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Damaging hydrological events can profoundly impact societies. This study reconstructs the longest flood history to date for the Orba River Basin (ORB) in northwestern Italy, from 1511 to 2021 CE. Using the Annual Flood Damage Index, we establish a continuous annual hydrological time-series. Our analysis, incorporating a seasonally-weighted function for interannual storm effects, uncovers anthropogenic influences on the ORB's fluvial landscape and flood characteristics. Specifically, a change-point emerged around the end of the Little Ice Age and the subsequent warming period (c. 1816), after which more intense storms prevailed. Remarkably, recent flood peaks align with heightened climate hazards, proving more extreme and unpredictable on a small scale in disaster-prone areas. Additionally, this study acknowledges competitive forcing factors on a larger scale, including landscape changes due to peasant civilisation expansion and 19th-century deforestation. Broader-scale factors, exemplified by the global impact of the 1815 eruption of Tambora in Indonesia, may have influenced the post-1816 climate conditions. These results emphasise the importance of considering both human-induced disturbances and precipitation occurrences in comprehending a territory's environmental history. [Display omitted] [ABSTRACT FROM AUTHOR]

Flash floods cause substantial harm to the social and economic aspects of the affected countries. This is a significant problem in urban areas where drainage systems are inadequate and unable to withstand severe flooding. Understanding the specific regions that are susceptible to flooding is essential to implement strategies aimed at mitigating the risk. Detecting floods in ungauged basins is challenging. The current work aims to establish a practical method for identifying and mapping floodplain areas. We can use several tools, including the FLO-2D integration tool, Flood Risk tool, Geomorphic Flood Area plugin, and Quantum Geographical Information System (QGIS) with Hydrologic Engineering Centre River Analysis System (HEC-RAS) to efficiently and cost-effectively detect flood hazard zones. The QGIS tool, the Geomorphic Flood Index (GFI), is the most valuable tool for identifying flood-prone areas in cases where the areas are extensive and lack sufficient data. This tool offers high data analysis and cost calculation precision while using few resources. [ABSTRACT FROM AUTHOR]

The sudden and unpredictable breach of landslide dams in the Yarlung Tsangpo river basin usually causes megafloods, posing great risks to human lives and infrastructures in the downstream areas. This study proposed an efficient and quantitative risk assessment framework of breach floods caused by landslide dam failures in the mainstream and tributaries of the Yarlung Tsangpo river basin with limited data. The impact of dam breach floods on human risks was evaluated. The flood attenuation along rivers, strategies for mitigating overlapping floods, and sensitivity analysis of human risks were also discussed. The results show that the developed framework successfully assessed flood risks caused by the breach of landslide dams. The flood attenuation ratio increased with river length but decreased with the peak discharge at dam site. A higher peak discharge and a larger inundated area downstream were predicted when the breach floods of two landslide dams, one in mainstream and the other in a tributary, overlapped at the confluence. The overlapping flood could be mitigated by reducing peak discharges of the two landslide dams or increasing time interval between the two peaks. The simulations also outlined the downstream peak discharge resulting from the cascading breach was larger than that of a single dam. However, it was smaller than the combined peak discharges of two separate dams, because the erosion during the breach of the downstream dam incurred energy dissipation. The human risks in the Pasighat village were greater when overlapping flood occurred due to the increased water depth and more hazardous inundated buildings. In the case of multi-peak floods, the warnings for the former peak flood would also warn the peak flood thereafter when individuals were notified multiple peaks. Otherwise, individuals might be misled by the warning of the previous peak flood, resulting in catastrophic flood impacts. A parametric analysis indicated that early evacuation warnings were needed to avoid serious loss of life and flood damages, especially in cases of dam breaches occurring at nighttime or for areas in close proximity to the dam site. [ABSTRACT FROM AUTHOR]

As population growth, economic development, and climate change increase the risks of inland flooding across the US and worldwide, a range of flood mitigation approaches must be considered and evaluated, including flood forecast and early warning systems (FFEWS). Cost–benefit analysis (CBA) has long been used to scrutinize flood mitigation investments, and although most applications have addressed physical infrastructure projects, a growing body of research has focused on assessing the benefits of information-based approaches such as FFEWS. This review summarizes research to date on the economic benefits of FFEWS, using value-of-information (VOI) analysis as a conceptual and organizing framework. This framework emphasizes the role of FFEWS in improving protective action decisions and flood-related outcomes. I also use the framework to categorize the methods used to assess FFEWS benefits, distinguishing between methods that focus specifically on prevented flood damage and those that take a more holistic VOI-based approach. Through a systematic search and screening of the literature, I identified 66 articles and reports published from 1970 to 2023 in more than 15 countries. Overall, I found that relatively few of the studies (15) applied a VOI-based approach, and most of them estimated VOI in relative rather than absolute (i.e., monetary) terms. In contrast, almost half of the studies used expert assessments or surveys of household or businesses to quantify the effectiveness of warnings for prompting protective actions and for reducing damages. Even so, evidence from these studies regarding the economic value of avoided flood damages is limited and is particularly lacking for the US. These findings highlight the continuing need for empirical research to inform VOI analyses of FFEWS and to isolate the effect of FFEWS on incurred flood damages and on protective action costs. [ABSTRACT FROM AUTHOR]

Extreme weather events, such as hurricanes with intense rainfall and storm surges, are posing increasing challenges to local communities worldwide. These hazards not only result in substantial property damage but also lead to significant population displacement. Federal disaster assistance programs are crucial for providing financial support for disaster response and recovery, but the allocation of these resources often unequal due to the complex interplay of environmental, social, and institutional factors. Relying on datasets collected from diverse sources, this study employs a structural equation model to explore the complex relationships between disaster damage (DD), social vulnerability (SV), public disaster assistance (PDA), the national flood insurance (NFI), and population migration (PM) across counties in the contiguous US. Our findings reveal that communities with lower SV tend to experience higher levels of DD across US counties. SV is negatively associated with PM, PDA, and NFI, both directly and indirectly. Furthermore, PDA is positively linked to PM, whereas DD has a direct negative effect on PM but an indirect positive effect through PDA. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Enhancing the resilience of disaster-affected communities is always a prime concern for policymakers. Empirical evidence reveals that many socio-economic indicators determine the resilience capacity of an entity. Although several studies in India have investigated the role of socio-economic and generic adaptation measures in reducing impact, there is a lack of studies concerning human development, inequality, and forest cover in reducing vulnerability to extreme events like floods. By considering data related to state-wise losses and damages from floods between 1980 and 2011, we study them in enhancing the resilience capacity of Indian states. Employing Poisson fixed effects with a clustered standard error model, we find: First, human development matters for reducing mortality, but it enhances people affected and houses damaged, and the relationship is expected to have an inverted-U shape for later outcomes. Second, in contrast to the anticipation, variables like inequality are negatively associated with houses damaged and human fatalities, i.e. the latter declines with increasing former. Third, the confounder, like forest cover, is found to be negative for all loss and damage indicators. Fourth, most of these variables are found to be statistically insignificant. Given that developing resilient infrastructure is the need of the hour, this study suggests the inclusion of climate risks in development planning to address the adaptation deficit and avoid possible maladaptation. [ABSTRACT FROM AUTHOR]

This study investigates the physical processes behind extreme precipitation events (EPEs) in the Himalayas, notorious for causing frequent floods and significant loss of life and property. Due to the presence of complex terrain, understanding the driving factors of these EPEs is challenging. Here, we decipher the precipitation characteristics and their driving factors responsible for the occurrence of EPEs in the Western Himalayas (WH) for the period 1979–2020. EPEs are defined as events exceeding the 99th percentile threshold. The extreme precipitation in the WH is contributed by both large‐scale precipitation (accounting for 61%) and convective precipitation (39%). Moreover, 25.49% of EPEs in this region are directly associated with monsoon depressions. The presence of distinct upper‐tropospheric gyres flanking the WH, along with a prominent zonal wave pattern, promotes a southward extension of the trough. This intensifies the low‐level convergence of moisture‐laden winds from the adjoining seas, resulting in substantial moisture availability for the EPEs. An omega‐type blocking pattern emerges 4 days before EPEs, facilitating the intrusion of an extratropical cyclonic circulation. This circulation, characterized by its slow eastward and equatorward movement, leads to low‐level moisture flux convergence and ascending motions, which in turn trigger the EPEs. This highlights the crucial role of extratropical signals in driving EPEs and implies that tropical‐extratropical interactions play an important role in these EPEs. Furthermore, the shifting of the Intertropical Convergence Zone is strongly linked to the enhancement of the intensity of EPEs. Moreover, moisture budget analysis shows that EPEs over the WH are primarily driven by vertical advection, with the dynamic (thermodynamic) terms explaining 92% (8%) contribution. The intensified diabatic heating structure further enhances the convection, facilitating the development of deep convection which controls the local thermodynamics of these EREs. Lastly, our study demonstrated that most intensified and persistent EPEs over the Himalayas are found to be linked with Quasi‐Resonance Amplification, which is driven by baroclinic waves with 5 and 8 zonal wave numbers that contribute to these EPEs. Plain Language Summary: The Himalayan region experiences heavy precipitation during the monsoon season, causing floods that impact downstream and Northwestern Indian regions. Our research focuses on understanding the causes behind these extreme precipitation events, particularly in the Western Himalayas. Given the region's complexity and meteorological diversity, understanding these events presents a significant challenge. We delve into the synoptic conditions and large‐scale factors associated with intense precipitation in the Western Himalayan region. As a result, we identified circulation patterns that bring moisture from the Arabian Sea and the Bay of Bengal, leading to intense rainfall. Other factors like vertical moisture advection and atmospheric heating in the atmosphere also play a role in the formation of heavy rainfall. Our study also stresses how large‐scale factors like Quasi Resonance Amplification (QRA) and Intertropical Convergence Zone (ITCZ) movement lead to the formation of heavy rainfall. This research sheds light on the complex interactions between large‐scale weather patterns and local conditions, providing valuable insights for building resilience against extreme weather events. Key Points: Large‐scale circulation patterns drive extreme precipitation events (EPEs) in the Himalayas, exacerbated by complex terrainDistinct upper‐tropospheric gyres and zonal wave patterns intensify moisture convergence, fueling EPEs in the Western HimalayasVertical moisture advection and diabatic heating play pivotal roles in triggering and intensifying EPEs along with impacts of QRA [ABSTRACT FROM AUTHOR]

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study. [ABSTRACT FROM AUTHOR]

Flooding stress is a growing threat to global soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations are increasing due to climate change. Soybean is highly sensitive to flooding and substantial yield losses are observed due to a cascade of negative physiological responses induced by hypoxia. Hence, there is a pressing need for the development of flood‐tolerant genotypes. This study evaluated the grain yield and seed protein and oil content of 31 soybean genotypes over 2 years under both non‐flooding and flooding conditions, where flooding entailed a 4‐day partial water submergence during the early reproductive growth stages R1/R2. Mixed‐effects linear models were utilized to assess the impact of flood damage scores (FDSs, 1–4 scale) on observed phenotypes, as well as differences in observed phenotypes between tolerant, moderate, and susceptible genotypes across flooding and non‐flooding treatments. No significant impact of FDS was observed for seed protein and oil content. In addition, no significant differences in these phenotypes were observed between flooding and non‐flooding treatments across the various genotype categories. On average, for each unit increase in FDS, grain yield decreased by 432.7 kg ha−1 (17.4%). Tolerant genotypes experienced roughly 33% yield losses between flooding and non‐flooding treatments, while moderate and susceptible genotypes experienced 44% and 51% yield losses, respectively. The advancements in genomics and phenomics are promising for the identification and incorporation of novel flood‐tolerant alleles through plant breeding, potentially mitigating flooding‐induced yield losses across diverse environmental conditions. [ABSTRACT FROM AUTHOR]

Increasing flood losses in the Gulf of Mexico related to development patterns and climate hazards pose serious threats to resilience and insurability. The purpose of this study is to understand how scale, social vulnerability, risk, and urban form relate to National Flood Insurance Program (NFIP) policy coverage and flood exposure. Our multilevel models identify that flooding is significantly clustered by region and counties, especially shoreline counties. Our measures of risk suggest that the Federal Emergency Management Agency (FEMA) special flood hazard area (SFHA) underestimates risk and exposure when compared with the Flood Factor and that there is some compensation in terms of insurance coverage, suggesting a pattern of adverse selection. Older housing stock appears both less insured and less exposed, raising questions of whether current growth patterns are increasing risk independent of environmental change. Our models suggest that census tracts with higher percentages of black residents are less insured and more exposed, and a similar pattern exists for rural areas. Our results highlight the need to seek common solutions across the Gulf of Mexico, concentrating on the most flood-exposed counties, and that specific resilience strategies may be necessary to protect areas with socially vulnerable populations, especially in rural areas. Underlying challenges exist due to the spatial relationship between exposure and social vulnerability and the potential for adverse selection in insurance markets due to different measures of risk. [ABSTRACT FROM AUTHOR]

When water damage occurs in a mine, variations in the immersion levels of tunnels at different burial depths can be observed. There is a significant relationship between the stability of the surrounding rock and the depth of immersion. Therefore, studying the deformation and damage characteristics of sandstone with circular holes at varying immersion depths, along with their acoustic properties, plays a crucial role in maintaining the stability of water-rich roadways. The TAW-2000 press and static strain system were utilized to investigate the mechanical properties, crack evolution, and deformation field distribution of sandstone with circular holes at varying immersion depths. Additionally, this study analyzed the impact of immersion depth on the characteristic parameters of acoustic emission. The results indicate that immersion depth is negatively correlated with the compressive strength and modulus of elasticity of sandstone; as immersion depth increases, the duration of the compression and yield phases of the rock samples also increases, while the duration of the elastic phase remains relatively unaffected. Furthermore, greater immersion depths correspond to a decrease in the total number of cracks, although the proportion of tensile cracks increases, making the formation of secondary cracks less likely. The frequency of acoustic emission events (transient elastic waves generated by the formation, extension, or closure of tiny cracks within the rock) shows a closely correlated dynamic with the stress–time curve of the rock sample. The acoustic emission ringing counts generated by rock samples under submerged water conditions tend to stabilize with a slight increase before signs of rupture appear. Additionally, the cumulative total energy of acoustic emissions from the rock samples decreases as the water level rises. These research findings provide significant reference value for addressing issues related to water immersion and the extent of water saturation in roadways within rock engineering. [ABSTRACT FROM AUTHOR]

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks. [ABSTRACT FROM AUTHOR]

Dike failures often induce highly damaging floods. Improved understanding of the inherent mechanisms is important to predict and prevent dike failures. This research focuses on sand dike failure by overtopping. During the experiments, the evolution of nine sections was monitored using laser-sheet while the water level in the upstream reservoir was captured using ultrasonic gauges. The impact of the grain size was studied by comparing a fine and a coarser sand. Even though the failure mechanisms look similar in both cases, some differences could be observed in the evolution of the geometry of the dike. Finally, the failure was simulated with 1D and 2D models. The 1D model shows good results for the initial stage of the dike evolution and in predicting the evolution of the width of the breach. The 2D model predicts the morphological evolution of the dike more accurately but slightly overestimates the widening of the breach. [ABSTRACT FROM AUTHOR]

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this research categorizes the disaster's impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems. [ABSTRACT FROM AUTHOR]

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. [ABSTRACT FROM AUTHOR]

This study proposes a multidimensional framework for the assessment of cultural heritage vulnerability to flood hazards. In this framework, cultural heritage is framed within a landscape approach drawing on the concept of the historic urban landscape as a physical entity, a lived space and a layered socio-economic environment. In such manner, this study emphasises that climate change disturbs both tangible and intangible aspects of cultural heritage – such as social systems and daily life practices – by modifying the material conditions that sustain them. Thus, the proposed framework integrates built and natural heritage with area-based vulnerability and socio-economic deprivation, considering households' exposure to flood risk. The analysis follows a spatial and quantitative approach using open geospatial data available at the statistical unit level. The data are processed and analysed employing QGIS software and statistical analysis. Results confirm that cultural heritage vulnerability is highly associated with socio-economic deprivation and area-based vulnerability. The understanding of this interrelation is key to the identification of historic urban landscape qualities and priority areas for reconstruction and social support. [ABSTRACT FROM AUTHOR]

This research focuses on improving flood management of the Malwathu Oya River in Anuradhapura Historical City, Sri Lanka, by designing an efficient gate system for the weir of Halpan Ela in the Malwathu Oya River. Frequent flooding threatens agriculture, infrastructure, and public safety in this region. This research aims to enhance water level control in the upper reach of Halpan Ela Anicut by evaluating rainfall patterns, tank spillway efficiency, and gate operation challenges. Historical data on rainfall and tank spillage were analyzed. Flow simulations revealed significant pressure differences, with the existing gate structure showing an upstream pressure of 114,492.5 Pa at a maximum flow of 1740 m3/s, compared to 105,406 Pa for the new flap gate system at the same flow rate. This represents a pressure difference of 9 kPa, equivalent to a 0.9 m water head. Despite the system's estimated cost of USD 0.1 million, the potential reduction in river flood damage, which currently exceeds USD 0.2 million annually, demonstrates its value. This research emphasizes the effectiveness of the flap gate system in reducing flood risks in Anuradhapura City compared to the existing gate type, though it is only a part of a broader flood mitigation strategy. [ABSTRACT FROM AUTHOR]

The present study discusses flood hazard characteristics in the lower reaches of the West Rapti River based on the results obtained from field surveys and numerical computations using depth averaged 2-D numerical models for flood flow and associated sediment transportation. To evaluate the inundation process with sediment erosion and deposition in the floodplain, a new erosion term was introduced into the mass conservation equations for suspended sediment and bed sediment. The results obtained from numerical computations indicated that field data on the spatial distributions of depths for inundation, and sediment erosion and deposition can be evaluated by the numerical model. Thus, numerical predictions were performed for the inundated areas, and the accumulated volumes of sediment erosion/deposition for floods with return periods of 50, 100, and 200 years, as a step towards damage assessment and risk assessment due to floods with active sediment transportation in the floodplains. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This editorial discusses the importance of preparing communities, cities, and nations for extreme flooding events. It highlights the need to adapt infrastructure to be more resilient, while acknowledging the tradeoff between efficiency and resilience. The editorial provides examples of infrastructure failures during past flooding events and discusses the challenges of adapting critical infrastructure systems to a changing climate. The document includes articles that explore different approaches to managing flood risk, the use of satellite data for monitoring and warning systems, and the vulnerability of industrial process facilities to flood damage. The authors emphasize the need for comprehensive solutions that consider uncertainties and involve collaboration between academia and the public and private sectors. The text also mentions the challenges of developing protective infrastructures, retrofitting existing infrastructures, and considering implications throughout the lifecycle. [Extracted from the article]

Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future. [ABSTRACT FROM AUTHOR]

Both the number of disasters in the U.S. and federal outlays following disasters are rising. FEMA's Public Assistance (PA) is a key program for rebuilding damaged public infrastructure and aiding local and state governments in recovery. It is the primary post-disaster source of recovery funds. Between 2000 and 2019, more than $125B (adjusted, 2020 dollars) was awarded through PA. While all who qualify for PA should have equal opportunity to receive aid, not all do, and the factors influencing how the program has been administered are complex and multifaceted. Lacking an understanding of the factors positively associated with historical receipt of aid, there is little way to objectively evaluate the efficacy of the PA program. In this work, we evaluate the salient features that contribute to the number of county-level PA applicants and projects following disasters. We use statistical learning theory applied to repetitive flooding events in the upper Midwest between 2003 and 2018 as a case study. The results suggest that many non-disaster related indicators are key predictors of PA outlays, including the state in which the disaster occurred, the county's prior experience with disasters, the county's median income, and the length of time between the end of the disaster and the date when a disaster is declared. Our work suggests that indicators of PA aid are tied to exposure, bureaucratic attributes, and human behavior. For equitable distribution of aid, policymakers should explore more disaster-relevant indicators for PA distribution. [ABSTRACT FROM AUTHOR]

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. [ABSTRACT FROM AUTHOR]

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future. [ABSTRACT FROM AUTHOR]

In Nigeria, sugarcane is a prominent crop and the primary crop used to produce sugar. However, floods have been a major tragedy that has troubled Nigeria's small-scale farmers. The bulk of rural poor people are these small-scale farmers, and as poverty rates have been rising over time, corrective action is required. This study was therefore carried out to assess the poverty status of sugar cane farming households (affected by flood, and unaffected by flood) in Kwara/Osun State of Nigeria. The study employed two-stage sampling technique to select 120 sugarcane farming households in the study area. Descriptive statistics, the Flood Loss Estimation, Logit Regression and Foster Greer Thorbecke (FGT) are the empirical analytical tools employed to analyze the data collected for the study. The results of the poverty index analysis revealed that 46 percent of the respondents are relatively poor, the poverty depth was 0.16 which implies that 16 percent were below the poverty line and the poverty severity was 0.07 which implies that 7 percent of the respondents were severely poor. Also, it revealed that flood shocks, gender, household size, household income, access to credit and membership to social organization were significant determinants of poverty. The study then came to the conclusion that many coping mechanisms people employ are corrosive because they have detrimental long-term implications on the sustainability of household subsistence. For future risk assessments and flood mitigation, the study advises using a holistic strategy and adept models. The remedies outlined in this paper would help the government, private sector, and sugar cane farmers economically. [ABSTRACT FROM AUTHOR]

Wang, P.; Royer, E.L.; Jackson, K., and Gutierrez, S., 2024. Impacts of Hurricane Ian along the low-lying southwest Florida coast (USA) in 2022: Lessons learned. Journal of Coastal Research, 40(5), 827–851. Charlotte (North Carolina), ISSN 0749-0208. Hurricane Ian made landfall in the low-lying, densely populated, and developed southwestern Florida coast on 28 September 2022 as a large and slow-moving category 4 hurricane. Various U.S. federal and state agencies collected a large and comprehensive data set, including pre- and poststorm airborne LIDAR topography, in situ water level and wave measurements at numerous locations before, during, and after the storm, and poststorm high-water marks over a large area. This study reports results from a series of poststorm field investigations including ground observations of beach-dune erosion and deposition, catastrophic damage to various infrastructure, and widespread distribution of non-biodegradable materials washed into the estuary and numerous mangrove islands. Hurricane Ian induced large-scale inundation in low-lying southwest Florida, submerging all the barrier islands bordering Charlotte Harbor estuary, all the islands within the estuary, and up to 5 km into the mainland. Dense tree-type vegetation limited the landward penetration of beach-dune erosion and overwash deposition along the barrier islands. Net sand-volume loss from the beach-dune system ranged 10–25 m3/m and was controlled by the deep submergence of the system during the peak of the storm. The extremely high storm surge of up to 5.2 m above mean sea level generated by Hurricane Ian caused severe damage to the built environments over a large area. High storm waves superimposed on the elevated water level, reaching 1.2 m at the seaward edge of vegetated dunes, contributed to the destruction along the barrier islands. Hurricane Ian distributed a tremendous amount of non-biodegradable artificial debris over a large area and into sensitive natural environments, including numerous mangrove islands, barrier-island interior wetlands, and the estuary waterbody. Measures to prevent materials such as single-use plastics, insulation fibers, and household appliances from being washed into sensitive environments should be a significant part of prestorm preparation. [ABSTRACT FROM AUTHOR]

The increasing magnitude of monetary and life losses caused by urban floods worldwide has sparked a deeper interest among researchers in comprehending these phenomena. Accurate modeling and forecasting of floods play a pivotal role in developing effective strategies for flood damage mitigation and management, thereby reducing associated hazards. A critical concern in this endeavor is optimizing the trade-off between computation time and accuracy when simulating real-time flood events. This paper aims to establish the superiority of the sub-grid approach in achieving accurate simulation results of real-time flood events within minimal computational time. By conducting simulations of one experimental test and two real-world flood events using the HEC-RAS flow simulation model, we demonstrate that the sub-grid approach significantly reduces computation time by approximately 90% while faithfully capturing the dynamics of urban flood. In contrast, the non-sub-grid approach often over-predicts flow dynamics in urban areas and fails to effectively trace building footprints, resulting in unrealistic flow patterns and under or overstating the hazard maps. In a nutshell, the sub-grid hydraulic modeling approach enables better hazard analysis and mapping. The outcomes of this research provide valuable guidance to modelers and authorities, suggesting the adoption of the sub-grid approach for accurate and reliable real-time urban flood modeling, inundation forecasting, and hazard zoning in the context of urban flood hazard analysis. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean region is affected by devastating annual flood events that cause massive socio-economic losses, especially in Syria. The displacement of thousands of refugees to the western region of Syria has led to tremendous pressures on the landscape, increasing the flood vulnerability. Moreover, the absence of administrative control over the landscape increased the economic losses caused by flood events. Producing flood vulnerability maps is considered one of the most important spatial strategies that will provide the essential basis for flood hazard management. The present study sought to detect flood vulnerability areas in the Al-Fawar river basin, western Syria, utilising the combination of the Analytical Hierarchy Process (AHP) method and the Geographical Information System (GIS) environment. To accomplish this purpose, nine factors influencing flood vulnerability were appointed: rainfall, elevation, slope, Topographic Wetness Index (TWI), drainage density, distance from the river, soil resistance, Land Use/Land Cover (LULC) and lithology. Importantly, A total of 129 flood events were specified as a validation dataset for the accuracy inspection, which was conducted utilising the Area Under the Curve (AUC). The results demonstrate that more than 32% of the study basin is regarded high and very high flooding threat. These parts are found in the central and western parts of the study area along the river courses. The AUC value of 0.868 implies the flood vulnerability map's reliable and accurate predictive ability constructed utilising AHP. The current approach implemented denotes a productive tool for LULC planners and policymakers to develop managing plans for flood hazards in the environmental rehabilitation phase in Syria. [ABSTRACT FROM AUTHOR]

Hüsler–Reiss vectors and Brown–Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler–Reiss vector, extend these to the case of the Brown–Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry. [ABSTRACT FROM AUTHOR]

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. [ABSTRACT FROM AUTHOR]

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management. [ABSTRACT FROM AUTHOR]

The urban areas of Mediterranean Europe, and particularly Italy, have experienced considerable expansion since the late 19th century in terms of settlements, structures, and infrastructure, especially in large population centers. In such areas, the geohydrological risk is high not only for inhabited areas but also along roadways exposed to flooding. This scenario is worrying, especially in road underpass sections, where drivers are unlikely to perceive a real risk due to the high degree of confidence that comes from the habit of driving. Underpasses have been widely used to obviate the need to find shorter alternative routes and manage vehicular traffic in urban settings impeded by previous anthropogenic and natural constraints. To assess the numerical consistency, frequency, and areal distribution of flood risk around road underpasses, several hundred pieces of data were selected (mostly from international, national and local newspapers, CNR IRPI archive and local archives) and cataloged in a thematic database, referring mainly to the Italian territory. The behavioral aspects in the face of risk were also examined in order to provide a better understanding and raise awareness for preventive purposes. The results of this specific CNR research, which lasted about two years, confirm the exposure of underpasses to extreme risk events, affecting road users. In Italy alone, between 1942 and 2023, 698 underpasses were identified as having experienced a flooding event at least once. The database shows that 680 vehicles were involved in Italy, with a total of at least 812 individuals, of whom 19 died. Despite incomplete and uneven information, the findings of the analysis regarding the increment in underpasses flooding and the drivers action in front of a flooded underpass may be useful for undertaking the appropriate mitigation strategies. [ABSTRACT FROM AUTHOR]

Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures. [ABSTRACT FROM AUTHOR]

Built environment flood resilience is a critical challenge facing communities worldwide. Amongst various efforts to resilience, the conception towards data utilization becomes popular with enormous technological advancements. Built environment creates varieties of data at larger volumes throughout their life cycle signifying that the importance of these data in the context of flood resilience cannot be ignored. However, despite the power of data, the greatest opportunities that exist for flood resilience enhancement have been mired by numerous and complex unidentified challenges. Thus, identifying these challenges with timely relevant strategies is a significant need. One of the best ways to tackle these challenges is by viewing them through the lens of data life cycle stages. This study, therefore, aimed to identify these challenges in each stage of the data life cycle with strategies to overcome them. Semi‐structured interviews conducted with 12 experts revealed the significant challenges allied with built environment data with potential future strategies. The qualitative content analysis was conducted to analyse the findings. The use of advanced sensing technologies, cloud‐based storage solutions, data governance policies and the development of predictive models are some of the consequential strategies outlined in this study. These findings provide valuable insights and guidance to facilitate built environment data utilization for flood resilience. [ABSTRACT FROM AUTHOR]

Subway station platforms are vulnerable to flood damage. Thus, an investigation of inundation in subway platforms is required to ensure the safety of citizens against flooding. This study analyzed and validated the inundation characteristics and safety areas in a subway station model using experimental inundation depth measurements and numerical simulations. Then by using the simulation, the effects of increased inflow to water velocity and depth were analyzed, and its impact on human models was found by using risk assessments which included specific force ( M 0 ), Flood Hazard Degree (FD), Flood Intensity Factors (FIF), toppling velocity, and sliding velocity. The flood risk assessment analysis results show that assessments using M 0 could increase uncertainty by broadening the evaluation of risky areas compared to other indices. Also, the drag force applied to the human models was calculated using the simulations, which provided inundation risk values to people in subway stations. Overall, the risk assessments would provide a criterion for flood situations in subway stations. [ABSTRACT FROM AUTHOR]

While urban populations are rapidly increasing around the world, floods have been frequently and seriously occurring due to the climate crisis. As existing disaster prevention facilities have specific limitations in completely protecting against flood damages, the concept of resilience, which emphasizes the ability to recover after becoming injured and harmed by a flood, is necessary to mitigate such damages. However, there is still a scarcity of studies that quantitatively show the relationship between the resilience and the socioeconomic costs, even though a variety of evaluation methods exist in the literature. This study aims to quantitively analyze the socioeconomic impact of flooding on the urban environment based on the concept of resilience. A method of evaluating four properties of resilience (redundancy, rapidity, resourcefulness, and robustness) through damage function and network analysis was used to measure changes in resilience against flood damages. In addition, to determine the socioeconomic impact of flooding, the costs incurred due to transportation delays and the lack of labor participation were evaluated. Differences in structural and social systems have led to variations in resilience and socioeconomic costs. As a future study, if the circumstances after flood events based on risk-based recovery can be evaluated, more effective urban flooding defense decisions would be expected. [ABSTRACT FROM AUTHOR]

This paper presents an innovative, original approach to co-design retention ponds and floating photo voltaic solar plants as a water-energy nexus approach to reduce flooding and energy burden for one location. Case-study analysis was conducted in Waimanalo, Hawai'i. A flood model--from a previous study--was used to provide a potential location for a retention pond and floating solar photovoltaic panels. This study found the co-design of retention ponds and floating PV solar can not only reduce future stormwater runoff by up to 50% but provide a total of 50% of onsite solar energy at a neighbourhood scale, demonstrating how clean energy and green infrastructure can help advance environmental and social justice. Several takeaways from this study were taken into account and things to consider for a follow-up paper. One of the major challenges was finding a potential suitable location. Overcoming this challenge required using Google Maps and the flood model to pinpoint high flood accumulation and searching for a large green space that is in close vicinity to a developed neighbourhood. [ABSTRACT FROM AUTHOR]

In this study, nine different types of essential facilities in the state of Iowa (such as hospitals, fire stations, schools, etc.) were analyzed on a county level in terms of flood depth, functionality and restoration time after flooding, and damage sustained during flooding. These essential facilities were also analyzed on the state level in terms of their location relative to the 100 y and 500 y flood zones. Results show that the number of essential facilities within the flood extent reached up to 39%, and during the 100 y flood scenario all but one of the six chosen counties lost functionality of 100% of their facilities. Most essential facilities were found to have a flood depth of 1 to 4 ft deep and a restoration time of 480 days. The purpose of this study is to bring awareness to decisionmakers regarding the risk that flooding events pose to essential facilities and to highlight the increasing dangers of flooding on a broader scale. This study will be beneficial to improve mitigation strategies, emergency response plans, and ensuring that emergency services and facilities are available in the event of future floods for the affected areas. [ABSTRACT FROM AUTHOR]

States of India like Bihar, Uttar Pradesh, and West Bengal along the Ganga River, endure natural disasters periodically, resulting in repeated trends of economic loss and damages. Especially, most of the districts of Bihar, India, are prone to floods. Based on this background, this study aims to assess the flood vulnerabilities across districts of Bihar, India, employing data from the Central Water Commission from 1953 to 2020. Further, we explore trends and patterns of loss and damage due to flood risks in Bihar. Using the flood vulnerability integrated method and the principal component analysis, the index is constructed by incorporating the three major indicators: exposure, sensitivity, and adaptive capacity. This study is unique, and advances from previous studies in using a greater number of variables in exposure indicator. The proxy variable for each indicator is identified through both inductive and deductive approaches, and the composite index is constructed using all three indicators. Also, we identify the districts with high level of education and per capita income are less likely to expose flood vulnerability. The comparison of the districts reflects wide range of variation in terms of flood vulnerability as per their adaptive capacity and sensitivity. Specifically, these findings align with Target Sustainable Development Goal 11.5. This study addresses the policy for disaster prevention, risk reduction, and mitigation measures, as well as the enhancement of the capability of adaptation to floods by the affected community. [ABSTRACT FROM AUTHOR]

Digital Elevation Models (DEMs) play a crucial role in flood management. This study aims to assess the effect of various global DEMs (GDEMs), including ALOS-12.5 m, ALOS-30 m, SRTM-30 m, SRTM-90 m, and NASADEM-30 m, on flood risk modeling in a densely urban area. The 1D-2D MIKE FLOOD hydraulic model was employed for the flood modeling. The process involved using a high-resolution DEM (Pleiades-1A 1 m) as the reference map (RM1), along with other GDEMs, to simulate a 50-year return period flood. The performance of GDEMs was then assessed in terms of flood inundation extent, flood hazard, and flood damage estimation, assessing their accuracy against the RM1. The study also explored the trade-offs between accuracy and efficiency by examining the effects of substituting the high-resolution map with a 5-m resolution map (Res_5 m) created through resampling. Results revealed that GDEMs tend to overestimate flood extent and underestimate depth, leading to inaccurate flood risk assessments. Among the GDEMs, NASADEM-30 and SRTM-30 outperformed others in simulating flood inundation extent but resulted in a more uniform flood depth distribution; approximately 70% of the flood extent was categorized with depths of less than 0.3 m, nearly double that of the RM1. This discrepancy led to an underestimation and overestimation of higher (H3-H6) and lower (H1) hazard levels by approximately 50%, respectively. Furthermore, GDEMs significantly overestimated flood damages, with NASADEM-30 showing a 161% overestimation compared to the RM1. Ultimately, the Res_5 m was a viable alternative for urban flood simulations as it led only to a modest 6% decrease in the flood zone area. [ABSTRACT FROM AUTHOR]

We analyze the exposure of different income groups to the 2021 floods in Germany, which serve as an exemplary case of natural disasters intensified by anthropogenic climate change. To this end, we link official geo-coded satellite data on flood-affected buildings to neighborhood-level information on socio-economic status. We then document the empirical relationship between flood damages and household income. We limit comparisons to the vicinity of affected rivers and absorb a rich set of regional fixed effects to assess the differential exposure at the local level. Average household income is around 1,500 euros or three percent lower in flood-affected neighborhoods than in non-affected neighborhoods nearby. Average flood exposure is more than three times as high in the bottom sixty than in the upper forty percent of neighborhoods in terms of average household income. Our study is the first to document this regressive exposure along the income distribution based on actual flood damage data in Europe. [ABSTRACT FROM AUTHOR]

In many nations, floods cause a significant loss of life and property. However, in underdeveloped nations, inadequate technology increases the number of fatalities and property damage caused by flooding. In the contemporary world, bridges are vital. Roads are more beautiful with bridges. One of the biggest issues with infrastructure worldwide is bridge breakdowns. It frequently has disastrous results, including fatalities and impeded trade. Every disaster results in the loss of life and destruction of public property. This project's goal is to monitor the water condition and raise the bridge when there is a threat, indicated by a buzzer sound. A smart bridge is one that recognises a noteworthy behaviour or environmental state, then automatically reacts to that condition. [ABSTRACT FROM AUTHOR]

Long-term trends in flood losses are regulated by multiple factors including climate variation, demographic dynamics, economic growth, land-use transitions, reservoir construction and flood risk reduction measures. The attribution of those drivers through the use of counterfactual scenarios of hazard, exposure or vulnerability first requires a good representation of historical events, including their location, their intensity and the factual circumstances in which they occurred. Here, we develop a chain of models that is capable of recreating riverine, coastal and compound floods in Europe between 1950 and 2020 that had a potential to cause significant socioeconomic impacts. This factual catalogue of almost 15 000 such events was scrutinized with historical records of flood impacts. We found that at least 10 % of them led to significant socioeconomic impacts (including fatalities) according to available sources. The model chain was able to capture events responsible for 96 % of known impacts contained in the Historical Analysis of Natural Hazards in Europe (HANZE) flood impact database in terms of persons affected and economic losses and for 81 % of fatalities. The dataset enables the study of the drivers of vulnerability and flood adaptation due to a large sample of events with historical impact data. The model chain can be further used to generate counterfactual events, especially those related to climate change and human influence on catchments. [ABSTRACT FROM AUTHOR]