Search Results (155)

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

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

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

Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters. Full article

Intensity–Duration–Frequency (IDF) curves are essential tools in the design of stormwater management systems and are often used over long periods without frequent updates. However, the continuous collection of rainfall data and the expansion of monitoring networks call for regular revisions of these curves. In Romania, current engineering and hydrological practices still rely on regionalized IDF graphs developed in 1973. Given the ongoing effects of climate change—particularly the increased frequency and, more significantly, intensity of extreme rainfall events—updating these curves has become critical. Incorporating recent observations is essential not only for methodological accuracy but also to support climate-resilient infrastructure design. This study employs updated IDF curves provided by the National Administration of Meteorology, based on 30 years of precipitation records from 68 meteorological stations across Romania. The main objective is to evaluate alternative regionalization approaches—including clustering methods, geographic proximity analysis, and hourly precipitation isolines for a 1:10 Annual Exceedance Frequency—to develop a new regionalization model and the corresponding nationwide IDF relationships. A comparative analysis using raster-based regional rainfall datasets from both the 1973 and 2025 regionalizations revealed significant changes in precipitation patterns. Short-duration rainfall events (5, 10, and 30 min) have increased in intensity across most regions, while long-duration events (3, 6, 12, and 24 h) have generally decreased in magnitude in several areas. These findings highlight a growing trend toward more intense short-term convective storms, underlining the urgent need for improved flash flood prevention and urban stormwater management strategies. Full article

Against the backdrop of increasingly evident climate change and frequent extreme weather events, flash floods have emerged as a major challenge for flood disaster prevention and mitigation in China. Flash flood early warning systems are crucial means to address this challenge, primarily comprising rainfall-based warnings (RW) and discharge-based warnings (DW). To support precise flash flood warnings, this study compares the effectiveness of RW and DW and summarizes their applicable scenarios through both case study analysis and model simulations. The results demonstrate that DW outperforms RW under the following scenarios: ① During persistent moderate-intensity rainfall events when antecedent soil moisture is moderate to high, RW is prone to missed or delayed warnings. ② When rainfall exhibits significant spatial heterogeneity, RW tends to produce false alarms. Conversely, RW outperforms DW in the following scenarios: ① For localized short-duration heavy rainfall events, DW is prone to missed or delayed warnings. ② In basins where numerous small- and medium-sized reservoirs exist upstream without operational data, DW is prone to false alarms. ③ When sparse or unevenly distributed rain gauges result in poor representativeness of areal rainfall, DW is prone to missed warnings. To enhance flash flood disaster management, future warning systems should integrate both RW and DW approaches to deliver more timely, reliable, and scientifically grounded warning information for local authorities. Full article

As global warming intensifies, extreme weather phenomena such as heatwaves, flash droughts, torrential floods, cold waves, and blizzards are becoming increasingly frequent. Against this backdrop, traditional static food security assessment methods fail to capture the dynamic transmission patterns of agricultural productivity risks and their regional heterogeneity. Therefore, it is imperative to reconstruct a resilience analysis paradigm for food production systems, dynamically investigate the mechanisms through which climate change affects China’s agricultural productivity and discern the interactive effects between technological evolution and climate constraints. This will provide theoretical foundations for building a climate-resilient food security system. Accordingly, this study establishes a multidimensional resilience measurement index system for China’s grain productivity by integrating agricultural factor elasticity analysis with disaster impact response modeling. Through production function decomposition and hybrid forecasting models, we reveal the evolutionary patterns of China’s grain productivity under climate risk shocks and trace the transmission pathways of risk fluctuations. Key findings indicate the following: (1) Extreme climate events exhibit significant negative correlations with grain production, with drought and flood impacts demonstrating pronounced regional heterogeneity. (2) A dynamic game relationship exists between agricultural technological progress and climate risk constraints, where the marginal contribution of resource efficiency improvements to productivity growth shows diminishing returns. (3) Climate-sensitive factors vary substantially across agricultural zones: Northeast China faces dominant cold damage, North China experiences drought stress, while South China contends with humid-heat disasters as primary regional risks. Consequently, strengthening foundational agricultural infrastructure and optimizing regionally differentiated risk mitigation strategies constitute critical pathways for enhancing food security resilience. (4) Future research should leverage higher-resolution, county-level data and incorporate a wider range of socio-economic variables to enhance granular understanding and predictive accuracy. Full article

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

Assimilation of weather radar data, as well as additional data like lightning data, in high-resolution weather forecast models is a promising method to improve short-term forecasting (nowcasting) of flash-flood events. A data assimilation system based on the Weather Research and Forecasting model is used in this study, with radar reflectivity and radial velocity data collected with X-band Doppler polarimetric radar in the area of Athens, Greece, and lightning observations obtained from a lightning detection network covering Greece. Radar data are assimilated with the four-dimensional variational method, which includes a full-hydrometeor assimilation scheme, in a nested domain of the model with a resolution of 3 km. Humidity, vertical velocity and horizontal wind divergence profiles estimated from lightning data are assimilated with a three-dimensional variation method in the parent domain of the model with a resolution of 9 km. The results from a case study are presented to show the effect of assimilating each type of data. Full article

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

Urban flooding represents a growing global concern, especially in areas with rapid urbanization, unregulated urban sprawl and climate change conditions. Conventional flood modeling approaches do not effectively capture the complex dynamics between natural watershed behavior and urban infrastructure; they typically simulate these domains in isolation. This study introduces the Watershed-BIM methodology, a three-dimensional simulation framework that integrates Building and City Information Modeling (BIM/CIM), Geographic Information Systems (GIS), Flood Risk Assessment (FRA), and Flood Risk Management (FRM) into a single framework. Autodesk InfraWorks 2024, Civil 3D 2024, and RiverFlow2D v8.14 software are incorporated in the development. The methodology enhances interoperability and prediction accuracy by bridging hydrological processes with detailed urban-scale data. The framework was tested on a real-world flash flood event in Mandra, Greece, an area frequently exposed to extreme rainfall and runoff events. A specific comparison with observed flood characteristics indicates improved accuracy in comparison to other hydrological analyses (e.g., by HEC-RAS simulation). Beyond flood depth, the model offers additional insights into flow direction, duration, and localized water accumulation around buildings and infrastructure. In this context, integrated tools such as Watershed-BIM stand out as essential instruments for translating complex flood dynamics into actionable, city-scale resilience planning. Full article

The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article

Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article

Landslide hazard chains pose significant threats in mountainous areas worldwide, yet their cascading effects remain insufficiently studied. This study proposes an integrated framework to systematically assess the landslide-landslide dam-outburst flood hazard chain in mountainous river systems. First, landslide susceptibility is assessed through a random forest model incorporating 11 static environmental and geological factors. The surface deformation rate derived from SABS-InSAR technology is incorporated as a dynamic factor to improve classification accuracy. Second, motion trajectories of rock masses in high-risk zones are identified by Rockfall Analyst model to predict potential river blockages by landslide dams, and key geometric parameters of the landslide dams are predicted using a predictive model. Third, the 2D HEC-RAS model is used to simulate outburst flood evolution. Results reveal that: (1) incorporating surface deformation rate as a dynamic factor significantly improves the predictive accuracy of landslide susceptibility assessment; (2) landslide-induced outburst floods exhibit greater destructive potential and more complex inundation dynamics than conventional mountain flash floods; and (3) the outburst flood propagation process exhibits three sequential phases defined by the Outburst Flood Arrival Time (FAT): initial rapid advancement phase, intermediate lateral diffusion phase, and mature floodplain development phase. These phases represent critical temporal thresholds for initiating timely downstream evacuation. This study contributes to the advancement of early warning systems aimed at protecting downstream communities from outburst floods triggered by landslide hazard chains. It enables researchers to better analyze the complex dynamics of such cascading events and to develop effective risk reduction strategies applicable in vulnerable regions. Full article

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems. Full article