Search Results (688)

A significant portion of Europe is prone to flooding, including severe events occurring over very small areas. Recent flood hazard mapping methods can cover large regions, but often fail to capture processes driven by small streams or direct rainfall. This study presents the authors’ experience in the application of a fully hydrodynamic model over an entire territory, with direct rainfall input (rain-on-grid approach at the basin scale). The case study is the Neto River basin in Calabria (Italy), covering approximately 1000 km², a region that represents an ideal natural laboratory for investigating flash flood processes in Europe. Simulations were carried out using the TUFLOW 2D commercial modelling tool. A key objective is to demonstrate that the Chicago hyetograph enables a constant return period across the entire domain. Additionally, specific procedures are proposed to represent numerous minor crossings (e.g., small bridges, culverts, and road and railway underpasses) and dam outlets without refining the computational grid or abandoning the Shallow Water Equations (SWE). This approach allows identification of major river floods, flash floods, runoff-related hydraulic effects, and pluvial flooding. Results show that the fully hydrodynamic rain-on-grid model is highly effective for flood hazard mapping, with strong agreement between simulations and observed events, confirming its predictive reliability and enabling high-resolution, comprehensive territorial analysis. Full article

Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we identified and characterized warm rain cases, and second, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, and weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern. Full article

Flood and flash flood events can generate severe hydraulic actions on civil structures, requiring modeling strategies able to link flow features to structural damage. This paper proposes a two-scale numerical framework based on advanced finite element modeling to assess the vulnerability of structures subjected to inundation and flood-driven impact. At the macroscale, the flood propagation and the interaction with the built environment are simulated through the depth-averaged Shallow Water Equations, adopting a time-explicit interface treatment to capture the evolution of the free surface. The macroscale model provides time-dependent water depth and flow velocity along the external surfaces of the structure, which are then used to derive hydrostatic and hydrodynamic actions, also in comparison with code-based formulations. At the mesoscale, these actions are transferred to a detailed structural model to investigate the nonlinear mechanical response of the building. Structural components are described through a coupled damage–plasticity constitutive law, enabling the prediction of stiffness degradation, cracking-driven damage patterns, and the identification of the most critical structural zones under flood loading. The proposed workflow is finally applied to a real structure located in the municipality of Cosenza (Italy), demonstrating the capability of the approach to combine hydraulic intensity measures with physics-based structural damage assessment, supporting scenario analyses and risk mitigation evaluations. Full article

Intensifying hydroclimatic extremes have heightened the need for basin-scale indicators of antecedent wetness that are relevant to flood responses. However, ground-based soil-moisture observations are spatially sparse, and satellite products frequently exhibit temporal gaps. To address this limitation, this study integrated satellite- and ground-soil-moisture observations, hydro-meteorological variables, and observed streamflow data from 2018 to 2024 across 26 standard basins (SBs) within three dam basin regions in South Korea: the Nam River Dam (NGD) and the upstream and downstream regions of the Seomjin River Dam (SJD). Using this integrated dataset, we quantified the relationships among precipitation, basin wetness, and rapid discharge increases, subsequently deriving composite thresholds for flood early warnings. For each SB, we trained a Random Forest regression model using satellite-soil-moisture and basin-representative hydro-meteorological inputs—including 1-day accumulated precipitation (P_1d), 7-day accumulated precipitation (P_7d), the antecedent precipitation index (API), and related meteorological variables—to estimate a continuous, daily basin-representative soil-moisture series (SM_RF). Validation results indicated that the coefficient of determination (R²) ranged from 0.6 to 0.7 for most SBs. Extreme event days were consistently associated with elevated values of SM_RF, P_1d, P_7d, and API, demonstrating that antecedent wetness significantly influences the likelihood of rapid discharge events. Finally, composite threshold scanning yielded candidate rules characterized by high precision, moderate hit rates, and low false-alarm rates, confirming the efficacy of the proposed framework for developing flash-flood early-warning thresholds in South Korean dam basins. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

This study presents a two-dimensional hydraulic modelling framework for the simulation of flash and compound flooding in coastal urban areas with non-perennial river systems. The model employs a rain-on-grid approach within HEC-RAS v6.7 beta5 (2D solver) to simulate rainfall-driven runoff and explicitly incorporates coastal water-level forcing to represent storm tides. The framework is applied to an ungauged coastal basin in northern Greece using a 50-year return period design storm. Model results show good agreement with official Flood Risk Management Plan maps while identifying additional inundated areas linked to lower-order streams. Compound flooding simulations indicate a 21% increase in flooded areas, highlighting the importance of integrated modelling for operational flood forecasting. Full article

Compound geological disaster chains pose major challenges for disaster prevention in mountainous regions due to their complex mechanisms and cascading impacts. This study investigates a landslide–debris flow–flash flood hazard chain that occurred on 21 July 2024 in the Xujia River catchment, Mianning County, Sichuan Province, China. This event is used as a representative case to improve the understanding of the formation and amplification mechanisms of breach-type debris flows through dynamic inversion constrained by sedimentary records. The objective is to reconstruct the evolution of the event and assess its downstream hazard extent. Post-disaster sedimentary and geomorphological records, including deposit distribution, channel aggradation, and flow traces, were systematically analyzed based on remote sensing interpretation, unmanned aerial vehicle surveys, and detailed field investigations. These sedimentary data were used as key constraints to estimate debris flow magnitude and mobility under different rainfall scenarios. A rainfall flood scenario-based estimation method was applied to quantify debris flow magnitude, and numerical simulations were conducted using the Rapid Mass Movement Simulation model to reproduce debris flow propagation and deposition processes. The results indicate that prolonged antecedent rainfall triggered slope failure in a tributary, leading to the accumulation of landslide-derived material and the formation of a temporary channel blockage. The subsequent breach of this blockage significantly amplified debris flow discharge, velocity, and sediment outflow, resulting in downstream hazard expansion. Simulation results constrained by sedimentary evidence show that peak discharge and solid material output under breach conditions were approximately three times higher than those of rainfall-driven scenarios under comparable rainfall frequencies. These findings demonstrate that sedimentary records provide critical constraints for the inversion of landslide debris flow disaster chain dynamics and highlight the effectiveness of post-disaster evidence based numerical assessment for hazard analysis and risk mitigation in debris flow-prone mountainous catchments. Full article

Effective mitigation of urban flash floods under extreme rainfalls requires integrated hydrologic monitoring and rapid response mechanisms. The study presents an adaptive flood response framework. It combines real-time rainfall forecasting, CCTV-based flood image classification, drainage network water level monitoring, pumping machinery operations, and automated response controls. The adaptive strategy is structured into three phases to support real-time decision-making: (1) atmospheric sensing and pre-alert actions, (2) subsurface drainage system monitoring and alert activation, and (3) surface run-off detection and response. Over three years of implementation in New Taipei City, the adapted strategy achieved an over 80% success rate in preventing street inundation during intense rainfall events (>25 mm per 10 min). By integrating ensemble modeling, remote sensing, and decision-support tools, the platform transforms climate-induced flood risks into opportunities for resilience. Full article

During seismic exploration, seismic data is collected to determine underground structural features and hydrocarbon-bearing stratum interfaces. The seismic data inversion process is highly complex and susceptible to interference from noise, which may lead to significant errors in inversion and affect comprehensive stratigraphic interpretation. The application of machine learning to seismic data interpretation and denoising remains technically challenging and yields suboptimal results. Micromotion exploration technology employs conventional “noise” as its signal source, utilizing widely occurring regular noise. On the basis of the theory of stationary random processes, it extracts frequency curves of surface waves from micromotion signals and performs inversion to obtain underground shear wave velocity profiles. Owing to its simplicity, cost-effectiveness, and environmental friendliness, micromotion exploration has notable advantages in structural exploration and hydrocarbon discovery. The micromotion detection results of an experimental area can quickly reflect the location of fault zones. When combined with electrical logging, this method is effective for shallow gas reservoir structure detection. Full article

Flooding is one of the major natural disasters that have a major impact on urban areas due to the increasing intensity of factors like extreme weather conditions, climate change, and unplanned urbanization. Considering Cook County, Illinois, the rapid development of the region, flat topography, and the induced rainfall extremes from climate change increase the potential risk of flooding when interacting with dense urban exposure and infrastructure. This study employed the Frequency Ratio (FR) model in a GIS environment to create a high-resolution flood susceptibility map of the county. The map was developed using 281 historical flood points collected from several authoritative sources, such as National Oceanic and Atmospheric Administration (NOAA) Storm Events Database records, Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM)-based FIRMette products, and U.S. Geological Survey (USGS) flood-inundation studies. Thirteen conditioning factors, including land use, elevation, slope, soil drainage, rainfall, and distance to the stream, were used to calculate FR values and to develop the Flood Susceptibility Index (FSI). The resulting FSI was grouped into four susceptibility zones: low, medium, high, and very high. The findings indicated that more than 64% of Cook County has a high and very high risk of flood susceptibility, particularly in the vicinity of major river corridors. The model was validated using testing data with a 91.4% prediction accuracy, which also demonstrated the reliability and applicability of the FR model in the urban flood susceptibility assessment. The map serves as a valuable tool for risk-based urban planning and design of flood mitigation infrastructure in one of the most populated counties in the United States. Full article

Changes in land use and land cover (LULC) are crucial indicators to consider when examining various environmental challenges and assessing the sustainability of rapidly transforming landscapes. Land utilization in arid regions results from a diverse range of socioeconomic activities that reshape urban and regional environments. Using remote sensing and geographic information systems (GISs), the authors investigate the evolving and sustainability-sensitive landscape of the Al-Kawamel area, southwest of Sohag City, Egypt. Three time series of Landsat imagery, from 1985, 2005, and 2025, were used to map major LULC categories and evaluate their transformations with respect to elevation and slope. Based on the data analysis, the results reveal substantial shifts over the 40-year period in this low desert zone. During this time, the built-up areas and the agricultural lands expanded from 8 to 64 km² and from 10 to 131 km², respectively. Conversely, the desert zone declined from 325 to 148 km². These essential changes reflect intensified human activities and land reclamation. These rapid shifts increase exposure to natural and man-made hazards, including karstification, sand accumulations, rockfalls, flash floods, problematic soils, heavy metal hazards from wastewater disposal sites, and abandoned pits. Accordingly, suitable remediation methods should be assigned to minimize their impact. Full article

Flash floods in arid regions dictate a rapid flood inundation mapping for early warning. However, hydrodynamic models, such as HEC-RAS, provide accurate flood mapping but require extensive topographical data and high computational resources. The GeoFlood method offers a rapid alternative for early warning relying on terrain-driven framework and simple hydraulics. This study examined GeoFlood applicability on two arid catchments and tested its sensitivity for different return periods, Manning coefficients, and wadi length segmentations. The original GeoFlood method showed good consistency with HEC-RAS in well-defined wadis but relatively poor performance in flat areas, with segmentation and slope calculation significantly affecting GeoFlood accuracy and robustness. To overcome these limitations, slope calculation was improved using the Theil–Sen trend, and segmentation was automated using the penalized cost approach Continuous Piecewise Optimal Partitioning (CPOP) to detect slope breakpoints. CPOP provides superior and robust performance without prior knowledge of the best segmentation lengths, producing smoother slopes at accurate breakpoints with a Fowlkes–Mallows (FM) index of 0.88 in flat areas and an error bias of 1.05 compared to a variable FM from 0.72 to 0.88 and an error bias from 0.81 to 1.3 for the original GeoFlood. The enhanced GeoFlood provides reliable robust results in arid regions when data are scarce. Full article

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments. Full article

Huizhou covered bridges represent a unique and irreplaceable component of China′s architectural heritage, yet they are increasingly threatened by flash floods. In the Huizhou region, complex mountainous terrain, concentrated intense rainfall, and structural aging jointly exacerbate flood damage risks. Existing flood risk assessment approaches often prioritize external hydrodynamic hazards or assume linear additive effects, overlooking the complex interactions among inherent structural and physical attributes. To address this limitation, this study integrates Random Forest (RF) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to develop a flood risk assessment framework capable of capturing both nonlinear relationships and configurational (asymmetric) causal mechanisms. Based on field investigations of 89 covered bridges and 116 documented damage cases from 2020 to 2024, the RF model identifies six key risk factors (ACC = 0.79, AUC = 0.87), several of which exhibit pronounced nonlinear and threshold effects. Building on these results, fsQCA further reveals eight equivalent configurational pathways leading to covered bridge damage (solution coverage = 0.66, solution consistency = 0.94), highlighting multiple causal combinations rather than a single dominant driver. The results demonstrate that the disaster resilience of covered bridges emerges from interactions among structural characteristics, management conditions, and spatial scale attributes, rather than from any individual factor alone. Accordingly, this study advocates a shift in protection strategies from conventional “one-size-fits-all” structural reinforcement toward risk-pattern-oriented, precision-based non-structural interventions. By combining predictive modeling with configurational causal analysis, this research provides a system-level understanding of flood-induced damage mechanisms and offers actionable insights for flood risk mitigation and sustainable conservation of covered bridge heritage in Huizhou and comparable regions worldwide. Full article