Search Results (1,455)

The aim of this study was to critically assess the usefulness of pollution indicators in monitoring riverside soils (fluvisols) for heavy metal content. A novel methodological approach was used, comparing areas located inside and outside flood embankments, which allowed for a precise determination of the impact of fluvial and anthropogenic processes on heavy metal accumulation. The experimental logic validated the usefulness of four indicators: the Individual Pollutant Index (PI), the Background Enrichment Factor (PIN), the Potential Ecological Risk (RI), and the Pollution Load Index (PLI). Comparative analysis revealed that soils within the embankment zone have higher metal concentrations, resulting from the continuous deposition of alluvial material, which often contains industrial and municipal pollutants. The vertical distribution of pollutants in fluvisols was shown to be closely related to sediment dynamics and soil properties (clay fraction, organic matter, redox conditions). Validation of the indicators revealed their varying sensitivity. The study revealed the limitations of the PLI, which, due to its summary nature, did not account for significant variability in contamination within the soil profile. Consequently, the PI, PIN, and RI indices were shown to be the most effective tools in assessing the actual degree of soil contamination by fluvisols in the middle Oder Valley. The study results emphasise the need for the selective selection of indicators in environmental monitoring. This comparative approach provides a reliable method for assessing the effectiveness of floodplain management strategies under exposure to chemical pressure. Full article

This paper presents the INFRARES tool, a fully parameterized, interactive, and freely available tool for the resilience assessment of bridges and tunnels within Greece’s transportation networks, under the impact of single or multiple hazards, including earthquakes and floods. The tool facilitates the application of a comprehensive methodology developed through the INFRARES project: Towards resilient transportation infrastructure in a multi-hazard environment research project. The resilience of each examined asset is quantified for the selected hazard scenario using a resilience index and a corresponding resilience grade. The INFRARES tool introduces two key innovations over previous approaches: first, it incorporates both structural and geotechnical components of bridges, overpasses, and tunnels in the vulnerability assessment step; second, it enables GIS-based visualization of the resilience index across selected single- or multi-hazard scenarios. In this context, INFRARES serves as a proactive decision-support tool, supporting authorities, infrastructure operators, and stakeholders to effectively assess, manage, and mitigate the impacts of diverse hazards on transportation systems, thereby enhancing the safety, reliability, resilience, and sustainability of transportation infrastructure under multi-hazard conditions. The proposed tool and the obtained results may support resilience-informed decision-making, prioritization of mitigation measures, and sustainable management of transportation infrastructure exposed to multiple natural hazards. Full article

Waterflooding in oilfields for oil displacement and reservoir pressure maintenance has led to the production of scale in several reservoirs. The formation of scale occurs both in the porous media of the reservoir and in the production equipment, leading to production disruptions that result in a decline in revenue. The aim of this paper is to investigate the effects of mixing samples of seawater and aquifer water. This is achieved by conducting turbidity, salinity, pH, and zeta potential measurements. The risk of self-precipitation of the prepared samples was assessed using the PHREEQC program. A PVT cell was used to assess the impact of temperature and pressure on the prepared seawater and aquifer samples. When 40% of the seawater sample was combined with 60% of the aquifer water sample, the turbidity findings indicated maximum precipitation. The amount of precipitation dropped as temperature and pressure increased. To assess the impact of scale formation on the permeability of a Berea sandstone core, a core flooding experiment was conducted employing liquid and gas as the flowing fluid. Additionally, SEM and EDS analyses were used to examine the shape and composition of scale. It was found that SO₄²⁻ and Ca²⁺ ions predominated in scale precipitation. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

One of the consequences of urbanization is the impermeabilization of the landscape, which increases the stormwater runoff and the risk of pluvial flooding. To address this challenge, this study proposes a methodology to quantify the impact of reducing impermeable areas in the design of pluvial drainage systems. This methodology is applied in the Lake Ganzirri Area, located in Messina, Italy, where accelerated expansion of residential projects and the closure of drainage outlets due to environmental restrictions have enhanced the risk of pluvial flooding. The relationship between impermeable areas and the impact on risk of failure is assessed using rainfall events derived from regional depth-duration-frequency curves, a pluvial drainage network, and InfoWorks ICM simulations for different impermeable scenarios. Full article

Natural hazards and their negative impacts on assets are increasing because of a variety of causes, including climate change, population expansion, and urbanization. Moreover, several areas are susceptible to multiple hazards that interact spatially and/or temporally, necessitating a multi-hazard assessment to adequately mitigate their effects. The goal of this study is to investigate the direct monetary losses produced by the simultaneous interaction of two independent hazards in Lisbon’s city centre, i.e., earthquake and pluvial flood. Seismic hazard has been assessed in terms of macro-seismic intensity, while flood scenario allows for the prediction of water depth for different return periods through a hydrologic-hydraulic model in HEC-RAS software. The seismic and flood vulnerability of the urban investigated compound was evaluated through MCDM methodology—specifically, AHP and TOPSIS methods. A framework for multi-hazard analysis was subsequently developed, explicitly accounting for the interaction between the two hazards and their joint occurrence probabilities based on historical data from the case study area. The results demonstrate that multi-hazard losses are 108 M€ for a 2-year return period and 232 M€ for a 475/500-year scenario, emphasizing that floods contribute more across all return periods in the research area; however, for longer return periods, the earthquake contribution increases significantly. Full article

Digital Elevation Model (DEM) resolution plays a critical role in hydraulic flood modelling by influencing inundation accuracy, spatial precision and computational efficiency. However, limited studies have simultaneously evaluated both inundation accuracy and computational performance across multiple DEM resolutions in event-based urban flood modelling. This study aims to evaluate the impact of DEM spatial resolution on the performance of HEC-RAS 1D and 2D models in simulating an event-based urban flood that occurred on 3 March 2025. A 1 m LiDAR-derived DEM was resampled to 2 m, 5 m, 8 m, 10 m, 20 m, 25 m, and 30 m resolutions to assess the effects of terrain generalization on hydraulic response. Simulated inundation extents were validated against observed flood areas derived from aerial imagery, and computation time was recorded for each scenario. Results reveal a clear trade-off between spatial accuracy and computational demand. In the 1D simulations, deviation from observed inundation increased from 0.76 ha at 1 m to 2.50 ha at 30 m, while computation time remained relatively stable. The 2D simulations were more sensitive to DEM resolution, with deviation increasing from 0.33 ha to 3.12 ha and longer runtimes at finer resolutions. Among the evaluated scenarios, the 10 m DEM provided the most balanced performance in both 1D and 2D models. For rapid assessment and operational flood management, where computational efficiency and timely decision-making are critical, a 1D modelling approach combined with a 10 × 10 m DEM is recommended as a practical and efficient solution. Full article

Frequent flooding and potential levee breaches pose severe threats to life safety and economic development in the Qujiang River Basin, highlighting the need for integrated risk assessments to improve flood management strategies. This study developed a flood risk assessment framework that combines hydrological design, 1D/2D hydrodynamic models and flood impact analysis. Design flood hydrographs for 10-, 20-, 50-, and 100-year return periods were generated using the instantaneous unit hydrograph method, and breach scenarios were incorporated to evaluate extreme failure conditions. The results indicate that inundation extent, depth, and duration increase significantly with return period, with the 100-year flood producing a maximum depth of 10.04 m and an inundation duration of up to 70 h. Levee breach simulations reveal that the Lingshangang breach results in rapid but short inundation, whereas the Qujiang breach results in prolonged deep flooding depths, posing severe risks to critical infrastructure and densely populated areas. Socio-economic impact assessments demonstrate substantial losses under extreme flood scenarios. These findings provide valuable insights for targeted flood risk mitigation, emergency evacuation planning, and resilient land use management in vulnerable river basins. Full article

Floods are among the most frequent and destructive natural hazards, causing significant socio-economic losses worldwide. This paper presents a comprehensive review of floodwall technologies, focusing on the integration of ultra-high-performance fibre-reinforced concrete (UHPFRC) to enhance structural and hydraulic performance. Flood protection systems are categorized into permanent, demountable, and temporary, and are evaluated based on parameters such as activation time, seepage resistance, and lifecycle cost. This review examines key structural applications, including floodwall barriers, wave-energy floaters, and retaining walls, in which UHPFRC provides significant advantages such as reduced material consumption, improved impact resistance, and increased durability in harsh environmental conditions. Additionally, recent advancements in floodwall systems are critically assessed through experimental investigations, numerical modelling, and hydraulic performance under varied loading and flow conditions. The analysis reveals that while UHPFRC systems can reduce material volumes by up to 73% and carbon emissions by 49% compared to conventional reinforced concrete, their adoption is currently limited by a lack of dedicated design standards. Based on a synthesis of peer-reviewed studies (2010–2026), findings indicate that autonomous, buoyancy-driven UHPFRC barriers offer the highest reliability in high-risk zones, whereas manual modular systems remain limited by human-factor vulnerabilities during rapid deployment. Critical research gaps are identified—specifically the need for standardized constitutive models for UHPFRC in hydrostatic environments and extensive long-term field validation—to support the transition toward resilient, smart urban flood defence infrastructure. Full article

Great Britain has a temperate climate, but like other countries, its weather patterns have already been profoundly affected by climate change, and the changes are very likely to continue for decades. It also has an older building stock than most other countries, which may mean it is more difficult to adapt the built environment to reduce vulnerability to climate hazards. However, Great Britain has excellent mapping and buildings data. The built environment is better described than most other countries, and the authors’ work on the National Buildings Database for Great Britain, which draws together the most reliable sources of data covering non-domestic buildings in England, Scotland and Wales, provides an unparalleled opportunity to evaluate how many people will be affected by climate hazards. There has been considerable research effort assessing how housing will be affected by climate change, but so far much less systematic assessment of impacts on non-domestic buildings. Here, the authors examine three aspects of climate hazard affecting people in non-domestic buildings in Great Britain: (1) Overheating—How many and what types of non-domestic buildings are vulnerable to overheating risks in a heat wave? What total floor area is affected, and how many people typically occupy these buildings? (2) Flooding—How many and what types of non-domestic buildings are threatened by flooding now and in 2080? How much floorspace is threatened, and how many people typically occupy these buildings? (3) Safe space—How much air-conditioned ‘safe space’ is available where people vulnerable to overheating risks could retreat to in an emergency overheating event (e.g., schools or hospitals)? How many people could be accommodated, and what fraction of the total GB working population does this represent? We propose five new metrics to assess two of the immediate hazards posed by climate change (overheating and flooding) and to begin to assess to what extent Great Britain could find temporary accommodation for people displaced by these hazards. Full article

Due to climate change and its increased variability, as well as the extreme weather events, flooding is becoming a major natural threat causing profound economic, social, and ecological impact. This paper systematically reviews 89 peer-reviewed articles published between 2010 and 2024 on flood risk assessment approaches, including geospatial techniques and methods for flooding, using the PRISMA framework and the ScienceDirect and Web of Science databases. GIS and remote sensing are the most popular tools for flood hazard mapping, and hydrodynamic models such as HEC-RAS and MIKE FLOOD dominate flood simulation. Machine learning algorithms, multi-criteria decision analysis (MCDA), and climate scenario analysis have also emerged as increasingly prominent methodological contributions to flood risk frameworks. This review makes a novel contribution by providing the first systematic synthesis of geospatial flood risk assessment methods, explicitly quantifying both the urban–rural research imbalance and the degree of hazard, vulnerability, and exposure integration across the literature. Specifically, only 13 (2.7%) of all eligible articles addressed rural flooding, despite the profound socioeconomic impacts that disproportionately affect these communities, and only 16% of included studies integrated any combination of hazard, vulnerability, and exposure components within current assessment approaches. This review highlights the importance of interdisciplinary collaboration and sensitivity to rural contexts in cultivating resilience and fostering equitable flood risk management. Full article

Efficient storm drainage systems (SDSs) play a pivotal role in sustainable urban development. In arid regions, urban SDS often underperform during prolonged dry periods, leaving them inoperable due to sediment buildup and clogging from the intrusion of sprawling waste. Municipalities either rely on emergency response to flooding complaints or inspect storm sewers individually to handle flash floods and conserve high-value rainwater. The present study developed a risk-based decision-analysis framework for resource-efficient inspection and maintenance (I&M) planning of SDS to prioritize geographically clustered sub-zones. The study applied the framework to a case study of three urban zones with varying population densities and land use distributions in Buraydah, Qassim, Saudi Arabia. The framework integrates fuzzy synthetic evaluation (FSE) to address data limitations and subjective expert knowledge, with geographic information system (GIS)-based spatial analysis to assess three risk factors: likelihood, consequences, and detectability of sewer clogging potential. In addition to traditional likelihood-based evaluation of the susceptibility of smaller sewers to sediment accumulation due to performance anomalies, the consequence analysis augmented the process by considering land-use characteristics, exemplified by commercial areas exhibiting higher socio-economic losses than open spaces that buffer excess runoff. The detectability factor consolidated the decision analysis by incorporating the impacts of past delayed inspections, deep manholes, and scattered construction-related waste on clogging potential. The analysis identified sub-zones with aged sewers, deep manholes, long-awaited inspections, and high population densities, resulting in a high risk. GIS maps showing distinct impacts of the three factors on overall flood risk facilitate municipalities facing unique urban flooding challenges arising from sediment accumulation during long dry periods, followed by short-duration, high-intensity rainfall. Full article

Weather-related hazards cause significant societal impacts, yet systematic long-term analyses linking these events to all levels of impact severity remain limited. This study investigates weather-related events and their associated impacts in Greece (2000–2025) using the High-Impact Weather Events Database (HIWE-DB). The HIWE-DB records 626 events, corresponding to 1871 localized records and includes 269 confirmed fatalities. Flood-related hazards are dominant, followed by windstorms, while one-third of all events involve multiple hazardous phenomena. A multilevel analysis, independently assessing weather intensity (W) and impact severity (I), reveals a statistically significant annual increase in the total number of events, driven mainly by low- to moderate-impact events (I1-I2), alongside an increase in high-intensity events (W3). While the most severe events (I3) show high annual variability, they exhibit a 38% increase in the second half of the study period compared to the first. Spatially, societal impacts are predominantly concentrated in major metropolitan areas, whereas the highest per capita fatality rates occur in specific regions, such as West Attica. The findings demonstrate how the independent indicators of intensity and severity contribute to understanding the link between weather hazards and societal exposure, providing an empirical basis for evidence-based risk assessment and impact-based early warnings. Full article

Flooding is a complex, unpredictable disaster that occurs frequently and can have devastating impacts. Over the past two decades, the advent of machine learning (ML) methods has led to a surge in studies focused on flood prediction, emphasizing high-performance algorithms and fast processing times. The present study aims to investigate the challenges of generalization in flood prediction models using machine learning techniques. A dataset of 18,810 samples was compiled from 40 river basins covering the period 1959–2020. Nine machine learning algorithms were applied to the analysis: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Extra Trees, and Gaussian Naive Bayes. Four distinct validation methods were employed to assess the performance of the models, and the results were thoroughly analyzed. The Gradient Boosting model demonstrated exceptional validation performance indicating its robustness across diverse datasets. High accuracy was also observed in the Decision Tree, Random Forest, Extra Trees, and AdaBoost models. However, for datasets with fewer than 200 samples, these four models experienced a decline in performance. Elevation was identified as the most important factor influencing flooding in 36 basins. NDVI was the dominant factor in 3 basins, while rainfall was the main driver in only 1 basin. The results highlight the contributions and shortcomings of machine learning methods in sustainable flood disaster management systems. Full article

Current flood early warning and risk communication approaches are often characterized by simple and/or alarmist messages, which can promote non-protective behaviours—either through overreliance on defence structures or emergency management organizations. In response, we propose and develop an early warning system (EWS) prototype aimed at fostering “flood literacy” within communities. This system seeks to empower individuals and local populations to better understand their flood risk by recognizing their personal vulnerability and the characteristics of potential floods affecting them. Such understanding enables timely and appropriate self-protective actions. The proposed EWS comprises an Internet of Things (IoT)-based camera network for monitoring rainfall, water depth, and water velocity based on Artificial Intelligence (AI) techniques. These AI algorithms have been used also to analyze and assess historical flood events in the study area, i.e., the heritage city of Matera (Basilicata Region, Italy). The monitoring system is integrated with AI-driven flood modelling to generate impact scenarios at the local scale. These forecasted scenarios can be compared with historical flood data to contextualize current measurements of rainfall and water levels and therefore the citizens can judge how significant a flood might be. The system incorporates threshold-based alerts related to flood instability for pedestrians, along with signals and symbols designed for quick interpretation and communication of self-protection measures to improve citizen resilience and response. Full article