In Europe, current flood risk assessment practices often rely on the number of directly exposed residents as a proxy of the overall flood impact on people, thereby overlooking the broader range of flood consequences on human well-being. Literature studies have shown that these consequences encompass several direct and indirect intangible impacts and may affect people even beyond the flooded area. With aim of deepening current understanding of flood impacts on people, we developed a questionnaire that was distributed to the citizens of the Marche region of Italy affected by a severe flood in 2022, reaching 707 responses. Respondents were asked to self-identifying as belonging to one of three exposed groups: directly affected (e.g., experienced damage to their property), indirectly affected (e.g., family or friends experienced direct impacts, or experienced work or domestic system interruptions) and not affected. Respondents of these three exposed groups were asked to rate the severity of various impacts, as well as the overall impact they experienced. The responses were analysed through descriptive statistics and multiple linear regressions. The analyses allowed to understand, first, how severely each impact was perceived when considered independently, and second, the role of each impact types in relation with the overall flood impact. The results show that flood consequences are felt even outside the flooded area, and underline the central role of intangible impacts, specifically psychological ones, in shaping the overall impact across all the exposed groups. The findings presented support the implementation of more effective recovery policies and measures that prioritize the most significant impacts for differently exposed groups.

Flood risk to agriculture is strongly influenced by the timing of inundation relative to crop development stages, making flood seasonality a critical but often overlooked component in damage estimation. This study introduces a generalizable regionalization framework that combines hydrological clustering and machine learning to incorporate seasonal flood probability into agricultural risk assessment. The approach involves identifying clusters of gauged catchments with similar patterns of intra-annual flood occurrence and using supervised classification to extrapolate these seasonal regimes to ungauged catchments based on their physical attributes. The resulting spatially distributed maps of monthly flood probability can be then integrated with a flood damage model to calculate expected annual losses and support risk estimates across entire river districts. The proposed framework, applied in this study to the Po River District (Italy) for illustrative purposes, is scalable and adaptable to different regions, contributing to more robust and context-sensitive adaptation planning in agriculture. Results highlight the importance of accounting for flood seasonality in cost-benefit analyses within agricultural contexts, as neglecting intra-annual variability can lead to overestimated damage projections and suboptimal mitigation strategies.

Geographic areas are rarely exposed to a single natural hazard; more often, two or more hazards coexist within the same territory. Consequently, a comprehensive analysis, quantification, and comparison of all potential risks affecting a given area are fundamental to fostering sustainable and climate-resilient environments. To this end, this paper introduces a new indicator-based framework for evaluating risk in multi-hazard contexts, referred to as the "Hazards-Impacts Matrix." Developed through a multidisciplinary effort, the matrix integrates several risk dimensions (i.e. those linked to impacts on individual well-being, the built environment, public services, business activities, environmental systems, communities, and the financial system) and can be consistently applied across multiple spatial scales. Furthermore, by employing a coherent set of indicators across different hazards, it ensures the comparability of risk estimates, thereby enabling the analysis of the simplest form of hazard interrelationships (i.e., compound events) and providing a robust foundation for investigating more complex hazard and risk interdependencies. The matrix is applied to the Lomellina area (Northern Italy) as a proof of concept. While originally designed to support decision-makers during the risk mitigation planning phase within the Italian context, the framework can also be adapted for post-event assessments and extended to other countries.

Managing flood risk is crucial for achieving global sustainability. Flood damage to firms' assets, in particular, imposes significant financial stress, necessitating efforts to minimize future consequences. However, current tools and knowledge for estimating flood damage to firms are inadequate, primarily due to a lack of high-quality damage data and the diversity of firm characteristics, complicating generalization. This study aims to improve understanding of micro-scale flood damage to firms in Italy through the analysis of empirical data, focusing specifically on direct damage. The dataset comprises 812 observed damage records collected after five flood events. Damage is categorized into building structure, stock, and equipment. The analysis reveals relationships between damage, economic sector, and water depth. Results indicate that damage increases at a rate less than proportional to the firm surface area and with water depth significantly explaining only stock damage. The quantification of damages across different sectors shows that healthcare facilities register the highest average damage to building structures, the commercial sector is most affected in terms of stock damage, and the manufacturing sector exhibits the greatest average damage to equipment. The derived damage model offers better predictive accuracy than foreign models in the Italian context. These findings aid in developing effective, tailored risk mitigation strategies and provide valuable insights for future research and policy aimed at reducing flood impacts on firms in Italy.

Natural risk management in areas exposed to multiple hazards remains a major challenge, as mitigation planning often relies on single-hazard approaches that fail to capture combined impacts and territorial complexity. In this context, we present a Multi-Criteria Decision Analysis (MCDA) framework design to support effective risk reduction in multi-hazard settings. The framework, developed within the Italian RETURN project (Multi-risk science for resilient communities under a changing climate), integrates impact modelling to derive risk scenarios, technical and expert judgement to assess the economic, environmental and societal sustainability effects of mitigation options, and participatory stakeholder processes to define the weighting of evaluation criteria. The framework is demonstrated through a case study in the Lomellina area, one of the most fragile territories in Northern Italy, characterized by multiple pressures including multi-risk exposure (i.e., floods, earthquakes, drought and technological accidents), depopulation, ageing populations, limited access to essential services, and fragmented governance. The MCDA framework is applied to compare two alternative risk reduction strategies: levee raising and the relocation of the most exposed population to seismically retrofitted buildings. Results highlight the potential of the proposed framework to support transparent and informed prioritization of risk reduction strategies in multi-hazard and fragile contexts.

Flood-related damage has increased dramatically in recent decades with direct and indirect economic impacts accounting for a large share of gross national products. Therefore, there is an urgent need to acquire more quantitative knowledge about flood damage to mitigate economic losses and reduce exposure to flood risk. Firms are especially affected in case of flood. Still, flood direct damage assessment to businesses is hindered by the paucity of available data to characterize the enterprises, the lack of high-quality damage data to derive new models or validate existing ones, and the high variability of activity types which hampers generalization. On the indirect damage side, the existing literature predominantly focus on estimating damage at the macro scale, leaving a gap in understanding the specific impact on individual firms. This study contributes at improving knowledge about types and extent of damage of flood events on economic activities through the analysis of empirical data, focusing on direct and indirect damage at the micro-scale, with specific reference to the Italian context. The investigated data derive from observed direct damage records collected after six flood events in Italy. The information on the surface of the building, the typology of the affected firms (i.e., NACE category) as well as on local water depth levels and the classification in damage components (building, equipment, and stock) permitted to develop an econometric model to forecast the direct damage and to analyze the mechanisms of flood damage across the economic sectors. The original dataset was then enriched with information included in the financial statement of flooded activities that has been used to investigate indirect damage. Despite characterized by significant uncertainty, obtained results supply first tools for the prediction of flood direct damage and for the quantification of indirect damages to firms for the Italian context, in the support of more effective risk mitigation actions. In fact, the model identifies the more vulnerable elements within the business sectors orienting modelers and decision-makers choices.

In recent years, extreme rainfall events led to severe flooding in several European countries which caused extraordinary human and economic losses. Such events, which are projected to become more likely because of climate change, expose many citizens to extremely stressful situations that involve intense fear, shock, and loss. Previous studies clearly point toward a direct relation between such flood experiences and negative mental health outcomes of those affected. However, existing studies commonly focus on directly exposed populations only, preventing a direct comparison with a control group. This makes it more difficult to separate the effect of the flood event from other factors that potentially affect the mental health of the respondents. Here, we use survey data from 698 residents from the Marche region in Italy, which was affected by disastrous flooding in 2022. The survey focused not only on the directly affected population (n = 392) but also included a nonaffected control group (n = 306). We use the short version of the Kessler Distress Scale (K6) as a screener for severe mental distress. Results show that directly affected respondents exhibit a 13.1%-16.7% higher prevalence rate of indications of severe mental distress compared to the control group. The significant impact of the flood event on negative mental health outcomes is further confirmed by regression analyses, which show a direct influence of several flood stressors on severe mental distress, including physical health impacts and higher water levels on one's own building. Since mental illness is associated with high burdens for those affected and their families, as well as high socioeconomic costs, this aspect deserves more attention in postdisaster contexts. The results presented in this article can be used as a reference by responsible authorities for estimating the additional demand for psychological assistance needed in the aftermath of such severe events.

Quantitative flood risk assessment is essential for local disaster risk reduction and management strategies. However, data scarcity which typically characterizes the Global South, poses significant challenges to the application of conventional risk assessment methodologies developed in data-rich contexts. This study addresses these challenges by providing an exportable and comprehensive flood risk framework designed for the Metuge district, a flood-prone region in northern Mozambique that is crossed by the Muaguide River. This framework integrates hydrological, hydrodynamic, and damage modelling with a multi-level participatory process that involves stakeholders from governmental to community levels. To overcome data deficiency, the modelling leverages global data sources, field survey data, and open-access tools. Feedback gathered through participatory activities has allowed to refine modelling assumptions, enhancing the reliability of the outcome. Specifically, the participatory activities were designed to reach multiple objectives: increasing the building capacity of local authorities, empowering the resilience of the local population, and validating the results. In fact, the absence of observed data for the study area has made the comparison of the results with community experience of past flood events the sole viable option for their validation. Results from this case study indicate an average of 2,000 individuals at risk annually and an Annual Average Damage (AAD) of approximately 300,000 USD/year to roads and buildings. The ratio between the AAD and the population of the study area corresponds to 0.5% of Mozambique’s GDP per capita. Moreover, the district population's access to the hospital during flooded periods has been assessed by analyzing the practicability of roads. These findings provide critical insights for local authorities for flood risk management and serve as a foundation for the design and implementation of mitigation measures.

Quantitative flood risk assessments rely on damage models, which relate information on flood hazard and vulnerability of exposed assets to estimate expected losses. Differently from other sectors, crop damage depends not only on typical hazards variables (including water depth, flow velocity, inundation duration, water salinity, yield of sediments and/or contaminants) but also on the month of flood occurrence. Indeed, plant vulnerability changes over the different phenological phases that are strictly related to the seasonality of crop production. Considering the time of occurrence of the flood would imply a shift from the traditional representation of inundation scenarios based on annual probability to monthly-based hazard estimations. When risk assessment is carried out at large spatial scale, a detailed understanding of seasonal flood patterns is then required for the different sub-catchments of the basins, including un-gauged ones. In this study we present a clustering approach to flood frequency regionalization applied to the Po River District in Northern Italy, within the risk assessment process required by the European Floods Directive. The area is characterized by complex climatic and topographic conditions, highlighting the representativeness of the case study for the implementation of the proposed approach in other geographical contexts. Utilizing observed monthly flow data from over 100 gauging stations, the approach combines both physical and statistical criteria to identify homogeneous regions in terms of flood generation mechanisms and seasonality. The process enables the assignment of distinct monthly flood probabilities to all catchments within the district, thereby supporting a comprehensive flood risk assessment for the agricultural sector.

This paper introduces INSYDE-content, a novel, probabilistic, multi-variable synthetic model designed to estimate flood damage for household contents on a component-by-component basis. The model addresses a critical gap in current modeling tools, which often overlook the significance of household contents in overall damage assessments. Developed through an expert-based approach and grounded in the scientific and technical literature, INSYDE-content leverages desk-based data to characterize model features, including uncertainty treatment arising from incomplete input data. A validation test on two historical flood events and a sensitivity analysis are performed to assess the model's performance and explore the contribution of input variables to damage estimation, confirming its robustness and interpretability. For illustrative purposes, in this study INSYDE-content has been tailored to the specific hazard, vulnerability and exposure characteristics of Northern Italy; nonetheless, its adaptable structure supports broader applicability across diverse regional settings, provided suitable customization is applied.

Within the context of the Italian RETURN (Multi-risk science for resilient communities under a changing climate) project, the objective of WP 7.2 is the definition of national guidelines for the evaluation of the effectiveness of alternatives of intervention in natural risks management, by considering in detail Multi Criteria Analysis (MCA) tools. The focus is on (i) multi-hazard contexts, for which state-of-the-art and knowledge is limited, (ii) the different phases of the risk management chain, and (iii) the variety of structural and non-structural measures that can be adopted. The present contribution describes results reached so far in this direction. First, First, we propose a flowchart that illustrates the process leading to the ranking of alternative strategies through MCA. The objective of the flowchart is to highlight the operative steps required for its implementation, including: (i) the identification of intervention alternatives and their characterization in terms of spatial and temporal scale of effectiveness, potential risk reduction, and secondary impacts on interested communities, (ii) recognition of stakeholder’s objectives and their respective dimensions, (iii) definition of attributes and indicators according to which alternatives are evaluated, (iv) selection of the most appropriate MCA tool and definition of related parameters, and (v) performance of sensitivity analysis. The development of the flowchart emphasized that establishing guidelines for applying MCA to multi-hazard risk management requires two ongoing fundamental steps (i) an in-depth, generalized investigation of the types of elements exposed to the different natural hazards as well as the identification of potential direct and indirect impacts on them in case of an event; (ii) the definition of an abacus of alternatives which identifies the most promising measures that can be implemented in a given context, and characterizes them in terms of potential risk reduction or increase (with respect to different hazards), and temporal and spatial scale of effectiveness.

Floods can cause power outages with widespread impacts on socio-economic activities dependent on electricity for their functioning. Effective flood risk management requires comprehensive damage assessment, yet methodologies to estimate the entire range of expected damages are lacking. This paper presents a new modeling and simulation probabilistic framework for the assessment of damages to power grids exposed to floods. The framework combines modeling tools and approaches from engineering, economics and sociology, namely a flood inundation model to generate stochastic hazard scenarios, fragility curves to describe the stochastic failure process of components in the power grid conditioned to the hazard, a simulation-based model to analyze the power flow, and a socio-economic model to characterize the customers connected to the power grid. Consequently, the framework enables: (i) considering the stochastic magnitude and frequency of floods, (ii) evaluating the vulnerability of power grids components, (iii) estimating their spatio-temporal probabilities of failure, (iv) analyzing the cascading effects across power transmission and distribution networks, and (v) assessing the impact of power outages on the final customers and their likelihood. A synthetic case study is worked out by adapting the IEEE 14 power grid benchmark to the Italian context, proving how the framework allows the identification of the most critical components for the security of power supply during flooding. The outcomes from the implementation of the framework can support civil protection agencies and grid operators in the decision on pre- and post-disruption mitigation strategies, so to guarantee public safety, secure power supply and ensure financial well-being.

The study aims to provide the Lombardy Region, the primary stakeholder in the project, with a procedure for evaluating and classifying structural flood risk mitigation measures. The primary objective is to assist the regional authority in identifying priority interventions for public funding. A step-by-step procedure has been developed to assess and rank all projects submitted to the Region, selecting priority projects based on technical considerations—evaluating feasibility, effectiveness, and sustainability of the proposed measures—and the preferences of policymakers. The assessment procedure's conceptual structure was tested using case studies, including both feasibility studies and executive projects, to determine the level of technical insights required at each planning phase of public works. The methodology relies on Multiple Criteria Analysis (MCA) techniques, enabling the simultaneous consideration of various, non-directly comparable criteria in a complex decision-making context. These criteria encompass technical features of proposed works, potential territorial constraints, and interferences in the intervention area (feasibility); the effectiveness of measures in reducing flood risk and associated costs; and the environmental and social co-benefits and disbenefits of each intervention (sustainability). Specific indicators, either ad hoc defined for the study or referenced from current regulations and guidelines at national and regional levels, are employed to evaluate the criteria. Stakeholder participation, particularly from the Region, River District Authorities, and Municipalities, is crucial throughout the process, especially in the final phase of aggregating (weighting) all criteria. This aggregation produces an overall performance score for each option, enabling the derivation of a regional ranking of flood risk mitigation strategies. The collaboration between academia and public institutions is highlighted as essential for enhancing the efficiency of disaster risk reduction policies.

Despite the primary aim of flood risk assessment and management to mitigate the negative impacts of floods on people, Italy lacks adequate tools for assessing flood human impact. In fact, current assessments are limited to estimating the number of residents in flooded areas. This approach underestimates the human impact as it disregards the broader spectrum of societal impacts and does not include indirectly exposed groups, who may, for example, suffer income losses due to the disruption of economic activities affected by the flood. However, addressing these impacts is key to guarantee healthy lives and well-being for all, as requested by the third Sustainable Development Goal. To better understand the broad spectrum of human impact, a questionnaire was distributed via a social media and local newspapers campaign to directly, indirectly and not affected citizens of the municipalities hit by the exceptional flood event that struck the Marche region, Italy, on September 15th, 2022. The survey elicited the perceived severity of flood impacts accounting for both direct (e.g., physical injuries, property damage) and indirect impacts (e.g., disruptions to daily life, post-event illnesses, psychological stress), together with socio-economic data and flood event information. About 700 responses were received, nearly half of which came from directly affected people. The analysis of the perceived severity of impacts across the three respondent groups revealed that, while direct tangible impacts were significant only for those directly affected, indirect intangible impacts were significant for both indirectly and not affected respondents. This finding confirms that the current approach, which focuses only on directly affected individuals, underestimates the human impact. Furthermore, the psychological stress induced by the flood was significant in all three groups, highlighting the need for targeted preventive measures and post-event mental health support for the whole community.

Low-income countries are the most vulnerable to floods; moreover, the occurrence and intensity of these disastrous events are progressively increasing worldwide. Quantitative flood risk assessment is the first and primary step that can be made to support local decision makers towards effective flood risk management. However, the lack of data that typically characterizes the Global South hinders the implementation of methods developed for data-richer contexts. The present study aims at proposing a comprehensive and exportable methodology for flood risk assessment, responding to the challenge of data and method deficiencies, by referring to global sources, freely available tools and relying on an intensive field survey. The methodology, including hydrological, hydro-dynamic and damage modelling, and a multi-level participatory process, was developed for a flood prone area in northern Mozambique that is crossed by the Muaguide river. The latter presents a bed completely buried by sediment in many stretches, causing extensive floods which have been more and more frequently in the last years, hitting the surrounding region. The obtained results (Annual Average Damage to roads and buildings of about 300,000 USD/year and an average of about 2000 people at risk per year) increase the knowledge of flood risk in the investigated area and can be a useful support for the design and implementation of effective mitigation measures at local and regional scales.

The increasing impacts of climate change and urbanization underscore the critical importance of micro-scale population data for enhancing natural risk management and emergency preparedness. Access to high resolution population information enables better correlation with the spatial variability of hazards, leading to more accurate damage estimations. However, such data are typically available at macro and meso-scales. In the case of Italy, for example, population data from the National Institute of Statistics (ISTAT) is provided at the census tract scale (meso-scale) for the entire country, despite the uneven distribution of residents within these areas. This study focuses on developing an exposure model for resident population in Italy at a finer spatial resolution than the currently available data. The model uses point data of resident population in the Emilia Romagna region, relating this information to residential building footprint area and volume, as well as land use features. The analysis reveals a notable portion of vacant residential buildings, with approximately 30% of Italian residential buildings reported as uninhabited by ISTAT. The study suggests that incorporating information on the type of residential buildings (main, secondary, or vacant) could significantly enhance the model's performance, especially in tourist-centric cities characterized by a high share of holiday houses. Additionally, the results of this study highlight the need for public entities to invest efforts in the development of a reliable and comprehensive spatial database that includes information on permanently inhabited properties.

Floods are among the most frequent and damaging natural hazards, affecting millions of people worldwide, and the risk of catastrophic losses due to flooding is expected to increase as a result of climate change. The possibility of predicting and estimating the expected fatalities in flood-prone regions is among the top priorities of decision-makers in flood risk management. Thus, predicting the conditions leading to loss of life is crucial for assessing the risk to the population. Here we focus on the Po River District in Northern Italy which covers the largest Italian hydrographic basin. We demonstrate that the occurrence of flood-related fatalities can be estimated by utilizing a random forest (RF) algorithm applied to a dataset of fatalities that occurred in this area from 1970 to 2019. This method relies on nine explanatory variables that describe the hazard intensity, and the environmental and sociodemographic conditions leading to fatalities. The proposed model is a primary attempt to estimate the probability of flood-related fatalities in the Italian context, and it provides a proxy for the quantitative estimation of flood risk to the population.

Accurate flood damage modelling is essential to estimate the potential impact of floods and to develop effective mitigation strategies. However, flood damage models rely on diverse sources of hazard, exposure and vulnerability data, which are often incomplete, inconsistent or totally missing. These issues with data quality or availability introduce uncertainties into the modelling process and affect the final risk estimations. In this study, we present INSYDE 2.0, a flood damage modelling tool that integrates detailed survey and desk-based data for enhanced reliability and informativeness of flood damage predictions, including an explicit representation of the effect of uncertainties arising from incomplete knowledge of the variables characterising the system under investigation.