Search Results (110)

Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogging-affected maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous patch sizes, and a Boundary Enhancement Module to refine transition zones. A pixel-level dataset containing 12,198 image patches was constructed from 62 multispectral scenes collected across five major maize-producing cities in Heilongjiang Province, China, during 2022–2024. On the test set, SAB-DeepLabV3+ achieved a waterlogged-class IoU of 68.30%, mIoU of 80.37%, mF1 of 88.62%, and OA of 93.49%, outperforming DeepLabV3+. Leave-one-city-out evaluation further produced an average mIoU of 76.56% and a waterlogged-class IoU of 63.45%. These results indicate that single-date high-resolution multispectral imagery can support rapid and reliable maize waterlogging mapping. 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

Risk assessment is an effective management tool for mitigating waterlogging disasters. In this study, a novel airport waterlogging risk assessment framework based on the analytic network process with triangular fuzzy numbers (TFN-ANP) was developed to evaluate hazard, exposure, vulnerability, and comprehensive risk under different return periods. The proposed framework was compared with the triangular fuzzy analytic hierarchy process (TFN-AHP). The results indicated that water depth, land cover type, and maintenance cost exerted dominant influences on hazard, exposure, and vulnerability, respectively. Compared with TFN-AHP, TFN-ANP produced different global weight distributions and a broader spatial extent of high-risk areas. Under the 50-year return period, TFN-ANP classified 31.65% of the study area as highest-risk, whereas TFN-AHP did not delineate any highest-risk zones and classified 40.05% of the study area as higher risk. A similar pattern was observed under the 100-year return period. TFN-ANP delineated 35.41% of the study area as being at the highest risk under the 100-year return period. By explicitly accounting for interdependencies among risk factors, TFN-ANP generated more differentiated spatial risk patterns. The proposed framework provides an effective decision-support tool for waterlogging risk management in data-scarce airport environments. Full article

Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional data and the inability to handle severe data imbalance. This study proposes a lightweight and interpretable machine learning framework for real-time waterlogging hotspot prediction, based on a multi-dimensional feature space. Specifically, we implement a Lasso-based mechanism to distill 37 multi-source variables into five core determinants. This process effectively isolates dominant environmental drivers while filtering noise. To further overcome the recall bottleneck, we propose a Synthetic Minority Over-sampling Technique based on Weighted Distance and Cleaning (SMOTE-WDC) algorithm that incorporates weighted feature distances and density-based noise cleaning. Validating the framework on datasets from Shenzhen (2023–2024), we demonstrate that the integrated Gradient Boosting Decision Tree (GBDT) model integrated with this strategy achieves optimal performance using only five features, yielding an F1-score of 0.808 and an Area Under the Precision-Recall Curve (AUC-PR) of 0.895. Notably, a Recall of 0.882 is attained, representing a 4.6% improvement over the baseline. This study contributes a cost-effective, high-sensitivity approach to disaster risk reduction, advancing predictive urban waterlogging management. Full article

(1) Background: The accurate remote sensing extraction of mangroves is often impeded by spectral confusion, particularly the misclassification of stagnant water bodies as mangroves in flat coastal regions. (2) Methods: To overcome this challenge, we propose a novel “spectral-spatial-terrain” stepwise correction framework. This approach integrates multi-source data: Sentinel-2 imagery for spectral pre-screening, Gaofen-2 (GF-2) imagery for geometric refinement, and a newly developed Potential Waterlogging Index (PWI), derived from a digital elevation model (DEM), for topographic correction. The framework was applied to evaluate mangrove damage following Typhoon Yagi (2024) in the East Harbour National Nature Reserve. (3) Results: The method achieved high extraction accuracy, with a Kappa coefficient of 0.97. The remote sensing-based damage assessment revealed that 48.2% of the mangrove area was affected, with a significantly higher damage rate of 63.0% observed within the PWI-identified potential waterlogging zones. (4) Conclusions: The high classification accuracy confirms the effectiveness of the proposed framework. More importantly, the spatially consistent damage pattern provides strong ecological evidence supporting the mechanistic rationale behind the terrain-based correction. This study presents a reliable and transferable remote sensing methodology for high-precision, dynamic monitoring and assessment of mangrove ecosystem after disaster. Full article

With the accelerating pace of urbanization, cities are increasingly affected by rainstorm and flood disasters, which pose severe threats to the safety of residents’ lives and property. Existing models are increasingly inadequate in meeting the accuracy requirements for flood simulation in highly urbanized regions. Thus, it is urgent to develop a new method for flood inundation simulation based on high-resolution urban hydrological units. The novelty of the model lies in the novel structure of the high-resolution Urban Hydrological Units model (HRGM), which replaces coarse sub-catchments with a fine-grained network of urban hydrological units. The primary innovation is the node-based coupling strategy, in which the HRGM provides precise overflow hydrographs at drainage inlets as point sources for LISFLOOD-FP, rather than relying on diffuse runoff inputs from larger areas. In this paper, a high-resolution hydraulic model (HRGM) based on urban hydrological units coupled with a 2D hydrodynamic model (LISFLOOD-FP) was constructed and successfully applied in the Chebeichong watershed. Results show that the model’s simulations align well with observed data, achieving a Nash efficiency coefficient above 0.8 under typical rainfall events. Compared with the SWMM model, the simulation results of HRGM were significantly improved and more consistent with measured results. Taking the rainstorm event on 10 August 2021 as an example, the Nash coefficient increased from 0.7 to 0.85, while the peak flow error decreased markedly from 15.8% to 3.1%. It should be emphasized that urban waterlogging distribution is not continuous but appears as patchy, discontinuous, and fragmented patterns due to the segmentation and blocking effects of roads and buildings in urban areas. The framework presented in this study shows potential for application in other regions requiring flood risk assessment at urban agglomeration scales, offering a valuable reference for advancing flood prediction methodologies and disaster mitigation strategies. Full article

Under the dual pressures of global climate change and accelerated urbanization, the impacts of flood disasters on urban systems are becoming increasingly pronounced. Enhancing regional resilience has emerged as a critical factor in achieving sustainable urban development. Compared with existing methods such as CRITIC–Entropy, PCA–AHP, or SWMM-based resilience evaluations, grounded in urban resilience theory, this study takes Fangshan District in Beijing as empirical research to construct a post-flood disaster resilience evaluation index system spanning five dimensions (ecological, social, engineering, economic, and institutional) and leverages the integrated DEMATEL-TOPSIS-AISM model to synergistically identify key drivers, evaluate performance, and uncover internal hierarchies, thereby overcoming the limitations of existing research approaches. The findings indicate that the DEMATEL analysis identified the frequency of heavy rainfall (a12 = 0.889) and the proportion of flood disaster information databases (c51 = 1.153) as key driving factors. The TOPSIS assessment reveals that Fangshan District exhibits the strongest resilience in the economic dimension (Relative Closeness C = 0.21200), while the institutional dimension is the weakest (C = 0.00000), the AISM model constructs a hierarchical topology from a cause–effect priority perspective, elucidating the causal relationships and transmission mechanisms among factors across different dimensions. This study pioneers a novel perspective for urban resilience assessment, thereby establishing a theoretical foundation and practical references for enhancing flood resilience and advancing resilient city development. Full article

Rhododendron hainanense Merr. is a tropical flowering shrub valued for its strong orna-mental and medicinal properties; however, its horticultural application is limited by its susceptibility to waterlogging disasters. The AP2/ERF transcription factor family plays crucial roles in plant growth, development, and responses to biotic and abiotic stresses; however, its regulatory mechanism in response to waterlogging stress remains unclear. This study conducted a genome-wide analysis of the AP2/ERF transcription factor family in R. hainanense, identifying 142 RhAP2/ERFs genes distributed across 13 chromosomes and classified into five subfamilies. Conserved motif analysis confirmed the characteristic AP2 domain structure. Gene duplication events revealed 16 segmental duplication pairs, indicating a potential role in adaptive evolution. Cis-element and protein interaction analyses suggested involvement in abiotic stress responses. Transcriptome and qRT-PCR results under waterlogging stress showed significant up-regulation of RhERF9 and RhERF95, with RhERF9 expression increasing 130-fold after 3 days, implying a positive regulatory role for the RhERF9 protein in early waterlogging response. Tissue-specific expression highlighted RhERF9’s strong induction in roots, associated with aerenchyma formation and hypoxia adaptation. The identified candidate AP2/ERF genes in R. hainanense play important roles in abiotic stress resistance and lay a foundation for future applications in breeding and horticulture. Full article

Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision magnetic surveys, spontaneous potential (SP) measurements, and transient electromagnetic (TEM) methods. This innovative framework effectively overcomes the limitations of traditional single-method detection approaches, enabling the precise delineation of fire zone boundaries and the accurate characterization of spatial dynamics of coal fires. The key findings of the study are as follows: (1) High-magnetic anomalies (with a maximum ΔT of 1886.3 nT) exhibit a strong correlation with magnetite-enriched burnt rocks and dense fracture networks (density > 15 fractures/m), with a correlation coefficient (R²) of 0.89; (2) Negative SP anomalies (with a minimum SP of −38.17 mV) can effectively reflect redox interfaces and water-saturated zones (moisture content > 18%), forming a “positive–negative–positive” annular spatial structure where the boundary gradient exceeds 3 mV/m; (3) TEM measurements identify high-resistivity anomalies (resistivity ρ = 260–320 Ω·m), which correspond to non-waterlogged goaf collapse areas. Spatial integration analysis of the three sets of geophysical data shows an anomaly overlap rate of over 85%, and this result is further validated by borehole data with an error margin of less than 10%. This study demonstrates that multi-parameter geophysical coupling can effectively characterize the thermo-hydro-chemical processes associated with coal fires, thereby providing critical technical support for the accurate identification of fire boundaries and the implementation of disaster mitigation measures in arid regions. Full article

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

Urban metro systems (UMSs) primarily consist of underground structures and are therefore highly susceptible to disasters, such as rainstorms and waterlogging. The damages caused by such events are often substantial and difficult to recover from, highlighting the urgent need to enhance the resilience of metro networks against waterlogging. Based on the principles of urban hydrology, this paper constructs scenarios to analyze the risk of waterlogging under varying rainstorm recurrence intervals and intensities. The ArcGIS geographic information system was employed to improve the existing passive inundation algorithm, enabling more accurate identification of flood-prone areas during heavy rainfall, which supports the topological modeling of UMSs. Structural connectivity was used as an external indicator of network resilience, and tools such as Gephi and NetworkX were applied to evaluate network performance. Using the Nanjing Metro as a case study, the resilience of the UMS under different risk scenarios was assessed by analyzing the impact of waterlogging events. Subsequently, recovery sequences following disruptions were prioritized to optimize post-disaster restoration, and targeted strategies for improving network resilience were proposed. The calculation results indicate that the overall resilience of the Nanjing UMS network is at a relatively high level. When connectivity is used as the performance indicator, the operating network resilience value is between 0.78 and 0.952, while the planned network resilience value is between 0.887 and 0.939. Full article

Soybean (Glycine max), as an important crop for both oil and grains, is a major source of high-quality plant proteins for humans. Among various natural disasters affecting soybean production, waterlogging is one of the key factors leading to yield reduction. It can cause root rot and seedling death, and in severe cases, even total crop failure. Given the significant differences in responses to waterlogging stress among different soybean varieties, traditional single-trait indicators are insufficient to comprehensively evaluate flood tolerance. In this study, relative seedling length (RSL) was used as a comprehensive evaluation index for flood tolerance. Using a chromosome segment substitution line (CSSL) population derived from SN14 and ZYD00006, we successfully identified seven quantitative trait loci (QTLs) associated with seed waterlogging tolerance. By integrating RNA-Seq transcriptome sequencing and phenotypic data, the functions of candidate genes were systematically verified. Phenotypic analysis indicated that Suinong14 had significantly better flood tolerance than ZYD00006. Further research revealed that the Glyma.05G160800 gene showed a significantly up-regulated expression pattern in Suinong14; qPCR analysis revealed that this gene exhibits higher expression levels in submergence-tolerant varieties. Haplotype analysis demonstrated a significant correlation between different haplotypes and phenotypic traits. The QTLs identified in this study can provide a theoretical basis for future molecular-assisted breeding of flood-tolerant varieties. Additionally, the functional study of Glyma.05G161800 in regulating seed flood tolerance can offer new insights into the molecular mechanism of seed flood tolerance. These findings could accelerate the development of submergence-tolerant rice varieties, enhancing crop productivity and stability in flood-prone regions. Full article

Cotton (Gossypium spp.) is an important industrial crop, but it is vulnerable to waterlogging stress. The relationship between cotton yields and waterlogging indicators (CY-WI) is fundamental for waterlogging disaster reduction. This review systematically summarized and analyzed literature containing CY-WI relations across 1970s–2020s. China conducted the most CY-WI experiments (67%), followed by Australia (17%). Recent decades (2010s, 2000s) contributed the highest proportion of CY-WI works (49%, 15%). Surface waterlogging form is mostly employed (74%) much more than sub-surface waterlogging. The flowering and boll-forming stage, followed by the budding stage, performed the most CY-WI experiments (55%), and they showed stronger negative relations of CY-WI than other stages. Some compound stresses enhance negative relations of CY-WI, such as accompanying high temperatures, low temperatures, and shade conditions, whereas some others weaken the negative CY-WI relations, such as prior/post drought and waterlogging. Anti-waterlogging applications significantly weaken negative CY-WI relations. Regional-scale CY-WI research is increasing now, and they verified the influence of compound stresses. In future CI-WI works, we should emphasize the influence of compound stresses, establish regional CY-WI relations regarding cotton growth features, examine more updated cotton cultivars, focus on initial and late cotton stages, and explore the consequence of high-deep submergence. Full article

Under the frequent occurrence of urban waterlogging disasters globally, underground spaces, due to their unique environmental conditions and structural vulnerabilities, are facing growing flood pressure, resulting in substantial economic losses that hinder sustainable urban development. This study focused on a high-density urban area in China, investigating surface waterlogging conditions under rainfall characteristics as the primary driver of flooding. Focusing on the main nodes—entrances and exits—within the waterlogging disaster chain of underground garages, a risk assessment framework was constructed that encompasses three key dimensions: the attributes of extreme rainfall, the structural characteristics of entrances/exits, and emergency response capacities. Subsequently, a waterlogging risk assessment was conducted for selected underground garages in the study area under a 100-year return period extreme rainfall scenario. The results revealed that the flood depth at entrances/exits and the structural height of entrances/exits are the primary factors influencing flood risk in urban underground garages. Under this simulation scenario, 37.5% of the entrances and exits exhibited varying degrees of flood risk. The assessment framework and indicator system developed in this study provide valuable insights for flood risk evaluation in underground garage systems and offer decision-makers a more scientific and robust foundation for formulating improvement measures. Full article

As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban built-up areas, they demonstrate greater vulnerability to rainfall-induced waterlogging due to their obsolete infrastructure and high heritage value, making it imperative to comprehensively enhance their waterlogging resilience. In this study, Qingdao’s historic urban area is selected as a sample case to analyze the interaction between rainfall intensity, the built environment, and population and business characteristics and the mechanism of waterlogging disaster in the historic urban area by combining with the concept of resilience; then construct a resilience assessment system for waterlogging in the historic urban area in terms of dangerousness, vulnerability, and adaptability; and carry out a measurement study. Specifically, the CA model is used as the basic model for simulating the possibility of waterlogging, and the waterlogging resilience index is quantified by combining the traditional research data and the emerging open-source geographic data. Furthermore, the waterlogging resilience and obstacle factors of the 293 evaluation units were quantitatively evaluated by varying the rainfall characteristics. The study shows that the low flooding resilience in the historic city is found in the densely built-up areas within the historic districts, which are difficult to penetrate, because of the high vulnerability of the buildings themselves, their adaptive capacity to meet the high intensity of tourism and commercial activities, and the relatively weak resilience of the built environment to disasters. Based on the measurement results, targeted spatial optimization strategies and planning adjustments are proposed. Full article