Search Results (2,981)

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 10⁶ m³, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 10⁶ m³ and 1 × 10⁶ m³) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management. Full article

Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R² = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings. Full article

The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters, which account for the largest proportion of flood-related fatalities in China. Flash flood regionalization, which divides a region into homogeneous subdivisions based on flash flood-inducing factors, provides insights for the spatial distribution patterns of flash flood risk, especially in ungauged areas. However, existing methods for flash flood regionalization have not fully reflected the spatial topology structure of the inputted geographical data. To address this issue, this study proposed a novel framework combining a state-of-the-art unsupervised Graph Neural Network (GNN) method, Dink-Net, and Shapley Additive exPlanations (SHAP) for flash flood regionalization in the HMR. A comprehensive dataset of flash flood inducing factors was first established, covering geomorphology, climate, meteorology, hydrology, and surface conditions. The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. The SHAP model was then applied to quantify the impact of all the inducing factors on the regionalization results by Dink-Net. The newly developed framework captured the spatial interactions of the inducing factors and characterized the spatial distribution patterns of the factors. The unsupervised Dink-Net model allowed the framework to be independent from historical flash flood data, which would facilitate its application in ungauged mountainous areas. The impact analysis highlights the significant positive influence of extreme rainfall on flash floods across the entire HMR. The pronounced positive impact of soil moisture and saturated hydraulic conductivity in the areas with a concentration of historical flash flood events, together with the positive impact of topography (elevation) in the transition zone from the Qinghai–Tibet Plateau to the Sichuan Basin, have also been revealed. The results of this study provide technical support and a scientific basis for flood control and disaster reduction measures in mountain areas according to local inducing conditions. Full article

Bedding rock landslides, characterized by their distinct geological structure, are widely distributed and highly susceptible to sliding under external disturbances, resulting in catastrophic events. This study aims to unravel the geomechanical mechanisms governing rainfall-induced instability through an integrated investigation of a representative landslide in Xing’an Village, Chongqing. Employing multidisciplinary approaches, including field monitoring, geotechnical testing, and dynamic numerical modeling, we systematically revealed two critical failure zones: a front failure zone and a rear potential instability zone. Under rainstorm conditions, the safety factor for both zones was 1.02, indicating a marginally unstable state. The DAN-W simulations indicate that the potential instability zone at the rear of the landslide experienced complete failure within 12 s under heavy rainfall, with a maximum run-out distance of 20 m, a maximum velocity of 4.32 m/s, and a maximum deposition thickness of 8.3 m, which could potentially bury the buildings at the toe of the landslide. The low strength and permeability of the mudstone-dominated Badong Formation, characterized by interbedded mudstone, siltstone, and sandstone within the Middle Triassic geological system, provides a fundamental prerequisite for the landslide. Rainwater infiltration into the mudstone layers degraded its mechanical properties, and excavation at the slope base ultimately triggered the landslide initiation. These findings can provide theoretical support for preventing and managing similar bedding rock landslides with similar geological backgrounds. Full article

Flood has become a major hazard globally, and in Bhutan, with its steep terrain and erratic rainfall, it has caused significant economic damage in recent years. Given these challenges, there is a lack of accurate flood prediction and management strategies. In this study, therefore, we evaluated three hydrological models—Integrated Flood Analysis System (IFAS), Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS), and Group on Earth Observation Global Water Sustainability (GEOGloWS)—and identified the most suitable model for simulating flood events in the Wangchu River Basin in Bhutan. Furthermore, we examined the models’ performance in a large and a small basin using the Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Peak Flow Error (PFE) metrics. Overall, the GEOGloWS model demonstrated the highest accuracy in simulating flood in the large basin, achieving NSE, PBIAS, and PFE values of 0.93, 3.21%, and 4.48%, respectively. In the small basin, the IFAS model showed strong performance with an NSE value of 0.84. The GEOGloWS model provides simulated discharge but needs to be bias corrected before use. The calibrated parameters can be used in the IFAS and HEC-HMS models in future studies to simulate floods in the Wangchu River Basin and adjacent basins with similar geographical characteristics. Full article

This article aims to analyze the use of a bike-sharing system (BSS) during the flooding event caused by extreme rainfall that hit the municipality of Porto Alegre, Brazil, in May 2024. Public transport services were interrupted, prompting an investigation into the resilience of the BSS during the crisis. Considering data from the Tembici BSS company, a set of approximately 400,000 trips made between 104 stations in the municipality of Porto Alegre from January to May 2024 were analyzed. Daily rainfall data from the National Institute of Meteorology (INMET) were compared with the daily trip flow to identify the travel flow patterns on the days most affected by the flooding. The results indicate an abrupt drop in shared bicycle use during May 2024, but 7600 trips were recorded despite the crisis. Regarding the travel pattern between 1 May and 10 May, most trips were still for recreational purposes (73%), while trips for work and study accounted for 22% of the total, and only 5% were for delivery services. Overall, the resilience of the BSS during the extreme climate event in question points to the continuation of practical daily activities, although with more significant effects on economic-related activities and lesser effects on leisure-related activities. Full article

Urban flooding, driven by extreme rainfall events and urbanization, poses substantial risks to urban safety and infrastructure. This study employed a neighborhood-scale InfoWorks ICM model to analyze the full-process impacts of urban flooding under six rainfall return periods in Haining, China. The results reveal distinct non-linear responses from the 3-year to 50-year rainfall return period: (1) the surface runoff volume increases by 64.3%, with peak timing advancing by about one minute; (2) the overflow nodes rise from 37.35% to 63.24%, with durations over 30 min increasing by 78.6%; (3) the inundation areas expand by 164.9%, with maximum depths increasing by 0.31 m, showing significant regional disparities; and (4) high-risk zones, such as Haining People’s Square and Railway Station, require targeted interventions due to severe surface overflow and inundation. This comprehensive analysis emphasizes the need for tailored and phased flood prevention measures that address each stage of urban flooding. It provides a strong framework to guide urban planning and enhance resilience against rainfall-induced urban flooding. Full article

Climate change, particularly extreme rainfall, imposes stress on plants, which can be assessed using fluctuating asymmetry (FA) in leaves and key leaf traits. FA, which is defined as random deviations in symmetrical structures, is a known bioindicator of environmental stress. Additionally, leaf area (LA) and specific leaf area (SLA) provide insights into plant responses to stressors. Mangrove plants have several mechanisms to cope with constant flooding and rainy periods. However, under extreme rainfall conditions, their adaptive capacity may be overwhelmed and plants may experience developmental stress. Nonetheless, it has not yet been verified whether plants subjected to drastic increases in rainfall exhibit more asymmetric leaves. We investigated seasonal differences in FA in Laguncularia racemosa after an extreme rainfall event and found a significant increase in FA after the rainfall event (t = 1.759, df = 149, p = 0.08) compared with the dry season. Concurrently, LA increased by 28% (p < 0.01) and SLA increased by 33% (p < 0.01) after the rainfall event. During the dry season, the plants exhibited antisymmetry rather than FA, highlighting their distinct responses to seasonal stressors. These findings demonstrate the differential effects of rainfall extremes on leaf traits and asymmetry, positioning FA, LA, and SLA as mangrove stress indicators. Full article

This study investigates groundwater flow patterns in a landslide area above the settlement of Koroška Bela in NW Slovenia using a series of tracer tests with sodium chloride (NaCl) and fluorescein (uranine). The tracer experiments, using a combination of pumping tests and continuous groundwater observations, reveal two distinct groundwater flow horizons within the landslide body: a prevailing shallower flow within highly permeable gravel layers and a slower deep flow in the weathered low-permeability clastic layers. Uranine injections suggest longer retentions, indicating complex hydrogeological conditions. Groundwater is recharged by the infiltration of precipitation and subsurface inflow from the upper-lying carbonate rocks. In the upper landslide, highly permeable gravel layers accelerate flow, especially during heavy rainfall, while downstream interactions between permeable gravel and less permeable clastic materials create local aquifers and springs. These groundwater dynamics significantly influence landslide stability, as rapid infiltration during intense precipitation events can lead to transient increases in pore water pressure, reducing shear strength and potentially triggering slope movement. Meanwhile, slow deep flows contribute to prolonged saturation of critical failure surfaces, which may weaken the landslide structure over time. The study emphasizes the region’s geological heterogeneity and landslide stability, providing valuable insights into the groundwater dynamics of this challenging environment. By integrating hydrogeological assessments with engineering measures, the study provides supportive information for mitigating landslide risks and improving groundwater management strategies. Full article

Between 11 and 14 August 2017, the southern belt of the central Himalaya experienced extreme precipitation, with some stations recording more than 500 mm of accumulated rainfall, which resulted in widespread, devastating flooding. Precipitation was concentrated over the sub-Himalaya, and the established forecasting systems failed to predict the event. In this study, we evaluate the performance of six cloud microphysics schemes in the Weather Research and Forecasting (WRF) model forced with the advanced ERA5 dataset. We also examine the importance of the cumulus scheme in WRF at 3 km horizontal grid spacing in highly convective events like this. Six WRF simulations, each with one of the six different microphysics schemes with the Kain–Fritsch cumulus scheme turned off, all fail to reproduce the spatial variability of accumulated precipitation during this devastating flood-producing precipitation event. In contrast, the simulations exhibit greatly improved performance with the cumulus scheme turned on. In this study, the cumulus scheme helps to initiate convection, after which grid-scale precipitation becomes dominant. Amongst the different simulations, the WRF simulation using the Morrison microphysics scheme with the cumulus turned on displayed the best performance, with the smallest normalized root mean square error (NRMSE) of 0.25 and percentage bias (PBIAS) of −6.99%. The analysis of cloud microphysics using the two best-performing simulations reveals that the event is strongly convective, and it is essential to keep the cumulus scheme on for such convective events and capture all the precipitation characteristics showing that in regions of extreme topography, the cumulus scheme is still necessary even down to the grid spacing of at least 3 km. Full article

In recent years, the combined effects of rapid urbanization and climate change have led to frequent floods in urban areas. Rainstorm flood risk warning systems play a crucial role in urban flood prevention and mitigation. However, there has been limited research in China on nationwide urban flood risk warning systems based on rainfall predictions. This study constructs a two-level early warning system (EWS) at the national and urban levels using a two-dimensional hydrological–hydrodynamic model considering infiltration and urban drainage standards. A methodology for urban rainstorm flood risk warnings is proposed, leveraging short-term and high-resolution rainfall forecast data to provide flood risk warnings for 231 cities in central and eastern China. Taking Beijing as an example, a refined rainstorm flood warning technique targeting city, district, and street scales is developed. We validated the methodology with monitoring data from the “7.31” rainstorm event in 2023 in Beijing, demonstrating its applicability. It is expected that the findings of this study will serve as a valuable reference for the urban rainstorm flood risk warning system in China. Full article

In this study, an ANN-derived innovative model was developed for estimating the failure soil depths of rainfall-induced shallow landslide events, named the SM_EFD_LS model. The proposed SM_EFD_LS model was created using the modified ANN model via the genetic algorithm calibration approach (GA-SA) with multiple transfer functions (MTFs) (ANN_GA-SA_MTF) with a significant number of failure soil depths and corresponding rainfall factors. Ten shallow landslide-susceptible spots in the Jhuokou watershed in southern Taiwan were selected as the study area. The associated 1000 simulations of rainfall-induced shallow landslide events were used in the model’s development and validation. The model validation results indicate that the validated failure soil depths are mainly located within the resulting 60% confidence intervals from the proposed SM_EFD_LS model. Moreover, the estimated failure depths resemble the validated ones, with acceptable averages of the absolute error (RMSE) and relative error (MRE) (11 cm and 0.06) and a high model reliability index of 1.2. In the future, the resulting probabilistic-based failure soil depths obtained using the proposed SM_EFD_LS model could be introduced with the desired reliability needed for early landslide warning and prevention systems. Full article

Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m³, PM2.5 > 30 μg/m³), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m³, PM2.5 > 20 μg/m³) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events. Full article

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting. Full article

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts. Full article