Urban Waterlogging Disaster Research Articles

Modeling and application of urban waterlogging resilience assessment based on PSR–NES framework and analysis of driving mechanisms

ABSTRACT Extreme weather events are escalating globally, intensifying urban waterlogging disasters and endangering inhabitants and assets while hindering socio-economic progress. This study explores factors hindering cities' waterlogging disaster mitigation using urban resilience theory. It introduces a pressure–state–response (PSR)–natural–economic–social (NES) assessment model to evaluate urban waterlogging resilience, focusing on Nanjing as a key city in flood control. Flood scenarios are simulated using the MIKE model for different rainstorm recurrence periods. Urban flooding resilience is assessed with a hybrid approach. Geodetector analysis identifies primary resilience drivers. Results validate the PSR–NES composite resilience index, showing complex resilience patterns across the study area. High-resilience areas are mainly in the east and southeast, while low-resilience areas cluster along roads and low-lying communities. Key resilience indicators include waterlogging duration, inundation depth, warning capacity, economic density, and road accessibility. These factors interact with nonlinear enhancements and synergies. The study's insights can identify urban management or flood defense infrastructure deficiencies, guiding tailored flood defense strategies for stakeholders.

Two-Dimensional Hydrodynamic Simulation of the Effect of Stormwater Inlet Blockage on Urban Waterlogging

The drainage capacity of stormwater inlets, which serve as the connection between the surface and the underground drainage system, directly affects surface runoff and the drainage capacity of underground drainage systems. However, in reality, stormwater inlets are often blocked due to the accumulation of leaves, human waste disposal and other factors, resulting in a greatly reduced drainage capacity of the drainage network and, in turn, urban waterlogging disasters. In view of the problem of stormwater inlet blockage, employing a typical waterlogging point in the Lianjiang Middle Road area of Fuzhou city as the research object, the stormwater inlet equivalent drainage method was adopted in this paper to characterize the drainage capacity of the pipe network and enable the control of the stormwater inlet blockage state. Coupled with the stormwater inlet drainage equation, an improved ITF-FLOOD two-dimensional hydrodynamic model was constructed, and the influence of stormwater inlet blockage on urban waterlogging under different rainfall return periods was simulated and analyzed. With increasing rainfall return period, the influences of stormwater inlet blockage on both the maximum area and the depth of accumulated water in the study area gradually decreased compared with those of a nonblocked stormwater inlet, and the growth proportions decreased from 43.35% and 34.58% under the 1-year rainfall scenario to 3.34% and 9.76% under the 50-year rainfall scenario, respectively. However, in terms of the change in the accumulated water level, stormwater inlet blockage will cause an increase, and the influence will always be significant. Overall, stormwater inlet blockage aggravated the waterlogging risk and the extent of waterlogging. Therefore, the results provided a reference for the construction of an urban waterlogging model and have certain guiding significance for waterlogging prevention and control in the study area prone to stormwater inlet blockage.

Assessment and Improvement of Emergency Rescue Service Accessibility under Urban Waterlogging Disasters

With the acceleration of urbanization and the impact of climate change, the frequent occurrence of urban waterlogging not only leads to road closures and traffic congestion but also severely affects the timeliness of urban emergency rescue. To accurately assess and enhance the response capability of urban emergency rescue under storm-induced waterlogging scenarios, a hydrodynamic model of urban waterlogging was developed to simulate waterlogging conditions under various design rainfall scenarios. By identifying road waterlogging risks and blocked roads, as well as combining the Two-Step Floating Catchment Area (2SFCA) method, the accessibility of emergency rescue services for points of interest (POIs) with different vulnerabilities was evaluated. The Liwan District of Guangzhou City was selected as a case study for accessibility impact assessment and improvement simulation. The results indicate that with the increase in the return period of rainfall, both the area and depth of waterlogged regions increased and the number of roads affected by waterlogging rose, leading to an increase in the length of blocked roads from 11 km to 49 km, an increase of over 300%. Additionally, the number of POIs inaccessible to emergency rescue increased, while the number of accessible POIs decreased, resulting in a significant downward trend in overall accessibility. By deploying mobile pumping vehicles, the depth and area of waterlogging under different rainfall return periods were reduced by over 10%, the number of blocked roads decreased by more than 10%, and the number of accessible POIs increased by more than 12%. The findings highlight that storm-induced waterlogging not only hinders traffic flow but also reduces the response capability of emergency rescue services. Through the strategic deployment of mobile pumping vehicles, the accessibility of urban emergency rescue services under waterlogging conditions can be effectively improved, mitigating the impact of waterlogging on urban functions and public safety.

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

Increasingly frequent urban waterlogging disasters are having profound social and economic consequences. An appropriate and integrated evaluation of the total economic impacts of such disasters is crucial for achieving effective urban disaster risk management and sustainable development. However, existing metrics are inadequate for measuring the economic impacts of rainfall events of different intensities and their ripple effects across regions. Moreover, their ecological impacts have received insufficient attention. To address these gaps, we developed an integrated assessment framework for analyzing urban waterlogging losses and evaluating their various impacts. Taking Beijing as a case study, we used the InfoWorks ICM model to simulate urban waterlogging disaster risks, quantified direct economic losses, and assessed their environmental impacts. Additionally, we estimated indirect economic losses using input-output analysis and explored spillover effects. The results revealed increasing trends of direct economic losses and environmental losses corresponding to a longer return period. We observed synergies between these losses and their spatial heterogeneity. However, indirect impacts far outweighed direct impacts, with the former being 2.43 times larger than the latter. The cascading effect resulting from damage to infrastructure was also particularly pronounced. The industrial and spatial heterogeneity of interregional impacts was striking, with eastern provinces evidencing the most significant effects. By mapping the transmission paths of disaster losses along industrial chains and across regions, this study provides inputs that could assist policymakers in developing more effective measures for preventing and mitigating urban waterlogging disasters.

Research on urban waterlogging risk prediction based on the coupling of the BP neural network and SWMM model

Abstract Scientific and effective urban waterlogging risk prediction can help improve urban waterlogging disaster prevention capabilities. Combining the numerical simulation model with the data-driven model, the construction of the urban waterlogging risk predictive model can satisfy the prediction accuracy and improve the prediction timeliness. Thus, this paper established an urban waterlogging risk predictive model based on the coupling of the BP neural network and SWMM model, and set five input patterns, finally selected the accumulative precipitation process and precipitation characteristics as input to predict the regional waterlogging risks under different urban rainstorm scenarios. The results show that the overall performance of the pipe drainage system in the study area is lower, and it cannot resist the rainstorm with a higher return period. Moreover, the total waterlogging risk of the southern old city is higher than that of the northern new city in the study area. The calculation speed of the prediction model constructed in this paper is thousands of times higher than that of the numerical model, so the calculation speed is very fast, which meets the requirements of the forecast timeliness.

An approach of using social media data to detect the real time spatio-temporal variations of urban waterlogging

Urban waterlogging has occurred frequently in recent years due to the impact of climate change and human activities. Real-time waterlogging information is crucial for disaster emergency management, but how to quickly obtain such information remains challenging. Social media data has been widely used to derive damage information because of its high real-time response, low acquisition cost, and high content integration. In this study, we propose an approach to extract real-time waterlogging points from social media data (Sina Weibo). First, social media data is obtained through web crawler technology; Then, de-duplication and de-noising methods are used to filter the data; Finally, a waterlogging point extraction method based on deep learning BERT-BiLSTM-CRF model is proposed to extract waterlogging points. Taking the “7.20” rainstorm in Zhengzhou as an example, there was a rapid increase in the number of social media data during urban waterlogging. Social media data is highly sensitive to urban waterlogging disasters caused by extreme rainstorms. On the day with the heaviest rainfall (July 20), the number of Weibo waterlogging points (331) in the central city was 267 more than the official waterlogging points (64). There were many more Weibo-derived waterlogging points than the real-time official published waterlogging points. The waterlogging points obtained by this approach covered the official published real-time waterlogging points accounted for no less than 82% and they were mostly located around roads, especially in low-lying areas. In general, we demonstrate the feasibility and accuracy of social media data on rapid detection of real-time spatiotemporal variations of waterlogging caused by extreme rainstorms. Urban waterlogging disaster information extracted from social media data can rapidly reflect the real-time spatiotemporal variations of urban waterlogging disasters, and can effectively cover and supplement data reported by government agencies, and can provide data support for urban waterlogging disaster prevention.

Analyzing the impacts of topographic factors and land cover characteristics on waterlogging events in urban functional zones

Rapid urbanization and climate changes result in frequent occurrence of urban waterlogging disasters, which cause serious economic damage and pose a threat to residents' safety. Understanding the spatial characteristic and the key influencing factors of urban waterlogging has significant implications for mitigating waterlogging. In this study, the officially issued representative waterlogging points were obtained, as well as the topographic factors and land cover characteristics were selected to compare their impacts on the waterlogging event density in a highly urbanized area at urban functional zone (UFZ) scale, and to quantify the contributions of the key influencing factors on urban waterlogging events. Results showed the average density of urban waterlogging events in the study area is 9.2 points/km2, and 38.4 % of the waterlogging events are distributed in REZ. The distribution of waterlogging points in the study area revealed a significant multi-core and multilevel spatial aggregation pattern, and 12.1 % of the study area was high-density waterlogging area. In the total UFZs, the correlation coefficients of topographic indices with waterlogging density were relatively weaker than the other land cover characteristic metrics. The impervious surface ratio had significant contributions in all UFZ types. The larger ratio of impervious surface significantly increased the density of waterlogging events. The increase in the ratio of green space can effectively decrease the density of urban waterlogging. In the total UFZs, the top 3 key influencing factors of urban waterlogging were PR (35.9 %), COHESION (32.5 %) and DIVISION (11.8 %). The higher connectivity of landscape patches in REZ, INZ and COZ, as well as the increase of landscape dispersion or diversity in REZ, EGZ, INZ and GSZ can effectively reduce the occurrence of urban waterlogging. This study provides a better understanding of the formation mechanism of urban waterlogging disasters and potential implications for prioritized waterlogging mitigation strategies.

Nature-based solutions: Establishing a comprehensive framework for addressing urban waterlogging management.

Due to the impact of global climate change, many cities in China have been hit by severe rainstorms, leading to increasingly frequent urban waterlogging disasters. In recent years, nature-based solutions (NbS) have received widespread attention and recognition, providing new ideas and approaches to addressing urban waterlogging issues. First, this article reviews the development process and concept of NbS and analyzes its core ideas and principles. Second, it analyzes the guiding role of NbS in urban waterlogging management and compares the commonalities and differences between NbS and three related waterlogging concepts. To ensure that urban waterlogging management is operational and dynamic, and that there is effective communication among stakeholders, this article proposes a comprehensive framework for the application of NbS in urban waterlogging management. Finally, this article analyzes the opportunities and potential of NbS applied to urban environmental issues. Integr Environ Assess Manag 2023;19:1414-1421. © 2023 SETAC.

Fast Simulation of Urban Waterlogging Based on Multi-Objective Machine Learning Model

The frequent occurrence of urban waterlogging disasters induced by rainstorm has recently caused serious economic losses and casualties in China. Numerical simulation of waterlogging is an important tool for disaster prewarning and forecasting as well as disaster prevention and control; however, the traditional numerical physical models have the disadvantage of low computational efficiency, which makes it difficult to meet the demand for real-time simulation and real-time early warning and forecast. To this end, this study combines the respective advantages of coupled rainstorm-flood models with physical mechanisms and machine learning algorithms, and proposes a rapid prediction and simulation method for inundated depth of urban waterlogging based on multi-objective machine learning algorithms. The forecasting performances of K-Nearest Neighbors(KNN), Multi-Objective Random Forest(MORF), Extreme Gradient Boosting(XGBoost) and their integrated models are discussed, respectively. The results show that: (1)The coupled rainstorm- flood model based on SWMM and LISFLOOD-FP has good applicability in the simulation of urban waterlogging induced by rainstorm in the study area. On this basis, the database with a total of 70 scenarios of rainstorm-inundation with different characteristics were generated. (2)The KNN, MORF, XGBoost and their integrated models all have good results in predicting water depth, with Pearson correlation coefficient (PCC)values all above 0. 812, mean absolute error(MAE)below 6. 9 cm, and root-mean-square error (RMSE)less than 0. 116. The KNN-MORF-XGBoost integrated model has the best overall results, with the average values of MAE, PCC and RMSE reaching 2. 4cm, 0. 965 and 0. 043, respectively. (3)In addition to the high prediction accuracy, the prediction speed of the constructed multi-objective machine learning prediction model is extremely fast, and the water depth simulation efficiency is more than 20 times higher than that of the coupled rainstorm-flood model. This study can provide a reference for the application of machine learning in the rapid simulation of urban waterlogging induced by rainstorm, which is of great value for the early warning and forecast of urban waterlogging disaster.

Urban waterlogging mitigation based on the concept of sponge city

In recent years, many cities around the world have experienced severe urban waterlogging disasters due to decreasing rainfall infiltration caused by land cover changes with rapid urbanization, and increased frequency of heavy rainfall events, including localized torrential rain. Traditional drainage systems and internal control concepts have failed to meet the requirements of urban safety. To address the challenging problem of urban waterlogging, China proposed the Sponge City in 2014. From the perspective of natural climate and urban construction planning, this paper makes an analysis of the causes of urban waterlogging, points out the limitations of traditional drainage facilities, and discusses the many hazards associated with urban waterlogging. This paper explains the meaning of Sponge City and the basic ideas of Sponge City construction. Based on the discussion of Sponge City development, this paper analyzes the Sponge City construction in Xixian New Area, and explains the significance of Sponge City to solve the problem of urban waterlogging. Through this idea, I would like to explore the common issues in countries suffering from heavy rainfall disasters such as Japan and Malaysia. The study hopes to provide a deeper thinking on ways to use nature to solve the waterlogging.

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification.

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.

Hazard Assessment of Urban Waterlogging Disaster on Underground Substations in Shanghai

Based on the two methods of the index system and the scenario simulation, hazard assessment of urban waterlogging disaster on underground substations in Shanghai was carried out. The results show that: (1) the return period rainfall of underground substations in Shanghai gradually increases with the extension of the return period; in terms of spatial distribution, the return period precipitation of the stations along the river and within the inner ring is higher than that of the stations outside the inner ring. (2) The waterlogging thresholds of stations between the inner ring and the middle ring are highest. The waterlogging thresholds of stations within the inner ring and between the middle ring and the outer ring are higher than those in the suburban stations. (3) Under the 20-year return period extreme precipitation scenario, the urban waterlogging risks of underground substations in Shanghai are mainly at low and medium levels. Under the 50-year and longer return period extreme precipitation scenario, the risks of inner ring stations increase to medium-high level.

Emergency management decision of urban rainstorm and flood disasters based on similar cases analysis

Under the background of global climate change and rapid development of urbanization, urban extreme occurs more frequently. In consideration of the existing problems in the emergency management decision-making of urban rainstorm and flood disasters in China, this study put forward the decision-making method of urban rainstorm and flood disaster emergency management based on similar cases analysis method. Based on the evolution process of urban waterlogging disaster, the problem attribute system of urban rainstorm and flood disasters was established. The case-based reasoning (CBR) method was used to calculate the global similarity to determine the best historical case. The case-based decision theory (CBDT) method was used to calculate the comprehensive utility value of alternative cases to determine the optimal alternative cases. The empirical analysis was took in Xi'an city, one of the national center cities of China. Results show that the CBR method was used to obtain the highest similarity of historical case P3, which is 0.481. The CBDT method was used to calculate the high similarity of historical cases P3 and P4 to form the similar case set, with the similarity of 0.820 and 0.851, respectively. Combined with the event development, the comprehensive utility values were calculated. When the decision-maker pays the same attention to the emergency effect and response cost, the comprehensive utility value of P3 and P4 is 0.922 and 0.900, respectively, and P3 is the best reference scheme. By comparison, the latter is more suitable. The results can provide scientific basis for emergency management of urban rainstorm and flood disasters.

Real-time urban rainstorm and waterlogging disaster detection by Weibo users

Abstract. With the process of urbanization in China, the urban waterlogging caused by rainstorms occurs frequently and often leads to serious damage to the natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging disasters is an essential step to minimize these losses. Weibo, a popular microblog service in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (k-means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from word vector features, we also introduce sentiment and publisher features for more real-time and accurate results. Furthermore, we build a waterlogging intensity dictionary to assess the waterlogging risk from the Weibo texts and produce a risk map with ArcGIS. To examine the efficacy of this approach, we collect Weibo data from two rainstorms and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for a real-time rainstorm and waterlogging information detection. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better indicate the intensity and timing of rainstorms. The location of waterlogging points is consistent with the risk assessment results, which proves our proposed risk assessment method can be used as a reference for timely emergency response.

Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model

Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model

PSO-WELLSVM: An integrated method and its application in urban waterlogging susceptibility assessment in the central Wuhan, China

Urban waterlogging susceptibility assessment can be used in preventing urban waterlogging disaster, which can cause serious damage. An integrated method based on particle swarm optimization (PSO) and weakly labeled support vector machine (WELLSVM), is presented to assess urban waterlogging susceptibility for a certain rainstorm. This method incorporated twelve explanatory factors, including daily precipitation, 3-day cumulated rainfall prior to flood occurrences, elevation, slope, curvature, aspect, topographic wetness index, stream power index, distance to river, leaf area index, impervious surface percentage, distance to road, to perform waterlogging susceptibility analysis. The rainstorm on July 6, 2016 in the main districts of Wuhan, China was used as the scenario to test its feasibility. Cohen's kappa index, accuracy, precision, and recall were used to evaluate the performance of the proposed model. The accuracy of the proposed model (93.6% for training data and 90.1% for testing data) is higher than WELLSVM, support vector machine, and logistic regression, demonstrating the advantages of utilizing unlabeled data and optimized parameter selection. The proposed model can also well identify the waterlogging susceptibility zones. The highest waterlogging susceptibility areas are located in the area with high impervious surface percent, intersections, culverts and overpasses, lakeshore, and riverbank. Elevation and precipitation factors are the most influential factors to waterlogging susceptibility. The proposed model was also tested by the other two storms on July 7, 2013 and June 21, 2019, proving the validity of it. The proposed model is helpful for instant waterlogging susceptibility analysis and can help decision-making of urban waterlogging control.

城市内涝调节服务供需关键区识别与优先级划分

在全球气候变化和城市化背景下, 城市内涝灾害频发, 严重危及城市居民的人身安全、财产安全及公共安全。如何精准施策, 针对关键区域进行城市内涝调节治理已成为社会各界关注并亟需解决的重要问题。基于生态系统服务供需视角, 以径流调节率表征城市内涝调节的供给水平, 以胁迫性、暴露性和脆弱性指标表征城市内涝调节的需求水平, 构建了完整的技术路线和指标体系, 从而识别城市内涝调节供需失衡的关键区, 并确定规划干预的优先级。基于此, 以岛屿型城市厦门岛为例, 使用遥感数据和人口、社会、经济数据, 运用ArcMap10.8、ENVI5.3和GeoDA平台, 模拟评估了城市街区尺度下城市内涝调节服务的供需水平及空间分布特征。通过供需匹配将研究单元划分为：高供-高需, 低供-高需, 低供-低需和高供-低需4种类型, 并识别出114个供需严重失衡的关键区, 该类关键区面临城市内涝调节服务供给短缺的严峻挑战, 是未来城市规划过程中需要重点优化的对象。在此基础上, 使用优先级指数将关键区划分为5个规划干预优先级, 明确了城市内涝治理的优先次序。研究结果为开展具有针对性的城市内涝调节治理实践提供了新的科学依据, 同时在解决城市内涝灾害问题, 推进以人为核心的新型城镇化方面具有重要理论与现实意义。;In the context of global climate change and urbanization, urban waterlogging disasters occur frequently, which seriously endangers the personal safety, property safety and public safety of urban residents. How to prevent and control urban waterlogging in key areas has become an important issue that all sectors of society are concerned about and urgently need to be resolved. In this study, based on the perspective of the supply and demand of ecosystem service, we used runoff regulation rate to characterize the supply level of urban waterlogging regulation and expressed the demand level of urban waterlogging regulation by hazard index (surface runoff and municipal drainage capacity), exposure index (number of people and buildings affected by disaster), and vulnerability index (the composition of people affected by disaster and the types of buildings affected by disaster), so as to construct a technical route and indicator system. The purpose of this study is to identify the key areas where urban waterlogging regulates the imbalance between supply and demand, and to determine the priority of planning intervention. Based on this, we took Xiamen Island as an example, used remote sensing data and population, social, and economic data, and used ArcMap10.8, ENVI5.3 and GeoDA platforms to evaluate the supply and demand level and their spatial distribution characteristics of urban waterlogging regulation service at the block scale. We found that urban green space could effectively provide waterlogging regulation service, and woodland was stronger than grassland; at the same time, the waterlogging regulation service provided by urban green spaces was restricted by rainfall. Hazard index, exposure index, and vulnerability index jointly determined the level of demand for urban waterlogging regulation service. The spatial distribution results show that the supply and demand levels regulated by waterlogging have obviously spatial agglomeration characteristics. Furthermore, we used the matching degree of supply and demand to divide the research unit into four types: high supply-high demand, low supply-high demand, low supply-low demand, and high supply-low demand. 114 key areas with severe imbalance between supply and demand were identified. These areas are facing the challenge of shortage of waterlogging regulation supply and need to be optimized in the future urban planning process. On this basis, we used the priority index to divide these key areas into units with five planning intervention priorities, and clarified the priority areas of urban waterlogging management. The research results provide a new scientific basis for formulating targeted urban waterlogging management strategies. At the same time, it has important theoretical and practical significance in mitigating urban waterlogging disasters and promoting the new type of human-centric urbanization.

Experience and enlightenment of urban waterlogging disaster prevention in foreign countries

As an important type of disaster, urban waterlogging has become a great challenge for urban governance around the world, threatening people’s lives, health and safety and property safety. The United States, Britain, Japan, Singapore and other countries have accumulated rich experience in the prevention and control of urban waterlogging. Foreign countries have strengthened the prevention and control of urban waterlogging disasters mainly in terms of condensing concepts and awareness, improving laws and regulations, paying attention to planning and construction, using scientific and technological means, strengthening knowledge popularization, strengthening risk assessment and early warning, implementing project-driven and active participation of multiple subjects, and achieved certain results. It is of reference and enlightening significance. In terms of the current and future urban waterlogging prevention and control in China, we should continuously improve the concept and awareness of urban waterlogging prevention and control, scientifically and systematically plan and construct the city; comprehensively use various policies and scientific and technological effective means to improve the effectiveness of urban waterlogging prevention; pay attention to waterlogging Risk assessment and disaster early warning work to realize the advancement of waterlogging disaster governance; strengthen knowledge promotion, popularization and multi-subject participation, create a joint force in urban waterlogging prevention and governance, and promote the modernization of urban waterlogging management systems and capabilities.

Three-dimensional simulation of regional urban waterlogging based on high-precision DEM model

With the frequent occurrence of extreme rainfall and the change of urban surface caused by human activities, urban waterlogging has gradually become a common disaster in many cities. In this paper, in order to deal with the urban waterlogging disasters, three-dimensional (3D) simulation of regional urban waterlogging was established based on high-precision digital elevation model (DEM). The project takes Zhongnan University of Economics and Law as the research area to setup the simulation. Firstly, the sub-catchment areas are divided by the watershed extraction method. Secondly, the research area is gridded to calculate the scope and volume of waterlogging area. Finally, combined with the rainstorm intensity formula, the statistics of local area underlying surface, and Soil Conservation Service (SCS) model, the urban waterlogging disaster model is established. Then, the waterlogging disaster calculation under different return period rainfall situations is realized and based on 3D technology, the 3D scene of research area and 3D simulation of waterlogging are shown by using CityEngine and Cesium. The data verification of the model is based on the rainfall data, and the urban waterlogging disaster results in Wuhan in 2016. The simulation results are basically consistent with the waterlogging disaster in 2016. And the research shows that the sub-catchment area divided by the watershed extraction method can take into account the blocking effect of terrain on surface runoff, and the results are consistent with the actual terrain. Waterlogging simulation in a small area can accurately locate the affected areas and buildings, and 3D visualization technology can be used as an effective means of transmitting disaster information to provide basis for emergency decision-making. Only the geographical data of the local area and the rainfall data are needed for the method for simulation calculation, which makes it easily to transplant to other areas and can provide an important idea and method for the flood prevention and control in flood season for reservoir, tailings pond, factories and so on.

Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method

Over the past few years, urban waterlogging disasters have caused serious losses to the national economy of China; therefore, creating technology for assessing waterlogging risk levels has become an important goal. Based on 25 post-screened evaluation indexes regarding the construction of waterlogging facilities, social and economic developments, and investments in scientific and technological innovation, the capacity of 31 provinces to prevent and mitigate waterlogging was comprehensively evaluated. The scores of six principal component factors were calculated by using the entropy weight TOPSIS method, and the coupled entropy weight TOPSIS–principal component analysis evaluation model was established. Moreover, in accordance with the evaluation results, measures for waterlogging prevention and disaster reduction are proposed. The results show that Beijing, Shanghai and Tianjin are the top three provinces regarding the capacity to control floods and mitigate disasters; this agrees well with the actual flood drainage standards and disaster losses of all provinces.