated. METHODS: In the summer of 2021, we conducted a quantitative survey. A total of 333 respondents from municipal administrations, facilitating organisations and institutions participated. Descriptive analyses and ANOVAs were conducted. RESULTS: Risk perception and adaptation knowledge concerning heat was perceived higher than concerning heavy rain. The intention to support institutions in finding measures for adaptation was also higher with regard to heat. The majority of interviewees from municipal administrations and institutions communicated with institutions through various channels on different topics including the natural hazards mentioned. The information material was evaluated positively. DISCUSSION: This article shows that facilities are seen as very affected by heat waves. Awareness towards heavy rainfall needs to be raised. The feedback on the information material clearly shows a high need in this area.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

The Great 2011 Thailand flood disaster revisited: Could it have been mitigated by different dam operations based on better weather forecasts?

This paper revisits the 2011 Great Flood in central Thailand to answer one of the hotly debated questions at the time "Could the operation decisions of the flood control structures substantially mitigate the flood impacts in the downstream areas?". Using a numerical modeling approach, we develop a hypothesis such that the two upstream dam reservoirs: Bhumibol and Sirikit had more accurately forecasted the typhoon-triggered abnormal rainfall volumes and released more water earlier to save the storage capacity via 17 different scenarios or alternative operation schemes. We subsequently quantify the potential improvements, or reduced flood impacts in the downstream catchments, solely by changing the operation schemes of these two dam reservoirs, with all other conditions remaining unchanged. We observed that changing the operation schemes could have reduced only the flood depth while offering very limited improvements in terms of inundated areas for the lower Chao Phraya River Basin. Among 17 scenarios simulated, the inundated areas could have been reduced at most by 3.68%. This result justifies the limited role of these mega structures in the upstream during the disaster on one hand, while pointing to the necessity of handling local rainfall differently on the other. The paper expands the discussion into how the government of Thailand has drawn the lessons from the 2011 flood to better prepare themselves against the lurking flood risk in 2021, also triggered by tropical cyclones. The highlighted initiatives, both technical and institutional, could have provided important references for the large river catchment managers in Southeast Asia and with implications of our method beyond the present application region.

Social vulnerability, impacts and adaptations strategies in the face of natural hazards: insight from riverine islands of Bangladesh.

BACKGROUND: Bangladesh is one of the countries at risk of natural disasters due to climate change. In particular, inhabitants of its riverine islands (char) confront ongoing climatic events that heighten their vulnerability. This study aims to assess social vulnerability, impacts, and adaptation strategies to climate change in the riverine island areas of Bangladesh. METHODS: A mixed-method approach incorporating qualitative and quantitative procedures was used on data collected from 180 households of riverine islands in Gaibandha, Bangladesh. The social vulnerability of riverine island communities was assessed based on their adaptation capacity, sensitivity, and exposure to climatic stressors. RESULTS: The findings show that char dwellers' vulnerability, impacts, and adaptation capability to climate change vary significantly depending on their proximity to the mainland. Social vulnerability factors such as geographical location, fragile and low-grade housing conditions, illiteracy and displacement, climate-sensitive occupation and low-income level, and so on caused to the in-height vulnerability level of these particular areas. This study also displays that climate change and its associated hazards cause severe life and livelihood concerns for almost all households. In this case, the riverine dwellers employed several adaptation strategies to enhance their way of life to the disaster brought on changing climate. However, low education facilities, deficiency of useful information on climate change, poor infrastructure, and shortage of money are still the supreme hindrance to the sustainability of adaptation. CONCLUSION: The findings underscore the importance of evaluating the susceptibility of local areas to climate change and emphasize the need for tailored local initiatives and policies to reduce vulnerability and enhance adaptability in communities residing in char households.

"One community at a time": promoting community resilience in the face of natural hazards and public health challenges.

BACKGROUND: Resilience is vital for facing natural disasters and public health challenges. Despite the significance of resilience-building activities, there is a scarcity of locally-tailored planning and response strategies, leaving communities incapable of addressing the unique challenges posed by natural disasters and public health crises. This study aims to explore how the "One Community at a Time" approach enhances community resilience in facing natural hazards and public health challenges. METHODS: A systematic review was conducted over journal articles published from January 2001 to April 2023 through PRISMA approach. Multiple databases such as Web of Science and Scopus were thoroughly searched. We used independent screening by two researchers and painstaking data extraction using standardized forms. This approach was adopted to assure the reliability, validity, and precision of our study selection and analysis. The included studies' quality was evaluated by the Mixed Methods Appraisal Tool. RESULTS: In the evaluation, 35 studies were deemed eligible for inclusion and underwent in-depth examination. Several major components of "One Community at a Time" have been identified, including social capital and networks, local knowledge and learning, effective governance and leadership, preparedness and response capacity, and adaptive infrastructure and resources. This framework highlights the significance of individualized approaches to resilience-building initiatives, recognizing that each community has specific strengths, needs, and challenges. CONCLUSION: Relevant stakeholders can adapt suitable resilient strategies to help prepare and recover from natural hazards and public health challenges. By adopting a localized strategy, stakeholders can collaborate to develop a culture of readiness and resilience, ultimately leading to more sustainable and resilient communities. This framework advises community-based groups, local government, and other stakeholders on prioritizing partnerships, preparedness planning, community participation, and leadership as essential components of creating and maintaining resilience. "One Community at a Time" framework offers practical guidance for community-based organizations, local government, and other stakeholders to prioritize partnerships, preparedness planning, community participation, and leadership as essential components of creating and sustaining resilience.

Explainable deep learning powered building risk assessment model for proactive hurricane response.

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities' preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings' risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.

Disaster preparedness and community helping behaviour in the wake of the 2020 Oregon wildfires.

Extreme weather events are increasing in frequency and severity owing to climate change. Individual-level behavioural responses-notably, disaster preparedness and community helping actions (such as donating and volunteering)-supplement government efforts to respond to such phenomena, but rarely have they been explored together. Using data from a survey administered soon after the 2020 Oregon wildfires, this paper compares a range of socio-demographic, experiential, attitudinal, and communication-related factors associated with these two individual-level behavioural responses. Findings indicate that respondents who reported experiencing a higher degree of harm and heightened concern about climate change after the wildfires were more likely to report disaster preparedness and community helping actions. Those who reported more frequent informal discussions about the wildfires, consulting more sources to seek information on them, and higher percentages of friends, neighbours, and community members taking actions to prepare for future wildfires also reported more disaster preparedness and community helping actions. Disaster preparedness actions were also positively associated with seeking information from formal/official sources.

A framework for integrating ecosystem services indicators into vulnerability and risk assessments of deltaic social-ecological systems.

Due to increasing population pressure and urbanization, as well as global climate change impacts, many coastal river deltas are experiencing increased exposure, vulnerability and risks linked to natural hazards. Mapping the vulnerability and risk profiles of deltas is critical for developing preparedness, mitigation and adaptation policies and strategies. Current vulnerability and risk assessments focus predominantly on social factors, and typically, do not systematically incorporate a social-ecological systems perspective, which can lead to incomplete assessments. We argue that ecosystem services, which link both ecosystem functions and human well-being, can be used to better characterize the mutual dependencies between society and the environment within risk assessment frameworks. Thus, building on existing vulnerability and risk assessment frameworks, we propose a revised indicator-based framework for social-ecological systems of coastal delta environments, supported by a list of ecosystem service indicators that were identified using a systematic literature review. This improved framework is an effective tool to address the vulnerability and risk in coastal deltas, enabling the assessment of multi-hazard risks to social-ecological systems within and across coastal deltas and allows more targeted development of management measures and policies aimed at reducing risks from natural hazards.

Learning inter-annual flood loss risk models from historical flood insurance claims.

Flooding is a natural hazard that causes substantial loss of lives and livelihoods worldwide. Developing predictive models for flood-induced financial losses is crucial for applications such as insurance underwriting. This research uses the National Flood Insurance Program (NFIP) dataset between 2000 and 2020 to evaluate the predictive skill of past data in predicting near-future flood loss risk. Our approach applies neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Processes) to estimate pointwise losses. It aggregates them over intervals using a bias-corrected Burr-Pareto distribution to predict risk. The regression models help identify the most informative predictors and highlight crucial factors influencing flood-related financial losses. Applying our approach to quantify the county-level coastal flood loss risk in eight US Southern states results in an R2=0.807, substantially outperforming related work using stage-damage curves. More detailed testing on 11 counties with significant claims in the NFIP dataset reveals that Extreme Gradient Boosting yields the most favorable results, and bias correction significantly improves the similarity between the predicted and reference claim amount distributions. Our experiments also show that, despite the already experienced climate change, the difference in future short-term risk predictions of flood-loss amounts between historical shifting or expanding training data windows is insignificant.

How path dependency manifests in flood risk management: observations from four decades in the Ennstal and Aist catchments in Austria.

Path dependency occurs when a contingent event predetermines what further steps can be taken and self-reinforcing mechanisms lock-in any further development on a sub-optimal trajectory. Path dependency is a prominent concept in the adaptation pathways literature, but insufficiently defined and operationalised. The present paper empirically tracks all constitutive elements of path dependency for four decades of flood risk management (FRM) in two alpine mountain regions in Austria, the Ennstal and Aist river catchments, using a mixed-methods approach. FRM governance has a critical role whether decisions lead to path dependency. Lock-in manifests not just in technical structures, but also in inertia of incumbent actor coalitions and management paradigms. Sub-optimality is hard to assess for lack of clearly defined protection targets; however, it appears in the ways that structural measures are implemented-too little, too late or with negative impacts on nature conservation. Past floods do not qualify as contingent events, as they have not fundamentally changed FRM practice. By contrast, technological and institutional shifts over longer periods, such as digital hazard maps and EU directives, have gradually reoriented FRM strategies. Institution-based self-reinforcing mechanisms are more prevalent than technology-based self-reinforcing mechanisms. Established actor coalitions combined with institutional density illustrate how those in charge uphold a path to defend their position, power and resources. Our recommendations for how to overcome path dependency in FRM governance are: encourage niche experiments, link FRM more closely with climate change adaptation, revise the national policy framework towards polycentric governance approaches and improve professional training.