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

doi:10.12082/dqxxkx.2021.210182

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^*()

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^*()

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/Abstract

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

麦鉴锋, 冼宇阳, 刘桂林. 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[J]. 地球信息科学学报, 2021, 23(11): 2042-2054.DOI:10.12082/dqxxkx.2021.210182

MAI Jianfeng, XIAN Yuyang, LIU Guilin. Predicting Potential Rainfall-Triggered Landslides Sites in Guangdong Province (China) using MaxEnt Model under Climate Changes Scenarios[J]. Journal of Geo-information Science, 2021, 23(11): 2042-2054.DOI:10.12082/dqxxkx.2021.210182

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参考文献 78

[1]	Liu R L, Han Y H, Xiao J, et al. Failure mechanism of TRSS mode in landslides induced by earthquake[J]. Scientific Reports, 2020, 10(1):1-11. doi: 10.1038/s41598-019-56847-4
[2]	刘广润, 晏鄂川, 练操. 论滑坡分类[J]. 工程地质学报, 2002, 10(4):339-342.
	[ Liu G R, Yan E C, Lian C. Discussion on classification of landslides[J]. Journal of Engineering Geology, 2002, 10(4):339-342. ]
[3]	Nadim F, Kjekstad O, Peduzzi P, et al. Global landslide and avalanche hotspots[J]. Landslides, 2006, 3(2):159-173. doi: 10.1007/s10346-006-0036-1
[4]	于宪煜. 基于多源数据和多尺度分析的滑坡易发性评价方法研究[D]. 武汉:中国地质大学, 2016.
	[ Yu X Y. Study on the landslide susceptibility evaluation method based on multi-source data and multi-scale analysis[D]. Wuhan: China University of Geosciences, 2016. ]
[5]	Haque U, da Silva P F, Devoli G, et al. The human cost of global warming: Deadly landslides and their triggers (1995-2014)[J]. Science of the Total Environment, 2019, 682:673-684. doi: 10.1016/j.scitotenv.2019.03.415
[6]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报·信息科学版, 2019, 44(7):957-966.
	[ Xu Q A, Dong X J, Li W L. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):957-966. ]
[7]	徐靓, 程刚, 朱鸿鹄. 基于空天地内一体化的滑坡监测技术研究[J]. 激光与光电子学进展, 2021, 58(9):98-111.
	[ Xu L A, Cheng G, Zhu H H. Research review of landslide monitoring methods based on integration of space-air-ground-interior[J]. Laser & Optoelectronics Progress, 2021, 58(9):98-111. ]
[8]	Van Westen C J, Castellanos E, Kuriakose S L. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview[J]. Engineering Geology, 2008, 102(3-4):112-131. doi: 10.1016/j.enggeo.2008.03.010
[9]	唐亚明. 陕北黄土滑坡风险评价及监测预警技术方法研究[D]. 北京:中国地质大学(北京), 2012.
	[ Tang Y M. Methods on risk assessment and monitor & early-warning for the loess landslide in North Shannxi[D]. Beijing: China University of Geosciences, 2012. ]
[10]	黄发明. 基于3S和人工智能的滑坡位移预测与易发性评价[D]. 武汉:中国地质大学, 2017.
	[ Huang F M. Landslide displacement prediction and susceptibility assessment based on 3S and artificial intelligence[D]. Wuhan: China University of Geosciences, 2017. ]
[11]	曹颖. 单体滑坡灾害风险评价与预警预报——以万州区塘角1号滑坡为例[D]. 武汉:中国地质大学, 2016.
	[ Cao Y. Risk assessment and early warning of individual landslide: Case study of the Tangjiao landslide in Wanzhou[D]. Wuhan: China University of Geosciences, 2016. ]
[12]	殷坤龙, 陈丽霞, 张桂荣. 区域滑坡灾害预测预警与风险评价[J]. 地学前缘, 2007, 14(6):85-97.
	[ Yin K L, Chen L X, Zhang G R. Regional landslide hazard warning and risk assessment[J]. Earth Science Frontiers, 2007, 14(6):85-97. ] doi: 10.1016/S1872-5791(08)60005-6
[13]	王念秦, 王永锋, 罗东海, 等. 中国滑坡预测预报研究综述[J]. 地质论评, 2008, 54(3):355-361.
	[ Wang N Q, Wang Y F, Luo D H, et al. Review of landslide prediction and forecast research in China[J]. Geological Review, 2008, 54(3):355-361. ]
[14]	Saito M. Forecasting time of slope failure by tertiary creep[C]. Proceedings of the 7^th International Conference on Soil Mechanics and Foundation Engineering, Mexico City, Mexico, 1969, 2:677-683.
[15]	Stanley T, Kirschbaum D B. A heuristic approach to global landslide susceptibility mapping[J]. Natural Hazards, 2017, 87(1):145-164. doi: 10.1007/s11069-017-2757-y pmid: 33867675
[16]	Kirschbaum D, Stanley T, Zhou Y. Spatial and temporal analysis of a global landslide catalog[J]. Geomorphology, 2015, 249:4-15. doi: 10.1016/j.geomorph.2015.03.016
[17]	Kirschbaum D, Stanley T. Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness[J]. Earth's Future, 2018, 6(3):505-523. doi: 10.1002/eft2.v6.3
[18]	Zhu A X, Lu G, Liu J, et al. Spatial prediction based on third law of geography[J]. Annals of GIS, 2018, 24(4):225-240. doi: 10.1080/19475683.2018.1534890
[19]	West A M, Kumar S, Brown C S, et al. Field validation of an invasive species MaxEnt model[J]. Ecological Informatics, 2016, 36:126-134. doi: 10.1016/j.ecoinf.2016.11.001
[20]	Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity and Distributions, 2011, 17(1):43-57. doi: 10.1111/ddi.2010.17.issue-1
[21]	Barbosa F G, Schneck F. Characteristics of the top-cited papers in species distribution predictive models[J]. Ecological Modelling, 2015, 313(5):77-83. doi: 10.1016/j.ecolmodel.2015.06.014
[22]	宫清华, 黄光庆, 张俊香. 广东省小流域地区降雨诱发的滑坡灾害预警体系探讨[J]. 气象科技进展, 2015, 5(4):53-56.
	[ Gong Q H, Huang G Q, Zhang J X. Rainfall induced landslides disaster warning system in small watershed areas of Guangdong Province[J]. Advances in Meteorological Science and Technology, 2015, 5(4):53-56. ]
[23]	陈悦丽. 降雨型滑坡动力数值预报模式GRAPES-LFM的研究[D]. 南京:南京信息工程大学, 2015.
	[ Chen Y L. Research on rainfall-induced landslide forecast model of GRAPES-LFM[D]. Nanjing: Nanjing University of Information Science & Technology, 2015. ]
[24]	Gao Y, Yin Y, Li B, et al. Investigation and dynamic analysis of the long runout catastrophic landslide at the Shenzhen landfill on December 20, 2015, in Guangdong, China[J]. Environmental Earth Sciences, 2017, 76(1):1-16. doi: 10.1007/s12665-016-6304-z
[25]	Yang C, Zhang C C, Li Q Q, et al. Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macao Greater Bay Area of China: A remote sensing perspective[J]. Ecological Indicators, 2020, 115:106373. doi: 10.1016/j.ecolind.2020.106373
[26]	田春山, 刘希林, 汪佳. 基于CF和Logistic回归模型的广东省地质灾害易发性评价[J]. 水文地质工程地质, 2016, 43(6):154-161,170.
	[ Tian C S, Liu X L, Wang J. Geohazard susceptibility assessment based on CF model and Logistic Regression models in Guangdong[J]. Hydrogeology & Engineering Geology, 2016, 43(6):154-161,170. ]
[27]	林本海, 杨树庄, 朱伯善, 等. 广东省地质构造与岩土工程基本特征[J]. 岩石力学与工程学报, 2006, 25(S2):3337-3346.
	[ Lin B H, Yang S Z, Zhu B S, et al. Geological structure and basic geotechnical characteristics in Guangdong Province[J]. Chinese Journal of Rock Mechanics and Engineering, 2006, 25(S2):3337-3346. ]
[28]	Yue W C, Liu Z Q, Su M R, et al. The impacts of multi-dimension urbanization on energy-environmental efficiency: Empirical evidence from Guangdong Province, China[J]. Journal of Cleaner Production, 2021, 296:126513. doi: 10.1016/j.jclepro.2021.126513
[29]	Wu T W, Lu Y X, Fang Y J, et al. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP₅ to CMIP₆[J]. Geoscientific Model Development, 2019, 12(4):1573-1600. doi: 10.5194/gmd-12-1573-2019
[30]	O'Neill B C, Tebaldi C, Vvan Vuuren D P, et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP₆[J]. Geoscientific Model Development, 2016, 9(9):3461-3482. doi: 10.5194/gmd-9-3461-2016
[31]	Graham M H. Confronting multicollinearity in ecological multiple regression[J]. Ecology, 2003, 84(11):2809-2815. doi: 10.1890/02-3114
[32]	Sillero N, Barbosa A M. Common mistakes in ecological niche models[J]. International Journal of Geographical Information Science, 2021, 35(2):213-226. doi: 10.1080/13658816.2020.1798968
[33]	Swets J. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857):1285-1293. pmid: 3287615
[34]	Chen F, Du Y S, Niu S K, et al. Modeling forest lightning fire occurrence in the Daxinganling mountains of northeastern China with MAXENT[J]. Forests, 2015, 6(12):1422-1438. doi: 10.3390/f6051422
[35]	West A M, Kumar S, Brown C S, et al. Field validation of an invasive species Maxent model[J]. Ecological Informatics, 2016, 36:126-134. doi: 10.1016/j.ecoinf.2016.11.001
[36]	Feinstein A R, Cicchetti D V. High agreement but low Kappa: I. the problems of two paradoxes[J]. Journal of Clinical Epidemiology, 1990, 43(6):543-549. pmid: 2348207
[37]	Cicchetti D V, Feinstein A R. High agreement but low kappa: II. Resolving the paradoxes[J]. Journal of Clinical Epidemiology, 1990, 43(6):551-558. pmid: 2189948
[38]	广东省地质局. 广东省2019年度地质灾害防治数据[EB/OL].http://dzj.gd.gov.cn/ywlc/content/post_2725543.html.
	[ Guangdong Geological Bureau. Geological hazard control data of Guangdong Province in 2019[EB/OL].http://dzj.gd.gov.cn/ywlc/content/post_2725543.html. ]
[39]	Bai H L, Feng W K, Yi X Y, et al. Group-occurring landslides and debris flows caused by the continuous heavy rainfall in June 2019 in Mibei Village, Longchuan County, Guangdong Province, China[J]. Natural Hazards, 2021, 108(3):3181-3201. doi: 10.1007/s11069-021-04819-1
[40]	林泽雨, 刘爱华. 广东地区滑坡灾害分布特征与预警措施分析[J]. 人民长江, 2019, 50(S1):90-92.
	[ Lin Z Y, Liu A H. Landslide disaster distribution characteristics and pre-warning measures of Guangdong Province[J]. Yangtze River, 2019, 50(S1):90-92. ]
[41]	Worldclim[EB/OL]. https://www.worldclim.org/data/index.html.
[42]	胡家梁. 基于AHP-信息量法的密云区地质灾害易发性评价[J]. 路基工程, 2020(5):11-17.
	[ Hu J L. Geological disasters susceptibility assessment in Miyun district based on AHP-information quantity method[J]. Subgrade Engineering, 2020(5):11-17. ]
[43]	杨根云, 周伟, 方教勇. 基于信息量模型和数据标准化的滑坡易发性评价[J]. 地球信息科学学报, 2018, 20(5):674-683. doi: 10.12082/dqxxkx.2018.170535
	[ Yang G Y, Zhou W, Fang J Y. Assessment of landslide susceptibility based on information quantity model and data normalization[J]. Journal of Geo-Information Science, 2018, 20(5):674-683. ]
[44]	Hartmann J, Moosdorf N. The new global lithological map database GLiM: A representation of rock properties at the earth surface[J]. Geochemistry, Geophysics, Geosystems, 2012, 13(12):1-37. doi: 10.1029/2011GC003955
[45]	地质科学数据出版中心[EB/OL] 质科学数据出版中心[EB/OL]. http://geodb.ngac.org.cn/cn/page/index.
	[ Geoscientific Data & Discovery Publishing Center[EB/OL]eoscientific Data & Discovery Publishing Center[EB/OL]. http://geodb.ngac.org.cn/cn/page/index. ]
[46]	资源环境科学与数据中心[EB/OL]. https://www.resdc.cn/data.aspx?DATAID=264.
	[ Resource and Environment Science and Data Center[EB/OL]. https://www.resdc.cn/data.aspx?DATAID=264. ]
[47]	National Aeronautics and Space Administration[EB/OL]. https://modis.gsfc.nasa.gov/data/dataprod/mod13.php.
[48]	Martin C S, Giannoulaki M, De Leo F, et al. Coralligenous and maërl habitats: Predictive modelling to identify their spatial distributions across the Mediterranean Sea[J]. Scientific Reports, 2014, 4:5073. doi: 10.1038/srep05073
[49]	Zhao H X, Zhang H, Xu C G. Study on Taiwania cryptomerioides under climate change: MaxEnt modeling for predicting the potential geographical distribution[J]. Global Ecology and Conservation, 2020, 24:e01313. doi: 10.1016/j.gecco.2020.e01313
[50]	Shroder J F Jr. Slope failure and denudation in the western Himalaya[J]. Geomorphology, 1998, 26(1/2/3):81-105. doi: 10.1016/S0169-555X(98)00052-X
[51]	黄建龙, 刘亦农, 曾伟国. 粤港澳大湾区地质特点与地质环境保护策略分析[J]. 人民珠江, 2019, 40(9):103-109.
	[ Huang J L, Liu Y N, Zeng W G. Analysis of geological characteristics and geological environment protection in Guangdong-Hong Kong-Macao greater bay area[J]. Pearl River, 2019, 40(9):103-109. ]
[52]	闫文晓. 地质环境条件在国土空间规划中的应用研究[J]. 上海国土资源, 2019, 40(2):56-60.
	[ Yan W X. Research on the application of geological environmental conditions in land spatial planning[J]. Shanghai Land & Resources, 2019, 40(2):56-60. ]
[53]	Du Z Y, He Y M, Wang H T, et al. Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model[J]. Journal of Arid Environments, 2021, 184:104328. doi: 10.1016/j.jaridenv.2020.104328
[54]	Yan H Y, Feng L, Zhao Y F, et al. Prediction of the spatial distribution of Alternanthera philoxeroides in China based on ArcGIS and MaxEnt[J]. Global Ecology and Conservation, 2020, 21:e00856. doi: 10.1016/j.gecco.2019.e00856
[55]	Warren D L, Wright A N, Seifert S N, et al. Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern[J]. Diversity and Distributions, 2014, 20(3):334-343. doi: 10.1111/ddi.2014.20.issue-3
[56]	Radosavljevic A, Anderson R P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation[J]. Journal of Biogeography, 2014, 41(4):629-643. doi: 10.1111/jbi.12227
[57]	朱耿平, 乔慧捷. Maxent模型复杂度对物种潜在分布区预测的影响[J]. 生物多样性, 2016, 24(10):1189-1196. doi: 10.17520/biods.2016265
	[ Zhu G P, Qiao H J. Effect of the Maxent model's complexity on the prediction of species potential distributions[J]. Biodiversity Science, 2016, 24(10):1189-1196. ] doi: 10.17520/biods.2016265
[58]	He F L. Maximum entropy, logistic regression, and species abundance[J]. Oikos, 2010, 119(4):578-582. doi: 10.1111/j.1600-0706.2009.17113.x
[59]	Shipley B. From plant traits to vegetation structure: chance and selection in the assembly of ecological communities[M]. Cambridgeshire: Cambridge University Press, 2010.
[60]	Robbins J C. A probabilistic approach for assessing landslide-triggering event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates[J]. Journal of Hydrology, 2016, 541:296-309. doi: 10.1016/j.jhydrol.2016.06.052
[61]	Skilodimou H D, Bathrellos G D, Koskeridou E, et al. Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece[J]. Land, 2018, 7(3):85. doi: 10.3390/land7030085
[62]	Smith S G, Wegmann K W. Precipitation, landsliding, and erosion across the Olympic mountains, Washington State, USA[J]. Geomorphology, 2018, 300:141-150. doi: 10.1016/j.geomorph.2017.10.008
[63]	Pagano L, Picarelli L, Rianna G, et al. A simple numerical procedure for timely prediction of precipitation-induced landslides in unsaturated pyroclastic soils[J]. Landslides, 2010, 7(3):273-289. doi: 10.1007/s10346-010-0216-x
[64]	Chikalamo E E, Mavrouli O C, Ettema J, et al. Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 89:102093. doi: 10.1016/j.jag.2020.102093
[65]	Xian Y Y, Lu Y Q, Musyimi Z, et al. Tracking the role of policies and economic factors in driving the forest change trajectories within the Guangdong-Hongkong-Macao region of China: A remote sensing perspective[J]. Land, 2021, 10(1):87. doi: 10.3390/land10010087
[66]	涂长红, 商建林, 谢叶彩, 等. 改进的灰色关联分析在滑坡危险性评价中的应用——以广东省滑坡危险性评价为例[J]. 灾害学, 2007, 22(1):86-89.
	[ Tu C H, Shang J L, Xie Y C, et al. An application of improved grey association analysis method in evaluating landslide hazard in Guangdong Province[J]. Journal of Catastrophology, 2007, 22(1):86-89. ]
[67]	魏平新, 汤连生, 张建国, 等. 基于GIS的广东省滑坡灾害区划研究[J]. 水文地质工程地质, 2005, 32(4):6-9.
	[ Wei P X, Tang L S, Zhang J G, et al. Study on regionalization of landslides based on GIS in Guangdong Province[J]. Hydrogeology and Engineering Geology, 2005, 32(4):6-9. ]
[68]	Henriques C, Zêzere J L, Marques F. The role of the lithological setting on the landslide pattern and distribution[J]. Engineering Geology, 2015, 189:17-31. doi: 10.1016/j.enggeo.2015.01.025
[69]	Fell R, Stapledon D, MacGregor P. Landslides and geologic environments[M]//Clague J J, Stead D. eds. Landslides. Cambridge: Cambridge University Press, 2012:134-143.
[70]	Gong Q H, Huang G Q, Zhang J X. Research on characteristics and formation mechanism of landslide disaster in red soil hilly region of South China[J]. Journal of Risk Analysis and Crisis Response, 2013, 3(2):110. doi: 10.2991/jrarc.2013.11439
[71]	易顺民. 广东省滑坡活动的时间分布规律研究[J]. 热带地理, 2007, 27(6):499-504.
	[ Yi S M. Temporal distribution regularity of landslide activities in Guangdong Province[J]. Tropical Geography, 2007, 27(6):499-504. ]
[72]	郑书彦, 李占斌. 滑坡侵蚀研究[M]. 郑州: 黄河水利出版社, 2005.
	[ Zheng S Y, Li Z B. Study on landslide erosion [M]. Zhengzhou: Yellow River Water Conservancy Press, 2005. ]
[73]	王连新, 马建宏, 边智华. 水库滑坡与防治技术[M]. 武汉: 长江出版社, 2005.
	[ Wang X L, Ma J H, Bian Z H. Reservoir landslide and control technology [M]. Wuhan: Yangtze River Press, 2005. ]
[74]	Sobie S R. Future changes in precipitation-caused landslide frequency in British Columbia[J]. Climatic Change, 2020, 162(2):465-484. doi: 10.1007/s10584-020-02788-1
[75]	Jakob M, Lambert S. Climate change effects on landslides along the southwest coast of British Columbia[J]. Geomorphology, 2009, 107(3-4):275-284. doi: 10.1016/j.geomorph.2008.12.009
[76]	Patton A I, Rathburn S L, Capps D M. Landslide response to climate change in permafrost regions[J]. Geomorphology, 2019, 340:116-128. doi: 10.1016/j.geomorph.2019.04.029
[77]	Westra S, Fowler H J, Evans J P, et al. Future changes to the intensity and frequency of short-duration extreme rainfall[J]. Reviews of Geophysics, 2014, 52(3):522-555. doi: 10.1002/2014RG000464
[78]	Tu J Y, Chou C A, Chu P S. The abrupt shift of typhoon activity in the vicinity of Taiwan and its association with western north Pacific-east Asian climate change[J]. Journal of Climate, 2009, 22(13):3617-3628. doi: 10.1175/2009JCLI2411.1

情景	描述
SSP1-2.6	是可持续发展路径情景,对RCP2.6情景进行更新,设定辐射强迫在2100年达到2.6 W/m²
SSP2-4.5	是中度发展路径情景,对RCP4.5情景进行更新,设定辐射强迫在2100年达到4.5 W/m²
SSP3-7.0	是局部发展路径情景,新设置的排放情景,设定辐射强迫在2100年达到7.0 W/m²
SSP5-8.5	是常规发展路径情景,对RCP8.5情景进行更新,设定辐射强迫在2100年达到8.5 W/m²

一级指标	二级指标	来源	空间分辨率
气候因子	年平均降雨量	WorldClim	30″, 2.5′
	最湿月降雨量	WorldClim	30″, 2.5′
	降雨量季节变异系数	WorldClim	30″, 2.5′
	最湿季度降雨量	WorldClim	30″, 2.5′
	最热季度降雨量	WorldClim	30″, 2.5′
	3月降雨量	WorldClim	30″, 2.5′
	4月降雨量	WorldClim	30″, 2.5′
	5月降雨量	WorldClim	30″, 2.5′
	6月降雨量	WorldClim	30″, 2.5′
	7月降雨量	WorldClim	30″, 2.5′
	8月降雨量	WorldClim	30″, 2.5′
	9月降雨量	WorldClim	30″, 2.5′
地形因子	海拔	WorldClim	30″
	坡度	WorldClim	30″
	坡向	WorldClim	30″
地质因子	岩性	GLiM	30′
地质因子	距断层距离	Geodb	30″, 2.5′
地表覆盖	2018年土地利用数据	RESDC	1 km
地表覆盖	增强型植被指数	NASA	0.5 km

时间段	总面积/×10⁴ km²	面积比/%	增加率/%
2021—2040,SSP1-2.6	6.41	38.00	12.80
2021—2040,SSP3-7.0	4.40	26.08	0.89
2021—2040,SSP5-8.5	6.16	36.51	11.32
2041—2060,SSP1-2.6	8.36	49.56	24.36
2041—2060,SSP3-7.0	5.28	31.30	6.11
2041—2060,SSP5-8.5	5.68	33.67	8.48

地区	时间段	最低值/mm	最高值/mm	中位数/mm	高风险面积比/%
梅州市	当前	597	849	675	32.97
韶关市	当前	649	889	758	0.72
	2021—2040(SSP1-2.6)	640	804	720	0.31
	2021—2040(SSP3-7.0)	662	866	774	0.15
	2021—2040(SSP5-8.5)	636	866	739	0.12
	2041—2060(SSP1-2.6)	584	780	668	5.88
	2041—2060(SSP3-7.0)	596	835	700	17.16
	2041—2060(SSP5-8.5)	609	811	695	6.76

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

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

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