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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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This paper investigates changes in political participation following a disaster. Drawing on the electoral results and flood declarations after the 2010 flood in Slovakia, a dataset was constructed that illustrates when and how often each municipality was affected by disastrous conditions before the elections. The analysis revealed that experiencing a flood significantly increased the level of political participation in a municipality. However, the effect of flooding on elections is conditional. First, significantly higher turnout occurs only when a flood affects the municipality on election day. Second, repeated flooding before the elections does not change the overall participation rate. In contrast, it is the one-time, continuous extreme weather event that most mobilises voters. Third, only severely affected municipalities demonstrated a significant turnout effect. Lastly, the timing variable is crucial in determining the impact of repetition and severity. This paper distinguishes between different kinds of flood events that can influence disaster behaviour., (© 2023 ODI.)

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities 1-3 . However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas 4 -a factor that is often underrepresented in coastal-management policies and long-term urban planning 2,5 . In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km 2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities., (© 2024. The Author(s).)

The burgeoning significance of urban floods in the context of evolving climate dynamics and shifting rainfall patterns underscores the exigency for comprehensive investigation and mitigation strategies. The study employs a multi-criteria assessment (MCE) approach and the analytical hierarchy process (AHP) to evaluate flood-vulnerable zones, wards, and sub-category-wise flood locations in Greater Mumbai. The AHP technique is used to evaluate flood-vulnerable impacting parameters such as rainfall (29.42%), slope (20.96%), land use/land cover (17.52%), vicinity to sewers and storm-water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). The study area is classified under different vulnerable categories as severe vulnerable (46.72%), high to very high (18.74%), and slight to moderate (34.54%). Researchers analysed 234 waterlogged locations, revealing that 85.46% (200 locations) were in the severe to very high vulnerability category, and only 14.52% (34 locations) were in the other three categories. Flood locations are more affected by slope (under the categories of < 5 m and 5.01-10 m), built-up land, sewers and storm water drainage (< 125 m), natural drainage (< 250 m), rainfall (< 2000 to 2200 mm), lowest dense vegetation, and coastal alluvium in soils. These model-based flood vulnerability maps are crucial for planning flood conservation and mitigation measures., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Disaster losses are increasing and evidence is mounting that climate change is driving up the probability of extreme natural shocks 1-3 . Yet it has also proved politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local and national authorities 4,5 . However, locally determined patterns of urbanization and spatial development are key factors to the exposure and vulnerability of people to climatic shocks 6 . Using high-resolution annual data, this study shows that, since 1985, human settlements around the world-from villages to megacities-have expanded continuously and rapidly into present-day flood zones. In many regions, growth in the most hazardous flood zones is outpacing growth in non-exposed zones by a large margin, particularly in East Asia, where high-hazard settlements have expanded 60% faster than flood-safe settlements. These results provide systematic evidence of a divergence in the exposure of countries to flood hazards. Instead of adapting their exposure, many countries continue to actively amplify their exposure to increasingly frequent climatic shocks., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R 2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Over the past few decades, flood disasters have emerged as the predominant natural hazard in Cyprus, primarily driven by the escalating influence of climate change in the Mediterranean region. In view of this, the objective of this study is to develop a geospatial flood risk map for the island of Cyprus by considering 14 flood hazard factors and five flood vulnerability factors, utilizing geographic information systems (GIS) and remotely sensed datasets. A comparative assessment was conducted for hazard mapping, employing statistical methods of frequency ratio (FR) and FR Shannon's entropy (FR-SE), and multi-criteria decision analysis method of fuzzy analytic hierarchy process (F-AHP). The main findings indicated that the FR method exhibited the highest predictive capability, establishing it as the most suitable approach for flood hazard mapping. Additionally, vulnerability factors were aggregated using F-AHP to generate the vulnerability map. The resulting flood risk map, which is the product of flood hazard and flood vulnerability, revealed that 9% of the island was located within highly risky regions, while 13.2% was classified as moderate risk zones. Spatial analysis of these high-risk areas indicated their concentration in the primary city districts of the island. Therefore, to mitigate future risks within these cities, an analysis of potential expansion zones was conducted, identifying the best-suited zone exhibiting the lowest risk. The generated flood risk map can serve as a valuable resource for decision-makers on the island, facilitating the integration of flood risk analysis into urban management plans., (© 2024. The Author(s).)

Objective: To explore young people's (YP) experiences of catastrophic flooding in the Northern Rivers area of northern NSW in 2017 and its effect on their mental health., Design: Content analysis of free-text survey responses (written responses to open questions, rather than selecting a response option)., Setting: Northern Rivers NSW., Participants: YP aged 16-25 years who were Northern Rivers residents at the time of the 2017 flood., Results: YP found their flood experience to be novel and challenging. They expressed worry, distress, anxiety, and worsened pre-existing mental health issues as a result of the flood. YP reported a number of primary stressors (inadequate preparedness, warning and support during the flood) and secondary stressors (loss/damage of property and possessions, financial strain and disruptions to daily life) which potentially contributed to poor mental health. Some YP reported positive outcomes from their flood experience, notably increased community connectedness and personal resilience. However, they also expressed concern for the future, particularly potential reoccurrence of extreme floods as well as climate change. YP conveyed a desire for better community involvement to improve preparedness for future floods and a clear motivation to take action on climate change., Conclusion: The flood was a challenging experience for YP which was commonly described as negatively affecting their mental health. Understanding what YP view as significant events or issues arising from their personal flood experience may help target support mechanisms and services to maintain their mental. More focus on community-based initiatives to improve disaster preparedness can support mental health in YP., (© 2024 The Authors. Australian Journal of Rural Health published by John Wiley & Sons Australia, Ltd on behalf of National Rural Health Alliance Ltd.)

Premise: Riparian plants can exhibit intraspecific phenotypic variability across the landscape related to temperature and flooding gradients. Phenotypes that vary across a climate gradient are often partly genetically determined and may differ in their response to inundation. Changes to inundation patterns across a climate gradient could thus result in site-specific inundation responses. Phenotypic variability is more often studied in riparian trees, yet riparian shrubs are key elements of riparian systems and may differ from trees in phenotypic variability and environmental responses., Methods: We tested whether individuals of a clonal, riparian shrub, Pluchea sericea, collected from provenances spanning a temperature gradient differed in their phenotypes and responses to inundation and to what degree any differences were related to genotype. Plants were subjected to different inundation depths and a subset genotyped. Variables related to growth and resource acquisition were measured and analyzed using hierarchical, multivariate Bayesian linear regressions., Results: Individuals from different provenances differed in their phenotypes, but not in their response to inundation. Phenotypes were not related to provenance temperature but were partially governed by genotype. Growth was more strongly influenced by inundation, while resource acquisition was more strongly controlled by genotype., Conclusions: Growth and resource acquisition responses in a clonal, riparian shrub are affected by changes to inundation and plant demographics in unique ways. Shrubs appear to differ from trees in their responses to environmental change. Understanding environmental effects on shrubs separately from those of trees will be a key part of evaluating impacts of environmental change on riparian ecosystems., (© 2022 The Authors. American Journal of Botany published by Wiley Periodicals LLC on behalf of Botanical Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068-0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km 2 in 2018 to 37.442 km 2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This Conference Paper uses descriptive-qualitative research to describe and analyze factors forming community resilience affected by floods. The determination of informants is determined deliberately on flood-affected communities using criteria according to the purpose of the study. Primary data were obtained from in-depth observations and interviews, while secondary data were obtained from library sources and relevant data. The data collection techniques used were observation, interviews, and documentation, while the data were analyzed using descriptive-qualitative analysis through several stages, namely data reduction, data presentation, and conclusions. The results of this study show that factors forming the resilience of the community to flood disasters are: (1) Value factors that have existed in the community for many years in the flooded land, namely mutual assistance; (2) Economic factors, finding alternative jobs or coping strategies by flood-affected communities; (3) Social factors, namely knowledge and skills to adapt to flood disasters through non-formal training and counselling on disaster and disaster mitigation from their experience or obtained through social media and mass media, such as television and radio; (4) Institutional factors, namely socialization of early flood warning, socialization of flood disaster mitigation, appeals for the prohibition of throwing garbage in rivers and essential food assistance before and during the occurrence of flood by the relevant government; and (5) Infrastructure factors that include the construction of facilities and infrastructure such as river dredging, drainage construction, and river cliff protection walls. [ABSTRACT FROM AUTHOR]