Using a discrete choice experiment, this study examines household preferences for staying versus relocating under future coastal flood risk in metropolitan France. We estimate mixed logit models to analyse how experimentally varied environmental and economic attributes, together with selected self-reported behavioural variables, shape residential relocation preferences. Flood frequency, beach amenities, insurance premiums, local economic conditions, perceived ability to relocate, homeownership, and prior network exposure significantly influence relocation choices. Counterfactual simulations show that insurance premiums provide a strong price signal: changes in insurance premiums generate substantial predicted changes in staying probabilities, even relative to large increases in flood probability. At the same time, high-income households exhibit slightly higher average staying probabilities across all policy scenarios. These findings suggest that the uniform pricing structure of the French CatNat system may weaken location-specific financial incentives and contribute to socioeconomic differences in coastal exposure while spreading risk-related costs more broadly. As a possible reform direction, we propose finer-scale risk-based pricing complemented by means-tested vouchers to preserve affordability.

The desiccation of Lake Urmia, Iran, represents a critical environmental crisis driven by water scarcity. In 2010, the "Integrated Management Plan for Lake Urmia Basin" was implemented to restore the lake, primarily by improving agricultural irrigation efficiency from 35% to 60% to reduce water withdrawals by 40%. This study evaluates the hydrological outcomes of this restoration plan between 2010 and 2022, specifically investigating the interplay between policy-driven efficiency gains and concurrent land use changes. We employed a spatial land use model to analyze changes in agricultural patterns. Our findings indicate a significant divergence from the plan’s assumptions. While irrigation efficiency improved, the area of water-intensive orchards expanded by 68.4%, 15.5% more than the 52.9% increase anticipated by the plan. This unplanned agricultural expansion increased regional water demand, offsetting the water savings from efficiency improvements. Consequently, the strategy did not lead to a net increase in inflows to the lake. Furthermore, a rising trend in maximum land surface temperature was observed, indicating an increasingly challenging climate context. The results demonstrate that the restoration strategy was unable to improve the lake’s water balance. The lake of integrated water and land governance allowed for efficiency gains to be effectively canceled out by shifts to more water-intensive cultivation. This study highlights that technological interventions for water savings are insufficient without robust governance mechanisms that manage overall water consumption. Our findings provide critical insights for designing more effective ecosystem restoration policies in water-scare regions globally.

We evaluate the performance of the Super-Fast INundation of CoastS (SFINCS) hydrodynamic model for simulating riverine floods, combined with a fully automated open-source data preprocessing pipeline. To do this, we assessed the simulated extent of 499 historic flood events against the satellite derived flood extents using the Critical Success Index (CSI) as a performance metric. We utilised simulated discharges from the Global Flood Awareness System (GloFAS) hydrological model and found that SFINCS performance improved with upstream basin size, with a global mean CSI of 0.42 for basins with large upstream area (>1000 km2) and a CSI of 0.29 for basins with small upstream area (<50 km2). Our results illustrate the importance of accurate discharge data input to flood hazard simulations. When the (globally simulated) GloFAS data is replaced with observed discharge data for ten events in the US, the CSI improved from 0.39 to 0.67. These results suggest that global hydrological model performance limits the accuracy of the flood hazard simulations. Our findings also showed a significant improvement in the CSI (from 0.37 to 0.57) when changing to a higher-resolution elevation input by contrasting a ∼1 m digital elevation model (DEM; 3DEP) with our default ∼30 m global DEM (FABDEM) in six US events. Sensitivity analysis of bathymetric calculations revealed a systematic underestimation of the default 2-year return period estimated by GloFAS discharge, likely driven by underrepresentation of annual block maxima, which resulted in underestimated channel dimensions. All of these factors resulted in a loss of detail, which impacted model performance, especially in smaller headwater rivers. We recommend to improve the estimation of bathymetry, for instance by employing the “gradually varying solver” method or using data from the SWOT mission. Furthermore, incorporating additional validation data which ideally includes flood depth measurements can largely enhance our understanding of the model performance.

Agricultural insurance is promoted as a drought-risk tool, yet its net long-term socio-hydrological impacts and interactions with other adaptations remain disputed. We expand the Geographical, Environmental and Behavioural model (GEB), a fully distributed hydrological model coupled with an agent-based model (ABM), a process-based crop model and a dynamic farmer adaptation behavior model. We add two adaptation options (wells, crop switching) and two insurance designs (traditional, index), calibrated to an Indian basin. Traditional insurance increases well adoption and profits but creates a lock-in to wells and higher-water-use crops, leading to 20-50% higher annual water use and 30-60% lower groundwater levels. Index insurance avoids this lock-in, shifts production toward lower-water options and delivers higher profits with lower basin-wide water use. Despite this, traditional insurance sustains greater crop diversity and a more diffuse irrigation mix via groundwater, reducing drought risk: profit variability and losses during consecutive droughts are smaller than under index insurance (similar to 0.039 vs similar to 0.085 USD m(-2); similar to 20% vs similar to 28%). Spatial patterns further show that insurance interacts with reservoir effects: uptake is lower in surface-water command areas, whereas index insurance has relatively high uptake in these zones, suggesting potential to counteract reservoir effects. Finally, we find that the level of available irrigation, rather than simple access, determines whether reservoir effects emerge. Our findings highlight design trade-offs: while hydrological and economic metrics favor index insurance, a risk-oriented perspective may prefer traditional insurance, underscoring the utility of ABMs to make these trade-offs explicit.

Severe convective storms (SCS) are extreme weather events that can produce hazardous hail, precipitation, wind, and lightning, either alone or combined. In recent decades, growing exposure and climate change have rapidly increased SCS-related economic losses in the United States and Europe. Consequently, it has become more important for risk management to accurately predict their potential losses. Vulnerability functions form the foundation for these loss predictions, as they describe the relationship between a natural hazard’s intensity and damage to an asset. We construct a novel vulnerability function for residential buildings that can account for SCS damage from hail, precipitation and from their combination. This compound vulnerability function is estimated on a large sample of object-level insurance claims and on ultra-high-resolution meteorological observations, using truncated beta regressions. Multiple model diagnostics and a cross-validation procedure show that the compound vulnerability function predicts aggregated damage with high accuracy, deviating less than 2% from the observed aggregate damage. For SCS producing exceptionally low or high precipitation levels, we show that the compound vulnerability function outperforms the conventional specification, which only accounts for hail damage, by reducing the magnitude of the mean bias error by ∼30%. Additionally, our compound vulnerability function is better equipped to predict SCS losses for climate change scenarios because it can accommodate the anticipated changes in the joint distribution of damaging hail and precipitation.

Improving irrigation efficiency (IE) is widely promoted as a strategy to reduce agricultural water use while sustaining farm production and freeing water for other sectors. However, higher field-scale IE does not necessarily increase basin-scale water availability, a mechanism known as the IE Paradox. The paradox occurs because (1) higher IE reduces runoff and recharge that would otherwise support downstream users and environmental flows, while (2) initial withdrawal reductions may rebound if farmers use apparent 'savings' for economic gain. Here, we develop a conceptual framework for this paradox and quantify its components with the Geographical, Environmental and Behavioral model (GEB), which couples a 30 arcsec hourly distributed hydrological model, a 1.5 arcsec 'one-to-one' agent-based model, a process-based crop model and a farmer adaptation model. We apply GEB to the southern Murray-Darling Basin, Australia (similar to 75 000 km2) and compare scenarios with historical IE increase, static IE, high IE and reduced irrigation entitlements. Historical IE increased basin-wide consumed irrigation water by 17% (1.1% per percentage-point IE increase), reduced recoverable irrigation water by 25% (-1.6% per percentage point) and reduced outlet discharge by 1% (-0.03% per percentage point). Although the IE-only effect reduced withdrawals by 13%, rebound largely offset this effect, leaving a net withdrawal reduction of only 2.5% (-0.08% per percentage point). Higher IE also increased farm income, especially in dry years, because additional water availability produced nonlinear yield gains under scarcity. In contrast, reduced entitlements lowered withdrawals and consumed water and increased discharge, but caused income losses, particularly in dry years. These results show that water conservation technologies (WCTs) and entitlement reductions serve different policy goals. WCTs can support farm income and drought resilience, whereas entitlement reductions are more effective for basin-scale water recovery. Field-scale IE gains should therefore not be presented as basin-scale water savings or streamflow-recovery policy without accounting for rebound, reduced return flows, groundwater impacts and economic trade-offs.

Abstract Floods are expected to increase in frequency and severity due to climate change. Recent floods have shown that many catchments worldwide are vulnerable to floods, highlighting the need for additional adaptation measures. This study extends the Geographical, Environmental, and Behavioral (GEB) model by coupling it to a hydrodynamic and a flood risk model to assess the effects of dry‐proofing, wet‐proofing, retention ponds, reforestation, and the creation of natural grassland. A key innovation is the integration of all local‐scale models, thereby allowing for a catchment‐wide assessment of the impacts of various measures on interlinked hydrological conditions, flood extents and depths, damages, and risk. We apply our method to the Geul catchment (shared between the Netherlands, Belgium and Germany), which was heavily flooded in July 2021. Our results show that reforestation and creation of natural grassland (both 10 km2) reduce flood extent by 12% and average water depth by 10%. Damage is decreased up to 38%. Larger retention ponds (1 m deeper) have a much smaller reduction in flood extent (3%), depth (0.5%) and damage (1.6%), due to limited storage capacity compared to excess rainfall. The building‐level adaptation scenarios outperform all nature‐based solutions, with dry‐proofing reducing more damage (up to 95%) than wet‐proofing (around 55%). A cost‐benefit analysis shows that several adaptation measures are economically attractive. Overall, our findings show a coupled model is essential for comparing the relative effectiveness of different flood adaptation measures and supporting informed risk management decisions. The open‐source model is transferable to other catchments worldwide to guide decision‐making.

In the Horn of Africa Drylands (HAD) conflicts over water and vegetation are prominent. Additionally, large-scale land acquisitions (LSLAs) are increasing the competition of water, putting local communities at greater risk. A key impact of increasing LSLA's is the decrease in water and land availability for vulnerable agropastoral communities. Despite recent studies, there is still a lack of research that includes the influence of upstream-downstream dynamics on drought risk and impacts of agropastoralists. Therefore, this study further develops an agent-based model (ADOPT-AP) to investigate how upstream large-scale commercial farms influence downstream drought risk and impact of agropastoralists in the Upper Ewaso Ng'iro catchment in Kenya. After the model has been calibrated, we assess how commercial exporting farms affect drought risk and impact of downstream communities by simulating different scenarios where the farms are replaced by agropastoral communities or forests. Our results show how both drought hazard characteristics and impacts differ among these scenarios. The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables during dry periods. These changes are linked to reduced water abstraction and increased infiltration, benefiting downstream communities by decreasing the distance to household water, and increasing crop production in times of dry periods. However, compared with the impact of drought hazard itself these differences are very small.

This meta-analysis of stated preference studies involving over 49,500 respondents quantifies the economic value of co-benefits from nature-based solutions (NBSs) that address climate risks. The results indicate that the willingness to pay for co-benefits increases with GDP per capita and decreases with NBS size. Recreational and aesthetic benefits, as well as NBSs developed in urban gray areas, are more valued than conservation and maintenance of current nature sites. The novel value transfer function can assist future research and policy makers in assessing the economic co-benefits of NBSs for climate risk based on the policy site characteristics. (JEL Q54, Q57)

Drought poses a thread in the already existing water challenges in dryland regions. Drought hazard and risk are, however, not merely a natural phenomenon. Instead they are shaped and influenced by human behaviour and interventions. This raises questions about how to distribute the limited available water in an equitable manner, especially in drought prone areas such as drylands where water is key to people’s livelihood and fragile ecosystems. In the Horn of Africa Drylands (HAD) conflict over water and vegetation is prominent. On top of that, large-scale land acquisitions (LSLAs) are increasing the competition of water, putting local communities at increased risk. A key impact of increasing LSLA's is the decrease in water and land availability for vulnerable agropastoral communities. For such communities, drought adaptation is key to reduce drought risk, especially under climate change. Despite these recent studies, there is still a lack of research that includes the influence of upstream-downstream dynamics on drought risk and adaptation behaviour with a focus on the impacts of agropastoralists. This study, therefore, further develops an agent-based model (ADOPT-AP) to investigate how upstream large scale commercial farms influence downstream drought risk and adaptation of agropastoralists. We apply and test the ADOPT-AP model for the Ewaso N’giro north catchment in Kenya. Main novelties of our method are the ability to capture heterogeneous and dynamic drought-human interactions (including different water users) in a spatially-explicit manner. After the model has been calibrated and validated, we test how commercial exporting farms affect drought risk and impact of downstream communities by simulating different scenarios. We show for various drought periods how both drought characteristics (soil moisture, discharge and groundwater levels) and impacts (milk production, crop production, distance to water) differ among the scenarios.

Decision-making for flood adaptation in coastal cities is complicated by deep uncertainty about sea level rise, subsidence, and socioeconomic trends, which increases the possibility of under- or over-investment. Using the megacity of Shanghai as a case study, we apply the dynamic adaptive policy pathways (DAPP) framework to demonstrate robust and flexible decision-making under uncertainty. The framework integrates compound flood risk modeling of flood risk, economic evaluation, and dynamic adaptation pathways. Our results show that without adaptation, annual damages and annual casualties could increase by 86–167%, and 45–97 times, respectively, by the year 2100. ‘Hard adaptation strategies’ such as levees can reduce projected damages by 58–94%. In contrast, local scale ‘soft adaptation’ (flood-proofing buildings) is only effective and economically efficient in combination with hard adaptation (‘hybrid strategy’). The best economic performance is a hybrid strategy that starts implementing a large storage tank adding a mix of measures around 2050 (coastal wetlands, dry-floodproofing, and land elevation). Depending on how the future plays out, a hybrid strategy of a combination of a storm-surge barrier and coastal wetlands would yield high economic benefits after ~2070.

Wave-driven flooding is often neglected or included in an approximate way in large-scale flood hazard assessments and early warning systems, despite its significant contribution to coastal flood hazards. This study introduces a method to incorporate incident and infragravity wave processes into a fast compound flood model by extending the SFINCS software with the SnapWave stationary wave energy solver. This extension efficiently translates offshore incident and infragravity wave conditions to the nearshore, allowing for the estimation of incident-wave-induced setup and the resolution of wave runup and overtopping. A quadtree approach is employed to optimize the grid resolution for wave processes in the coastal zone. The approach is validated for Hurricane Florence (2018) along the North and South Carolina coastline of the United States, where observed offshore wave heights reached 10 m. The results illustrate that the impact of the hurricane extended hundreds of kilometers beyond the landfall area due to waves, highlighting its importance as coastal flood driver. In 19% of the coastline analyzed, wave contributions surpassed all other flood drivers combined, with waves contributing to an additional flooded area of 226 km2 and a flood volume of 62 million m3. The study also indicates that simpler parameterized methods for including wave-induced setup can lead to significant discrepancies in modeled water depths. The computational efficiency of the extended SFINCS model allows for the simulation of 1,000 km of coastline with limited computational resources. Hereby the critical role of wave effects in coastal compound flood hazard assessments could be demonstrated.

Intense short duration rainfall events are expected to increase in severity and frequency due to climate change. Densely populated urban areas are vulnerable to these events, resulting in high losses. Implementing nature-based (e.g. green streets, rain gardens and green roofs) and other municipal adaptation measures (e.g. water storage facilities) can be a way to mitigate these damages. Little is known about the effectiveness of these measures combined in a municipality. This study assesses municipal climate adaptation measures being taken by the municipality of Amsterdam. Unique claims data of almost all Dutch insurers is used to understand the impact of these climate adaptation interventions. We study one neighborhood in Amsterdam which has been renovated using climate adaptation measures, including nature-based solutions. We implement a quasi-experimental difference-in-Differences (DiD) analysis that compares insured rainfall damages in the area to a similar neighboring area that was not renovated with climate adaptation measures. We find a negative significant relation between climate adaptation measures and insured damage when comparing the area where measures were taken to the similar area were measures were not taken, i.e. damage is reduced by climate adaptation measures by EUR 1375-5648 per rain day in the treatment area. Furthermore, precipitation per day is positively and significantly associated with insured damage. We suggest that nature-based and other adaptation measures can be installed by local governments and stimulated by insurers and banks to increase climate resilience in urban areas.

Recent disasters have highlighted the severe local impacts of extreme precipitation, including flash floods, landslides, and urban inundation. Despite significant investments in early warning systems, these events often catch many people off guard, emphasizing the need for a better translation of warnings into early actions. In this study, we directly address this gap by translating precipitation forecasts from the ECMWF, specifically the extreme forecast index (EFI) and shift of tails (SOT), to concrete triggers for early action. Our analysis reveals that such actions, triggered with a 2-3-day lead time, have a high potential economic value (PEV) across large parts of Europe. The SOT forecasts generally have higher economic value than EFI, especially at longer lead times. However, the effectiveness of both disappears across most of Europe with a 5-day lead. These results are based on comparing forecasts to the E-OBS dataset for extreme rainfall (>5-yr return period) over 8years. We apply the optimal warning thresholds found in this analysis to the rainfall event that triggered the July 2021 western Europe flood disaster. Our results indicate that the EFI and SOT forecasts provided accurate and timely warnings at least 2-3 days in advance, aligning with flood impacts recorded in the Emergency Events Database (EM-DAT) dataset. Notably, on a 1-day lead time, the SOT forecasts accurately pointed to the Ahr catchment in Germany with highly exceptional values, providing strong indications of the disaster that unfolded there. This study underscores the value of these rainfall indicators and calls for further testing on more high-impact events. SIGNIFICANCE STATEMENT: Recent floods triggered by extreme rainfall, such as the 2021 western Europe floods, the 2023 Emilia-Romagna floods, and the 2022-23 California floods, have had an enormous impact. These events highlight the urgent need to scale up early actions. In this study, we use two rainfall indicators from ECMWF: the extreme forecast index (EFI) and the shift of tails (SOT). We find evidence that these indicators have high economic value when used to trigger early actions 2-3 days ahead of extreme rainfall events in Europe. Furthermore, these indicators provided accurate warnings at least 2 days before the 2021 western Europe floods. This underscores the vital role of early warning and preparedness in reducing future disaster risk.

Decisions on coastal cities flood adaptation are complicated by deep uncertainty about sea level rise, subsidence and socioeconomic trends, increasing the chance of under- or over-investment. Frameworks have been proposed to plan coastal adaptation in urban settings. In this study, we expand those frameworks to include elements critical to rational decision-making in coastal cities under deep uncertainty. Our framework, trained on the city of Shanghai, includes compound flood modeling, flood risk analysis, design and quantitative simulation of adaptation strategies, cost-benefit analysis, trade-off analysis and formulation of dynamic adaptive policy pathways (DAPP). We include land subsidence in modeling flood scenarios; we compute a diverse set of flood impacts on multiple sectors; we evaluate several techniques of cost-benefit analysis; and we include multiple adaptive strategies against compound flooding (i.e., pluvial, fluvial, coastal). We show that the hard adaptation strategies (e.g., storm-surge barriers and storage tank) can successfully reduce future increase in risk generated by sea level rise, land subsidence and socioeconomic development, by 58%~94%. In contrast, soft adaptation only generate considerable benefits when integrated with hard adaptation into hybrid strategies. A hybrid strategy that combines storm-surge barrier and wetland creation most effectively reduces flood damages and casualties, and yields promising co-benefits. We formulate DAPP for robust and flexible decision-making over time for the coming decades, which open up the decision-making space and help overcome policy paralysis due to deep uncertainty.

Ongoing climate change, resulting in heavier rainfall and potentially higher flood peaks, can challenge flood risk management in many European regions. In particular, flood design values and flood hazard and risk maps can be challenged by future climate conditions. The devastating July 2021 floods in western Europe highlighted the need for transboundary cooperation in adapting flood risk management to climate change. In the JCAR-ATRACE Initiative (Joint Cooperation programme on Applied scientific Research – Accelerate Transboundary Regional Adaptation to Climate Extremes), we review and synthesize how climate change information is integrated into flood risk management in regions of Germany, the Netherlands, Belgium, and Luxembourg. We assess whether regions have published flood policy papers, developed future climate and flood scenarios, and translated these scenarios to flood hazard and risk maps and/or flood design values. Our findings reveal that while all 17 sub-national regions have adaptation plans addressing climate change, only 6 regions have developed future flood projections, with even fewer (3) incorporating climate-adjusted design values and only one providing flood hazard and risk maps under future climate scenarios. Practices vary widely: for example, Flanders in Belgium uses a full range of emission scenarios (CMIP5 RCP2.6 to RCP8.5), while Baden-Württemberg and Bavaria in Germany rely on the high-end scenario (CMIP5 RCP8.5) only. The Netherlands adopts a robust approach using 33 CMIP6 global climate models and a dynamic adaptation pathway framework to address uncertainties. Some regions like Saxony in Germany argue that the spread of projections is too large to derive design values and emphasize the need for standardized scenarios and methods. In summary, our synthesis highlights substantial gaps in incorporating climate change projections into flood risk management and significant regional variation in approaches. The synthesis will hopefully contribute to cross-border learning and foster uptake of climate change adaptation in flood risk management in Europe.

Extreme windstorms pose significant societal and economic challenges, ranking among the costliest natural disasters in Europe. This study addresses the complex task of quantifying windstorm impacts, with a specific focus on the Netherlands. Despite their substantial economic cost, windstorm risks in the Netherlands have been underexplored in dedicated regional studies. Existing large-scale investigations often rely on hazard-loss relationships derived from data from other European countries. We aim to enhance the accuracy of windstorm risk assessment by utilizing not only higher-resolution hazard data but also higher-resolution Dutch damage data. Our methodology involves analyzing high-resolution data to identify hazard variables that best correlate with losses. This is done by leveraging post-disaster loss data from a private Dutch insurance company. In particular, we use the aggregated losses per postal code 4 area, which delivers a nuanced understanding of the spatial distribution of losses. Simultaneously, we account for hazard intensities using the wind climatology data from KNMI North Sea Wind (KNW). This data is derived from 40 years (1979-2019) of ERA-Interim re-analyzed data and downscaled to a higher resolution (2.5 x 2.5 km) tailored specifically for the Netherlands. Through statistical analysis, the study aims to determine the most suitable hazard components for a regional windstorm damage assessment model. This approach aims to move beyond the conventional use of daily maxima wind speed or gust speed by evaluating the appropriateness of hazard variables concerning observed losses. This meticulous integration of proprietary loss records and refined wind climatology enables developing new spatial windstorm hazard maps and a high-resolution windstorm risk database, which provide a solid basis for risk assessment.

Floods cause large disruptions to society by causing both direct and indirect damages. These impacts will be further exacerbated by climate change and socioeconomic development. In addition to direct impacts, businesses may face indirect losses resulting from disruptions to their operations, adding extra complexity to business risk assessments. Additionally, business closures can have far-stretching economic repercussions. Flood insurance is an instrument to reduce the impact of floods for businesses by spreading the risk over space and time. While the (future-) increase in flood damages puts pressure on businesses, insurance systems tailored to businesses remain underexplored.This research applies and extends the ‘Dynamic Integrated Flood Insurance’ (DIFI) model to analyse flood insurance for businesses in the Netherlands, taking into account both insurance against direct damages and insurance against business interruption damages. We analyse the responses of various insurance systems to changes in flood risk. These systems include voluntary insurance, solidarity-based insurance, and public-private partnership insurance. In addition, we assess the effect of adaptation on the viability of flood insurance by allowing businesses to take building-level measures to reduce their flood risk.To facilitate the insurance analysis, flood damages are estimated using an object-based approach that takes high resolution (25m x 25m) inundation maps as input. To simulate the insurance uptake, company-level financial data obtained from the Dutch Chamber of Commerce is used in a subjective expected utility framework. This module is calibrated on actual insurance uptake numbers and takes risk misperception into account. DIFI simulations until 2080 show how premiums, insurance uptake, and policyholder adaptation efforts develop over time for various insurance market structures. These projections provide valuable insights into the viability and effectiveness of different insurance market structures in the face of climate change and shifting socioeconomic conditions.The novelty of this research lies not only in incorporating businesses into the insurance analysis, but also in introducing a focus on business interruption damages, offering a more comprehensive perspective on flood impacts for businesses. Initial results reveal that, in certain sectors, flood-related business interruption damages are nearly as high as, or even exceed, direct damages. These findings offer new insights into the impact of flooding on businesses and the challenges of insuring such damages.Consequently, the findings are relevant for policymakers and insurers by identifying which insurance market structures are more resilient to the increasing flood risk, providing guidance on designing financially sustainable insurance framework. Moreover, the study highlights the need for targeted insurance incentives to encourage business-level adaptation, and it informs decisions regarding potential government involvement in the insurance system to ensure equitable access to flood insurance.

Climate change-induced sea-level rise and associated flood risk will have major impacts on coastal regions worldwide, likely prompting millions of people to migrate elsewhere. Migration behavior is expected to be context-specific, but comparative empirical research on coastal migration under climate change is lacking. We address this gap by utilizing original survey data from coastal Argentina, France, Mozambique and the United States to research determinants of migration under different flood risk scenarios. Here we show that migration is more likely in higher-than in lower-income contexts, and that flood risk is an important driver of migration. Consistent determinants of migration across contexts include response efficacy, self-efficacy, place attachment and age, with variations between scenarios. Other factors such as climate change perceptions, migration costs, social networks, household income, and rurality are also important but context-specific. Furthermore, important trade-offs exist between migration and in-situ adaptation. These findings support policymakers in forging equitable migration pathways under climate change.