Spatiotemporal Variance Assessment of Urban Rainstorm Waterlogging Affected by Impervious Surface Expansion: A Case Study of Guangzhou, China

Abstract

Urban rainstorm waterlogging has become a typical “city disease” in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization’s impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making.

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

“…Geographically Weighted Regression (GWR) is also a spatial regression technique, which provides a local model of the variables. GWR obeys Tobler's first law of geography, and fully considers the spatial location of adjacent factors [26]. GWR can remedy the disadvantage of OLS to explore the spatial non-stationarity of relationships among geographical data [98][99][100].…”

“…Current research on urban waterlogging problems includes the causes of urban waterlogging [24][25][26][27][28], urban stormwater control [29,30], urban stormwater management [31][32][33][34][35][36], urban waterlogging simulation [37][38][39], and the spatial assessment of urban waterlogging risks and vulnerability in flood-prone areas [12,[40][41][42][43]. Some assessment models for urban waterlogging risk have been proposed.…”

“…Efforts to apply the regression model in ArcGIS for estimating urban waterlogging risks have been made, particularly in the last year. For example, a geographically weighted regression model was developed to assess the spatiotemporal variance in the impact of impervious surface expansion on urban rainstorm waterlogging in Guangzhou, China, during 2012-2018 [26]. applied a geographically weighted regression model with a database of waterlogging events to examine the spatially explicit relationships between waterlogging frequency and the spatial explanatory factors of urban pluvial floods in Shanghai, China, during 1997-2013 [62].…”

Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

“…Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.Sustainability 2019, 11, 6227 2 of 18 waterlogging [8,9], and water quality deterioration [10][11][12]. The spatial distribution of impervious surface is not only an important characteristic of the urbanization process, but also a critical indicator for urban ecological environment quality evaluation [13][14][15].…”

“…Sustainability 2019, 11, 6227 2 of 18 waterlogging [8,9], and water quality deterioration [10][11][12]. The spatial distribution of impervious surface is not only an important characteristic of the urbanization process, but also a critical indicator for urban ecological environment quality evaluation [13][14][15].…”