Search Results (1,194)

Refined urban flood risk assessment serves as a fundamental safeguard for urban sustainability. However, most studies based on scenario analysis method tend to rely on a single risk evaluation criterion, with limited consideration of applicability differences arising from underlying computational principles. Furthermore, as flood events are inherently dynamic spatial–temporal processes, most studies often overlook the three-dimensional characteristics of flood risk, particularly the connectivity of risk in physically adjacent spaces. To address these issues, this paper proposes a comprehensive flood risk assessment framework that integrates the spatial–temporal characteristics of disaster-causing factors. An improved analysis method for grid-scale flood assessment is proposed based on the comprehensive mechanical analysis method and the drowning factor. In addition, a quantitative approach for characterizing the spatial aggregation of urban flood risk is established using risk thresholds and aggregation area thresholds. These methods are then integrated through a combination weighting–cluster analysis framework for comprehensive flood risk assessment. The results show that the improved analysis method can better reflect the change in risk of flow velocity and water depth combined. Spatiotemporally, the Yinshan Road and western section of the Dongzhong Road, exhibiting high localized risk, moderate overall risk, high risk on the time scale and high spatial agglomeration status, are comprehensively assessed as extremely high-risk flooded zones. The proposed framework effectively characterizes the spatial–temporal distribution of disaster-causing factors, providing a scientific basis for disaster prevention and contributing to urban sustainability. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Extreme hydrometeorological events are occurring more often under climate change, increasing the risk for cities in coastal zones and lower river reaches. Such areas are prone to compound flooding (CF), where flood duration and magnitude are amplified by the combined effects of storm surges, onshore winds, long-term sea-level rise, and increasingly frequent rainfall-driven floods. This study assesses the socio-economic risk of residential neighbourhoods (RNs) along the lower reach of the Danė River in the city of Klaipėda, Lithuania, using a composite socio-economic risk index (CSERI) developed in this study under an extreme flood scenario, if the sea level in the south-eastern Baltic Sea rises by 1 m by the end of the century. The results show a strong relationship between water levels in the Klaipėda Strait and the lower reach of the Danė River, confirming a CF regime, where flood magnitude is driven by the interaction between strait water level and river discharge. The CSERI is based on five risk sub-indices (SIs): the building risk SI, road infrastructure risk SI, population risk SI, economic entities risk SI, and cultural heritage risk SI. The assessment identifies RNs at greatest risk under climate change and anthropogenic pressure and indicates priority areas for adaptation measures to reduce potential socio-economic losses. The proposed CSERI provides a practical decision-support tool for sustainable and climate-resilient urban development in coastal cities. Full article

Ultra-deep high-pour-point oil (waxy crude oil) reservoirs under high-temperature and high-pressure conditions are characterized by severe heterogeneity and poor displacement efficiency, with the crude oil exhibiting a pour point of approximately 47 °C. Using the XH block as a representative ultra-deep reservoir, this study systematically examines the displacement mechanisms of CO₂ flooding and CO₂–water-alternating-gas (WAG) flooding. This study aims to elucidate the CO₂–oil interactions between CO₂ and waxy crude oil, to compare oil recovery and CO₂ retention under different injection modes in media with varying permeability and heterogeneity, and to provide experimental support for field-scale development. Slim tube, swelling, and long-core flooding experiments were conducted under reservoir conditions (139 °C, 57 MPa). The phase behavior between CO₂ and crude oil, as well as its impact on oil volume and flow properties, was analyzed. Moreover, continuous CO₂ flooding and WAG flooding were compared in low-permeability and medium–high-permeability cores, and WAG was subsequently applied to a parallel-core system to quantify the effect of interlayer heterogeneity. Results indicate that while CO₂ achieves miscibility with the waxy crude at reservoir pressure, its contribution to swelling and viscosity reduction is moderate compared to light oils; thus, recovery relies primarily on miscible displacement. Compared with continuous CO₂ flooding, WAG effectively delays gas breakthrough and enlarges the swept volume, leading to higher oil recovery and CO₂ storage efficiency. Increasing permeability reduces flow resistance and significantly enhances the oil recovery factor. In strongly heterogeneous systems, dominant flow through high-permeability channels markedly weakens displacement in low-permeability zones, resulting in lower overall recovery and CO₂ retention. These results indicate that properly designed WAG schemes can improve the development performance of heterogeneous waxy oil reservoirs while simultaneously meeting CO₂ storage requirements. Full article

Urban disaster management frequently relies on isolated single-hazard assessments and static census data. This conventional approach systematically obscures the highly dynamic, time-varying nature of population exposure to co-located environmental hazards. This study develops an observation-based, time-adaptive multi-hazard exposure prioritization framework to quantify these spatiotemporal variations. We integrate seismic amplification susceptibility, derived from shear-wave velocity estimates, and empirical pluvial flooding footprints with hourly dynamic living population data at a 250 m grid resolution in Seoul, South Korea. Results indicate that multi-hazard integration refines spatial prioritization, with 11% of high-priority areas diverging from single-hazard models, primarily driven by highly amplifiable alluvial deposits. Furthermore, dynamic living population data revealed clear diurnal exposure shifts. Business districts exhibited a daytime-to-nighttime exposure ratio of 3.35, whereas residential areas showed an inverse ratio of 0.69, demonstrating that identical physical conditions generate markedly different exposure patterns depending on the daily urban rhythm. Based on these temporal dynamics, we classified high-priority zones into Persistent (79.4%), Day-peak (10.3%), and Night-peak (10.3%) transition types. These findings suggest that urban exposure must be managed as a time-varying attribute rather than a static feature. The proposed classification supports targeted mitigation: structural improvements for Persistent areas, dynamic crowd management for Day-peak zones, and localized alerts for Night-peak zones. Driven by globally accessible mobile data, this framework provides a transferable foundation for exposure-informed urban resilience planning across diverse metropolitan environments. Full article

To address the issues of high cutting temperature and insufficient heat dissipation during the machining of γ-TiAl alloys, this study systematically investigates the effects of three cooling strategies—dry cutting, flood cooling, and high-pressure cooling—on tool wear and surface integrity. The variations in tool wear, surface morphology, surface roughness, surface defects, microstructure, and microhardness were analyzed in detail. The experimental results indicate that adhesive wear is the dominant wear mechanism under all three cooling conditions. Owing to its superior penetration capability, high-pressure cooling significantly suppresses tool wear, although it may induce groove wear. In terms of surface integrity, high-pressure cooling significantly improves the machined surface quality while reducing surface defects, plastic deformation, and work hardening. Compared with dry cutting, the surface roughness decreases by approximately 9.1%–39.0%, the thickness of the plastically deformed layer is reduced by up to 50.74%, and the degree of work hardening decreases by approximately 11.5%–14.5%. With increasing cutting speed, the surface roughness, plastically deformed layer thickness, and degree of work hardening increase under all three cooling conditions; however, high-pressure cooling still maintains the best overall performance at high cutting speeds. These results indicate that high-pressure cooling effectively suppresses thermo-mechanical coupling in the cutting zone by enhancing coolant penetration and lubrication, thereby providing an efficient approach to reducing tool wear and improving the surface quality of machined γ-TiAl alloys. Full article

This paper presents a physics-based hazard model for catastrophe (CAT) modelling of urban flood risk—a first step toward a complete CAT modelling framework. We introduce a linear second-order ordinary differential equation (ODE) system to simulate the underlying mechanisms of water accumulation, absorption, routing, and drainage across interconnected surfaces in densely built urban areas. The model treats an urban zone as a multivariate network of surfaces, each with unique hydrological properties, linked by directed water flows. For risk analysis, the external meteorological forcing (representing the precipitation input) is randomised. Our risk-analysis protocol relies on a Monte Carlo simulation of stochastic forcing. Its reliability is founded on rigorous mathematical properties proven for the ODE system (existence, uniqueness, positivity, monotonicity, and a priori bounds), ensuring that the probabilistic outputs are well-defined and physically plausible. A three-surface example illustrates the framework and a complete risk analysis is performed, yielding concrete risk metrics that inform mitigation strategies. Computational efficiency is shown to be optimal for linear ODE systems, outperforming generic methods. This work provides a foundational, physics-informed hazard model for next-generation CAT models, directly supporting the insurance industry’s adaptation to climate change. Full article

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

The Historical Lateral Mobility Zone (HLMZ) represents the portion of the alluvial plain occupied by the river channel over the last decades or centuries and represents the most flood-prone sector of the floodplain. Mapping Land-Use–Land Cover (LULC) changes within HLMZs helps reconstruct human-driven land-use dynamics and identify the areas potentially exposed to the highest flood risk. Among the rivers of Southern Italy, the Sele River is characterized by one of the largest mean annual discharges and has experienced extreme and destructive floods, such as those from 1935 and 2010. Over the last 150 years, it has also undergone remarkable channel adjustments, consisting of narrowing up to ~120 m, morphological changes, and riverbed degradation. In this study, LULC changes that occurred between 1988 and 2023 within the HLMZ of the Sele River, formed over the last 150 years, were analyzed and mapped in a GIS environment. Active channels were digitized from historical maps, topographic maps, and orthophotos to map the HLMZ. LULC changes were assessed through visual interpretation of orthophotos and Google Earth imagery in a GIS environment. Results show a transition, over 35 years towards more pristine conditions, with forest expansion, reduction in agricultural areas, and absence of further artificialization. LULC dynamics appear to be strictly controlled by an increased awareness of the high flood hazard within the HLMZ, with positive implications in terms of flood risk, which, however, should be further assessed quantitatively in future studies and, possibly, reduced, given the high proneness of the Sele River to destructive floods. Full article

This study analyses two wetlands within the Medina del Campo groundwater body (Duero River Basin, Spain) to reconstruct flood patterns and quantify the hydrological volumes involved in episodic inundation. We integrate Sentinel satellite imagery (2015–2024), targeted field campaigns (2024–2025), and preliminary water-balance assessments (2015–2022). Calculations were constrained to the inundated cells of each wetland bed to reduce spatial heterogeneity issues. For Laguna de los Lavajares, an initial standing water depth was assumed to estimate infiltration losses more accurately. We discuss the primary sources of uncertainty—particularly the representation of atmospheric losses as evaporation versus evapotranspiration—and recommend computing water balances for wet, average, and dry years to capture interannual variability. Key findings include the identification of distinct hydroperiods for each wetland, the dominant role of infiltration in the water balance of Laguna de los Lavajares, and the critical influence of vegetation-driven evapotranspiration in Laguna Redonda. Full article

Addressing the severe risk of artificial fractures causing vertical pressure channeling and subsequent water flooding during shale oil development in the Ordos Basin, this study investigates the overlapping development zone in Block Shun 269. Through laboratory rock mechanics experiments, the mechanical anisotropy of the overlapping layers was characterized. Utilizing actual production data, a 4D dynamic geomechanical model incorporating 21 years of injection-production history was established to reconstruct the pre-fracturing 3D in situ stress field. Based on this stress field model, a quantitative analysis was conducted on the evolution of injection-production stresses, the vertical superposition distance, the distribution of natural fractures, and the propagation patterns of hydraulic fractures across layers under various fracturing engineering parameters (including pumping rate, fluid viscosity, and perforation cluster, etc.). Research indicates that long-term injection-production disturbances caused the average minimum horizontal principal stress in the Chang 6 layer to decrease by 1.6 MPa, with partial “stress deficit zones” experiencing reductions as high as 3.5 MPa. This significantly weakened the stress shading capability between layers, resulting in the probability of fracturing cracks through the Chang 7 layer in the lower section increasing from 12% to 49%. The propagation of fracture height is jointly governed by geological and engineering factors, the weighting order is as follows: superposition distance > pumping rate > interlayer stress difference. A fracturing cross-layer risk assessment chart based on the coupling of geological and engineering factors has been established, proposing different anti-leakage and fracture control technical models for fracturing sections with different risk levels. Using this model to simulate fracturing in B horizontal wells, the simulation results were consistent with microseismic measurement data. Full article

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index ($\mathrm{FSI}=0.20\cdot \widehat{E}+0.30\cdot \widehat{S}+0.25\cdot \widehat{T}+0.15\cdot \widehat{F}+0.10\cdot \widehat{D}$) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km² (5.00% of the 6.18 km² analysis domain). A Monte Carlo sensitivity analysis (n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km², capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B. Full article

Flooding is one of the most persistent and destructive natural hazards in West Virginia. However, community-scale assessments that connect social vulnerability with physical flood vulnerability are still limited. Existing floodplain management plans often focus on infrastructure and hydrology, overlooking how socioeconomic disparities shape resilience. This study assesses flood resilience in West Virginia communities by connecting socioeconomic vulnerability with physical flood vulnerability. Using data from the American Community Survey (ACS) and state floodplain maps, we developed a Socioeconomic Vulnerability Index (SEVI) and combined it with physical indicators, such as the percentage of residential buildings in the 100-year floodplain, the share of mobile homes in flood-prone areas, the presence of essential facilities and community assets within flood zones, and the proportion of roads submerged by at least one foot of water. Incorporated and unincorporated communities were analyzed separately to reflect differences in governance and service capacity. The results reveal that high flood vulnerability areas often coincide with high socioeconomic vulnerability, especially in the southern and southeastern counties, where long-term economic decline has increased risks. Communities like McDowell and Mingo face a combined challenge of social and physical vulnerability, adding pressure to populations already dealing with limited resources. These findings emphasize the importance of integrated resilience planning that combines physical protection with social support. Considering the increasing intensity of extreme precipitation events associated with climate change, these findings also highlight the importance of incorporating long-term climate considerations into flood resilience planning. Policy suggestions include expanding targeted flood insurance subsidies for low-income households, prioritizing the relocation or retrofitting of mobile homes and essential facilities, investing in green and open spaces, and encouraging community-based mitigation strategies. Together, these actions can lower long-term flood risks while addressing structural inequalities that make certain populations more vulnerable. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article