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Coastal habitat restoration, especially of coral reef ecosystems, can significantly reduce the exposure of coastal communities to natural hazards and, consequently, the risk of wave-driven flooding. Likewise, reef degradation can increase coastal flood risks to people and property. In this study, the valuation of coral reefs in the United States Virgin Islands (USVI), along the coasts of St. Croix, St. John, and St. Thomas, demonstrated the social and economic benefits provided by these natural defenses. Across the territory, more than 481 people and $31.2 million of infrastructure were estimated to receive protection from coral reefs per year (2010 U.S. dollars). In 2017, Hurricanes Irma and Maria significantly damaged coral reefs throughout the archipelago. By combining engineering, ecological, geospatial, social, and economic data and tools, this study provided a rigorous valuation of where potential coral reef restoration projects could help rebuild these damaged habitats and decrease the risks from coastal hazards faced by USVI’s reef-fronted communities. Multiple restoration scenarios were considered in the analysis, two of which are detailed in this report. These include (1) ‘Ecological’ restoration, where restoration creates a structure that is 0.25 m high and 25-m-wide reef, and (2) ‘Hybrid’ restoration, where restoration creates a structure that is 1.25 m high and 5 m wide. There are many ways that such structures could be developed. In the hydrodynamic analyses, there are no assumptions about how the restoration is developed. Many practitioners of both coral (and oyster reef) restoration consider that a reef height of 0.25 m might be delivered from planting corals alone and that 1.25 m might require a combination of artificial structures and coral planting. In a third scenario, the analysis investigated the reduction of protection benefits that would occur through the reduction of 1 meter of naturally occurring reef height due to reef degradation. The reduction of protection due to the loss of reefs can also be interpreted as the protection value of the existing reefs. In all studied restoration scenarios, it was assumed that the planting of corals would enhance hydrodynamic roughness, effectively dissipating incident wave energy and reducing the potential for coastal flooding. A standardized approach was employed to strategically locate potential restoration projects along the entire linear extent of existing reefs bordering the USVI, and to identify where coral reef restoration could offer valuable benefits in flood reduction. Potential restoration projects were only located within the existing distribution of reefs across the region, even though numerous sites were positioned far offshore (2-3 km), and some were at relatively deep depths (up to 7 m). Risk-based valuation approaches were followed to delineate flood zones at a 10 m2 resolution along the entire region's reef-lined shorelines for all the potential coral reef restoration scenarios. These were subsequently compared to flood zones without coral reef restoration. The potential reduction in coastal flood risk provided by coral reef restoration, and the protection value of existing reefs, were quantified utilizing the latest information available at the time of analysis from the U.S. Census Bureau, Federal Emergency Management Agency (FEMA), and Bureau of Economic Analysis for return-interval storm events. The change in Expected Annual Damages (EAD), a metric indicating the annual protection gained due to coral reef restoration, was calculated based on the damages associated with each storm probability. The findings suggest that the benefits of reef restoration are spatially variable within the USVI. In some areas, the analysis showed limited benefits from reef restoration, which may be attributed to the depth or offshore distances of proposed restoration sites. However, there were a number of key areas where reef restoration could have substantial benefits for flood risk reduction. The annual flood risk reduction attributed to potential ‘ecological’ coral reef restoration in the USVI was 99 people and $6.1 million (2010 U.S. dollars). The Benefit-to-Cost Ratio (BCR) for this restoration approach was found to be larger than 1 (i.e., cost-effective) along 11% of the St. Croix coastline, 4.9% of the St. John coastline, and 8.7% of the St. Thomas coastline. This analysis offers stakeholders and decision-makers a spatially explicit and rigorous evaluation that illustrates how, where, and when potential coral reef restoration efforts in St. Croix, St. John, and St. Thomas could be instrumental to reducing coastal storm-induced flooding. Understanding areas where reef management, recovery, and restoration could effectively reduce climate hazard-related risks is crucial to protect and enhance the resilience of coastal communities in USVI.

Shangshan is the earliest Neolithic culture discovered in the lower Yangtze region and is significant as one of the origins of rice agriculture worldwide. During the Early Holocene, the Shangshan human groups settled in the river basins of the upper Qiantang region and transitioned from hunter-gatherers to farmers. Little is known about the dominant factors motivating them to settle down and their occupation patterns for adapting to local environments. To address these issues, we applied an ad hoc method by coupling landform classification, geomorphic fieldwork, accessibility analysis, and flood risk assessment. The results of geomorphic and accessibility analyses show that the ancient residents preferred to occupy the edges of the Middle Pleistocene terraces along second- and third-order streams. These locations are mainly situated in the centers of river basins and have height differences of 5–15 m, less than approximately 15-min walking distance from streams, providing them with stable residential spaces and accessible water resources. Within the 10-min territorial scope, there are always some fluvial plains near the terrace edges, indicating that the plain is another critical landform utilized by the residents. The wide plains and a plain-terrace-upland geomorphic assemblage, within 1-h and 2-h scopes, respectively, imply that the residents started to develop agricultural land while they also exploited diverse environments to ensure a plentiful food supply. Furthermore, the hydrologic analysis and assessment reveal that the residents were aware of the potential flood risks: their settlements are located at 30-min walking distance or greater from the high-order streams; most sites are exposed to low flood risks, with average percentages of unflooded territory greater than 67% and 43% in the minor and large flood scenarios, respectively; and each site has sufficient arable land with areas of 5–23 km2 within a 1-h scope, even under the large flood scenario. Overall, these results reveal that the river basins provided favorable living environments, and the residents could flexibly manage the landscapes to ensure stable settlements and food production, thus enabling the appearance and development of a sedentary society and rice agriculture during the early Holocene. Ultimately, the methods used in this study provide an effective approach to exploring the occupation patterns of prehistoric residents in the river basins. [ABSTRACT FROM AUTHOR]

This study analyzed the flood risk associated with potential dam break events at the Pidekso dam in Wonogiri Regency, Central Java Province, Indonesia. Embankment dams, such as the Pidekso dam, are susceptible to piping and overtopping, which can result in dam failure and severe natural disasters, causing loss of life and infrastructure damage. The study utilized HEC-HMS and HEC-RAS software to simulate dam break scenarios, generating maps of dam break flood discharge, flood inundation, and flood arrival time. The analysis revealed that overtopping scenarios resulted in a higher outflow discharge compared to piping scenarios, with a peak discharge of 14,821 m3/s. Flood inundation and arrival time maps were used to assess the risks to nearby villages. Moreover, based on the risk index calculation using the formula provided by the National Disaster Management Agency, the studied villages were classified into distinct risk levels. Specifically, one very low-risk, four low-risk, six medium-risk, and seven high-risk villages, with none classified as very high-risk. This study also recommends a combination of structural and non-structural solutions to mitigate the risks of a dam break. By implementing structural mitigation measures such as an emergency spillway and compound channel along the downstream river, the study achieved an 8.4% reduction in flood extent. While most villages showed no significant changes in their risk indices, Sinorboyo village, which was previously susceptible to flooding, benefited from enhanced protection measures. [ABSTRACT FROM AUTHOR]

Urbanization and climate change are contributing to severe flooding globally, damaging infrastructure, disrupting economies, and undermining human well-being. Approaches to make cities more resilient to floods are emerging, notably with the design of flood-resilient structures, but relatively little is known about the role of urban form and its complexity in the concentration of flooding. We leverage statistical mechanics to reduce the complexity of urban flooding and develop a mean-flow theory that relates flood hazards to urban form characterized by the ground slope, urban porosity, and the Mermin order parameter which measures symmetry in building arrangements. The mean-flow theory presents a dimensionless flood depth that scales linearly with the urban porosity and the order parameter, with different scaling for disordered square- and hexagon-like forms. A universal scaling is obtained by introducing an effective mean chord length representative of the unobstructed downslope travel distance for flood water, yielding an analytical model for neighborhood-scale flood hazards globally. The proposed mean-flow theory is applied to probe city-to-city variations in flood hazards, and shows promising results linking recorded flood losses to urban form and observed rainfall extremes. A statistical mechanics approach unveils how urban layout influences flood hazards linking flood risk to factors like ground slope, porosity, building arrangement symmetry, and chord length, offering a scalable model applicable worldwide. [ABSTRACT FROM AUTHOR]

As the risk of flooding continues to impose on transportation infrastructure systems, enhancing the ability to monitor flood inundations in road networks becomes more significant. This research explores the potential crowdsourced data, specifically 3-1-1 reports, has to complement information from physical flood sensors. Three flooding events in two watersheds in Harris County, Texas were studied. To assess 3-1-1 report's capabilities, a time series analysis between reports and water elevation of physical sensor data was performed to capture channel overflow. Next, a graph-based observability analysis identified multiple combinations of minimum additional sensor locations needed for complete network monitoring in the study area. To find the optimal combination, a principal component analysis assigns a criticality score based on exposure, road importance, neighborhood vulnerability, and frequency of 3-1-1 reports near a node. The results indicate that 3-1-1 reports effectively improve flood monitoring by reducing the need for physical sensors by 32% in areas that lack flood sensors. This approach can help city managers improve flood monitoring by leveraging socially sensed data to supplement physical sensors, especially in blind spots where no flood gauge exists. [ABSTRACT FROM AUTHOR]

Building flood reduction dams for territory protection is very often necessary as it allows the reduction of peak flows during flood events by creating a storage in upstream areas. An online structure with such characteristics is outlined, presenting one or more openings in order to allow normal and moderate flows to pass downstream. A stilling basin is provided downstream of the openings and, on one side of the flooded area, a spillway weir is set. For low river flows, the openings will act as free-surface weirs. The openings are equipped with a sill in such a way to let the discharge independent of the downstream flow conditions. For moderate flood flows, the water level upstream of the dam will rise, and the openings will become submerged, effectively behaving as an orifice arrangement. For large flood flows, the crest of the flood spillway weir is reached and they will be discharged via both the orifice structure and the flood spillway structure. The silled openings and the stilling basin are one only hydraulic complex. Since no examples exist of their behavior as a whole, the aim of this paper is to give the designers the opportunity to size them given the basic requirement of the flood reduction dam, i.e. the max discharge allowed downstream. For this reason, the paper will focus on the opening discharge coefficients, the stilling basin energy dissipation and water level. Both the orifices and the stilling basins were studied starting from the hydraulic models of three dams that were built in Calabria (Italy). The structures of the stilling basins are original. The results appear in dimensionless form and offer useful guidance for similar hydraulic cases, with the exception of specific terrain related issues. Finally, an example of a preliminary design of orifices and stilling basin is presented. [ABSTRACT FROM AUTHOR]

The largest rivers in developed countries have usually been turned into waterways by straightening them and removing large bedforms hampering navigation. For river restoration and their sustainable management it is important to know how large bedforms support biodiversity, whether they could be protected and what potential conflicts in river management they can pose. We have addressed these questions by studying the role of large bedforms in supporting populations of two inland tern species Sternula albifrons and Sterna hirundo. We spatially analysed the behaviour of these two species with reference to the bedform structure mapped over a long semi-natural reach of the River Wisła (Vistula) (S. Poland). The results show that radiotagged terns breed on islands within the aggradation reaches, foraging in the adjacent shallows inhabited by populations of small fish. For Little Terns, the more complex the water line of emergent forms, the greater their foraging intensity. The islands do not pose any flood risk to human settlements. The whole geofeature forms an integral habitat for fish and birds; it is maintained by its geographic settings and so is stable over long periods of time (over 200 years). Protection of such habitats is thus feasible. [ABSTRACT FROM AUTHOR]

Theileriosis, a parasitic disease, caused by Theileria spp. and transmitted through ticks, poses a significant threat to livestock, leading to elevated morbidity and mortality rates. This study investigated the incidence trend of theileriosis in Kerala, India, over three years (2019–21). Notably, the research unveiled a substantial upsurge in bovine theileriosis cases within Kerala during this period, partly attributed to the state’s severe flooding and landslides in 2018, triggered by incessant monsoon rains. The present study envisaged pinpointing the risk factors underlying the prevalence of theileriosis in Kerala. Employing linear discriminant analysis, key environmental and remote sensing variables influencing the disease’s incidence were identified. Subsequently, these risk factors underwent climate disease modelling, leading to the creation of risk maps. To predict areas sensitive to theileriosis outbreaks in Kerala, two regression models and nine machine learning models were employed. The gradient boost and random forest models demonstrated the most accurate fit among these. The study also estimated the basic reproduction number (R0), which ranged from 0.89 to 1.8. This value indicates a high potential for Theileria spp. transmission within the study area. Consequently, the research outcomes offer valuable insights into pinpointing high risk theileriosis locations in livestock in Kerala. [ABSTRACT FROM AUTHOR]

Coastal cities are becoming more vulnerable to flood risks due to climate change, rising sea levels, intense storm surges, population growth, and land subsidence. Developing emergency preparedness and response strategies can reduce the impact of coastal flooding and enhance a city's resilience. This article presents a flood relief logistics planning aimed at providing decision-makers with a feasible framework. The framework integrates geographic information system (GIS) network analysis and resource allocation optimization models. Considering the fairness of resource allocation, a biobjective allocation model that minimizes the total transportation cost and maximum unsatisfied rate is developed. This flood relief logistics planning approach is applied to Shanghai, China to presents feasible distribution strategies. And, the case study indicates that the current capacity of emergency flood shelters (EFSs) and the supplies stored in emergency reserve warehouses (ERWs) are adequate to meet the demand of the elderly population if affected by a 100-year coastal flood scenario. However, they would not be sufficient to cover the demand in a 1000-year coastal flood scenario and could only serve half of the affected elderly people. The results also suggest that the city-level ERW in Jiading District and the branch warehouse in Minhang District play a crucial role in distribution. Additionally, the study highlights the importance of increasing resource investments to tackle the inherent unfairness caused by resource shortages. This study provides a scientific reference for developing flood relief logistics plans in Shanghai, and it presents a transferable framework that is applicable to other coastal cities. [ABSTRACT FROM AUTHOR]

In summer 2021, the Dutch Province of Limburg faced an acute high-water event caused by two days of heavy rainfall, resulting in €383 million worth of damage. Afterwards, various policy actors organised a number of evaluations. This study analyses these evaluations and the long-term lessons drawn from this flood event. Dealing with climate change requires insights into how lessons from acute shocks such as floods can stimulate the anticipation of climate change and solutions that can cope with future extremes. This study therefore applies policy learning theory—single, double and triple loop learning—to different layers of flood safety to analyse the extent to which governments prepare for future events and how. The results show that pre-2021 water and flood risk management policies—our point of comparison to derive what lessons emerge after the 2021 flood—focused mostly on flood prevention (layer 1) and from 1996 onwards with the start of the Room for the River programme also on spatial planning (layer 2), without paying much attention to crisis management (layer 3). Based on the 2021 flood, the official evaluation committee recommends adding two more layers to the multilayer flood safety approach: increasing water awareness (layer 0 as a base layer for the other layers) and focusing on climate-robust recovery after floods (layer 4) to improve long-term robustness. Based on our research, triple-loop learning as the deepest type of learning appeared in the form of applying the Room for the River approach to the tributary brooks systems. To prepare for the future, policy evaluations recommend improving stress tests to better deal with climate change. Futures scenarios and visions for the future are also recommended. Creating future visions can help to steer changes in existing land use and re-think areas to build or to use for water retention, for example. Governments are advised to strengthen their anticipatory capacities as part of their crisis management response and recovery systems. Furthermore, governments will need to apply the different layers for multilayer safety in an integrated way to ensure that regions are able to prepare for future floods. [ABSTRACT FROM AUTHOR]

The rafting behaviour of the red imported fire ant, Solenopsis invicta, in response to flooding events is well documented, although studies generally have focussed on the mechanisms of raft assembly and the behaviour of the raft's occupants. Flooding as a means of dispersal of S. invicta is frequently mentioned in the literature, although there are few data on the distances travelled or how effective it is compared to natural flight. In Australia, S. invicta is a priority invasive species with a national eradication program operating for 23 years, focussed on the population in southeast Queensland, which currently encompasses more than 700 000 ha. Flooding presents a risk to the success of the program through extending the infestation area or recolonising successfully treated areas. We used the program's extensive spatiotemporal dataset of known fire ant colony locations to assess the effects of two significant flood events on the dispersal or displacement of S. invicta in Queensland. Results indicated that flooding did not spread S. invicta beyond the known boundaries of infestation but contributed to localised spread, particularly for sites with known polygyne infestations. This situation could change if the ant spreads to new river catchments. A novel method developed to assess the risk of S. invicta dispersal through flooding is presented, alongside program actions that can be applied to mitigate this risk. [ABSTRACT FROM AUTHOR]

The main objectives of this research are to identify (i) how women in Can Tho City experience the impacts of flooding on their health, income, household and personal finances; and (ii) limitations to women's participation in flood‐related planning activities. Qualitative data collection included a household survey, followed by field observation and in‐depth interviews of the affected women and other actors. Following a feminist framing, the research not only offers insight into the unique harms that women experience from floods, but also demonstrates that women have important knowledge and insights into flood‐related planning. The research found that women took on additional flood management roles in the home. Carrying out this work in polluted flood waters placed a physical burden on their bodies, and led to associated financial burdens related to health treatment and protective equipment. Moreover, the time dedicated to this labour resulted in a reduction in women's incomes. Their houses were also damaged by the floods, resulting in repair costs. Women in higher income of full‐time employment are dealing with the losses of income activity and housing, women in lower income or unemployment are dealing with the losses of personal expense and housing. Though women have a unique understanding of and relationship to flooding, government officials exclude them from local planning and training workshops about flooding risks. As a result, they have limited opportunities to use their knowledge to shape flood mitigation efforts. This research concludes that addressing vulnerability to future flooding must draw on Vietnamese women's knowledge and experiences. [ABSTRACT FROM AUTHOR]

Atlantic tropical cyclones often associate with heavy rainfall, which causes inland- and coastal-flooding in the United States, and the storm-induced rainfall is closely related to its storm scale, movement, and location. For a better performance in flood or risk analysis in a region, understanding the characteristics and distribution of tropical storm (TS) induced extreme rainfall is essential. This study proposes dimensionless rainfall-duration curves for designated four-quartile storms that represents the temporal distribution of TS induced extreme rainfall in the Gulf of Mexico from 1979 to 2021. Our study employs spatiotemporal analysis to compute rainfall while TSs are located overseas and inland from satellite based climate forcing data and hurricane track records, annual maximum approach to define TS induced extreme rainfall events, and designated track types to categorize events based on their trajectories. As a result, extreme rainfall relating to TSs in the Gulf of Mexico are found to be considerably higher in inland than overseas. For inland, majority of the TSs was found to be the 1st- and 2nd-quartile storms. However, the 3rd-quartile storms, which case are rare, were found to have the overall largest amount of rainfall per duration compared to the other quartile storms. As for overseas, more than half of the TSs were found to be the 4th-quartile storm while the 2nd-quartile storm has higher overall rainfall per duration. Spatial analysis shows that Texas, Louisiana, Mississippi, Florida, and South Carolina are determined as high-threatened areas by TS induced extreme rainfall. [ABSTRACT FROM AUTHOR]

What are the consequential socio‐spatial vulnerabilities of fragmentation in flood risk governance (FRG) functions in Tana River County, Kenya? To answer the question, this paper utilises social differentiation theory to establish types and consequences of such fragmentation. It applies theoretical patterns and processes of social differentiation to expose that FRG diversification could result in fragmentation of FRG functions. The study employs an interview strategy that focuses on governmental and non‐governmental FRG actors. The results reveal that fragmented FRG functions in Tana River County was not an intentional policy decision, but rather a product of a transitional vacuum between policy promulgation and implementation. Furthermore, socio‐spatial vulnerabilities differentiated by patterns and processes related to age, gender, disability, and ethnicity, for instance, are consequential outcomes of fragmented FRG functions in the Tana River floodplains. The study recommends implementation of the National Disaster Management Policy in Tana River County and institutionalisation of a specific FRG policy and legal framework. [ABSTRACT FROM AUTHOR]

Increasing resilience to flooding is a complex process that requires horizontal and vertical coordination between institutions in policy making and implementation. This paper explores the effect of institutional coordination on managing flood risk in two cities plagued by flooding. Our results show that efforts on building urban flood resilience can be undermined by lack of proper coordination between urban development, water management and land use planning. We find that this complexity is magnified by the emergence of the concept of resilience as an urban development goal that is increasingly pursued by various authorities, but that is inherently contested in practice. [ABSTRACT FROM AUTHOR]

Climate change will substantially increase extreme rainfall events, especially in the Tropics, enhancing flood risks. Such imminent risks require climate adaptation strategies to endure extreme rainfall and increase drainage systems. Here, we evaluate the potential of nature-based solutions by using an ecosystem service modeling approach, evaluating the impact of extreme rainfall events on flood risks in a large urban area and with a real-world land recovery plan. We evaluate the cost-effectiveness of four different land recovery scenarios and associated co-benefits, based on a gradient increase in area recovered and cost of implementation. Although the scenarios reveal increasing flood risk reduction and co-benefits along with greater proportion of land recovery, the most cost-effective scenario was the one with an intermediate land recovery where 30% of the study area would be reforested. We emphasize the striking benefits of nature-based solutions for flood risk reduction in cities, considering landscape scale and stakeholders' needs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fusion of varied data sources are used to develop more informed flood extent maps. • SVM outscored the MLE, RF, Iso cluster, and Otsu's algorithms in all strategies. • PCA-based technique outperforms the other three approaches. • First and Second approaches performed better in VV than VH polarisation. Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, by integrating various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE, and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE, and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (C v) metrics. The findings show that a PCA-based technique with SVM is marginally superior (based on the overall accuracy and Kappa coefficient with a value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the C v metric. The SVM algorithm with a C v of 1.25 demonstrated the best performance, followed by the RF algorithm with C v of 1.5, and the MLE algorithm with C v of 2.5. The worst performing algorithm among all is found to be ISO with C v of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area. [ABSTRACT FROM AUTHOR]

Flooding is Aotearoa-New Zealand's most frequent natural hazard, and there is high confidence that climate change is making extreme rainfall events more frequent and intense. Additionally, there are significant development pressures which could both increase the number of people and assets at risk and the flood hazard. To date, there is no publicly available consistent approach to accurately determine flood risk on a national scale, nor for how this may be changing; although there is a growing legislative requirement to provide quality information over multiple spatial scales. This paper draws on empirical data to gain insights on how to best manage changing flood risks in Aotearoa-New Zealand from the perspective of centrally organised entities. Findings confirm the need for a nationally consistent approach to flood risk management, better understanding of Aotearoa's communities and their vulnerability to floods, equitable access to quality information and decision-support tools, and better understanding of the economic impacts on differing communities, regions and places. The paper concludes that to achieve a flood-resilient Aotearoa, flood governance needs to be reconfigured to achieve national consistency in flood risk management whilst enabling targeted variability at the local scale. [ABSTRACT FROM AUTHOR]

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 event as 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region. [ABSTRACT FROM AUTHOR]

The regulation of upstream cascade reservoirs has significantly altered the downstream hydrologic regime and should be taken into account in design flood estimation. The current flood regional composition (FRC) methods do not consider the unfavorable situations for reservoir flood control operation. In this paper, a novel framework, the most unfavorable flood regional composition (MUFRC) method, was proposed based on flood risk analysis to estimate design flood in the cascade reservoir operation period. The cascade reservoirs in the Yalong River basin were selected as a case study. The results indicated that (1) the proposed MUFRC method would allocate more flood volume to the downstream uncontrolled sub-basin, and the precise definition of flood disaster loss could have a significant impact on the MUFRC method for the rational estimation of design flood. (2) The 1000-year design flood peak, and 3-day and 7-day flood volumes at the outlet section estimated by the MUFRC method are 15,400 m3/s, 3.91, and 8.42 billion m3, respectively, which are higher than the values estimated by other FRC methods. (3) The flood control water level in the downstream reservoir can be adjusted for the reduction in design floods in the operation period, which can additionally generate 460 million kW·h (+1.82%) of hydropower during the flood season. A comparison study and sensitivity analysis further proved that the MUFRC method can rationally allocate flood volume while balancing the flood risk and comprehensive utilization benefits, which is worth further study and practical application. [ABSTRACT FROM AUTHOR]

This study analyzes survey data of U.S. East Coast homeowners to characterize accuracy and determinants of homeowner flood risk (mis)perceptions. Using an array of instruments, we assess subjective risk perceptions and compare them with objective risk estimates. Reduced-form regressions suggest flood experience, worry, and flood zone classification influence relative perceptions of risk. Common probability weighting functions do not fit the divergence in risk perceptions, suggesting that the source of the probability distortions is most likely due to misperceiving the true risk rather than a widespread behavioral heuristic. [ABSTRACT FROM AUTHOR]

This paper proposes a flood vulnerability index utilizing publicly available data that can be applied to different spatial scales. The index enables the development of an appropriate flood risk management strategy, tailored to those factors of vulnerability that have the greatest impact on the overall flood vulnerability of a given place. Currently, following a shift in the approach to flood protection, vulnerability has become a critical element of disaster mitigation. This index is an important contribution to the existing body of research as it can be applied to flood risk management strategies overcoming the ambiguity of definitions of vulnerability and the dynamic nature of temporal and spatial scales of analysis. The proposed flood vulnerability index is a multifaceted approach, encompassing physical, social, economic, environmental, and institutional attributes. In order to create the index, the literature on flood vulnerability was reviewed, including definitions, research methods, as well as the factors and constituents that are major components of vulnerability. The proposed indicator can be used by policy-makers responsible for flood risk management, especially in EU countries, in order to minimize the negative consequences of floods. Compared to other methods used (i.e., disaster loss estimation or modeling), the flood vulnerability index is easy to use when comparing different areas. [ABSTRACT FROM AUTHOR]

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

Urban flooding poses a significant threat to the functionality of roadway networks, and the frequency and severity of these events are anticipated to increase as a result of climate change. A key challenge in mitigating urban flood impact is the lack of detailed flood impact prediction methods at a large scale. This study addresses this gap by developing an integrated framework that assesses the flood vulnerability of large-scale urban roadway networks. A framework combining a large-scale hydraulic flood simulation with a roadway traffic network analysis was utilized to map the impact of flooding on the mobility and connectivity of the roadway network. Then, a flood-roadway network analysis was conducted to assess the vulnerability of the Houston roadway network under different phases of the flooding event. The efficacy of the proposed framework is validated through a case study focusing on Hurricane Harvey in Houston, successfully identifying areas with pronounced flood vulnerability. By adopting this framework, decision makers can better evaluate the flood vulnerability of the roadway network and identify areas that require attention to enhance resilience to floods. Practical Applications: Urban flooding is always a great threat to our economy and safety. This study explores a new way of predicting, responding to, and planning for major urban flooding events. Our large-scale flood-roadway prediction framework can be used to predict flood impacts rapidly and accurately in advance, providing policymakers precious time to plan accordingly. Urban planners and city officials can leverage our research to identify areas more vulnerable to floods and guide urban development to increase flood resilience. Moreover, first responders can utilize our large-scale flood-roadway prediction framework to gain critical situation awareness in their disaster rescue efforts. Lastly, our research can play a crucial role in providing public information on flood risks. By providing timely and accurate flood alerts to the public, individuals and communities can take proactive measures to protect themselves and their property. [ABSTRACT FROM AUTHOR]

Flood mitigation behavior is essential for effective flood risk management, particularly in Australia, where the federal government has increasingly emphasized individual responsibility for preventing and preparing for flood disasters. Despite this emphasis, around 60% of flood-prone residents hesitate to adopt private mitigation measures. Their reluctance highlights the complexities of decision making regarding the implementation mitigation measures, emphasizing the need for a comprehensive understanding of flood mitigation behavior among Australian residents. In order to address this knowledge gap, we conducted household surveys in flood-prone regions of Australia and used six machine learning algorithms—logistic regression, k -nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network—to analyze the proposed framework for flood mitigation behavior. The results of five-fold cross-validation demonstrated that the random forest algorithm provided superior accuracy for predicting protection motivation compared to the other five algorithms. Furthermore, seven of the 37 predictors utilized in the model had a higher impact on protection motivation than the other predictors. These influential predictors included self-efficacy, response efficacy, response costs, key variables within the coping appraisal factor, fear or worry about future flood risk, one variable associated with the threat appraisal factor, past experiences of flooding inside the house linked to the prior flood experience factor, and the implementation of flood mitigation measures by individuals connected to the householder, such as family, friends, and neighbors, which pertains to the social environment factor within the proposed framework for flood mitigation behavior. These predictors were deemed adequate to be used as input variables in the random forest model, because they provided accuracy and performance similar to when all 37 predictors were included. These insights into influential predictors can be used to develop an optimal prediction model for protection motivation in flood-prone areas of Australia and reduce the effort needed to collect the required data during postdisaster surveys. This research is of value to policymakers and floodplain managers in designing effective flood mitigation strategies and promoting the adoption of protective measures in flood-prone communities to reduce flood risk. [ABSTRACT FROM AUTHOR]

The floods of the Lahdar River cause repeated inundations and damage to road infrastructures, particularly crossing structures in the territorial center of Had Msila. Our study involved the application of various methods to estimate flood flows for different return periods along the Lahdar River. The selected flows were chosen after a comparative analysis of values calculated by the different methods used. These results served as the basis for hydraulic modeling aimed at assessing water levels to establish risk zone mapping. This step is crucial in flood risk assessment. Two main approaches were distinguished: hydrometeorological methods, based on regional parameters derived from rainfall data, and empirical methods, used in the absence or with limited data on flood flows in a given region. Hydraulic modeling was carried out using two software programs: a Geographic Information System (GIS) such as Arc-GIS, and a specific river modeling software like Hec-Ras, allowing for the numerical representation of the natural state of the territory. The results obtained serve as the foundation for all river hydraulic modeling, thereby facilitating flood prediction and hydrological risk management in floodplains. Modeling Lahdar River floods in the studied sections enables the prediction of flood risk and its impacts on constructions and infrastructure in the Had Msila Center. [ABSTRACT FROM AUTHOR]

Strategic flood risk management of river catchments involves significant increases in the complexity both of the contents (e.g. the aims and measures) of a given strategy and also its social, spatial, and temporal scales. Conceptually, flood risk management research to date has underestimated the importance of time and temporality. This paper, which is based on ‘historical Institutionalism,’ introduces a temporal lens to focus on strategic flood risk management; it highlights issues of duration and timing as well as tempo and change in tempo with respect to the implementation of measures to reduce flood risk at catchment level. The application of a temporal lens is illustrated through empirical research into strategic flood risk management for the medium-sized Aist river catchment in Austria. The paper uses a longitudinal qualitative research design to analyze the changes in strategic flood risk management in the catchment. The analysis shows that strategy efforts in reaction to an extreme flood event in the catchment in August 2002 can be differentiated into three phases. Phase 1 is characterized by the design of ambitious catchment-wide management; Phase 2 by struggles to implement the strategy due to institutional conditions and protests by citizens; and Phase 3 by redesign of the initial strategic plan to make it less ambitious and by changes to the actor constellation supporting the plan. The present paper offers a process-oriented institutional explanation for this pattern of phases, and it highlights issues of timing and tempo. It concludes with general suggestions for enhancing the temporal dimension in flood risk management. [ABSTRACT FROM AUTHOR]

In this study, a coupla risk combinations and coping strategies have been developed for confronting conflicts between population-economy development and water resource management (PEWM) due to population-industry transformation (PIT) into a floodplain of the economic belt under climate change. A location-entropy-based PVAR (Panel Vector Autoregression) model coupla-risk analysis (LPCR) can be introduced into PEWM to reflect the adverse effects of population-industry transformation on a special function area (e.g., floodplain) of the economic belt, where the coupla risk map has been addressed. Meanwhile, an adaptive scenario analysis-based stochastic-fuzzy method (ASSF) can be joined to deal with multiple uncertainties and their interactions due to subjective and artificial factors. The proposed LPCR and ASSF can be integrated into a risk-based stochastic-fuzzy scenario method framework (RASF) to apply for a practical PEWM case study of the Yongding River floodplain in the context of the coordinative development of the Beijing-Tianjin-Hebei economic belt, China. The results can be presented as follows: 1) the PIT was accelerated into the floodplain due to the fact that it can require more water resources, where each additional unit can promote the regional gross domestic product (GDP) by 0.019 units, increasing the water demand by 0.046 units. It can strengthen various risks such as water shortage, soil loss, and flood control investment in a floodplain of the economic belt; 2) the farmland returning to wetland (CFW) can decrease the water demand and shortage ratio (would be 0% at highest) for irrigation in the long run, but this would reduce the direct economic income of irrigation in the short run; 3) backward irrigative schemes and low water utilization efficiency require a cleaner production mode, which could reduce water shortages by 77.23 × 103m³ at the highest; and 4) the combined policy (S12) can reduce coupla risks (including the risk of water shortage, soil loss, and flood) compared to other individual policies (S2, S4, S6, and S8). The findings can assist local decision-makers to gain insights into adjusting interegional strategies not only for remitting population-resource stress in core cities but also for improving the resilience against drought and flood in a floodplain area, which is conducive of the special function of the floodplain to support an integrated sustainable development of the economic belt. [ABSTRACT FROM AUTHOR]

This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks. [ABSTRACT FROM AUTHOR]

Background: Climate change has made the relationship between disaster and sustainable development more critical. Especially in developing countries, disasters frequently devastate local communities and hinder progress. Children are at a higher risk during catastrophes; however, their knowledge of disaster risk is limited. Education for disaster risk reduction can bring the necessary changes for a safe society. School disaster prevention education can raise awareness through lectures and games, but interactive dialogues between students and teachers can be more effective. Also, GPS tracking can be a valuable tool for understanding people's behavior during disasters. Methodology: This study proposes a method to analyze the relationship between students' daily behavior and their experiences of flood damage and preparedness for disaster risk. It is a simple method using questionnaires and GPS loggers and can be applied in mountainous areas in developing countries where equipment is inadequate. The proposed method will be used to understand the current state of disaster risk among children and identify disaster risk reduction issues. The study area lies in Paluntar Municipality, Gorkha district, Nepal. Forty-five students from two schools in the region participated in the study. Results: The results showed that students who commute to school in areas inundated by heavy rainfall in recent years were not adequately prepared for disasters, even though they had experienced flood damage in the past and were significantly more concerned about future harm. Our field observations and interviews of teacher-student relationships showed that students were very attentive to their teachers' instruction. Teachers understand the dangers of heavy rainfall and how to prepare for it, but students need to learn how to deal with it. In other words, the school staff's disaster education to the students effectively reduces disasters. Early warning is needed when severe floods are expected during the rainy season. [ABSTRACT FROM AUTHOR]

The Snow Cover Service Value (SCSV) is an important component of the ecological assets of the Qinghai–Tibet Plateau (QTP). Exploring the SCSVs on the QTP is the key to maintaining the functions of climate regulators and Asian water towers, and it is also an important theoretical basis for maintaining the ecological security of ecological barrier areas. Using multi-source data such as daily and monthly observation data sets and related statistical yearbooks, an evaluation framework for the SCSVs on the QTP was constructed for the first time. The results showed that the average annual SCSV of the QTP from 2001 to 2020 was 6.99 trillion yuan, and the average annual climate regulation value was 5.81 trillion yuan, which was the most important SCSV. The Inner Plateau Basin, the Brahmaputra Basin, and the Yangtze Basin had the highest SCSVs, while the Yellow Basin had the lowest unit SCSV, where it was the most vulnerable area of snow cover resources on the QTP, and the SCSV of the Yellow Basin was significantly correlated with rainfall. The correlation between the SCSV and the temperature in the Indus and Ganges Basins was significant. The freshwater supply value of the snow in the Indus Basin and Tarim Basin was found to be able to reach 30% of the whole year in June, where it would be necessary to pay attention to the risk of flooding caused by snowmelt during the melting period. Finally, this paper discussed the strategies for the protection and development of snow resources in each basin based on the spatial distribution characteristics, seasonal variation characteristics, and influencing factors of the SCSVs. The research can provide reference for the rational allocation of snow resources and ecological protection on the QTP. [ABSTRACT FROM AUTHOR]

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization and underground transportation, as well as the frequent occurrence of extreme weather conditions such as extreme rainfall, flooding disasters for rail transit are becoming severe, and need to be urgently clarified in terms of the mechanism causing them. In this study, a comprehensive model for water damage at the entrance to a rail transit station is proposed, emphasizing the entire process of extreme weather–surface ponding–underground intrusion. The model is validated by the inundation process of Line 5 of the Zhengzhou Metro during the "7.20" event and further applied to Wanqingsha Station of Guangzhou Metro Line 18 in China to determine the surrounding water depth, distribution, total water inflow volume, and water damage time under different rainfall intensities, rain patterns and protection scenarios. It was found that when rainfall reaches the level of a 1-in-2000-years event, the surface water begins to invade the internal rail transit system through the rail transit entrances. When facing extreme rainfall akin to the "7.20" event in Zhengzhou, the rail transit system in Wanqingsha Station meets a heightened risk of water damage, resulting in significantly deeper water levels compared to 1-in-5000-year rainfall event in Guangzhou and exceeds the height of the subway entrances. Analysis of the water intrusion process reveals that, as rainfall intensity escalates, the total inflow water volume into the rail transit system increases while escape time diminishes. Moreover, under identical rainfall intensity, pre-type rainfall yields the highest total water inflow, whereas mid-type rainfall exhibits the shortest escape time. Enhancing the protection conditions can markedly attenuate surface water intrusion into the subterranean rail transit system, thereby enhancing the evacuation time for individuals within the system. [ABSTRACT FROM AUTHOR]

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches. [ABSTRACT FROM AUTHOR]

The increase in rainfall intensity caused by climate change, combined with high levels of urban soil sealing and the limited capacity of drainage systems, is significantly increasing flooding risk. Integrated stormwater management is a key factor in dealing with the consequences of climate change by mitigating flood risk based on nature-based solutions. An experimental site was designed to assess the hydrological response of vegetated swales depending on different catchment surfaces, and to evaluate the impact of spatial variability of the hydraulic conductivity (K). A hydrological model was developed on EPA SWMM and calibrated based on measured data in two experimental swales with different active surfaces (N6—50 m2 and N11—100 m2). The model validation was assessed with NSE scores higher than 0.7. The simulations considered various factors, such as the water level in the swales, the actual rainfall, the evapotranspiration, the swale geometry, the catchment area (Sa), and the hydraulic conductivities of the natural silty soil, to assess, for the heaviest rainfall event, the best swale morphological characteristics. The study examined the combined impact of K (8) and Sa (6) on swale storage capacity, designed with a 250 mm depth. The simulations showed that the 250 mm overflow limit was exceeded for N10 (90 m2) when K ≤ 2 × 10−6 m/s, and for N11 (100 m2) when K ≤ 4 × 10−6 m/s. These results provide valuable information on the optimal storage capacity based on the swales' geometrical and physical properties. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasing climate risks, strengthening the adaptive capacity of citizens is crucial. Yet, the usefulness of the concept of adaptive capacity is currently limited for science and policy, as it is neither clear what exactly constitutes adaptive capacity nor whether capacity translates into adaptation action. Drawing on survey data from 1,571 households in Southern Germany collected in 2022, we use regression analysis to examine the relationship between adaptive capacity indicators and the implementation of pluvial flood risk adaptation measures. Our results confirm a capacity-action gap, as high levels of adaptive capacity do not necessarily translate into household adaptation action. Widely used generic capacity indicators such as income and education are less important for adaptation decisions while specific capacity indicators, such as risk perception, damage experience and motivation, lead to action. We found initial evidence of a nonlinear effect: while a certain stock of financial and human capital is required, additional capital gains do not translate into additional adaptation action. Thus, enhancing the specific capacity of households should be a priority, as generic assets alone will not suffice to cope with climate risk. [ABSTRACT FROM AUTHOR]

The upper reaches of the Yangtze River Basin (UYRB) are famous for their hydropower generation and water resources in China, relying greatly on precipitation. The UYRB summer precipitation has decreased since the early 2000s and has been exposed to a particularly dry period in the most recent two decades. By analyzing the long‐term variability from the beginning of the 20th Century, we found that the precipitation exhibits a profound periodic interdecadal variation, with a significant cycle of 30–50 years. The interdecadal variability of precipitation is shown to be significantly modulated by both the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), exerting their impact through related circulation anomalies. Moreover, the periodical nature of the UYRB precipitation variation and its correlation with AMO/PDO suggest that the UYRB might enter a wet period in the forthcoming decade, i.e. rich in hydroelectric resources but a high risk of flood. [ABSTRACT FROM AUTHOR]

The complexity of flood risk models is intrinsically linked to a variety of sources of uncertainty (hydrology, hydraulics, exposed assets, vulnerability, coping capacity, etc.) that affect the accuracy and reliability of the analyses. Estimating the uncertainties associated with the different components allows us to be more confident in the risk values on the ground, thus providing a more reliable assessment for investors, insurance and flood risk management purposes. In this study, we investigate the flood risk of the entire Central Apennines District (CAD) in Central Italy using the laRgE SCale inUndation modEl – Flood Risk, RESCUE-FR, focusing on the interaction between the uncertainty of the hydraulic Manning parameter and the risk variability. We assess the coherence between the quantile flood risk maps generated by our model and the official risk maps provided by the CAD authority and focusing on three specific zones within the CAD region. Thus, RESCUE-FR is used to estimate the Expected Annual Damage (EAD) and the Expected Annual Population Affected (EAPA) across the CAD region and to conduct a comprehensive uncertainty analysis. The latter provides a range of confidence of risk estimation that is essential for identifying vulnerable areas and guiding effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Environmental assets provide important benefits to society and support the equilibrium of natural processes. They can be affected by floods, nevertheless, flood risk analyses usually neglect environmental areas due to (i) a lack of agreement on what should be considered as an environmental asset, (ii) a poor understanding of environmental values, and (iii) the absence of damage models. The aim of this work is to advance the understanding of environmental exposure to floods by first identifying asset typologies that could be considered in flood risk analyses and second, by introducing a method, named EnvXflood, to estimate flood exposure of environmental assets. The method is structured around three levels of detail requiring increasing information, from a fast and parsimonious analysis suitable for regional assessment to a detailed ecosystem-service-based site analysis. Exposure focuses on the social and environmental value of the assets. Social values were investigated by means of a survey. The method was tested on three case studies in Italy (Tuscany region, Chiana, and Orcia basins). The Ecosystem Services weighting highlights the leading importance of the biodiversity-supporting service. The results of the analyses show that the environmental assets related to water, such as rivers, lakes, and wetlands, are the assets most exposed to floods. Notwithstanding, commonly they are not considered as exposed assets in the usual river management practices. Further research should aim at consolidating the asset typologies to be included in environmental exposure analysis and their social and ecological value, moving towards a coherent understanding of environmental flood impacts. [ABSTRACT FROM AUTHOR]

Introduction: The study focuses on evaluating the reliability of prefabricated perimeter walls in substations during flood events. It employs a sophisticated numerical model based on actual engineering data to assess their load-bearing capabilities. The research investigates the impact of crucial flood parameters on the structural behavior of these walls, examines the force transmission mechanisms, and suggests "W-shaped" reinforcement techniques to mitigate stress-related issues. Methods: To meet our research goals, we developed an extensive numerical model for prefabricated perimeter walls, incorporating real-world engineering data. This model enabled us to analyze critical flood parameters, such as flood depth, flow velocity, and flood erosion. Furthermore, we investigated the force transmission mechanisms within the walls and introduced "W-shaped" reinforcement strategies to improve their load-bearing capacity. Results: Our results indicate that flood depth and flow velocity have a substantial impact on the performance of prefabricated perimeter walls, while flood erosion has a minor effect. Safety concerns become prominent when flood depth exceeds 1.0 m or flow velocity surpasses 3 m per second. Analysis of force transmission mechanisms reveals greater displacements at higher water levels. Critical areas, including wall panel-column and wall panel-foundation connections, experience heightened stress levels. Discussion: Our study highlights the significant role of flood depth and flow velocity in evaluating the load-bearing capacity of prefabricated perimeter walls in substation environments. To address potential structural weaknesses, we recommend implementing "W-shaped" wall reinforcement methods, which efficiently decrease both displacement and stress. These findings carry implications for substation design and flood resilience, underscoring the importance of comprehensive flood risk management strategies to protect internal facilities during floods. [ABSTRACT FROM AUTHOR]

Frequent flooding in the central part of the Hindu Kush region, Nepal, results in substantial damage to lives and property, particularly in the plains. This study explores the catchment-scale flood hazard and vulnerability, generating flood risks under climate change scenario. Flood model was developed using HEC-RAS 2D rain-on-grid approach and CMIP6 projected climate data for SSP245 and SSP585 scenarios was used. Flood hazard, vulnerability and risk maps were generated delineating potential inundation scenarios for both 50-year, and 100-year return periods, under present and future climate scenarios. Hazard maps revealed a probable increase in flooding under future climate scenarios. Vulnerability assessments also demonstrated an anticipated escalation in future. Cropland dominates the land-use type in the study area, with agriculture vulnerability increasing by 22.14% (SSP245) and 29.322% (SSP585). The number of inundated buildings increases from 2967 in the existing scenario to 4601 (SSP245) and 5274 (SSP585). Similarly, road vulnerability areas are increased by 28.06% (SSP245) and 37.86% (SSP585). The impact of flooding is higher in SSP585 scenario as compared to SSP245 scenario. The catchment level outcomes have diverse applications, particularly in decision-making for flood management, integrating risk maps for effective strategies, and providing valuable insights into shape climate change adaptation policies. [ABSTRACT FROM AUTHOR]

Purpose: The ineffectiveness of flood control in climate-impacted majority-world cities like Jakarta highlights the need for policies that integrate local knowledge and embrace water harmony rather than resistance. This study explores flood adaptation in North Jakarta's kampungs (urban informal settlements), aiming to enhance the efficacy of current flood disaster management. The outcomes of the participatory planning mechanism simulation that we propose are expected to provide valuable insights for the urban planning approach in that city. Design/methodology/approach: We employed focus groups and design charrettes with a bottom-up approach to explore how local knowledge can enhance spatial flood management and urban planning policies. In total, 17 diverse participants, covering various ages and professions, engaged in these activities. Our methods aimed to be culturally sensitive and inclusive, embracing indigenous values like musyawarah and gotong royong. The research methodically examined flood implications and adaptations in informal settlements, progressing through preliminary understanding, data triangulation, and a reflective synthesis of the findings. Findings: Amid worsening global changes like sea level rise, community-focussed, collaborative planning can help create tailored flood-resilience solutions. The research reveals that partnerships between communities and organisations promote city-wide, flood-adapted environments, aligning policy with the needs and goals of those most affected by flooding. This collaboration enhances flood disaster management and planning policies. Research limitations/implications: This research focusses on Jakarta's flood adaptation and urban planning, reflecting on historical situations relevant to urbanising majority-world countries. Whilst specific to Jakarta, it offers perspectives on managing global environmental challenges such as sea level rise. Subsequent research should prudently consider each locale's distinct geographic and social milieu and the trust in planning systems in applying these findings, methodologies and approaches. Originality/value: This study clarifies the relationship between disaster management, policy and flood adaptation, focussing on local knowledge in North Jakarta, pertinent to urbanising majority-world nations. Jakarta's historical and modern dynamics, including globalisation, reveal specific prospects and obstacles to applying vernacular knowledge to planning and disaster response. It highlights crucial points for policymakers in the majority of the world to address growing flood risks and create strategies that integrate local and traditional wisdom. [ABSTRACT FROM AUTHOR]

Flood prediction in hilly regions, characterized by rapid flow rates and high destructive potential, remains a significant challenge. This study addresses this problem by introducing a novel machine learning-based approach to enhance flood forecast accuracy and lead time in small watersheds within hilly terrain. The study area encompasses small watersheds of approximately 600 km2. The proposed method analyzes spatiotemporal characteristics in rainfall dynamics to identify historical rainfall–flood events that closely resemble current patterns, effectively "learning from the past to predict the present". The approach demonstrates notable precision, with an average error of 8.33% for peak flow prediction, 14.27% for total volume prediction, and a lead time error of just 1 h for peak occurrence. These results meet the stringent accuracy requirements for flood forecasting, offering a targeted and effective solution for flood forecasting in challenging hilly terrains. This innovative methodology deviates from conventional techniques by adopting a holistic view of rainfall trends, representing a significant advancement in addressing the complexities of flood prediction in these regions. [ABSTRACT FROM AUTHOR]

Knowledge regarding soil wetness dynamics is relevant for its application in hydrological and climatological studies. It is also a parameter that allows the analysis of processes related to land cover and land use. Due to this importance, the spatio-temporal dynamics of the surface soil humidity (SSH) in the southern of the Argentinean Pampean Plains have been analyzed, based on data corresponding to six Argentine localities and in relation to a climatological index that incorporates rainfall and evapotranspiration variables. The methodology includes statistical correlation and multivariate analysis based on modeled and reanalysis data. The results obtained reveal a different response of the SSH at a spatial level, with maximum values in the highest rainfall region, diminishing towards the most arid zone. This allows us to infer the influence of the topographic homogeneity of the area and the predominance of vertical processes in the hydrological balance. At both a spatial and temporal scale, the SSH dynamics concur with the climatological index pattern, confirmed by a Pearson coefficient. It has also been shown that the SSH responds to the climatological parameters and that the highest correlation occurs at a three-month temporal scale. This response may be observed at an interannual and annual level, with maximum and minimum values during the wet and dry years, respectively and a decrease in the SSH during the warmest seasons (summer). The SSH displays the same tendency as the climatological index, from NE to SW, reaching a maximum in the rainiest area and minimum in the arid region. The manuscript, based on diverse sources analysis, pluviometric periods classification and statistical methods application, is a contribution related to the edaphic variable dynamics in a region in which farming activity constitutes the main land use and where the droughts and floods alternance has effects on the flooding risks and the economic losses as a consequence of intensive droughts, respectively. [ABSTRACT FROM AUTHOR]

The runoff threshold is the amount of precipitation that exceeds the infiltration capacity of the soil and from which water begins to flow over the land surface. Its spatial variation will determine the appearance of a series of processes and risks such as soil erosion, sediment transport, water pollution or flooding, all of which are of great interest in the field of land planning and management. This article provides an update of the runoff threshold in the territorial scope of the Demarcación Hidrográfica de las Cuencas Mediterráneas Andaluzas and evaluates the spatial changes occurred between 1990 and 2018 because of land use dynamics. For this purpose, the methodology described in Regulation 5.2-IC related to surface drainage, of the Instrucción de Carreteras (Ministerio de Obras Pu'blicas y Urbanismo [MOPU], 1990; Ministerio de Fomento, 2016, 2019) was used. The results obtained show very contrasting runoff threshold values, with maximums in the western sector and minimums in the eastern sector. On the other hand, the dynamics of land use changes have led to a significant increase in impermeable surfaces and sclerophyllous vegetation, which has generated a clear reduction in runoff threshold values in different areas, mainly in the eastern region. Specifically, the former forest areas, currently occupied by sclerophyllous vegetation, stand out, with a reduction of 17 mm over an area of approximately 229.1 km2. The conversion of agricultural lands into urban areas is also noteworthy, with a decrease in their retention capacity by up to 14 mm of precipitation, covering a total area of 97.1 km2. [ABSTRACT FROM AUTHOR]

Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting. [ABSTRACT FROM AUTHOR]

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time. [ABSTRACT FROM AUTHOR]