Spatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators

Spatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators Ana Noemí Gomez Vaca, Ignasi Rodríguez-Roda, Lucía Alexandra Popartan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4199231/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract This study introduces a methodology for evaluating vulnerability to urban flooding across different dimensions, by employing spatial data analysis. The methodology consists of four steps: (1) selection of indicators that reflect the vulnerability of an urban area, (2) normalization of the data for each selected indicator across all dimensions, (3) assignment of weights for each indicator and dimension, and (4) mapping and classification using spatial analysis, resulting in a regular grid. This study proposes a comprehensive list of 31 potential indicators for quantifying vulnerability, with each indicator falling under one of the five dimensions (social, economic, environmental, physical, and institutional) and three components of vulnerability (exposure, susceptibility, and resilience), which are part of Step 1. Additionally, the methodology is complemented by a data generation and spatial analysis technique for Step 4. The proposed methodology can serve planners and policymakers to make objective decisions, based on vulnerability quantification, considering each dimension separately, as well as integrating with each other, using a multidimensional and spatial representation of flood risk vulnerability. Multidimensional vulnerability Urban flooding Indicator Spatial analysis Figures Figure 1 Figure 2 1. INTRODUCTION Floods are natural disasters that have caused significant losses globally, both in terms of human life and economic impact (Hu et al., 2017 ). These events account for approximately one-third of all global natural hazards, and the frequency of extreme floods has risen significantly in the last decade (Tsatsaris et al., 2021 ). According to OECD (2016), the annual global economic damage from floods surpasses $ 40 billion. The effects of climate change, reduction of green areas, and deterioration of water infrastructure are contributing factors to various types of floods, such as fluvial, flash, or pluvial floods. These factors, along with others, increase the risk of flooding (Kaykhosravi et al., 2019 ; Rangari et al., 2018 ; Sarkadi et al., 2022 ) and raise questions about the effectiveness of traditional management approaches (Westra et al., 2014 ). Traditionally, flood risk management has heavily focused on gray infrastructure (Johns, 2019 ), which is often deemed ineffective and unsustainable, both economically and environmentally. In response, to this cities are now reassessing their strategies by integrating green infrastructure solutions, such as rain gardens, permeable pavements, and green roofs, rather than relying solely on conventional engineering methods (Chan et al., 2018 ). However, transitioning from an approach focused on gray infrastructure soley to a hybrid one approach, incorporating green solutions has limitations: state of the art research indicates that even when combined, these approaches may not offer sufficient protection (Meng et al., 2020 ; Patel et al., 2023 ). Therefore, integrating nonstructural measures, such as urban planning (Loucks, 2015 ), and accurately mapping vulnerability to flood risk is essential (Abdrabo et al., 2020 ). In this sense, vulnerability assessment remains a key component of effective risk management, as highlighted in international discussions on disaster management, sustainable development, and climate change adaptation (Balica and Wright, 2009 ; IPCC, 2021 ). The Sendai Framework for Disaster Risk Reduction, the United Nations Sustainable Development Goals, and the European Green Deal all underline the importance of understanding and reducing disaster risk, including vulnerability, to achieve sustainable development (Ludwig et al., 2023 ; Sachs et al., 2022 ). Flood vulnerability is typically assessed using the following methods: (i) vulnerability curve, (ii) disaster loss data, (iii) computer modeling, and (iv) index-based. (Nasiri et al., 2016 ). The latter remains is the most widely used methodology (Bera et al., 2019 ; Moreira, De Brito, et al., 2021; Nasiri et al., 2016 ). Indices serve as a summary of complex, multidimensional issues to assist decision makers, facilitate the interpretation of a phenomenon and increase public interest through a summary of the results (Moreira, de Brito, et al., 2021). Given the complexity of flood vulnerability analysis (Birkmann et al., 2013 ), a diversity of conceptual frameworks has emerged, emphasizing its multidimensional character (Aroca-Jimenez et al., 2017 ; Aroca-Jiménez et al., 2022 ; Fuchs & Thaler, 2018 ; Kumpulainen, 2006 ). The most commonly addressed dimension of vulnerability is the social one (de Loyola Hummell et al., 2016 ; Oulahen et al., 2015 ; Tascón-González et al., 2020 ), along with the physical (Papathoma-Köhle et al., 2019 ), economic (Veen & Logtmeijer, 2005 ), institutional (Papathoma-Köhle et al., 2021 ), and environmental (Damm, 2010 ; European Environment Agency., 2016). In addition to encompassing these dimensions, vulnerability to flooding has been analyzed considering three main components: exposure, susceptibility and resilience (S. F. Balica et al., 2009 ; Cutter et al., 2010 ; Qasim et al., 2016 ). However, these indicators are rarely geospatial and do not always take into account all the components and dimensions of vulnerability at the same time. In addition, these indices focus largely on catchment scale, neglecting factors that affect vulnerability on a small urban scale. In addition, a detailed breakdown of all the indicators and variables considered is not yet clear. The objective of this study is to present a methodology for quantifying multidimensional vulnerability to long-term impact flooding, including an analysis of spatial data. This novel methodology constitutes an alternative approach for addressing multidisciplinary issues related to flood vulnerability. Its diverse dimensions and components provide a holistic perspective of vulnerability. This approach can enhance the communication among stakeholders and promote the awareness of flood-related risks. Vulnerable dimensions can be identified through visualization via maps, allowing for structured (e.g., gray, hybrid, or green infrastructure) and non-structured (e.g., emergency plans or risk management plans) proposals. Previous research conducted a detailed assessment of a large city at the scale of a downtown district (Nasiri et al., 2019 ). This approach is advantageous for guiding future urban growth away from high-risk areas and promoting resilient districts (Mercader-Moyano et al., 2021 ). Furthermore, the integrated method considers the relationships between each element not only within its own scale but also across multiple layers that contribute to urban vulnerability (Salas & Yepes, 2018 ). Additionally, some studies have examined flood vulnerability at multiple scales, considering only certain dimensions of vulnerability (Chang et al., 2021). The structure of the paper is as follows. First, we present the research method. Subsequently, we discuss the proposed indicators as well as the advantages and limitations of the methodology. Finally, we conclude with the main findings of the study. 2. METHODOLOGY In this section we present a step-by-step method employed to build the proposed vulnerability index ( Fig. 1 ). Step 1: Selection of components and dimensions In this step, indicators are selected for each component and dimension. The purpose is to select and reduce the number of variables to simplify the interpretation of the issue being addressed (Pérez, 2004), in our case, the dimension of interest. Furthermore, each dimension encompasses several indicators, and each indicator may encompass one or more variables (see Results section). To calculate the multidimensional index, distributed in five dimensions (social, economic, environmental, physical, and institutional) and three components (exposure, susceptibility, and resilience), there are a total of 31 proposed indicators (Table 1 ). Table 1 Number of proposed indicators by component and dimension Components Total indicators Exposition Susceptibility Resilience Dimensions Social 1 5 2 8 Economic 2 4 2 8 Environmental 4 1 1 6 Physical 3 2 1 6 Institutional 0 1 2 3 Total indicators 10 13 8 31 The most difficult task in constructing an index is the selection of indicators, which depends on the quality of the available variables and the subjectivity inherent in decision-making (Nardo et al., 2005). However, there is no hard and fast rule that defines the variables and indicators to be taken into account when assessing vulnerability to flooding because the indicators will depend on the context of each city, the needs of the users, the information available, the precipitation, the lithology, and the slope angle (Nguyen et al., 2022 ). Therefore, rather than a strict conceptual framework, it is relevant to identify the types of components, dimensions, and indicators that are useful for vulnerability analysis. The proposed indicators were selected according to their definition, considering quality standards including relevance, accuracy, timeliness, accessibility, interpretability, and coherence (OECD & JRC, 2008). The selected indicators covered the aspects of exposure, susceptibility, and resilience (Wu, 2021). These indicators offer a thorough assessment of urban systems in cities and independent evaluation of their vulnerability to flooding. The findings outlined in this paper stem from a meticulous examination of the existing literature combined with insights from expert opinions. Although some indicators may not be easily classified within the five dimensions, we assigned each indicator to a single dominant vulnerability dimension to avoid double counting (Chang et al., 2021b ). For the classification of indicators and variables, any indicator, qualitative or quantitative, was considered to carry meaning beyond its value. That is, the relevance of an indicator for estimating a particular characteristic of a system arises from the interpretation of the indicator itself and its relationship with the phenomenon being characterized (in this case, vulnerability). Therefore, assigning meaning to a variable and defining the relationship between the variable and indicator makes a variable eligible to be considered an indicator (Birkmann, 2013 ). Integrated and multidimensional approaches require the aggregation of multiple indicators, referred to as composite indicators (Damm, 2010 ). A composite indicator is the result of combining individual indicators into a single index (OECD, 2008 ). In this paper we consider indicators and variables, so each indicator can include one or more variables. Vulnerability is the result of interaction between the three components mentioned above. In this way, the Flood Vulnerability Index (FVI) can be calculated using Eq. 1 provided by Balica (2007): FVI = Exposure + Susceptibility – Resilience (1) In this study, we define the following: Exposure is defined as the predisposition of a system to be disrupted by a flooding event owing to its location in the same area of influence (S. Balica & Wright, 2010 ). Susceptibility is defined as the exposed elements within the system that influence the likelihood of damage during times of dangerous (preflood) flooding (S. Balica & Wright, 2010 ). Resilience is characterized by a set of characteristics, abilities, resources, and opportunities of people, places, and infrastructure to survive, absorb impacts, and manage the adverse effects of floods (Emrich y Tobin, 2018). After selecting the indicators proposed in Fig. 1 (Step 1, gray box), as described in Table 1 , the next step is to choose the urban scale (municipality, neighborhood, census sector, or others). The representative variables for each indicator are then defined, and the data corresponding to these indicators are obtained. Subsequently, the data are processed in vector format. To ensure that the urban scale is not a constraint, a grid-level analysis is proposed, which is a geographic information system (GIS) technique used to analyze data within a grid or raster format. It divides the study area into smaller grids or cells, where each cell represents a specific area or unit of analysis, which allows the visualization and analysis of data at a higher resolution and provides a more detailed understanding of spatial patterns and relationships (Kikon et al., 2023 ). In addition, it is considered that most cities depend on urban planning at least have cadastral data. Step 2: Normalization This study presents a methodology that can be employed at various scales, such as districts, sectors, and neighborhoods. To compare variables and indicators with different units of measurement, it is necessary to standardize or normalize the data values (Sarstedt and Mooi, 2014). The main normalization methods are Ranking, Z scores, Min -max, distance from the group leader, division by total, categorical scale and binary standard (Moreira, De Brito, et al., 2021) A commonly used method of standardization involves rescaling the original data so that the variable's mean is 0 and the standard deviation is 1. This approach is known as the z-score method and is based on Eq. ( 2 ). Results will be given in percentage. $$Zi=\frac{({x}_{i} - \stackrel{-}{\text{x}})}{s}$$ 2 Step 3: Aggregation and assignment of weights of indicators and dimensions Once the normalization process is completed, the weights of the variables, indicators, and dimensions are obtained. The methods used for weighting are usually based on statistics (Gu et al. 2018). In this study, we opted for expert involvement because the results are more likely to be reliable (Oulahen et al., 2015 ). Participation is believed to be a key component in promoting effective disaster risk reduction (Fekete et al., 2021 ). Many authors have recommended the use of participatory methods for weighting indicators (Evers et al., 2018). The assumption is that, if practitioners and experts participate in creating an index that they find useful, they are more likely to trust their results (Oulahen et al. 2015 ). In addition, participation is believed to be a key component in promoting effective disaster risk reduction (Fekete et al., 2021 ). The methodologies to follow for aggregation can be Linear and Geometric (Moreira, de Brito, et al., 2021). Because vulnerability includes five dimensions –social, economic, environmental, physical, and institutional– in this case, linear aggregation is preferred (Gan et al., 2017 ). The sum of the weighted components results in a cumulative vulnerability score for each dimension. In addition, it is considered that element make it more important in terms of weights. The cumulative vulnerability score shown in Eq. 3 is used to map the spatial distribution (Abdrabo et al., 2020 ; Aroca-Jimenez et al., 2017 ). $$Vulnerability={\sum }_{f=1}^{n}Wf*Sf$$ 3 where f represents the indicator/dimension of vulnerability; n is the total number of indicators/dimensions; wf ​ is the relative weight assigned to the indicator/dimension; and sf ​ is the indicator/dimension score. Step 4: Mapping and index classification (Maps Multi-layer) In this step, vulnerability maps are generated for each dimension based on Abdrabo et al. ( 2023 ) and Aroba-Jimenez et al. (2017); the resulting vulnerability maps will be classified into five evenly distributed categories from 0 to 1 based on their values: very low (0–0.201), low (0.202–0.401), moderate (0.402–0.600), high (0.601–0.800), and very high (0.801–1.0) (Abdrabo et al., 2020 ). The vulnerability maps are calculated based on (Abdrabo et al., 2023 ; Aroca-Jimenez et al., 2017 ) Next, the grid is generated, using the fixed index of the grid is an n×n matrix of cells of equal size. Each is associated with a list of spatial objects that intersect or overlap the cell. This data structure, known as a fixed grid index, provides an efficient way to organize and query spatial data based on their intersections or overlaps with the cells in the grid. The fixed grid index is a powerful tool for organizing and querying spatial data based on their intersections or overlaps with the cells in the grid (Jiang et al., 2018 ). This allows us to analyze the data at different scales (Kikon et al., 2023 ). Next, the data are analyzed, the vulnerability maps that have been considered above are visualized, and then the overlay method is applied. Overlap analysis involves combining two or more thematic maps of the same area and creating a new map by overlapping it. In addition, we combine the features of multiple datasets into a single dataset and then search for specific areas that have a certain value. This approach is often used to find locations that are suitable for the particular use of some risk ( Overlay Analysis—ArcMap | Documentation , 2012). Additionally, this method has been applied to a variety of decision-making problems with a large number of criteria (Chen et al., 2010 ; Kittipongvises et al., 2020 ) owing to its ability to integrate a large amount of heterogeneous data and provide degrees of consistency and inconsistency in the obtained results. Once the multidimensional vulnerability index map is obtained, it is overlaid with a flood map that is downloaded from the area database of a given study area. This superposition of maps makes it possible to identify the most vulnerable areas by considering an integrated approach based on multiple layers of selected dimensions and indicators. Visualization aids decision-makers in understanding and prioritizing necessary actions to reduce vulnerability in each area, as depicted in Fig. 2 . This multidimensional approach provides a solid foundation for developing more effective mitigation and adaptation strategies, enabling a better understanding of the interconnections among various dimensions. By integrating these perspectives, this approach seeks to contribute to the advancement of vulnerability assessment research, promoting more comprehensive and applicable approaches across a variety of contexts. The methodology is comprehensive and can be used at different scales and in different contexts, with sub-indicators adjusted according to the specific disaster under study. 3. Results The result of our research constitute a novel flood vulnerability index, including the following: (i) Components : exposure, susceptibility, and resilience, according to the established definition (see methodology Step 1). (ii) Indicators : Indicators are quantified using variables. (iii) Variables : Individual indicators. Each indicator includes one or more variables. (iv) Description in relation to vulnerability : This description is elaborated by the authors in order to specify the relationship of the established indicator. (v) Units : Units were established in relation to the variables, some of which consider the units of the literature review and others are added by the authors. (vi) References : This section establishes a reference for the variables that have been considered. Social Dimension Vulnerability Indicators This dimension comprises the characteristics of an individual or group and their situation, which influences their ability to anticipate, cope with, resist, and recover from the impact of floods (Wisner, 2016 ). The social dimension comprises of eight indicators. Once of them indicates exposure, five indicators of susceptibility, and two demonstrating resilience ( Table 2 ). Table 2 Social Dimension indicator Nº Component Indicators Variables Description in relation to vulnerability Unit Reference 1 Exposition Population Population density It is used to quantify the exposed population; Inhabitants/km 2 (Almeida et al., 2020 ; Aroca-Jiménez et al., 2022 ; JRC TECHNICAL REPORT, 2023 ) 2 Susceptibility Immigration Immigration rate Population with lack of knowledge or limited access to information % (Parsons et al., 2020) 3 Susceptibility Gender condition Femininity Rate Women may experience a longer recovery period than men due to their specialization in specific sectors, lower wages, and traditional family caregiving responsibilities. % (Cutter et al., 2003 ; Müller et al., 2011; Rufat et al., 2015 ; El-Boshy et al., 2019; El-Hattab et al., 2018a; JRC TECHNICAL REPORT, 2023 (JRC TECHNICAL REPORT, 2023 ) 4 Susceptibility Age Extremes Elderlies above 65 Older people have less mobility and dependency % (Cutter et al., 2003 ; Rufat et al., 2015 ; Kablan et al., 2017; El-Hattab et al., 2018a; El-Boshy et al., 2019; JRC TECHNICAL REPORT, 2023 ) Children under 14 Fragile physical condition, degree of dependency, and difficulty in movement. % 5 Susceptibility Disabilities Number of the disabled population Disabled populations find mobility difficulties to avoid hazards (physical, auditory, visual, psychological, or psychiatric). % (Rufat et al., 2015 ; Rana and Routray, 2018 ; El-Boshy et al., 2019) 6 Susceptibility Social assistance dependency Employees earning below minimum wage People with limited economic resources find it difficult to cope with disastrous events. % (Cutter et al., 2003 ; Rufat et al., 2015 ; Rana and Routray, 2018 ;Abdrabo et al., 2023 ) Unemployed population % 7 Resilience Health Emergency Service Accessibility Improved healthcare facilities in emergencies strengthen resilience in urban areas. Number of health workers/ 1,000 inhabitants (Almeida et al., 2020 ; Aroca-Jiménez et al., 2022 ; Tascón-González et al., 2020 ) 8 Resilience Level of Education Population with High School or Vocational Training Education with understanding and responding to warnings and implementing mitigation measures. % (Ribas et al., 2024 ) Population with university education % Social indicators are fundamental for understanding the vulnerability of individuals and groups. Population density ( indicator 1) is crucial in determining exposure to hazards such as flooding, highlighting the importance of considering both the geographic extent of the hazard and the concentration of people in vulnerable areas. In terms of susceptibility, indicators such as immigration ( indicator 2 ) and limited access to information highlight how certain groups may be at greater risk owing to their socioeconomic or demographic situation. In addition, gender ( indicator 3) and extreme age ( indicator 4 ) have been extensively studied because of their influence on traditional roles, reduced mobility, and dependence on caregivers (JRC TECHNICAL REPORT, 2023 ). Disabilities ( indicator 5 ) also play a crucial role, because physical, mental, and emotional barriers can hinder people's ability to cope with disasters. Similarly, dependency on social assistance ( indicator 6 ) may limit the available resources and the responsiveness of those affected. The state-of-the-art processes indicate that there are uniform criteria when using the following indicators: percentage of elderly, percentage of children, percentage of women, percentage of individuals with disabilities, percentage Immigration rate (Aroca-Jimenez et al., 2017 ; Koks et al., 2015 ). This is because they have the greatest influence on the social burdens of natural hazards, as they constitute more vulnerable groups, as they suffer large social impacts more frequently. Regarding resilience indicators ( indicator 7 ) of health in the unit, there are coincidences in the number of health personnel per 1,000 inhabitants (Aroca-Jimenez et al., 2017 ; Tascón-González et al., 2020 ). Level of education ( indicator 8 ) considers the percentage unit (Ribas et al., 2024 ). Economic Dimension Vulnerability Indicators The economic dimension is defined as the propensity for the loss of economic value from damage to physical assets and/or the disruption of productive capacity (Birkmann et al. 2013 ). This dimension includes eight indicators: two exposure indicators, four susceptibility indicators, and two resilience indicators ( Table 3 ). Table 3 Economic Dimension indicator Nº Component Indicator Variables Description in relation to vulnerability Unit Reference 9 Exposition Housing Population of renters Renters may have fewer financial resources to deal with flood damage, and rental housing may lack risk mitigation features compared to owner-owned housing, leading to housing quality inequality. % (Beccari, 2016 ) 10 Exposition Economic activity Companies & Businesses Economic activity is often vulnerable due to exposure in flood areas % (m 2 business/ha of city) (Ribas et al., 2024 ) 11 Susceptibility Income Income (€) per person Floods can negatively impact people's employment and livelihoods, especially in regions where economic activities depend on the climate. Low-income communities may struggle more to recover economically. €/person*year (Parsons et al., 2021 ; Ribas et al., 2024 ) 12 Susceptibility Occupation (Economic sectors) Employed in primary extractive industries (farming, fishing, mining, and forestry Some occupations are sensitive to disruptions caused by disasters, as they lose their livelihoods and financial resources % (Cutter et al., 2003 b; Rufat et al., 2015 ; Rana & Routray, 2018 ;) Employed in transportation, communications, and other public utilities Employed in service occupations 13 Susceptibility Municipal Budget Municipal budget Local economic conditions can affect the city's capacity €/km 2 (Aroca-Jiménez et al., 2022 ) 14 Susceptibility Asset Value The monetary value of infrastructure, facilities, and properties It may impair electrical infrastructure and disrupt functionality, which can influence the property's worth and overall performance. €/km2 Author’s 15 Resilience Insurance & Coverage Degree of insurance coverage for physical assets Measure to reduce vulnerability % (Kuhlicke et al., 2011 ) 16 Resilience Financial Capacity (Municipal) Access to financial resources for recovery Access to national and international financial resources for recovery as measures to reduce vulnerability €/km2 (Guillard-Gonçalves et al., 2015 ) In terms of exposure, economic activity ( indicator 10 ) highlights the importance of understanding that companies and businesses may be vulnerable to the loss of income and physical assets, which is directly related to the susceptibility of occupation ( indicator 12 ) and Municipal Budget ( indicator 13 ), where economic capacity can be closely linked to the financial situation of local authorities and their available budget for response and recovery. On the other hand, income ( indicator 11 ) highlights how people with lower incomes may face greater difficulties in recovering economically after a disaster (Ribas et al., 2024 ). This can be aggravated if housing is primarily composed of housing tenants ( Indicator 9 ), who may lack financial resources and rental insurance to cope with the damages that could be caused. In terms of resilience, indicators 15 and indicator 16 are also essential, as the municipality's insurance coverage and financial capacity can determine its ability to recover from a disaster and reduce economic losses.(Kuhlicke et al., 2011 ). Some overlaps are identified; for example, indicator 11 and part of indicator 12 are related to the socio-economic susceptibility of the population. Income is closely linked to employment, especially in agriculture, fisheries, and forestry, which are particularly vulnerable. Therefore, inclusion of both indicators may be beneficial. Municipal Budget and Asset Value ( indicators 13 and 14 ) both provide information on the financial capacity and economic assets of a municipality. The municipal budget may reflect, in part, the value of assets and infrastructure within the area, suggesting an overlap, insofar as both indicators assess a jurisdiction's economic capacity to cope with and recover from disasters. In addition, the causal relationship between indicators can be assessed; some indicators may be causal factors of others, whereas others may be outcome or mitigation measures. For example, income ( indicator 11 ) can influence a person's ability to obtain insurance ( indicator 15 ), which in turn can affect economic resilience. Identifying these relationships can help to distinguish between indicators and understand how they interact. It can also influence the context and relevance in which the indicators are applied. Some indicators may be more relevant in certain environments than others. For example, the importance of the municipal budget ( indicator 13 ) may vary according to the municipality’s size and economic structure. Understanding the context can help determine the importance of each indicator and differentiate between overlaps. This will allow for a more complete and accurate analysis of the situation and facilitate the identification of key areas for intervention and risk mitigation. Regarding the units, consensus was found for indicators 9, 10, 11, and 13. The authors considered other units of indicators 12, 14, 15, and 16. Environmental Dimension Vulnerability Indicators The environmental dimension is defined as the degree to which an ecosystem service is sensitive to external pressures (urbanization and agriculture) (Aroca-Jiménez et al., 2022 ; Metzger et al., 2006 ), including cultural aspects. This dimension includes six indicators, four of which are exposure indicators, one is a susceptibility indicator, and one is a resilience indicator (see Table 4 ). Table 4 Environmental Dimension indicator Nº Component Indicator Variables Description in relation to vulnerability Unit Reference 17 Exposition Recreation facilities Recreation Spaces Small-Scale Recreation sites, being at ground level and without adequate protective measures / Locations near bodies of water can increase vulnerability (neighborhood parks, small playgrounds). % (Aroca-Jiménez et al., 2022 ) 18 Exposition Natural Assets of Cultural Interest Percentage of surface Certain assets are more vulnerable due to their historical, cultural, and natural importance in being close to bodies of water and eroded soils. % (Aroca-Jiménez et al., 2022 ) 19 Exposition Underground water bodies Percentual extension of groundwater bodies Floods can transfer soil contaminants from agricultural or urban areas into the soil and impact groundwater quality. % (Aroca-Jiménez et al., 2022 ) 20 Exposition Damage or loss of sea/river line Sea/River Line Urban development without proper planning results in the loss of buffer zones, making riverbanks and coastlines more susceptible to flooding. Length/ km 2 (Mani Murali et al., 2013 ) 21 Susceptibility Species richness Relative Habitat Richness Index. Flooding can result in the destruction and degradation of natural habitats, leading to biodiversity loss because many organisms depend on specific environments for their survival. Number of habitats per ha. (Aroca-Jiménez et al., 2022 ) 22 Resilience Green infrastructure capacity Extension of Nature Based Solutions Green infrastructure, including natural areas, bio-drainage canals, urban wetlands, reservoirs, green roofs, permeable pavements, and urban agriculture, can help mitigate and adapt to the impacts of climate change. % (Spahr et al., 2020 ) Protected natural areas % The following indicators emphasize the importance of protecting and conserving natural resources to maintain cultural identity and biodiversity: exposure to recreational facilities, such as parks and playgrounds, to extreme water-related events ( indicator 17 ); the presence of natural assets of cultural interest in vulnerable areas ( indicator 18 ); groundwater contamination due to flooding ( indicator 19 ); and loss or damage to shorelines or rivers ( indicator 20 ), which are crucial for land use planning and coastal management to reduce vulnerability to erosion and flooding. In a susceptibility approach, the index of species richness ( indicator 21 ) underlines the need to conserve and restore ecosystems to maintain ecosystem health and ensure the continued provision of ecosystem services essential for human well-being. Green infrastructure ( indicator 22 ) is a standout strategy in resilience approaches to strengthen communities' abilities to withstand natural disasters. Although it may seem similar to the Recreation Facilities Exhibition ( indicator 17 ), as both involve planning and designing green spaces to mitigate risk, they address different aspects. While both indicators use nature to reduce vulnerability, their approaches differ: the first focuses on the implementation of concrete solutions and ecosystem regulatory services provided by ecosystems in an intensive manner, while the second focuses on assessing the vulnerability of existing recreational infrastructure. The socio-ecological approach to risk assessment comprehensively analyzes these indicators, recognizing the interdependence between natural and social systems to provide a more comprehensive and effective analysis of vulnerability and resilience to natural disasters (Shah et al., 2020 ). Physical Dimension Vulnerability Indicators The physical dimension is the potential for damage to physical assets, including built-up areas, infrastructure, and open spaces (Birkmann et al., 2013 ), including cultural aspects. This dimension encompasses six indicators, three of which are indicators of exposure, two of susceptibility, and one of resilience (see Table 5 ). Table 5 Physical Dimension indicator Nº Component Indicator Variables Description in relation to vulnerability Unit Reference 23 Exposition Urban context Urban morphology (The density of the built Environment) Increased population and activities raise the number of exposed assets. (km/km 2 ) % built/km 2 (Cutter et al., 2003 ; Müller et al., 2011; Kablan et al., 2017) 24 Exposition Cultural heritage sites Monuments, Historical constructions, Archaeological sites Historical cultural sites are frequently situated near bodies of water, which exposes them to the danger of flooding. Presence/ absence (Aroca-Jiménez et al., 2022 ) 25 Exposition Urban Service Electricity transport and distribution Urban system services are often vulnerable, particularly those exposed in flood-prone areas. Length/km 2 (Almeida et al., 2020 ) Waste Collection Presence/ absence Traffic lights and street lighting Length/km 2 Telecommunication network Length/km 2 Urgent and Emergency Services Presence / absence Gas distribution network Length/km 2 26 Susceptibility Building condition Quality buildings Buildings are more vulnerable to damage when subjected to flooding due to poor construction conditions. Low, medium, high-quality building (Ghajari et al., 2017 ) 27 Susceptibility Water infrastructure condition Drainage system In the event of damage to infrastructure, an additional financial burden is placed on the city to compensate for these damages. (Km poor network/ Km total network) ha. (Cutter et al., 2003 ; Rana and Routray, 2018 ) (Aroca-Jiménez et al., 2022 ) Drinking water distribution system (Km poor network/ Km total network) ha. Water tanks Presences / Absence and Cubic meters of the tank 28 Resilience Structural protection measures Dams, canalizations, bridges Infrastructures aid in reducing the adverse consequences of flooding, thereby preserving lives and safeguarding properties. % (Tiggeloven et al., 2020 ) The built environment ( indicator 23 ) is crucial for understanding urban areas' vulnerability to disasters, and high-density buildings can increase exposure while affecting resilience Cultural heritage sites (indicator 24) and urban services (indicator 25) are also essential components, if they see in their unity we adhere to the presence/absence approach to represent a discrete site-specific variable. Indicator 26 , which assesses building conditions and susceptibility, is complex and evaluates low-, medium-, and high-quality buildings (Ghajari et al., 2017 ), while Indicator 27 , which measures water infrastructure conditions, is also challenging. The resilience component ( indicator 28 ) focuses on structural protection measures to mitigate the negative impacts of flooding. In some cases, it may be necessary to combine multiple units to adequately capture the complexity of physical vulnerability. For example, in indicator 27 , the ratio of the length of poor drainage infrastructure to the total length of infrastructure is used as a measure of susceptibility. This combination of units provides a more comprehensive metric of water infrastructure vulnerability. Analyzing the indicators, it is possible to identify the overlap between Exposure of Cultural Heritage Sites ( indicator 24 ) and Exposure of Urban Infrastructure ( indicator 25 ), both of which assess the exposure of physical assets to natural hazards but focus on different types of infrastructure. While Indicator 24 focuses on cultural heritage sites, such as historical monuments and archaeological constructions, indicator 25 focuses on more general urban infrastructure. Institutional Dimension Vulnerability Indicators Definition of these dimensions: The strengths and constraints in governance, underlying rules, and regulatory systems that govern the ability or inability of institutions to cope with floods and adaptation challenges (Papathoma-Köhle et al., 2021 ) The institutional dimension, which does not take into account the exposure component, includes three indicators, one of which is susceptibility and two are indicators of resilience ( Table 6 ) . Table 6 Institutional Dimension indicator Nº Component Indicator Variables Description in relation to vulnerability Unit Reference 29 Susceptibility Municipal debt Municipal debt Municipal debt can increase vulnerability due to limited resources and dependence on external resources. €/ Inhabitant (Aroca-Jiménez et al., 2022 ) 30 Resilience Flood Risk Management Plan Existence of Local Flood Risk Management Plan The plans aim to increase public awareness about flood risks, emphasizing preparedness and mitigation. Community education on flood readiness boosts resilience at both individual and collective levels. 1 − 0 (Aroca-Jiménez et al., 2022 ) Availability of emergency plans 1 − 0 Existence of early warning systems 1 − 0 31 Resilience Resilience Measures Projects related to green infrastructure and adaptation By incorporating resilience principles into urban planning and development, cities can ensure their sustainability. (e.g., alternative energy sources, firefighters, civil guard, voluntary groups, collectives, and associations) 1 − 0 Annual projected investment in adaptation €/ inhabitant Emergency and civil protection €/ inhabitant Annual investment in Preventive and compensatory measures €/ Inhabitant The indicators presented in this section provide a detailed overview of how institutions influence the vulnerability and resilience of local communities. The Municipal debt susceptibility indicator ( indicator 29 ) can limit available resources and dependence on external sources, which can increase the local vulnerability to flooding and other challenges. Indicators 30 and 31 , Flood Risk Management Plan and Resilience Measures, respectively, focus on institutional strategies to prepare for and respond to floods. Although they address different aspects of institutional resilience, both indicators are linked to local institutional capacity to cope with flood risks. While there is no direct overlap in the variables, there is an overlap in terms of the aspects they address and their relationship with institutional capacity to promote community resilience to natural disasters. This overlap provides a comprehensive view of the challenges and opportunities faced by institutions in managing flood risk. 4. Discussion This research offers a methodology and set of indicators for evaluating flood vulnerability in urban areas, with the aim of fulfilling the urgent need for urban planning and disaster risk management. The proposed methodology includes different dimensions and components, which allows for a multidisciplinary approach to vulnerability assessment. Previous studies have conducted detailed assessments of flood vulnerability on various scales (Abdrabo et al., 2020 ; S. Balica & Wright, 2010 ; Nasiri et al., 2019 ; Santos et al., 2020 ). This study contributes to the field of flood vulnerability by providing a comprehensive approach and a spatial analysis to address this problem.The construction of an index for the assessment of vulnerability to floods is inherently difficult because of the subjective nature of decision-making and variability in the quality of the indicators (Kumar & Bhattacharjya, 2020 ). The proposed methodology can be applied at various scales, from districts and neighborhoods to entire cities, to promote resilient growth. Our study emphasizes the challenges of administrative decision-making at smaller scales, such as municipal, district, neighborhood, and census tract. By employing a regular grid methodology, we can better assess the vulnerability of a city. Although these estimates may not be completely accurate, they provide valuable insights into a city's situation. As the scale decreases, these complexities become more apparent, presenting significant barriers to effective decision making. This highlights the difficulty of obtaining data at smaller urban scales, which further complicates the decision-making process. Likewise, the inclusion of weights defined by expert panels ensures an impartial distribution, avoids potential conflicts of interest among decision-makers, and improves the credibility of our assessment. In addition, it can be used by urban planners or decision-makers, who can assign weights to each indicator and dimension based on the criteria or expert opinions of technicians. This allows them to visualize the results and experiment with weights as well as incorporate sensitivity analysis to refine their decisions before arriving at a well-founded definition for the index. Stakeholders and experts have shown a clear preference for a deductive approach because of its simplicity and clarity, especially when vulnerability factors are well-defined (Abdrabo et al., 2023 ). Our integrated approach not only facilitates the evaluation of flood vulnerability but also serves as a crucial tool for advocating structural and nonstructural measures aimed at reducing flood risk. This study proposes a comprehensive list of 31 indicators that should be considered when assessing vulnerability in urban environments. Rather than adhering to a rigid framework, we advocate identifying relevant components, dimensions, and variables adapted to different spatial scales, thereby enhancing the adaptability and usefulness of our approach. In the results section, we present a multidimensional assessment of vulnerability to urban flooding using carefully selected indicators and variables. However, certain indicators are highlighted by their unit of presence/absence in a specific location, such as within a 30 × 30-meter grid, from which the percentage of extent per hectare can be determined. Nonetheless, we will adhere to the presence/absence approach to represent a discrete site-specific variable. This approach could also be applied to businesses or buildings in general, with the subsequent application of weighting factors based on known values (e.g., age of the physical asset, number of dwellings per building, etc.). While acknowledging the subjective nature of indicator selection, we strive to provide a clear delineation of each dimension to guide interpretation and analysis.Our study offers a more integral perspective on urban vulnerability, independent of specific flood events. However, it is essential to recognize and address the limitations of the proposed methodology. The lack of case studies may have introduced a higher degree of subjectivity in the selection of indicators, emphasizing the need for empirical validation to ensure the accuracy and reliability of our findings. Further research is required to validate the proposed methodology and its effectiveness in different contexts considering the availability of data and resources. 5. Conclusions The study presents an integrated methodology, complemented by spatial analysis, to evaluate vulnerability to urban flooding. While not yet demonstrated for applicability, this approach enables multidisciplinary assessment, addressing the pressing needs of urban planning and disaster risk management. By offering an integrated approach alongside a comprehensive set of 31 indicators across five dimensions (social, economic, environmental, physical, and institutional) and three components (exposure, susceptibility, and resilience), this study significantly advances the field of vulnerability assessment. It builds on prior research by providing a versatile framework applicable across various scales. The inherent subjectivity of indicators poses challenges for the construction of vulnerability indices. Despite these obstacles, the proposed methodology provides a flexible approach that is adaptable to diverse spatial scales, thereby fostering resilient urban growth. Different urban scales enhance vulnerability assessment within cities. Additionally, the incorporation of expert-defined weights ensures impartiality and credibility in vulnerability assessment, thereby enhancing the validity of the findings. Developing an index to gauge vulnerability to floods entails a multifaceted process that requires careful consideration of several factors. Consequently, future research should focus on refining the proposed methodology and exploring its applicability across diverse contexts. Moreover, the future trajectory of this study involves implementing the proposed methodology in various urban settings and exploring additional indicators to augment the assessment of flood vulnerability. The proposed methodology’s potential to be deployed across various scales, from districts and neighborhoods to entire cities, holds promise for fostering resilient urban development and guiding urban planning and disaster risk management practices. Furthermore, delving into potential applications of the proposed methodology in other fields related to disaster risk reduction and resilience building could further enhance its utility and effectiveness. Declarations Conflict of interest The authors declare there is no conflict. Acknowledgement This study is supported by the IFUdG 2021 pre-doctoral grant and acknowledges the funding from University de Girona. Ignasi Rodriguez-Roda acknowledges the support of project CLEPSIDRA (Ref: TED2021-131862B-I00). Lucia Alexandra Popartan acknowledges the support from Juan de la Cierva Formación grant (FJC2021-047857-I) financed by MCIN/AEI/ 10.13039/501100011033 and European Union “NextGenerationEU”/PRTR. 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Oxford University Press. https://doi.org/10.1093/acrefore/9780199389407.013.25 Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2025 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 18 May, 2024 Reviewers invited by journal 27 Apr, 2024 Editor invited by journal 07 Apr, 2024 Editor assigned by journal 03 Apr, 2024 First submitted to journal 02 Apr, 2024 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. 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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-4199231","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296238145,"identity":"f259a3d5-eb8e-42b7-a79d-9d97d7e6a5eb","order_by":0,"name":"Ana Noemí Gomez Vaca","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBADHihtw8BwAIlLjJY04rXAwGHCWvhnNz978KFimwwD/+KjGz78OZ/Yd34B44O3bbi1SNw5Zm4448xtHgaJZ2k3Z/DcTpx54wGz4Vw8WhhuJJhJ87aBtJwxu80jcTtxw40DbEAR3Drkb6R/k+b9B9Xyx+AcSAv7b3xaDG7kAG1pAGrh7zG7zZBwIHHD+QY2ZnxaDG/klEnOOHabh02CLe1mz4Fk45k3GJsl55zDrUXuRvo2iQ81t+35+Q8fu/Hjj51s3/nDBz+8KcPjfRhgk0iAsiQSG4hQDwL8B9AZo2AUjIJRMAogAAB3pVqFHFyJywAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1118-5007","institution":"UDG FC: Universitat de Girona Facultat de Ciencies","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"Noemí Gomez","lastName":"Vaca","suffix":""},{"id":296238146,"identity":"5cf95bd9-bd8e-4f7f-a537-de5af1032cb8","order_by":1,"name":"Ignasi Rodríguez-Roda","email":"","orcid":"","institution":"University of Girona: Universitat de Girona","correspondingAuthor":false,"prefix":"","firstName":"Ignasi","middleName":"","lastName":"Rodríguez-Roda","suffix":""},{"id":296238147,"identity":"67e54d04-784c-4f3e-a399-d6f7402dda56","order_by":2,"name":"Lucía Alexandra Popartan","email":"","orcid":"","institution":"University of Girona: Universitat de Girona","correspondingAuthor":false,"prefix":"","firstName":"Lucía","middleName":"Alexandra","lastName":"Popartan","suffix":""},{"id":296238148,"identity":"ebfbfa38-e025-4797-a49b-52d08e44971e","order_by":3,"name":"Sergi Nuss-Girona","email":"","orcid":"","institution":"University of Girona: Universitat de Girona","correspondingAuthor":false,"prefix":"","firstName":"Sergi","middleName":"","lastName":"Nuss-Girona","suffix":""}],"badges":[],"createdAt":"2024-04-01 08:22:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4199231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4199231/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07451-5","type":"published","date":"2025-06-25T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55787772,"identity":"8656c1be-de3c-4f2f-b83c-22d4e0a360d4","added_by":"auto","created_at":"2024-05-03 08:08:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248959,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the steps in the development of a multidimensional flood vulnerability index.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4199231/v1/2d4dd628ce7bb45e37925280.png"},{"id":55787773,"identity":"530f2926-8b1a-4eb9-8cdd-2b8dfc9cc317","added_by":"auto","created_at":"2024-05-03 08:08:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169584,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of dimensions and indicators to obtain the index.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4199231/v1/eb023d23af386d11b1bcce8c.png"},{"id":85686115,"identity":"b2b5d635-4ac9-46c5-94d9-fcd0eff8c7df","added_by":"auto","created_at":"2025-06-30 16:03:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1668724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4199231/v1/cb5d4d6c-375a-4d06-833c-112802551aaa.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eFloods are natural disasters that have caused significant losses globally, both in terms of human life and economic impact (Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These events account for approximately one-third of all global natural hazards, and the frequency of extreme floods has risen significantly in the last decade (Tsatsaris et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to OECD (2016), the annual global economic damage from floods surpasses \u003cspan\u003e$\u003c/span\u003e40\u0026nbsp;billion. The effects of climate change, reduction of green areas, and deterioration of water infrastructure are contributing factors to various types of floods, such as fluvial, flash, or pluvial floods. These factors, along with others, increase the risk of flooding (Kaykhosravi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rangari et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sarkadi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and raise questions about the effectiveness of traditional management approaches (Westra et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, flood risk management has heavily focused on gray infrastructure (Johns, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is often deemed ineffective and unsustainable, both economically and environmentally. In response, to this cities are now reassessing their strategies by integrating green infrastructure solutions, such as rain gardens, permeable pavements, and green roofs, rather than relying solely on conventional engineering methods (Chan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, transitioning from an approach focused on gray infrastructure soley to a hybrid one approach, incorporating green solutions has limitations: state of the art research indicates that even when combined, these approaches may not offer sufficient protection (Meng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, integrating nonstructural measures, such as urban planning (Loucks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and accurately mapping vulnerability to flood risk is essential (Abdrabo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, vulnerability assessment remains a key component of effective risk management, as highlighted in international discussions on disaster management, sustainable development, and climate change adaptation (Balica and Wright, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Sendai Framework for Disaster Risk Reduction, the United Nations Sustainable Development Goals, and the European Green Deal all underline the importance of understanding and reducing disaster risk, including vulnerability, to achieve sustainable development (Ludwig et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sachs et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFlood vulnerability is typically assessed using the following methods: (i) vulnerability curve, (ii) disaster loss data, (iii) computer modeling, and (iv) index-based. (Nasiri et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The latter remains is the most widely used methodology (Bera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moreira, De Brito, et al., 2021; Nasiri et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Indices serve as a summary of complex, multidimensional issues to assist decision makers, facilitate the interpretation of a phenomenon and increase public interest through a summary of the results (Moreira, de Brito, et al., 2021).\u003c/p\u003e \u003cp\u003eGiven the complexity of flood vulnerability analysis (Birkmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a diversity of conceptual frameworks has emerged, emphasizing its multidimensional character (Aroca-Jimenez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fuchs \u0026amp; Thaler, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kumpulainen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The most commonly addressed dimension of vulnerability is the social one (de Loyola Hummell et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oulahen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), along with the physical (Papathoma-K\u0026ouml;hle et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), economic (Veen \u0026amp; Logtmeijer, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), institutional (Papathoma-K\u0026ouml;hle et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and environmental (Damm, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; European Environment Agency., 2016). In addition to encompassing these dimensions, vulnerability to flooding has been analyzed considering three main components: exposure, susceptibility and resilience (S. F. Balica et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cutter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Qasim et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, these indicators are rarely geospatial and do not always take into account all the components and dimensions of vulnerability at the same time. In addition, these indices focus largely on catchment scale, neglecting factors that affect vulnerability on a small urban scale. In addition, a detailed breakdown of all the indicators and variables considered is not yet clear.\u003c/p\u003e \u003cp\u003eThe objective of this study is to present a methodology for quantifying multidimensional vulnerability to long-term impact flooding, including an analysis of spatial data. This novel methodology constitutes an alternative approach for addressing multidisciplinary issues related to flood vulnerability. Its diverse dimensions and components provide a holistic perspective of vulnerability. This approach can enhance the communication among stakeholders and promote the awareness of flood-related risks. Vulnerable dimensions can be identified through visualization via maps, allowing for structured (e.g., gray, hybrid, or green infrastructure) and non-structured (e.g., emergency plans or risk management plans) proposals. Previous research conducted a detailed assessment of a large city at the scale of a downtown district (Nasiri et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This approach is advantageous for guiding future urban growth away from high-risk areas and promoting resilient districts (Mercader-Moyano et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the integrated method considers the relationships between each element not only within its own scale but also across multiple layers that contribute to urban vulnerability (Salas \u0026amp; Yepes, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, some studies have examined flood vulnerability at multiple scales, considering only certain dimensions of vulnerability (Chang et al., 2021).\u003c/p\u003e \u003cp\u003eThe structure of the paper is as follows. First, we present the research method. Subsequently, we discuss the proposed indicators as well as the advantages and limitations of the methodology. Finally, we conclude with the main findings of the study.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cp\u003eIn this section we present a step-by-step method employed to build the proposed vulnerability index \u003cem\u003e(\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: Selection of components and dimensions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this step, indicators are selected for each component and dimension. The purpose is to select and reduce the number of variables to simplify the interpretation of the issue being addressed (P\u0026eacute;rez, 2004), in our case, the dimension of interest. Furthermore, each dimension encompasses several indicators, and each indicator may encompass one or more variables (see \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003eResults\u003c/span\u003e section). To calculate the multidimensional index, distributed in five dimensions (social, economic, environmental, physical, and institutional) and three components (exposure, susceptibility, and resilience), there are a total of 31 proposed indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of proposed indicators by component and dimension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe most difficult task in constructing an index is the selection of indicators, which depends on the quality of the available variables and the subjectivity inherent in decision-making (Nardo et al., 2005). However, there is no hard and fast rule that defines the variables and indicators to be taken into account when assessing vulnerability to flooding because the indicators will depend on the context of each city, the needs of the users, the information available, the precipitation, the lithology, and the slope angle (Nguyen et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, rather than a strict conceptual framework, it is relevant to identify the types of components, dimensions, and indicators that are useful for vulnerability analysis.\u003c/p\u003e \u003cp\u003eThe proposed indicators were selected according to their definition, considering quality standards including relevance, accuracy, timeliness, accessibility, interpretability, and coherence (OECD \u0026amp; JRC, 2008). The selected indicators covered the aspects of exposure, susceptibility, and resilience (Wu, 2021). These indicators offer a thorough assessment of urban systems in cities and independent evaluation of their vulnerability to flooding. The findings outlined in this paper stem from a meticulous examination of the existing literature combined with insights from expert opinions. Although some indicators may not be easily classified within the five dimensions, we assigned each indicator to a single dominant vulnerability dimension to avoid double counting (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the classification of indicators and variables, any indicator, qualitative or quantitative, was considered to carry meaning beyond its value. That is, the relevance of an indicator for estimating a particular characteristic of a system arises from the interpretation of the indicator itself and its relationship with the phenomenon being characterized (in this case, vulnerability).\u003c/p\u003e \u003cp\u003eTherefore, assigning meaning to a variable and defining the relationship between the variable and indicator makes a variable eligible to be considered an indicator (Birkmann, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Integrated and multidimensional approaches require the aggregation of multiple indicators, referred to as composite indicators (Damm, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A composite indicator is the result of combining individual indicators into a single index (OECD, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this paper we consider indicators and variables, so each indicator can include one or more variables.\u003c/p\u003e \u003cp\u003eVulnerability is the result of interaction between the three components mentioned above. In this way, the Flood Vulnerability Index (FVI) can be calculated using Eq.\u0026nbsp;1 provided by Balica (2007):\u003c/p\u003e \u003cp\u003eFVI\u0026thinsp;=\u0026thinsp;Exposure\u0026thinsp;+\u0026thinsp;Susceptibility \u0026ndash; Resilience (1)\u003c/p\u003e \u003cp\u003eIn this study, we define the following:\u003c/p\u003e \u003cp\u003eExposure is defined as the predisposition of a system to be disrupted by a flooding event owing to its location in the same area of influence (S. Balica \u0026amp; Wright, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Susceptibility is defined as the exposed elements within the system that influence the likelihood of damage during times of dangerous (preflood) flooding (S. Balica \u0026amp; Wright, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Resilience is characterized by a set of characteristics, abilities, resources, and opportunities of people, places, and infrastructure to survive, absorb impacts, and manage the adverse effects of floods (Emrich y Tobin, 2018).\u003c/p\u003e \u003cp\u003eAfter selecting the indicators proposed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Step 1, gray box), as described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the next step is to choose the urban scale (municipality, neighborhood, census sector, or others). The representative variables for each indicator are then defined, and the data corresponding to these indicators are obtained. Subsequently, the data are processed in vector format. To ensure that the urban scale is not a constraint, a grid-level analysis is proposed, which is a geographic information system (GIS) technique used to analyze data within a grid or raster format. It divides the study area into smaller grids or cells, where each cell represents a specific area or unit of analysis, which allows the visualization and analysis of data at a higher resolution and provides a more detailed understanding of spatial patterns and relationships (Kikon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, it is considered that most cities depend on urban planning at least have cadastral data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Normalization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study presents a methodology that can be employed at various scales, such as districts, sectors, and neighborhoods. To compare variables and indicators with different units of measurement, it is necessary to standardize or normalize the data values (Sarstedt and Mooi, 2014). The main normalization methods are Ranking, Z scores, Min -max, distance from the group leader, division by total, categorical scale and binary standard (Moreira, De Brito, et al., 2021)\u003c/p\u003e \u003cp\u003eA commonly used method of standardization involves rescaling the original data so that the variable's mean is 0 and the standard deviation is 1. This approach is known as the z-score method and is based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Results will be given in percentage.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Zi=\\frac{({x}_{i} - \\stackrel{-}{\\text{x}})}{s}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3: Aggregation and assignment of weights of indicators and dimensions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOnce the normalization process is completed, the weights of the variables, indicators, and dimensions are obtained. The methods used for weighting are usually based on statistics (Gu et al. 2018). In this study, we opted for expert involvement because the results are more likely to be reliable (Oulahen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Participation is believed to be a key component in promoting effective disaster risk reduction (Fekete et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many authors have recommended the use of participatory methods for weighting indicators (Evers et al., 2018). The assumption is that, if practitioners and experts participate in creating an index that they find useful, they are more likely to trust their results (Oulahen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, participation is believed to be a key component in promoting effective disaster risk reduction (Fekete et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe methodologies to follow for aggregation can be Linear and Geometric (Moreira, de Brito, et al., 2021). Because vulnerability includes five dimensions \u0026ndash;social, economic, environmental, physical, and institutional\u0026ndash; in this case, linear aggregation is preferred (Gan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sum of the weighted components results in a cumulative vulnerability score for each dimension. In addition, it is considered that element make it more important in terms of weights. The cumulative vulnerability score shown in Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e is used to map the spatial distribution (Abdrabo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aroca-Jimenez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$Vulnerability={\\sum }_{f=1}^{n}Wf*Sf$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ef\u003c/em\u003e represents the indicator/dimension of vulnerability; \u003cem\u003en\u003c/em\u003e is the total number of indicators/dimensions; \u003cem\u003ewf\u003c/em\u003e​ is the relative weight assigned to the indicator/dimension; and \u003cem\u003esf\u003c/em\u003e​ is the indicator/dimension score.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4: Mapping and index classification (Maps Multi-layer)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this step, vulnerability maps are generated for each dimension based on Abdrabo et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Aroba-Jimenez et al. (2017); the resulting vulnerability maps will be classified into five evenly distributed categories from 0 to 1 based on their values: very low (0\u0026ndash;0.201), low (0.202\u0026ndash;0.401), moderate (0.402\u0026ndash;0.600), high (0.601\u0026ndash;0.800), and very high (0.801\u0026ndash;1.0) (Abdrabo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The vulnerability maps are calculated based on (Abdrabo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aroca-Jimenez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eNext, the grid is generated, using the fixed index of the grid is an n\u0026times;n matrix of cells of equal size. Each is associated with a list of spatial objects that intersect or overlap the cell. This data structure, known as a fixed grid index, provides an efficient way to organize and query spatial data based on their intersections or overlaps with the cells in the grid. The fixed grid index is a powerful tool for organizing and querying spatial data based on their intersections or overlaps with the cells in the grid (Jiang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This allows us to analyze the data at different scales (Kikon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNext, the data are analyzed, the vulnerability maps that have been considered above are visualized, and then the overlay method is applied. Overlap analysis involves combining two or more thematic maps of the same area and creating a new map by overlapping it. In addition, we combine the features of multiple datasets into a single dataset and then search for specific areas that have a certain value. This approach is often used to find locations that are suitable for the particular use of some risk (\u003cem\u003eOverlay Analysis\u0026mdash;ArcMap | Documentation\u003c/em\u003e, 2012). Additionally, this method has been applied to a variety of decision-making problems with a large number of criteria (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kittipongvises et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) owing to its ability to integrate a large amount of heterogeneous data and provide degrees of consistency and inconsistency in the obtained results.\u003c/p\u003e \u003cp\u003eOnce the multidimensional vulnerability index map is obtained, it is overlaid with a flood map that is downloaded from the area database of a given study area. This superposition of maps makes it possible to identify the most vulnerable areas by considering an integrated approach based on multiple layers of selected dimensions and indicators. Visualization aids decision-makers in understanding and prioritizing necessary actions to reduce vulnerability in each area, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis multidimensional approach provides a solid foundation for developing more effective mitigation and adaptation strategies, enabling a better understanding of the interconnections among various dimensions. By integrating these perspectives, this approach seeks to contribute to the advancement of vulnerability assessment research, promoting more comprehensive and applicable approaches across a variety of contexts. The methodology is comprehensive and can be used at different scales and in different contexts, with sub-indicators adjusted according to the specific disaster under study.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe result of our research constitute a novel flood vulnerability index, including the following: \u003cem\u003e(i) Components\u003c/em\u003e: exposure, susceptibility, and resilience, according to the established definition (see methodology Step 1). \u003cem\u003e(ii) Indicators\u003c/em\u003e: Indicators are quantified using variables. \u003cem\u003e(iii) Variables\u003c/em\u003e: Individual indicators. Each indicator includes one or more variables. \u003cem\u003e(iv) Description in relation to vulnerability\u003c/em\u003e: This description is elaborated by the authors in order to specify the relationship of the established indicator. \u003cem\u003e(v) Units\u003c/em\u003e: Units were established in relation to the variables, some of which consider the units of the literature review and others are added by the authors. \u003cem\u003e(vi) References\u003c/em\u003e: This section establishes a reference for the variables that have been considered.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocial Dimension Vulnerability Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis dimension comprises the characteristics of an individual or group and their situation, which influences their ability to anticipate, cope with, resist, and recover from the impact of floods (Wisner, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The social dimension comprises of eight indicators. Once of them indicates exposure, five indicators of susceptibility, and two demonstrating resilience \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocial Dimension indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ordm;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription in relation to vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIt is used to quantify the exposed population;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInhabitants/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Almeida et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; JRC TECHNICAL REPORT, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmigration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmigration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation with lack of knowledge or limited access to information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Parsons et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemininity Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWomen may experience a longer recovery period than men due to their specialization in specific sectors, lower wages, and traditional family caregiving responsibilities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; M\u0026uuml;ller et al., 2011; Rufat et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; El-Boshy et al., 2019; El-Hattab et al., 2018a; JRC TECHNICAL REPORT, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e (JRC TECHNICAL REPORT, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge Extremes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElderlies above 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOlder people have less mobility and dependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rufat et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kablan et al., 2017; El-Hattab et al., 2018a; El-Boshy et al., 2019; JRC TECHNICAL REPORT, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren under 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFragile physical condition, degree of dependency, and difficulty in movement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of the disabled population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisabled populations find mobility difficulties to avoid hazards (physical, auditory, visual, psychological, or psychiatric).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Rufat et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rana and Routray, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; El-Boshy et al., 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial assistance dependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployees earning below minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePeople with limited economic resources find it difficult to cope with disastrous events.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rufat et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rana and Routray, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Abdrabo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnemployed population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmergency Service Accessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImproved healthcare facilities in emergencies strengthen resilience in urban areas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of health workers/ 1,000 inhabitants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Almeida et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation with High School or Vocational Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEducation with understanding and responding to warnings and implementing mitigation measures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Ribas et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation with university education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSocial indicators are fundamental for understanding the vulnerability of individuals and groups. Population density (\u003cem\u003eindicator 1)\u003c/em\u003e is crucial in determining exposure to hazards such as flooding, highlighting the importance of considering both the geographic extent of the hazard and the concentration of people in vulnerable areas.\u003c/p\u003e \u003cp\u003eIn terms of susceptibility, indicators such as immigration (\u003cem\u003eindicator 2\u003c/em\u003e) and limited access to information highlight how certain groups may be at greater risk owing to their socioeconomic or demographic situation. In addition, gender (\u003cem\u003eindicator 3)\u003c/em\u003e and extreme age (\u003cem\u003eindicator 4\u003c/em\u003e) have been extensively studied because of their influence on traditional roles, reduced mobility, and dependence on caregivers (JRC TECHNICAL REPORT, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Disabilities (\u003cem\u003eindicator 5\u003c/em\u003e) also play a crucial role, because physical, mental, and emotional barriers can hinder people's ability to cope with disasters. Similarly, dependency on social assistance (\u003cem\u003eindicator 6\u003c/em\u003e) may limit the available resources and the responsiveness of those affected.\u003c/p\u003e \u003cp\u003eThe state-of-the-art processes indicate that there are uniform criteria when using the following indicators: percentage of elderly, percentage of children, percentage of women, percentage of individuals with disabilities, percentage Immigration rate (Aroca-Jimenez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is because they have the greatest influence on the social burdens of natural hazards, as they constitute more vulnerable groups, as they suffer large social impacts more frequently.\u003c/p\u003e \u003cp\u003eRegarding resilience indicators (\u003cem\u003eindicator 7\u003c/em\u003e) of health in the unit, there are coincidences in the number of health personnel per 1,000 inhabitants (Aroca-Jimenez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tasc\u0026oacute;n-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Level of education (\u003cem\u003eindicator 8\u003c/em\u003e) considers the percentage unit (Ribas et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEconomic Dimension Vulnerability Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe economic dimension is defined as the propensity for the loss of economic value from damage to physical assets and/or the disruption of productive capacity (Birkmann et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This dimension includes eight indicators: two exposure indicators, four susceptibility indicators, and two resilience indicators \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEconomic Dimension indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ordm;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription in relation to vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHousing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation of renters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRenters may have fewer financial resources to deal with flood damage, and rental housing may lack risk mitigation features compared to owner-owned housing, leading to housing quality inequality.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Beccari, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEconomic activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompanies \u0026amp; Businesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEconomic activity is often vulnerable due to exposure in flood areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% (m\u003csup\u003e2\u003c/sup\u003e business/ha of city)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Ribas et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncome (\u0026euro;) per person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFloods can negatively impact people's employment and livelihoods, especially in regions where economic activities depend on the climate. Low-income communities may struggle more to recover economically.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/person*year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Parsons et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ribas et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOccupation (Economic sectors)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployed in primary extractive industries (farming, fishing, mining, and forestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSome occupations are sensitive to disruptions caused by disasters, as they lose their livelihoods and financial resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003eb; Rufat et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rana \u0026amp; Routray, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployed in transportation, communications, and other public utilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployed in service occupations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMunicipal Budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMunicipal budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocal economic conditions can affect the city's capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsset Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe monetary value of infrastructure, facilities, and properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIt may impair electrical infrastructure and disrupt functionality, which can influence the property's worth and overall performance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/km2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAuthor\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsurance \u0026amp; Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree of insurance coverage for physical assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure to reduce vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Kuhlicke et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial Capacity (Municipal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccess to financial resources for recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccess to national and international financial resources for recovery as measures to reduce vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/km2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Guillard-Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of exposure, economic activity (\u003cem\u003eindicator 10\u003c/em\u003e) highlights the importance of understanding that companies and businesses may be vulnerable to the loss of income and physical assets, which is directly related to the susceptibility of occupation (\u003cem\u003eindicator 12\u003c/em\u003e) and Municipal Budget (\u003cem\u003eindicator 13\u003c/em\u003e), where economic capacity can be closely linked to the financial situation of local authorities and their available budget for response and recovery. On the other hand, income (\u003cem\u003eindicator 11\u003c/em\u003e) highlights how people with lower incomes may face greater difficulties in recovering economically after a disaster (Ribas et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This can be aggravated if housing is primarily composed of housing tenants (\u003cem\u003eIndicator 9\u003c/em\u003e), who may lack financial resources and rental insurance to cope with the damages that could be caused.\u003c/p\u003e \u003cp\u003eIn terms of resilience, \u003cem\u003eindicators 15\u003c/em\u003e and \u003cem\u003eindicator 16\u003c/em\u003e are also essential, as the municipality's insurance coverage and financial capacity can determine its ability to recover from a disaster and reduce economic losses.(Kuhlicke et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome overlaps are identified; for example, indicator 11 and part of \u003cem\u003eindicator 12\u003c/em\u003e are related to the socio-economic susceptibility of the population. Income is closely linked to employment, especially in agriculture, fisheries, and forestry, which are particularly vulnerable. Therefore, inclusion of both indicators may be beneficial. Municipal Budget and Asset Value (\u003cem\u003eindicators 13 and 14\u003c/em\u003e) both provide information on the financial capacity and economic assets of a municipality. The municipal budget may reflect, in part, the value of assets and infrastructure within the area, suggesting an overlap, insofar as both indicators assess a jurisdiction's economic capacity to cope with and recover from disasters.\u003c/p\u003e \u003cp\u003eIn addition, the causal relationship between indicators can be assessed; some indicators may be causal factors of others, whereas others may be outcome or mitigation measures. For example, income (\u003cem\u003eindicator 11\u003c/em\u003e) can influence a person's ability to obtain insurance (\u003cem\u003eindicator 15\u003c/em\u003e), which in turn can affect economic resilience. Identifying these relationships can help to distinguish between indicators and understand how they interact. It can also influence the context and relevance in which the indicators are applied. Some indicators may be more relevant in certain environments than others. For example, the importance of the municipal budget (\u003cem\u003eindicator 13\u003c/em\u003e) may vary according to the municipality\u0026rsquo;s size and economic structure.\u003c/p\u003e \u003cp\u003eUnderstanding the context can help determine the importance of each indicator and differentiate between overlaps. This will allow for a more complete and accurate analysis of the situation and facilitate the identification of key areas for intervention and risk mitigation.\u003c/p\u003e \u003cp\u003eRegarding the units, consensus was found for indicators 9, 10, 11, and 13. The authors considered other units of indicators 12, 14, 15, and 16.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnvironmental Dimension Vulnerability Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe environmental dimension is defined as the degree to which an ecosystem service is sensitive to external pressures (urbanization and agriculture) (Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Metzger et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), including cultural aspects. This dimension includes six indicators, four of which are exposure indicators, one is a susceptibility indicator, and one is a resilience indicator \u003cem\u003e(see\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental Dimension indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ordm;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription in relation to vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecreation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecreation Spaces Small-Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecreation sites, being at ground level and without adequate protective measures / Locations near bodies of water can increase vulnerability (neighborhood parks, small playgrounds).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural Assets of Cultural Interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCertain assets are more vulnerable due to their historical, cultural, and natural importance in being close to bodies of water and eroded soils.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderground water bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentual extension of groundwater bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFloods can transfer soil contaminants from agricultural or urban areas into the soil and impact groundwater quality.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDamage or loss of sea/river line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSea/River Line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban development without proper planning results in the loss of buffer zones, making riverbanks and coastlines more susceptible to flooding.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLength/\u003c/p\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Mani Murali et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative Habitat Richness Index.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFlooding can result in the destruction and degradation of natural habitats, leading to biodiversity loss because many organisms depend on specific environments for their survival.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of habitats per ha.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGreen infrastructure capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtension of Nature Based Solutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGreen infrastructure, including natural areas, bio-drainage canals, urban wetlands, reservoirs, green roofs, permeable pavements, and urban agriculture, can help mitigate and adapt to the impacts of climate change.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Spahr et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtected natural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following indicators emphasize the importance of protecting and conserving natural resources to maintain cultural identity and biodiversity: exposure to recreational facilities, such as parks and playgrounds, to extreme water-related events (\u003cem\u003eindicator 17\u003c/em\u003e); the presence of natural assets of cultural interest in vulnerable areas (\u003cem\u003eindicator 18\u003c/em\u003e); groundwater contamination due to flooding (\u003cem\u003eindicator 19\u003c/em\u003e); and loss or damage to shorelines or rivers (\u003cem\u003eindicator 20\u003c/em\u003e), which are crucial for land use planning and coastal management to reduce vulnerability to erosion and flooding.\u003c/p\u003e \u003cp\u003eIn a susceptibility approach, the index of species richness (\u003cem\u003eindicator 21\u003c/em\u003e) underlines the need to conserve and restore ecosystems to maintain ecosystem health and ensure the continued provision of ecosystem services essential for human well-being. Green infrastructure (\u003cem\u003eindicator 22\u003c/em\u003e) is a standout strategy in resilience approaches to strengthen communities' abilities to withstand natural disasters. Although it may seem similar to the Recreation Facilities Exhibition (\u003cem\u003eindicator 17\u003c/em\u003e), as both involve planning and designing green spaces to mitigate risk, they address different aspects. While both indicators use nature to reduce vulnerability, their approaches differ: the first focuses on the implementation of concrete solutions and ecosystem regulatory services provided by ecosystems in an intensive manner, while the second focuses on assessing the vulnerability of existing recreational infrastructure.\u003c/p\u003e \u003cp\u003eThe socio-ecological approach to risk assessment comprehensively analyzes these indicators, recognizing the interdependence between natural and social systems to provide a more comprehensive and effective analysis of vulnerability and resilience to natural disasters (Shah et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhysical Dimension Vulnerability Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe physical dimension is the potential for damage to physical assets, including built-up areas, infrastructure, and open spaces (Birkmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), including cultural aspects. This dimension encompasses six indicators, three of which are indicators of exposure, two of susceptibility, and one of resilience \u003cem\u003e(see\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical Dimension indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ordm;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription in relation to vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban context\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban morphology (The density of the built Environment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased population and activities raise the number of exposed assets.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e% built/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; M\u0026uuml;ller et al., 2011; Kablan et al., 2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCultural heritage sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonuments, Historical constructions, Archaeological sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHistorical cultural sites are frequently situated near bodies of water, which exposes them to the danger of flooding.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresence/ absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eExposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eUrban Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElectricity transport and distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eUrban system services are often vulnerable, particularly those exposed in flood-prone areas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLength/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e(Almeida et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaste Collection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresence/ absence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraffic lights and street lighting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLength/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTelecommunication network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLength/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrgent and Emergency Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresence / absence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGas distribution network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLength/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuilding condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuality buildings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuildings are more vulnerable to damage when subjected to flooding due to poor construction conditions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow, medium, high-quality building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Ghajari et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWater infrastructure\u003c/p\u003e \u003cp\u003econdition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrainage system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIn the event of damage to infrastructure, an additional financial burden is placed on the city to compensate for these damages.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Km poor network/\u003c/p\u003e \u003cp\u003eKm total network) ha.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Cutter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rana and Routray, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrinking water distribution system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Km poor network/\u003c/p\u003e \u003cp\u003eKm total network) ha.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater tanks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresences / Absence and Cubic meters of the tank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructural protection measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDams, canalizations, bridges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInfrastructures aid in reducing the adverse consequences of flooding, thereby preserving lives and safeguarding properties.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Tiggeloven et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe built environment (\u003cem\u003eindicator 23\u003c/em\u003e) is crucial for understanding urban areas' vulnerability to disasters, and high-density buildings can increase exposure while affecting resilience Cultural heritage sites (indicator 24) and urban services (indicator 25) are also essential components, if they see in their unity we adhere to the presence/absence approach to represent a discrete site-specific variable. \u003cem\u003eIndicator 26\u003c/em\u003e, which assesses building conditions and susceptibility, is complex and evaluates low-, medium-, and high-quality buildings (Ghajari et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while \u003cem\u003eIndicator 27\u003c/em\u003e, which measures water infrastructure conditions, is also challenging. The resilience component (\u003cem\u003eindicator 28\u003c/em\u003e) focuses on structural protection measures to mitigate the negative impacts of flooding.\u003c/p\u003e \u003cp\u003eIn some cases, it may be necessary to combine multiple units to adequately capture the complexity of physical vulnerability. For example, in \u003cem\u003eindicator 27\u003c/em\u003e, the ratio of the length of poor drainage infrastructure to the total length of infrastructure is used as a measure of susceptibility. This combination of units provides a more comprehensive metric of water infrastructure vulnerability. Analyzing the indicators, it is possible to identify the overlap between Exposure of Cultural Heritage Sites (\u003cem\u003eindicator 24\u003c/em\u003e) and Exposure of Urban Infrastructure (\u003cem\u003eindicator 25\u003c/em\u003e), both of which assess the exposure of physical assets to natural hazards but focus on different types of infrastructure. While \u003cem\u003eIndicator 24\u003c/em\u003e focuses on cultural heritage sites, such as historical monuments and archaeological constructions, \u003cem\u003eindicator 25\u003c/em\u003e focuses on more general urban infrastructure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstitutional Dimension Vulnerability Indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDefinition of these dimensions: The strengths and constraints in governance, underlying rules, and regulatory systems that govern the ability or inability of institutions to cope with floods and adaptation challenges (Papathoma-K\u0026ouml;hle et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) The institutional dimension, which does not take into account the exposure component, includes three indicators, one of which is susceptibility and two are indicators of resilience \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstitutional Dimension indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026ordm;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription in relation to vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMunicipal debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMunicipal debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMunicipal debt can increase vulnerability due to limited resources and dependence on external resources.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/\u003c/p\u003e \u003cp\u003eInhabitant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFlood Risk Management Plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExistence of Local Flood Risk Management Plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThe plans aim to increase public awareness about flood risks, emphasizing preparedness and mitigation. Community education on flood readiness boosts resilience at both individual and collective levels.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e(Aroca-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability of emergency plans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExistence of early warning systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eResilience Measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProjects related to green infrastructure and adaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBy incorporating resilience principles into urban planning and development, cities can ensure their sustainability. (e.g., alternative energy sources, firefighters, civil guard, voluntary groups, collectives, and associations)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual projected investment in adaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/\u003c/p\u003e \u003cp\u003einhabitant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmergency and civil protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/\u003c/p\u003e \u003cp\u003einhabitant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual investment in Preventive and compensatory measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026euro;/\u003c/p\u003e \u003cp\u003eInhabitant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe indicators presented in this section provide a detailed overview of how institutions influence the vulnerability and resilience of local communities. The Municipal debt susceptibility indicator (\u003cem\u003eindicator 29\u003c/em\u003e) can limit available resources and dependence on external sources, which can increase the local vulnerability to flooding and other challenges.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIndicators 30\u003c/em\u003e and \u003cem\u003e31\u003c/em\u003e, Flood Risk Management Plan and Resilience Measures, respectively, focus on institutional strategies to prepare for and respond to floods. Although they address different aspects of institutional resilience, both indicators are linked to local institutional capacity to cope with flood risks. While there is no direct overlap in the variables, there is an overlap in terms of the aspects they address and their relationship with institutional capacity to promote community resilience to natural disasters. This overlap provides a comprehensive view of the challenges and opportunities faced by institutions in managing flood risk.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis research offers a methodology and set of indicators for evaluating flood vulnerability in urban areas, with the aim of fulfilling the urgent need for urban planning and disaster risk management. The proposed methodology includes different dimensions and components, which allows for a multidisciplinary approach to vulnerability assessment. Previous studies have conducted detailed assessments of flood vulnerability on various scales (Abdrabo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. Balica \u0026amp; Wright, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nasiri et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study contributes to the field of flood vulnerability by providing a comprehensive approach and a spatial analysis to address this problem.The construction of an index for the assessment of vulnerability to floods is inherently difficult because of the subjective nature of decision-making and variability in the quality of the indicators (Kumar \u0026amp; Bhattacharjya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The proposed methodology can be applied at various scales, from districts and neighborhoods to entire cities, to promote resilient growth.\u003c/p\u003e \u003cp\u003eOur study emphasizes the challenges of administrative decision-making at smaller scales, such as municipal, district, neighborhood, and census tract. By employing a regular grid methodology, we can better assess the vulnerability of a city. Although these estimates may not be completely accurate, they provide valuable insights into a city's situation. As the scale decreases, these complexities become more apparent, presenting significant barriers to effective decision making. This highlights the difficulty of obtaining data at smaller urban scales, which further complicates the decision-making process.\u003c/p\u003e \u003cp\u003eLikewise, the inclusion of weights defined by expert panels ensures an impartial distribution, avoids potential conflicts of interest among decision-makers, and improves the credibility of our assessment. In addition, it can be used by urban planners or decision-makers, who can assign weights to each indicator and dimension based on the criteria or expert opinions of technicians. This allows them to visualize the results and experiment with weights as well as incorporate sensitivity analysis to refine their decisions before arriving at a well-founded definition for the index. Stakeholders and experts have shown a clear preference for a deductive approach because of its simplicity and clarity, especially when vulnerability factors are well-defined (Abdrabo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our integrated approach not only facilitates the evaluation of flood vulnerability but also serves as a crucial tool for advocating structural and nonstructural measures aimed at reducing flood risk.\u003c/p\u003e \u003cp\u003eThis study proposes a comprehensive list of 31 indicators that should be considered when assessing vulnerability in urban environments. Rather than adhering to a rigid framework, we advocate identifying relevant components, dimensions, and variables adapted to different spatial scales, thereby enhancing the adaptability and usefulness of our approach.\u003c/p\u003e \u003cp\u003eIn the \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003eresults\u003c/span\u003e section, we present a multidimensional assessment of vulnerability to urban flooding using carefully selected indicators and variables. However, certain indicators are highlighted by their unit of presence/absence in a specific location, such as within a 30 \u0026times; 30-meter grid, from which the percentage of extent per hectare can be determined. Nonetheless, we will adhere to the presence/absence approach to represent a discrete site-specific variable. This approach could also be applied to businesses or buildings in general, with the subsequent application of weighting factors based on known values (e.g., age of the physical asset, number of dwellings per building, etc.). While acknowledging the subjective nature of indicator selection, we strive to provide a clear delineation of each dimension to guide interpretation and analysis.Our study offers a more integral perspective on urban vulnerability, independent of specific flood events. However, it is essential to recognize and address the limitations of the proposed methodology. The lack of case studies may have introduced a higher degree of subjectivity in the selection of indicators, emphasizing the need for empirical validation to ensure the accuracy and reliability of our findings. Further research is required to validate the proposed methodology and its effectiveness in different contexts considering the availability of data and resources.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study presents an integrated methodology, complemented by spatial analysis, to evaluate vulnerability to urban flooding. While not yet demonstrated for applicability, this approach enables multidisciplinary assessment, addressing the pressing needs of urban planning and disaster risk management.\u003c/p\u003e \u003cp\u003eBy offering an integrated approach alongside a comprehensive set of 31 indicators across five dimensions (social, economic, environmental, physical, and institutional) and three components (exposure, susceptibility, and resilience), this study significantly advances the field of vulnerability assessment. It builds on prior research by providing a versatile framework applicable across various scales.\u003c/p\u003e \u003cp\u003eThe inherent subjectivity of indicators poses challenges for the construction of vulnerability indices. Despite these obstacles, the proposed methodology provides a flexible approach that is adaptable to diverse spatial scales, thereby fostering resilient urban growth. Different urban scales enhance vulnerability assessment within cities. Additionally, the incorporation of expert-defined weights ensures impartiality and credibility in vulnerability assessment, thereby enhancing the validity of the findings.\u003c/p\u003e \u003cp\u003eDeveloping an index to gauge vulnerability to floods entails a multifaceted process that requires careful consideration of several factors. Consequently, future research should focus on refining the proposed methodology and exploring its applicability across diverse contexts.\u003c/p\u003e \u003cp\u003eMoreover, the future trajectory of this study involves implementing the proposed methodology in various urban settings and exploring additional indicators to augment the assessment of flood vulnerability. The proposed methodology\u0026rsquo;s potential to be deployed across various scales, from districts and neighborhoods to entire cities, holds promise for fostering resilient urban development and guiding urban planning and disaster risk management practices. Furthermore, delving into potential applications of the proposed methodology in other fields related to disaster risk reduction and resilience building could further enhance its utility and effectiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare there is no conflict.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThis study is supported by the IFUdG 2021 pre-doctoral grant and acknowledges the funding from University de Girona. Ignasi Rodriguez-Roda acknowledges the support of project CLEPSIDRA (Ref: TED2021-131862B-I00). Lucia Alexandra Popartan acknowledges the support from Juan de la Cierva Formaci\u0026oacute;n grant (FJC2021-047857-I) financed by MCIN/AEI/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13039/501100011033\u003c/span\u003e\u003cspan address=\"10.13039/501100011033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and European Union \u0026ldquo;NextGenerationEU\u0026rdquo;/PRTR. Sergi Nuss-Girona, who is part of the Territorial and Environmental Analysis and Planning (APTA) research group at the University of Girona and the CLEPSIDRA project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdrabo, K. I., Kantoush, S. A., Esmaiel, A., Saber, M., Sumi, T., Almamari, M., Elboshy, B., \u0026amp; Ghoniem, S. (2023). An integrated indicator-based approach for constructing an urban flood vulnerability index as an urban decision-making tool using the PCA and AHP techniques: A case study of Alexandria, Egypt. \u003cem\u003eUrban Climate\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 101426. https://doi.org/10.1016/j.uclim.2023.101426\u003c/li\u003e\n\u003cli\u003eAbdrabo, K. I., Kantoush, S. A., Saber, M., Sumi, T., Habiba, O. M., Elleithy, D., \u0026amp; Elboshy, B. (2020). Integrated Methodology for Urban Flood Risk Mapping at the Microscale in Ungauged Regions: A Case Study of Hurghada, Egypt. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(21), Article 21. https://doi.org/10.3390/rs12213548\u003c/li\u003e\n\u003cli\u003eAlmeida, M. do C., Telhado, M. J., Morais, M., Barreiro, J., \u0026amp; Lopes, R. (2020). 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Oxford University Press. https://doi.org/10.1093/acrefore/9780199389407.013.25\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multidimensional vulnerability, Urban flooding, Indicator, Spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-4199231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4199231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a methodology for evaluating vulnerability to urban flooding across different dimensions, by employing spatial data analysis. The methodology consists of four steps: (1) selection of indicators that reflect the vulnerability of an urban area, (2) normalization of the data for each selected indicator across all dimensions, (3) assignment of weights for each indicator and dimension, and (4) mapping and classification using spatial analysis, resulting in a regular grid. This study proposes a comprehensive list of 31 potential indicators for quantifying vulnerability, with each indicator falling under one of the five dimensions (social, economic, environmental, physical, and institutional) and three components of vulnerability (exposure, susceptibility, and resilience), which are part of Step 1. Additionally, the methodology is complemented by a data generation and spatial analysis technique for Step 4. The proposed methodology can serve planners and policymakers to make objective decisions, based on vulnerability quantification, considering each dimension separately, as well as integrating with each other, using a multidimensional and spatial representation of flood risk vulnerability.\u003c/p\u003e","manuscriptTitle":"Spatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 08:08:17","doi":"10.21203/rs.3.rs-4199231/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-18T07:44:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-28T00:49:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2024-04-07T15:14:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-03T13:10:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-04-02T11:33:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"08de76b4-846a-489a-9f9e-34674a24ad9c","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T15:59:36+00:00","versionOfRecord":{"articleIdentity":"rs-4199231","link":"https://doi.org/10.1007/s11069-025-07451-5","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-06-25 15:57:14","publishedOnDateReadable":"June 25th, 2025"},"versionCreatedAt":"2024-05-03 08:08:17","video":"","vorDoi":"10.1007/s11069-025-07451-5","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07451-5","workflowStages":[]},"version":"v1","identity":"rs-4199231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4199231","identity":"rs-4199231","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}