Search Results (58)

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Climate-induced migration is increasingly recognized as a major demographic consequence of environmental change, yet projections vary widely due to differences in spatial scale, hazard coverage, and modeling approaches. This study introduces the First Street Global Climate Migration Model (FS-GCMM), a globally consistent, multi-hazard framework that estimates climate-driven population redistribution at a 12.5 km resolution across all countries through 2100. The model integrates high-resolution global climate hazard datasets, including flood (GloFAS), wind (IBTrACS and ERA5), drought (ERA5), wildfire (Global Fire Atlas), and extreme heat and cold (ERA5-LAND) datasets, with gridded population data from NASA SEDAC’s Gridded Population of the World (GPWv4) and Shared Socioeconomic Pathway (SSP) projections. To identify climate-related migration effects, we applied within-country propensity score matching to construct balanced samples of exposed and unexposed grid cells with similar socioeconomic, demographic, geographic, and governance characteristics. Hazard-specific impacts on annualized population change from 2000 to 2020 were then estimated using mixed-effects ridge regression with country-level random effects to account for cross-national heterogeneity and multicollinearity. These empirically derived coefficients were applied to SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios to project future climate-driven outmigration, which was subsequently redistributed using a spatial attractiveness framework incorporating economic opportunity, population density, climate safety, and geographic proximity. Results indicate statistically significant negative effects of all modeled hazards on population retention globally, with approximately 199.5 million people projected to experience climate-driven displacement by 2055 under SSP2-4.5. Full article

Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. Identification and analysis of rivers, lakes, and reservoirs through segmentation have become feasible, enabling the exploration of their statistical information. During crises such as floods and changes in river pathways, real-time detection of water bodies via remote sensing proves to be highly advantageous. Nevertheless, achieving precise segmentation of water bodies presents a notable challenge, mainly due to the necessity of high-resolution multi-channel satellite images. Existing literature predominantly relies on satellite data from multi-band satellites for water-body extraction. Conversely, the current research emphasizes the segmentation of water-body regions using relatively lower-resolution RGB images without the incorporation of extra multi-spectral channels. To tackle this challenge, a unique methodology is suggested, involving a customized U-Net model integrated with a time-frequency attention network for segmentation. To assess the comprehensive performance of the proposed model, it is evaluated against a publicly available Sentinel-2 satellite dataset, and the outcomes are compared against standard benchmark metrics. The proposed TFA-U-Net model demonstrates superior performance compared to several recent state-of-the-art water-body segmentation models. Experimental results show that the proposed model achieves a precision of 0.94, sensitivity of 0.96, Dice score of 0.93, accuracy of 0.97, and mean IoU of 0.85, indicating its effectiveness for accurate water-body segmentation using low-resolution satellite images. Full article

The hepatitis E virus (HEV) is an emerging multi-host pathogen, with suids being the main reservoir. Humans are primarily infected through the consumption of contaminated water or food. In Argentina, HEV circulation has been confirmed in humans, domestic pigs, wild boar (Sus scrofa), and surface water. In El Palmar National Park, invasive wild boar and axis deer (Axis axis) are controlled, and their meat is released for public consumption, with trimmings and offal frequently fed to dogs. Between 2017 and 2019, we conducted a multi-species serological survey in this protected area to assess HEV exposure in invasive mammals and in dog and human consumers of game meat. We also evaluated associations between seropositivity and environmental variables, as well as behavioral risk factors among game-meat consumers. Total anti-HEV antibodies were detected in 29/75 (38.67%) wild boar, 1/134 (0.75%) deer, 1/18 (5.6%) dogs, and 6/59 (10.17%) humans. A spatial cluster of seropositive wild boar was identified in a low-lying, flood-prone area near the confluence of the El Palmar stream and the Uruguay river, suggesting increased risk of environmental transmission. This is the first report of HEV exposure in wild boar from this park and in axis deer and dogs in Argentina. Participation in culling and game meat handling and consumption may contribute to HEV exposure pathways among humans. These findings improve understanding of HEV epidemiology at the wildlife–domestic animal–human interface and highlight the influence of environmental factors and human behavior on zoonotic virus circulation. Full article

As the frequency and severity of extreme weather events may increase due to climate change, understanding their impacts on water systems, resources, and infrastructure becomes very important. This study contributes to the growing body of knowledge on how extreme storms and floods disrupt interrelated elements comprising water systems by examining the case of Storm Daniel, which struck the Thessaly region of Greece in September 2023. Using a multi-source approach, including field data, institutional reports, scientific assessments, and publications, the study systematically identifies and categorizes the impacts of the storm and the ensuing flood across surface waters, drinking water supply, and wastewater infrastructure and other water-related systems through various mechanisms. The findings provide an overview of how such extreme storms may affect such systems and reveal widespread, interconnected disruptions that highlight systemic vulnerabilities in both natural and engineered systems, synthesizing these impact pathways. The study presents evidence of poor resilience against extreme events and climate change hazards in water-related infrastructure. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

The uncoordinated water–sediment relationship, fragile eco-environment and unbalanced economic development in the Wei River Basin (WRB) pose serious challenges to its high-quality development. Most existing studies focus on static structures or single elements, making it difficult to systematically reveal the complex interrelationships among ecosystem services (ESs) supply, transmission and demand. To address this issue, this paper innovatively combines the “system perspective” with the “flow network model”. From the perspective of flood-sediment transport, eco-environmental and socio-economic (FES) subsystems, we take the WRB as its research object and systematically analyzes the supply–demand relationship of ESs, the pathways of the ESs flows and ecological compensation (EC) strategies at multiple scales. By constructing a supply–demand assessment model for six types of ESs combined with the water-related flows model, the enhanced two-step floating catchment area method and the gravity model, this paper simulates the ESs flows driven by different transmission media (water, road and atmosphere). The results showed the following: (1) a significant spatial mismatch was observed between the high-supply areas at the northern foothills of the Qinling Mountains and the high-demand areas in the Guanzhong Plains. Furthermore, the degree of this mismatch increased with decreasing scale. (2) The pathways of different ESs flows were influenced by their respective transmission media. The water-related flows passed through areas along the Wei River and the Jing River. The carbon sequestration flows were identified in the upper reaches of the Luo River and between the core urban agglomerations of the Guanzhong Plains. The crop production flows were significantly influenced by the scale of urban crop demand, radiating outward from Xi’an City. (3) At the county and watershed scales, The EC fund pools of 7.5 billion yuan and 2.6 billion yuan were formed, respectively. These EC funds covered over 90% of the areas. These findings verify the applicability of the “FES subsystems” framework for multi-scale EC and provide a theoretical basis for developing an integrated EC mechanism across the entire basin. Full article

Flood hazards in arid oil-producing regions result from both natural hydrological processes and terrain changes due to land subsidence. In the Yibal field in northern Oman, long-term hydrocarbon extraction has caused measurable ground deformation, altering surface gradients and drainage patterns. This study presents a deformation-adjusted flood hazard assessment by integrating a 2013 photogrammetric DEM with a 2023 subsidence-corrected DEM derived from multi-temporal PS-InSAR observations (RADARSAT-2 and TerraSAR-X). Key hydrological indicators—including slope, drainage networks, Height Above Nearest Drainage (HAND), floodplain depth, Curve Number, and extreme precipitation from the wettest monthly rainfall in a 10-year archive—were recalculated for both years. Flood hazard maps for 2013 and 2023 were generated using an AHP-based multi-criteria framework across five hydrologically motivated scenarios. Results indicate that while the total area of high- and very-high-hazard zones changed only slightly in most scenarios (within ±6%), these zones shifted into subsidence-affected depressions, reflecting deformation-driven redistribution of flood-prone areas. Low-hazard zones grew most significantly, especially in Scenarios S2–S4, with increases of 160–320% compared to 2013, while moderate-hazard areas showed smaller but consistent growth. Floodplain-dominated conditions (S5) produced the most pronounced nonlinear response, with a substantial increase in very low hazard and localized concentration of very high hazard in areas of deepest subsidence. Geomorphic analysis using the Geomorphic Flood Index (GFI) shows deepening of flow pathways and expansion of geomorphic depressions between 2013 and 2023, supporting the modeled redistribution of hazards. These findings demonstrate that even moderate subsidence can significantly alter hydrological susceptibility and underscore the importance of incorporating deformation-adjusted terrain modeling into flood hazard assessments in petroleum fields and other subsidence-prone areas. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a new generation of clean and efficient energy conversion devices, demonstrating broad application prospects in transportation, distributed power generation, and other fields. The geometric configuration of the cathode gas channel (GC) and the surface microstructure of the gas diffusion layer (GDL) are core factors influencing the efficiency of reactant gas transport and water management performance. However, conventional rectangular flow channels suffer from insufficient convective enhancement and restricted oxygen supply beneath the fins. Furthermore, homogeneous GDLs exhibit limited diffusion and drainage capabilities, often leading to oxygen depletion and flooding downstream of the cathode, significantly limiting overall cell performance. To address these challenges, this study designs a novel centrally positioned fin-type barrier block. A three-dimensional multiphysics numerical model integrating GDL surface microporosity with the internal barrier block flow channels is constructed to systematically investigate the synergistic mechanisms of microporous topology and flow channel structure on two-phase flow distribution, oxygen mass transfer, and electrochemical performance. The results demonstrate that this model accurately captures the dynamic evolution of flow fields within the GDL. Compared to conventional structures, significant coupling effects exist between the GDL microporous structure and the novel barrier block. Their synergistic interaction forms multi-scale mass transfer enhancement and dewatering pathways, providing quantifiable optimization pathways and structural parameter references for high-power-density PEMFC cathode design. Full article

In arid regions, rainfall is scarce, summer-concentrated, and prone to extreme events, while evaporation exceeds precipitation, creating fragile ecosystems that need scientific stormwater management for flood resilience. Sponge cities, through the implementation of green infrastructure, can alleviate urban flooding, improve rainwater utilization, and enhance the urban ecological environment. Under the “dual carbon” target, sponge city construction has gained new developmental significance. It must not only ensure core functions and minimize construction costs but also fully leverage its carbon reduction potential, thereby serving as a crucial pathway for promoting urban green and low-carbon development. Therefore, this study focused on Xining, a typical arid city in Northwest China, and couples the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) with the Storm Water Management Model (SWMM) to construct a multi-objective optimization model for Low Impact Development (LID) facilities. The layout optimization design of LID facilities is conducted from three dimensions: life cycle cost (LCC), rainwater utilization rate (K), and carbon emission intensity (CI). Hydrological simulations and scheme optimizations were performed under different design rainfall events. Subsequently, the entropy-weighted TOPSIS method was utilized to evaluate and compare these optimized schemes. It is shown by the results that: (1) The optimized LID schemes achieved a K of 76.2–80.43%, an LCC of 2.413–3.019 billion yuan, and a CI of −2.8 to 0.19 kg/m²; (2) Compared with the no-LID scenario, the optimized scheme significantly enhanced hydrological regulation, flood mitigation, and pollutant removal. Under different rainfall return periods, the annual runoff control rate increased from 64.97% to 80.66–82.23%, with total runoff reduction rates reaching 46.41–49.26% and peak flow reductions of 45–47.62%. Under the rainfall event with a 10-year return period, the total number of waterlogging nodes decreased from 108 to 82, and the number of nodes with a ponding duration exceeding 1 h was reduced by 62.5%. The removal efficiency of total suspended solids (TSS) under the optimized scheme remained stable above 60%. The optimized scheme is highly adaptable to the rainwater management needs of arid areas by prioritizing “infiltration and retention”. Vegetative swales emerge as the primary facility due to their low cost and high carbon sink capacity. This study provides a feasible pathway and decision-making support for the low-carbon layout of LID facilities in arid regions. Full article

Oil and gas fields in subsidence-prone regions face multiple hazards that threaten the resilience of their infrastructure. This study presents an integrated risk mapping framework for the Yibal field in the Sultanate of Oman, utilizing remote sensing and geophysical data. Multi-temporal PS-InSAR analysis from 2010 to 2023 revealed cumulative surface deformation and tilt anomalies. Micro-seismic and fault proximity data assessed subsurface stress, while a flood risk map-based surface deformation-adjusted elevation captured hydrological susceptibility. All datasets were standardized into five risk zones (ranging from very low to very high) and combined through a weighted overlay analysis, with an emphasis on surface deformation and micro seismic factors. The resulting risk map highlights a central corridor of high vulnerability where subsidence, seismic activity, and drainage pathways converge, overlapping critical infrastructure. The results demonstrate that integrating geomechanical and hydrological factors yields a more accurate assessment of infrastructure risk than single-hazard approaches. This framework is adaptable to other petroleum fields, enhancing infrastructure protection (e.g., pipelines, flowlines, wells, and other oil and gas facilities), and supporting sustainable field management. Full article

Ship damage control (DC) is pivotal to platform survivability in the face of battle damage and severe accidents. The DC context features multi-hazard coupling among flooding, fire, and smoke, as well as fast system dynamics and intensive human–machine collaboration, demanding real-time predictive simulation and decision support. Conventional DC simulations fall short in multiphysics fidelity, predictive speed, and integration with onboard sensing and control. A digital twin (DT) framework for predictive shipboard DC is introduced with an explicit capability envelope, observability, and latency requirements, and a cyber-physical mapping to ship systems. Building on this foundation, a three-stage/four-level maturity model charts progression from L1 monitoring, through L2 prediction and L3 human-in-the-loop, override-enabled plan generation, to L4 closed-loop decision control, specifying capability milestones and evaluation metrics. Guided by this model, a four-layer architecture and an end-to-end roadmap are formulated, spanning multi-domain modeling, multi-source sensing and fusion, surrogate-accelerated multiphysics simulation, assisted plan generation with human approval/override, and cyber-physical closed-loop control. The framework aligns interfaces, performance targets, and verification pathways, providing actionable guidance to upgrade shipboard DC toward resilient, efficient, and human-centric operation under multi-hazard coupling. Full article

Urban development and intensive human activities have led to increasingly prominent nitrogen pollution issues in the Baiyangdian Lake basin. Accurately identifying the sources of nitrate pollution is a crucial prerequisite for implementing targeted remediation strategies, while flooding further complicates this task by exacerbating the transport and mixing of multi-source pollutants within the basin. This study, conducted from August to October 2023 (encompassing flood and post-flood periods), established 20 sampling sites in the lake area and its major inflow rivers. By integrating hydrochemical parameters, nitrate dual-isotope tracers (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻), and the Bayesian mixing model (MixSIAR), we quantitatively revealed the contributions of nitrate sources and their response mechanisms to a major flood event. The results indicate that domestic sewage and livestock wastewater (Manure & Sewage, MS) were the dominant sources of nitrate, with an average contribution of 84.0%, which further increased to 90.3% after the flood. Soil nitrogen was a secondary source (average 12.3%), while contributions from chemical fertilizers and atmospheric deposition were negligible (<4%). The results quantified a flood-driven dynamic response process of the nitrate source structure, characterized by “dilution-mixing-pollution rebound-process transformation”: the initial flood stage (August) showed multi-source mixing; the post-flood period (September) witnessed a rapid rebound of sewage sources; and during the October, nitrification persisted, but the basin’s overall denitrification capacity was limited, indicating a risk of nitrogen accumulation. Spatially, rivers like the Fu River were identified as key input pathways. This study revises the traditional understanding by emphasizing the absolute dominance of sewage sources after extreme hydrological events and the risk of insufficient denitrification capacity. The findings provide a scientific basis for water quality management in Baiyangdian and similar lakes. Full article

As global climate change intensifies, hydrological processes in arid inland river basins are undergoing profound transformations, posing severe challenges to regional water security and ecological stability. This study aims to develop a coupled SWAT-LSTM model integrating glacier melt processes to simulate runoff dynamics in the Keria River basin under climate change, providing a basis for local water resource management. Based on natural monthly runoff observations from the Langgan hydrological station (1961–2015), glacier data extracted from Landsat 8 remote sensing imagery (2013–2019), and downscaled data from the CMIP6 Multi-Model Ensemble (MME), this study constructed a SWAT-LSTM coupled model to simulate future scenarios (2026–2100). Research indicates that this hybrid model significantly enhances the accuracy of hydrological simulations in high-altitude glacier-fed catchments. The Nash efficiency coefficient (NSE) during the validation period reached 0.847, representing a 15% improvement over the SWAT model. SSP5-8.5 is identified as a high-risk scenario, underscoring the urgency of emissions reduction; SSP1-2.6 represents the most desirable pathway, with its relatively stable pattern offering sustained advantages for long-term water resource management in the basin. The study further reveals a negative feedback mechanism between glacier ablation and runoff increase, validating the regulatory role of Jiyin Reservoir’s “store during floods to compensate for droughts” operation strategy in balancing basin water resources. This study explores the coupling path between the physical model and the deep learning model, and provides an effective integration scheme for the hydrological simulation of the global watershed with ice–snow meltwater as the main recharge runoff, especially for the adaptive management of water resources in inland river basins in arid areas. Full article

Rainfall-driven non-point source (NPS) pollution has become a critical issue for water environment management in urban watershed systems. However, single-model use is limited to fully represent the intricate processes of rainfall-correlated NPS pollution generation and dispersion for effective decision-making. This study develops a novel cross-scale, multi-factor coupled model framework to characterize hydrologic and NPS pollution responses to different rainfall events in Shenzhen, China, a representative worldwide metropolis facing challenges from rapid urbanization. The calibrated and validated coupled model achieved remarkable agreements with observed hydrologic (Nash–Sutcliffe efficiency, NSE > 0.81) and water quality (NSE > 0.85) data in different rainfall events and demonstrated high-resolution dynamic changes in flow and pollutant transfer within the studied watershed. In an individual rainfall event, heterogeneous spatial distributions of discharge and pollutant loads were found, highly correlated with land use types. The temporal change pattern and risk of flooding and NPS pollution differed significantly with rainfall intensity, and the increase in the pollutants (mean 322% and 596%, respectively) was much larger than the discharge (207% and 302%, respectively) under intense rainfall conditions. Based on these findings, a decision-support framework was established, featuring land-use-driven spatial prioritization of industrial hotspots, rainfall-intensity-stratified management protocols with event-triggered operational rules, and integrated source-pathway-receiving end intervention strategies. The validated model framework provides quantitative guidance for optimizing infrastructure design parameters, establishing performance-based regulatory standards, and enabling real-time operational decision-making in urban watershed management. Full article