Spatio-temporal variations of ecosystem services and their drivers in the Pearl River Delta, China

2022 ◽  
pp. 130466
Author(s):  
Wei Liu ◽  
Jinyan Zhan ◽  
Fen Zhao ◽  
Chao Wang ◽  
Fan Zhang ◽  
...  
Author(s):  
Gizem Mestav Sarica ◽  
Tinger Zhu ◽  
Wei Jian ◽  
Edmond Yat-Man Lo ◽  
Tso-Chien Pan

The Pearl River Delta metropolitan region is one of the most densely urbanized megapolises worldwide with high exposure to weather-related disasters such as storms, storm surges and river floods. Shenzhen megacity has been the fastest growing city in the Pearl River Delta region with a significant increase of resident population from 0.32 million in 1980 to 13.03 million in 2018. Being a flood-prone city, Shenzhen’s rapid urbanization has further exacerbated potential flood losses and forthcoming risk. Thus, evaluating the changes in its exposure from present to future is essential for flood risk assessment, mitigation and management purposes. The main objective of this study is to present a methodology to assess the spatio-temporal dynamics of flood exposure from present to future using high-resolution and open-source data with a particular focus on the built-up area. To achieve this, the SLEUTH model, a cellular automata-based urban growth model, was employed for predicting the built-up area in Shenzhen in 2030. An almost threefold increase was observed in total built-up area from 421 km2 in 1995 to 1166 km2 in 2030, with the 2016 built-up area being 858 km2. Built-up areas, both present (2016) and projected (2030), were then used as the land cover input for flood hazard assessment based on a fuzzy comprehensive evaluation model, which classified the flood hazard into five levels. The analysis indicates that the built-up area subjected to the two highest flood hazard levels will increase by almost 88% (212 km2) from present to future. The approach presented here can be leveraged by policymakers to identify critical areas that should be prioritized for flood mitigation and protection actions to minimize potential losses.


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