Search Results (1,560)

This study presents a comprehensive approach to risk management and planned preventative maintenance (PPM) for cultural heritage, focusing on the hypogeum archaeological site beneath the Chamber of Commerce in Rimini, Italy. Hypogeal environments pose unique conservation challenges due to their microclimates, biological threats, and structural vulnerabilities. Applying the ABC Method—developed by ICCROM and CCI—this research systematically identifies, analyzes, and prioritizes risks associated with agents of risks. The methodology was complemented by the Nara Grid to assess the site’s authenticity and cultural value, aiding in the delineation of risk areas and informing strategic conservation priorities. The study identifies efflorescence formation, flooding risks, and lack of management guidelines as extreme threats, proposing tailored treatments and practical interventions across multiple layers of control. Through environmental monitoring, empirical analysis, and a multidisciplinary framework, the research offers a replicable model for sustainable conservation and preventive heritage management in similar subterranean contexts. Full article

A methodology is proposed to predict the daily river discharge (RD) to estuaries from rivers draining in similar temperate zones. Multiple regression models are proposed to estimate RD using only available meteorological data. The models are based on monthly air temperature (T) and recent (P_R) and non-recent (P_NR) atmospheric precipitation (rainfall). They consist of the linear and nonlinear terms of T, P_R, and P_NR, without interaction terms between them. Four rivers located in the north and centre of Portugal (flowing to the Atlantic Ocean) are used in this study—Vouga, Antuã, Neiva, and Mondego. The optimal period used to compute the recent precipitation history is between 4 and 7 days for Vouga, Antuã, and Mondego and is 11 days for Neiva. The recommended lag to compute the non-recent precipitation history is between 50 and 90 days. The optimisation of the lengths of recent and non-recent periods improved the model performance, compared with previously proposed models with interaction terms between the meteorological variables. The obtained models provide a clear interpretation of the impact that meteorology has on RD. All rivers showed similar responses, but the flows of bigger rivers (Vouga, Mondego) were more significantly affected by precipitation and temperature. The proposed models are useful for analysing biogeochemical processes in rivers and estuaries, as well as for assessing flood and drought risks in sensitive areas. Full article

Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article

The increasing frequency and intensity of urban floods due to the climate crisis necessitate effective adaptation. In South Korea, flood vulnerability assessments have focused on preparedness, underscoring the need for adaptive capacity research. This study proposes the Urban Flood Adaptive Capacity Index (UFACI), a Fuzzy Logic-based framework that quantifies urban resilience. Developed from a socio-ecological systems (SES) perspective, the UFACI integrates economic resources, social capital, risk perception, and infrastructure. Fourteen indicators are applied using Fuzzy Logic to address uncertainties and enhance decision-making. The methodology is tested in 12 rainwater pumping station drainage areas in Seoul, providing actionable insights for flood management. This study contributes by shifting the focus from vulnerability to adaptive capacity, offering a systematic, data-driven approach to flood resilience assessment. Unlike conventional methods, the UFACI integrates socio-economic and physical factors, enabling targeted policy interventions and resource allocation. Its application in Seoul demonstrates its practical value, with potential adaptability for broader urban flood risk management. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

This study assesses and maps the physical flood vulnerability within the Xerias, Krafsidonas, and Anavros ungauged catchments in Volos, Thessaly, Greece, using a Geographical Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) integrated with the Analytic Hierarchy Process (AHP). Six factors influencing flood dynamics were selected including slope, flow accumulation, geology, land use/cover, flood history and burned areas. The factors were weighted using the AHP based on their relative influence in flood occurrence. Physical flood vulnerability was assessed utilizing the Weighted Linear Combination (WLC) method and visualized through thematic flood-vulnerability maps. The analysis indicates that the southwestern and central-southern parts of the study area, which are highly urbanized and industrialized, exhibit the highest physical flood-vulnerability. Specifically, 32.76% of the Xerias catchment, 41.16% of the Krafsidonas catchment, and 34.71% of the Anavros catchment exhibit high to very high flood vulnerability. On the other hand, mountainous areas with steep slopes, permeable lithology, and dense forests exhibit low to very low physical flood vulnerability. The method’s accuracy was verified through sensitivity analysis and comparison with national flood-risk data for the study area. The results emphasize the physical vulnerability of Volos to flooding and the necessity for targeted flood mitigation measures, demonstrating the value of GIS in flood risk management. 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

Urban estuarine environments face increasing water safety risks due to microbial contamination from combined sewer overflows (CSOs), particularly during heavy rainfall events. In megacities like Tokyo, where waterfronts are widely used for recreation, such contamination poses significant public health risks. The challenge is compounded by the variability in both intensity and spatial distribution of rainfall across the catchment, combined with complex tidal dynamics making effective water quality management difficult. To address this challenge, we conducted a series of hydrodynamic–microbial fate simulations to examine the spatial and vertical behavior of Escherichia coli (E. coli) under different rainfall–tide conditions. Focusing on the Sumida River estuary, rainfall data from eight drainage areas were classified into six event types using cluster analysis. Two contrasting events were selected for detailed analysis: a light rainfall (G2, 15 mm over 13 h) and an intense event (G6, 272 mm over 34 h). Vertical water quality profiling was performed along an 8.5 km transect from the Kanda–Sumida River confluence to the Tokyo Bay Tunnel, illustrating E. coli and salinity. The results showed that the rainfall intensity and tidal phase at the event onset are critical in shaping both the magnitude and vertical distribution of microbial contamination. The intense event (G6) led to deep microbial intrusion (up to 6–7 m) and major salinity disruption, while the lighter event (G2) showed surface-layer confinement. Salinity gradients were more strongly affected during G6, indicating freshwater intrusion. Tidal phase also influenced transport: the flood-high condition retained E. coli, whereas ebb-low tides facilitated downstream flushing. These findings highlight the influence of rainfall intensity and tidal timing on microbial distribution and support the use of vertical profiling in estuarine water quality management. They also support the development of dynamic, event-based water quality risk assessment tools. With appropriate local calibration, the modeling framework is transferable to other urban estuarine systems to support proactive and adaptive water quality management. Full article

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones. Full article

This study presents a comprehensive assessment and strategic framework for implementing Nature-Based Solutions (NBSs) to mitigate flooding in Lethem and Tabatinga, Region 9 of Guyana. The communities are increasingly vulnerable to flooding due to climate variability, hydrological dynamics, and socio-economic factors. A mixed-methods approach, comprising hydrological modelling and observation, a questionnaire survey with a sample of households in both communities, and interviews with municipal administrators, was utilised to acquire data for the study. The study utilised the Statistical Package for Social Sciences (SPSS) to analyse the socio-economic impacts of flooding in the two communities. The results revealed that recent events, such as the significant floods of 2022, have prompted an urgent need for sustainable management strategies. Community engagement efforts, supported by data analysis through remote sensing technology, identified flood-prone areas and vulnerable populations, including women, the elderly, and persons with disabilities. Chi-Square testing was conducted to determine mutual dependence between the communities’ livelihood activities and disruptions to income and working days, and their ability to deal with flooding. Based on the results, the farmers were the group that the highest inability to deal with flooding. Existing infrastructure, including drainage systems and emergency response initiatives led by the Civil Defence Commission, has contributed to improved flood management; however, limitations persist, particularly in urban planning and land use practices. This study underscores the detailed process of implementing and adopting NBS approaches, such as flood conveyance solutions and water storage and bio-retention solutions. These solutions can improve water quality, preserve ecosystems, and enhance community well-being while reducing flood risks. Applying these solutions in the targeted communities promises to bolster ecological resilience, support climate adaptation, and reduce the incidence and the impact of floods in the sampled communities. Full article

Intensified by climate change and anthropogenic activities, flood disasters necessitate rapid and accurate mapping for effective disaster management. This study develops an integrated framework leveraging synthetic aperture radar (SAR) and cloud computing to enhance flood monitoring, with a focus on a 2024 extreme rainfall event in Liaoning Province, China. Utilizing the Google Earth Engine (GEE) platform, we combine three complementary techniques: (1) Otsu automatic thresholding, for efficient extraction of surface water extent from Sentinel-1 GRD time series (154 scenes, January–October 2024), achieving processing times under 2 min with >85% open-water accuracy; (2) random forest (RF) classification, integrating multi-source features (SAR backscatter, terrain parameters from 30 m SRTM DEM, NDVI phenology) to distinguish permanent water bodies, flooded farmland, and urban areas, attaining an overall accuracy of 92.7%; and (3) Fuzzy C-Means (FCM) clustering, incorporating backscatter ratio and topographic constraints to resolve transitional “mixed-pixel” ambiguities in flood boundaries. The RF-FCM synergy effectively mapped submerged agricultural land and urban spill zones, while the Otsu-derived flood frequency highlighted high-risk corridors (recurrence > 10%) along the riverine zones and reservoir. This multi-algorithm approach provides a scalable, high-resolution (10 m) solution for near-real-time flood assessment, supporting emergency response and sustainable water resource management in affected basins. Full article

The Loess Plateau, possessing the world’s most extensive loess deposits, is highly vulnerable to accelerated soil erosion and vegetation loss triggered by extreme hourly rainfall (EHR) events due to the inherently erodible nature of its porous, weakly cemented sediment structure. EHR exacerbates soil erosion, induces flash flooding, compromises power infrastructure, and jeopardizes agricultural productivity. Through analysis of 43 years (1981–2023) of station observational data and ERA5 reanalysis, we present the first comprehensive assessment of EHR characteristics across the plateau. Results reveal pronounced spatial heterogeneity, with southeastern regions exhibiting higher EHR intensity thresholds and frequency compared to northwestern areas. EHR frequency correlates positively with elevation, while intensity decreases with altitude, demonstrating orographic modulation. Synoptic-scale background environment of EHR events is characterized by upper-level divergence, mid-tropospheric warm advection, and lower-tropospheric convergence, all of which are linked to summer monsoon systems. Temporally, EHR peaks in July during the East Asian summer monsoon and exhibits a bimodal diurnal cycle (0700/1700 LST). Long-term trends reveal a significant overall increase in the frequency of EHR events (~0.82 events a⁻¹). While an overall increase in EHR intensity is also observed, it fails to achieve statistical significance due to opposing regional signals. Collectively, these trends elevate the risks of slope failures and debris flows. Our findings highlight three priority interventions: (i) implementation of elevation-adapted early warning systems, (ii) targeted agricultural soil conservation practices, and (iii) climate-resilient infrastructure design for high-risk valleys—all essential for safeguarding this ecologically sensitive region against intensifying hydroclimatic extremes. Full article

Floods pose a substantial risk to human well-being. These risks encompass economic losses, infrastructural damage, disruption of daily life, and potential loss of life. This study presents a state-wide and county-level spatial exposure assessment of the Iowa railway network, emphasizing the resilience and reliability of essential services during such disasters. In the United States, the railway network is vital for the distribution of goods and services. This research specifically targets the railway network in Iowa, a state where the impact of flooding on railways has not been extensively studied. We employ comprehensive GIS analysis to assess the vulnerability of the railway network, bridges, rail crossings, and facilities under 100- and 500-year flood scenarios at the state level. Additionally, we conducted a detailed investigation into the most flood-affected counties, focusing on the susceptibility of railway bridges. Our state-wide analysis reveals that, in a 100-year flood scenario, up to 9% of railroads, 8% of rail crossings, 58% of bridges, and 6% of facilities are impacted. In a 500-year flood scenario, these figures increase to 16%, 14%, 61%, and 13%, respectively. Furthermore, our secondary analysis using flood depth maps indicates that approximately half of the railway bridges in the flood zones of the studied counties could become non-functional in both flood scenarios. These findings are crucial for developing effective disaster risk management plans and strategies, ensuring adequate preparedness for the impacts of flooding on railway infrastructure. Full article

This study investigates cross-border cascading hazards and infrastructure vulnerabilities in the Sava River Basin, a seismically active and flood-prone region spanning the Slovenia–Croatia border. Conducted within the CROSScade project, the research focuses on assessing cross-border hazards and the vulnerabilities of levees and bridges. Key earthquake and flood scenarios were identified using advanced hydraulic and seismic modelling, forming the basis for evaluating the cascading effects of these events, including the potential failure of hydropower plants and associated flood protection systems. The analysis reveals that levees are particularly vulnerable to failure during the recession phase of flooding that follows an earthquake. At the same time, bridges are primarily affected by seismic loading, with minimal structural impact from flood forces. These findings underscore the pressing need for enhanced cross-border collaboration, updated design standards, and the reinforcement of critical infrastructure. The study provides essential insights for multi-hazard resilience planning and emphasises the importance of integrated risk assessments in managing cascading disaster impacts across national boundaries. Full article

Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article