Study On The Spatial Pattern And Driving Mechanism Of Urban Flooding In Karst Mountains

With the acceleration of global climate change and urbanization,the problem of urban waterlogging has become increasingly prominent.The frequency of occurrence has gradually increased,and the scope of influence has been expanding,which restricts the sustainable development of cities.The city itself is a complex system,and urban waterlogging has the characteristics of complex disaster-causing mechanism and obvious regional differences.Therefore,the disaster-causing mechanism of urban waterlogging points is still unclear.Although many coastal cities in China have carried out research on urban waterlogging-related issues,there are significant differences in natural environmental characteristics and socio-economic conditions in different regions.Therefore,the experience and measures of waterlogging control in other regions cannot be directly copied.Guiyang is a typical karst mountainous city.Its terrain is undulating,the contradiction between people and land is prominent,and the ecological environment carrying capacity is low.In recent years,waterlogging disasters have occurred frequently,which has brought many challenges to the development of the city.Therefore,exploring the spatial pattern and driving mechanism of waterlogging in Guiyang can provide some reference for the treatment and prevention of waterlogging in mountainous cities.In this paper,the main urban area of Guiyang City is taken as the research area,and the historical waterlogging data collection and field investigation are carried out.The vector data set of waterlogging points is established,and various spatial analysis methods are used to explore the spatial and temporal distribution characteristics of waterlogging.Through the reference data,factors such as precipitation,topography,land use,soil,geology,population density,and urban spatial structure characteristics were selected.Spearman correlation analysis was used to explore the correlation between various factors and waterlogging at multiple scales,and combined with geographically weighted regression to explore the spatial differentiation of the effects of related factors on waterlogging.Finally,the geographical detector was used to analyze the driving force of each influencing factor on waterlogging and the interaction between different factors.The main conclusions of the study are as follows:(1)There was a significant spatial aggregation of waterlogging in Guiyang from 2016 to 2020,and the aggregation centers were located in Yunyan District and Nanming District.During the five years,influenced by the expansion of urban construction land and the construction of urban projects,the distribution direction of waterlogging has changed from ’south-north ’ to ’ southeast-northwest ’.(2)The correlation analysis results of various factors and waterlogging at different scales show that impervious surface area,soil type,some geological strata,urban spatial structure and population density are significantly positively correlated with waterlogging.Elevation,slope,topographic relief,downhill length,slope variability,green space and some geological strata were significantly negatively correlated with waterlogging.The correlation between various factors and waterlogging will change with the change of research scale,which has obvious scale effect.(3)The fitting effect of multi-scale geographically weighted regression(MGWR)model is better than that of geographically weighted regression(GWR)model.Based on the MGWR model,it is found that there are obvious regional differences in the influence of elevation,slope direction variability,population density,average building height,standard deviation of building volume and standard deviation of building height on waterlogging.(4)The results of the geo detector show that the factors that pass the significance test drive flooding in the order of strength to weakness : plot ratio 、 building volume standard deviation 、 building density 、building congestion 、 building average height 、 building height standard deviation 、 population density 、 building shape coefficient 、impervious surface area 、 green area、elevation、surface roughness、slope、topographic relief 、slope aspect variability 、 slope variability 、downhill length;the results of interactive detection show that the interaction mode of the two factors is two-factor enhancement,that is,the influence after interaction is greater than the influence of each factor when it acts independently.When the standard deviation of building height interacts with the impervious surface,the driving force for waterlogging is the strongest.