Search Results (1,519)

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

This study investigates flooding and erosion impacts and human responses in Aoraki Mount Cook and Westland Tai Poutini national parks in Aotearoa New Zealand. These fast-eroding landscapes provide important test cases and insights for considering the public access dimensions of climate change. Our objectives were to explore and characterise the often-overlooked role of public access as a ubiquitous concern for protected areas and other area-based conservation approaches that facilitate connections between people and nature alongside their protective functions. We employed a mixed-methods approach including volunteered geographic information (VGI) from a park user survey (n = 273) and detailed case studies of change on two iconic mountaineering routes based on geospatial analyses of digital elevation models spanning 1986–2022. VGI data identified 36 adversely affected locations while 21% of respondents also identified beneficial aspects of recent landscape changes. Geophysical changes could be perceived differently by different stakeholders, illustrating the potential for competing demands on management responses. Impacts of rainfall-triggered erosion events were explored in case studies of damaged access infrastructure (e.g., roads, tracks, bridges). Adaptive responses resulted from formal or informal (park user-led) actions including re-routing, rebuilding, or abandonment of pre-existing infrastructure. Three widely transferable dimensions of public access management are identified: providing access that supports the core functions of protected areas; evaluating the impacts of both physical changes and human responses to them; and managing tensions between stakeholder preferences. Improved attention to the role of access is essential for effective climate change adaptation in parks and reserves. Full article

The increasing severity of extreme weather events necessitates rapid methods for post-disaster damage assessment. Current remote sensing datasets often lack the spatial resolution required for a detailed evaluation of critical waterfront infrastructure, which is vulnerable during hurricanes. To address this limitation, we introduce the Flood and Waterfront Infrastructure Segmentation Dataset (FWISD), a new dataset constructed from high-resolution unmanned aerial vehicle imagery captured after a major hurricane, comprising 3750 annotated 1024 × 1024 pixel image patches. The dataset provides semantic labels for 11 classes, specifically designed to distinguish between intact and damaged structures. We conducted comprehensive experiments to evaluate the performance of both convolution and Transformer-based models. Our results indicate that hybrid models integrating Transformer encoders with convolutional decoders achieve a superior balance of contextual understanding and spatial precision. Regression analysis indicates that the distance to water has the maximum influence on the detection success rate, while comparative experiments emphasize the unique complexity of waterfront infrastructure compared to homogenous datasets. In summary, FWISD provides a valuable resource for developing and evaluating advanced models, establishing a foundation for automated systems that can improve the timeliness and precision of post-disaster response. Full article

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for estimating flood damage costs across the contiguous United States, where comprehensive hydrologic, climatic, and socioeconomic data are available. A database of 17,407 flood events was compiled, incorporating approximately 38 parameters obtained from the National Oceanic and Atmospheric Administration (NOAA), the National Water Model (NWM), the United States Geological Survey (USGS NED), and the U.S. Census Bureau. Data preprocessing addressed missing values and outliers using the interquartile range and Walsh tests, followed by partitioning into training (70%), testing (15%), and validation (15%) subsets. Four modeling configurations were examined to improve predictive accuracy. The optimal hybrid regression–classification framework achieved correlation coefficients of 0.97 (training), 0.77 (testing), and 0.81 (validation) with minimal bias (−5.85, −107.8, and −274.5 USD, respectively). The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment, providing a practical tool for risk evaluation and resource planning. Full article

In recent years, with the improvement of unmanned aerial vehicle (UAV) performance, various applications have been explored. In environments such as disaster areas, where existing infrastructure may be damaged, alternative uplink communication for transmitting observation data from UAVs to the ground station (GS) is critical. However, conventional mobile ad hoc network (MANET) routing protocols do not sufficiently account for GS-oriented traffic or the highly mobile UAV topology. This study proposed a flying ad hoc network (FANET) routing protocol that introduces a control option called GS flood, where the GS periodically disseminates routing information, enabling each UAV to efficiently acquire fresh source routes to the GS. Evaluation using NS-3 in a disaster scenario confirmed that the proposed method achieves a higher packet delivery ratio and practical latency compared to the representative MANET routing protocols, namely DSR, AODV, and OLSR, while operating with fewer control IP packets than existing methods. Furthermore, although the multihop throughput between UAVs and the GS in the proposed method plateaued at approximately 40% of the physical-layer maximum, it demonstrated performance exceeding realistic satellite uplink capacities ranging from several hundred kbps to several Mbps. Full article

Complex landslides have characteristics and parameters that are difficult to analyze. The landslide on 16 June 2024 in the rural community of Quilloturo (Tungurahua, Ecuador) caused severe damage (14 deaths, 24 injuries, and hundreds of affected families) related to the area’s geological, social, and anthropogenic conditions. Its location in the eastern foothills of Ecuador’s Cordillera Real exacerbated the effects of a landslide involving various processes (mud and debris flows, landslides, and rock falls). This event was preceded by intense rainfall lasting more than 10 h, which accumulated and caused natural damming of the streams prior to the event. The lithology of the investigated area includes deformed metamorphic and intrusive rocks overlain by superficial clayey colluvial deposits. The relationship between the geological structures found, such as fractures, joints, schistosity, and shear, favored the formation of blocks within the flow, making mass movement more complex. Geomorphologically, the area features a relief with steep slopes, where ancient landslides or material movements, composed of these colluvial deposits, have already occurred. At the foot of these steep slopes, on plains less than 300 m wide and bordered by the Pastaza River, there are human settlements with less than 60 years of emplacement and a complex history of territorial occupation, characterized by a lack of planning and organization. The memory of the inhabitants identified mass movements that have occurred since the mid-20th century, with the highest frequency of occurrence recorded in the last decade of the present century (2018, 2022, and 2024). Furthermore, it was possible to identify several factors within the knowledge of the inhabitants that can be considered premonitory of a mass movement, specifically a flood, and that must be incorporated as critical elements in decision-making, both individual and collective, for the evacuation of the area. Full article

Water inrush from flooded goaf under high hydraulic head seriously threatens deep coal mining, especially where roadways must be driven close to old workings. This study investigates the seepage and load-bearing behavior of a combined coal pillar and rigid cutoff wall system under coupled mining-excavation-seepage processes. A three-dimensional hydro-mechanical model based on Biot poroelasticity and a stress-damage-permeability relationship is developed in FLAC3D, using a field case from the Yuwu Coal Mine. Different wall thicknesses and mining stages are simulated, and pillar performance is quantified by the elastic-core volume fraction and a permeability-connectivity index. Similar-material shear tests are further carried out to examine sliding behavior at the wall–pillar interface. Simulations show that the composite system reduces peak vertical stress in the pillar by 12–20% during panel retreat (from 54.2 MPa without a wall to 47.7–45.0 MPa with 0.5–2.5 m walls), while the elastic core volume fraction increases from 16.7% to 30.4–50.4% and the minimum elastic core width improves from 0.5 m to 1.5–2.0 m. The wall provides strong lateral confinement, increasing lateral stress within the pillar by up to 50% and preventing hydraulic penetration for wall thicknesses ≥1.0 m. Shear tests reveal critical distances for safe load transfer and support the use of targeted reinforcement at the interface. The findings offer a quantitative basis for designing safe water-control structures in high-pressure goaf environments. Full article

The impacts of flooding on people’s livelihoods are profound. Therefore, the rapid restoration of safe conditions in existing buildings post-flood, through rational and effective emergency strengthening, constitutes a most urgent priority. Focusing on the specific challenges of flood-induced damage to buildings, coupled with the constraints of limited resources and time-sensitive conditions after a disaster, this study established an indicator system for prioritizing emergency strengthening schemes for existing buildings after floods. A dedicated prioritization model is developed by integrating Prospect Theory and a combination weighting method. The application of this model to a practical engineering case verifies its feasibility and effectiveness. The results demonstrate that the proposed model can rationally and efficiently select the optimal scheme, thereby providing new insights for the quantitative selection of optimal emergency strengthening schemes for existing buildings after floods. This study also highlights the model’s transferability to different disaster scenarios, while its limitations were discussed and future research directions outlined. Full article

Flooding in Bengaluru areas such as Kodigehalli, Hebbal, and Nagavara has led to severe disruptions, including traffic congestion, infrastructure damage, and health risks. To address this issue, we have proposed a smart flood alert and communication system, integrating Internet of things (IoT), artificial intelligence (AI), and smart infrastructure solutions. The system helps by giving information about real-time water level sensors, AI-driven flood prediction models, automated emergency coordination, and a mobile-based citizen reporting platform. Through cloud-based data processing, predictive analytics, and smart drainage management, this solution aims to enhance early warnings, reduce emergency response time, and improve urban flood resilience. It yields up to an 80% reduction in alert delays, a 50% faster emergency response, and improved community safety. This project seeks collaboration with government agencies, technology firms, and community stakeholders to implement a pilot plan, ensuring a scalable and sustainable flood mitigation strategy for Bengaluru. Full article

Glacier mass loss driven by climate change has increased glacier-related hazards, including glacier debris flows, and poses growing threats to downstream communities. The Badswat Basin in northern Karakoram has experienced repeated glacier debris flows in recent years but lacks systematic disaster analysis and detailed monitoring. This study reconstructs and analyzes three glacier debris flows from 2015, 2018, and 2021 using multi-source remote sensing data and high-resolution DEMs. Results show that three events were triggered by tributary glaciers, with the 2015 event creating the initial dammed lake, and the 2018 and 2021 events further enlarging it (up to 0.72 km² and 40 million m³). These events transported glacier mass downstream, expanded alluvial fans, and caused net glacier erosion. The 2018 event was the most destructive, damaging 75 buildings, flooding 0.28 km² of farmland, and destroying 4.95 km of roads. Analysis suggests that topography influences environmental vulnerability and glacier stability. High temperatures, which accelerate glacier melting, are the primary drivers of the hazard. The bidirectional link between glacier movement and debris flows is a key factor in triggering or intensifying events. Under future climate scenarios, both tributary and main glaciers are expected to continue losing mass, further increasing downstream risks. This study details the evolutionary process of recurring periodic debris flows in the Badswat Basin, providing scientific insights into glacier–landform interactions and hazard management in high-mountain socio-ecological systems. Full article

Autophagy is an evolutionarily conserved cellular process that maintains homeostasis by degrading intracellular materials. Numerous studies have investigated the role of autophagy-related genes (ATGs) in plant adaptation to abiotic stresses. In plants, hypoxia (e.g., flooding events, oxygen supply during growth) rapidly activates the autophagy pathway as a protective mechanism for cell survival. Considering the moisture-loving yet waterlogging-sensitive nature of tea plants, this study explored the role of CsATG8f in the tea plant’s response to submergence. We found that overexpression of CsATG8f formed more autophagosomes than controls under submergence. Furthermore, CsATG8f was confirmed to physically interact with CsRAP2.12. Co-overexpression of both genes partially suppressed transcription of hypoxia-response genes while activating the antioxidant system, thereby enhancing tea plants’ resistance to submergence. Consistent with this, the opposite trend was observed in silenced plants, which attempted to mitigate stress damage by increasing GABA levels in vivo. In conclusion, our study reveals the crucial roles of CsATG8f and CsRAP2.12 in tea plant tolerance to submergence and provides new insights into potential regulatory networks governing tea plant adaptation to flooding. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

Climate-induced flooding is a major issue throughout the globe, resulting in damage to infrastructure, loss of life, and the economy. Therefore, there is an urgent need for sustainable flood risk management. This paper assesses the effectiveness of the hybrid defense system using advanced artificial intelligence (AI) techniques. A data series of energy dissipation (ΔE), flow conditions, roughness, and vegetation density was collected from literature and laboratory experiments. Out of the selected 136 data points, 80 points were collected from literature and 56 from a laboratory experiment. Advanced AI models like Random Forest (RF), Extreme Boosting Gradient (XGBoost) with Particle Swarm Optimization (PSO), Support Vector Regression (SVR) with PSO, and artificial neural network (ANN) with PSO were trained on the collected data series for predicting floodwater energy dissipation. The predictive capability of each model was evaluated through performance indicators, including the coefficient of determination (R²) and root mean square error (RMSE). Further, the relationship between input and output parameters was evaluated using a correlation heatmap, scatter pair plot, and HEC-contour maps. The results demonstrated the superior performance of the Random Forest (RF) model, with a high coefficient of determination (R² = 0.96) and a low RMSE of 3.03 during training. This superiority was further supported by statistical analyses, where ANOVA and t-tests confirmed the significant performance differences among the models, and Taylor’s diagram showed closer agreement between RF predictions and observed energy dissipation. Further, scatter pair plot and HEC-contour maps also supported the result of SHAP analysis, demonstrating greater impact of the roughness condition followed by vegetation density in reducing floodwater energy dissipation under diverse flow conditions. The findings of this study concluded that RF has the capability of modeling flood risk management, indicating the role of AI models in combination with a hybrid defense system for enhanced flood risk management. Full article

Floods rank among the most destructive natural hazards worldwide. In Canada’s capital region—Ottawa and its surrounding areas—flood prediction is crucial, especially in flood-prone zones, to improve flood mitigation strategies, given its historical record-breaking events in 2017 and 2019, which resulted in substantial damage to homes and infrastructure in the region. Previous studies in these regions typically did not use remote sensing techniques or advanced methods to enhance flood susceptibility prediction and extent mapping. This study addressed the gap by incorporating 18 flood conditioning factors and integrating high-performance machine learning algorithms such as Random Forest, Support Vector Machines and XGBoost to develop ensemble flood susceptibility models. The HEC-RAS 2D model was used to simulate hydrodynamic variables based on a 100-year flood scenario. The developed ensemble model for flood susceptibility prediction achieved strong performance (Kappa, F1-score, and AUC all above 0.979) and demonstrated model transferability, maintaining high accuracy (Kappa > 0.850, F1-score > 0.920, AUC > 0.990) when applied to other sub-regions. The hydraulic model reveals that flood velocity and depth differ across sub-regions, reaching maximums of 15 m/s and 15 m, respectively. SHAP analysis indicates Elevation, Handmodel, MNDWI, NDWI, and Aspect are key factors influencing floods. These findings and methods help Natural Resources Canada develop tools and policies for effective flood risk reduction in the Ottawa River watershed and similar regions. Full article

Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical metrics—including bias ratio, mean error, and correlation coefficient—as well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry regions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of precipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety. Full article