Search Results (342)

Extreme weather events exacerbated by global warming pose severe threats to urban safety, underscoring the urgent need for highly accurate precipitation nowcasting. Short-term local heavy precipitation remains a particular challenge for traditional forecasting due to its suddenness and high disaster potential. To address this, we propose a multi-modal fusion framework that integrates ground-based GNSS-derived Precipitable Water Vapor (GNSS-PWV) and ground-based Radar Composite Reflectivity (CR). While GNSS-PWV keenly captures pre-convective atmospheric water vapor accumulation, radar CR details the morphological distribution of hydrometeors. Specifically, we developed the Spatio-Temporal Enhanced Attention Swin U-Net (STEA-Swin) model to synergize these heterogeneous datasets over the Beijing–Tianjin–Hebei region. High-precision PWV was retrieved from 250 Continuously Operating Reference Stations (CORS) using the dual-frequency ionosphere-free Precise Point Positioning (PPP) method, achieving a strong correlation (>0.97) with ERA5 reanalysis data. Validated against measured data from the 2025 flood season, the STEA-Swin model achieved a Probability of Detection (POD) of 0.68 for torrential rain events at a +1 h forecast lead time. Notably, compared to single-source models, the Critical Success Index (CSI) and POD for torrential rain improved by 18.5% and 21.5%, respectively. These findings demonstrate that coupling deep learning with ground-based GNSS-derived atmospheric thermodynamic information can significantly enhance early warning capabilities, providing a promising technical approach for regional disaster prevention and climate resilience. Full article

Eco-geological environmental carrying capacity (EGECC) refers to the ability of a regional eco-geological environmental system to support human activities and socioeconomic development while maintaining ecological functions and geological stability. Its assessment is important for preventing geological hazards, regional development, and the green transition. However, existing studies often face challenges, such as subjectivity in assigning weights and limited capacity to capture nonlinear interactions among multiple factors. To address these limitations, this study focuses on Kunming City. It applies the analytic hierarchy process and coefficient of variation methods to derive subjective and objective weights, respectively. At the same time, random forest is used to identify nonlinear indicator contributions embedded in the evaluation results. These weights are then integrated through an improved linear efficacy coefficient framework to construct a comprehensive weighting scheme. The results indicate that Kunming’s EGECC shows a spatially heterogeneous pattern, with higher values distributed mainly in the southeastern and southwestern areas, especially in Shilin County. In contrast, lower values are concentrated in the northern mountainous areas and highly urbanized central districts, such as Dongchuan and Wuhua Districts. Moreover, spatial pattern detection of the EGECC is sensitive to the choice of composite weighting method, while interactions among geological factors—such as disaster point density and slope—play a major role in shaping the carrying capacity. Overall, the proposed composite weighting approach provides a relatively balanced integration of expert-knowledge-based, data-dispersion-based, and nonlinear data-driven information, and its reliability is further examined through geological hazard-point validation and weight-perturbation sensitivity analysis. Full article

On 26 January 2026, a 5.5-magnitude earthquake occurred in Diebu County, Gansu Province, causing different degrees of damage and collapse to houses. To understand the damage characteristics and causes of typical buildings, a post-earthquake damage assessment was conducted on buildings in the epicentral area through field investigations of 16 urban buildings and rural houses in 10 natural villages. The results indicate that among the rural buildings, timber frame structures accounted for the largest proportion and suffered the worst damage, primarily manifested as overall collapse of enclosure walls, partial wall collapse, and wall cracking. Brick–wood structures and non-seismic fortification masonry structures suffered relatively minor damage, mainly characterized by cracks at the intersections of longitudinal and transverse walls, as well as diagonal cracks around door and window openings. In urban buildings, reinforced concrete frame structures are more prevalent, with damage primarily concentrated on infill walls, stairwells, suspended ceilings and decorative surfaces. In seismic-resistant masonry structures, the damage primarily involves the failure of non-structural components such as parapets and canopies. The primary causes of seismic damage are construction defects and the absence of seismic structural measures in self-built houses, insufficient seismic resilience in non-structural components of seismic-resistant structures, and the site amplification effect and secondary seismic hazards, which exacerbate the damage to buildings. Furthermore, improvement measures are proposed based on the seismic damage characteristics of different structures. These include conducting research on the construction techniques of Tibetan-style timber-frame houses, developing design and construction standards tailored to local conditions, and enhancing the seismic performance of non-structural components for seismic-resistant structures. The aim is to provide a scientific basis and engineering guidance for post-disaster reconstruction and earthquake disaster prevention in affected areas. Full article

Urban rail transit projects situated in Quaternary deposits are progressively influenced by ultra-shallow gas. During the investigation and construction phases, this gas may instigate gas outbursts, combustion, explosions, stratum disturbances, and secondary ground deformations. To transparently and applicably identify the most crucial disaster-inducing factors in engineering practice, this research constructs a hierarchical risk factor evaluation framework for shallow gas hazards during the investigation stage of rail transit engineering. Initially, candidate indicators were screened via a literature review of shallow gas hazard studies and metro engineering reports. Subsequently, by employing the AHP, four first-level indicators and fifteen second-level indicators were compared and weighted. The findings indicate that shallow gas pressure, methane content per ton of soil, and the occurrence form of shallow gas are the three most influential factors, with comprehensive weights of 0.2735, 0.2319, and 0.1113 respectively. A metro tunnel case in Guangdong Province was then utilized to illustrate how the ranked indicators can guide the verification of suspected zones, section-based hazard discrimination, and the planning of controlled gas release. In comparison with existing studies that concentrate on descriptive disaster phenomena or single-factor analyses, the contributions of this study are threefold. Firstly, it offers a structured indicator system specifically tailored to Quaternary shallow gas in rail transit engineering. Secondly, it makes the expert-based weighting process explicit. Thirdly, it links the ranking results to practical investigation and prevention decisions. This framework is intended as a preliminary engineering decision support tool rather than a substitute for detailed predictive modeling or large-sample statistical validation. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

Short-term precipitation forecasting is essential for disaster prevention, urban management, and weather-sensitive decision making, yet radar-based nowcasting remains challenging because precipitation systems evolve nonlinearly and high-frequency echo structures are easily over-smoothed by deterministic sequence models. This paper proposes a ViT-modulated diffusion spatiotemporal prediction network (VSTPN) that cascades a spatiotemporal prediction module with a ViT-conditioned diffusion refinement module. The spatiotemporal module models the temporal evolution of radar echoes, whereas the ViT-Diffusion module uses global contextual features as conditional guidance during iterative denoising to refine spatial structures. Experiments on the HKO-7 benchmark show that VSTPN achieves lower MSE and higher SSIM than the tested baselines and improves CSI, HSS, and POD at the evaluated reflectivity thresholds. At the 40 dBZ threshold, the model improves CSI, HSS, and POD, while its FAR is slightly higher than that of ETCJ-PredNet, indicating a recall–false alarm trade-off for intense echoes. Additional post-hoc diagnostic analyses of relative gains, metric consistency, threshold sensitivity, and component effect sizes further support the stability of the reported improvements under the current experimental protocol. The results suggest that coupling spatiotemporal sequence modeling with diffusion-based radar echo refinement is a feasible direction for short-term precipitation forecasting; nevertheless, probabilistic uncertainty evaluation, multi-domain validation, and additional generative-quality metrics remain important directions for future work. Full article

Short-term precipitation forecasting is an important research direction in meteorological studies, holding significant implications for disaster prevention and mitigation, urban flood drainage, and agricultural meteorological management. Existing deep learning models have achieved favourable results in modeling local features, yet they generally suffer from insufficient sensitivity to heavy precipitation areas, limitations in modeling temporal dependencies, and gradient instability issues. To address these limitations, we propose a novel spatiotemporal dual-branch neural network (ST-DualNet) for short-term precipitation forecasting based on radar echo maps. The network comprises a temporal branch (based on an enhanced ST-DConvLSTM) and a spatial branch (based on dilated convolutions and Transformer), respectively capturing the dynamic evolution and spatial structural features of precipitation. The two branches are integrated through the CBAM attention module and 3D convolution layer to achieve cross-branch feature fusion and prediction output. Experimental results demonstrate that ST-DualNet outperforms multiple mainstream models on the KNMI radar precipitation dataset, especially in heavy precipitation forecasting, providing an effective new framework for short-term precipitation forecasting. Full article

Urban resilience to earthquakes refers to the capacity of cities to anticipate, absorb, and adapt to seismic shocks through a combination of structural, institutional, and social mechanisms. In the context of Japan, one of the world’s most seismically active countries, this concept has evolved through both post-disaster learning and anticipatory planning. This entry examines two complementary trajectories of urban resilience in Japanese cities: reactive resilience, which develops through adaptation after a destructive event, and proactive resilience, which is embedded in preventive policies and preparedness strategies before a shock occurs. The city of Kobe, following the 1995 Great Hanshin Earthquake, exemplifies a reactive trajectory shaped by institutional reform, community mobilization, and regulatory change. In contrast, Tokyo represents a proactive resilience model based on stringent seismic standards, advanced monitoring and early warning systems, and a widespread culture of disaster preparedness. By comparing these trajectories, the entry outlines a conceptual framework for understanding urban seismic resilience as a dynamic process that integrates social adaptation, governance, and technological innovation. Full article

Urban emergency shelters constitute essential spatial elements within the framework of urban disaster prevention and mitigation. Addressing the shortcomings of existing evaluation methods, which often overlook the relationship between shelters and their served populations, this study utilizes Xi’an as a case study to develop a resilience assessment model that integrates supporting facilities, operational efficiency, and safety performance. To link this model to the served population, the research incorporates the service population pressure index and employs the Gini coefficient alongside the Lorenz curve to assess the congruence between shelter resilience and population distribution. Moreover, the introduction of the intervention priority index and population vulnerability index facilitates a comprehensive determination of shelter intervention priorities. The results reveal that emergency shelters in Xi’an display a spatial pattern characterized by a “single core with multiple centers,” with higher resilience levels, service pressures, and intervention priorities concentrated in the central urban area and lower values observed in peripheral zones. Additionally, a significant spatial mismatch is identified between shelter resilience and population service demands. Despite relying on static population data and not accounting for the effects of population migration, the evaluation framework presented in this study offers a transferable methodological reference for the comprehensive evaluation of shelters in densely populated urban areas, contributing to sustainable urban development. Full article

Given that flood disasters often occur in areas without sufficient instrumentation, conventional observation networks alone may be inadequate to capture the actual evolution of flood events. However, video content from diverse sources such as smartphones, dashboard cameras, surveillance cameras, and helicopters or drones used for rescue, reconnaissance, or reporting, are increasingly collected incidentally and are gaining attention as complementary sensing modalities. Quasi-viewpoint fixation is key to quantitative hydraulic measurements because such videos involve significant changes in their point of view over the course of a given clip. To this end, we developed a quasistatic stabilization technique for social sensing referred to as QS4. The results of a laboratory experiment show that QS4 reproduced velocity distributions comparable to those of a fixed camera for pan-dominant videos recorded with a moving camera. In the 2024 Tsukada River flood, QS4 yielded stable velocity fields despite high turbidity, driftwood, and partial occlusions. Following the blockage of a bridge, flows were detoured along buildings shifting from a channel to flow through farmland, where ~5 m/s flows caused building failures that could not be detected by fixed observations. QS4 offers a practical pathway for transforming incidental videos into quantitative hydraulic observations. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

The underground urban rail transit (URT) is usually vulnerable to waterlogging caused by rainstorms, and floods run into the URT systems mainly via stations. Because of the increasing rainstorms due to global warming, assessing and improving the waterlogging resilience of URT stations is essential for preventing flooding disasters in URT. Here, an entropy weight–TOPSIS method is proposed to assess the waterlogging resilience of metro stations in Changsha, China. Firstly, 20 assessment indicators were selected from stability, resistance, and recovery of the system, respectively. Then, the entropy weight method was used to determine the objective weight of each indicator, and the TOPSIS method was applied to calculate the resilience index of metro stations. The results indicate that among the 137 metro stations, there are 26 low-resilience ones, 64 medium-resilience ones, and 47 high-resilience ones. The waterlogging resilience of metro stations shows a decreasing trend from the urban periphery to the urban center, and the low-resilience stations are predominantly located in the eastern low-altitude flat areas of Changsha. Finally, the countermeasures are proposed to improve the resilience of metro stations. Full article

Global climate change continues to intensify, leading to an increase in extreme meteorological disasters characterized by high intensity, frequency, and extensive impact. Chinese cities are facing increasingly severe flood disaster risks. As the fundamental unit of the urban system, scientifically quantifying a community’s post-disaster recovery capacity provides a crucial basis for formulating disaster prevention and mitigation strategies. Existing research has largely focused on either quantitative resilience assessment of communities or the functional recovery of specific systems within communities, falling short of meeting the quantitative needs for assessing community functional recovery after flood disasters. Given this, this paper aims to construct a community functional recovery model based on different land use types to precisely quantify the recovery trajectory of community functions. First, the MIKE 21 two-dimensional hydrodynamic model is employed to simulate 100-year and 200-year flood scenarios, obtaining dynamic inundation data at the community scale. Subsequently, a semi-Markov process is adopted to model the recovery of individual buildings, with the aggregated building functions within the community summarized to derive building recovery curves. A road network topology model is constructed using the Space L method, and network global efficiency is applied to quantify community road functionality. Green space functional loss is quantified based on the percentage of inundated areas. Finally, calculation is performed based on the proposed dual-layer computational framework consisting of a connectivity layer and a functional layer, and the overall community functional recovery curve after the disaster is generated, thereby achieving precise quantification of the recovery process. The research findings indicate that increased disaster intensity significantly amplifies functional losses and recovery delays. Concurrently, distinct land use types exert markedly different impacts on community recovery. This study quantitatively reveals the phased dominant roles of various land use types throughout the community recovery process, providing a scientific basis for formulating phased, prioritized resilience enhancement strategies. Full article

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

As typical natural disasters in coastal areas, node failure and link interruption caused by typhoons directly threaten the operation stability of the freight multimodal transportation network (FMTN) in urban agglomerations. Such disruptions, in turn, restrict the sustainable development of the regional transportation and logistics system. In order to scientifically assess the FMTN resilience level in coastal area urban agglomerations under typhoon disturbances, this study constructs a resilience assessment method that integrates structural performance and functional performance. The Spatial Local Failure model and the Monte Carlo method, combined with fragility curves, are used to dynamically simulate the damage process of FMTN nodes and links by different typhoons intensities. By constructing FMTN resilience performance function, the resilience ratio is used to quantitatively assess the damage resistance and resilience maintenance level of FMTN under disturbances. This study also analyzes the resilience difference between FMTN and its sub-networks. The Typhoon Bebinca case is applied to validate the application of FMTN assessment method. The results show that FMTN exhibits stronger invulnerability and robustness under typhoon disturbances, and its resilience is significantly better than that of sub-networks. Specifically, when a strong typhoon hits, the FMTN resilience ratio only decreases by 0.13, while the resilience ratio of each sub-network decreases significantly by 0.21, 0.42, 0.46 and 0.57, respectively. FMTN resilience under typhoon disturbances is further assessed through an example analysis. And it verifies not only the comprehensive advantage of FMTN under typhoon disturbances but also the rationality and practicability of the assessment method. The findings can provide an important theoretical basis and technical support for resilience assessment, disaster prevention, mitigation planning, and the sustainable development of FMTN in coastal area urban agglomerations. It is of great practical significance to promote the efficient operation of China’s FMTN. Full article