A framework for assessing commuting delay driven economic loss of urban waterlogging

An urban waterlogging footprint accounting based on emergy: A case study of Beijing

Investigating relationships between landscape patterns and surface runoff from a spatial distribution and intensity perspective

Urban waterlogging events have become more frequent in big cities, causing a considerable economic loss and casualties. Waterlogging disasters exert increasingly severe influences on cities, which has become a research hotspot under changing environment. In view of that problem of insufficient information in the waterlogging emergency management, it is necessary to build emergency management pattern to adapt to the changing environment of urban waterlogging and design support emergency management pattern implementation of adjustment system. Through the process of "pre-event, in-event and after-event", a set of emergency pattern is designed to face the urban waterlogging event, which ensures that the management personnel could grasp the state of the occurrence of the event at each stage of the event and take countermeasures for the occurrence of the event quickly. The digitalization and visualization of urban waterlogging emergency management can be realized by using customized components, drawing knowledge map and designing adjustment system. According to the urban waterlogging emergency management pattern, the Beijing emergency plan component and the component based on similar historical case analysis are customized, and the functions of the components are realized through the integrated adjustment system. The application shows that emergency management adjustment system of urban waterlogging can realize the quick use of information, offer users customized waterlogging emergency management services, and provide the management with the support of effective emergency management decision. The results can provide conceptual and technical support for urban emergency management. It can also improve the timeliness of urban response and the level of urban flood control.

With the rapid development of China's economy, the number of victims and economic losses caused by Urban Waterlogging continue to increase, and Urban Waterlogging has become the core problem to be solved. Considering the blind spots of related research in the study area, it is necessary to carry out an Urban Waterlogging diagnosis systematically and scientifically in Xining City, so as to provide targeted guidance for solving the waterlogging problem in Xining city. In the framework of Dualistic Hydrological Cycle Process, combined with PSR evaluation model, the pressure P system is composed of urbanization level characteristics, land use characteristics and rainfall evolution characteristics, the state S system is composed of historical waterlogging and current waterlogging, and the response R system is composed of sponge city construction, drainage operation management and drainage network construction. The comprehensive results show that the main causes of Urban Waterlogging in Xining city can be summarized as increased pressure, unbalanced state and scattered response.

With the acceleration of urbanization and due to the impact of climate warming, economic losses caused by urban waterlogging have become increasingly severe. To reduce urban waterlogging losses under the constraints of limited economic and time resources, it is essential to identify key waterlogging-prone areas for focused governance. Previous studies have often overlooked the spatial heterogeneity in the distribution of value and risk. Therefore, identifying the spatial distribution of land value and risk, and analyzing their spatial overlay effects, is crucial. This study constructs a “Waterlogging-Value-Loss” spatial analysis framework based on the hydrological and value attributes of land use. By developing a 1D–2D coupled hydrodynamic model, the study determines waterlogging risk distributions for different return periods. Combining these results with disaster loss curves, it evaluates land-use values and employs the bivariate local Moran’s I index to comprehensively assess waterlogging risk and land value, thereby identifying key areas. Finally, the SHAP method is used to quantify the contribution of water depth and value to waterlogging losses, and a Birch-K-means combined clustering algorithm is applied to identify dominant factors at the street scale. Using the central urban area of Beijing as a case study, the results reveal significant spatial heterogeneity in the distribution of urban waterlogging risks and values. Compared to traditional assessment methods that only consider waterlogging risk, the bivariate spatial correlation analysis method places greater emphasis on high-value areas, while reducing excessive attention to low-value, high-risk areas, significantly improving the accuracy of identifying key waterlogging-prone areas. Furthermore, the Birch-K-means combined clustering algorithm classifies streets into three types based on dominant factors of loss: water depth-dominated (W), value-dominated (V), and combined-dominated (WV). The study finds that as the return period increases, the dominant factors for 22.23% of streets change, with the proportion of W-type streets rising from 29% to 38%. This study provides a novel analytical framework that enhances the precision of urban flood prevention and disaster mitigation efforts. It helps decision-makers formulate more effective measures to prevent and reduce urban waterlogging disasters.

Quantifying the direct and indirect impacts of urban waterlogging using input‒output analysis

The global rising level of climate change has caused significant disruptions in city traffic patterns due to intense heavy rains causing urban waterlogging disasters worldwide. These disasters have also resulted in massive economic losses and casualties. In consideration of these issues, the current challenge for urban disaster emergency response is to determine strategies to arrange for emergency vehicle scheduling as soon as urban waterlogging expands in a way that minimizes casualties and financial losses. This article proposes the Locally Constraint Evolutionary algorithm for Vehicle Evacuation Scheduling (LCEVES), which includes the following features to address the vehicle emergency evacuation problems in urban waterlogging zones specifically for regulated vehicles (e.g., buses, vehicles transporting hazardous materials, etc.). Firstly, a general technical route for vehicle emergency evacuation under urban waterlogging risk, secondly an optional routes search mechanism for vehicle evacuation in the waterlogging area and thirdly a locally constraint evolutionary algorithm for vehicle evacuation in the waterlogging area. The experiments show that LCEVES improves vehicle evacuation efficiency and reduces the risk of affected vehicles, providing an effective means for vehicle evacuation in the waterlogging zone. This research provides an effective method for solving vehicle evacuation scheduling challenges during urban waterlogging and also has significance for vehicle scheduling in other types of disasters.

Economic loss of urban waterlogging based on an integrated drainage model and network environ analyses

Urban waterlogging has become a frequent and threatening issue in recent years due to rapid urbanization and extreme weather conditions, resulting in economic losses and health hazards. In this context, green roofs (GRs) emerge as a sustainable and innovative solution to mitigate these issues by absorbing rainfall, reducing runoff, and enhancing urban biodiversity. Despite the apparent benefits, the adoption of GRs remains limited, largely due to a lack of quantitative understanding of the factors that influence urban residents' GR adoption willingness.This study aims to fill this knowledge gap via a survey approach, and distribute and collect survey responses from 999 residents in Shenzhen, a rapidly developing coastal city in China. The survey is designed to capture a range of variables that may influence residents' decision-making regarding GR adoption, including demographic information, housing characteristics, waterlogging experiences, roof utilization preference, knowledge of and preference for GR, and willingness to adopt GR. The GR adoption willingness is collected assuming two policy scenarios, one with government subsidy and the other without. By leveraging a machine learning model for data analysis, the study identifies five key predictors that commonly influence GR adoption willingness with and without subsidy: recognition of the advantages of GRs (GR_advantage), whether a resident lives on the top floor (Top_floor), the degree of concern about GRs (GR_concern), the duration of waterlogging experienced in and around the community (WL_time), and the individual's level of education (Education). Interestingly, the study also reveals that GR adoption willingness is affected differently under scenarios with and without policy incentives. In the absence of subsidies, the property fee (Pro_fee) is a significant factor; conversely, when policy incentives are present, age and house ownership (House_own) emerge as influential factors.The complexity of these influencing factors is further evaluated using the SHAP (SHapley Additive explanation) model, which provides a nuanced interpretation of how these factors interact and exert nonlinear impacts on residents' willingness to adopt GRs. The insights derived from this analysis are critical for policymakers and urban planners who are looking to promote GRs as part of an integrated urban water management strategy. Specifically, a combination of long-term subsidies and one-time subsidies can be combined to motivate residential adoption. Recognizing the general unfamiliarity with GRs and related policies among residents, relevant outreach and education programs are essential. In addition, targeted subsidy levels could be helpful in stimulating more GR adoptions. An important consideration in this targeting process is the frequency of waterlogging events, which has been shown to significantly influence residents' willingness to pay for GRs. 

In the last few decades, rainfall-induced urban waterlogging has become a significant environmental barrier and acquired global prominence worldwide due to its frequent threat, which results in significant infrastructure damage and economic loss. This study aims to model and identify the waterlogging hazard, vulnerability, and risk zones in the unplanned city of Siliguri, ‘Gateway of North-east India’, with the help of an integrated Analytical hierarchy process (AHP) and GIS techniques. Due to the lack of a comprehensive database, a systematic waterlogging assessment in the Siliguri city has not yet been carried out. However, waterlogging is a seasonal phenomenon in the Siliguri city, especially during the monsoon seasons, when short-duration and high-intensity rainfall cause inundation in low-lying areas causing mayhem to the city. Therefore, in this present study, a primary field investigation was conducted to prepare waterlogging inventory map along with seventeen other parameters, including spatial and attribute data from the secondary sources to delineate waterlogging hazard, waterlogging vulnerability, and risk map. Further, the final risk map was integrated with the spatial distribution of the slums locations, revealing that a larger proportion of slum households are under high-risk zones. The results suggest that about 46% of the city is under high to very high waterlogging hazard zones, while 38% of the Siliguri is highly vulnerable to waterlogging. The final risk map reveals that around 35% of the study area is susceptible to threat of waterlogging, which is mostly concentrated in the central part of the city center. Besides, the consistency of the model was assessed by area under curve (AUC), which gives an accuracy of 0.782 or 78.2%. This study's overall strategy may be used for planning and mitigation efforts to reduce future waterlogging incidents all around the world.

The frequent extreme weather events have led to dramatic urban waterlogging and flash flood hazards in recent years, which result in heavy casualties and serious economic losses. Mountainous cities like Fuzhou, China, usually experience both types of hazard concurrently and are susceptible to these hazards. Therefore, it is necessary to take comprehensive defense measures to prevent and/or mitigate the impacts of these hazards. In this study, the authors first reviewed the progress of prevention measures of these two types of hazards in China through the nationwide construction of sponge cities and national flash flood prevention projects. The authors then analyzed the characteristics of waterlogging and flash flood hazards in China using 25 years of records from the Chinese government and elaborated the causes and existing issues. Results show that flash floods and urban waterlogging in mountainous cities have exacerbated the severity of flood hazards. Based on the typical effective measures of such flood hazard prevention systems, the authors also discussed the possible comprehensive preventive measures to be implemented in Fuzhou via the spongy city construction, which can provide a reference for future flood management.

The occurrence of waterlogging disaster has brought great economic losses to the society and brought great threats to the safety of the city, people’s lives and the sustainable development of the society. Use big data technology to dynamically monitor urban waterlogging network public opinion, use data mining and other data mining technology, real-time collection of urban waterlogging public opinion data, deeply analyze the evolution trend of public opinion heat, focus on key points and emotional tendency. Taking the sample of the July 16 flood event in Beijing, this paper studies the transmission characteristics of urban waterlogging events and reveals the dynamic mechanism of its development. It is found that there is a significant positive correlation between the high incidence period of urban waterlogging disasters and the heat of public opinion and its impact level on waterlogging events. The social attention to the waterlogging problem mainly focuses on the construction of the drainage system, the progress of the waterlogging disaster and the disaster relief work. The development process of public opinion in urban waterlogging events has gone through five periods: incubation period, outbreak period, spread period, repeated period and extinction period. According to the development characteristics of urban public opinion, corresponding countermeasures are formulated to provide decision-making basis for public opinion handling of urban waterlogging emergencies.

Application of Multiple Geographical Units Convolutional Neural Network based on neighborhood effects in urban waterlogging risk assessment in the city of Guangzhou, China

As a common concern of the public and a major problem for urban construction and management, urban water logging disasters, especially in coastal cities, which are caused by extreme weather such as typhoons and continuous heavy rains, not only affect the public security of the city, the production and living order of the residents, but also lead to a lot of economic losses. Along with the continuous occurrence of urban water logging disasters, the problems of not paying attention to the drainage project and weak awareness of flood prevention in the process of urban construction have become increasingly conspicuous. Taking Ningbo City as an example, the thesis analyzes the causes of urban water logging disasters from different perspectives, and proposes specific measures to improve the prevention and management ability of urban water logging disasters in Ningbo, so as to offer helpful reference for solving similar problems to solve urban water logging disasters.

Assessing Economic Loss from Urban Waterlogging in Beijing under Climate Change Using a Hydraulic Model