Characterization and process understanding of tropical cyclone-induced floods derived from observations in Shenzhen, China

Liu，Jingru1,2; Tian，Zhan2; Dobbie，Steven1; Ross，Andrew N.1; Sun，Laixiang3; Ye，Qinghua4

2023-12-01

10.1088/1748-9326/ad0bd7

Environmental Research Letters   影响因子和分区

1748-9326

Coastal cities like Shenzhen are confronting escalating flood risks under the combined impact of climate change and rapid urbanization, especially the tropical cyclones (TC)-induced flood. Incorporating the impact of climate change and urbanization on the flood, this study constructed a new TC-induced flood model on western Shenzhen embedded with a unique statistical approach. Based on extensive historical data and machine learning techniques, the temporal characteristics and changes of flooding were revealed. The results reveal an increase in the frequency of TC-induced floods between 1964-2022, especially after the 1990s, which is attributed to a decrease in the distance of the location of the maximum intensity of TCs (observed within an 800 km range of the study area) relative to the land, averaging a reduction of 11.4 km per decade. This shift towards land is likely due to changes in the locations of TC genesis. Furthermore, the ‘rainfall sea level’ threshold for western Shenzhen was accordingly derived from the results of modelling, which would enable decision-makers to quickly assess TC-induced flood risks. The study’s proposed methods offer alternative approaches for predicting TC-induced floods in regions where the gathering of hydro-meteorological data is challenging or where economic and technological resources are limited.

flood threshold machine learning TC-induced flood tropical cyclone

SCI ; EI

英语

通讯

WOS:001107216500001

20235015198294

Climate change ; Climate models ; Decision making ; Hurricanes ; Machine learning ; Risk assessment ; Sea level ; Tropical cyclone ; Tropics

Meteorology:443 ; Atmospheric Properties:443.1 ; Oceanography, General:471.1 ; Artificial Intelligence:723.4 ; Management:912.2 ; Accidents and Accident Prevention:914.1 ; Mathematics:921

2-s2.0-85179131692

Scopus

1.School of Earth and Environment,University of Leeds,Leeds,LS2 9JT,United Kingdom
2.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Geographical Sciences,University of Maryl,College Park,20742,United States
4.Deltares,Delft,Netherlands

Liu，Jingru,Tian，Zhan,Dobbie，Steven,et al. Characterization and process understanding of tropical cyclone-induced floods derived from observations in Shenzhen, China[J]. Environmental Research Letters,2023,18(12).

Liu，Jingru,Tian，Zhan,Dobbie，Steven,Ross，Andrew N.,Sun，Laixiang,&Ye，Qinghua.(2023).Characterization and process understanding of tropical cyclone-induced floods derived from observations in Shenzhen, China.Environmental Research Letters,18(12).

Liu，Jingru,et al."Characterization and process understanding of tropical cyclone-induced floods derived from observations in Shenzhen, China".Environmental Research Letters 18.12(2023).