Identifying dominant factors of waterlogging events in metropolitan coastal cities: The case study of Guangzhou, China

Zhang, Qifei; Wu, Zhifeng; Zhang, Hui; Fontana, Giancarlo Dalla; Tarolli, Paolo

doi:10.1016/j.jenvman.2020.110951

Cited by 80 publications

(48 citation statements)

References 47 publications

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“…The theory of landscape ecology suggests that multiscale information is required to obtain a better understanding of the relationship between the regional landscape structure and ecological process in the context of spatial heterogeneity [11,32]. This is because both of the elements may have different performances at different spatial and temporal scales, thus leading to complexity in geological/ecological research [33,34]. Therefore, discussing the gradient effect will be beneficial in revealing the potential rules of specific ecological processes.…”

Section: Grid Analysis Designmentioning

confidence: 99%

The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Wang

et al. 2020

Land

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Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.

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Section: Grid Analysis Designmentioning

confidence: 99%

The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Wang

et al. 2020

Land

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“…Unlike most studies [20][21][22][23][24][25][26][27] which focus on urban floods, this study has highlighted that flood hazards in PRB have caused significant impacts on croplands in rural regions, which is the important agricultural product supplier and plays an important role in food safety in PRB. Figures 1 and 14 show that water conservancy facilities like embankments are currently constructed mainly in urban regions, such as the PRD.…”

Section: Implications For Sustainable Management Of Water Resources I...mentioning

confidence: 83%

“…This is because the number of hydrological stations is too sparse to reliably infer changes in water levels in rural and remote areas. Thus, most of the studies [20][21][22][23][24][25][26][27] just focused on the delta regions. The floods in the vast rural middle and upper reaches are still not well investigated.…”

Section: Introductionmentioning

confidence: 99%

Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR

et al. 2021

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Flood hazards result in enormous casualties and huge economic losses every year in the Pearl River Basin (PRB), China. It is, therefore, crucial to monitor floods in PRB for a better understanding of the flooding patterns and characteristics of the PRB. Previous studies, which utilized hydrological data were not successful in identifying flooding patterns in the rural and remote regions in PRB. Such regions are the key supplier of agricultural products and water resources for the entire PRB. Thus, an analysis of the impacts of floods could provide a useful tool to support mitigation strategies. Using 66 Sentinel-1 images, this study employed Otsu’s method to investigate floods and explore flood patterns across the PRB from 2017 to 2020. The results indicated that floods are mainly located in the central West River Basin (WRB), middle reaches of the North River (NR) and middle reaches of the East River (ER). WRB is more prone to flood hazards. In 2017, 94.0% flood-impacted croplands were located in WRB; 95.0% of inundated croplands (~9480 hectares) were also in WRB. The most vulnerable areas to flooding are sections of the Yijiang, Luoqingjiang, Qianjiang, and Xunjiang tributaries and the lower reaches of Liujiang. Our results highlight the severity of flood hazards in a rural region of the PRB and emphasize the need for policy overhaul to enhance flood control in rural regions in the PRB to ensure food safety.

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“…This phenomenon leads to numerous social-environmental-ecological problems [6][7][8][9]. The driving factors and spatial variability of waterlogging have been extensively studied [10,11]. Specifically, the man-made land covers destroy the original urban hydrological cycle, which impedes the natural infiltration of rainwater and reduces the storage capacity of the underlying urban surface.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

Zhang

Tarolli

2021

Remote Sensing

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Urban green infrastructures (UGI) can effectively reduce surface runoff, thereby alleviating the pressure of urban waterlogging. Due to the shortage of land resources in metropolitan areas, it is necessary to understand how to utilize the limited UGI area to maximize the waterlogging mitigation function. Less attention, however, has been paid to investigating the threshold level of waterlogging mitigation capacity. Additionally, various studies mainly focused on the individual effects of UGI factors on waterlogging but neglected the interactive effects between these factors. To overcome this limitation, two waterlogging high-risk coastal cities—Guangzhou and Shenzhen, are selected to examine the effectiveness and stability of UGI in alleviating urban waterlogging. The results indicate that the impact of green infrastructure on urban waterlogging largely depends on its area and biophysical parameter. Healthier or denser vegetation (superior ecological environment) can more effectively intercept and store rainwater runoff. This suggests that while increasing the area of UGI, more attention should be paid to the biophysical parameter of vegetation. Hence, the mitigation effect of green infrastructure would be improved from the “size” and “health”. The interaction of composition and spatial configuration greatly enhances their individual effects on waterlogging. This result underscores the importance of the interactive enhancement effect between UGI composition and spatial configuration. Therefore, it is particularly important to optimize the UGI composition and spatial pattern under limited land resource conditions. Lastly, the effect of green infrastructure on waterlogging presents a threshold phenomenon. The excessive area proportions of UGI within the watershed unit or an oversized UGI patch may lead to a waste of its mitigation effect. Therefore, the area proportion of UGI and its mitigation effect should be considered comprehensively when planning UGI. It is recommended to control the proportion of green infrastructure at the watershed scale (24.4% and 72.1% for Guangzhou and Shenzhen) as well as the area of green infrastructure patches (1.9 ha and 2.8 ha for Guangzhou and Shenzhen) within the threshold level to maximize its mitigation effect. Given the growing concerns of global warming and continued rapid urbanization, these findings provide practical urban waterlogging prevention strategies toward practical implementations.

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Identifying dominant factors of waterlogging events in metropolitan coastal cities: The case study of Guangzhou, China

Cited by 80 publications

References 47 publications

The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR

Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

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