Search Results (1,981)

In order to meet the urgent needs of refined geological disaster risk assessment at a county scale, and in view of the shortcomings of existing methods in the aspects of sample dependence, rainfall time-varying differences, and vulnerability quantification, this study takes Linxiang City as an example, integrates multi-source data such as geology, geography, meteorology, remote sensing, and field survey, and explores practical methods. A random forest (RF) model was implemented for geological hazard susceptibility mapping, and its hyper-parameters were tuned using Bayesian optimization. Based on a statistical analysis of the frequency of historical disaster events, a risk classification of rainfall in the flood season and non-flood season was evaluated. A vulnerability simplification method based on the value and exposure of disaster-bearing bodies was proposed. Finally, rapid risk assessment was achieved by matrix superposition. The results showed that the model had high accuracy (AUC = 0.903). The use of field survey risk types effectively enhanced the susceptibility sample set and verified the accuracy of risk assessment. The risk factor in the flood season and non-flood season was significantly different, and the very-high- and high-risk areas in the flood season were mainly distributed in the shallow metamorphic rock mountainous area in the east of Yanglousi Town and the granite residual soil area in the south of Zhanqiao Town, the latter of which was highly consistent with the field survey results. This study demonstrated value in terms of sample enhancement, model optimization, consideration of time-varying rainfall, and vulnerability simplification. The evaluation results can provide direct support for the construction of a “point–area dual control” system for geological disasters in Linxiang City, and the methodological framework can also provide a practical reference for risk evaluation in other counties. Full article

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a quantitative framework coupling groundwater extraction with construction land expansion, and the inadequate separation of seasonal and long-term subsidence drivers. We developed an integrated remote-sensing-based approach: high-resolution subsidence time series (2016–2023) were derived via Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, validated against leveling measurements (R > 0.885, error < 20 mm). This subsidence dataset was fused with groundwater level records and annual construction land maps. Seasonal-Trend Decomposition using Loess (STL) isolated trend, seasonal, and residual components, which were input into a Random Forest (RF) model to quantify the relative contributions of subsidence drivers. Dynamic Time Warping (DTW) and Cross-Wavelet Transform (CWT) were further employed to characterize temporal patterns and lag effects between subsidence and its drivers. Our results reveal a distinct shifting subsidence pattern: “areal expansion but intensity weakening.” Groundwater control policies mitigated five historical subsidence funnels, reducing areas with severe subsidence from 72.36% to <5%, while the total subsiding area expanded by 1024.74 km², with new zones emerging (e.g., northern Dongli District). The RF model identified the long-term groundwater level trend as the dominant driver (59.5% contribution), followed by residual (23.3%) and seasonal (17.2%) components. Cross-spectral analysis confirmed high coherence between subsidence and long-term groundwater trends; the seasonal component exhibited a dominant resonance period of 12 months and a consistent subsidence response lag of 3–4 months. Construction impacts were conceptualized as a “load accumulation-soil compression-time lag” mechanism, with high-intensity engineering projects inducing significant local subsidence. This study provides a robust quantitative framework for disentangling the complex interactions between subsidence, groundwater, and urban expansion, offering critical insights for evidence-based hazard mitigation and sustainable urban planning in vulnerable coastal environments worldwide. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analyzes the spatiotemporal trends of 1352 non-glacial lakes (>1 km²) on the TP from 2014 to 2023 using high-resolution Gaofen-1 (GF-1) and Gaofen-2 (GF-2) imagery. By integrating geomorphic analysis with hazard mechanisms, we screened and categorized lakes prone to outburst floods and inundation using a classification and assessment framework proposed in this study. The results indicate that the net area of these lakes expanded by 2839.53 km² (6.07%), with the Inner TP Basin contributing the largest absolute area gain (1960.60 km²). We identified 21 potentially hazardous lakes (10 outburst-prone and 11 inundation-prone) and systematically categorized them by risk level. Field investigations of high-risk candidates, such as Rulei Co and Xiao Qaidam Lake, validated the accuracy of the hazard classification and risk assessment methodology. Preliminary attribution analysis further suggests that the two hazard types may be associated with distinct climatic factors. Overall, this study provides a scientific basis for disaster mitigation and lake management on the TP. Full article

The Netherlands is widely recognised as the global leader in water management, with its flood risk management (FRM) infrastructure lauded as being of the best in the world. This status notwithstanding, Dutch FRM primarily maintains established infrastructural practices and experimental applications of NBSs remain less frequent than established structural projects. This paper details and examines the challenges associated with the prevailing ‘business-as-usual’ approach to FRM in The Netherlands, in which traditional ‘grey’ infrastructural techniques are prioritised over innovative ‘green’ nature-based solutions (NBSs). In line with emerging international trends, such as the EU Water Resilience Strategy, NBSs are increasingly advocated as a strategic, complementary layer to enhance the resilience of existing safety frameworks rather than a self-evident replacement for them. Contrary to grey infrastructure, NBSs provide a number of environmental and social co-benefits extending beyond their flood and drought protection utility. The literature on NBSs details the design, effectiveness, and positive socio-economic impact of the operationalisation of such projects for FRM. This notwithstanding, the uptake and practical implementation of NBSs have been slow in The Netherlands. From a legal and policy perspective, this has been attributed to a lack of political will and the corresponding failure to include NBSs in long term FRM planning. Given the long planning horizons associated with FRM (50–100 years), the failure to incorporate NBSs can lead to policy lock-in that blocks future adaptations. Against this backdrop, this paper employs a semi-systematic literature review to clarify the obstacles to implementing NBSs in Dutch FRM and sets a research agenda that charts a course to mainstreaming NBSs in Dutch FRM. Seven core focus areas for future research are identified. The paper concludes by drawing on these identified focus areas to construct a research agenda aimed at systematically addressing each barrier to the practical operationalisation of NBSs in Dutch FRM, emphasising a hybrid green–grey approach which may serve to inspire similar research in other jurisdictions. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

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

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

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

Climate change and the resulting projected rise in sea level put densely populated urban communities at risk of river flooding, storm surges, and subsurface flooding. Miami finds itself in an increasingly vulnerable position, as compound inundation seems to be a constant and unavoidable occurrence due to its low elevation and limestone geomorphology. Several recent studies on compound overflows have been conducted in Miami-Dade County. However, in-depth research has yet to be conducted on its economic epicenter. Owing to the lack of resilience to tidal surges and extreme precipitation events, Miami’s infrastructure and the well-being of its population may be at risk of flooding. This study applied HEC-RAS 2D to develop one- and two-dimensional water flow models to understand and estimate Miami’s vulnerability to extreme flood events, such as 50- and 100-year return storms. It used Hurricane Irma as a validation and calibration event for extreme event reproduction. The study also explores novel machine learning metamodels to produce a robust sensitivity analysis for the hydrologic model. This research is expected to provide insights into vulnerability thresholds and inform flood mitigation strategies, particularly in today’s unprecedented and intensified weather events. The study revealed that Miami’s inner bay coastline, particularly the downtown coastline, is severely impacted by extreme hydrometeorological events. Under extreme event circumstances, the 35.4 km² area of Miami is at risk of flooding, with 38% of the areas classified as having medium to extreme risk by FEMA, indicating severe infrastructural and community vulnerability. Full article

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks. Full article

Heavy metals are widespread environmental contaminants from natural and anthropogenic sources, posing risks to human health and ecosystems. In urban areas, levels are elevated due to industrial activity, traffic emissions, and building materials. Camden, New Jersey, a city with a history of industry and illegal dumping, faces increased risk due to aging sewer and stormwater systems. These systems frequently flood neighborhoods and parks, heightening residents’ exposure to heavy metals. Despite this, few studies have examined metal distribution in Camden, particularly during storm events. This study analyzes stormwater metal concentrations across residential and commercial areas to assess contamination levels, potential sources, and land use associations. Stormwater samples were collected from 33 flooded street locations after four storm events in summer 2023, along with samples from a flooded residential basement during three storms. All were analyzed for total lead, cadmium, and arsenic using inductively coupled plasma–mass spectrometry (ICP-MS, (Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ, USA)). Concentration data were visualized using geographic information system (GIS)-based mapping in relation to land use, socioeconomic, and public health factors. In Camden’s stormwater, lead levels (1–1164 µg L⁻¹) were notably higher than those of cadmium (0.1–3.3 µg L⁻¹) and arsenic (0.2–8.6 µg L⁻¹), which were relatively low. Concentrations varied citywide, with localized hot spots shaped by environmental and socio-economic factors. Principal component analysis indicates lead and cadmium likely originate from shared sources, mainly industries and illegal dumping. Notably, indoor stormwater samples showed higher heavy metal concentrations than outdoor street samples, indicating greater exposure risks in flooded homes. These findings highlight the spatial variability and complex sources of heavy metal contamination in stormwater, underscoring the need for targeted interventions in vulnerable communities. Full article

Environmental Impact Assessments (EIAs) are crucial for mitigating flood risks in vulnerable ecosystems, yet their effective application remains inconsistent. This study synthesises global literature to systematically map EIA methodologies, evaluate the extent of hydrological integration, and analyse the evolution of practices against policy frameworks for flood-prone areas. A scoping review of 144 peer-reviewed articles, conference papers, and one book chapter (2005–2025) was conducted using PRISMA protocols, complemented by bibliometric analysis. Quantitative findings reveal a significant gap where 72% of studies lacked specialised hydrological impact assessments (HIAs), with only 28% incorporating them. Post-2016, advanced tools like GIS, remote sensing, and hydrological modelling were used in less than 32% of studies, revealing reliance on outdated checklist methods. In South Africa, despite wetlands covering 7.7% of its territory, merely 12% of studies applied flood modelling. Furthermore, 40% of EIAs conducted after 2016 excluded climate adaptation strategies, undermining resilience. The literature is geographically skewed, with developed nations dominating publications at a 3:1 ratio over African contributions. The study’s novelty is its systematic global mapping of global EIA practices for flood-prone areas and its proposal for mandatory HIAs, predictive modelling, and strengthened policy enforcement. Practically, these reforms can transform EIAs from reactive compliance tools into proactive instruments for disaster risk reduction and climate resilience, directly supporting Sustainable Development Goals 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land). This is essential for guiding future policy and improving EIA efficacy in the face of rapid urbanisation and climate change. Full article

Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented in the Enipeas River basin, located within the Thessalia River Basin District, Greece. Hydrological analysis was performed using the HEC-HMS software (version 4.12), while hydraulic simulations were conducted with HEC-RAS 2D. The hydraulic modeling produced synthetic flood scenarios for a 1000-year return period, generating spatially distributed outputs of flood extents. The deep learning algorithm was based on a U-Net (CNN) architecture. The model was trained using multi-channel raster tiles, including open access geospatial data such as Digital Elevation Model, slope, flow direction, stream centerline, land use, and simulated flood extents. Model validation was carried out in two independent domains (TS1 and TS2) located within the same river basin. Model outputs are adequately compared with both 2D hydraulic simulations and official Flood Risk Management Plan maps, and the comparison indicates close spatial and quantitative agreement, with flood extent area differences below 8%. Based on the results, the proposed methodology presents a potential and efficient tool for rapid flood risk mapping. Full article