Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation

Quan, Ruisong

doi:10.1007/s11069-014-1156-x

Cited by 69 publications

(53 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include: (1) a reduction in river density (Cheng et al 2007); (2) land use changes and an increase in impervious surfaces (Wu et al 2012;Quan 2014); (3) insufficient drainage capacities, particularly in shantytowns (Yuan 1999;Shi et al 2010); and (4) changes in precipitation (Wu et al 2012). As the variation in precipitation is likely to be limited over an area of 289 km 2 , this study only examines the other spatial variables in order to explain the road inundation frequencies.…”

Section: Explanatory Factors For Inundated Streetsmentioning

confidence: 99%

“…In urban areas, buildings typically resist water flow and significantly affect flood inundation (Quan 2014). However, the DEM employed herein only reflects the elevations underlying buildings, and does not include their terraces (Yin et al 2011b) was therefore employed to add the doorsills and terraces to a buildings' DEM, with the assistance of land use data (Fig.…”

Section: Explanatory Factors For Inundated Streetsmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Wang

Wen

et al. 2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

In the context of climate change and rapid urbanization, urban pluvial floods pose an increasing threat to human wellbeing and security in the cities of China. A valuable aid to managing this problem lies in understanding the roles of environmental factors in influencing the occurrence of pluvial floods. This study presents a spatial analysis of records of inundated streets in the inner city of Shanghai during 1997-2013. A geographically weighted regression (GWR) is employed to examine the spatially explicit relationships between inundation frequency and spatial explanatory factors, and an ordinary least squares regression (OLS) is used to validate the GWR results.Results from the GWR model show that the inundation frequency is negatively related to elevation, pipeline density, and river density, and is positively related to road/ square ratio and shantytown ratio. The green ratio is another significant explanatory factor for inundation frequency, and its coefficients range from -1.11 to 0.81. In comparison with the OLS model, the GWR model has better performance as it has higher R 2 , and lower corrected Akaike information criterion and mean square error values, as well as insignificant spatial autocorrelation of the model residuals. Additionally, the GWR model reveals detailed site-specific roles of the related factors in influencing street inundation. These findings demonstrate that the GWR model is a useful tool for investigating spatially explicit causes of disasters. The results also provide guidance for policy makers aiming to mitigate urban pluvial flood risks.

show abstract

Section: Explanatory Factors For Inundated Streetsmentioning

confidence: 99%

Section: Explanatory Factors For Inundated Streetsmentioning

confidence: 99%

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Wang

Wen

et al. 2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…In China, exposure analysis is normally carried out on the basis of urban land use or even building footprints. For example, the distribution map of buildings in downtown Shanghai has been interpreted from remote sensing images and then used for waterlogging exposure analysis (Quan et al 2011b). Furthermore, several different methods have been developed for the exposure analysis of urban flooding in China.…”

Section: Exposure and Vulnerability Analysismentioning

confidence: 99%

A review of advances in urban flood risk analysis over China

Yin

et al. 2014

Stoch Environ Res Risk Assess

183

View full text Add to dashboard Cite

China's urban environments are particularly vulnerable to flooding due to climate change and rapid urbanization. Study of the urban flood risk analysis has significantly increased over the past decade, and this paper therefore reviews the main results (i.e. theoretical basis, methods, techniques, case studies) obtained in the literature from China. We focus on the following topics: (1) urban flood hazard analysis, (2) exposure and vulnerability analysis, and (3) urban flood risk assessment. Recent advances made in the research area are presented with suggestions for further research to improve the availability and reliability of urban flood risk analysis.

show abstract

“…Daily precipitation up to 100 mm is common. In this study, we chose 57 mm and 81 mm of precipitation [28], which occur once in five years and once in ten years precipitation, respectively, to simulate surface runoff using the improved SCS-CN method. Because most of the rainfall in the urban city is transported through sewers rather than flowing onto the open terrain, the impacts of terrain roughness and elevation difference were not taken into account during the simulations.…”

Section: Case Studymentioning

confidence: 99%

“…The objectives of this paper are: (1) to extract the vegetation, impervious surface and soil fractions from remotely sensed imagery; (2) to develop a method of computing composite CN based on the V-I-S model; and (3) to simulate surface runoff volume in Guangzhou City under the scenarios of 57 and 81 mm (rainfall intensity: per hour) precipitation [28].…”

Section: Introductionmentioning

confidence: 99%

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Fan

Deng

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

Abstract:The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is

show abstract

Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation

Cited by 69 publications

References 30 publications

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

A review of advances in urban flood risk analysis over China

Estimating Composite Curve Number Using an Improved SCS-CN Method with Remotely Sensed Variables in Guangzhou, China

Contact Info

Product

Resources

About