Dr. Elco Koks

Critical infrastructure (CI) is exposed to natural hazards that may lead to the devastation of these infrastructures and burden society with the indirect consequences that stem from this. Fragility and vulnerability curves, which quantify the likelihood of a certain damage state and the level of damage of an element under varying hazard intensities, play a crucial role in comprehending, evaluating, and mitigating the damage posed by natural hazards to these infrastructures. To date, however, these curves for CI have been distributed across the literature instead of being accessible through a centralized database. This study, through a systematic literature review, synthesizes the state of the art of fragility and vulnerability curves for the CI assets of energy, transport, water, waste, telecommunication, health, and education in context of natural hazards and offers a unique physical vulnerability database. The publicly available centralized database that contains over 1510 curves can directly be used as input for risk assessment studies that evaluate the potential physical damage to assets due to flooding, earthquakes, windstorms, and landslides. The literature review highlights that vulnerability development has mainly focused on earthquake curves for a wide range of infrastructure types. The curves for windstorms have the second largest share in the database, but they are especially limited to energy curves. While all CI systems require more vulnerability research, additional efforts are needed for telecommunication, which is largely underrepresented in our database.

Understanding coastal flood protection is crucial for assessing risks from natural hazards and climate change. However, there is a significant lack of quantitative data on coastal flood protection and their standards. FLOPROS, currently the only global database of flood protection standards, relies on limited coastal observations and simplified assumptions. Despite these limitations, it is widely used by the research community, leading to potential uncertainties in impact estimates. We propose that a new community-driven effort is needed to develop a more complete dataset, and we present COASTPROS-EU, a dataset compiling coastal flood protection standards within Europe. This effort further highlights the urgent need for enhanced information. Establishing an accurate dataset requires using both bottom-up and top-down approaches and ensuring diverse societal representation.

Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap, processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.

Globally, urban disaster risk is being affected by changing patterns of natural hazards due to climate change and rapid urbanization. While we have a relatively good understanding of the hazard and exposure in cities, we know little about their vulnerability. In this study, we investigate the drivers and dynamics of urban vulnerability for six different natural hazards by conducting a systematic literature review on the peer-reviewed scientific literature. Out of an initial set of 3168 studies, we included 462 studies for in-depth analysis. We present VulneraCity, the urban vulnerability drivers database, in which we record the drivers and classify them based on topic and acquisition method. Overall, we list 1460 unique drivers of vulnerability in VulneraCity, of which 37.3 % are empirically acquired in the source studies. Other drivers are either modeled (7.9 %), theorized (22.9 %), adopted (27.0 %), or acquired with an unknown method (5.0 %). Furthermore, the relationships between drivers and impact are often assumed to be linear, but we identify six types of directional dynamics - one-directional, bidirectional, transferable, asynergies, conditional, and compound - which describe the complexities in these relationships to show that the linearity assumption is regularly violated. These results shed light on the necessary drivers that should be taken into account in urban vulnerability assessments. VulneraCity can facilitate discussions in local-scale vulnerability analyses, but could also provide input for larger-scale comparative studies of cities. We recommend further research into multi-hazard (instead of multiple hazards’) vulnerability and vulnerability dynamics as next steps towards more comprehensive urban disaster risk studies.

Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the “Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)” at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments.

Society is dependent on critical infrastructure that provides basic services such as healthcare, mobility, communications, and power. Severe weather can damage these vital infrastructure assets, disrupting services. Such disruptions can further escalate due to system interdependencies. Although research increasingly evaluates physical risks to infrastructure assets, knowledge on service disruption risks from natural hazard-induced failure cascades across networked infrastructure systems remains limited. Here, we couple an open-source risk model with a complex network-based infrastructure module to simulate spatially explicit service disruptions from 700 historic floods and tropical cyclones in 30 countries. We find that failure cascades account for 64–89% of service disruptions, which also spread beyond the hazard footprint in nearly 3 out of 4 events. Disruption-affected population surpasses estimates of physically affected by up to ten-fold. We demonstrate that knowledge of the effect of infrastructure network designs, population distribution, wealth, and hazard characteristics can help prioritize systemic adaptation strategies over asset-focused ones.

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.

High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm’s intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.

Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

Transportation infrastructures are generally designed to have multi-decadal service lives. Transport infrastructure design, however, is largely based on historical conditions. Yet, in the face of global warming, we are likely going to experience more intense and frequent extreme events, which may put infrastructure at severe risk. In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally. Under  2 degrees of warming in mid-century (RCP 8.5 scenario), 43.6 percent of the global transportation assets are expected to experience at least a 25 percent decrease in design return period of extreme rainfall (a 33 percent increase in exceedance probability), which may increase to 69.9 percent under  4 degrees of warming by late-21st century. To accommodate for such increases, we propose to incorporate a safety factor for climate change adaptation during the transportation infrastructure design process to ensure transportation assets will maintain their designed risk level in the future. Our results show that a safety factor of 1.2 would work sufficient for most regions of the world for quick design process calculations following the RCP4.5 path.

This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest.

Bonaire’s topographic and geographic characteristics, in combination with the island’s high dependency on economic sectors that are susceptible to the impacts of climate change, make this Caribbean island particularly vulnerable to climatic changes. In this study, biophysical and economic models are combined and complemented with stakeholder consultation to assess and quantify environmental effects and associated socio-economic impacts of climate change on Bonaire. We apply three climate scenarios of the 2021 IPCC report (SSP1-2.6, 2–4.5, and 5–8.5) and combine them with local conditions to conduct a site-specific integrated assessment. The results show that various buildings, critical infrastructure, and identified tangible cultural heritage, especially at the south of Bonaire, are at risk of climate change induced coastal inundation by 2050, even under the least severe climate projection. In addition, the overall health of coral reefs declines under the climate scenarios SSP2-4.5 and SSP5-8.5 due to sea level rise, acidification, and increasing temperatures. In the most pessimistic scenario, Bonaire could experience a reduction in dive tourist arrivals of 118,000, which can lead to an economic contraction of 174 USDm (25%) in Bonaire’s GDP. In the absence of timely planning and implementation of adaptation measures, the impacts of climate change may have serious implications for inhabitants’ lifestyles and wellbeing. These results are imperative for various stakeholders, and stress that decision-makers should focus on the development and implementation of effective and feasible adaptation strategies urgently. Moreover, future researchers confronted with data scarcity in comparable contexts can utilise the novel methodologies employed in this study.

Disruptions to ports from climate extremes can have systemic impacts on global shipping, trade and supply chains. By combining estimated climatic-related port downtime at 1,320 ports with a global model of transport flows, we pinpoint systemic risks to global maritime transport, trade and supply-chain networks. We estimate a total of US81 billion of global trade and at least US122 billion of economic activity being at-risk on average annually.

Modelling complex interactions involving climatic features, socio-economic vulnerability or responses, and long impact transmissions is associated with substantial uncertainty. Physical climate storylines are proposed as an approach to explore complex impact transmission pathways and possible alternative unfoldings of event cascades under future climate conditions. These storylines are particularly useful for climate risk assessment for complex domains, including event cascades crossing multiple disciplinary or geographical borders. For an effective role in climate risks assessments, development guidelines are needed to consistently develop and interpret the storyline event analyses. This paper elaborates on the suitability of physical climate storyline approaches involving climate event induced shocks propagating into societal impacts. It proposes a set of common elements to construct the event storylines. In addition, criteria for their application for climate risk assessment are given, referring to the need for storylines to be physically plausible, relevant for the specific context, and risk-informative. Apart from an illustrative gallery of storyline examples found in literature, three examples of varying scope and complexity are presented in detail, all involving the potential impact on European socio-economic sectors induced by remote climate change features occurring far outside the geographical domain of the European mainland. The storyline examples illustrate the application of the proposed storyline components and evaluate the suitability of the criteria defined in this paper. It thereby contributes to a rigorous design and application of event-based climate storyline approaches.

Every country relies on a well-functioning road system. However, we do not have a clear understanding yet of the vulnerability of each of these road networks to different forms of disruption. In this study, we aim to better understand how road networks are affected by different disruptive events, to identify hotspots of road network vulnerabilities, and to better target where and what type of future investments can be made to develop more resilient networks. To do so, we developed a fully open-source modelling framework to expose over 200 country road systems across the world to random, local, and targeted disruption schemes. For each country, we assessed the impact of such disruptions on intra-country travel activities and regional accessibility. The results highlight the vulnerability of road systems in mountainous and small-island countries owing to the limited availability of alternative routes. Additionally, we find that, on average, low-income countries experience a collapse of road-system services with much fewer disruptions, relative to high-income countries, due to the lack of redundancy in their systems. While the value of goods and services disrupted may be higher in wealthier countries, the results highlight that from an equity perspective, transport infrastructure investments are more desired in low-income country networks.

Critical infrastructures are more exposed than ever to natural hazards in a changing climate. To understand and manage risk, failure cascades across large, real-world infrastructure networks, and their impact on people, must be captured. Bridging established methods in both infrastructure and risk modelling communities, we develop an open-source modelling framework which integrates a network-based interdependent infrastructure system model into the globally consistent and spatially explicit natural hazard risk assessment platform CLIMADA. The model captures infrastructure damages, triggers failure cascades and estimates resulting basic service disruptions for the dependent population. It flexibly operates on large areas with publicly available hazard, exposure and vulnerability information, for any set of infrastructure networks, hazards and geographies of interest. In a validated case study for 2018’s Hurricane Michael across three US states, the model reproduced important failure dynamics among six infrastructure networks, and provided a novel spatial map where people were likely to experience disruptions in access to healthcare, loss of power and other vital services. Our generalized approach allows for a view on infrastructure risks and their social impacts also in areas where detailed information and risk assessments are traditionally scarce, informing humanitarian activities through hotspot analyses and policy frameworks alike.

Despite their economic importance, the risk that ports face from multiple natural hazards has not yet been monetised on a global scale. Here, we perform an asset-level risk analysis of global port infrastructure from multiple hazards, quantifying the risk to physical asset damages and logistics services (i.e. port-specific risk) and maritime trade flows at-risk (i.e. trade risk). We find that 86% majority of ports are exposed to more than three hazards. Globally, port-specific risk totals 7.5 USD bn per year, with 32% of the risk attributed to tropical cyclone impacts. In addition, 63.1 USD bn of trade is at-risk every year, with trade risk as a fraction of total trade being particularly high in Small Island Developing States. Our result underline that port resilience is determined by various critical factors, such as engineering standards, operational thresholds, recovery duration, that vary widely across ports, requiring tailored solutions to improve port resilience.

This study presents an event-based storyline framework to assess the influence of future climatic and socioeconomic conditions on coastal flood impacts to critical infrastructure. The framework combines well-established quantitative methods of sea level rise, coastal inundation, and critical infrastructure (CI) physical damage assessments into an integrated modelling approach. We apply our approach to re-imagine three historic events: storm Xaver, storm Xynthia , and a storm surge event along the coast of Emilia Romagna (Italy). Our results indicate that northern Germany would benefit mostly from coordinated adaptation action to reduce the flood impact, whereas the southwestern coast of France would find the highest damage reduction through asset-level ‘autonomous’ adaptation action. Our approach helps to improve the scientific understanding of how coastal flood risk are assessed and best managed, and forces a distillation of the science into an accessible narrative to support policymakers and asset owners to make progress towards more climate-resilient coastal communities.

Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI. In this study, we: (1) collect and harmonize detailed geospatial data of the world’s main CI systems into a single geospatial database; and (2) develop the Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The CISI aggregates high-resolution geospatial OpenStreetMap (OSM) data of 39 CI types that are categorized under seven overarching CI systems. The detailed geospatial data are rasterized into a harmonized and consistent dataset with a resolution of 0.10 × 0.10 and 0.25 × 0.25 degrees. The dataset can be applied to explore the current landscape of CI, identify CI hotspots, and as exposure input for large-scale risk assessments.

Ports are embedded in different networks, including the local critical infrastructure network, the regional hinterland transport network and the global maritime transport network. These networks are exposed to a variety of natural hazards, which cause disruptions that can propagate to other network components, resulting in wider supply chain losses. However, the risks of such indirect network disruptions, or systemic risks, are often not considered in risk analyses of ports. We propose a systemic risk framework for different networks interconnected through ports, and describe the state-of-the-art risk modelling approaches to quantify systemic risks. In addition, we present a port risk layering framework that can help identify how resilience against systemic risks can be improved. As climate change will likely increase the occurrence of natural hazards to ports and transport networks, efforts to enhance system-wide resilience should be considered, alongside port adaptation, to prevent exacerbation of supply chain losses in the future.

We quantify the criticality of the world’s 1300 most important ports for global supply chains by predicting the allocation of trade flows on the global maritime transport network, which we link to a global supply-chain database to evaluate the importance of ports for the economy. We find that 50% of global trade in value terms is maritime, with low-income countries and small islands being 1.5 and 2.0 times more reliant on their ports compared to the global average. The five largest ports globally handle goods that embody >1.4% of global output, while 40 ports add >10% of domestic output of the economies they serve, predominantly small islands. We identify critical cross-border infrastructure dependencies for some landlocked and island countries that rely on specific ports outside their jurisdiction. Our results pave the way for developing new strategies to enhance the resilience and sustainability of port infrastructure and maritime trade.

Critical infrastructure systems are often considered to be the backbone of modern-day society. Yet, they are both exposed and vulnerable to environmental hazards globally. Affected critical infrastructure is not just costly to repair. Its failure can quickly result in a cascading effect on households, companies, or other infrastructure systems. As such, the identification and assessment of the risks of environmental hazards to critical infrastructure require approaches that can look beyond the direct impacts. This chapter provides an overview of the current state-of-the-art in critical infrastructure risk modelling, examines the close connection between resilience and critical infrastructure, and discusses the importance of including uncertainty metrics in a risk assessment.

River flooding is a profound climate hazard in Europe and a threat to its road transport infrastructure. However, its impact on road network interruptions is mostly unexplored, while some have suggested that national road networks may experience tipping points. This study assesses the robustness of road networks of European countries and their potential for a tipping point: an abrupt and disproportionally large loss of network functionality, due to unfavourable combinations of floods. Methodologically inspired by percolation analysis, ten-thousands of flood combinations are sampled and their impacts on road network performance are assessed. The results show that Albania, Croatia, Serbia and Austria are relatively vulnerable, whereas Belgium, Estonia, Lithuania and Portugal are relatively robust. Tipping points in the sense of nationwide network fragmentation seem unlikely, but regional-scale tipping points can happen. Flood-proofing the identified weak spots could result in quick wins for national road operators.

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.

Floods have negative effects on the reliable operation of transportation systems. In China alone, floods cause an average of ∼1125 h of railway service disruptions per year. In this study, we present a simulation framework to analyse the system vulnerability and risk of the railway system to floods. First, we developed a novel methodology for generating flood events at both the national and river basin scale. Based on flood hazard maps of different return periods, independent flood events are generated using the Monte Carlo sampling method. Combined with network theory and spatial analysis methods, the resulting event set provides the basis for national- and provincial-level railway risk assessments, focusing in particular on train performance loss. Applying this framework to the Chinese railway system, we show that the system vulnerability of the Chinese railway system to floods is highly heterogeneous as a result of spatial variations in the railway topology and traffic flows. Flood events in the Yangtze River basin show the largest impact on the national railway system, with approximately 40 % of the national daily trains being affected by a 100-year flood event in that basin. At the national level, the average percentage of daily affected trains and passengers for the national system is approximately 2.7 % of the total daily number of trips and passengers. The event-based approach presented in this study shows how we can identify critical hotspots within a complex network, taking the first steps in developing climate-resilient infrastructure.

Mitigating the disaster risk of transportation infrastructure networks along the Belt and Road is crucial to realizing the area’s high trade potential in the future. This study assessed the exposure and risk of existing and planned railway assets to river flooding and earthquakes. We found that about 9.3% of these railway assets are exposed to a one in 100 year flood event, and 22.3% are exposed to a one in 475 year earthquake event. The combined flood and earthquake risk of physical damage to railway assets, expressed by expected annual damage (EAD), is estimated at USD 1438 (between 966 and 2026) million. Floods contribute the majority of the risk (96%). China has the highest EAD for both floods and earthquakes (between USD 240 and 525 million in total). Laos and Cambodia are the countries with the highest EAD per km from flooding (USD 66,125–112,154 and USD 31,954–56,844 per km, respectively), while Italy and Myanmar have the highest EAD per km from earthquakes (USD 1000–3057 and USD 893–3019 per km, respectively). For the newly built and planned projects along the Belt and Road, the EAD is estimated at USD 271 (between 205 and 357) million. The China–Indochina Peninsula Economic Corridor and China–Pakistan Economic Corridor have the highest absolute EAD and EAD per km, with EADs reaching USD 95 and USD 67 million, and USD 18 and USD 17 thousand per km, on average, respectively. For railway segments with high risks, we found that if the required adaptation cost within 20 years to realize a 10% increase of the railway quality is below 8.4% of the replacement cost, the benefits are positive.

The implementation of large-scale containment measures by governments to contain the spread of the COVID-19 virus has resulted in large impacts to the global economy. Here, we derive a new high-frequency indicator of economic activity using empirical vessel tracking data, and use it to estimate the global maritime trade losses during the first eight months of the pandemic. We go on to use this high-frequency dataset to infer the effect of individual non-pharmaceutical interventions on maritime exports, which we use as a proxy of economic activity. Our results show widespread port-level trade losses, with the largest absolute losses found for ports in China, the Middle-East and Western Europe, associated with the collapse of specific supply-chains (e.g. oil, vehicle manufacturing). In total, we estimate that global maritime trade reduced by -7.0% to -9.6% during the first eight months of 2020, which is equal to around 206–286 million tonnes in volume losses and up to 225–412 billion USD in value losses. We find large sectoral and geographical disparities in impacts. Manufacturing sectors are hit hardest, with losses up to 11.8%, whilst some small islands developing states and low-income economies suffered the largest relative trade losses. Moreover, we find a clear negative impact of COVID-19 related school and public transport closures on country-wide exports. Overall, we show how real-time indicators of economic activity can inform policy-makers about the impacts of individual policies on the economy, and can support economic recovery efforts by allocating funds to the hardest hit economies and sectors.

River floods pose a significant threat to road transport infrastructure in Europe. This study presents a high-resolution object-based continental-scale assessment of direct flood risk of the European road network for the present climate, using high-resolution exposure data from OpenStreetMap. A new set of road-specific damage functions is developed. The expected annual direct damage from large river floods to road infrastructure in Europe is EUR 230 million per year. Compared to grid-based approaches, the object-based approach is more precise and provides more action perspective for road owners because it calculates damage directly for individual road segments while accounting for segment-specific attributes. This enables the identification of European hotspots, such as roads in the Alps and along the Sava River. A first comparison to a reference case shows that the new object-based method computes realistic damage estimates, paving the way for targeted risk reduction strategies.

There is increasing concern among financial regulators that changes in the distribution and frequency of extreme weather events induced by climate change could pose a threat to global financial stability. We assess this risk, for the case of floods, by developing a simple model of the propagation of climate-induced shocks through financial networks. We show that the magnitude of global risks is determined by the interplay between the exposure of countries to climate-related natural hazards and their financial leverage. Climate change induces a shift in the distribution of impacts towards high-income countries and thus larger amplification of impacts as the financial sectors of high-income countries are more leveraged. Conversely, high-income countries are more exposed to financial shocks. In high-end climate scenarios, this could lead to the emergence of systemic risk as total impacts become commensurate with the capital of the banking sectors of countries that are hubs of the global financial network. Adaptation policy, or the lack thereof, appears to be one of the key risk drivers as it determines the future exposure of high-income countries. This implies in particular that the avoided costs in terms of financial stability should be weighted in as benefits of adaptation policy.

Guan and colleagues used a model of the global economy to quantify the impacts of the coronavirus disease 2019 (COVID-19) pandemic under different scenarios of pandemic spreading and lockdown stringencies. Using real-time ship tracking data from before and during the pandemic, we show how the onset of disruption to trade was slower than modelled by Guan et al. Whereas supply chains to some countries with strong trading links to China (for example, Australia and Malaysia) have been affected in ways that resemble the results of their model (although to a lower extent than predicted), others with equally strong links (for example, Vietnam) have managed to increase their trade, contrary to the model’s predictions. Understanding the propagation of the economic shock from COVID-19, which can be informed by real-time observations as well as model predictions, will help to better allocate international aid and economic stimuli.

Ports are located in low-lying coastal and riverine areas making them prone to the physical impacts of natural disasters. The consequential disruptions can potentially propagate through supply chains, resulting in widespread economic losses. Previous studies to quantify the risks of port disruptions have adopted various modelling assumptions about the resilience of individual ports and marine network logistics. However, limited empirical evidence is available to validate these modelling assumptions or to provide deeper understanding of the ways in which operations are adapted during and after disruptions. Here, we use vessel tracking data to analyse past port disruptions due to natural disasters, evaluating 141 incidences of disruptions across 74 ports and 27 disasters. Results show a median disruption duration of six days with a 95th percentile of 22.2 days. All analysed events show multiple ports being affected simultaneously, challenging some of the studies that only focus on single port disruptions. Moreover, we find that the duration of the disruption scales with the severity of the event, with an increment of 1.0 m storm surge or 10 m/s wind speed associated with a two day increase in disruption duration. In contrast to commonplace assumptions in model studies, substitution between ports is rarely observed during short-term disruptions. On the other hand, production recapture happens in practice in many cases of port disruptions. In short, empirical vessel tracking data provides valuable insights for future modelling studies in order to better approximate the extent of the disruption and the potential resilience of the port and maritime network.

Quantified flood risk assessments focus on asset losses, neglecting longer-term impacts to household welfare via income and consumption losses. The extent of welfare losses depends upon resilience – the ability to anticipate, resist, cope, recover and learn from a shock. Here, we use a novel welfare loss modelling framework and perform a high-resolution spatial analysis in coastal Bangladesh to quantify welfare losses from a tropical cyclone under present and future climatic and socio-economic conditions. We further test various adaptation options that are intended to enhance resilience. Results show that poor households experience, on average, 7% of the asset losses, but 42% of the welfare losses. Combining dike heightening, post-disaster support and stronger housing can reduce welfare losses by up to 70%, and foster sustainable development by benefitting the poor, increasing resilience and demonstrating robustness under socio-economic and climatic uncertainties. Thus, a welfare-orientated perspective helps to identify adaptation options that enhance resilience and leave no-one behind.

Extreme wind events are among the costliest natural disasters in Europe, causing severe damages every year. Despite the significant impact, damages related to windstorms are an understudied topic in academia. For damage estimates, the community mostly relies on post-disaster insurance data, which is often not publicly available. Few studies offer more generic tools, but again these are often based on non-disclosed insurance data. To offer a generic, high-resolution, reproducible, and publicly accessible tool, this study presents a wind damage model that is built around publicly available hazard, exposure, and vulnerability data. We apply the model to assess building damages related to extratropical storms in Europe, but the methodology is applicable globally, given data availability, and to other hazards for which similar risk frameworks can be applied. The results show that for Europe, coastal regions are affected the most, with the United Kingdom, Ireland, Germany, France, the Netherlands, and Denmark as most affected countries. We find that the modelled damage estimates are in line with reported damages for a series of historical storms. The model is distributed as an open-source model to offer a transparent and useable windstorm damage model to a broad audience.

Flood risk is expected to increase in coastal cities, particularly in Asian megacities such as Shanghai. This paper presents an integrated modeling framework to simulate changes in the flood risk in Shanghai and provide a cost-benefit analysis of multiple adaptation strategies used to reduce risk. The results show that the potential flood risk will increase dramatically as a result of sea level rise, land subsidence, and socioeconomic development. By 2100, the expected annual damage could reach 0.8% (uncertainty range: 0.4%–1.4%) of local GDP under an optimistic emission scenario (RCP4.5), compared to the current value of 0.03%. All of the adaptation strategies can effectively reduce the flood risk under the current conditions and those in 2050. In contrast to the ‘hard’ flood protection strategies (i.e., storm-surge barriers and floodwalls), the ‘soft’ strategies (i.e., building codes and nature-based measures) cannot substantially reduce the flood risk in 2100. However, the soft strategies can play a critical role in reducing the residual risk resulting from the hard strategies. A ‘hybrid’ strategy combining a storm-surge barrier, wet-proofing, and coastal wetland development outperforms both hard and soft strategies in terms of low residual risk and high benefit/cost ratio. Additionally, the hybrid strategy can also enable a larger reduction in casualties. These findings imply that managing flood risk is more than the use of single adaptation measures. The methodology developed in this paper can enlighten Shanghai and other coastal cities on an economically and socially feasible adaptation strategy in an uncertain future.

Earthquakes pose a great risk to railway systems and services around the world. In China alone, earthquakes caused 88 rail service disruptions between 2012 and 2019. Here, we present a first-of-its-kind methodology to analyze the seismic risk of a railway system using an empirically derived train service fragility curve. We demonstrate our methodology using the Chinese railway system. In doing so, we generate a set of stochastic earthquake scenarios for China based on a national-scale seismicity model. Using disruption records, we construct an empirically grounded fragility curve that relates the failure probability of train services to peak ground acceleration. By combining the simulated earthquakes, the fragility curve, and empirical train flow data from 2016, we quantitatively assess the seismic impact and the risk faced by the Chinese railway system. The maximum train trip loss could reach 2400 trips in response to a single seismic event, accounting for 34% of the national daily train trips. Due to the spatially uneven daily train flow and seismicity distribution, the seismic impact on the railway system in different seismic zones is highly heterogeneous and does not always increase when the hazard intensity increases. More specifically, the results show that the railway lines located in the Qinghai-Tibet and Xinjiang seismic zones exhibit the highest risk. The generated impact curves and the risk map provide a basis for railway planning and risk management decisions.

This chapter provides an overview of several multiregional modelling approaches used for disaster impact analysis. The chapter specifically focuses on the multiregional supply-use model, the dynamic multiregional inoperability input-output model, the multiregional impact assessment model and the non-linear programming model. Whereas the first two approaches have been applied widely over the last years, the latter two are recently developed methods which aim to improve the estimation of a disruption in the economic system by, amongst others, allowing for a supply shock and spatial substitution effects. Our outcomes show significantly distinct results for the demand-driven multiregional supply-use model and the dynamic multiregional inoperability input-output model on the one hand, and for the non-linear programming model and the multiregional impact assessment model, on the other hand. Whereas for the former only negative impacts in all German regions and foreign countries are observed, the latter also shows positive impacts in several only indirectly impacted regions in addition to different negative impacts.

Failure of critical national infrastructures can cause disruptions with widespread economic impacts. To analyze these economic impacts, we present an integrated modeling framework that combines: (1) geospatial information on infrastructure assets/networks and the natural hazards to which they are exposed; (2) geospatial modeling of the reliance of businesses upon infrastructure services, in order to quantify disruption to businesses locations and economic activities in the event of infrastructure failures; and (3) multiregional supply-use economic modeling to analyze wider economic impacts of disruptions to businesses. The methodology is exemplified through a case study for the United Kingdom. The study uses geospatial information on the location of electricity infrastructure assets and local industrial areas, and employs a multiregional supply-use model of the UK economy that traces the impacts of floods of different return intervals across 37 subnational regions of the UK. The results show up to a 300% increase in total economic losses when power outages are included in the risk assessment, compared to analysis that just includes the economic impacts of business interruption due to flooded business premises. This increase indicates that risk studies that do not include failure of critical infrastructures may be underestimating the total losses.

Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that  27% of all global road and railway assets are exposed to at least one hazard and  7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which  73% is caused by surface and river flooding. Global EAD are small relative to global GDP ( 0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on  60% of roads exposed to a 1/100 year flood event.

Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution—individual building-based—building asset value map because of the high spatial heterogeneity of flood hazards.

Floods are one of the most devastating disasters and their intensity and severity is expected to increase in the future. New research shows how regional floods can cause global impacts through propagation within the global trade and supply network.

One of the most critical impacts of sea level rise is that flooding suffered by ever larger settlements in tropical deltas will increase. Here we look at Ho Chi Minh City, Vietnam, and quantify the threats that coastal floods pose to safety and to the economy. For this, we produce flood maps through hydrodynamic modeling and, by combining these with data sets of exposure and vulnerability, we estimate two indicators of risk: the damage to assets and the number of potential casualties. We simulate current and future (2050 and 2100) flood risk using IPCC scenarios of sea level rise and socioeconomic change. We find that annual damage may grow by more than 1 order of magnitude, and potential casualties may grow 5–20-fold until the end of the century, in the absence of adaptation. Impacts depend strongly on the climate and socioeconomic scenarios considered. Next, we simulate the implementation of adaptation measures and calculate their effectiveness in reducing impacts. We find that a ring dike would protect the inner city but increase risk in more rural districts, whereas elevating areas at risk and dryproofing buildings will reduce impacts to the city as a whole. Most measures perform well from an economic standpoint. Combinations of measures seem to be the optimal solution and may address potential equity conflicts. Based on our results, we design possible adaptation pathways for Ho Chi Minh City for the coming decades; these can inform policy-making and strategic thinking.

Detailed estimates of economy-wide disaster losses provide important inputs for disaster risk management. The most common models used to estimate losses are input–output (IO) and computable general equilibrium (CGE) models. A key strength of these models is their ability to capture the ripple effects, whereby the impacts of a disaster are transmitted to regions and sectors that are not directly affected by the event. One important transmission channel is household migration. Changes in the spatial distribution of people are likely to have substantial impacts on local labour and housing markets. In this paper, we argue that IO and CGE models suffer from limitations in representing household migration under disaster risk. We suggest combining IO and CGE models with agent-based models to improve the representation of migration in disaster impact analysis.

A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input–output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of them in combination with noneconomic methods. While both IO and CGE models are widely used, they are mainly compared on theoretical grounds. Few studies have compared disaster impacts of different model types in a systematic way and for the same geographical area, using similar input data. Such a comparison is valuable from both a scientific and policy perspective as the magnitude and the spatial distribution of the estimated losses are born likely to vary with the chosen modelling approach (IO, CGE, or hybrid). Hence, regional disaster impact loss estimates resulting from a range of models facilitate better decisions and policy making. Therefore, this study analyses the economic consequences for a specific case study, using three regional disaster impact models: two hybrid IO models and a CGE model. The case study concerns two flood scenarios in the Po River basin in Italy. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of 7 between the three models, depending on the type of recovery path. Total economic impact, comprising all Italian regions, is negative in all models though.

This paper presents a recursive dynamic multiregional supply-use model, combining linear programming and input–output (I–O) modeling to assess the economy-wide consequences of a natural disaster on a pan-European scale. It is a supply-use model which considers production technologies and allows for supply side constraints. The model has been illustrated for three floods in Rotterdam, The Netherlands. Results show that most of the neighboring regions gain from the flood due to increased demand for reconstruction and production capacity constraints in the affected region. Regions located further away or neighboring regions without a direct export link to the affected region mostly suffered small losses. These losses are due to the costs of increased inefficiencies in the production process that have to be paid for by all (indirectly) consuming regions. In the end, the floods cause regionally differentiated welfare effects.

Flood damage assessments are often based on stage-damage curve (SDC) models that estimate economic damage as a function of flood characteristics (typically flood depths) and land use. SDCs are developed through a site-specific analysis, but are rarely adjusted to economic circumstances in areas to which they are applied. In Italy, assessments confide in SDC models developed elsewhere, even if empirical damage reports are collected after every major flood event. In this paper, we have tested, adapted and extended an up-to-date SDC model using flood records from Northern Italy. The model calibration is underpinned by empirical data from compensation records. Our analysis takes into account both damage to physical assets and losses due to foregone production, the latter being measured amidst the spatially distributed gross added value.

Flood risk assessments provide inputs for the evaluation of flood risk management (FRM) strategies. Traditionally, such risk assessments provide estimates of loss of life and economic damage. However, the effect of policy measures aimed at reducing risk also depends on the capacity of households to adapt and respond to floods, which in turn largely depends on their social vulnerability. This study shows how a joint assessment of hazard, exposure and social vulnerability provides valuable information for the evaluation of FRM strategies. The adopted methodology uses data on hazard and exposure combined with a social vulnerability index. The relevance of this state-of-the-art approach taken is exemplified in a case-study of Rotterdam, the Netherlands. The results show that not only a substantial share of the population can be defined as socially vulnerable, but also that the population is very heterogeneous, which is often ignored in traditional flood risk management studies. It is concluded that FRM measures, such as individual mitigation, evacuation or flood insurance coverage should not be applied homogenously across large areas, but instead should be tailored to local characteristics based on the socioeconomic characteristics of individual households and neighborhoods.

In this article, we propose an integrated direct and indirect flood risk model for small- and large-scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb-Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input-output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high- and low-probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low-probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high-probability events are qualitatively different from low-probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high-probability and low-probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.

In this paper, we make first steps with applying a methodology consisting of a hybrid interregional input-output model to assess the economic consequences of large-scale floods for the European economy. The proposed methodology consists of multiple steps. First, a direct loss assessment is conducted in several flood-prone regions, based on simulated floods. Second, the direct losses in capital and labor are translated into the loss in production per sector. Third, the recovery of this production shock is modeled using a hybrid interregional input-output model, combining non-linear programming and input-output modelling. Consequently, when knowing how much production is lost (or gained) in each region, the economic consequences can be assessed. Finally, the model outcome is loss estimation expressed in terms of expected annual damage. To assess these consequences, interregional supply and use tables are used, consisting of 256 different European NUTS2 regions. This data makes it possible to model the indirect losses for both the affected region and the rest of Europe in detail. Results show that regions outside the affected area can have either benefits or losses, depending on the economic relation with the affected region. Consequently, the overall consequences for the European Union are found to be positive for smallscale floods and negative for large-scale floods. This study shows the large potential of interregional modelling and the added value of combining different economic loss estimation approaches into an integrative framework.

The effectiveness of disaster risk management and financing mechanisms depends on an accurate assessment of current and future hazard exposure. The increasing availability of detailed data offers policy makers and the insurance sector new opportunities to understand trends in risk, and to make informed decisions on ways to deal with these trends. In this paper we show how comprehensive property level information can be used for the assessment of exposure to flooding on a national scale, and how this information provides valuable input to discussions on possible risk financing practices. The case study used is the Netherlands, which is one of the countries most exposed to flooding globally, and which is currently undergoing a debate on strategies for the compensation of potential losses. Our results show that flood exposure has increased rapidly between 1960 and 2012, and that the growth of the building stock and its economic value in flood-prone areas has been higher than in non-flood-prone areas. We also find that property values in flood-prone areas are lower than those in non-flood-prone areas. We argue that the increase in the share of economic value located in potential flood-prone areas can have a negative effect on the feasibility of private insurance schemes in the Netherlands. The methodologies and results presented in this study are relevant for many regions around the world where the effects of rising flood exposure create a challenge for risk financing.

Flood risk in coastal zones is projected to increase due to climate change and socioeconomic changes. Over the last decades, population growth, increases in wealth, and urban expansion have been found to be the main causes for increasing losses in coastal areas. These changes may, however, be offset by appropriate management measures. The main goal of this study is to assess future changes in flood risk and the effectiveness of flood risk adaptation measures for the coastal zone in Flanders, Belgium. In order to achieve this, we set up a modeling framework to assess the future flood risk of the Belgian coast including climatic and socioeconomic projections, and used this model to assess the effectiveness of two spatial adaptation measures: compartmentalization and land-use zoning. In this modeling framework, a land-use model, an inundation model, and a damage model were combined to calculate expected annual damage. Results show that without adaptation measures, future flood risk would increase substantially. Compartmentalization would result in an average flood risk reduction of approximately 50 % for both the baseline situation and future scenarios. Land-use zoning would result in smaller flood risk reductions, averaging between 6 and 10 %. Except for the most extreme climate change scenario, compartmentalization would successfully offset the combined adverse effects of socioeconomic growth and climate change on flood risk for this case study. For both compartmentalization and zoning, large differences have been found in their effectiveness at the local level, implying that the choice of adaptation measures should be tailored to local characteristics.