Search Results (605)

Quantitative risk assessment of flash floods is crucial for developing disaster prevention and mitigation strategies. This study developed a refined framework that innovatively integrates field-validated data from Chongqing’s flash flood disaster investigation project with AHP, factor analysis, and cluster analysis to quantify hazard, vulnerability, resistance, and risk indicators at a 30 m grid. Unlike existing coarse-scale assessments that rely on generic indicators, this hybrid model, calibrated by observed disaster evidence, significantly enhanced the local relevance and reliability of risk zoning. The validity of this framework was confirmed through validation against objective weighting methods and historical flash flood locations. The results indicated that the risk value of flash floods in Chongqing was between 0.24 and 0.69, with extremely high-risk and high-risk zones covering 42,388 km² (51.47%) of the study area. This accurately identifies areas at high risk of flash floods and provides a basis for government decision-making regarding priority areas for disaster risk reduction investments. Verification showed that 83.44% of historical disaster points fall within medium-risk or above zones, confirming the framework’s accuracy in identifying flood-prone hotspots and providing actionable support for targeted early warning and resource allocation. Full article

South Africa is highly susceptible to climate variability and long-term climatic shifts, necessitating a comprehensive understanding of changing extreme precipitation patterns to guide effective mitigation and adaptation responses. This study examined variations in extreme precipitation indices from 1981 to 2023 across the eastern Free State Province using daily rainfall records derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Ten extreme precipitation indices were evaluated, with trend detection conducted through the Innovative Trend Analysis (ITA) technique. Findings indicate that the majority of municipalities exhibited statistically significant declining trends (p < 0.05) in total wet-day precipitation (PRCPTOT), R99P, R95P, the Simple Daily Intensity Index (SDII), CDD, RX5day, R20mm, and R10mm, suggesting an overall reduction in both heavy and moderate rainfall occurrences. In contrast, significant upward trends (p < 0.05) were identified in CWD, and RX1day, reflecting a shift toward prolonged wet periods and more intense short-duration rainfall events. Taken together, these divergent patterns point to the simultaneous emergence of heightened drought vulnerability driven by reduced cumulative rainfall and increased flood risk linked to intensified precipitation extremes. These results underscore the importance of forward-looking, climate-resilient water resource management and context-specific adaptation strategies suited to the eastern Free State’s complex mountainous terrain. Full article

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article

Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon $h\ge 2$, with $R^2=0.576\pm 0.007$ and RMSE $=15.08\pm 0.07$ mm/day at $h=4$. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at $h=1$), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits ($R^2\in [0.576,0.590]$). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article

Rapid peri-urban expansion has intensified flood risk in Southeast Asian cities through wetland loss, rural–urban migration, and delayed infrastructure development. This study examines the spatial and temporal dimensions of flood risk in Phnom Penh, Cambodia using a multi-factor framework based on hazard, exposure, vulnerability, and coping capacity. Vulnerability and coping capacity are analysed at both community and household levels. Migrant settlement duration captures differences in exposure and adaptive capacity over time. A composite flood risk index is constructed from survey data using the Rank Order Centroid weighting method. Results show that exposure is the dominant driver of flood risk, exceeding the influence of hazard intensity and largely shaping spatial patterns. Community-level vulnerability and coping capacity exert stronger effects than household-level characteristics, highlighting the importance of infrastructure and local settings. Flood risk varies across migrant groups: new migrants face the highest risk due to elevated exposure and vulnerability, while long-term migrants experience lower risk as adaptive capacity improves over time. However, risk reduction varies across groups, with persistent challenges linked to infrastructure and disaster preparedness systems. These findings highlight the importance of community-scale resilience strategies and targeted infrastructure investment to reduce flood risk in rapidly urbanising cities. Full article

Low-altitude UAV scheduling and planning has become a critical technological pillar in disaster response systems; however, systemic challenges in complex environments and under uncertain risk conditions remain insufficiently understood. Although substantial progress has been achieved in model formulation and algorithm design in recent years, scheduling and planning frameworks still lack a systematic representation of key risk factors, such as meteorological disturbances, terrain damage, and communication constraints, thereby undermining operational safety and decision reliability. This study conducts a systematic review of low-altitude UAV scheduling and planning research over the past decade, covering representative disaster scenarios including forest fires, large building fires, earthquakes, floods, major public health emergencies, and traffic accidents. By comparatively analyzing scheduling objectives and technical pathways across the pre-disaster, during-disaster, and post-disaster stages, this paper summarizes the dominant research paradigms and limitations of multi-UAV coordination, air–ground coordination, and risk reduction-oriented scheduling and planning. This review reveals that existing approaches generally lack explicit modeling of dynamic risks and uncertainties, highlighting an urgent need to incorporate risk-aware considerations and reliability analysis frameworks into scheduling and planning to enhance the overall robustness and decision credibility of UAV systems in disaster environments. Full article

Flood-prone riverfront zones face increasing challenges due to climate change, urbanisation, and legacy industrial development. Riverfront regeneration presents a unique opportunity not only to restore ecological function and public amenity but also to integrate adaptive architectural strategies that enhance flood resilience. This study aims to investigate the interplay between riverfront regeneration and adaptive architectural planning in flood-prone areas. This study provides a framework for understanding how built form, landscape infrastructure, and socio-spatial systems were developed to mitigate flood risk while reactivating riverfronts. Through a literature review and a methodology that integrates comparative case study analysis with generative scenario modelling, key design typologies were identified, including amphibious buildings, multifunctional embankments, and dynamic land-use zoning, and their performance was evaluated in terms of flood risk reduction, amenity provision, and community resilience. Based on the results, recommendations are proposed for practitioners and policymakers on advancing integrated riverfront regeneration in flood-prone regions, emphasising the necessity of multi-stakeholder governance, adaptable architectural strategies, and nature-based infrastructure. Full article

Pluvial floods can cause severe socio-economic impacts on coastal urban areas like the Metropolitan Area of Barcelona. This study combined the development of high-resolution flood maps, based on a large-scale coupled 1D/2D model and empirical functions, to quantify direct economic damage to buildings and determine risk to pedestrians and vehicles. Importantly, the flood model included a network of 36 municipalities and covered 636 km². Three scenarios were considered: single-hazard (extreme precipitation), multi-hazard (coincident extreme precipitation and storm surge), and adaptation (implementation of resilience measures). In total, 20 rain events were applied for each scenario: 5 were historic design storms, while 15 considered the effect of climate change (60 simulations in total). By the end of the century, results show potential increases in expected annual damage of up to 36%, from €139.8 M to €190.3 M. Risk for pedestrians could increase by 25% (494 ha to 620 ha) and for vehicles by 26% (59 km to 75 km) in the T10 single-hazard scenario. In the multi-hazard case, the socio-economic impacts are approximately 5% higher, while the adaptation simulations considering sustainable urban drainage systems show reductions between 6 and 18%. The metropolitan results were compared and validated with a previous assessment done in the City of Barcelona. Based on these results, urban planners, emergency responders, and public administrations can develop effective adaptation measures based on cost–benefit analyses for current and future climate scenarios. Compared to previous studies, this approach adapts existing urban-scale methodologies to regional-scale flood risk assessment. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has not been systematically assessed, largely due to limited data and field access. This study addresses this gap by mapping flood susceptibility, vulnerability, and risk using remote sensing (RS) and geographic information systems (GIS) at both subbasin and provincial scales. We apply a hybrid approach that combines Principal Component Analysis (PCA) to identify key environmental, climatic, and socio-economic indicators with the Analytic Hierarchy Process (AHP) to derive consistent weights and reduce subjectivity in decision-making. The results show that the eastern and northeastern ssubbasins especially within the Panj-Amu and Kabul River basins, have the highest flood susceptibility due to intense precipitation, steep terrain, and efficient drainage. Vulnerability increases in the densely populated northern and northeastern provinces, where land-use change and socio-economic constraints elevate flood-related impacts. Overall, 31% and 20% of study areas are classified as Very High and High vulnerability zones, respectively. The composite potential flood-risk index identifies that approximately 24% and 22% of Afghanistan fall within Very High and High flood risk zones, concentrated in the northern and eastern provinces. Model performance, evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC), indicates strong agreement between mapped Very High/High risk zones and frequently flooded provinces, with the upper-threshold scenario yielding an AUC of 0.913. These findings support targeted resource allocation, mitigation planning, and disaster-risk reduction in data-scarce and conflict-affected mountain regions. Full article

Community disaster resilience is increasingly guiding risk-reduction investments, but in many Southeast European settings, comparable subnational data remain scarce. This study assesses perceived community disaster resilience across Serbia by combining BRIC–DROP dimensions into a single index and analyzing differences across hazard types and sociodemographic factors. A cross-sectional household survey was conducted using multistage random sampling and the “next birthday” method for respondent selection. The final sample included 1200 adults from 22 local government units across four regions: Belgrade, Vojvodina, Šumadija & Western Serbia, and Southern & Eastern Serbia. Participants evaluated preventive measures and societal resilience for ten hazard types and considered five social dimensions: social structure, social capital, social mechanisms, social equity/diversity, and social beliefs. Descriptive statistics, bivariate analyses (including Pearson correlations, t-tests, and ANOVA), and multiple linear regression identified key predictors of preventive behavior and perceived resilience. Composite scores highlighted spatial resilience differences. Overall perceptions were generally low, mostly falling below the midpoint of the scale. Furthermore, the highest ratings for implemented preventive measures were recorded for pandemics/epidemics, storms/hail, and floods, whereas the lowest were observed for environmental pollution and droughts. Perceived resilience was highest for snowstorms, storms/hail, and pandemics/epidemics, and lowest for environmental pollution and droughts. Also, respondents reported relatively strong family ties and favorable perceptions of communication and access to basic supplies, but weak institutional capacity, particularly in budget allocation, early warning and public notification, rapid decision-making, and evacuation and shelter readiness. Regression results were statistically significant but explained only a small portion of the variance. Age and public-sector employment positively predicted perceived resilience; fear, income, and, to a lesser extent, education were negatively associated. These findings highlight the structural and psychosocial factors that shape perceptions of resilience. The BRIC–DROP composite indicates generally low perceived preparedness and resilience, especially in risk communication, evacuation and shelter readiness, and financing—the key bottlenecks in strengthening local resilience. The results recommend combining institutional reform with targeted risk communication to reduce fear and build trust, especially focusing on hazard areas with the lowest confidence, such as environmental pollution and drought. Full article

The primary aim of this study is to identify the driving mechanisms behind long-term water-level changes and drought–flood transitions in Dongting Lake. To achieve this, we employed methods including the Improved Standardized Water Level Index (ISWI), Mann–Kendall test, Sen’s slope estimator, and a random forest–SHAP model to analyze hydro-meteorological data from 1992 to 2023. The results demonstrate a significant overall decline and spatial heterogeneity in water levels, alongside a systemic shift in the regional pattern from flood-dominated conditions to frequent droughts with intense drought–flood abrupt alternations. Crucially, during the critical autumn water recession period, runoff anomalies from the Yangtze River’s three outlets emerged as the dominant factor driving water-level changes, far exceeding the influence of local precipitation. Furthermore, a recent downward shift in the water level–discharge relationship indicates that under identical inflow conditions, water levels are now 1.5 to 2.0 m lower than in previous decades. These general findings highlight that critical-period inflow reductions and altered boundary hydrodynamic conditions mutually amplify low-water-level risks, providing a scientific reference for adaptive water resource management in complex river-connected lakes. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

Due to climate change, the frequency and intensity of extreme rainfall events have increased in South Korea, resulting in recurrent urban flooding that exceeds the design capacity of conventional drainage systems. In the Dangjin Traditional Market area, comparable rainfall conditions in 2024 and 2025 caused repeated flooding, suggesting that structural improvements implemented without quantitative verification do not necessarily guarantee effective flood prevention. This study aims to support sustainable urban flood management by assessing the pre-implementation effectiveness of structural flood mitigation measures using a spatially explicit simulation approach. An ArcGIS-based rainfall–inundation simulation was conducted by integrating a 1 m LiDAR-derived digital elevation model, land cover data classified using a pixel-based Support Vector Machine, detailed building and channel datasets, and observed hourly rainfall from the July 2025 extreme event. Scenarios with and without the application of levee heightening and drainage capacity expansion were compared under identical rainfall conditions. The results indicate that the application of structural measures leads to a clear reduction in inundation extent and water depth. The proposed framework provides a practical simulation-based decision-support tool for verifying flood mitigation measures in advance and for promoting sustainable flood risk management in urban areas prone to recurrent flooding. Full article