Urban Storm Flood Research Articles

3D visualization of hurricane storm surge impact on urban infrastructure

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Analysis of mechanism and optimal value of urban built environment resilience in response to stormwater flooding

The concept of urban resilience focuses on understanding the process and mechanisms of disaster occurrence, providing innovative approaches to address stormwater flooding. However, existing studies primarily concentrate on enhancing overall system resilience, with limited research examining the temporal progression from stormwater disturbance to flood generation. To fill this gap, this study categorizes the development process of stormwater flooding into three periods: disturbance resistance (DR), adjustment and adaptation (AA), and rapid recovery (RR). Using the SWMM (Storm Water Management Model) software, 27 representative parcels in the Beijing-Tianjin-Hebei region of China were simulated. By sequentially considering single-indicator control variables, resilience indicators that significantly impact the three periods were identified through the construction of a stormwater flooding resilience indicator library. Subsequently, resilience models for each disaster phase were constructed using the BP (Back Propagation) neural network, and genetic algorithms were employed to optimize the models and determine the optimal values of resilience indicators for each period. Finally, the research findings were summarized into a resilience design method for the built environment to address stormwater flooding, accompanied by a guide for improving stormwater flooding resilience.The study reveals the following key findings: (1) the influence of physical and spatial elements in the built environment on stormwater flooding formation varies across different stages of the disaster process; (2) distinct resilience indicators operate at different times and in different ways throughout the entire stormwater flooding resilience process; (3) enhancing stormwater flooding resilience in the built environment does not necessarily require setting specific threshold values for each influencing indicator; instead, an optimal single value emerges when multiple indicators interact. Moreover, when multiple indicators interact, an optimal combination module with the best value for a single indicator exists. This study investigates the complete cycle from storm disturbance to flood disaster formation, offering both solutions for cities to mitigate storm flood disasters and advancing theoretical research on urban storm flood resilience while fostering interdisciplinary integration.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

City Flood Disaster Scenario Simulation Based on 1D–2D Coupled Rain–Flood Model

In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, located in the western part of Henan Province, China was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93 cm; the average relative error of the verified simulated water depth was 15.27%, the average absolute error was 7.54 cm, and the simulation result was good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management.

Assessing the impacts of land use-land cover changes on direct surface runoff: a remote sensing approach in Khulna City.

The increased risks of storm flood occurrences in large cities are the result of land use changes due to rapid urbanization. This study examines the influence of land use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 15 years, from 2005 to 2020. Land use-land cover (LULC) maps for 2005, 2010, 2015, and 2020 were created employing support vector machine (SVM)-based supervised image classification using time-series satellite data, and the surface runoff was determined using Soil Conservation Service-Curve Number model. The major land use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend as that of impervious urban areas, which went up by 5.44% from 2005 to 2020 (17.00 mm increment in average runoff) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation land use-land cover (LULC) class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

A distributed hydrodynamic model for urban storm flood risk assessment

Hydrodynamic models have been increasingly applied to urban flood simulations, but still encounter challenges because of complicated urban landscapes, buildings and structures, as well as insufficient sewer data. In order to improve the applicability in real-life urban environments, a distributed hydrodynamic model for urban storm flood risk assessment (DHMUrban) is developed in this study. It is based on the two-dimensional shallow water flow equations simplified by neglecting the convection terms in the momentum equations. The sharp variation of ground surface often happening in urban areas is specially treated with the broad-crested weir flow equation. The rainfall, infiltration and sewer drainage are considered at each overland cell. Two connection approaches between the overland model and the sewer model are proposed depending on the availability of sewer data. The overland cells and the sewer nodes are connected physically in areas with complete sewer data, and connected conceptually through a drainage relationship in areas where sewer data are insufficient or unavailable. The present model has been evaluated in five cases, showing comparable reliability and stability. It has been applied to a real-life urban catchment in Shenzhen City, and demonstrated to be capable of presenting well the ground surface flooding, adapting adequately to the real-life urban areas with different data availability and providing practical and reliable solutions for urban storm flood risk assessments.

The Influence of Urbanization on Storm Runoff

With the development of economy society, urbanization process are accelerated, the tension of the urban land using leads that the cities develop to the low-lying parts, and even occupy river, landfill ditch pond, it makes perched water space of the city narrow, rainfall runoff coefficient increasing, the confluence time speeding up, it increases urban flood risk. Urbanization has changed the urban hydrologic cycle characteristics, so as to make the urban rain flood characteristics changing, it easily causes short duration rainstorm seeper-waterlogging, so as to directly influence the urban residents’ lives and causes great economic losses, even casualties. City is the political, economic and cultural center, once suffered from floods, it will cause great political influence and economic losses, so, it’s necessary to analysis urban storm flood reason and put forward the corresponding countermeasures. This paper mainly discusses the urban waterlogging reason and influence on storm runoff, and discusses the countermeasures.

A land-use decision approach integrating thermal regulation, stormwater management, and economic benefits based on urbanization stage identification

Driven by global climate change and urbanization, urban heat island (UHI) and urban storm flood (USF) have become the most frequent and influential hazards in recent decades. Land-use optimization can effectively cope with these hazards. However, the trade-offs between multi-hazard mitigation and economic development impose many limitations in practice. Furthermore, current region-based optimization methods no longer meet the precise management demand, and both subdivision and spatial heterogeneity identification have the potential for wider applicability. Hence, a systematic integration of climate adaptation and urban construction through land-use planning is urgently required. This paper proposes a new land-use decision approach for improving climate adaptability of urbanization. This approach involves multi-objective optimization, spatial subdivision, and urbanization stage identification, which enable the simultaneous achievement of environmental and economic benefits. Taking Xiamen as case study, the results showed that excessive pursuit of land economic output (LEO) limits the chance of mitigating UHI and USF. Improving the LEO per unit area of construction land could disrupt the link between land exploitation and the increasing side effects of climate hazards. Future urbanization hotspots in Xiamen will likely emerge at the urban fringe in Tong'an District and Xiang'an District. Within each developing unit, the upper limit of construction land was 81.06 hm2 and the green space was recommended to be 7.29–21.94 hm2. Construction land and bare land contributed most to UHI and USF, while forest and grassland were highly efficient in heat and runoff mitigation. The developed approach proved to be effective and practicable, especially for reducing the impacts of extreme UHI and USF.

Evaluation of the Impact of Rainfall Inputs on Urban Rainfall Models: A Systematic Review

Over the past several decades, urban flooding and other water-related disasters have become increasingly prominent and serious. Although the urban rain flood model’s benefits for urban flood simulation have been extensively documented, the impact of rainfall input to model simulation accuracy remains unclear. This systematic review aims to provide structured research on how rain inputs impact urban rain flood model’s simulation accuracy. The selected 48 peer-reviewed journal articles published between 2015 and 2019 on the Web of Science™ database were analyzed by key factors, including rainfall input type, calibration times and verification times. The results from meta-analysis reveal that when a traditional rain measurement was used as the rainfall input, model simulation accuracy was higher, i.e., the Nash–Sutcliffe efficiency coefficient (NSE) of traditional technology for rain measurement was higher than the 0.18 for the new technology rain measurement with respect to flow simulation. In addition, the single-field sub-flood calibration model was better than the multi-field sub-flood calibration model. NSE was higher than 0.14. The precision was better for the verification period; NSE of the calibration value showed a 0.07 higher verification value on average in flow simulation. These findings have certain significance for the development of future urban rain flood models and propose the development direction of the future urban rain flood model. Finally, in view of the rainfall input problem of the urban storm flood model, we propose the future development direction of the urban storm flood model.

Analysis of the Effect of Low Impact Development on Urban Runoff Control Based on the SWMM Model

Zhang, J.; Zhang, Y.; Sun, S.-J.; Zhang, W.-W., and Zhang, S.-H., 2019. Analysis of the effect of low impact development on urban runoff control based on the SWMM model. In: Yu, T.-s. (ed.), Environmental Sustainability: Water Resources Integrated Management and the Development of Coastal Environments. Journal of Coastal Research, Special Issue No. 96, pp. 62-67. Coconut Creek (Florida), ISSN 0749-0208.In order to solve the problems of frequent urban flood disasters and the continuous deterioration of the water environment in the process of urbanization, the urban storm flood management method centered on Low Impact Development (LID) has become a new concept in urban construction. Taking one residential area in Beijing as the research object, and on the basis of the Storm Water Management Model (SWMM), this paper simulates and analyzes the effect of LID measures on runoff control under different rainfall conditions, and deduces the functional relationship between the LID scale and runoff reduction. The results show that the runoff reduction rate decreases with the increase in rainfall level, and LID measures have the most obvious effect on runoff quantity reduction while having a small effect on the peak time delay. The research methods and results of this paper can provide a reference for sponge city construction, flood control and drainage management.

Simulation of Urban Rainstorm Waterlogging and Pipeline Network Drainage Process Based on SWMM

Urban flood has already become weaknesses of flood control system in China. Flood and pipeline drainage calculation based on hydraulics model plays a key role in flood monitoring, warning and control management. On the basis of the topographic maps (high-precision DEM), underground drainage networks, land use and rainfall data in Enshi City, a SWMM-based urban flood model is constructed, which is applied to calculating rainstorm waterlogging and pipeline drainage process under the designed storm probability of 0.01, 0.02 and 0.05, and the designed storm period of 1 hour and 3 hours. The pipeline drainage peak flow of different pipeline type is also retrieved. And results suggest that SWMM is a reliable urban storm water model, which is suitable for urban storm flood simulation and pipeline network drainage analysis.

Obtaining Land Cover Type for Urban Storm Flood Model in UAV Images Using MRF and MKFCM Clustering Techniques

With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic.

Urban Storm Flooding: Characteristics and Management in Beijing

Beijing is located on the North China Plain with five rivers, which belong to the Hai River Basin. Its continental monsoon climate with uneven precipitation distribution is extreme likely lead to serious urban flood disasters. According to the disaster results, urban storm flood in Beijing can be classified into four types. Here typical extreme storm flood events and their characteristics in Beijing were analyzed in detail. It showed that storm flood events in recently decades had a trend, which centered in a relatively small area with high intensity and short duration. The main reasons of urban storm flood disaster were urbanization and basic facilities with low flood and drainage standard. Urbanization means land utilization significantly altering hydraulic processes, and extreme storm can easily exceed those facilities capacity. In order to deal with urban storm flood, Beijing government have taken four measurements, which were upgrading and reconstruction of rainwater pumping stations, improving projects of small and medium rivers, building sponge city, and implementing the West Suburb Storm-water Regulation Project. In addition, the flood warning and emergency management system has been established. Furthermore, some measurements were pointed to be done in the future, including improvement of the flood control management system, improvement of flood control plans, strengthening flood warning system, and strengthening social management and public awareness of flood prevention. With these improvements of management and engineering measurements, it can be more secure under intensive storms in Beijing. These experiences of flood control in Beijing can provide references for other cities.

基于土地利用变化情景的城市暴雨洪涝灾害风险评估——以深圳市茅洲河流域为例

PDF HTML阅读 XML下载 导出引用 引用提醒 基于土地利用变化情景的城市暴雨洪涝灾害风险评估——以深圳市茅洲河流域为例 DOI: 10.5846/stxb201708271546 作者: 作者单位: 北京大学城市与环境学院,北京大学城市与环境学院,北京大学城市与环境学院,北京大学城市与环境学院,北京大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金重点项目（41330747） Storm flood disaster risk assessment in urban area based on the simulation of land use scenarios: A case of Maozhou Watershed in Shenzhen City Author: Affiliation: Laboratory for Earth Surface Processes,Ministry of Education,College of Urban and Environment Science,Peking University,Laboratory for Earth Surface Processes,Ministry of Education,College of Urban and Environment Science,Peking University,Laboratory for Earth Surface Processes,Ministry of Education,College of Urban and Environment Science,Peking University,Laboratory for Earth Surface Processes,Ministry of Education,College of Urban and Environment Science,Peking University,Laboratory for Earth Surface Processes,Ministry of Education,College of Urban and Environment Science,Peking University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:近年来频发的暴雨洪涝成为威胁城市可持续发展的主要灾害类型，而土地利用变化改变了区域原有的景观结构和水文过程，是城市暴雨洪涝灾害风险加剧的重要诱因，定量探讨土地利用变化对暴雨洪涝灾害及其风险影响具有重要意义。以深圳市茅洲河流域为例，基于CLUE-S模型、SCS模型及等体积淹没算法等，对12种暴雨洪涝致灾-土地利用承灾情景下的城市暴雨洪涝灾害风险进行定量模拟。研究结果显示，相同土地利用空间格局下城市暴雨洪涝灾害随暴雨致灾危险性增加风险加剧显著；在同等致灾危险性水平下随建设用地面积增加，中等风险和高风险区面积均呈现较为明显的增加趋势，中、高风险区面积与建设用地面积的增加率表现出较高的协同变化特征；以50年遇危险性水平为例，随着建设用地面积由基期的15368.85hm2增加至近期16076.07hm2和远期16750.89hm2，高风险区面积由254.07hm2增加至276.48hm2和286.2hm2。由此可见，尽管暴雨强度的增加是城市暴雨洪涝灾害风险加剧的根本诱因，但是以建设用地面积增加为表征的土地利用变化对暴雨洪涝灾害风险的影响不容忽视。 Abstract:Recently, the frequent storm flood has become the main hazard in the coastal cities, which treats to the regional sustainable development. Land use change has transformed the original landscape structure and hydrological processes, and also becomes the significant factor to the increase of urban storm flood disaster risk. It is significant to discuss on the relationship between the land use change and the storm flood hazard risk. Based on the CLUE-S model, the SCS model and the equal volume submerged algorithm, this article took the Maozhou Watershed as the study area and quantitatively evaluated the storm flood disaster risk in the twelve interactional scenarios of the storm hazard and the hazard affected land use. Results showed that the urban storm flood hazard risk increased remarkably with the increased storm magnitude on a certain pattern of land use. While at the same level of the storm hazard, the areas of medium and high risk patches showed an obvious increasing trend with the increased construction land. The area increasing rates of high or medium level risk patches and the construction land were highly-related with the form of synergy. Take the recurrence interval of 50years as an example, the area of high risk patches grew from 254.07hm2 to 276.48hm2 and 286.2hm2 when the construction land area increased from the base scenario to short-term and long-term scenario, respectively. Therefore, even though the significant factor to the storm flood disaster risk is the storm magnitude, the influence of land use change characterized by the increasing of construction land on the storm flood disaster risk cannot be ignored. 参考文献 相似文献 引证文献

Study on Storm-Water Management of Grassed Swales and Permeable Pavement Based on SWMM

Grassed swales and permeable pavement that have greater permeable underlying surface relative to hard-pressing surface can cooperate with the city pipe network on participating in urban storm flood regulation. This paper took Nanshan village in Jiangsu Province as an example, the storm-water management model (SWMM) was used to conceptualize the study area reasonably, and the low-impact development (LID) model and the traditional development model were established in the region. Based on the storm-intensity equation, the simulation scene employed the Chicago hydrograph model to synthesize different rainfall scenes with different rainfall repetition periods, and then contrasted the storm-flood-management effect of the two models under the condition of using LID facilities. The results showed that when the rainfall repetition period ranged from 0.33a to 10a (a refers to the rainfall repetition period), the reduction rate of total runoff in the research area that adopted LID ranged from 100% to 27.5%, while the reduction rate of peak flow ranged from 100% to 15.9%, and when the values of unit area were the same, the combined system (permeable pavement + grassed swales) worked more efficiently than the sum of the individuals in the reduction of total runoff and peak flow throughout. This research can provide technical support and theoretical basis for urban LID design.

A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface

Urban storm inundation, which frequently has dramatic impacts on city safety and social life, is an emergent and difficult issue. Due to the complexity of urban surfaces and the variety of spatial modeling elements, the lack of detailed hydrological data and accurate urban surface models compromise the study and implementation of urban storm inundation simulations. This paper introduces a Constrained Delaunay Triangular Irregular Network (CD-TIN) to model fine urban surfaces (based on detailed ground sampling data) and subsequently employs a depression division method that refers to Fine Constrained Features (FCFs) to construct computational urban water depressions. Storm-runoff yield is placed through mass conservation to calculate the volume of rainfall, runoff and drainage. The water confluences between neighboring depressions are provided when the water level exceeds the outlet of a certain depression. Numerical solutions achieved through a dichotomy are introduced to obtain the water level. Therefore, the continuous inundation process can be divided into different time intervals to obtain a series of inundation scenarios. The main campus of Beijing Normal University (BNU) was used as a case study to simulate the “7.21” storm inundation event to validate the usability and suitability of the proposed methods. In comparing the simulation results with in-situ observations, the proposed method is accurate and effective, with significantly lower drainage data requirements being obtained. The proposed methods will also be useful for urban drainage design and city inundation emergency preparations.

Research on Urban Storm Flood Processes under Two Measures: Detention Pond and Retention Pond

The detention pond and the retention pond are effective means for flood control. In this paper, two different storm runoff processes, used the detention pond and the retention pond, were simulated by SWMM. Through analyzing the results, the detention pond and the retention pond can control the flood obviously. Furthermore, the action effects of detention pond and the retention pond were discussed. In the end, the paper put forwards that the suitable flood control measures should be chosen by regional hydrology characteristics, maximum permitted discharge of downstream, engineering economics and so on.

基于SWMM模型的济南城市雨洪模拟研究

在城市尺度上构建了基于SWMM模型的济南市城市雨洪模型，利用2006年和2007年暴雨洪水资料对该模型进行验证，结果表明所构建的模型具有良好的精度和可靠性，能为济南市防洪决策提供科学依据。运用构建的城市雨洪模型对济南市城市排水系统进行检验与评价，并对济南市应对突发性暴雨防灾减灾规划的合理性进行剖析，以检验该模型在城市雨洪防灾减灾分析评价以及规划实践中的应用效果。 In accordance with characteristic of Jinancity, the urban storm-flood simulation model forJinancity was established based on Strom Water Management Model (SWMM). The model is tested by simulating the flood events between 2006 and 2007. The results showed that the established model has satisfied precision and it can be used in simulation of urban storm-flood and provide technique support for flood protection. By using the model and design storms inJinanCity, drainage system is verified and evaluated and the rationality of the recent urban flood control planning projects is analyzed.

Utilization Efficiency and Potential Analysis of Urban Storm Flood Resources

The study of urban storm flood is significant to improve the efficient use of rainfall resources and regional water stress conditions. From the perspective of the use benefits of storm flood resources after the urbanization of Shijiazhuang City, this paper used methods of calculation and analysis to analyze the potential of storm flood resources use. The results show that the collection and use as well as the integrated management of storm flood has become an important subject of increasing income and reducing expenditure for Shijiazhuang, which conforms to the concept of sustainable development and is an area with great potential for development.