Flood risk method for scarce-data catchments and municipalities

Changing climate, intense rainfall, and geomorphological conditions within the Indo-Gangetic Basin (IGB) have led to recurring flooding within the area in the recent past. The devastating August 2022 floods in Pakistan affected 33 million people causing severe loss of life and property. The occurrence of such flooding events has increased the need to understand the complexities of the interplay between flood hazards, exposure, vulnerability, and risk. This study delves into flood risk within India's Ganga Basin, examining the flood-inducing factors, vulnerability, and exposure through an innovative approach using NASA's Black Marble Nighttime Lights Product Suite (VNP46).  The product suite, available globally on daily, monthly, and annual composite scales, corrects extraneous sources of noise in nighttime light (NTL) radiance signals and has proven effective in disaster monitoring, risk assessment and reduction, humanitarian response, preparedness, resilience, and sustainable development. Our work to date has successfully utilized these NTL data to quantify flood exposure and the impact of flooding in both urban and rural areas by analyzing changes in radiance across time and space. However, to improve our understanding of human response to floods, we now focus on a more intricate analysis: incorporating geomorphological and socio-hydrological factors into a risk assessment approach. Our study evaluates flood hazard, exposure, and vulnerability as three separate entities and combines them using a multi-criterion decision tool to assess flood risk within the basin. Flood hazards are studied as a relationship between geomorphological and hydrological parameters, whereas flood vulnerability is studied using land use and land cover data. The novelty of this research is using NASA’s Black Marble nightlights as a proxy to study flood exposure. We argue that the NTL data can more effectively capture the human presence and economic activities compared to some conventional parameters for flood exposure such as population count, household density, and literacy amongst others. By integrating these diverse data layers using the robust Analytical Hierarchical Process (AHP), we generate comprehensive flood risk maps across the Ganga Basin spanning a decade. The accuracy of these maps is validated against historical flood event data from the EM-DAT database. Ultimately, our research culminates in a spatially explicit and data-driven approach to flood risk assessment, which can empower targeted mitigation strategies and proactive planning within the basin.

One of the most dangerous challenges to settlements in the UK comes from flooding. Currently, there is extensive map coverage of flood hazards zones in the UK; however, it is increasingly recognised that risk associated with natural hazards cannot be reduced solely by focussing on the hazard. There is also an urgent need for methods of evaluating and mapping flood vulnerability and risk in detail. Despite its significance, conventional flood risk assessment methodologies often underestimate likely levels of vulnerability in areas prone to hazards, yet it is the degree of vulnerability within a community that determines the consequences of any given hazard. The research presented proposes a general methodology to assess and map Coastal Flood Vulnerability and Risk at a detailed, micro-scale level. This captures aspects that are considered crucial and representative of reality (socio-economic, physical and resilient features). The methodology is then applied to a UK case study (city of Portsmouth). Environment Agency flood hazard data, National Census socio-economic data and Ordnance Survey topographic map data have been used to evaluate and map coastal flood vulnerability, examining neighbourhoods within census wards. This led to a subsequent analysis of Coastal Flood Risk, via the combination of a Coastal Flood Vulnerability Index and a Coastal Flood Hazard Index, for the Portsmouth ward Hilsea. This, consequently, identifies potential weaknesses that could lead to more effective targeting of interventions to improve resilience and reduce vulnerability in the long term and provides a basis for hazard and environmental managers/planners to generate comprehensive national/international vulnerability and risk assessments.

This study is concerned with flood risk that can be assessed by integrating GIS, hydraulic modelling and required field information. A critical point in flood risk assessment is that while flood hazard is the same for a given area in terms of intensity, the risk could be different depending on a set of conditions (flood vulnerability). Clearly, risk is a function of hazard and vulnerability. This study aims to introducing a new approach of assessing flood risk, which successfully addresses this above-mentioned critical issue. The flood risk was assessed from flood hazard and vulnerability indices. Two-dimensional flood flow simulation was performed with Delft3D model to compute floodplain inundation depths for hazard assessment. For the purpose of flood vulnerability assessment, elements at risk and flood damage functions were identified and assessed, respectively. Then, finally flood risk was assessed first by combining replacement values assessed for the elements and then using the depth–damage function. Applying this approach, the study finds that areas with different levels of flood risk do not always increase with the increase in return period of flood. However, inundated areas with different levels of flood depth always increase with the increase in return period of flood. The approach for flood risk assessment adopted in this study successfully addresses the critical point in flood risk study, where flood risk can be varied even after there is no change in flood hazard intensity.

Despite traditional measures to prevent disasters, climate change and urbanization increase flood risk. Thus, flood resilience has attracted increased global concern. Understanding the commonalities and differences between flood resilience and risk is arguably important for flood risk reduction. However, these factors have been seldom reported in previous studies, and discussions on the role of flood resilience in flood risk analysis, assessment, and management are lacking. In this study, the association between flood resilience and risk is discussed using a case study in the Pearl River Delta. Flood resilience is quantified using a pressure-state-response (PSR) model, while flood risk is assessed based on the hazard-vulnerability framework and the extension catastrophe progression method. The implications of considering flood resilience in flood risk analysis, assessment, and management are proposed. The results suggest that the overall flood resilience (risk) in the study area is greater (lower) than that in the highly urbanized areas, and areas with low (high) flood resilience (risk) are mainly concentrated within the highly urbanized areas. Indices extracted from human society and highly related to human activities have the same attributes in both frameworks, while indices associated with climate and geography contribute to the two con- cepts differently. Flood resilience supplements the concept of flood risk, and can be incorporated into risk assessment as an index. Moreover, pre-disruption (post-disaster) measures should follow flood risk (resilience) assessment, and strategies that foster flood resilience should be included in flood risk management. This study provides references for flood resilience improvement and risk mitigation.

Towards flood risk reduction: Commonalities and differences between urban flood resilience and risk based on a case study in the Pearl River Delta

Floods driven by extreme precipitation events pose significant risks to communities, infrastructure, and ecosystems, particularly in regions with complex topography, such as Nepal. Probable Maximum Precipitation (PMP) is a critical parameter for assessing flood risk and designing resilient infrastructure. This review examines the methodologies and findings of a study focused on flood risk assessment in the West Rapti River Basin, Nepal, with emphasis on PMP and Probable Maximum Flood (PMF). This study employs hydrometeorological and statistical methods to estimate PMP, utilizes Snyder’s unit hydrograph for PMF calculation, and applies HEC-RAS 2D modeling to map flood hazards, vulnerabilities, and risks. Key findings include PMP estimates of 507 mm and 575 mm, a PMF of 11,211.1 m³/s, and detailed flood risk maps highlighting significant inundation risks in Deukhuri Valley. This review synthesizes the contributions of this study, compares its approaches to global practices, and discusses its implications for flood risk management in Nepal.The study on flood risk assessment in the West Rapti River Basin, Nepal, employs a comprehensive approach to evaluate and map flood hazards, vulnerabilities, and risks. By combining hydrometeorological and statistical methods to estimate Probable Maximum Precipitation (PMP), the research provides crucial insights into extreme precipitation events in the region. The use of Snyder's unit hydrograph for Probable Maximum Flood (PMF) calculation and HEC-RAS 2D modeling for flood hazard mapping demonstrates a robust methodology for assessing flood risks in complex topographical areas. The findings of this study have significant implications for flood risk management in Nepal and similar regions. The PMP estimates of 507 mm and 575 mm, along with the calculated PMF of 11,211.1 m³/s, provide valuable data for designing resilient infrastructure and implementing effective flood mitigation strategies. The detailed flood risk maps highlighting substantial inundation risks in Deukhuri Valley offer critical information for local authorities, urban planners, and policymakers to develop targeted interventions and improve community preparedness. This research contributes to the broader understanding of flood risk assessment methodologies and their application in regions with challenging topography, potentially informing similar studies in other flood-prone areas worldwide. Keywords: Flood risk assessment, West Rapti River Basin, Nepal, Probable Maximum Precipitation (PMP), Snyder's unit hydrograph, Probable Maximum Flood (PMF), HEC-RAS 2D modeling, Flood hazard mapping, Deukhuri Valley, Flood mitigation strategies, Hydrometeorological methods, Statistical analysis, Extreme precipitation events, Flood risk management, Inundation risks

Comprehensive flood risk assessment is often constrained by a lack of appropriate data in high-altitude watersheds, particularly in developing countries like Nepal, where institutional capacities are limited for mapping and monitoring flood-prone communities. This study, one of the first of its kind, produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS). The result shows that most parts of the KV (around 80%) have moderate to low flood susceptibility around the watershed but susceptibility is prominent in southern areas. Highly flood-susceptible regions (about 14%) are found mainly along the riverbanks. Flood vulnerability, primarily influenced by population density and literacy rate, is moderate to low in most areas of the watershed (around 86%), predominantly higher in the central urban areas, and gradually lower towards the edge of the watershed. Flood risks in the major portion of the watershed (around 72%), mainly in the southern and eastern parts, are estimated as moderate to low risk, whereas higher risk zones are found in the central urban areas. The high contrast in flood vulnerability scores across the watershed has mainly contributed to the variation of flood risk zones, as flood susceptibility scores are fairly distributed over the watershed. The study findings will help policymakers develop location-specific sustainable flood risk management strategies for the flood-vulnerable communities in the KV watershed.

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

Floods are the most frequent disasters, affecting the largest number of people. In 2023, 164 floods were recorded worldwide, killing 7763 people, affecting 32.4 million people, and resulting in 20.4 billion USD losses (CRED 2023 Disasters in Number report). To mitigate flood risk, decision makers and civil protection authorities need reliable information on flood risk assessment, covering all disaster management stages.In the framework of a Programming Agreement with the Prefecture of Attica, NOA/IAASARS/BEYOND, in cooperation with NTUA/ITIA, developed a holistic multiparameter methodology that was implemented in five flood-stricken river basins at high spatial resolution. The research teams collected all available Earth Observation data, spatial data and technical studies; conducted detailed field visits; and modified the DEM and land cover accordingly. Following rainfall-runoff modeling and hydraulic modeling, the flood hazard was assessed for different scenarios. Vulnerability was considered a weighted estimation of population density, population age, and building characteristics on the basis of the population-housing census at the building block level. Exposure was based on the land value. Flood risk was eventually assessed on the basis of the combination of flood hazard, vulnerability, and exposure. Moreover, critical points, which were identified from the field visits, were also crosschecked with the flood inundation maps. Finally, refuge areas and escape routes were proposed for the worst-case flood scenario. This innovative methodology was applied, among other methods, in the Mandra river basin and was validated with the results of the urban flash flood, which took place in 2017, the deadliest flood in Greece in the last 40 years. BEYOND developed a user-friendly web GIS platform in which all the collected and produced data, including flood risk maps, critical points, refuge areas and escape routes, are made available.This flood risk assessment methodology was applied, following adaptation, in the Garyllis river basin in Cyprus, within the framework of the EXCELSIOR project, as part of the collaborative activities between ECoE and BEYOND. Data were collected from multiple sources, including satellite missions, governmental portals, in situ measurements, and historical records, at different resolutions. The collected data were calibrated via onsite visits and discussions with relevant actors, harmonized in terms of spatial and temporal resolution and used as inputs to estimate the flood hazard for different return periods. The vulnerability levels of the study area were quantified via the weighted linear combination of population density, population age, and building characteristics at the road level. The exposure levels were quantified in terms of the land value. Flood risk levels were estimated as a product of hazard, vulnerability and exposure levels. The validity of the proposed methodology was evaluated by comparing the critical points identified during the field visits with the estimates of the flood risk levels. Consequently, escape routes and refuge regions are recommended for the most extreme scenario.This work supports relevant authorities in improving disaster resilience and in implementing the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, and the UN Early Warnings for All initiative.

Diurnal changes within communities can significantly alter the level of impacts during a flood, yet these essential daily variations are not currently catered for within flood risk assessments. This paper develops a flood vulnerability and risk model that captures crucial features of flood vulnerability; integrating physical and socio‐economic vulnerability data, combined with a flood hazard analysis, to give overall flood risk at neighbourhood scale, at two different times of day, for floods of different magnitudes. The flood vulnerability and risk model, the resulting diurnal coastal flood vulnerability and risk indexes, and corresponding maps for the ward of Hilsea (Portsmouth, United Kingdom), presented within this paper, highlight three previously unidentified neighbourhoods in particular in the northwest of the Hilsea ward, which have the highest levels of risk during both time zones and for flood events of different magnitude. Critically, these neighbourhoods lie further inland and not directly on the Hilsea coastline, yet by analysing at this resolution (including diurnal impacts), substantial levels of underlying vulnerability were identified within these areas.

Flood risk assessment of the population in Afghanistan: A spatial analysis of hazard, exposure, and vulnerability

Despite an increasing number of people exposed to flood risks in Europe, flood risk perception remains low and effective flood risk management policies are rarely implemented. It becomes increasingly important to understand how local governments can design effective flood risk management policies to address flood risks. In this article, we study whether high flood exposure and flood risk perception correlate with the demand for a specific design of flood risk management policies. We take the ideal case of Switzerland and analyze flood risk management portfolios in 18 flood-prone municipalities along the Aare River. We introduce a novel combination of risk analysis and public policy data: we analyze correlations between recorded flood exposure data and survey data on flood risk perception and policy preferences for selected flood risk management measures. Our results indicate that local governments with high flood risk perception tend to prefer non-structural measures, such as spatial planning and ecological river restoration, to infrastructure measures. In contrast, flood exposure is neither linked to flood risk perception nor to policy preferences. We conclude that flood risk perception is key: it can decisively affect local governments’ preferences to implement specific diversified policy portfolios including more preventive or integrated flood risk management measures. These findings imply that local governments in flood-prone areas should invest in raising their population’s awareness capacity of flood risks and keep it high during periods without flooding.

Floods are the most common and costliest natural disaster in Australia. Australian flood risk assessments (FRAs) are mostly conducted on relatively small scales using modelling outputs. The aim of this study was to develop a novel approach of index-based analysis using a multi-criteria decision-making (MCDM) method for FRA on a large spatial domain. The selected case study area was the Hawkesbury-Nepean Catchment (HNC) in New South Wales, which is historically one of the most flood-prone regions of Australia. The HNC’s high flood risk was made distinctly clear during recent significant flood events in 2021 and 2022. Using a MCDM method, an overall Flood Risk Index (FRI) for the HNC was calculated based on flood hazard, flood exposure, and flood vulnerability indices. Inputs for the indices were selected to ensure that they are scalable and replicable, allowing them to be applied elsewhere for future flood management plans. The results of this study demonstrate that the HNC displays high flood risk, especially on its urbanised floodplain. For the examined March 2021 flood event, the HNC was found to have over 73% (or over 15,900 km2) of its area at ‘Severe’ or ‘Extreme’ flood risk. Validating the developed FRI for correspondence to actual flooding observations during the March 2021 flood event using the Receiver Operating Characteristic (ROC) statistical test, a value of 0.803 was obtained (i.e., very good). The developed proof-of-concept methodology for flood risk assessment on a large spatial scale has the potential to be used as a framework for further index-based FRA approaches.

Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters in cities on a large scale, thus offering the possibility to study the risk flooding poses to urban buildings. To fill this research gap, taking Shanghai as an example, this study developed a new research framework to assess urban vulnerability based on vulnerability curves and online data of residential buildings. First, detailed information about residential buildings was prepared via web crawlers. Second, the cleaned residential building information fed a support vector machine (SVM) algorithm to classify the buildings into four flood vulnerability levels that represented the vulnerability curves of the four building types. Third, the buildings of different levels were given vulnerability scores by accumulating the depth–damage ratios across the possible range of flood depth. Further, combined with the unit price of houses, flood risk was assessed for residential buildings. The results showed that the F1-score for the classification of buildings was about 80%. The flood vulnerability scores were higher in both the urban center and the surrounding areas and lower between them. Since 1990, the majority of residential buildings in Shanghai have switched from masonry–concrete structures to steel–concrete structures, greatly reducing the vulnerability to floods. The risk assessment showed decreasing risk trend from the center outward, with the highest risk at the junction of the Huangpu, Jing’an and Xuhui districts. Therefore, this framework can not only identify the flood vulnerability patterns but also provide a clue for revealing the flood risk of residential buildings. With real estate data becoming increasingly accessible, this method can be widely applied to other cities to facilitate flood vulnerability and risk assessment.

Floods are among the most impactful natural phenomena affecting society. The associated risks are influenced by factors such as urban expansion into high-risk areas, modifications to rivers and watersheds (e.g., artificial channels, flow redirection, and structures like dams and dikes), and the effects of climate change, including more frequent and severe events. In recent years, the application of Artificial Intelligence (AI) in climate and weather risk assessment has gained increased attention due to its ability to handle numerical and categorical variables, uncover nonlinear relationships, and achieve high performance.In this study, we introduce FloodGuard, an AI-powered tool for flood vulnerability and risk mapping. FloodGuard employs the concept of regionalization in ungauged basins and leverages a flood inventory derived from satellite imagery (e.g., Copernicus Emergency Mapping Service) over extensive areas (e.g., national or continental scales). The methodology selects the most relevant historical flood events and transfers this information to train a Random Forest machine learning model for estimating flood extent and producing a flood exposure map. Inputs to the model include the Geomorphic Flood Index (GFI), the Elevation, the Horizontal Distance to the Nearest River, Precipitation, the NDVI, and information on Land Use and Lithology. Flood prediction map is evaluated using maps generated from hydrological and hydraulic models. To assess vulnerability, we apply a geomorphic approach proposed by Manfreda and Samela (2019). This approach estimates flood depth, which is useful for estimating fast vulnerability levels. Finally flood risk is estimates with a GIS-based model.The primary objective of this study is to provide a preliminary simple tool to estimate a flood risk and provide risk maps. At the same time, this study evaluates evaluate the transferability of machine learning models from regions with flood records to ungauged areas using satellite observations. Limitations include uncertainties inherent to machine learning models and the lack of association with specific return periods. Preliminary results across Italy demonstrate that the Random Forest model achieves high performance (AUC > 0.9) and exhibits robust generalization capabilities (e.g., combined error (rfp + (1-rtp)) of 0.58).Keywords: Artificial Intelligence, Machine Learning, Flood risk, Flood vulnerability, GFI. This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 -D. D. 1243 2/8/2022, PE0000005).