Search

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate related changes are already affecting every area of our world and will increasingly do so as global warming increases, resulting in compounding and cascading risks across multiple locations and sectors. Deliberative processes and anticipatory actions are required to adapt to the associated complex and uncertain systemic risks, with dynamic and long‐term planning needed even where there is limited knowledge of the effectiveness of adaptation. In this focus article, we examine the adaptation pathways developed for the Europe Chapter of the IPCC AR6. We argue that illustrative pathways built on quantitative and qualitative assessment of adaptation effectiveness can inform adaptation planning to manage the increasing severity of risks. We find that as the global warming level increases adaptation pathways can diverge, leading to radically different futures, for example, adaptation responses to sea level rise. We illustrate how adaptation measures for different risks interact resulting in trade‐offs, for example, increasing water scarcity. Although pathways offer a useful framework to address multiple adaptation challenges, other supporting conditions are needed for the successful implementation of adaptation, such as establishing legitimacy and buy‐in through collaboration of various actors and effective governance. Ultimately, adaptation will be increasingly more complex and constrained in a warmer world, increasing risks of losses and damages to people and nature. This article is categorized under:Vulnerability and Adaptation to Climate Change > Institutions for AdaptationThe Social Status of Climate Change Knowledge > Climate Science and Decision MakingAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate ChangeVulnerability and Adaptation to Climate Change > Learning from Cases and Analogies [ABSTRACT FROM AUTHOR]

A disaster community's priority is to ensure the safety of populations during natural hazards. Disaster risk reduction strategies require knowledge of what is hazardous, who is vulnerable, and what is risky. As of now, engineering methods predominate in Russian disaster research, while social science rarely analyzes floods, earthquakes, wildfires, etc. Study aims to test the applicability of new methods for delineating flood hazards, characterizing social vulnerability, and determining overall societal risk in Russia. Multidisciplinary nature makes the problem complex. The research will concentrate on the ten representative counties of the Kuban River basin in a southern Russian region, where at the beginning of the XXI century catastrophic floods led to enormous fatalities and huge economic damage. Flood hazards are assessed based on the county-scale spatial distribution of records produced by a Regional Early Warning System, impact information, and flood management infrastructure. Social vulnerability is investigated using a County Comparable Social Vulnerability Profiling model that encompasses three pillars (physical, socio-economic and awareness), nine themes (age, disability, poverty, etc.), and seventeen census variables (aged 0–19 years, density, one-person household in detached housing, only nine years of school, etc.). Finally, a holistic flood risk map is compiled. The results show that the three riskiest counties require measures to reduce flood hazard and social vulnerability in all phases of disaster risk management (mitigation, preparation, emergency, and rehabilitation). Data-poor nations such as Russia can benefit from these methods, but their use is limited by insufficient flood hazard and census information. [ABSTRACT FROM AUTHOR]

Climate change causes erratic rainfall and often results in flooding of urban areas. Floods are hydrometeorological disasters that occur in various regions of Indonesia. Flood vulnerability in urban areas has increased over the past 30 years. Kauman Village is included in the Asri water catchment area, which has an Asri primary channel downstream of the urban area of Nganjuk District. However, from 2019 to 2022, the urban area of Nganjuk District, including Kauman Village, was affected by flooding. Urban floods inundated office areas, schools, housing, and public facilities such as the Nganjuk District General Hospital. The factor indicated as the cause of flooding is the clogging of the drainage channel with rubbish. Therefore, field research and mathematical calculations were conducted to evaluate the discharge capacity of drainage channels in the village. Based on the research, it was found that the existing drainage channel discharge in the research area could not accommodate the planned discharge for the 10-year return period. In addition, there are 33 channels that are unable to accommodate the planned discharge because the channel dimensions are too small, some channels are slightly damaged, sedimentation occurs, and they are blocked by rubbish. Drainage channels that do not function optimally affect urban flooding. Therefore, several efforts have been made to reduce the risk of flooding by changing the dimensions of drainage channels, normalizing drainage channels, and getting used to maintain drainage channels and not throwing rubbish in drainage channels. [ABSTRACT FROM AUTHOR]

Urban flooding can differ significantly from rural flooding due to the influence of rapidly changing land use and rainfall patterns on runoff in urban areas. Consequently, understanding and managing urban flooding necessitate a comprehensive grasp of these influential factors. This study focuses on assessing the impact of land use and rainfall changes on runoff and flood resilience in urban areas of Chennai, India, utilizing the InVEST-UFRM model. The research includes an evaluation of flood risk and potential damage to building infrastructure, examining 14 sub-basins within the study area with diverse land use and rainfall depths for 2015, 2020, and 2025. Observed rainfall and land use data were employed for 2015 and 2020, while future rainfall data relied on Global Circulation Models (GCMs) of the Coupled Model Inter-comparison Project-6 (CMIP6) outputs and QGIS MOLUSCE plugin predicted land use for 2025. The study identified that the change in land use had a more significant impact on runoff than the temporal change in rainfall amount. Notably, the reduction of water bodies in the study area emerged as a major contributing factor to excessive runoff. The estimated maximum potential damage to built infrastructure in the study area reached approximately 10 billion USD. This research provides valuable insights into urban flood resilience and the impact of land use and rainfall changes and proposes effective measures for flood adaptation and mitigation. The study findings can serve as essential tools for urban planners in an effective management of urban floods in similar regions as investigated here. [ABSTRACT FROM AUTHOR]

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national‐scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo‐Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo‐Drain estimates of population and property value exposed to 1%‐ and 5%‐annual‐chance hazards diverge at finer scales of governance, for example, by 4‐ to 18‐fold at the municipal scale. FSF and PRIMo‐Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national‐scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models. Plain Language Summary: Flooding presents a significant risk to human activities and development, and its impacts have been rapidly increasing over recent decades. However, government flood mapping in the U.S. has not kept pace with adaptation needs, and communities have now turned to other sources of information to inform planning and design decisions. This study examines the uncertainties of flood hazard data available from the First Street Foundation across Los Angeles, California, the second largest city in the U.S. With a comparision to two different models that more fully capture processes known to affect urban flooding, we show concerning levels of uncertainty in the First Street Foundation data at scales of municipalities and properties. These results highlight the need for more robust validation of urban flood hazard models, and caution against overliance of First Street Foundation data for urban flood management. Key Points: Flood risks are concentrated in urban areas, where national‐scale hazard models are less accurateFlood exposure estimates become increasingly uncertain at finer scales and may misrepresent the social distribution of riskRefined and validated urban flood models are needed to effectively and equitably manage increasingly severe flood risks [ABSTRACT FROM AUTHOR]

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two‐thirds of flood‐exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster‐scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take‐up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take‐up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short‐lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take‐up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non‐primary residences. Overall, these findings suggest that relying on households to self‐adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap. Plain Language Summary: Disaster‐scale floods are expected to become more frequent in a warming climate, impacting areas where insurance is not mandated. Many flood‐exposed properties are currently uninsured, potentially undermining the climate resilience of communities. We investigate whether households might adapt to the changing flood risk by purchasing and maintaining insurance over time. We find that while disaster‐scale floods increase insurance demand, this effect is temporary, even among counties experiencing consecutive disaster‐scale flood years. This effect is more salient among inland counties as well as among homeowners whose primary residence is exposed to a disaster‐scale flood. We conclude that relying on households to self‐adapt by maintaining insurance is insufficient for closing the insurance protection gap. Key Points: Increasing flood risk is impacting areas where flood insurance is not currently mandatedConsecutive disaster‐scale flood years increase insurance take‐up, but this effect diminishes over timeRelying on the autonomous adaptation of households will be insufficient for closing the insurance protection gap [ABSTRACT FROM AUTHOR]

In response to the escalating challenges posed by climate change, the United Nations (UN) proactively formulated the Sendai Framework for Disaster Risk Reduction (SFDRR-2015- 2030) as a comprehensive strategy for disaster risk management (DRM). This framework delineates four priority action objectives, seven global targets, and a set of guiding principles aimed at mitigating the impact of disasters. Notably, each of the seven SFDRR targets is intricately linked with the Sustainable Development Goals (SDG-2030). Given that natural disasters, such as floods, continue to hinder a country's social and economic progress, achieving long-term development becomes challenging. Both frameworks prioritize initiating and investing in innovation, involving all of society's stakeholders, to build a risk-informed and people-centered disaster-resilient society. However, a research gap exists regarding the relationship between Social Innovation (SI) and disaster risk preparedness. To address this, the present study conducts an in-depth literature review and content analysis focused on SI and flood preparedness. The study also aims to offer strategic recommendations for adopting SI to improve preparedness against dam-related flood risks, aligning with SFDRR targets and SDGs-2030. The study's findings are valuable for academia, policymakers, flood risk management agencies, at-risk communities, and stakeholders. By shedding light on the role of SI in flood risk preparedness, the research contributes to existing knowledge and enhances understanding of SI in the context of disaster risk management. [ABSTRACT FROM AUTHOR]

Pluvial flooding is a critical issue in cities worldwide, particularly in lowland areas with old and deteriorating drainage systems. The primary driver of pluvial flooding is extreme rainfall; other drivers include urbanization, inadequate drainage systems, improper solid-waste management, and the tidal backwater effect. However, the interplay between these drivers makes predicting pluvial floods difficult and complex. Previous studies in developing countries seldom used water-level data or simulation modeling to identify the causes of pluvial flooding. In this study, rainfall data and water-level variations in an open channel drain and a receiving river controlled by sluice gates were collected and evaluated in detail to investigate pluvial flooding events. To predict these events, we generated a hydrodynamic model using InfoWorks ICM and verified its results using water logger data and official field reports. Analysis shows that drainage-system failures due to solid blockage and receiving water-level variation contribute more to pluvial flood occurrence than heavy rainfall. Lastly, we discuss measures to mitigate pluvial flooding in Yangon, Myanmar. The proposed monitoring and modeling approach can suitably predict pluvial flooding occurrence and provide useful quantitative data for flood risk management. [ABSTRACT FROM AUTHOR]

Preparing a map of flood hazard is susceptibility an important step in flood risk management. Therefore, it is necessary to use methods that reduce errors and increase the accuracy of identifying flood hazard areas. This study was conducted to prepare a map of the flood hazard index (FHI) and evaluate subjective and objective multicriteria decision analysis (MCDA) weighting methods. Talar basin, which is located in the north of Iran, has been investigated as a case study for this research. Seven factors influencing flood, including elevation, slope, flow accumulation, distance from the river, rainfall intensity, land cover, and geology, were considered to create a flood hazard map. The weighting of these factors has been performed by the Analytical Hierarchy Process (AHP), sensitivity analysis of AHP (AHPS), Shannon Entropy (SE), and Entropy-AHP. The maps created with the data of past floods were validated with the Accuracy index and Kappa index methods. The results showed that the FHI-SE method was more accurate than others, with an accuracy value of 0.979. FHI-SEA, FHIS, and FHI methods were placed in the next priorities, respectively. Based on the SE method, the factors of distance from the river, elevation, and slope have respectively obtained the highest weight value in creating the flood hazard index map. Distance from river variable was classified separately for mountain and plain regions to reduce the overestimation of flood hazard areas in mountainous areas. The objective weighting method has provided higher accuracy than the subjective weighting method, such as AHP. [ABSTRACT FROM AUTHOR]

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States. [ABSTRACT FROM AUTHOR]

It is very essential in terms of flood risk management to consider social expectations such as risk perception, flood awareness, preparedness, and socio-economic dynamics together with engineering designs. Understanding the way people perceive flood risk can enhance our capability of improving existing flood risk management methods, thus helps us creating disaster resilient societies. In this study, results of a questionnaire which was used for a previous study and had been administered to participants from Eastern Black Sea Region of Turkey were further investigated using statistical methods. The main aim was to understand how demographic factors such as age, gender and education level affect people's flood risk perception. It was also desired to see that whether they were aware of the parties responsible for taking mitigation measures, or whether they know about possible flood mitigation measures or not. Using the same data with the previous study, but in addition using SPSS software to do statistical analysis, questionnaire results were investigated using convenient statistical tests for each parameter, analysis results were interpreted, and conclusions were drawn. Same tests were conducted using weight coefficients adopted using a certain methodology which is explained in the paper, in order to make a better investigation. Also, results were compared with the results of the previous study. It was seen that there were some consistencies and contradictions between the results of the previous study and this study's results. [ABSTRACT FROM AUTHOR]

Biomechanical measures through supporting ecosystem sustainability and regulating natural processes would be considered the adaptation techniques to tackle climate extremes, and on-site and off-site hazards. In this study, we explored the hydrological behavior of two nearly identical adjacent basins (paired catchments) in terms of implemented biomechanical measures. The water stage time series recorded by OTT devices was applied to assess the basins' hydrological response to a distinct precipitation event. The results indicated that the construction of check dams along with intensified vegetation coverage can profoundly contribute to the hydrology, particularly surface runoff generation and its delivery downstream. The peak flow heights were found to be 53 and 31 cm for the control and treatment catchments, respectively. Our results revealed the control catchment contributed substantially (approximately 41.5%) to surface runoff provision service and flooding, more so than the treatment catchment. Moreover, the treatment catchment had a longer time to peak than the control catchment, reaching their respective peaks 35 and 50 min after the commencement of rainfall. Our findings provide increased insights into the interaction between biomechanical measures and hydrology. Accordingly, biomechanical measures can effectively be considered as mitigation strategies to tackle climate change and support regional sustainability. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess Haditha Dam's catchment area and accessible surface area in order to guarantee that these regions can hold water without being at risk of floods. Using topographic data, the study simulated the two-dimensional catchment area and flow area below the dam. The monthly increase in water storage was then computed using the water balance equation and HEC RAS software. These increments were used to determine the required flow that might be utilized to run the dam more efficiently. Significant outflows were found at the start of the operational year. These volumes will probably cause water to accumulate, water levels to increase quickly, and heights to climb. In order to make sure that these regions can store water without running the danger of flooding, the goal of this study is to assess the catchment area of a contemporary dam and its accessible surface area. The study generated a two-dimensional catchment region and flow area below the dam using topography data. The water balance equation and HEC RAS software were then used to determine the monthly increase in water storage. The necessary flow that could be utilized to run the dam as effectively as possible was calculated using these increments. This assessment provides a comprehensive analysis of the dam's capacity to manage water storage efficiently and mitigate flood risks, contributing to sustainable water management practices. [ABSTRACT FROM AUTHOR]

The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We integrate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the economic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregular floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event. [ABSTRACT FROM AUTHOR]

Here, we investigate whether England's 152 local flood risk management strategies (LFRMSs) satisfy minimal legislative criteria and address the growing surface water flood (SWF) risk caused by climate change. A systematic audit was used to assess the alignment of the LFRMSs with national climate change legislation and other relevant national strategies. An objective method to identify inclusion of a range of factors that good strategies should include was applied. LFRMSs are mostly meeting their minimum statutory requirements. However, there is a widespread issue across most LFRMSs regarding inadequate consideration of increasing SWF risk from climate changes, which highlights the need for enhanced LFRMSs by improved planning and climate change adaptation plans. There is some evidence of good practice within the LFRMS portfolio, which is discussed in the context of the ongoing LFRMS update process. Beyond England, there are implications for developing FRM processes at a local level that can be objectively assessed against national requirements. Communities in England face inadequately managed SWF risk in the future because of the range in plan quality across the LFRMSs. This research contributes to the ongoing examination of the full suite of 152 LFRMSs and, therefore, builds towards a complete assessment of the SWF management approach in England. This will help inform local climate change adaptation strategies that cater to the escalating threat of SWF due to climate change. [ABSTRACT FROM AUTHOR]

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria's coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study's objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria's coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study's geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate. [ABSTRACT FROM AUTHOR]

Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, hydrological models that estimate such discharges often lack transparency regarding the uncertainty in their predictions. This was evidenced by the devastating flood that occurred in July 2021, which was not captured by the existing model for estimating design discharges. This article proposes an approach to obtain uncertainty estimates for extremes with structured expert judgment using the classical model (CM). A simple statistical model was developed for the river basin, consisting of correlated generalized extreme value (GEV) distributions for discharges from upstream tributaries. The model was fitted to seven experts' estimates and historical measurements using Bayesian inference. Results were fitted only to the measurements were solely informative for more frequent events, while fitting only to the expert estimates reduced uncertainty solely for extremes. Combining both historical observations and estimates of extremes provided the most plausible results. The classical model reduced the uncertainty by appointing the most weight to the two most accurate experts, based on their estimates of less extreme discharges. The study demonstrates that with the presented Bayesian approach that combines historical data and expert-informed priors, a group of hydrological experts can provide plausible estimates for discharges and potentially also other (hydrological) extremes with relatively manageable effort. [ABSTRACT FROM AUTHOR]

The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making. [ABSTRACT FROM AUTHOR]

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively. [ABSTRACT FROM AUTHOR]

The Ca Mau Peninsula plays a critical role in the agricultural and aquacultural productivity of the Vietnam Mekong Delta (VMD), central to regional food security and the population's economic and social welfare. Unfortunately, this region has also historically been a hotspot for natural disasters, particularly from flooding, which is initiated by seasonal river flux upstream and heightened sea levels downstream, but also exacerbated by global climate change (e.g., increased rainfall and sea-level rise, tropical storm surges) and human activities (e.g. river bed lowering, land subsidence). The potential risks associated with rising inundation levels is important information for the future sustainability of the region and its ability to adapt to both current and forthcoming changes. The research around the influence of such drivers on future flood risk, in the Ca Mau Peninsula, is incomplete, primarily due to the absence of a quantitative coastal inundation map corresponding to future compounded scenarios. In this study, we therefore evaluate flooding dynamics in the Ca Mau peninsula using a fully calibrated 1D model, to represent a range of anthropogenic and climate change compound scenarios. Our findings indicate that factors such as increased high-flows upstream, alterations in the riverbed of the main Mekong channel, and occurrences of storm surges effecting the mainstream Mekong River, are unlikely to significantly affect inundation dynamics in this region. However, land subsidence, rising sea levels, and their combined effects emerge as the primary drivers behind the escalation of inundation events in the Ca Mau peninsula, both in terms of their extent and intensity, in the foreseeable future. These results serve as vital groundwork for strategic development and investment as well as for emergency decision-making and flood management planning, providing essential insights for shaping development policies and devising investment strategies related to infrastructure systems in an area which is rapidly developing. [ABSTRACT FROM AUTHOR]

Introduction: Future urban stormwater flood risk is determined by the confluence of both climate-driven changes in precipitation patterns and the effectiveness of flood mitigation systems, such as urban drainage and pump systems. This is especially true in coastal cities protected by levee systems like New Orleans, where even present-day rainfall would be enough to cause serious flooding in the absence of extensive stormwater drainage and pumping. However, while the uncertainties associated with climate change have been well studied, uncertainties in infrastructure performance and operation have received less attention. Methods: We investigated how these interrelated sets of uncertainties drive flood risk in New Orleans using a Robust Decision Making (RDM) approach. RDM is a framework for Decision Making Under Deep Uncertainty (DMDU) that leverages simulation models to facilitate exploration across many possible futures and the identification of decision-relevant scenarios. For our work, we leveraged a detailed Storm Water Management Model (SWMM) representation of the New Orleans urban stormwater management system to examine flood depths across the city when faced with different levels of future precipitation, sea-level rise, drainage pipe obstruction, and pumping system failure. We also estimated direct flood damage for each neighborhood in the city for this scenario ensemble. These damage estimates were then subjected to vulnerability analysis using scenario discovery—a technique designed to determine which combinations of uncertainties are most stressful to the system in terms of an outcome of interest (excess flood damage). Results: Our results suggest that key drivers of vulnerability depend on geographic scale. Specifically, we find that possible climate-driven precipitation increases are the most important determinant of vulnerability at the citywide level. However, for some individual neighborhoods, infrastructure operation challenges under present day conditions are a more significant driver of vulnerability than possible climate-driven precipitation increases. [ABSTRACT FROM AUTHOR]

Coastal flood risk is expected to increase due to climate change and population growth. Much of our coastlines is protected by “grey” infrastructure such as a dike. Dike maintenance and strengthening requires ever increasing capital and space, putting their economic viability in question. To combat this trend, more sustainable alternatives are explored, also known as Nature based Solutions. A promising option has shown to be tidal marshes. Tidal marshes are coastal wetlands with high ecological and economic value. Also, they protect dikes through wave attenuation and in case of a dike breach reduce its development. However, the effectiveness of a tidal marsh on reducing dike breach development rates highly depends on the stability of the tidal marsh itself. Not much is known about the stability of a tidal marsh under dike breach conditions, which are accompanied with flow velocities that can reach 4–5ms−1. In this study we tested the vegetation response and erodibility of a mature tidal marsh, in-situ, under high flow velocities ( > 0.5 m s−1). Our results confirm that tidal marshes similar to the one tested in this study are highly erosion resistant with low erodibility. More research is necessary to confirm this for tidal marshes with different soil and vegetation properties. For tidal marshes similar to what is tested thus far, erosion under dike breach conditions is negligible and other erosion mechanisms such as headcut erosion probably dominate the erosion process [ABSTRACT FROM AUTHOR]

Coastal water level information is crucial for understanding flood occurrences and changing risks. Here, we validate the preliminary version (0.9) of NOAA's Coastal Ocean Reanalysis (CORA), which is a 43-year reanalysis (1979-2021) of hourly coastal water levels for the Gulf of Mexico and Atlantic Ocean (i.e., the Gulf and East Coast region, or GEC). CORA-GEC v0.9 was conducted by the Renaissance Computing Institute using the coupled ADCIRC+SWAN coastal circulation and wave model. The model uses an unstructured mesh of nodes with varying spatial resolution that averages 400 m near the coast and is much coarser in the open ocean. Water level variations associated with tides and meteorological forcing are explicitly modeled, while lower-frequency water level variations are included by dynamically assimilating observations from NOAA's National Water Level Observation Network. We compare CORA to water level observations that were either assimilated or not, and find that the reanalysis generally performs better than a state-of-the-art global ocean reanalysis (GLORYS12) in capturing the variability on monthly, seasonal, and interannual timescales as well as the long-term trend. The variability of hourly non-tidal residuals is also shown to be well resolved in CORA when compared to water level observations. Lastly, we present a case study of extreme water levels and coastal inundations around Miami, Florida to demonstrate an application of CORA for studying flood risks. Our assessment suggests that NOAA's CORA-GEC v0.9 provides valuable information on water levels and flooding occurrence from 1979-2021 in areas that are experiencing changes across multiple time scales. CORA potentially can enhance flood risk assessment along parts of the U.S. Coast that do not have historical water level observations. [ABSTRACT FROM AUTHOR]

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management. [ABSTRACT FROM AUTHOR]

This study was conducted in response to the Beijing–Tianjin–Hebei mega heavy rainfall event at the end of July 2023, and the severely affected and representative Jiangou village in Beijing was selected as the study area. A variety of methods were used to synthesize and analyze the situation and propose an adaptive response to heavy rainfall and flooding in the village. Based on multi-source remote sensing (RS) data, a comprehensive topographic and hydrological characterization was carried out, and the precipitation before and after the disaster was analyzed; the flood inundation area was extracted using the improved normalized water body index (MNDWI) and OTSU thresholding methods, and the changes of water bodies during the flooding period were quantitatively analyzed; and an improved convolutional-neural-network-based building identification and extraction model was constructed to extract the research distribution of buildings in the area. The sponge city construction (SPCC) method was improved to obtain a method that can mitigate flood risk and adapt to villages by constructing small artificial lakes and local topographic buffers to improve the water storage and drainage capacity of villages. The study shows that these methods are innovative in flood hazard analysis and mitigation but still need further improvement in data accuracy, simulation depth, and system evaluation. [ABSTRACT FROM AUTHOR]

Urbanization and climate change increasingly challenge urban water management. In this context, the design of stormwater drainage systems, which traditionally relies on historical rainfall records, is being questioned. Although significant efforts have been dedicated to optimizing drainage networks, the upgrading of existing systems remains understudied. This research devised a set of viable stormwater drainage networks, referencing the road network of the Sino-Singapore Tianjin Eco-City (data from Google Maps). On this basis, utilizing design rainfall data (sourced from the local meteorological center), an extensive array of scenario analyses was conducted. The investigation assessed the performance of implementing two redundancy-based interventions—introducing loops and enlarging pipe diameters—as well as the patterns of flood risk response, and by integrating a multi-objective optimization algorithm, this study proposes a framework for the optimization of grey infrastructure upgrades based on component replacement. The findings suggest that a precise deployment strategy for grey infrastructure is essential. The former improves the effective flow distribution of the drainage system, while the latter enhances its flow capacity, making each intervention suitable for drainage systems with a different degree of centralization. Further research shows that an integrated hybrid scheme brings significant flood risk improvement with strong applicability for most urban drainage systems. The upgrade model proposed in this study could be a valuable initiative, offering theoretical insights for the construction and development of resilient cities. [ABSTRACT FROM AUTHOR]

Earthquakes and floods in Albania are devastating, but combining these two different hazards in terms of action on bridge structures may lead the bridge to collapse. This article presents a seismic risk assessment of a code-conforming precast reinforced concrete bridge located in a region prone to earthquakes and where local scour induced by floods is a significant concern. The seismic action is considered using a group of ground motion accelerograms generated by matching the accelerogram of the 29 November 2019 earthquake in Durres (M = 6.4), Albania, to the target response spectrum. The scouring effects on the bents of the bridge are characterized by the scour depths. A set of non-linear time-history analyses of the bridge are performed to assess the bridge's performance. The bridge fragility curves are generated and analyzed for multi-hazard scenarios at both element and system levels for different flow discharge values and PGA levels. The result shows a low seismic risk of the bridge with bearings when considering the local scour induced by flood events in the seismic analysis due to their flexibility to adapt to changes in structure geometry and significant foundation stiffness. This research also emphasizes the significance of choosing the right foundation type and depth for bridges located in areas prone to local scour induced by floods. [ABSTRACT FROM AUTHOR]

Extremely large floods that far exceed previously observed records are often considered virtually "impossible", yet they are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. Neglecting such floods may lead to emergency situations where society is unprepared and to disastrous consequences. Four reasons why extremely large floods are often considered next to impossible are explored here, including physical (e.g. climate change), psychological, socio-economic and combined reasons. It is argued that the risk associated with an "impossible" flood may often be larger than expected and that a bottom-up approach should be adopted that starts from the people affected and explores possibilities of risk management, giving high priority to social in addition to economic risks. Suggestions are given for managing this risk of a flood considered impossible by addressing the diverse causes of the presumed impossibility. [ABSTRACT FROM AUTHOR]

An event-based approach has been used to explore the potential effects of climate change on the spatial and temporal coherence of widespread flood events in Great Britain. Time series of daily mean river flow were generated using a gridded national-scale hydrological model (Grid-to-Grid) driven by a 12-member ensemble of regional climate projections from UK Climate Projections 2018 (UKCP18), for 30-year baseline (1980–2010) and future (2050–2080) time slices. From these, sets of widespread extreme events were extracted. The question of what defines a "widespread flood event" is discussed; here it was defined as an event exceeding an at-site 99.5th percentile (equivalent to 2 d per year) simultaneously over an area of at least 20 km 2 , with a maximum duration of 14 d. This resulted in a set of 14 400 widespread events: approximately 20 events per year, per ensemble member, per time slice. Overall, results have shown that events are more temporally concentrated in winter in the future time slice compared to the baseline. Distributions of event area were similar in both time slices, but the distribution of at-site return periods showed some heavier tails in the future time slice. Such information could be useful for adaptation planning and risk management for floods under climate change, but the potential future changes have to be interpreted in the context of some differences in event characteristics between the baseline climate-projection-driven model runs and an observation-driven model run. While the focus here is Great Britain, the methods and analyses described could be applied to other regions with hydrological models and climate projections of appropriate resolution. [ABSTRACT FROM AUTHOR]

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00–19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods. [ABSTRACT FROM AUTHOR]

After a flood disaster, the question often arises: "What could have happened if the event had gone differently?" For example, what would be the effects of a flood if the path of a pressure system and the precipitation field had taken a different trajectory? In this paper, we use alternative scenarios of precipitation footprints shifted in space, the so-called "spatial counterfactuals" to generate plausible but unprecedented events. We explore the spatial counterfactuals of the deadly July 2021 flood in the Ahr Valley, Germany. We drive a hydrological model of the Ahr catchment with precipitation fields of this event systematically shifted in space. The resulting discharge is used as a boundary condition for a high-resolution two-dimensional hydrodynamic model. We simulate changes in peak flows, hydrograph volumes, maximum inundation extent and depths and affected assets and compare them to the simulations of the actual event. We show that even a slight shift of the precipitation field by 15–25 km eastwards, which does not seem implausible due to orographic conditions, causes an increase in peak flows at the gauge Altenahr of about 32 % and of up to 160 % at the individual tributaries. Also, significantly larger flood volumes of more than 25 % can be expected due to this precipitation shift. This results in significantly larger inundation extents and maximum depths at a number of analyzed focus areas. For example, in the focus area around Altenahr, the increase of mean and maximum depth of up to 1.25 m and 1.75 m, respectively, is simulated. The presented results should encourage flood risk managers as well as the general public to meet precautionary measures for extreme and unprecedented events. [ABSTRACT FROM AUTHOR]

Compound flooding (CF) events, driven by coincident/concurrent and mutually reinforcing factors such as heavy rainfall, storm surges, and river discharge, pose severe threats to coastal communities around the Globe. Moreover, the exacerbating influence of climate change and sea-level rise further amplifies these risks. This study delves into the complex and multifaceted issue of compound coastal flooding in two freshwater-influenced systems on the Gulf Coast of the United States - Southeast Texas and South Alabama. We first conduct a robust statistical analysis to evaluate the significance of nonstationarity, multi-dimensionality, and non-linearity of interactions among various drivers of CF. Second, to assess the extent to which current flood resilience policies and guidelines account for these characteristics of CF events, we perform a critical review of existing policy documents. The results of the statistical analysis reveal significant compounding and shifts in the statistics of flood drivers that emphasize the pressing need for a multi-mechanism, nonstationary approach to flood hazard assessment. We also found an evident lack of appropriate language/recommendation in policy documents of solid tools that systematically take non-stationarity, multi-dimensionality, and non-linearity of CF into account. By identifying the gaps between current policy measures and the detected complexities of CF, we seek to provide insights that can inform more effective flood resilience policies and design guidelines. Through this robust analysis, we aspire to bridge the divide between research and policy. [ABSTRACT FROM AUTHOR]

The impacts of climate change manifest heterogeneously across regions, and in Khovd City, a semi-arid area in Western Mongolia, the escalating threat of flooding is evident through the occurrence of 10 flash floods in the last 30 years. The risk zone, encompassing rivers and flash floods, endangers ca. 32,000 residents, with 750–1,800 traditional nomadic dwellings (ger s) located on the floodplain of the Buyant River during summer. There is a risk of flash floods in the eastern part of Khovd City from the mountains, while the western part is at a higher risk of flooding from the Buyant River. This paper aims at assessing flood hazards through a detailed topographical survey conducted using an Unmanned Aerial Vehicle (UAV). 15,206 aerial photos were collected in Khovd City using the UAV and measured by Real-Time Kinematic (RTK) on 22 Ground Control Points (GCPs). A Digital Elevation Model (DEM) with a resolution of 2.7 meters was generated from the aerial drone mapping data, enabling surface morphology, hydrological and eight-direction pour point model analysis using a Geographic Information System (GIS). The resulting flood hazard map revealed 4 flood risk areas based on flood flow direction and topographical features. Recommendations for local govern and residents include enhancing flood protection facilities for flood disaster prevention on flood risk zones. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify potentially flood-prone areas in the section between the Siret hydrometric station and the confluence of the Siret River with the Suceava River. Data related to water flow and specific flow parameters were incorporated into a dedicated hydrodynamic modeling program, in accordance with its requirements (DHI). The construction of the model focused on reproducing the field's reality as faithfully as possible. Flow parameters were individually configured for each calculation section, taking into account their specific characteristics. Roughness values were determined non-uniformly based on the areas traversed by each cross-sectional profile. Great emphasis was placed on model calibration, using monitored data from the Zvoriștea and Huțani hydrometric stations as reference points. A time series spanning 15 years, including two of the largest floods recorded in the sector, was used. Following the simulation of two scenarios, Q1% and Q0.1%, three types of files were generated, describing the flood boundary, water depth at each point on the boundary surface and water velocity at each point within the flood boundary. Localities and types of potentially flood-prone surfaces were identified on a map using GIS techniques. Furthermore, flood boundary limits from different scenarios were overlaid to quantify areas at risk of flooding. Although the scenarios were simulated on a previously calibrated model, the fact that the simulated flows exceeded the observed flows required several post-calibration steps. In the case of the first scenario, the simulation did not show significant errors, precisely because the Q1% flow is close in value to the maximum flows observed during the calibration period. In the case of the second scenario, the fact that the flow extended to nonspecific areas even during historical maximum flows, resulted in stability errors or non-conforming results obtained by the program, inconsistent with research and studies in the field. [ABSTRACT FROM AUTHOR]

Limited research has been directed toward coastal protection infrastructure compared to other types of infrastructure, despite the increasing global population in low-elevated coastal regions and the threats posed by climate change. This paper presents a risk-based asset management framework for coastal protection structures that improves accuracy with each inspection. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA) module, Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). The framework addresses challenges in accurately predicting coastal structure deterioration due to uncertainties in wave loading conditions and the need for frequent inspections. It is applied to rubble-mound breakwaters in Alexandria, Egypt. The BMDM and FMDM models are developed based on inspection data, and the IPE optimizes interventions considering structural condition, risk thresholds, and budget constraints. Results showed that long-term deterioration estimates occur at an accelerated rate with an increase in inspection points, triggering earlier interventions. However, the framework proves reliable even with only two inspection points, allowing asset managing agencies to implement the model based on the structural condition at the year of construction and a minimum of two inspections. The proposed risk-based asset management framework provided a comprehensive approach to managing coastal protection infrastructure, reducing risks to life and property. By accurately predicting deterioration and optimizing intervention decisions, the framework can greatly assist in the effective management and maintenance of coastal assets. This is vital in ensuring the safety of coastal populations facing global climate change and demographic growth. Coastal protection structures are crucial for safeguarding coastal populations in the face of climate change and demographic growth. This research paper introduces a risk-based asset management framework for coastal protection structures, with practical applications for coastal management agencies and practitioners. The framework addresses the challenges of predicting coastal structure deterioration and optimizing intervention decisions. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA), Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). It has been successfully applied to rubble-mound breakwaters in Alexandria, Egypt. The practical implications are significant: The framework enables asset managers to make informed decisions on coastal infrastructure maintenance and interventions, reducing risks to life and property. It provides a comprehensive approach to managing coastal protection infrastructure and ensuring the safety of coastal populations. By accurately predicting deterioration and optimizing interventions, the framework supports effective management and maintenance of coastal assets. Its reliability and flexibility make it a valuable tool for coastal management practitioners, promoting sustainable development and safeguarding coastal areas. With its potential for broader application, this risk-based asset management framework offers practical solutions for coastal protection worldwide. [ABSTRACT FROM AUTHOR]

The 'vulnerability' approach highlights institutional factors as the primary drivers of flooding risk. However, the intricate connections between institutional vulnerability and urban flood disaster risk have not been extensively explored until now. This study aims to address this gap by examining the case of the 7·20 heavy rainfall disaster in Zhengzhou. Employing a hybrid analysis methodology that integrates text coding, fault tree analysis and analytic hierarchy process methods, the study seeks to identify and assess the distinct contributions of various institutional factors and their interplay, culminating in ineffective urban flood risk management. In contrast to previous research findings that emphasize the determinant role of policy‐making and organizational coordination, this study demonstrates that inadequate legislative compliance constitutes the root cause determining the ineffectiveness of urban flood risk management. It is further exacerbated by insufficient policy attention, resulting in inadequate allocation of resources that ensure the legislative quality, risk coping skills and knowledge and stakeholders' coordination and collaboration. By emphasizing the significance of legislative compliance and policy attention, the study offers a fresh conceptual perspective to understand the factors influencing the efficiency of urban flood risk management. It provides valuable insights to develop targeted countermeasures for mitigating similar urban flood risks in the future. [ABSTRACT FROM AUTHOR]

We propose an interactive web‐based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real‐time visualization and interaction techniques in 3D. A new view‐dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid‐latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can be devastating events for vulnerable countries like Vanuatu, impacting their population, livelihoods, and infrastructure, leaving the country in need of aid and recovery. Despite this, comprehensive risk information on the nuanced impacts of each region is not well understood. Every TC event is different, and understanding the potential for impact at each location empowers decision makers in the lead-up to an event or during off-season planning to make more informed decisions to direct disaster risk reduction efforts. TC hazard model data typically describe intensity and likelihood, which can be fed into risk assessment frameworks to describe probabilistic risk. This study instead uses freely available remote sensing data to create proxies for the TC hazards of storm surge and flooding and to describe only the intensity of the hazard if the event occurs at the location. This hazard susceptibility index is fed into a risk assessment framework with Vanuatu exposure and vulnerability data for domains of populations, housing, and roads. These methods allow for the risk to be estimated for each month, as well as during specific historical time periods of TC Pam, TC Harold, and the TCs Judy and Kevin, enabling future impact validation. The results show households to have the highest risk, followed by roads and population domains, while a TC-induced surge risk is overall higher than TC-induced flooding, particularly in the road domain. The results, however, show a likely underestimation of event hazards and an overestimation of Port Vila's resistance to impacts, which is a subject of future investigation and validation. [ABSTRACT FROM AUTHOR]

The changing frequency of flooding in global watersheds, driven by various human and natural factors like land use/cover changes and global warming, necessitates innovative approaches in flood frequency analysis and risk assessment. Nonetheless, the reliability of nonstationary frequency analysis models remains a concern given challenges in accurately measuring the uncertainty introduced by these methods and the impact on design flood values. In this study, deviation-based differential sensitivity indices, including single-parameter (SDDSI) and entire-parameter (EDDSI) measures were developed to assess the influence of parameter uncertainty in nonstationary models using Bayesian statistics and "equivalent reliability" nonstationary design. The Weihe River, the largest tributary of the Yellow River which is experiencing both climate change and heavy impact of human activities, is chosen to be the study area to investigate the impact of precipitation change and land use change on nonstationary flood frequency. Results show that in the One-At-A-Time (OAT) sensitivity analysis under a small uncertainty scenario (SUS) for parameter inputs, the shape parameter stands out as the most influential factor (SDDSI_SUS = 0.347) affecting the 100-year design flood in the Stationary Generalized Extreme Value (SGEV) model. For the Non-Stationary GEV (NGEV) models, the influence of this parameter is less pronounced, with SDDSI_SUS values of 0.095 and 0.093 for the SSP126 and SSP585 scenarios, respectively. Instead, attention turns to the regression coefficient of the grassland area, associated with the GEV scale parameter. In global sensitivity analysis under the posterior uncertainty scenario (PUS) for parameter inputs, the EDDSI_PUS values for SGEV, NGEV_SSP126, and NGEV_SSP585 models were 0.52, 1.41, and 1.30, respectively, inferring heightened sensitivity of NGEV models to perturbations from entire parameters. It is anticipated that incorporating additional evidence, such as historical flood data, is essential for accurate nonstationary hydrological design to mitigating the influence of parameter uncertainty. The sensitivity indices in this study provide significant insights for assessing the robustness of nonstationary hydrological design in flood risk management and applications. [ABSTRACT FROM AUTHOR]

Now a days, assessment of flood risk is a significant concern worldwide, particularly in regions experiencing rapid urbanization and climate change. This study utilizes the analytic hierarchy process and Geographic Information Systems to assess flood risk under changing climate in the Lower Tapi river basin (LTRB) in India. The objective is to develop flood hazard and flood vulnerability maps and subsequently to create a flood risk map by integrating them in LTRB in a Base and Advance scenario, i.e., under changing climate. The change point study to identify Advance scenario has been employed to distinguish the year from which sudden change in the data emerge out. The flood risk contributing factors are included in this study are Elevation, Distance from river, Average annual rainfall, Drainage density, Normalized Difference Vegetation Index, Slope, Clay percentage, Topographic Wetness Index, Population density, Average Crop production, Land use Land cover, and Road River intersection. The developed flood risk map has been separated into five zones of the level of risk, namely very high, high, moderate, low, and very low-risk zones. The flood risk maps developed in Base scenario and in Advance scenario are compared to investigate the impact of changing climate in Lower Tapi river basin. It is revealed that the high and very high-risk zone area proliferated, and very low and moderate risk area condensed in the Advance scenario. The developed flood risk maps in Base and Advance scenario can aid in effective disaster management and planning for the region under changing climate. [ABSTRACT FROM AUTHOR]

Flood maps are a crucial component of integrated flood risk management. While their key role is commonly acknowledged by experts and scholars, however, literature and findings on the practical utilization of flood maps (including the user' experiences) within the processes of governance are scarce. Our study aims to contribute to closing this gap; by focusing on the Czech local government bodies, data collected through a questionnaire survey allowed us to examine (a) how, or whether at all, the officials employ flood maps in their agendas; (b) how do they experience and assess working with the maps; and (c) which data would they supplement the extant flood maps by. Our findings show that the praxis of local flood-related governance in the Czech Republic still largely neglects the up-to-date approaches and practices of flood risk management. The officials addressed mostly continue to rely on the earliest type of floodplain maps and purely technical aspects of floods, while largely omitting the newer flood danger and risk maps; thus, they are also missing the opportunities of applying multi-criteria assessment of the flood risk and more effective communication with the public. The paper concludes with a set of suggestions for relevant praxis and future research. [ABSTRACT FROM AUTHOR]

Frequency analysis of extreme storm surge is crucial for coastal flood risk assessments. To date, such analyses are based on traditional extreme value theory (EVT) and its associated generalized extreme value (GEV) distribution. The metastatistical extreme value distribution (MEVD) provides a new approach that can alleviate limitations of EVT. This paper provides a comparison between the GEV distribution and the MEVD on their ability to predict "unseen" upper-tail quantiles of storm surge along the US coastline. We analyze the error structure of these distributions by performing a cross-validation experiment where we repeatedly divide the data record into a calibration and validation set, respectively, and then compute the predictive non-dimensional error. We find that the MEVD provides comparable estimates of extreme storm surge to those of the GEV distribution, with discrepancies being subtle and dependent on tide gauge location and calibration set length. Additionally, we show that predictions from the MEVD are more robust with less variability in error. Finally, we illustrate that the employment of the MEVD, as opposed to classical EVT, can lead to remarkable differences in design storm surge height; this has serious implications for engineering applications at sites where the novel MEVD is found more appropriate. [ABSTRACT FROM AUTHOR]

Flooding is a difficult and common hazard in Indonesia, particularly in Jakarta during the rainy season. Floods have been the subject of several endeavours, ranging from discovering the causes to reducing their impacts. Floods cause significant damage to infrastructure, the social economy, and human lives. The government continues to create reliable flood risk maps and plans for long-term flood risk management. According to data from Jakarta Flood Monitoring, 12 sub-districts and 26 urban villages were hit by floods each year between 2016 and 2020, with an average flood length of nearly 2 days. The flood tendency in Jakarta decreased from 2018 to 2019, but increased in 2020. Floods are produced by a variety of reasons, including weather, geography, and human actions such as deforestation. Strong flood prediction is required for disaster management, however this might be difficult owing to changing weather conditions. This study focuses on flood prediction in Jakarta based on weather parameters utilising machine learning techniques to provide accurate and realtime predictions. K-Nearest Neighbours (KNN) is an algorithm employed to forecast the areas that will encounter the consequences of floods. The outcomes of this research with the value of k=2 to k=9 obtained the best performance values at k=7, where the level of accuracy reaches 92.25%, 88.89% precision, 92.25% recall, and F1-measure of 89.52%. The integration of machine learning algorithms which encompasses multiple weather variables provides significant utility in comprehensive flood predictions and early warning systems in flood disaster mitigation. [ABSTRACT FROM AUTHOR]

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: 'low risk', 'moderate risk', 'high risk', and 'very high risk'. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness. [ABSTRACT FROM AUTHOR]

Flood bypasses are used in regional flood management to reduce flood risk and damage by routing substantial flood flows to a supplementary river channel to lower flood stages in the usual river mainstem. Design, operations, and modifications to flood bypasses commonly employ hydraulic analysis and evaluation. Economics of flood bypasses is usually considered only in terms of feasibility, and bypass design is rarely optimized economically. The literature lacks formal integration of economics, engineering, and hydrology for flood bypasses. Economically analyzed flood bypass systems better incorporate bypasses into flood management, reflecting and balancing the range of costs, impacts, and opportunities of bypasses. This paper presents a method to integrate economics, hydrology, and engineering for flood bypass planning. An economic optimization model is developed based on a cost–benefit analysis. The optimization suggests and explores a preferred economic flood bypass capacity. A preliminary analysis is developed, with a base case that includes benefits from flood risk reduction only. Costs include for a levee setback, weir widening, and land use. The optimization model is applied preliminarily to California's Yolo Bypass. With the simplification of unlimited land availability, the model results suggest an optimal Yolo Bypass capacity expansion of approximately 5,800 m3/s. The expansion suggested is close to the Department of Water Resources (DWR) stated objective in their 2017 Central Valley Flood Protection Plan and part of the Basin-Wide Feasibility Studies Sacramento River Basin, part of the Central Valley Flood Management Program of 2017. The model is also applied to the Mississippi River Basin's, New Madrid Birds Point Bypass and Morganza Floodway. In all cases, actual and theoretically optimal bypass capacities agree in their general behavior. This paper provides a framework for decision analysis. This method can better inform policymakers and stakeholders on bypass design and structural modifications and long-term flood management strategy. [ABSTRACT FROM AUTHOR]

Floods have devastating consequences for human life and economies worldwide. The present study focuses on flash flooding in the Himalayan environment, specifically in Dharamshala, Himachal Pradesh, India. In July 2021, a low-pressure system developed in the area, intensifying into a deep depression and resulting in extreme rainfall on July 12, 2021. This extreme rainfall event resulted flash flooding, landslides, and extensive damage in Dharamshala. Utilizing satellite observation and geospatial technology, this study examined the heavy rainfall events and the vulnerability of the geology of Dharamshala to flash flooding. The area received a cumulative rainfall of more than 370 mm, with over 35 mm recorded at 09:00 Universal Time Coordinated (UTC). The steep slopes of the region, combined with sudden heavy downpours from multiple extreme rainfall events, contributed to flash flooding and landslides. The consequences were severe, resulting in the loss of seven lives and impacting thousands of people in the study area. The research findings emphasize the significance of satellite-borne remote sensing applications and geospatial technology for real-time short-lived localised rainfall events in the study area. The study also highlights the role of Bhagsu Nala in exacerbating flood risks. This research provides a valuable model for implementing effective disaster mitigation measures. Moreover, it contributes to global efforts to reduce the risk of natural catastrophes, particularly in Himachal Pradesh. The findings offer valuable insights into disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

It has become common to attribute the growing frequency and severity of floods to climate change. But the factors behind flooding are many, and climate change often disappears from the equation at the local level. This study draws on interviews with key informants and community members and focus group discussions to explore the increasing incidence of flooding in two sub-districts in Eastern Thailand. To our surprise, there was little sense of community anger: flood risk had increased; the causes rooted in maladaptation linked to land conversion were recognised and uncontested; and injustice was palpable. But anger and resistance were muted. The paper seeks to make sense of this situation. Villagers accepted their complicity in creating the conditions for heightened flood risk through their willingness to sell their land for conversion. The disconnection between the identification of causality and the allocation of blame raises questions about how notions of environmental justice play out in places like Ban Thapma and Ban Nhonglalok, where justice and injustice do not fall equally across space and society. [ABSTRACT FROM AUTHOR]