A novel and comprehensive approach for understanding the social impacts of flooding: assessing social flood vulnerability and social flood risk in Denmark

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However, the importance of understanding the intangible social impacts of flooding are increasingly being acknowledged. Social vulnerability indices have been constructed in diverse geographical contexts to understand differential susceptibility of different social groups to flood hazards. However, integrated assessments of social vulnerability, exposure, and hazard information are lacking. Here, we construct a national social vulnerability index (SVI) for Denmark and combine this with direct and indirect social exposure data and coastal flood hazard data to construct a national social flood risk index (SFRI). Results show the spatial distribution of social flood vulnerability and social flood risk in Denmark. Our work introduces a comprehensive flood risk modelling approach that explicitly considers the social impacts of flooding in all model components. Such an approach can facilitate a shift towards the implementation of more holistic and inclusive flood risk management and climate change adaptation planning approaches that are usable within the context of existing risk management frameworks such as the EU Floods Directive. We hope that introducing a comprehensive, socially-specific approach to flood risk assessment can help to mainstream social wellbeing, resilience, and justice as central considerations in decision making on flood risk management. flood risk social vulnerability vulnerability assessment social risk risk assessment flood risk management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Social vulnerability has emerged as a critical component of flood risk assessment and management in recent years due to the recognition that the inherent capabilities of different individuals and groups influence vulnerability in flood situations. Many studies find that vulnerability to flooding is influenced by individual characteristics such as age, health, and economic situation as well as community dynamics and social support systems (Balica et al., 2012 ; Dwyer et al., 2004 ; Foudi & Oses-Eraso, 2014; Sayers et al., 2018 ). The growing interest in including social vulnerability in flood risk assessment is a response to the economic and technocratic biases of traditional risk assessment approaches. Many flood risk management strategies have traditionally taken point of departure in risk assessment frameworks that prioritize the protection of valuable economic assets without acknowledging the differential impact of flooding on individual wellbeing (Vojinovic et al., 2012; Jeffers, 2013 ). Including social factors in flood vulnerability and risk assessment helps to promote holistic flood risk management that ensures social resilience and strives for socially just outcomes (Sayers et al., 2018 ). While a number of indices for measuring social flood vulnerability exist (Armaș & Gavriș, 2013 ; Balica et al., 2012 ; Bouaakkaz et al., 2023 ; Cutter et al., 2003; de Loyola Hummell et al., 2016 ; Guillard-Gonçalves et al., 2015 ; Holand et al., 2011 ; Sayers et al., 2018 ), these models are highly context dependent and are difficult to generalize to new geographical contexts. Furthermore, models that combine vulnerability indices with socially relevant exposure categories and hazard information are less common and often integrate exposure or hazard information in an incomplete or ad-hoc manner rather than comprehensively assessing socially specific flood risk (Kazmierczak, 2015 ; Koks et al., 2015 ; Lujala et al. 2014 ; Mason et al., 2021 ; Tascón-González et al., 2020). Understanding both social vulnerability, i.e. individual and community susceptibility to potential flooding and social risk, i.e. the probabilistic, spatial interaction of flood hazards, social exposure, and social vulnerability is critical to facilitate holistic decision making on flood risk management and critically engage with questions of equity and justice. There is increasing focus on implementing adaptation solutions that target social resilience, wellbeing, and equity (Aven, 2011 ; Joakim et al., 2015 ; Hegger et al., 2016 ; Jurgilevich, 2021 ); however, without the tools to assess social vulnerability and social risk, the impacts of these measures cannot be evaluated. With this in mind, we construct a social flood vulnerability index (SVI) and a social flood risk index (SFRI) for Denmark. Our study is the first that we are aware of to integrate social vulnerability into flood risk assessment in Denmark. Furthermore, we expand on existing methods for social vulnerability and social risk assessment by introducing a method for mapping of social flood exposure, which considers both direct and indirect social flood exposure categories. These exposure categories are linked with vulnerability and hazard information to create a model of risk that explicitly focuses on understanding the social impacts of flooding. Results contribute to an enhanced understanding of social vulnerability in a Nordic context and provide a methodological push towards flood risk modelling approaches that explicitly consider the social impacts of flooding in all model components. Such an approach can facilitate a shift towards the implementation of more inclusive flood risk management approaches that are usable within the context of existing risk management frameworks such as the EU Floods Directive. A more holistic approach to assessing risk is critical to be able to understand the full range of impacts from flood events and implement effective and fair flood risk management and climate change adaptation. We hope that our work can provide practitioners with a means to incorporate the intangible impacts of flooding into risk management decision making and concretely assess the social impacts of potential interventions that aim to improve social resilience. 2. Approaches to assessing social vulnerability and social risk Social vulnerability studies have emerged in the past few decades in flood risk management and environmental hazards research. Social vulnerability refers to inherent individual or community characteristics that combine to increase susceptibility of certain individuals or groups in a specific hazard situation (Balica et al, 2012 ; FEMA, 2021 ; Foudi & Oses-Eraso, 2014; Kyne & Aldrich, 2020 ; Sayers et al., 2018 ). Social vulnerability is a multidimensional concept that cannot be measured in absolute terms and is therefore often expressed in relative terms through index-based analyses comprised of multiple socio-economic indicators. Social vulnerability index construction became popular in environmental research in the early 2000s following the publication of a Social Vulnerability Index (SoVI) measuring relative social vulnerability to environmental hazards in the US on a national scale (Cutter et al., 2003). Cutter et al. (2003) quantify social vulnerability by compiling a large set of socio-economic variables thought to contribute to environmental vulnerability and employing factor analysis, specifically principal components analysis (PCA), to identify key indicators of vulnerability and subsequently build a composite index of social vulnerability. Many studies have since adopted or been inspired by the SoVI approach to assess social vulnerability to different environmental hazards such as floods, earthquakes and avalanches in diverse geographical contexts including Brazil (de Loyola Hummell et al., 2016 ), Morocco (Bouaakkaz et al., 2023 ), Norway (Holand et al., 2011 ), Portugal (Guillard-Gonçalves et al., 2015 ), and Romania (Armaș & Gavriș, 2013 ) to name a few. Despite the widespread application of the SoVI, some studies have criticized the SoVI approach and developed alternative methods for assessing social vulnerability. For example, some studies have questioned the validity of relying on statistical techniques such as PCA to determine indicator weights when constructing social vulnerability indices and suggest that it may instead be more accurate to include all available data in index construction or define indicators and associated weights based on expert or local knowledge in the case study in question (Kienberger et al., 2009; Rufat et al., 2019 ; Speilman et al., 2020). Rufat et al. ( 2019 ) tested this theory and found that expert-determined indicator identification and weighting was in fact more accurate in explaining the outcomes of Hurricane Sandy than other statistical methods. This point to the importance of paying close attention to local context when analyzing social vulnerability. Additionally, the indicators included in the original SoVI may not fully capture all dimensions of social vulnerability. For example, the role of social resilience in defining vulnerability has often been neglected in SoVI-based analyses despite consensus that resilient communities are less vulnerable to the impacts of natural hazards (Breil et al., 2018 ; Kyne & Aldrich, 2020 ; Lo et al., 2015 ; Wickes et al., 2015 ). In response to this gap, some studies have begun to consider social resilience by integrating data on factors such as social connectedness and local knowledge in their analyses of vulnerability (Mason et al., 2021 ; Sayers et al., 2018 ). Methodological limitations also exist when scaling SoVI results from a national to regional or local scale for use in decision making. As SoVI produces a relative ranking of vulnerability, it is only valid at the analysis scale at which it is performed. In other words, it would be unwise to make local planning decisions based on a national analysis that ranks the social vulnerability of each neighborhood, because local vulnerability rankings will be influenced by vulnerability scores of neighborhoods across the country (Speilman et al., 2020). It is therefore critical to pay close attention to the appropriate scale at which index-based vulnerability analyses such as SoVI can be used. Despite these challenges, social vulnerability indices have been widely recognized as a useful analysis and planning tool in the context of flood risk management research. However, a very limited number of studies combine social vulnerability information with hazard or exposure data to form complete analyses of socially specific flood risk. Understanding how social vulnerability interacts with exposure and hazard data is critical to inform a holistic, socially sensitive approach to flood risk management planning and policy making. Several studies attempt to integrate exposure information by including exposed people as a variable within social vulnerability indices in the form of a floodplain population or population density estimate (Balica et al., 2012 ; Koks et al., 2015 ; Sayers et al., 2018 ; Tascón-González et al. 2020). This approach does not stay true to the definition of social flood vulnerability as a function of inherent individual or community traits that increase susceptibility to flooding (Balica et al, 2012 ; Foudi & Oses-Eraso, 2014; IPCC, 2022 ; Sayers et al., 2018 ). Therefore, approaches that integrate exposure data within analyses of vulnerability may confuse sensitivity (i.e. social vulnerability) with location (i.e. exposure, or what is sometimes referred to as physical vulnerability). Retaining separate measures of social vulnerability and exposure is critical in the context of data-assisted decision making, as different flood risk management solutions will likely be necessary in areas with high concentrations of socially marginalized residents compared to areas with high concentrations of floodplain populations. A limited number of studies recognize this need to combine social vulnerability data with socially relevant exposure and hazard information. Lujala et al. ( 2014 ) construct both a social vulnerability index and a physical exposure index and combine these to create a single composite index that offers information on the spatial distribution of both vulnerability and exposure in the case of Verdal, Norway. Koks et al. ( 2015 ) take a similar approach by combining a social vulnerability index with data on exposure and hazard in Rotterdam. However, this analysis is not spatial and instead offers results in the form of population statistics. Tascón-González et al. (2020) and Kazmierczak ( 2015 ) both acknowledge the importance of measuring exposure of socially relevant infrastructure such as healthcare facilities, community centers, and public transport. Mason et al. ( 2021 ) similarly consider indirect exposure by measuring exposure of services that are especially important for vulnerable populations, such as day cares, rest homes, medical centers, and community buildings. These critical points are presented spatially in addition to a social vulnerability index to better support decision making (Mason et al., 2021 ). While these approaches to combining social vulnerability data with exposure and hazard information represent important advances in the literature on holistic flood risk management, comprehensive approaches to combining social vulnerability, social exposure, and hazard information to derive social flood risk measures are still lacking. 3. Conceptual and methodological framework 3.1 Conceptual framework We understand social flood risk according to the commonly applied H.E.V. risk assessment framework wherein risk is defined as a function of hazard, exposure, and vulnerability (IPCC, 2022 ). As the scope of this analysis is limited to social flood risk, we define risk as a situation where potential flooding, exposed people, and social vulnerability factors combine to create risk (see Fig. 1 ). In this study, our application of social flood risk in a Danish context is limited to consideration of coastal flood hazards. To calculate social risk, we utilize a probabilistic event set composed of nine different return period events (10 year, 20 year, 50 year, 100 year, 200 year, 500 year, 1,000 year, 5,000 year, and 10,000 year return periods) for coastal flooding. These events are based on current climate conditions. Exposure is understood as flood dependent and two exposure categories are considered: direct exposure (flooded people) and indirect exposure (critical service infrastructure disruption) (see section 3.3 for further explanation). Therefore, the model captures both direct wellbeing loss due to flooding as well as indirect wellbeing loss due to flood-related critical service disruptions. The extent of individual wellbeing loss is determined by social vulnerability factors, with the assumption that increased vulnerability corresponds with a higher degree of wellbeing loss in the event of direct or indirect flood exposure. Social vulnerability is flood independent, meaning that vulnerability is constant, i.e. it exists without flooding, but is only ‘triggered’ as a component of social flood risk in areas that are exposed to flood events. 3.2 Constructing the social flood vulnerability index In order to carry out a national screening of social flood risk in Denmark, it was first necessary to develop a nationally relevant social vulnerability index. The following sections describe the process of social vulnerability indicator selection, indicator weighting, and calculation of the social vulnerability index. 3.2.1 Social vulnerability indicator selection Existing literature on social vulnerability often defines vulnerability as a function of susceptibility and resilience. Susceptibility refers to how inherent individual characteristics increase peoples’ vulnerability to flooding. For example, individual susceptibility to flood event is impacted by characteristics such as age, education, and economic situation (Balica et al, 2012 ; Foudi & Oses-Eraso, 2014; Sayers et al., 2018 ). On the other hand, resilience is understood as an important factor for decreasing vulnerability, as the strength of a community influences residents’ ability to support each other and take collective action in a disaster situation (FEMA, 2021 ; Kyne & Aldrich, 2020 ). Our construction of a Danish social vulnerability model is therefore based on indicators of both susceptibility and resilience. Specific susceptibility and resilience indicators are defined based on the selection criteria listed in Table 1 . Table 1 Indicator selection criteria. Adapted from Mason et al. ( 2021 ). Selection criteria Justification Data availability Data must be available in a Danish context Appropriate spatial scale Data should be available at a local level Temporal relevance Data should be up to date and collected and reported frequently Methodologically sound measurement Data source should be reliable, accurate, and representative of the population Scientifically valid The importance of indicators should be demonstrated with scientifically robust evidence Geographical relevance Indicators should be relevant in a Danish context Based on these selection criteria, the indicators shown in Table 2 were selected for inclusion. Indicators were selected to measure the two dimensions of social vulnerability: individual susceptibility and community resilience. Indicators of susceptibility measure individual vulnerability and are therefore measured on a personal, household, or family level depending on the scale of available data. Resilience indicators measure community coping capacity and are therefore measured on a neighborhood scale. Susceptibility indicators have a negative impact on vulnerability (i.e. they increase vulnerability) while resilience indicators have a positive impact on vulnerability (i.e. they decrease vulnerability). Table 2 Social vulnerability indicators Indicators Sub-indicators (Level 1) Sub-indicators (Level 2) Definition Influence on vulnerability Age Young or elderly % of people under 5 years or over 67 years Negative (susceptibility) Health Recipients of nursing or care % of households receiving nursing or care Negative (susceptibility) Early pensioners % of people receiving early pension Negative (susceptibility) Socio-economic situation Low income % of households in the lowest 10% of the national income distribution Negative (susceptibility) Low social class % of households belonging to the lowest social class Negative (susceptibility) Single parent households % of single parent households Negative (susceptibility) Ability to acquire and understand information Recent arrivals to Denmark % of people who moved to Denmark in the last 5 years Negative (susceptibility) Non-western immigrants % of non-western immigrants Negative (susceptibility) Neighborhood resilience Bonding social capital Income equality Weighted difference of income levels Positive (resilience) * Educational equality Weighted difference of educational attainment Positive (resilience) * Nationality similarity Nationality fractionalization Positive (resilience) * Bridging social capital Community linkage % of families with at least one child under 18 years Positive (resilience) * Linking social capital Political linkage % voter turnout in municipal elections Positive (resilience) * *Note: Although indicators of neighborhood resilience have a positive influence on vulnerability, we measure the inverse of each indicator, meaning that we measure a negative influence on vulnerability. As seen in Table 2 , age, health, socio-economic situation, and ability to acquire and understand information are included as indicators of susceptibility while neighborhood resilience is included as an indicator of resilience. The following sections detail the reasons for including each of these indicators. Age Elderly people are more likely to suffer from pre-existing health conditions that may be exacerbated by a flood event and are prone to harm during a flood event due to mobility constraints and heightened sensitivity to cold, damp environments (Holand et al., 2011 ; Koks et al., 2015 ; Lowe et al., 2013 ; Tapsell et al., 2002 ; Vardoulakis & Heaviside, 2012 ). Flood events may also have negative psychological impacts on elderly people and could exacerbate feelings of social isolation (Breil et al., 2018 ; Holand et al., 2011 ; Lowe et al., 2013 ). Young children are also highly vulnerable to flooding because they do not have the cognitive or motor skills to seek help and may have difficulty expressing symptoms to caregivers (Koks et al., 2015 ). Young children also have weaker immune systems and are more prone to anxiety as a result of a flood event, meaning that they are more likely to contract diseases or experience mental or behavioral issues because of a flood event (Breil et al., 2018 ; Mort et al., 2016 ; WHO, 2013 ). Health People with long-term physical health conditions may find flood preparation and evacuation challenging and may be dependent on others for care or rely on medication that is difficult to access during a flood event (Rufat et al., 2015 ; WHO, 2013 ). People suffering from mental health conditions may also be vulnerable because cognitive impairment can lead to a skewed perception of risk and can make it difficult to trust authority figures and follow orders during a flood event (Rufat et al., 2015 ; WHO, 2013 ). Additionally, people that suffer from pre-existing health conditions have been found to have higher rates of morbidity and mortality following flood events as well as higher levels of PTSD, anxiety, and depression post flood (Rufat et al., 2015 ; Tapsell et al., 2002 ). Socio-economic situation People with financial constraints or low socio-economic status are disproportionately impacted by flooding (Rufat et al., 2015 ). Financial constraints make it difficult to prepare for flood events and recover post flood (Breil et al., 2018 ). Koks et al. ( 2015 ) find that a households’ socioeconomic status is a major determinant of social flood vulnerability. Households with lower incomes will be more reliant on publicly provided emergency services as they have fewer resources to prepare for and recover from disaster. In general, lower social classes are highly reliant on social services and often require additional assistance with evacuation and recovery in the case of disaster (Bartram et al., 2021; Holand et al., 2011 ; Koks et al., 2015 ). In addition to households with low income or low social class, single parent households are often financially stressed as they only have one potential wage earner and dependent children and therefore are expected to be highly vulnerable in flood situations (Holand et al., 2011 ; Koks et al., 2015 ). Ability to acquire and understand information Lack of ability to acquire and understand information can make people more vulnerable to flooding. People who are not familiar with the local area and cannot speak the local language may find it difficult to obtain and interpret disaster information (Holand et al., 2011 ; Lindley et al., 2011 ; Reddy Bathi et al., 2016 ). Non-western immigrants may find it especially difficult to access information in a disaster context due to language barriers and cultural differences (Koks et al., 2015 ; Lulaja et al., 2014; WHO, 2013 ). Neighborhood resilience Many studies highlight the importance of neighborhood or community resilience in coping with disaster situations. Neighborhood resilience is in large part determined by social capital, i.e. the social networks and ties that bind people together (Breil et al., 2018 ; Kyne & Aldrich, 2020 ; Lo et al., 2015 ; Wickes et al., 2015 ). Social capital can be understood according to three types of social ties that link people together: bonding, bridging, and linking. Bonding social capital refers to close ties such as family and friends. Bonding social capital can help to increase coping capacity in the case of flood events (Kyne & Aldrich, 2020 ; Rufat et al., 2015 ). Our closest ties are likely to be with people that are similar to us, for instance those that share our social class, culture, ethnicity, and language (Kyne & Aldrich, 2020 ). Bridging social capital refers to weak ties with people who we spend less time with and generally share less similarities with. Bridging ties may come from connections to school or community organizations (Sayers et al., 2018 ). Bridging ties may be useful during a disaster situation, as these connections will likely be more geographically distanced than close ties and could therefore be better positioned to provide aid or shelter during a flood event (Kyne & Aldrich, 2020 ). Linking social capital refers to vertical ties between citizens and authority figures. Linking social capital is critical for connecting those impacted by a disaster to authorities who control resources and knowledge dissemination related to emergency response (Kyne & Aldrich, 2020 ). Linking social capital may come from engagement in local political processes, such as participation in local elections (Holand et al., 2011 ). 3.2.2 Social vulnerability indicator calculation and weighting Following the selection of appropriate indicators of social vulnerability in a Danish context, calculation and weighting of these indicators was employed to construct a social vulnerability index (SVI). Figure 2 shows the calculation procedure used to construct the SVI. Susceptibility indicators are mapped on 100x100m grid cells while resilience indicators are measured on a neighborhood level as resilience is best understood at a community level. School district boundaries are used as a proxy for neighborhoods as these are the smallest spatial administrative units available on a national scale. Indicators are weighed equally (see Appendix A) in recognition of the fact that all indicators are important in determining social vulnerability to flood hazards. To construct the SVI, we employ a z-score method. Z-scores are a measure of the number of standard deviations that a data point falls from the mean and are a commonly applied approach for understanding relative levels of vulnerability (Bathi & Das, 2016 ; Jun et al., 2020 ; Sayers et al., 2018 ). Following the calculation of z-scores for each main indicator, all five indicator z-scores are aggregated to calculate the final social vulnerability index (see Appendix A for full calculation procedure). 3.3 Calculating social flood exposure Our model includes two types of social flood exposure: direct exposure and indirect exposure. We include both direct and indirect exposure categories in recognition of the fact that flood events not only have impacts on directly flooded areas or people but often result in more widespread loss of access to services and everyday necessities. Consequently, exposure to flooding may extend well beyond the flooded area, leading to a reduction in social well-being that extends far beyond the scope of negative consequences associated solely with direct flooding. 3.3.1 Defining direct and indirect exposure Direct exposure is defined as a situation where an individual’s residential structure is inundated with at least 20cm of water. In the case where occupants reside in multi-story buildings, it is assumed that all residents in the building are directly exposed to flooding, irrespective of the specific floor they inhabit. This assumption is grounded in the understanding that, regardless of whether water intrudes into individual homes, occupants will still experience impacts related to the flood event as they may be unable to leave their homes or could lose access to critical utilities including water, power, and heat. Indirect exposure is defined as a situation where an individual, who is not directly subjected to flooding, experiences a loss of access to critical service infrastructure due to a flood event. Critical service infrastructure includes fundamental services vital to health, safety, education, and emergency sectors. We include service interruptions that can be categorized as critical infrastructure because these services are understood to be most crucial in a disaster situation. According to the European Commission ( 2005 ), critical infrastructure encompasses "the physical resources, services, and information technology facilities, networks, and infrastructure assets that, if disrupted or destroyed, would have a serious impact on the health, safety, security, or economic well-being of citizens or on the efficiency of government functions." The concept of indirect exposure suggests that even in cases where direct flooding is not experienced, individuals may still be indirectly affected if the essential services they typically rely upon are compromised. The consequences of the flood manifest when these services, crucial for both physical and social well-being, become either non-operational or inaccessible due to the flooding, impacting individuals who are dependent on these services. 3.3.2 Critical service infrastructure Definitions of critical infrastructure often include utilities such as drinking water, electricity, heating, gas, or sewage, as well as facilities like power plants, communication networks, vital road networks, medical facilities (hospitals), schools, and emergency service buildings (European Commission 2005 ; Heilemann et al. 2013 ; Len et al. 2018 ). As our model focuses on social well-being, we limit our inclusion of critical infrastructure to aspects that can be considered 'critical service infrastructure' and that would negatively impact individual well-being if disrupted due to flooding. Our indirect exposure model includes 10 service infrastructure sectors, covering utilities, health, education, emergency sectors, and polluting units. More specifically, the model includes all units that relate to the production of electricity, provision of heat and water, and treatment of wastewater. From the health sector, the model includes all hospitals and general practitioners. The emergency sector includes police stations, fire stations, and emergency service units. Finally, the educational sector includes kindergartens, schools, and higher education institutions. Potentially polluting firms are also included in the model to measure access to a pollution free environment. For a full description and definition of the included units, see Table B.1 in the appendix. 3.3.3 Measuring indirect exposure The following section describes the method for calculating indirect exposure. In order to understand this method, a number of terms must first be defined: Service Disruption A service providing unit will no longer provide its service to the surrounding community when inundated with at least 20cm of water. Loss of Access to Service When a single service-providing unit within a given critical service infrastructure category is disrupted, this results in a complete loss of this service category for individuals dependent on the disrupted unit as their nearest provider. The nearest service-providing unit for each cell is defined as the unit that is closest to the cell center. This applies to all services except pollution-free environments. Pollution-Free Environment Access to a pollution-free environment cannot be measured by the nearest provider approach. Instead, all individuals living within 500m of a flooded polluting firm are considered exposed to pollution. Figure 3 shows an example of how indirect exposure is calculated. 3.3.4 Developing a common metric of exposure: Flooded people equivalent (FPeq) In order to compare direct and indirect exposure, we develop a common metric for both types of exposure. Here we describe how direct and indirect exposure are measured using a common measurement unit: flooded people equivalent (FPeq). FPeq is a metric that enables measurement of different levels of exposure or ‘wellbeing loss’ in the event of a flood. FPeq ranges from 0–1 with 1 denoting direct exposure. Indirect exposure is determined based on how many critical infrastructure services an individual loses access to as a result of a given flood event. An FPeq of 0.1 denotes loss of access to a single category of critical service infrastructure. All critical service infrastructure categories are weighed equally, meaning that FPeq scores are aggregated cumulatively as an individual loses access to additional services. For example, an individual that loses access to five critical service infrastructure categories experiences an FPeq score of 0.5. The maximum FPeq score based on indirect exposure is 1, corresponding exactly to the FPeq score associated with direct exposure. We calculate FPeq on an individual level and subsequently take the sum of FPeq within each 100x100m cell to find aggregated, cell-level FPeq scores. 3.4 Constructing a social flood risk index (SFRI) Following construction of the SVI and calculation of social exposure, we combine exposure and vulnerability information to calculate the flood event specific social damage (FESSD) for a single flood event. FESSD can then be used to calculate a social flood risk index (SFRI) for a probabilistic set of flood events. To calculate FESSD for a single flood event, we first categorize exposure values for each 100x100m grid cell into nationally relevant exposure categories based on a national mapping of population per grid cell. This is achieved by categorizing FPeq values into a 10-class Natural Breaks classification based on the absolute number of people living in each 100x100m cell nationally. This enables easier comparison between exposure scores for different flood events and avoids overemphasis of damage and risk in areas that are highly exposed in frequent return period events. SVI scores for each grid cell are also categorized into a 10-class Natural Breaks classification. Following the reclassification of exposure and SVI scores, we multiply the reclassified exposure and SVI scores to find FESSD (See Appendix C for complete description of calculation procedure). FESSD is calculated through multiplication to emphasize that both exposure and vulnerability reinforce each other and thus both high exposure and high vulnerability must be present to result in high social damage scores. It is important to note that by combining exposure and vulnerability information in this way, we assume that social vulnerability values apply to individuals who are both directly and indirectly exposed to flooding. We make this assumption in recognition of the fact that individuals with high vulnerability will not only be more vulnerable when directly faced with flooding but are also likely more vulnerable in the face of critical service infrastructure disruptions caused by a flood event. After FESSD has been calculated for each event considered in our probabilistic set of flood events, we are able to calculate an aggregated social flood risk index (SFRI). Calculation of SFRI requires the combination of expected social damage of each flood event with the probability of that flood event (see Appendix D for a complete description of the calculation procedure). 4. Results The following sections present the results of the social flood vulnerability and social flood risk models. We begin by presenting a national overview of SVI in Denmark before mapping SVI, social flood exposure, flood event specific social damage (FESSD) and social flood risk (SFRI) in the local case study of Vejle. Finally, we present a national mapping of the social flood risk index (SFRI). 4.1 Social flood vulnerability (SVI) The social vulnerability index (SVI) displays social vulnerability to flooding on a 100x100m scale nationally. Figure 4 visualizes the spatial distribution of social vulnerability in Denmark. Figure 4 shows an uneven distribution of social vulnerability across the country. Cities clearly stand out as areas of higher social vulnerability. SVI is not weighted by population, so this trend does not merely reflect higher population density but points to a general trend of higher social vulnerability in cities. While a national overview is instructive for visualizing social vulnerability across Denmark, it is useful to more closely investigate the results in a local case study. Figure 5 maps social vulnerability in Vejle. Figure 5 shows that SVI in different areas of Vejle is determined by different indicators. For example, in the city center, high vulnerability stems primarily from lack of ability to acquire and understand information, poor socio-economic situation, and low neighborhood resilience. The disaggregation of SVI to individual indicators enables a more detailed understanding of the causes of vulnerability and thus also provides important information about potentially suitable strategies for reducing vulnerability depending on the local context. It should be noted that when interpreting local-scale SVI mapping, vulnerability should always be understood in relation to national average vulnerability. For example, an SVI score of 2 in a given grid cell in Vejle indicates that vulnerability in that grid cell is 2 standard deviations above the national mean. This enables easy comparison of relative levels of vulnerability in different local areas across the country. 4.2 Social flood exposure, flood event specific social damage, and social flood risk in Vejle The case study of Vejle also offers an interesting area in which to visualize social flood exposure. Figure 6 shows the extent of a 100-year flood event in Vejle. Figure 7 shows exposure, social damage, and social risk in Vejle. Figure 7 a shows that indirect social flood exposure extends far beyond the flooded area. In fact, although only 36% of exposure falls on those indirectly exposed, 19,989 people experience indirect exposure, more than 4.5 times as many as those who experience direct flooding. Figure 7 b shows exposure in terms of flooded people equivalent (FPeq). Figure 7 c shows flood event specific social damage for a 100-year event in Vejle. In this scenario, direct exposure accounts for 40% of total social damage while indirect exposure accounts for 60% of social damage. Figure 7 d shows the social flood risk index in Vejle. Here, we see that the spatial extent of social risk in Vejle is much greater than the extent of exposure and social damage during a 100-year event. This is due to the fact that higher return period events (for example a 10,000-year flood event) will impact a larger geographical area of the city. 4.3 Social flood exposure in Denmark On a national scale, we see similar trends as in Vejle regarding the share of indirect vs. direct flood exposure experienced as a result of flood events. Figure 8 shows the share of the Danish population that is directly and indirectly exposed during various flood events. For the most extreme event (10,000-year event), about one third of the Danish population will be indirectly exposed. For the 100-year event, the ratio of indirect vs. direct flooded is approximately 12:1, showcasing how the consequences of a severe flood extends far beyond the flooded area. This ratio decreases for more extreme floods, indicating that some of those indirectly exposed in less severe floods will be directly exposed during more severe flood events. In the 10,000-year event, more than one third of the population is affected (33% are indirectly exposed). This may seem like a very large share, but we must recall that these calculations are based a worst-case flood simulation and that this simulation assumes simultaneous flooding across the entire country. It is also interesting to explore how exposure is distributed across social vulnerability classes for different flood events. For example, Table 3 shows how indirect exposure is distributed across vulnerability classes during a 100-year flood event. Table 3 Distribution of indirect exposure across vulnerability classes for two flood events (100-year event) Social vulnerability (SVI) Total population in group Population indirectly exposed in group Share of total population indirectly exposed Population directly exposed in group Share of total population directly exposed Group SVI Interval Very low \(-3\le SVI<-2\) 152400 8163 5,4% 6741 4.42% Low \(-2\le SVI<-1\) 405815 18688 4,6% 4002 0.99% Moderately low \(-1\le SVI<0\) 1353530 182749 13,5% 11209 0.83% Moderately high \(0\le SVI<1\) 1938070 273843 14,1% 19949 1.03% High \(1\le SVI<2\) 1329010 150358 11,3% 11098 0.84% Very high \(2\le SVI\le 3\) 554235 68950 12,4% 4727 0.85% Total 5733060 702751 12,3% 57726 1.01% Table 3 demonstrates a noteworthy contrast in social vulnerability during a 100-year flood event between individuals directly and indirectly exposed. Among those with the lowest Social Vulnerability Index (SVI), both groups exhibit nearly identical proportions. However, among the indirectly exposed, individuals with higher SVI encounter a significantly greater likelihood of exposure compared to those directly exposed to flooding. Such computations indicate that this model can yield insights into how exposure to floods may disproportionately affect various social classes. 4.4 Social flood risk index (SFRI) in Denmark Figures 9 shows the distribution of social flood risk on a national scale. Here, we see major clusters of social flood risk across the country, with the area around Aarhus noticeably standing out as the largest cluster of social flood risk. 5. Discussion In this paper, we have developed a national mapping of social flood vulnerability and social flood risk in a Danish context. Our results offer several empirical and theoretical insights. First, we provide a nationwide mapping of social flood vulnerability and social flood risk in Denmark. The inclusion of social data has thus far been severely limited in Danish flood risk assessment (Kystdirektoratet, 2018 ), so this represents a significant increase in empirical knowledge about the social implications of flooding in a Danish context. Results show that the distribution of social flood risk in Denmark differs significantly when compared to existing areas of potential significant flood risk (risk areas in short) appointed under the EU Floods Directive in Denmark. Several of the largest clusters of social risk shown in Fig. 9 (Aarhus, Aalborg, and northwestern Jutland) are not currently appointed as risk areas (Kystdirektoratet, 2018 ). This finding points to the significance that including social data could have on our understanding of the national flood risk landscape in Denmark. We hope that our model can represent a first step in centering social impacts in Danish flood risk management approaches alongside economic and ecological consequences of flood events. Second, we advance methodological approaches to assess the social impacts of flooding by combing social vulnerability data with social flood exposure modeling to create a comprehensive model of social flood risk. This approach offers a more precise understanding of the dynamic and multi-faceted nature of flood consequences. The indirect flood exposure model highlights that the consequences of flooding can extend far beyond the physical boundaries of a flooded area and may have repercussions that impact larger systems and communities. Our results show that, on a national scale, indirect exposure accounts for between 55% and 71% of total social flood exposure during a flood event, depending on the return period. This finding has significant implications for flood risk management and climate change adaptation planning as it highlights the need to look beyond direct flooding when considering the social impacts of flood events. It is critical to acknowledge that our communities are interconnected systems and critically examine these interconnections when assessing and managing flood risk. While our findings offer a valuable contribution to flood risk assessment research, several limitations must be mentioned. First, even in a data rich country such as Denmark, demographic data is highly sensitive and therefore challenging to access at a high spatial resolution due to confidentiality concerns. This makes the construction of a detailed and accurate SVI challenging. In our case, it was especially difficult to access health data. Therefore, we use proxy indicators to estimate spatial distribution of populations with poor health. However, we acknowledge that these proxies may not realistically capture all relevant health challenges experienced by the population. Challenges related to data acquisition and confidentiality also severely limit the reproducibility of our methods in less data rich contexts. Additionally, the intangible nature of social flood risk gives rise to issues of measurement and comparison. Here, we construct a social flood risk index (SFRI), which, by definition, provides a relative, index-based assessment of social flood risk. While this is valuable for identifying areas of relatively high risk and comparing risk across regions, municipalities, and communities; it does not provide an absolute measure of risk. The index-based nature of this model makes it difficult to compare with economic flood risk models which most often express risk in monetary value. This poses a potential challenge for decision-making processes that aim to balance reduction of social, economic, or other dimensions of flood risk. Finally, while we attempt to center social impacts in flood risk assessment, we must acknowledge that our methodology relies purely on expert-defined data. Recently, there has been some debate as to the efficacy of expert-defined social vulnerability indices in accurately representing empirical realities and locally relevant narratives of vulnerability (Rufat et al., 2019 ). Therefore, further work should empirically validate SVI in a Danish context and consider more inclusive and participatory methods of vulnerability and exposure mapping to ensure that they accurately represent experiences on the ground. 6. Conclusions As flood events become increasingly frequent and intense due to climate change, it is urgent to develop more comprehensive understandings of flood vulnerability, exposure, and risk. The social consequences of flooding are a critical and often overlooked component of flood risk that must be highlighted further in flood risk management decision-making. This paper has developed a socially specific model of flood risk that combines flood hazard information, social flood vulnerability, and social flood exposure. This model enables a comprehensive understanding of the social impacts of flooding and highlights that flooding not only impacts various social groups differently but can also impact people living far outside flooded areas. This study therefore highlights the importance of looking beyond social vulnerability and including socially relevant exposure modelling in assessments of social flood risk. Three key avenues for further research are identified: (i) participatory research to integrate diverse community values into indirect social flood exposure modelling, (ii) further research into modelling social flood vulnerability and social flood exposure in future scenarios, and (iii) studies that explore methods for comparison or combination of social flood risk with economic and ecological flood risk models. Together, these three research directions can further inform socially oriented flood risk management approaches that center human wellbeing and advance equity. Declarations Author contributions M.P. and U.B. led the conception and methodological design of the research with support from all authors. N.H. and M.H. carried out the analysis and produced maps. M.P. and U.B. wrote the text with support from N.D. and K.A. All authors provided feedback on the text prior to submission. Acknowledgements We would like to thank our colleague Jane Mosbæk Flyvholm for her valuable contributions to the initial conception of this work. References Armaș, I., & Gavriș, A. (2013). Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model)–a case study for Bucharest, Romania. Natural hazards and earth system sciences, 13(6), 1481-1499. Aven, T. (2011). On some recent definitions and analysis frameworks for risk, vulnerability, and resilience. Risk Analysis: An International Journal, 31(4), 515-522. Balica, S. F., Wright, N. G., & Van der Meulen, F. (2012). 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Social Science Quarterly , 96 (2), 330-353. Additional Declarations The authors declare no competing interests. Supplementary Files appendix.docx Appendix Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4293472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293199409,"identity":"e6b3ad68-2f58-457a-845c-faba712a7d04","order_by":0,"name":"Mia Cassidy Prall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDACZhBRkQBmSzYQr+UMUAsb0VpAgLGNFC267cwPPxfOS0ucP7+B8eYMYrSYHWYzlp65LSdxwzEGZssNxGnhYZDm3VaRuIGNgU3yAZFamH/zzqlInN9GghY2ad6GnMSGY0AtRDqMzcx6xrE04w3HEpstifP++cOPbxfUJMvObz588GYPMVpAABSbjg0MjA3EaoBosSde+SgYBaNgFIw4AADHuTHqJi91WAAAAABJRU5ErkJggg==","orcid":"","institution":"Danish Coastal Authority, Aalborg University","correspondingAuthor":true,"prefix":"","firstName":"Mia","middleName":"Cassidy","lastName":"Prall","suffix":""},{"id":293199410,"identity":"64e58b63-7d69-4fb4-baed-d50d7a25fd21","order_by":1,"name":"Urs Steiner Brandt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYJACZoaKBDBDsoF4LWeAWthI0sLYRooWg9vHH38unJeWOH9+A+PNGURpOZdjJj1zW07ihmMMzJYbiNJyhoeNmXdbReIGNgY2yQfEaWF//Jl3TkXi/DbitTAYSPM25CQ2HANqIcphkmd4zKRnHEsz3nAssdmSKO/zgRxWUJMsO7/58MGbPcRogQHHBgbGBlI0MDDYk6Z8FIyCUTAKRhQAABctM10JeGGLAAAAAElFTkSuQmCC","orcid":"","institution":"Danish Coastal Authority","correspondingAuthor":true,"prefix":"","firstName":"Urs","middleName":"Steiner","lastName":"Brandt","suffix":""},{"id":293200383,"identity":"9b4d2f01-5adb-47da-b8ce-a4b252d6ed71","order_by":2,"name":"Nick Schack Halvorsen","email":"","orcid":"","institution":"Danish Coastal Authority","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"Schack","lastName":"Halvorsen","suffix":""},{"id":293200384,"identity":"2fb715a3-f4b4-4e54-b7f7-e889438e279f","order_by":3,"name":"Morten Uldal Hansen","email":"","orcid":"","institution":"Danish Coastal Authority","correspondingAuthor":false,"prefix":"","firstName":"Morten","middleName":"Uldal","lastName":"Hansen","suffix":""},{"id":293200385,"identity":"d357c845-8b99-4129-acce-2228d1270a48","order_by":4,"name":"Niklas Dahlberg","email":"","orcid":"","institution":"Danish Coastal Authority, Finnish Environment Institute","correspondingAuthor":false,"prefix":"","firstName":"Niklas","middleName":"","lastName":"Dahlberg","suffix":""},{"id":293200386,"identity":"d2c23436-ccc5-48d9-8352-b3f1db892c5f","order_by":5,"name":"Kaija Jumppanen Andersen","email":"","orcid":"","institution":"Danish Coastal Authority","correspondingAuthor":false,"prefix":"","firstName":"Kaija","middleName":"Jumppanen","lastName":"Andersen","suffix":""}],"badges":[],"createdAt":"2024-04-19 13:37:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4293472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4293472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55070774,"identity":"8791ab8a-36d5-406d-8e4e-4c81918a3203","added_by":"auto","created_at":"2024-04-22 06:26:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":374660,"visible":true,"origin":"","legend":"\u003cp\u003eA conceptual framework of social flood risk\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/5e1f947ee1b45f14b9f83161.jpg"},{"id":55070773,"identity":"752114b5-3bbf-4bd3-a942-78a66bc131fd","added_by":"auto","created_at":"2024-04-22 06:26:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96882,"visible":true,"origin":"","legend":"\u003cp\u003eSVI calculation procedure. Note: see Appendix A for a complete description of the SVI calculation procedure.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/19526a672d6415f747614101.png"},{"id":55071934,"identity":"a1afbcfa-de40-4d06-8d0d-8e2d02405425","added_by":"auto","created_at":"2024-04-22 06:50:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":717014,"visible":true,"origin":"","legend":"\u003cp\u003eMethod for calculating indirect exposure\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/15685eea2da47ede8c57ca1f.jpg"},{"id":55071086,"identity":"806e88d7-669e-4e53-ad09-bc6240a67bcd","added_by":"auto","created_at":"2024-04-22 06:34:38","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":461699,"visible":true,"origin":"","legend":"\u003cp\u003eSocial Vulnerability in Denmark\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/0e4eb16c70f8c31860940699.jpeg"},{"id":55071088,"identity":"1af08eec-5159-4f11-a731-0b8f76e5d51d","added_by":"auto","created_at":"2024-04-22 06:34:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":625246,"visible":true,"origin":"","legend":"\u003cp\u003eIndicators of social vulnerability in Vejle\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/7ae9d85d5772bdbcf677cfed.jpeg"},{"id":55070779,"identity":"e572615a-48dd-4435-b84b-a5dd73bbba40","added_by":"auto","created_at":"2024-04-22 06:26:38","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342667,"visible":true,"origin":"","legend":"\u003cp\u003eA 100-year flood event in Vejle\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/d99cbfe123ac262f0984a29a.jpeg"},{"id":55070776,"identity":"faecfd07-65d6-43ae-8a16-2af7ad49bed4","added_by":"auto","created_at":"2024-04-22 06:26:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":746003,"visible":true,"origin":"","legend":"\u003cp\u003eSocial flood exposure, flood event specific damage, and social flood risk in Vejle\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/726c5ad00ff30d17c658dafa.png"},{"id":55071572,"identity":"de6a9792-94d3-4317-a73b-ecdb3a9a2fdc","added_by":"auto","created_at":"2024-04-22 06:42:38","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":169180,"visible":true,"origin":"","legend":"\u003cp\u003eShare of Danish population directly and indirectly exposed to various flood events\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/ca9dee34fea1fdd8923c20f7.jpg"},{"id":55070782,"identity":"06c5b64b-18a1-4521-bd9f-e7d7e73efaa3","added_by":"auto","created_at":"2024-04-22 06:26:38","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":164936,"visible":true,"origin":"","legend":"\u003cp\u003eHot Spots of Social Flood Risk Denmark\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/f5e1f38d43009bedde26d026.jpeg"},{"id":55071951,"identity":"dec969ce-9c7a-4c04-9b30-7856b70404b1","added_by":"auto","created_at":"2024-04-22 06:51:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2267047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/db2db661-5d41-4aa3-931b-f9dece036adf.pdf"},{"id":55071085,"identity":"6810b12c-d8cc-4e9b-a42e-7196b7ff773f","added_by":"auto","created_at":"2024-04-22 06:34:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":64571,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix\u003c/p\u003e","description":"","filename":"appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4293472/v1/8f55f3788ab9a6d11d243b8b.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA novel and comprehensive approach for understanding the social impacts of flooding: assessing social flood vulnerability and social flood risk in Denmark\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSocial vulnerability has emerged as a critical component of flood risk assessment and management in recent years due to the recognition that the inherent capabilities of different individuals and groups influence vulnerability in flood situations. Many studies find that vulnerability to flooding is influenced by individual characteristics such as age, health, and economic situation as well as community dynamics and social support systems (Balica et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dwyer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Foudi \u0026amp; Oses-Eraso, 2014; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe growing interest in including social vulnerability in flood risk assessment is a response to the economic and technocratic biases of traditional risk assessment approaches. Many flood risk management strategies have traditionally taken point of departure in risk assessment frameworks that prioritize the protection of valuable economic assets without acknowledging the differential impact of flooding on individual wellbeing (Vojinovic et al., 2012; Jeffers, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Including social factors in flood vulnerability and risk assessment helps to promote holistic flood risk management that ensures social resilience and strives for socially just outcomes (Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile a number of indices for measuring social flood vulnerability exist (Armaș \u0026amp; Gavriș, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Balica et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bouaakkaz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cutter et al., 2003; de Loyola Hummell et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Guillard-Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Holand et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), these models are highly context dependent and are difficult to generalize to new geographical contexts. Furthermore, models that combine vulnerability indices with socially relevant exposure categories and hazard information are less common and often integrate exposure or hazard information in an incomplete or ad-hoc manner rather than comprehensively assessing socially specific flood risk (Kazmierczak, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lujala et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mason et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al., 2020). Understanding both social vulnerability, i.e. individual and community susceptibility to potential flooding and social risk, i.e. the probabilistic, spatial interaction of flood hazards, social exposure, and social vulnerability is critical to facilitate holistic decision making on flood risk management and critically engage with questions of equity and justice. There is increasing focus on implementing adaptation solutions that target social resilience, wellbeing, and equity (Aven, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Joakim et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hegger et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jurgilevich, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); however, without the tools to assess social vulnerability and social risk, the impacts of these measures cannot be evaluated.\u003c/p\u003e \u003cp\u003eWith this in mind, we construct a social flood vulnerability index (SVI) and a social flood risk index (SFRI) for Denmark. Our study is the first that we are aware of to integrate social vulnerability into flood risk assessment in Denmark. Furthermore, we expand on existing methods for social vulnerability and social risk assessment by introducing a method for mapping of social flood exposure, which considers both direct and indirect social flood exposure categories. These exposure categories are linked with vulnerability and hazard information to create a model of risk that explicitly focuses on understanding the social impacts of flooding. Results contribute to an enhanced understanding of social vulnerability in a Nordic context and provide a methodological push towards flood risk modelling approaches that explicitly consider the social impacts of flooding in all model components.\u003c/p\u003e \u003cp\u003eSuch an approach can facilitate a shift towards the implementation of more inclusive flood risk management approaches that are usable within the context of existing risk management frameworks such as the EU Floods Directive. A more holistic approach to assessing risk is critical to be able to understand the full range of impacts from flood events and implement effective and fair flood risk management and climate change adaptation. We hope that our work can provide practitioners with a means to incorporate the intangible impacts of flooding into risk management decision making and concretely assess the social impacts of potential interventions that aim to improve social resilience.\u003c/p\u003e"},{"header":"2. Approaches to assessing social vulnerability and social risk","content":"\u003cp\u003eSocial vulnerability studies have emerged in the past few decades in flood risk management and environmental hazards research. Social vulnerability refers to inherent individual or community characteristics that combine to increase susceptibility of certain individuals or groups in a specific hazard situation (Balica et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; FEMA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Foudi \u0026amp; Oses-Eraso, 2014; Kyne \u0026amp; Aldrich, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Social vulnerability is a multidimensional concept that cannot be measured in absolute terms and is therefore often expressed in relative terms through index-based analyses comprised of multiple socio-economic indicators.\u003c/p\u003e \u003cp\u003eSocial vulnerability index construction became popular in environmental research in the early 2000s following the publication of a Social Vulnerability Index (SoVI) measuring relative social vulnerability to environmental hazards in the US on a national scale (Cutter et al., 2003). Cutter et al. (2003) quantify social vulnerability by compiling a large set of socio-economic variables thought to contribute to environmental vulnerability and employing factor analysis, specifically principal components analysis (PCA), to identify key indicators of vulnerability and subsequently build a composite index of social vulnerability. Many studies have since adopted or been inspired by the SoVI approach to assess social vulnerability to different environmental hazards such as floods, earthquakes and avalanches in diverse geographical contexts including Brazil (de Loyola Hummell et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Morocco (Bouaakkaz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Norway (Holand et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Portugal (Guillard-Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Romania (Armaș \u0026amp; Gavriș, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to name a few.\u003c/p\u003e \u003cp\u003eDespite the widespread application of the SoVI, some studies have criticized the SoVI approach and developed alternative methods for assessing social vulnerability. For example, some studies have questioned the validity of relying on statistical techniques such as PCA to determine indicator weights when constructing social vulnerability indices and suggest that it may instead be more accurate to include all available data in index construction or define indicators and associated weights based on expert or local knowledge in the case study in question (Kienberger et al., 2009; Rufat et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Speilman et al., 2020). Rufat et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) tested this theory and found that expert-determined indicator identification and weighting was in fact more accurate in explaining the outcomes of Hurricane Sandy than other statistical methods. This point to the importance of paying close attention to local context when analyzing social vulnerability.\u003c/p\u003e \u003cp\u003eAdditionally, the indicators included in the original SoVI may not fully capture all dimensions of social vulnerability. For example, the role of social resilience in defining vulnerability has often been neglected in SoVI-based analyses despite consensus that resilient communities are less vulnerable to the impacts of natural hazards (Breil et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kyne \u0026amp; Aldrich, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wickes et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In response to this gap, some studies have begun to consider social resilience by integrating data on factors such as social connectedness and local knowledge in their analyses of vulnerability (Mason et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMethodological limitations also exist when scaling SoVI results from a national to regional or local scale for use in decision making. As SoVI produces a relative ranking of vulnerability, it is only valid at the analysis scale at which it is performed. In other words, it would be unwise to make local planning decisions based on a national analysis that ranks the social vulnerability of each neighborhood, because local vulnerability rankings will be influenced by vulnerability scores of neighborhoods across the country (Speilman et al., 2020). It is therefore critical to pay close attention to the appropriate scale at which index-based vulnerability analyses such as SoVI can be used.\u003c/p\u003e \u003cp\u003eDespite these challenges, social vulnerability indices have been widely recognized as a useful analysis and planning tool in the context of flood risk management research. However, a very limited number of studies combine social vulnerability information with hazard or exposure data to form complete analyses of socially specific flood risk. Understanding how social vulnerability interacts with exposure and hazard data is critical to inform a holistic, socially sensitive approach to flood risk management planning and policy making.\u003c/p\u003e \u003cp\u003eSeveral studies attempt to integrate exposure information by including exposed people as a variable within social vulnerability indices in the form of a floodplain population or population density estimate (Balica et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al. 2020). This approach does not stay true to the definition of social flood vulnerability as a function of inherent individual or community traits that increase susceptibility to flooding (Balica et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Foudi \u0026amp; Oses-Eraso, 2014; IPCC, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sayers et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, approaches that integrate exposure data within analyses of vulnerability may confuse sensitivity (i.e. social vulnerability) with location (i.e. exposure, or what is sometimes referred to as physical vulnerability). Retaining separate measures of social vulnerability and exposure is critical in the context of data-assisted decision making, as different flood risk management solutions will likely be necessary in areas with high concentrations of socially marginalized residents compared to areas with high concentrations of floodplain populations.\u003c/p\u003e \u003cp\u003eA limited number of studies recognize this need to combine social vulnerability data with socially relevant exposure and hazard information. Lujala et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) construct both a social vulnerability index and a physical exposure index and combine these to create a single composite index that offers information on the spatial distribution of both vulnerability and exposure in the case of Verdal, Norway. Koks et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) take a similar approach by combining a social vulnerability index with data on exposure and hazard in Rotterdam. However, this analysis is not spatial and instead offers results in the form of population statistics. Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al. (2020) and Kazmierczak (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) both acknowledge the importance of measuring exposure of socially relevant infrastructure such as healthcare facilities, community centers, and public transport. Mason et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) similarly consider indirect exposure by measuring exposure of services that are especially important for vulnerable populations, such as day cares, rest homes, medical centers, and community buildings. These critical points are presented spatially in addition to a social vulnerability index to better support decision making (Mason et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile these approaches to combining social vulnerability data with exposure and hazard information represent important advances in the literature on holistic flood risk management, comprehensive approaches to combining social vulnerability, social exposure, and hazard information to derive social flood risk measures are still lacking.\u003c/p\u003e"},{"header":"3. Conceptual and methodological framework","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Conceptual framework\u003c/h2\u003e\n\u003cp\u003eWe understand social flood risk according to the commonly applied H.E.V. risk assessment framework wherein risk is defined as a function of hazard, exposure, and vulnerability (IPCC, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). As the scope of this analysis is limited to social flood risk, we define risk as a situation where potential flooding, exposed people, and social vulnerability factors combine to create risk (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn this study, our application of social flood risk in a Danish context is limited to consideration of coastal flood hazards. To calculate social risk, we utilize a probabilistic event set composed of nine different return period events (10 year, 20 year, 50 year, 100 year, 200 year, 500 year, 1,000 year, 5,000 year, and 10,000 year return periods) for coastal flooding. These events are based on current climate conditions. Exposure is understood as flood dependent and two exposure categories are considered: direct exposure (flooded people) and indirect exposure (critical service infrastructure disruption) (see section 3.3 for further explanation). Therefore, the model captures both direct wellbeing loss due to flooding as well as indirect wellbeing loss due to flood-related critical service disruptions. The extent of individual wellbeing loss is determined by social vulnerability factors, with the assumption that increased vulnerability corresponds with a higher degree of wellbeing loss in the event of direct or indirect flood exposure. Social vulnerability is flood independent, meaning that vulnerability is constant, i.e. it exists without flooding, but is only \u0026lsquo;triggered\u0026rsquo; as a component of social flood risk in areas that are exposed to flood events.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Constructing the social flood vulnerability index\u003c/h2\u003e\n\u003cp\u003eIn order to carry out a national screening of social flood risk in Denmark, it was first necessary to develop a nationally relevant social vulnerability index. The following sections describe the process of social vulnerability indicator selection, indicator weighting, and calculation of the social vulnerability index.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.1 Social vulnerability indicator selection\u003c/h2\u003e\n\u003cp\u003eExisting literature on social vulnerability often defines vulnerability as a function of susceptibility and resilience. Susceptibility refers to how inherent individual characteristics \u003cem\u003eincrease\u003c/em\u003e peoples\u0026rsquo; vulnerability to flooding. For example, individual susceptibility to flood event is impacted by characteristics such as age, education, and economic situation (Balica et al, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Foudi \u0026amp; Oses-Eraso, 2014; Sayers et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the other hand, resilience is understood as an important factor for \u003cem\u003edecreasing\u003c/em\u003e vulnerability, as the strength of a community influences residents\u0026rsquo; ability to support each other and take collective action in a disaster situation (FEMA, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our construction of a Danish social vulnerability model is therefore based on indicators of both susceptibility and resilience.\u003c/p\u003e\n\u003cp\u003eSpecific susceptibility and resilience indicators are defined based on the selection criteria listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eIndicator selection criteria.\u003c/p\u003e\n\u003cdiv class=\"Credit\"\u003e\n\u003cp\u003eAdapted from Mason et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSelection criteria\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eJustification\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData must be available in a Danish context\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAppropriate spatial scale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData should be available at a local level\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemporal relevance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData should be up to date and collected and reported frequently\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMethodologically sound measurement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData source should be reliable, accurate, and representative of the population\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScientifically valid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe importance of indicators should be demonstrated with scientifically robust evidence\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeographical relevance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndicators should be relevant in a Danish context\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on these selection criteria, the indicators shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e were selected for inclusion. Indicators were selected to measure the two dimensions of social vulnerability: individual susceptibility and community resilience. Indicators of susceptibility measure individual vulnerability and are therefore measured on a personal, household, or family level depending on the scale of available data. Resilience indicators measure community coping capacity and are therefore measured on a neighborhood scale. Susceptibility indicators have a negative impact on vulnerability (i.e. they increase vulnerability) while resilience indicators have a positive impact on vulnerability (i.e. they decrease vulnerability).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSocial vulnerability indicators\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndicators\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSub-indicators\u003c/p\u003e\n\u003cp\u003e(Level 1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSub-indicators\u003c/p\u003e\n\u003cp\u003e(Level 2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDefinition\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInfluence on vulnerability\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYoung or elderly\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of people under 5 years or over 67 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRecipients of nursing or care\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of households receiving nursing or care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEarly pensioners\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of people receiving early pension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSocio-economic situation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow income\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of households in the lowest 10% of the national income distribution\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow social class\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of households belonging to the lowest social class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSingle parent households\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of single parent households\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAbility to acquire and understand information\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRecent arrivals to Denmark\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of people who moved to Denmark in the last 5 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon-western immigrants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of non-western immigrants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (susceptibility)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood resilience\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBonding social capital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eIncome equality\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted difference of income levels\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive (resilience) *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eEducational equality\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted difference of educational attainment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive (resilience) *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNationality similarity\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNationality fractionalization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive (resilience) *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBridging social capital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCommunity linkage\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% of families with at least one child under 18 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive (resilience) *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLinking social capital\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePolitical linkage\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% voter turnout in municipal elections\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive (resilience) *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Note: Although indicators of neighborhood resilience have a positive influence on vulnerability, we measure the inverse of each indicator, meaning that we measure a negative influence on vulnerability.\u003c/p\u003e\n\u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, age, health, socio-economic situation, and ability to acquire and understand information are included as indicators of susceptibility while neighborhood resilience is included as an indicator of resilience. The following sections detail the reasons for including each of these indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElderly people are more likely to suffer from pre-existing health conditions that may be exacerbated by a flood event and are prone to harm during a flood event due to mobility constraints and heightened sensitivity to cold, damp environments (Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Koks et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lowe et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tapsell et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vardoulakis \u0026amp; Heaviside, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Flood events may also have negative psychological impacts on elderly people and could exacerbate feelings of social isolation (Breil et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lowe et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Young children are also highly vulnerable to flooding because they do not have the cognitive or motor skills to seek help and may have difficulty expressing symptoms to caregivers (Koks et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Young children also have weaker immune systems and are more prone to anxiety as a result of a flood event, meaning that they are more likely to contract diseases or experience mental or behavioral issues because of a flood event (Breil et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mort et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; WHO, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeople with long-term physical health conditions may find flood preparation and evacuation challenging and may be dependent on others for care or rely on medication that is difficult to access during a flood event (Rufat et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; WHO, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). People suffering from mental health conditions may also be vulnerable because cognitive impairment can lead to a skewed perception of risk and can make it difficult to trust authority figures and follow orders during a flood event (Rufat et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; WHO, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, people that suffer from pre-existing health conditions have been found to have higher rates of morbidity and mortality following flood events as well as higher levels of PTSD, anxiety, and depression post flood (Rufat et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tapsell et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocio-economic situation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeople with financial constraints or low socio-economic status are disproportionately impacted by flooding (Rufat et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Financial constraints make it difficult to prepare for flood events and recover post flood (Breil et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Koks et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) find that a households\u0026rsquo; socioeconomic status is a major determinant of social flood vulnerability. Households with lower incomes will be more reliant on publicly provided emergency services as they have fewer resources to prepare for and recover from disaster. In general, lower social classes are highly reliant on social services and often require additional assistance with evacuation and recovery in the case of disaster (Bartram et al., 2021; Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Koks et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition to households with low income or low social class, single parent households are often financially stressed as they only have one potential wage earner and dependent children and therefore are expected to be highly vulnerable in flood situations (Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Koks et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbility to acquire and understand information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLack of ability to acquire and understand information can make people more vulnerable to flooding. People who are not familiar with the local area and cannot speak the local language may find it difficult to obtain and interpret disaster information (Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lindley et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Reddy Bathi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Non-western immigrants may find it especially difficult to access information in a disaster context due to language barriers and cultural differences (Koks et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lulaja et al., 2014; WHO, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood resilience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany studies highlight the importance of neighborhood or community resilience in coping with disaster situations. Neighborhood resilience is in large part determined by social capital, i.e. the social networks and ties that bind people together (Breil et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lo et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wickes et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Social capital can be understood according to three types of social ties that link people together: bonding, bridging, and linking.\u003c/p\u003e\n\u003cp\u003eBonding social capital refers to close ties such as family and friends. Bonding social capital can help to increase coping capacity in the case of flood events (Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rufat et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our closest ties are likely to be with people that are similar to us, for instance those that share our social class, culture, ethnicity, and language (Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Bridging social capital refers to weak ties with people who we spend less time with and generally share less similarities with. Bridging ties may come from connections to school or community organizations (Sayers et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Bridging ties may be useful during a disaster situation, as these connections will likely be more geographically distanced than close ties and could therefore be better positioned to provide aid or shelter during a flood event (Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Linking social capital refers to vertical ties between citizens and authority figures. Linking social capital is critical for connecting those impacted by a disaster to authorities who control resources and knowledge dissemination related to emergency response (Kyne \u0026amp; Aldrich, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Linking social capital may come from engagement in local political processes, such as participation in local elections (Holand et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.2 Social vulnerability indicator calculation and weighting\u003c/h2\u003e\n\u003cp\u003eFollowing the selection of appropriate indicators of social vulnerability in a Danish context, calculation and weighting of these indicators was employed to construct a social vulnerability index (SVI). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the calculation procedure used to construct the SVI.\u003c/p\u003e\n\u003cp\u003eSusceptibility indicators are mapped on 100x100m grid cells while resilience indicators are measured on a neighborhood level as resilience is best understood at a community level. School district boundaries are used as a proxy for neighborhoods as these are the smallest spatial administrative units available on a national scale.\u003c/p\u003e\n\u003cp\u003eIndicators are weighed equally (see Appendix A) in recognition of the fact that all indicators are important in determining social vulnerability to flood hazards. To construct the SVI, we employ a z-score method. Z-scores are a measure of the number of standard deviations that a data point falls from the mean and are a commonly applied approach for understanding relative levels of vulnerability (Bathi \u0026amp; Das, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jun et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sayers et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following the calculation of z-scores for each main indicator, all five indicator z-scores are aggregated to calculate the final social vulnerability index (see Appendix A for full calculation procedure).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Calculating social flood exposure\u003c/h2\u003e\n\u003cp\u003eOur model includes two types of social flood exposure: direct exposure and indirect exposure. We include both direct and indirect exposure categories in recognition of the fact that flood events not only have impacts on directly flooded areas or people but often result in more widespread loss of access to services and everyday necessities. Consequently, exposure to flooding may extend well beyond the flooded area, leading to a reduction in social well-being that extends far beyond the scope of negative consequences associated solely with direct flooding.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.1 Defining direct and indirect exposure\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDirect exposure\u003c/strong\u003e is defined as a situation where an individual\u0026rsquo;s residential structure is inundated with at least 20cm of water. In the case where occupants reside in multi-story buildings, it is assumed that all residents in the building are directly exposed to flooding, irrespective of the specific floor they inhabit. This assumption is grounded in the understanding that, regardless of whether water intrudes into individual homes, occupants will still experience impacts related to the flood event as they may be unable to leave their homes or could lose access to critical utilities including water, power, and heat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect exposure\u003c/strong\u003e is defined as a situation where an individual, who is not directly subjected to flooding, experiences a loss of access to critical service infrastructure due to a flood event. Critical service infrastructure includes fundamental services vital to health, safety, education, and emergency sectors.\u003c/p\u003e\n\u003cp\u003eWe include service interruptions that can be categorized as critical infrastructure because these services are understood to be most crucial in a disaster situation. According to the European Commission (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), critical infrastructure encompasses \"the physical resources, services, and information technology facilities, networks, and infrastructure assets that, if disrupted or destroyed, would have a serious impact on the health, safety, security, or economic well-being of citizens or on the efficiency of government functions.\"\u003c/p\u003e\n\u003cp\u003eThe concept of indirect exposure suggests that even in cases where direct flooding is not experienced, individuals may still be indirectly affected if the essential services they typically rely upon are compromised. The consequences of the flood manifest when these services, crucial for both physical and social well-being, become either non-operational or inaccessible due to the flooding, impacting individuals who are dependent on these services.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.2 Critical service infrastructure\u003c/h2\u003e\n\u003cp\u003eDefinitions of critical infrastructure often include utilities such as drinking water, electricity, heating, gas, or sewage, as well as facilities like power plants, communication networks, vital road networks, medical facilities (hospitals), schools, and emergency service buildings (European Commission \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Heilemann et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Len et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAs our model focuses on social well-being, we limit our inclusion of critical infrastructure to aspects that can be considered 'critical service infrastructure' and that would negatively impact individual well-being if disrupted due to flooding.\u003c/p\u003e\n\u003cp\u003eOur indirect exposure model includes 10 service infrastructure sectors, covering utilities, health, education, emergency sectors, and polluting units. More specifically, the model includes all units that relate to the production of electricity, provision of heat and water, and treatment of wastewater. From the health sector, the model includes all hospitals and general practitioners. The emergency sector includes police stations, fire stations, and emergency service units. Finally, the educational sector includes kindergartens, schools, and higher education institutions. Potentially polluting firms are also included in the model to measure access to a pollution free environment. For a full description and definition of the included units, see Table B.1 in the appendix.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.3 Measuring indirect exposure\u003c/h2\u003e\n\u003cp\u003eThe following section describes the method for calculating indirect exposure. In order to understand this method, a number of terms must first be defined:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eService Disruption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA service providing unit will no longer provide its service to the surrounding community when inundated with at least 20cm of water.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoss of Access to Service\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen a single service-providing unit within a given critical service infrastructure category is disrupted, this results in a complete loss of this service category for individuals dependent on the disrupted unit as their nearest provider. The nearest service-providing unit for each cell is defined as the unit that is closest to the cell center. This applies to all services except pollution-free environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePollution-Free Environment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccess to a pollution-free environment cannot be measured by the nearest provider approach. Instead, all individuals living within 500m of a flooded polluting firm are considered exposed to pollution.\u003c/p\u003e\n\u003cp\u003eFigure 3 shows an example of how indirect exposure is calculated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.4 Developing a common metric of exposure: Flooded people equivalent (FPeq)\u003c/h2\u003e\n\u003cp\u003eIn order to compare direct and indirect exposure, we develop a common metric for both types of exposure. Here we describe how direct and indirect exposure are measured using a common measurement unit: flooded people equivalent (FPeq).\u003c/p\u003e\n\u003cp\u003eFPeq is a metric that enables measurement of different levels of exposure or \u0026lsquo;wellbeing loss\u0026rsquo; in the event of a flood. FPeq ranges from 0\u0026ndash;1 with 1 denoting direct exposure. Indirect exposure is determined based on how many critical infrastructure services an individual loses access to as a result of a given flood event. An FPeq of 0.1 denotes loss of access to a single category of critical service infrastructure. All critical service infrastructure categories are weighed equally, meaning that FPeq scores are aggregated cumulatively as an individual loses access to additional services. For example, an individual that loses access to five critical service infrastructure categories experiences an FPeq score of 0.5. The maximum FPeq score based on indirect exposure is 1, corresponding exactly to the FPeq score associated with direct exposure.\u003c/p\u003e\n\u003cp\u003eWe calculate FPeq on an individual level and subsequently take the sum of FPeq within each 100x100m cell to find aggregated, cell-level FPeq scores.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Constructing a social flood risk index (SFRI)\u003c/h2\u003e\n\u003cp\u003eFollowing construction of the SVI and calculation of social exposure, we combine exposure and vulnerability information to calculate the flood event specific social damage (FESSD) for a single flood event. FESSD can then be used to calculate a social flood risk index (SFRI) for a probabilistic set of flood events.\u003c/p\u003e\n\u003cp\u003eTo calculate FESSD for a single flood event, we first categorize exposure values for each 100x100m grid cell into nationally relevant exposure categories based on a national mapping of population per grid cell. This is achieved by categorizing FPeq values into a 10-class Natural Breaks classification based on the absolute number of people living in each 100x100m cell nationally. This enables easier comparison between exposure scores for different flood events and avoids overemphasis of damage and risk in areas that are highly exposed in frequent return period events. SVI scores for each grid cell are also categorized into a 10-class Natural Breaks classification. Following the reclassification of exposure and SVI scores, we multiply the reclassified exposure and SVI scores to find FESSD (See Appendix C for complete description of calculation procedure). FESSD is calculated through multiplication to emphasize that both exposure and vulnerability reinforce each other and thus both high exposure and high vulnerability must be present to result in high social damage scores.\u003c/p\u003e\n\u003cp\u003eIt is important to note that by combining exposure and vulnerability information in this way, we assume that social vulnerability values apply to individuals who are both directly and indirectly exposed to flooding. We make this assumption in recognition of the fact that individuals with high vulnerability will not only be more vulnerable when directly faced with flooding but are also likely more vulnerable in the face of critical service infrastructure disruptions caused by a flood event.\u003c/p\u003e\n\u003cp\u003eAfter FESSD has been calculated for each event considered in our probabilistic set of flood events, we are able to calculate an aggregated social flood risk index (SFRI). Calculation of SFRI requires the combination of expected social damage of each flood event with the probability of that flood event (see Appendix D for a complete description of the calculation procedure).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe following sections present the results of the social flood vulnerability and social flood risk models. We begin by presenting a national overview of SVI in Denmark before mapping SVI, social flood exposure, flood event specific social damage (FESSD) and social flood risk (SFRI) in the local case study of Vejle. Finally, we present a national mapping of the social flood risk index (SFRI).\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Social flood vulnerability (SVI)\u003c/h2\u003e\n\u003cp\u003eThe social vulnerability index (SVI) displays social vulnerability to flooding on a 100x100m scale nationally. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e visualizes the spatial distribution of social vulnerability in Denmark.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows an uneven distribution of social vulnerability across the country. Cities clearly stand out as areas of higher social vulnerability. SVI is not weighted by population, so this trend does not merely reflect higher population density but points to a general trend of higher social vulnerability in cities. While a national overview is instructive for visualizing social vulnerability across Denmark, it is useful to more closely investigate the results in a local case study. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e maps social vulnerability in Vejle.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows that SVI in different areas of Vejle is determined by different indicators. For example, in the city center, high vulnerability stems primarily from lack of ability to acquire and understand information, poor socio-economic situation, and low neighborhood resilience. The disaggregation of SVI to individual indicators enables a more detailed understanding of the causes of vulnerability and thus also provides important information about potentially suitable strategies for reducing vulnerability depending on the local context. It should be noted that when interpreting local-scale SVI mapping, vulnerability should always be understood in relation to national average vulnerability. For example, an SVI score of 2 in a given grid cell in Vejle indicates that vulnerability in that grid cell is 2 standard deviations above the national mean. This enables easy comparison of relative levels of vulnerability in different local areas across the country.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Social flood exposure, flood event specific social damage, and social flood risk in Vejle\u003c/h2\u003e\n\u003cp\u003eThe case study of Vejle also offers an interesting area in which to visualize social flood exposure. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the extent of a 100-year flood event in Vejle.\u003c/p\u003e\n\u003cp\u003eFigure 7 shows exposure, social damage, and social risk in Vejle.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea shows that indirect social flood exposure extends far beyond the flooded area. In fact, although only 36% of exposure falls on those indirectly exposed, 19,989 people experience indirect exposure, more than 4.5 times as many as those who experience direct flooding. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb shows exposure in terms of flooded people equivalent (FPeq).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec shows flood event specific social damage for a 100-year event in Vejle. In this scenario, direct exposure accounts for 40% of total social damage while indirect exposure accounts for 60% of social damage.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed shows the social flood risk index in Vejle. Here, we see that the spatial extent of social risk in Vejle is much greater than the extent of exposure and social damage during a 100-year event. This is due to the fact that higher return period events (for example a 10,000-year flood event) will impact a larger geographical area of the city.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Social flood exposure in Denmark\u003c/h2\u003e\n\u003cp\u003eOn a national scale, we see similar trends as in Vejle regarding the share of indirect vs. direct flood exposure experienced as a result of flood events. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the share of the Danish population that is directly and indirectly exposed during various flood events.\u003c/p\u003e\n\u003cp\u003eFor the most extreme event (10,000-year event), about one third of the Danish population will be indirectly exposed. For the 100-year event, the ratio of indirect vs. direct flooded is approximately 12:1, showcasing how the consequences of a severe flood extends far beyond the flooded area. This ratio decreases for more extreme floods, indicating that some of those indirectly exposed in less severe floods will be directly exposed during more severe flood events. In the 10,000-year event, more than one third of the population is affected (33% are indirectly exposed). This may seem like a very large share, but we must recall that these calculations are based a worst-case flood simulation and that this simulation assumes simultaneous flooding across the entire country.\u003c/p\u003e\n\u003cp\u003eIt is also interesting to explore how exposure is distributed across social vulnerability classes for different flood events. For example, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows how indirect exposure is distributed across vulnerability classes during a 100-year flood event.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of indirect exposure across vulnerability classes for two flood events (100-year event)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSocial vulnerability (SVI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal population in group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePopulation indirectly exposed in group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eShare of total population indirectly exposed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePopulation directly exposed in group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eShare of total population directly exposed\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSVI Interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-3\\le SVI\u0026lt;-2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.42%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-2\\le SVI\u0026lt;-1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e405815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18688\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerately low\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-1\\le SVI\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1353530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e182749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerately high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0\\le SVI\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1938070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e273843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14,1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19949\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1\\le SVI\u0026lt;2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1329010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery high\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(2\\le SVI\\le 3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e554235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5733060\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e702751\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e12,3%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e57726\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.01%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates a noteworthy contrast in social vulnerability during a 100-year flood event between individuals directly and indirectly exposed. Among those with the lowest Social Vulnerability Index (SVI), both groups exhibit nearly identical proportions. However, among the indirectly exposed, individuals with higher SVI encounter a significantly greater likelihood of exposure compared to those directly exposed to flooding. Such computations indicate that this model can yield insights into how exposure to floods may disproportionately affect various social classes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4 Social flood risk index (SFRI) in Denmark\u003c/h2\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e shows the distribution of social flood risk on a national scale. Here, we see major clusters of social flood risk across the country, with the area around Aarhus noticeably standing out as the largest cluster of social flood risk.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this paper, we have developed a national mapping of social flood vulnerability and social flood risk in a Danish context. Our results offer several empirical and theoretical insights. First, we provide a nationwide mapping of social flood vulnerability and social flood risk in Denmark. The inclusion of social data has thus far been severely limited in Danish flood risk assessment (Kystdirektoratet, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), so this represents a significant increase in empirical knowledge about the social implications of flooding in a Danish context. Results show that the distribution of social flood risk in Denmark differs significantly when compared to existing areas of potential significant flood risk (risk areas in short) appointed under the EU Floods Directive in Denmark. Several of the largest clusters of social risk shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e (Aarhus, Aalborg, and northwestern Jutland) are not currently appointed as risk areas (Kystdirektoratet, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This finding points to the significance that including social data could have on our understanding of the national flood risk landscape in Denmark. We hope that our model can represent a first step in centering social impacts in Danish flood risk management approaches alongside economic and ecological consequences of flood events.\u003c/p\u003e \u003cp\u003eSecond, we advance methodological approaches to assess the social impacts of flooding by combing social vulnerability data with social flood exposure modeling to create a comprehensive model of social flood risk. This approach offers a more precise understanding of the dynamic and multi-faceted nature of flood consequences. The indirect flood exposure model highlights that the consequences of flooding can extend far beyond the physical boundaries of a flooded area and may have repercussions that impact larger systems and communities. Our results show that, on a national scale, indirect exposure accounts for between 55% and 71% of total social flood exposure during a flood event, depending on the return period. This finding has significant implications for flood risk management and climate change adaptation planning as it highlights the need to look beyond direct flooding when considering the social impacts of flood events. It is critical to acknowledge that our communities are interconnected systems and critically examine these interconnections when assessing and managing flood risk.\u003c/p\u003e \u003cp\u003eWhile our findings offer a valuable contribution to flood risk assessment research, several limitations must be mentioned. First, even in a data rich country such as Denmark, demographic data is highly sensitive and therefore challenging to access at a high spatial resolution due to confidentiality concerns. This makes the construction of a detailed and accurate SVI challenging. In our case, it was especially difficult to access health data. Therefore, we use proxy indicators to estimate spatial distribution of populations with poor health. However, we acknowledge that these proxies may not realistically capture all relevant health challenges experienced by the population. Challenges related to data acquisition and confidentiality also severely limit the reproducibility of our methods in less data rich contexts.\u003c/p\u003e \u003cp\u003eAdditionally, the intangible nature of social flood risk gives rise to issues of measurement and comparison. Here, we construct a social flood risk index (SFRI), which, by definition, provides a relative, index-based assessment of social flood risk. While this is valuable for identifying areas of relatively high risk and comparing risk across regions, municipalities, and communities; it does not provide an absolute measure of risk. The index-based nature of this model makes it difficult to compare with economic flood risk models which most often express risk in monetary value. This poses a potential challenge for decision-making processes that aim to balance reduction of social, economic, or other dimensions of flood risk.\u003c/p\u003e \u003cp\u003eFinally, while we attempt to center social impacts in flood risk assessment, we must acknowledge that our methodology relies purely on expert-defined data. Recently, there has been some debate as to the efficacy of expert-defined social vulnerability indices in accurately representing empirical realities and locally relevant narratives of vulnerability (Rufat et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, further work should empirically validate SVI in a Danish context and consider more inclusive and participatory methods of vulnerability and exposure mapping to ensure that they accurately represent experiences on the ground.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eAs flood events become increasingly frequent and intense due to climate change, it is urgent to develop more comprehensive understandings of flood vulnerability, exposure, and risk. The social consequences of flooding are a critical and often overlooked component of flood risk that must be highlighted further in flood risk management decision-making. This paper has developed a socially specific model of flood risk that combines flood hazard information, social flood vulnerability, and social flood exposure. This model enables a comprehensive understanding of the social impacts of flooding and highlights that flooding not only impacts various social groups differently but can also impact people living far outside flooded areas. This study therefore highlights the importance of looking beyond social vulnerability and including socially relevant exposure modelling in assessments of social flood risk. Three key avenues for further research are identified: (i) participatory research to integrate diverse community values into indirect social flood exposure modelling, (ii) further research into modelling social flood vulnerability and social flood exposure in future scenarios, and (iii) studies that explore methods for comparison or combination of social flood risk with economic and ecological flood risk models. Together, these three research directions can further inform socially oriented flood risk management approaches that center human wellbeing and advance equity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eM.P. and U.B. led the conception and methodological design of the research with support from all authors. N.H. and M.H. carried out the analysis and produced maps. M.P. and U.B. wrote the text with support from N.D. and K.A. All authors provided feedback on the text prior to submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank our colleague Jane Mosb\u0026aelig;k Flyvholm for her valuable contributions to the initial conception of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmaș, I., \u0026amp; Gavriș, A. (2013). Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model)\u0026ndash;a case study for Bucharest, Romania. Natural hazards and earth system sciences, 13(6), 1481-1499.\u003c/li\u003e\n\u003cli\u003eAven, T. (2011). On some recent definitions and analysis frameworks for risk, vulnerability, and resilience. Risk Analysis: An International Journal, 31(4), 515-522.\u003c/li\u003e\n\u003cli\u003eBalica, S. F., Wright, N. G., \u0026amp; Van der Meulen, F. (2012). 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Neighborhood structure, social capital, and community resilience: Longitudinal evidence from the 2011 Brisbane flood disaster. \u003cem\u003eSocial Science Quarterly\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e(2), 330-353.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Danish Coastal Authority","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"flood risk, social vulnerability, vulnerability assessment, social risk, risk assessment, flood risk management","lastPublishedDoi":"10.21203/rs.3.rs-4293472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4293472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlood risk assessment approaches have traditionally been dominated by measures of economic damage. However, the importance of understanding the intangible social impacts of flooding are increasingly being acknowledged. Social vulnerability indices have been constructed in diverse geographical contexts to understand differential susceptibility of different social groups to flood hazards. However, integrated assessments of social vulnerability, exposure, and hazard information are lacking. Here, we construct a national social vulnerability index (SVI) for Denmark and combine this with direct and indirect social exposure data and coastal flood hazard data to construct a national social flood risk index (SFRI). Results show the spatial distribution of social flood vulnerability and social flood risk in Denmark. Our work introduces a comprehensive flood risk modelling approach that explicitly considers the social impacts of flooding in all model components. Such an approach can facilitate a shift towards the implementation of more holistic and inclusive flood risk management and climate change adaptation planning approaches that are usable within the context of existing risk management frameworks such as the EU Floods Directive. We hope that introducing a comprehensive, socially-specific approach to flood risk assessment can help to mainstream social wellbeing, resilience, and justice as central considerations in decision making on flood risk management.\u003c/p\u003e","manuscriptTitle":"A novel and comprehensive approach for understanding the social impacts of flooding: assessing social flood vulnerability and social flood risk in Denmark","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 06:26:33","doi":"10.21203/rs.3.rs-4293472/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb181b97-94d0-4d5e-b9ec-226cd70e8ef9","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T06:26:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 06:26:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4293472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4293472","identity":"rs-4293472","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}