Explore the vast range of titles available.

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

This study focused on pre-flood measures to estimate evacuation times impacted by flood depths and identify alternate routes to reduce loss of life and manage evacuation measures during flood disasters. Evacuation measures, including traffic characteristics, were reviewed according to different flood depths. Several scenarios were constructed for different flooding situations and traffic volumes. Evacuation times in the study area were evaluated and compared for all scenarios with reference to dry conditions. Results of network performance indicators compared to the dry situation showed that average speed dropped to 2 km/h, VHT rose above 200%, and VKT rose above 30%. Cumulative evacuee arrival percentage increased when flood levels were higher than 5 cm. Flood levels of 10–15, 15–20, 20–25, and 25–30 cm represented percentages of remaining evacuees at 9%, 19%, 49%, and 83%, respectively. Time taken to evacuate increased according to flood level. For flood depths of 5–30 cm, travel time increased by 40, 90, 260, and 670 min, respectively, suggesting the need for early evacuation before the flood situation becomes serious.

Flooding is a recurring natural phenomenon that can have both life-giving and destructive aspects. In natural environments, floods are often an important element of the seasonal hydrologic cycle that provides water and nutrients to soil, supporting unique, rich and diverse ecosystems. However, flood events can also represent a destructive force that can endanger lives and cause significant damage in urban areas. Karst areas, in particular, are unique because of their special hydraulic characteristics in terms of flood occurrence, the dependence of ecosystems on such events, and attempts to actively store and manage floods. In this article, the hydraulic response of karst aquifers to heavy precipitation events, flood generation, and engineering interventions for flood control are discussed using several examples from karst areas in the Mediterranean region. Flooding mechanisms and regulatory structures in karst poljes are considered using several typical examples from the Dinaric mountain range. In addition, different variants of groundwater abstraction for increasing storage capacity and flood control are presented using examples from France and Montenegro. Managed aquifer recharge in karst areas and adjacent aquifers is demonstrated with examples from Jordan and Algeria. Finally, failed attempts at flood storage in karst reservoirs are presented with examples from Spain and Montenegro. These examples of flood retention in karst areas show the wide range of planning and technical measures and remind us of possible risks and failures in implementation as well as some positive and negative impacts on the environment and especially on ecosystems.

Flash flood early warning is a very effective way to reduce casualties induced by rainstorm flash flood in mountainous area. The forecasting of flash flooding remains challenging because of the short response time and inaccurate warning threshold. So far, the flash flood disaster defenses often adopt the critical rainfall amounts inducing the peak discharge or water level to establish an early warning threshold in China. However, the runoff peak discharge depends on rainfall patterns including rainfall intensity and accumulation, result in the critical rainfall threshold has a significant uncertainty. To reduce this uncertainty, herein we present a dual-threshold method for flash flood early warning with consideration of rainfall patterns based on above two-rainfall metrics. Moreover, applying this new method in the flash flood disasters occurred in the Zhongdu river basin, Sichuan province of China to evaluate the early warning reliability. Firstly, five most likely rainfall patterns of this basin were determined according to the timing of rain peak in historical rainfall events, and then, we determined the critical rainfall thresholds under different rainfall patterns and soil moisture conditions. The result showed that the rainfall thresholds uncertainty caused by rainfall pattern is more pronounced than soil moisture. Next, using the cumulative rainfall depth and maximum rainfall intensity corresponding to disaster discharge in different flood processes to establish the dual-thresholds. We found the dual-threshold method comprehensively considers the impacts of soil moisture, rainfall temporal distribution and flood rising property, which can achieve early warning for the four protected objects along the Zhongdu River, with an average lead duration of 46.2 min. Compared with the other three single-threshold methods, the critical rainfall and the critical rainstorm curve methods frequently created false or missing warnings, making it difficult to achieve the effect of early warning. Although reliability of flood water level rising rate method is high, the lead time is relatively short and only lasts for a few minutes in some cases. As a result, the new proposed dual-threshold method, accounting for both the reliability and long lead time, can be a potential candidate for the flash flood disaster early warning.

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system’s operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

We investigated the abundance and taxonomic composition of the aquatic predatory insect fauna, with focus on adult diving beetles (Coleoptera: Dytiscidae), in eight temporary flooded wet meadows and two alder swamps in the River Dalälven floodplains, central Sweden from 2002 to 2006. Diving beetles are generalist predators and often abundant in various waters, including temporary wetlands. In the River Dalälven floodplains, recurrent floods induce massive hatching of flood-water mosquitoes (Diptera: Culicidae), which constitute a superabundant patchy and irregular food resource for aquatic predatory insects. Our aims were (1) to characterize the assemblage of adult diving beetles occurring in the wetlands during floods in relation to time and wetland type and (2) to evaluate the effect on the aquatic predator assemblage of strongly reducing the abundance of a potential prey, flood-water mosquito larvae with Bacillus thuringiensis var. israelensis (Bti) during floods. We found diving beetles to be the dominating aquatic predatory insect taxa in all 10 wetlands. There was a difference in Dytiscidae species richness but not in diversity between wet meadows and alder swamps after rarefaction. The cluster analysis based on dytiscid species and abundances showed very high similarities between the wetlands. The variance component analysis was unable to distinguish any factor that could explain more than 7.4% of the variation in the dytiscid species assemblages. The only effect of Bti-treatment against flood-water mosquito larvae, potential food for the predatory dytiscids, was a slight increase in abundance of the medium-sized dytiscid species. Our results are in accordance with previous studies, suggesting that irregular and recurrent flood dynamic structure the dytiscid fauna more than food limitations and environmental factors.

Water shortages during dry periods can be successfully mitigated by managing reservoirs in real-time to conserve water as floods recede. The inherent uncertainty in inflow forecasts however means that it remains a challenge to balance the risks of flooding against those of water shortages. Few studies have examined how the risks of floods and water shortages can be managed using reservoir operation rules. In this study, a two-phase stochastic optimization model was built to determine the optimal conservation level for flood water by minimizing the risks from both floods and water shortages. For the optimal condition, hedging rules were analytically derived as a quasi-linear function of the storage capability and the expected water shortage. The rules indicate that the flood water conservation was achieved when the marginal upstream flood risk was equal to the marginal water shortage risk, and that the limits of three operation zones divided by the expected water availability should be used when determining the water release. The results from testing the model with data from the Xianghongdian Reservoir (China) showed that the hedging rules outperformed the capacity-constrained pre-release rules for conserving flood water without increasing the flood risk. This proposed methodology will inform the process for making decisions about how to operate reservoirs to ensure optimal real-time flood water conservation.

The chronology provides a record of flash flood events in Britain based on data collated mainly between 1700 and 2020. The primary purpose of the chronology is to improve the risk assessment of flash floods of given magnitude. It is divided into 18 regions of the country and contains descriptions of nearly 8000 events. It extends a previous chronology covering northern and southwest England which is provided as an online resource in (http://ceg-fepsys.ncl.ac.uk/outputs/). Flash floods have had a variety of previous definitions but are here defined in terms of the speed of onset which can apply to both river floods and surface water floods. The chronology for the first time provides a comprehensive list of surface water floods and their recorded impact on cities, towns and villages. It also draws attention to the prevalence of very rapid rates of rise in river level either as ‘walls of water’, or at a rate likely to endanger life as a result of intense rainfall. Nearly 300 such events have been identified mainly in upland areas of northern England, Wales and Scotland. Practical and theoretical issues with respect to flood risk assessment and warning are discussed. The chronology is available to download and is hosted on https://www.jbatrust.org/how-we-help/publications-resources/rivers-and-coasts/uk-chronology-of-flash-floods-1/

Floods represent one of the most significant global risks, threatening human lives, infrastructure, and economic development. Although various strategies for flood water management have been developed, their effectiveness and applicability vary depending on geopolitical, economic, and climatic factors. This systematic review aims to analyze and critically assess existing mechanisms and programs focused on industry engagement in flood risk reduction and flood water management. Through a comprehensive literature review, key strategies have been identified, including nature-based solutions such as blue-green infrastructure, technological innovations in flood prediction, and regulatory frameworks designed to strengthen cooperation between the public and private sectors. Special attention is given to the limitations of previous research, including methodological shortcomings, the lack of empirical evidence on the long-term effects of strategies, and challenges in implementing existing policies. The findings highlight the need for an integrated approach that combines technical, regulatory, and socio-economic solutions for more effective flood risk reduction. This study contributes to academic and practical discussions by providing a comprehensive analysis of current strategies and offering guidelines for future research.

Surface water flooding can be a significant source of damage and disruption in urban areas. The complexity of urban surfaces, the need for spatially disaggregated approaches and the multiplicity of interventions makes management challenging from a number of perspectives. This research responds to the challenge of selecting appropriate surface water management interventions by applying a fast assessment framework to generate evidence for comparing strategies at low resource cost during initial design. This is demonstrated by simulating flood dynamics and comparing damage costs in 144 flood scenarios. The main finding of this work is that a high-level quantitative assessment of large numbers of scenarios is capable of providing evidence to identify performance trends and consider resilience to extreme events at an early stage of planning.