FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897041887> ?p ?o ?g. }

• W2897041887 endingPage "3761" @default.
• W2897041887 startingPage "3761" @default.
• W2897041887 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." @default.
• W2897041887 created "2018-10-26" @default.
• W2897041887 creator A5008627500 @default.
• W2897041887 creator A5010307809 @default.
• W2897041887 creator A5041690017 @default.
• W2897041887 creator A5082266707 @default.
• W2897041887 date "2018-10-18" @default.
• W2897041887 modified "2023-10-02" @default.
• W2897041887 title "Spatiotemporal Variance Assessment of Urban Rainstorm Waterlogging Affected by Impervious Surface Expansion: A Case Study of Guangzhou, China" @default.
• W2897041887 cites W1009866200 @default.
• W2897041887 cites W1579030254 @default.
• W2897041887 cites W1773028378 @default.
• W2897041887 cites W1964839977 @default.
• W2897041887 cites W1965115132 @default.
• W2897041887 cites W1967137980 @default.
• W2897041887 cites W1969836701 @default.
• W2897041887 cites W1970443926 @default.
• W2897041887 cites W1971820573 @default.
• W2897041887 cites W1973747196 @default.
• W2897041887 cites W1973749534 @default.
• W2897041887 cites W1979349690 @default.
• W2897041887 cites W1982006127 @default.
• W2897041887 cites W1999027583 @default.
• W2897041887 cites W1999800318 @default.
• W2897041887 cites W2009411811 @default.
• W2897041887 cites W2020165879 @default.
• W2897041887 cites W2023007377 @default.
• W2897041887 cites W2027454324 @default.
• W2897041887 cites W2028988189 @default.
• W2897041887 cites W2029943613 @default.
• W2897041887 cites W2033351567 @default.
• W2897041887 cites W2033539531 @default.
• W2897041887 cites W2038687286 @default.
• W2897041887 cites W2049827513 @default.
• W2897041887 cites W2052611179 @default.
• W2897041887 cites W2057128284 @default.
• W2897041887 cites W2059015611 @default.
• W2897041887 cites W2062157031 @default.
• W2897041887 cites W2071338898 @default.
• W2897041887 cites W2071936897 @default.
• W2897041887 cites W2074732445 @default.
• W2897041887 cites W2080768341 @default.
• W2897041887 cites W2083439972 @default.
• W2897041887 cites W2085181402 @default.
• W2897041887 cites W2087189487 @default.
• W2897041887 cites W2088885216 @default.
• W2897041887 cites W2111355857 @default.
• W2897041887 cites W2118898434 @default.
• W2897041887 cites W2136638101 @default.
• W2897041887 cites W2146187510 @default.
• W2897041887 cites W2150942787 @default.
• W2897041887 cites W2164450828 @default.
• W2897041887 cites W2170606244 @default.
• W2897041887 cites W2211722991 @default.
• W2897041887 cites W2298166163 @default.
• W2897041887 cites W2312730115 @default.
• W2897041887 cites W2318440313 @default.
• W2897041887 cites W2318689352 @default.
• W2897041887 cites W2524567940 @default.
• W2897041887 cites W2538065311 @default.
• W2897041887 cites W2547136272 @default.
• W2897041887 cites W2577810053 @default.
• W2897041887 cites W2594407117 @default.
• W2897041887 cites W2600391162 @default.
• W2897041887 cites W2606835700 @default.
• W2897041887 cites W2767468794 @default.
• W2897041887 cites W2779687307 @default.
• W2897041887 cites W2782417058 @default.
• W2897041887 cites W2783834877 @default.
• W2897041887 cites W2787435155 @default.
• W2897041887 cites W2791420662 @default.
• W2897041887 cites W2793026872 @default.
• W2897041887 cites W2796500067 @default.
• W2897041887 cites W2799620231 @default.
• W2897041887 cites W2800862509 @default.
• W2897041887 cites W2802710985 @default.
• W2897041887 cites W2803661416 @default.
• W2897041887 cites W2803859470 @default.
• W2897041887 cites W3104298728 @default.
• W2897041887 cites W4236798210 @default.
• W2897041887 doi "https://doi.org/10.3390/su10103761" @default.
• W2897041887 hasPublicationYear "2018" @default.
• W2897041887 type Work @default.
• W2897041887 sameAs 2897041887 @default.
• W2897041887 citedByCount "54" @default.
• W2897041887 countsByYear W28970418872019 @default.
• W2897041887 countsByYear W28970418872020 @default.
• W2897041887 countsByYear W28970418872021 @default.
• W2897041887 countsByYear W28970418872022 @default.
• W2897041887 countsByYear W28970418872023 @default.
• W2897041887 crossrefType "journal-article" @default.
• W2897041887 hasAuthorship W2897041887A5008627500 @default.
• W2897041887 hasAuthorship W2897041887A5010307809 @default.
• W2897041887 hasAuthorship W2897041887A5041690017 @default.
• W2897041887 hasAuthorship W2897041887A5082266707 @default.
• W2897041887 hasBestOaLocation W28970418871 @default.
• W2897041887 hasConcept C100970517 @default.
• W2897041887 hasConcept C127313418 @default.

• next