Search Results (555)

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

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

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

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

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

Urban river floodplains function not only as zones for flood regulation and ecological buffering but have increasingly been utilized as multifunctional spaces that support leisure, waterfront, and cultural activities. However, overlapping hydraulic and geomorphic factors such as channel meandering, vegetation distribution, and flood-induced flow redistribution have amplified environmental risks, including recurrent erosion deposition, vegetation disturbance, and infrastructure damage, yet quantitative assessment frameworks remain limited. This study systematically evaluates the environmental safety of an urban floodplain by estimating vegetation variability using Sentinel-2 derived NDVI time series and deriving SEDI and TEDI through FaSTMECH two-dimensional hydraulic modeling. NDVI response cases were identified for different rainfall intensities, and interpolation-based hazard maps were generated using spatial cross-validation. Results show that the left bank exhibits higher vegetation variability, indicating strong sensitivity to hydrological fluctuations, while outer meander bends repeatedly display elevated SEDI and TEDI values, revealing concentrated structural vulnerability. Integrated analyses across rainfall conditions indicate that overall safety remains high; however, low-safety zones expand in the upstream meander and several outer bends as rainfall intensity increases. Full article

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

The stability of modern economies relies on the uninterrupted supply of electricity, heat, and transport fuels, making the energy sector highly exposed to various risks and disruptions, including floods, which are among the major natural hazards affecting energy infrastructure in Poland. Despite risks, a scalable and integrated modelling framework for operational flood risk management in energy infrastructure is still lacking. Such a framework should account for increasing climate-related hazard dynamics, integrate robust fragility and damage models with comprehensive flood risk assessments at both asset and system levels, and explicitly consider interdependencies among energy system components and associated critical infrastructure. This integration is essential for analyzing cascading failures and their consequences, while complying with the EU CER Directive requirements for resilience and continuity of critical infrastructure services. An original three-stage spatial vulnerability analysis method was developed, involving GIS data preparation, classification of asset importance, and flood scenario modelling, demonstrated on selected rivers in the Odra River basin. The Expected Damage Factor (EDF) metric was applied to combine flood probability with infrastructure significance. The analysis enabled spatial identification of the most vulnerable components of the energy system and illustrated the dynamics of threats in time and space. The EDF coefficient allowed for quantitative vulnerability assessment, supporting more precise adaptive planning. The approach innovatively combines infrastructure criticality assessment with probabilistic flood scenarios and explicitly incorporates systemic interdependencies in accordance with the CER Directive, enhancing operational flood risk management capabilities. The method provides a practical tool for critical infrastructure protection, operational planning, and the development of adaptive strategies, thereby increasing the flood resilience of the energy system and supporting stakeholders responsible for risk management. Full article

Under the increasing threat of extreme weather events, road infrastructure faces significant risks of flood-induced damage. Traditional manual inspection methods are insufficient for modern highway emergency response, which requires higher efficiency and accuracy. To enhance the precision and accuracy of flood damage identification, this study proposes an intelligent recognition system that integrates a multimodal large language model with a structured knowledge base. The system constructs a professional repository covering eight typical categories of flood damage, including roadbed, pavement, and bridge components, with associated attributes, visual features, and mitigation strategies. A vectorized indexing mechanism enables fine-grained semantic retrieval, while task-specific templates and prompt engineering guide the multimodal model, such as Qwen-VL-Max, which extracts risk elements from image–text inputs and generating structured identification results with expert recommendations. The system is evaluated on a real-world highway flood damage dataset. The results show that the knowledge-enhanced model performs better than the baseline and prompt-optimized models. It reaches 91.5% average accuracy, a semantic relevance score of 4.58 out of 5, and 85% robustness under difficult conditions. These results highlight the strong domain adaptability and practical value for real-time flood damage assessment and emergency response. 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 increasing occurrence of extreme rainfall events often leads to flash floods, infrastructure damage, loss of human life, and significant economic impacts. There is a pressing need for data-driven assessments and the application of robust analytical approaches to better understand these changes. Analyzing ground-level daily rainfall data from 1985 to 2023 from 26 monitoring stations, this study first employs the Mann–Kendall test using robust statistics including minimum, median, various quartiles, and maximum rainfall values for detecting long-term trends across Saudi Arabia. Next, the k-means clustering technique is applied to characterize the annual rainfall cycles across different regions of the country. Finally, the Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT) is employed to identify site-specific thresholds for extreme rainfall using the Generalized Pareto Distribution (GPD). This automated, data-driven method offers a more objective alternative to the commonly used ad hoc percentile-based threshold selection, thereby enhancing the rigour and reproducibility of extreme rainfall analysis. Local specific thresholds were computed ranging from about 16 to 47 mm from Arar and Jazan, respectively. These thresholds were then used to calculate the frequency and intensity of extreme rainfall events. The fitted GPD parameters were further used to estimate return levels (RLs) for different return periods (2-, 5-, 10-, 20-, 50-, and 100-year) into the future. The results underscore considerable spatial variability in extreme rainfall behaviour across Saudi Arabia, with a higher likelihood of intense and infrequent precipitation events in the coming decades. Full article

The present article focuses on the assessment of the potential advantages and disadvantages of the utilisation of modern building inventory technologies in crisis situations, using a case study of Ukraine, currently engulfed in armed conflict. The following methods are described in detail: laser scanning, 360-degree camera images, and photo series. The authors conducted an in-depth SWOT/TOWS analysis, adapted to the specifics of the post-conflict environment, with a view to the future reconstruction of damaged buildings. The originality of the study lies in the use of a modified, quantitative version of the conventional SWOT analysis, supplemented with a weighting and rating system, which allowed for a more accurate assessment of the effectiveness of various technologies, including laser scanning. While the study focuses on the Ukrainian context, the authors emphasise that the developed methodology is universal and can be successfully applied to other critical areas, such as regions affected by earthquakes, floods, fires, or technological disasters. A modified SWOT/TOWS analysis can serve as a valuable tool in crisis management and infrastructure reconstruction during emergencies, providing the data necessary for making rational and effective decisions regarding the use of modern technologies in construction. The analysis revealed that, of the analysed inventory strategies, only laser scanning technology fits the so-called “maxi-maxi” strategy, a scenario in which both internal resources and external capabilities are maximised. The remaining two strategies were designated as “maxi-mini,” signifying that their implementation is associated with elevated levels of risk despite their inherent advantages. It is imperative to acknowledge the existence of substantial external threats that persist. Nevertheless, this does not constitute a complete rejection of the concept. This study examines armed conflict as a research context for a selection of buildings in Ukraine. The analysis was constrained to the three most prevalent methods: The use of TLS, SfM, and 360-degree cameras is also a key component of the methodology. Full article

Floods are among the most frequent and damaging natural hazards in the Mekong River Basin, where the interplay of monsoon-driven climate variability, complex topography, and rapid socio-economic change creates high exposure and vulnerability. This study presents a comprehensive assessment of flood disaster patterns, loss distribution, and regional disparities across five countries in the Lower Mekong Basin—Cambodia, Laos, Myanmar, Thailand, and Vietnam. Using multivariate spatiotemporal analysis based on EM-DAT, MRC, and national government datasets, the study quantifies flood frequency, casualties, and affected population to reveal cross-country differences in disaster impact and timing. Results show that while Vietnam and Thailand experience high flood frequency and storm-induced events, Laos and Cambodia face riverine flooding under constrained economic and infrastructural conditions. The findings highlight a basin-wide increase in flood frequency over recent decades, driven by climate change, land use transitions, and uneven development. The analysis identifies critical gaps in adaptive governance, particularly the need for dynamic policy frameworks that can adjust to spatial disparities in flood typologies (e.g., Vietnam’s storm floods vs. Cambodia’s riverine floods) and improve transboundary coordination of reservoir operations. Despite the region’s extensive reservoir capacity, most infrastructure prioritizes hydropower over flood mitigation. The study evaluates the role of regional cooperation frameworks such as the Lancang–Mekong Cooperation (LMC), demonstrating how strengthened institutional flexibility and knowledge-sharing mechanisms could enhance progress toward Sustainable Development Goals (SDGs) related to water governance (SDG 6), resilient infrastructure (SDG 9), and disaster risk reduction (SDG 11). By constructing the first integrated national-level flood disaster database for the basin and conducting comparative analysis across countries, this research provides empirical evidence to support differentiated yet coordinated flood risk governance strategies at both national and transboundary levels. Full article

Disasters caused by natural hazards, including the August 2023 floods in Slovenia and the 2020 earthquakes in Croatia, resulted in a combined damage and loss of about EUR 26 billion. Indemnity insurance covered only a small share, shifting recovery to public budgets. This review examines whether parametric insurance can provide transparent, pre-arranged, and auditable post-event liquidity to smooth public finances and support timely recovery. A structured qualitative review of peer-reviewed studies, supervisory materials, and EU and national law assesses data readiness, enforceability, and consumer protection duties. EU rules address parts of prudential and conduct risk. However, gaps persist in trigger verification, automated execution, and in the treatment of third-party trigger data sources and calculation methodologies documented for supervisory reviews and audits (no published parametric-specific accreditation standards). The core gap reflects the low take-up of catastrophe insurance rather than a low overall insurance penetration. Parametric cover is treated strictly as a complement to indemnity insurance. We outline narrowly scoped pilots using verifiable, publicly sourced triggers, version-controlled calculations, pre-tested basis risk disclosures, and reversible, auditable settlements with human oversight. Parametric designs add value only when verifiable triggers, transparent disclosures, and supervisory audits are embedded ex ante. Full article

Global climate change is expected to alter characteristics of flood events. This study evaluates the rising flood risk and damage potential in the lower Vipava River valley—a transboundary catchment between Slovenia and Italy—under climate scenarios RCP 2.6, 4.5, and 8.5. The area has experienced multiple floods in recent decades, indicating high vulnerability. Using hydraulic modeling for current and future conditions, flood hazard zones were identified and integrated into the KRPAN model to estimate expected annual damage (EAD). The findings show that EAD escalates from €0.97 million under current conditions to €1.97 million under the most extreme scenario. A 20% rise in flood peaks leads to a 1.4-fold increase in damage, while a 40% rise results in losses that are more than double. Buildings show a 2.5-fold increase in EAD, and water infrastructure EAD rises by a factor of 1.9. These results underscore the substantial economic consequences of climate change on flood risk. The study highlights the urgent need to incorporate climate scenarios into flood risk assessments and spatial planning to support adaptive strategies and reduce future damage. These insights are essential for making informed decisions and achieving long-term resilience. Full article