地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 2042-2054.doi: 10.12082/dqxxkx.2021.210182

• 地理空间分析综合应用 • 上一篇    下一篇

气候变化情景下广东省降雨诱发型滑坡灾害潜在分布及预测

麦鉴锋(), 冼宇阳, 刘桂林*()   

  1. 华南师范大学地理科学学院,广州 510631
  • 收稿日期:2021-04-06 修回日期:2021-07-22 出版日期:2021-11-25 发布日期:2022-01-25
  • 通讯作者: 刘桂林(1985—),男,山东青州人,副研究员,硕士生导师,主要从事环境遥感与地理信息科学研究。E-mail: liuguilin@m.scnu.edu.cn
  • 作者简介:麦鉴锋(1999—),男,广东广州人,本科生,研究方向为地理信息科学与国土空间规划。E-mail: 20182621017@m.scnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41901349);华南师范大学青年拔尖人才启动基金项目(8S0472);广东省普通高校青年创新人才类项目(2018KQNCX054);广东省省级科技计划项目(2021B1111610001);广东省省级科技计划项目(2018B020207002)

Predicting Potential Rainfall-Triggered Landslides Sites in Guangdong Province (China) using MaxEnt Model under Climate Changes Scenarios

MAI Jianfeng(), XIAN Yuyang, LIU Guilin*()   

  1. School of Geography, South China Normal University, Guangzhou 510631, China
  • Received:2021-04-06 Revised:2021-07-22 Online:2021-11-25 Published:2022-01-25
  • Contact: LIU Guilin, E-mail: liuguilin@m.scnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No(41901349);The Startup Foundation for Talented Scholars in South China Normal University, No(8S0472);Foundation for Young Innovation Talents in Higher Education of Guangdong, China (Natural Science), No(2018KQNCX054);The Science and Technology Program of Guangdong Province, China, No(2021B1111610001);The Science and Technology Program of Guangdong Province, China, No(2018B020207002)

摘要:

降雨诱发型滑坡灾害导致了人居环境的破坏并带来巨大的经济损失,尤其是在经济高度发达的粤港澳大湾区城市群。因此,急需有关降雨诱发型滑坡灾害分布的影响因素以及未来气候变化情景下潜在分布的研究。本文从气候变化角度出发,基于最大熵(MaxEnt)模型,结合气候、地形、地表覆盖等数据,揭示不同影响因素对当前气候环境下广东省滑坡空间分布的作用,进而阐述了未来气候情景下滑坡的潜在分布。结果表明:① 影响滑坡灾害空间分布的主要因子为最湿季度降雨量、7月降雨量、海拔和4月降雨量;② 当最湿季度降雨量处于593~742 mm、7月降雨量处于139~223 mm、海拔处于81~397 m和4月降雨量处于154~186 mm之间时,滑坡灾害较易发生;③ 受到气候变化的影响,当前密集分布于粤东地区的滑坡灾害高风险区的潜在分布范围和危害性总体呈现扩大趋势。本研究的结果可以为国土空间规划及城市群灾害预防提供科学依据。

关键词: 降雨诱发型滑坡, MaxEnt模型, 影响因子, 空间分析, 气候变化, 情景模拟, 预测, 广东省

Abstract:

As a natural disaster, rainfall-triggered landslide causes tremendous losses to mankind and then seriously affects living environments of mankind, especially in highly economically developed urban agglomeration areas. Many scholars have carried out data collection and related researches from the perspectives of real-time monitoring, process mechanism analysis, risk assessment, and prediction. However, the abovementioned studies lack the prediction of the potential distribution of landslides from the perspective of climate change. Therefore, we employed the Maximum Entropy (MaxEnt) model to simulate the current distribution of potential rainfall-triggered landslides combined with a series of indicators related to precipitation, including geology, topography, vegetation cover, the precipitation of April, precipitation of May, precipitation of July, precipitation of wettest quarter, Enhanced Vegetation Index (EVI), elevation, slope, lithology, and distance to fault. Then we revealed different effects of those influencing factors on the spatial distribution of landslides in Guangdong Province. Finally, we predicted the future potential distribution of rainfall-triggered landslides under the Shared Socio-economic Pathways (SSPs) scenarios from 2021 to 2060. The results show that the average AUC (Area under the receiving operator curve) value of the model simulation was 0.820, exceeding the standard of "very accurate" simulation effect, and the Kappa coefficient was also 0.823 after 10 repeated simulations. We found that precipitation of wettest quarter, precipitation of July, precipitation of April, and elevation significantly affected the distribution of landslides, in which the wettest season rainfall was the most contributing factor. Specifically, the precipitation of wettest quarter between 593~742 mm, precipitation of July between 139~223 mm, precipitation of April between 154~186 mm, and elevation between 81~397 m were highly correlated to the occurrence of rainfall-triggered landslides. Currently, the area of high risk of rainfall-triggered landslides in Guangdong Province was 1.28×104 km2, accounting for 7.59% of Guangdong Province. Spatially, it is mainly distributed in the eastern part of Guangdong Province. However, under three future SSPs scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) of two periods (i.e., 2021—2040, 2041—2060), the potential distribution range and harm of the areas above the risk in Guangdong Province have shown an expansion trend. The area increased the most under the SSP1-2.6 scenario during the period of 2041—2060. The simulated future high-risk distribution of landslide had potential harm to Guangdong-Hong Kong-Macao Greater Bay Area and Eastern Guangdong urban agglomeration. The findings can provide scientific evidence for the future smart sustainable territory development plan from the perspective of prediction of landslide distribution.

Key words: rainfall-triggered landslide, MaxEnt modeling, influence factors, spatial analysis, climate change, scenario simulation, prediction, Guangdong Province