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Floods are one of the most common and widely distributed natural risks to life and property worldwide.?There is a need to identify the risk of flooding in flood prone areas to support decisions for flood management from high level planning proposals to detailed design. An important part of modern flood risk management is to assess vulnerability to floods. This assessment can be done only by using a parametric approach.?Worldwide there is a need to enhance our understanding of vulnerability and to also develop methodologies and tools to assess vulnerability.?One of the most important goals of assessing flood vulnerability is to create a readily understandable link between the theoretical concepts of flood vulnerability and the day-to-day decision-making process and to encapsulate this link in an easily accessible tool.?The present book portrays a holistic parametric approach to be used in flood vulnerability assessment and this way to facilitate the consideration of system impacts in water resources decision-making.?The approach was verified in practical applications on different spatial scales and comparison with deterministic approaches. The use of flood vulnerability approach can produce helpful understanding into vulnerability and capacities for using it in planning and implementing projects.

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global estimates of the number of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-year floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world’s flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development. Floods are most devastating for those who can least afford to be hit. Globally, 1.8 billion people face high flood risks; 89% of them live in developing countries; 170 million of them live in extreme poverty making them most vulnerable.

Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years. Using historical data across the U.S., the authors find that population declines are associated with flood exposure. Projecting this relationship to 2053, the authors find that flood risk may result in 7% lower growth than otherwise expected.

Flood magnitude and frequency estimation are essential for the design of structural and nature-based flood risk management interventions and water resources planning. However, the global geography of hydrological observations is uneven, with many regions, especially in the Global South, having spatially and temporally sparse data that limit the choice of statistical methods for flood estimation. To address this data scarcity, we pool all available annual maximum flood data for the Philippines to estimate flood magnitudes at the national scale. Available river discharge data were collected from publications covering 842 sites, with data spanning from 1908 to 2018. Of these, 466 sites met criteria for reliable estimation of the annual maximum flood. Using the index flood approach, a range of controls was assessed at both national and regional scales using modern land cover and rainfall data sets, as well as geospatial catchment characteristics. Predictive equations for 2 to 100 year recurrence interval floods using only catchment area as a predictor have R2≤0.59. Adding a rainfall variable, the median annual maximum 1 d rainfall, increases R2 to between 0.56 for Q100 and 0.66 for Q2. Very few other topographic or land use variables were significant when added to multiple regression equations. Relatively low R2 values in flood predictions are typical of studies from tropical regions. Although the Philippines exhibits regional climate variability, residuals from national predictive equations show limited spatial structure, and region-specific equations do not significantly outperform the national equations. The predictive equations are suitable for use as design equations in ungauged catchments for the Philippines, but statistical uncertainties must be reported. Our approach demonstrates how combining individually short historical records, after careful screening and exclusion of unreliable data, can generate large data sets that can produce consistent results. Extension of continuous flood records by continuous and rated monitoring is required to reduce uncertainties. However, the national-scale consistency in our results suggests that extrapolation from a small number of carefully selected catchments could provide nationally reliable predictive equations with reduced uncertainties.

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided.

Historical records are an important source of information on extreme and rare floods and fundamental to establish a reliable flood return frequency. The use of long historical records for flood frequency analysis brings in the question of flood stationarity, since climatic and land-use conditions can affect the relevance of past flooding as a predictor of future flooding. In this paper, a detailed 400 yr flood record from the Tagus River in Aranjuez (central Spain) was analysed under stationary and non-stationary flood frequency approaches, to assess their contribution within hazard studies. Historical flood records in Aranjuez were obtained from documents (Proceedings of the City Council, diaries, chronicles, memoirs, etc.), epigraphic marks, and indirect historical sources and reports. The water levels associated with different floods (derived from descriptions or epigraphic marks) were computed into discharge values using a one-dimensional hydraulic model. Secular variations in flood magnitude and frequency, found to respond to climate and environmental drivers, showed a good correlation between high values of historical flood discharges and a negative mode of the North Atlantic Oscillation (NAO) index. Over the systematic gauge record (1913–2008), an abrupt change on flood magnitude was produced in 1957 due to constructions of three major reservoirs in the Tagus headwaters (Bolarque, Entrepeñas and Buendia) controlling 80% of the watershed surface draining to Aranjuez. Two different models were used for the flood frequency analysis: (a) a stationary model estimating statistical distributions incorporating imprecise and categorical data based on maximum likelihood estimators, and (b) a time-varying model based on \"generalized additive models for location, scale and shape\" (GAMLSS) modelling, which incorporates external covariates related to climate variability (NAO index) and catchment hydrology factors (in this paper a reservoir index; RI). Flood frequency analysis using documentary data (plus gauged records) improved the estimates of the probabilities of rare floods (return intervals of 100 yr and higher). Under non-stationary modelling flood occurrence associated with an exceedance probability of 0.01 (i.e. return period of 100 yr) has changed over the last 500 yr due to decadal and multi-decadal variability of the NAO. Yet, frequency analysis under stationary models was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.

This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial and Evaluation Laboratory (DEMATEL) methods for assessing flood risk in the Kosynthos River basin in northeastern Greece. The study aims to address challenges arising from data limitations and provide decision-makers with effective flood risk management strategies. The integration of DEMATEL is crucial, providing a robust framework that considers interdependencies among factors, particularly in regions where conventional numerical modeling faces difficulties. DEMATEL is preferred over other methods due to its proficiency in handling qualitative data and its ability to account for interactions among the studied factors. The proposed method is based on two developed causality diagrams. The first diagram is crucial for assessing flood hazard in the absence of data. The second causality diagram offers a multidimensional analysis, considering interactions among the criteria. Notably, the causality diagram referring to flood vulnerability can adapt to local (or national) conditions, considering the ill-defined nature of vulnerability. Given that the proposed methodology identifies highly hazardous and vulnerable areas, the study not only provides essential insights but also supports decision-makers in formulating effective approaches to mitigate flood impacts on communities and infrastructure. Validation includes sensitivity analysis and comparison with historical flood data. Effective weights derived from sensitivity analysis enhance the precision of the Flood Hazard Index (FHI) and Flood Vulnerability Index (FVI). Highlights • The proposed Causality Diagram addresses multidimensionality regarding the vulnerability. • The proposed Causality Diagram addresses data gaps regarding the hazard. • The causality diagrams are exploited by using the DEMATEL method. • GIS leverages quantitative map data to assess high-risk areas comprehensively. • Sensitivity refines Flood Hazard Index and Flood Vulnerability Index precision.

In the current study, the one-dimensional/two-dimensional (1D/2D) coupled hydrodynamic model is used for the development of flood hazard maps for the frequently flooded coastal urban floodplain of the Surat city, India. The releases from the Ukai dam and tidal levels at the Arabian Sea are considered as upstream and downstream boundary conditions, respectively. The floodplain roughness was estimated using the existing land use land cover (LULC) classification, and the performance of the developed coupled hydrodynamic model was evaluated against the past flood data of year 2006 and 2013. The flood frequency analysis was carried out for peak inflow into the Ukai reservoir, and subsequently, the design flood hydrographs for different return periods have been developed. Finally, the simulated model output has been used to develop multi-parameter flood hazard maps defining the stability of people, vehicles, and buildings. More than 80% of the entire coastal urban floodplain of the Surat city is submerged during 100-year return period flood, with West and North zone of the city being the worst affected regions. Out of the total flooded area, nearly 20% area is under significant hazard for adults. The 27% area offers instability hazard to large four-wheel drive vehicles, whereas 14% area is affected with moderate to high hazard for buildings. The instability index for specific vehicle types is dominated by floating of small and large cars over 90% of the flooded area. Further, the combined hazard maps revealed that 14% of the flooded area is under very severe hazard category, posing a threat to the stability of people, vehicles, and buildings. The developed hazard maps will work as an effective non-structural measure for local administrative agencies to minimize the losses and better future planning.

Flooding events are a major contributor to natural disasters across the global tropics. However, reliable flood data are sparse, which limits our ability to understand flood dynamics across spatial and temporal scales. Here, data from the CYGNSS SmallSat constellation are utilized to identify small‐ to regional‐scale flooding events in Sumatra. Three case studies show that CYGNSS‐derived inundation data capture evolution of floods, with increased inundation detected near flood locations 1 day after the event. A comprehensive analysis of 555 floods records in Sumatra identifies key parameters for a flood detection: an inundation anomaly threshold (0.1) and maximum distance between observations and a flood location (15 km). This approach could enhance flood risk assessment and forecasting, benefiting tropical populations. Our methodology, reliant on low‐cost small‐satellites, shows promise for future scalability with a larger satellite constellation, enabling better flood detection and long‐term, near‐real‐time monitoring of floods across the global tropics.