Search Results (128)

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

In response to the increasing frequency of urban rainstorms, this study focuses on investigating the friction coefficient related to pedestrian instability under urban road flooding conditions. The objective is to conduct an in-depth analysis of the friction coefficient between pedestrians and the ground in actual flood scenarios and its variations, providing practical data to support future pedestrian safety assessments under flood conditions. Wet friction coefficient experiments were conducted under waterlogged conditions, with real human subjects tested across various operational scenarios. A buoyancy calculation formula was introduced to explore the impact of pressure changes caused by buoyancy on the human body in water, influencing the friction coefficient. An exponential relationship between pressure and the friction coefficient was established. Furthermore, by considering factors such as outsole hardness, ground type, and pressure variations with water depth, a dynamic method for selecting the friction coefficient was proposed, offering a scientific basis for determining friction coefficient thresholds associated with pedestrian instability risks. Experimental results indicate that, in the combination of hydrophilic materials with experimental asphalt and cement pavements, the friction coefficient under waterlogged conditions is generally higher than under dry conditions. However, as pressure increases, the friction coefficient of rubber materials decreases. This study concludes that the selection of the friction coefficient in pedestrian instability analysis should be treated as a dynamic process, and relying on a fixed friction coefficient for force analysis of pedestrian instability may lead to significant inaccuracies. Full article

Small reservoirs in hilly areas serve as critical water conservancy infrastructure, playing an essential role in flood control, irrigation, and regional water security. However, dam-break events pose significant risks to downstream urban areas, threatening the sustainability and resilience of cities. This study takes Guangyuan City as a case study and employs numerical simulation methods—including dam-break modeling, hydrological modeling, and hydrodynamic modeling—to analyze the impact of dam-break floods on downstream urban regions. The results reveal that dam failure in small reservoirs can cause peak flood velocities exceeding 15 m/s, severely endangering urban infrastructure, ecosystems, and public safety. Additionally, for reservoirs with large catchment areas, dam-break floods combined with rainfall-induced flash floods may create compound disaster effects, intensifying urban flood risks. These findings underscore the importance of sustainable reservoir management and integrated flood risk strategies to enhance urban resilience and reduce disaster vulnerability. This research contributes to sustainable development by providing scientific insights and practical support for flood risk mitigation and resilient infrastructure planning in mountainous regions. 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

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies. Full article

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities. Full article

Currently, one of the major threats to cities is the escalating risk of flooding, which is attributed to the alteration of climate and hastened urbanization. The purpose of this study was to introduce the Strahler ordering method for simplifying drainage networks and to avoid randomness in developing flooding models. A coupled hydrodynamic model that combines SWMM and LISFLOOD-FP was developed to simulate urban inundation. Results showed that the coupled model had satisfactory applicability for waterlogging simulation. The Strahler ordering method could construct clear topological relations of the drainage network and showed good performance in drainage network simplification. Higher-density drainage networks could increase peak discharge and total volume of discharge, while decreasing the maximum water depth and the total inundation area. Taking “5.29” rainstorm events as an example, compared to Level 3, the relative rates of change in the total flow and peak flow of Level 2 and Level 1 networks are −33.18% and −23.29%. The total inundation area was decreased from 14.14 ha to 1.43 ha when the level of drainage network hierarchy was increased from Level 1 to Level 3. This study highlights the importance of re-assessment of current and future urban drainage networks for coping with the changes in urban floods induced by local and large-scale changes. Full article

The relentless growth of the global population, coupled with increasing biotic and abiotic stresses on crops, poses a major challenge: enhancing agricultural productivity while mitigating these stresses and reducing chemical inputs. Insect farming has led to the large-scale production of insect frass, a nutrient-rich by-product with biofertilizer and biostimulant potential. This review examines the effects of frass on plant stress responses, including its mechanisms of action and possible negative effects. Regarding abiotic stress, frass from certain insects improves plant resilience to drought, waterlogging and salinity, while facilitating heavy metal sorption and complexation in contaminated soils. For biotic stress, frass contains antifungal, antibacterial, and nematicide compounds, as well as entomopathogenic fungi, all of which can reduce pest survival. Additionally, frass activates plant defense mechanisms, such as the increased expression of the defense-related genes involved in stress signaling and immune activation. However, some studies report negative effects, including pathogen dispersion, pest attraction, and the inhibition of beneficial microorganisms commonly used as biopesticides. Despite these risks, frass is a promising alternative for sustainable agriculture, reducing chemical dependency while improving plant resilience. Nevertheless, further research is needed to mitigate its potential risks and optimize its agricultural application. Full article

The waterlogged archaeological wood from the Qiantang River Ancient Seawall site faces significant preservation challenges due to its unique and complex preservation environment. Without targeted dehydration and consolidation treatments after excavation, these artifacts are at risk of severe deformation, cracking, or even complete destruction. This study focuses on the waterlogged wood from Chaitang (bundled firewood structure) and Zhulong Shitang (bamboo–stone structure) within the ancient seawall, comparing two methods: ethanol dehydration and polyethylene glycol (PEG) dehydration. Both methods were combined with natural drying for comparative analysis. In addition to traditional metrics such as dimensional stability and weight percentage gain, the study employs a multidimensional evaluation framework, including colorimetric analysis, scanning electron microscopy (SEM), and X-ray diffraction (XRD), to comprehensively assess the effectiveness of dehydration and consolidation. Combining natural drying with PEG, although it may reduce the chromaticity of WAW to some extent, effectively fills cellular cavities, enhances diffraction peak intensity, improves dimensional stability, and effectively prevents cracking and deformation. The results provide differentiated treatment strategies for WAW from different historical periods and varying degrees of degradation. This study offers valuable insights and a scientific basis for the further restoration and preservation of the WAW from the Qiantang River Ancient Seawall. Full article

This study utilizes ArcGIS, the InVEST model, and the SCS model to analyze remote sensing data from the central urban areas of Chengdu. The analysis simulates water yield and runoff within the study area while calculating the water conservation capacity for each land use type using the water balance method. This study aims to address the challenges faced by Chengdu in implementing its sponge city initiatives. The results reveal that the spatial distribution of direct runoff generally follows a pattern of “low in the periphery and high in the center”. Transportation, commercial, industrial, and residential land types account for 74.7% of the total surface runoff within the study area, emphasizing their importance in urban rainwater management and sponge city development. Water yield varies across different land use types, with water bodies exhibiting the lowest capacity and artificial land exhibiting the highest capacity. This pattern initially exhibited a downward trend before increasing, with land use type, climatic factors, and vegetation coverage identified as the primary drivers of water yield. The water conservation capacity of the study area gradually decreased, with higher values observed in the east and south and lower values in the north and west. These trends and spatial differences can be attributed to urban expansion and alterations in land cover. Based on these findings, this study assessed the risk of urban waterlogging and provided recommendations for optimizing low-impact development (LID) strategies. This study provides a scientific foundation for the development of sponge city initiatives, urban waterlogging mitigation, and rainwater management strategies in Chengdu. Full article

The identification of waterlogging driving factors and the assessment of associated risks are of utmost importance to enable cities to sustain their development. Initially, this paper utilizes the kernel density estimation (KDE) technique to visually display the spatial distribution features of waterlogging points within the downtown region of City B. Employing a spatial analysis method, the examination through the application of Global Moran’s I reveals that the central urban area of City B exhibits a spatial clustering distribution. Moreover, nine influencing factors, including terrain characteristics, land cover features, and infrastructure construction aspects, are chosen as the elements that drive the continual occurrences of waterlogging due to rainstorms incidents. By applying the geographic detector (GD) and random forest regression (RF) models, an in-depth exploration into the agents leading to rainstorm waterlogging is conducted. The outcomes demonstrate that the surface impervious rate stands out as the primary factor. Additionally, under the geographic detector model, it has been verified that the integrated effect of two factors is more significant than that of a solitary factor, with the interaction between the surface impervious rate and community density having the most prominent influence on waterlogging situations within the investigated area. Finally, through the utilization of the random forest model, the sensitive areas inclined to experience waterlogging in the investigated area are demarcated. The findings of this research can offer valuable references for the management of urban rainstorm waterlogging as well as the sustainable development of cities. Full article

Urban flood susceptibility has emerged as a critical challenge for cities worldwide, exacerbated by rapid urbanization. This study evaluates urban flood susceptibility under different Shared Socioeconomic Pathways (SSPs) in the context of urbanization. A coupled modeling approach integrating the System Dynamics (SD) model and the Future Land Use Simulation (FLUS) model was employed to project future land use changes under sustainable development, moderate development, and conventional development scenarios. Additionally, an XGBoost model was developed to assess urban flood susceptibility. The results indicate that urban construction land will continue to increase over the next 30 years, with the extent of growth varying across different scenarios. Notably, under the conventional development scenario, rapid economic growth leads to a significant expansion of built-up land and a sharp decline in ecological land, which in turn exacerbates the urban flood susceptibility. Consequently, urban flood susceptibility is projected to increase across all three scenarios, albeit at varying rates. Specifically, under the sustainable development scenario, 27% of Guangzhou is projected to face high flood risk. In the moderate development scenario, the area classified as high-risk increased by 868.73 km². Under the conventional development scenario, the high-risk area expanded from 1282.9 km² in 2020 to 2761.33 km², representing a 16% increase. These differences are primarily attributed to changes in land use, which alter surface runoff and subsequently enhance the city’s vulnerability to waterlogging. This study provides a comprehensive framework for assessing urban flood susceptibility in the context of urbanization, offering valuable insights for formulating targeted flood prevention and mitigation strategies. Full article

With the intensification of climate change and the continuous advancement of urbanization, the pressure on urban drainage systems has increased, leading to the growing prominence of urban waterlogging issues. Besides the destruction of infrastructure, urban waterlogging also affects environmental quality, economy, and residents’ daily lives. Researchers have recently analyzed the causes of urban waterlogging from multiple perspectives, including land-use changes driven by urbanization, the inadequacy of urban drainage systems, and extreme rainfall events resulting from climate change. Various strategies have been proposed to address waterlogging, including optimizing urban green spaces, establishing forecasting systems, and creating effective emergency management systems. Additionally, some scholars highlight the significance of integrated urban planning and interdepartmental collaboration, suggesting that multi-party cooperation can help mitigate the risks of waterlogging. This paper conducts a comprehensive literature review to summarize the current research status of urban waterlogging, focusing on theoretical, experimental, numerical simulation, and artificial intelligence approaches. The review aims to provide a clearer understanding of the existing knowledge, identify gaps for future research and propose ideas that combine advanced technologies and interdisciplinary approaches. Full article

In recent years, the increasing frequency of extreme rainfall has exacerbated urban waterlogging, which has seriously constrained the sustainable development of cities. Given the problem that the impact of social information on waterlogging risk is easy to ignore in the urban risk waterlogging assessment process, it is of great significance to carry out a comprehensive waterlogging risk assessment and identify the waterlogging risk for urban waterlogging prevention and control. Based on the hazard–vulnerability assessment framework, this study comprehensively considers the flood disaster hazard and socio-economic vulnerability to carry out a multi-scenario urban waterlogging risk assessment in the central urban area of Zhoukou. The results show that, in comprehensive risk assessment, the area proportions are expressed as medium risk > low risk > higher risk > high risk. For a single waterlogging hazard assessment, the area proportions are shown as low risk > medium risk > higher risk > high risk. The difference ranges in area proportions of low, medium, higher, and high risk are (−61.00%, −54.00%), (49.00%, 56.00%), (1.30%, 2.70%), and (1.80%, 4.00%), respectively. It can be seen that compared with the single waterlogging hazard assessment, in the comprehensive waterlogging risk assessment with the introduction of the vulnerability factor, the waterlogging risk in the area with highly waterlogging vulnerability increases correspondingly, while the waterlogging risk in the area with low waterlogging vulnerability decreases relatively, and the waterlogging risk assessment results are more in line with the actual situation. Full article

In recent years, power outages due to typhoon-induced rainstorms, waterlogging, and other extreme weather events have become increasingly common, and accurately assessing the risk of damage to the distribution system during a disaster is critical to enhancing the resilience of the power system. Therefore, a risk assessment method for power distribution systems considering the spatiotemporal characteristics of the typhoon disaster chain is proposed. The mechanism of forming the typhoon disaster chain is first analyzed and its spatiotemporal characteristics are modeled. Secondly, the failure probability of the distribution system equipment during the evolution process of the disaster chain is modeled. Then, the non-sequential Monte Carlo state sampling method combined with the distribution system risk assessment index is proposed to establish the disaster risk assessment system of the distribution system. Finally, based on the IEEE 33-bus power system, the proposed distribution system disaster risk assessment method is verified. Simulation solutions show that the proposed assessment method can effectively assess the disaster risk of the distribution system under the influence of the typhoon disaster chain. The simulation results show that at the time step of typhoon landfall, the load shedding reaches 1315.3 kW with a load shedding rate of 35.4%. The total economic loss at the time step is 2,289,200 CNY. These results demonstrate the effectiveness of the proposed method in assessing disaster risks and improving the resilience of power systems during typhoon events. Full article