Abstract
Strong earthquakes can trigger mountain landslides, which can produce long-term effects on subsequent landslide activities. Therefore, understanding the spatiotemporal evolution characteristics of post-earthquake landslides is crucial for risk assessment of long-term geological hazards. In light of this, the current study aims to analyze the spatiotemporal evolution law, decay modes, and susceptibility changes of post-earthquake landslides, using the post-hazard condition of the Jiuzhaigou earthquake in 2017 as the reference for research. An integrated monitoring technology known as "space-sky-ground" was utilized to create a comprehensive multi-temporal dataset of post-earthquake typical landslide disasters. The spatiotemporal distribution characteristics of landslides were then analyzed to construct a quantitative predictive model for landslide spatiotemporal evolution and to deduce the long-term spatiotemporal evolution law of landslide disasters in seismic areas. Following this, the typical influencing factors were introduced to construct a coupled post-earthquake landslide susceptibility model (CF + LR), summarizing the spatiotemporal evolution patterns of landslide susceptibility. The results show that post-earthquake landslides gradually transitioned from large and medium-sized to small-scale slides, exhibiting an overall power-law decay pattern, with an estimated recovery to pre-earthquake levels projected by 2033. Additionally, the CF + LR coupled model demonstrated higher accuracy and reliability in identifying the high and extremely high susceptibility areas, with the susceptibility zones showing an evolution trend towards lower altitudes, gentler slopes, windward slopes, and closer proximity to channels. This study provides important guidance for the staging, zoning, and long-term risk assessment and prevention of post-earthquake landslides.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10064-024-03724-8/MediaObjects/10064_2024_3724_Fig11_HTML.png)
Data availability
Not applicable.
References
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Adolfo QR (2021) Landslides and floods zonation using geomorphological analyses in a dynamic basin of Costa Rica. Rev Cartogr 102:125–138. https://doi.org/10.35424/rcarto.i102.901
Aslam B, Zafar A, Khalil U (2021) Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential. Soft Comput 25(21):13493–13512. https://doi.org/10.1007/s00500-021-06105-5
Bacha AS, Shafique M, van der Werff H, van der Meijde M, Hussain ML, Wahid S (2022) Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan. Environ Monit Assess 194(11):845. https://doi.org/10.1007/s10661-022-10514-w
Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility map** of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(1–2):23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
Cao J, Zhang Z, Du J, Zhang LL, Song Y, Sun G (2020) Multi-geohazards susceptibility map** based on machine learning—a case study in Jiuzhaigou, China. Nat Hazards 102:851–871. https://doi.org/10.1007/s11069-020-03927-8
Cavallaro A, Fiamingo A, Grasso S, Massimino MR, Sammito MSV (2024) Local site amplification maps for the volcanic area of Trecastagni, south-eastern Sicily (Italy). Bull Earthquake Eng 22:1635–1676. https://doi.org/10.1007/s10518-023-01834-4
Cavallaro A, Grasso S, Sammito MSV (2022) A seismic microzonation study for some areas around the Mt. Etna volcano on the east coast of Sicily, Italy. In: Conference on performance-based design in earthquake. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-031-11898-2_61. Geotechnical Engineering 52:863–870
Chen XQ, Chen JG, Cui P, You Y, Hu KH, Yang ZJ, Zhang WF, Li XP, Wu Y (2018) Assessment of prospective hazards resulting from the 2017 earthquake at the world heritage site Jiuzhaigou Valley, Sichuan, China. J Mt Sci 15(4):779–792. https://doi.org/10.1007/s11629-017-4785-1
Chen M, Tang C, **ong J, Shi QY, Li N, Gong LF, Wang XD, Tie Y (2020) The long-term evolution of landslide activity near the epicentral area of the 2008 Wenchuan earthquake in China. Geomorphology 367:107317. https://doi.org/10.1016/j.geomorph.2020.107317
Dadson SJ, Hovius N, Chen H, Dade WB, Lin JC, Hsu ML, Lin CW, Horng MJ, Chen TC, Milliman J, Stark CP (2004) Earthquake-triggered increase in sediment delivery from an active mountain belt. Geology 32(8):733. https://doi.org/10.1130/G20639.1
Dahlquist MP, West AJ (2019) Initiation and runout of post-seismic debris flows: insights from the 2015 Gorkha earthquake. Geophys Res Lett 46(16):9658–9668. https://doi.org/10.1029/2019GL083548
Dai LX, Xu Q, Fan XM, Chang M, Yang Q, Yang F, Ren J (2017) A preliminary study on spatial distribution patterns of landslides triggered by Jiuzhaigou earthquake in Sichuan on 8 August, 2017, and their susceptibility assessment. J Eng Geol 25(4):1151–1164. https://doi.org/10.13544/j.cnki.jeg.2017.04.030
Fan XM, Scaringi G, Korup O, West AJ, van Westen CJ, Tanyas H, Huang RQ (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57(2):421–503. https://doi.org/10.1029/2018RG000626
Fan X, Yunus AP, Scaringi G, Catani F, Siva Subramanian S, Xu Q, Huang R (2021) Rapidly evolving controls of landslides after a strong earthquake and implications for hazard assessments. Geophys Res Lett 48(1):e2020GL090509. https://doi.org/10.1029/2020GL090509
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Ghosh S, van Westen CJ, Carranza EJM, Jetten VG, Cardinali M, Rossi M, Guzzetti F (2012) Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Eng Geol 128:49–62. https://doi.org/10.1016/j.enggeo.2011.03.016
Guo X, Fu B, Du J, Shi P, Chen Q, Zhang W (2021) Applicability of susceptibility model for rock and loess earthquake landslides in the eastern Tibetan plateau. Remote Sens 13(13):2546. https://doi.org/10.3390/rs13132546
Han LN, Zhang JQ, Zhang YC, Ma Q, Alu S, Lang QL (2019) Hazard assessment of earthquake disaster chains based on a Bayesian network model and ArcGIS. ISPRS Int J Geo-Inf 8(5):210. https://doi.org/10.3390/ijgi8050210
Han X, Yin YH, Wu YM, Wu SH (2021) Risk assessment of population loss posed by earthquake-landslide-debris flow disaster chain: a case study in Wenchuan, China. ISPRS Int J Geo-Inf 10(6):363. https://doi.org/10.3390/ijgi10060363
Hu Q, Zhou Y, Wang SX, Wang FT, Wang HJ (2019) Improving the accuracy of landslide detection in “off-site” area by machine learning model portability comparison: a case study of Jiuzhaigou earthquake, China. Remote Sens 11(21):2530. https://doi.org/10.3390/rs11212530
Huang Y, Zhao L (2018) Review on landslide susceptibility map** using support vector machines. CATENA 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003
Jia HC, Chen F, Pan DH (2019) Disaster chain analysis of avalanche and landslide and the river blocking dam of the Yarlung Zangbo River in Milin County of Tibet on 17 and 29 October 2018. Int J Environ Res Public Health 16(23):4707. https://doi.org/10.3390/ijerph16234707
** W, Cui P, Zhang GT, Wang J, Zhang YX, Zhang P (2023) Evaluating the post-earthquake landslides sediment supply capacity for debris flows. CATENA 220:106649. https://doi.org/10.1016/j.catena.2022.106649
Kamp U, Owen LA, Growley BJ, Khattak GA (2010) Back analysis of landslide susceptibility zonation map** for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps. Nat Hazards 54:1–25. https://doi.org/10.1007/s11069-009-9451-7
Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the **aojiang watershed, Yunnan, China. Eng Geol 76(1–2):109–128. https://doi.org/10.1016/j.enggeo.2004.06.009
Larsen IJ, Montgomery DR, Korup O (2010) Landslide erosion controlled by hillslope material. Nat Geosci 3(4):247–251. https://doi.org/10.1038/ngeo776
Li L, Yao X, Zhang Y, Iqbal J, Chen J, Zhou N (2016) Surface recovery of landslides triggered by 2008 Ms8.0 Wenchuan earthquake (China): a case study in a typical mountainous watershed. Landslides 13:787–794. https://doi.org/10.1007/s10346-015-0594-1
Liu SH, Lin CW, Tseng CM (2013) A statistical model for the impact of the 1999 Chi-Chi earthquake on the subsequent rainfall-induced landslides. Eng Geol 156:11–19. https://doi.org/10.1016/j.enggeo.2013.01.005
Liu M, Chen NS, Zhang Y, Deng MF (2020) Glacial lake inventory and lake outburst flood/debris flow hazard assessment after the Gorkha earthquake in the Bhote Koshi Basin. Water 12(2):464. https://doi.org/10.3390/w12020464
Lombardo L, Mai PM (2018) Presenting logistic regression-based landslide susceptibility results. Eng Geol 244:14–24. https://doi.org/10.1016/j.enggeo.2018.07.019
Marc O, Hovius N, Meunier P, Uchida T, Hayashi SI (2015) Transient changes of landslide rates after earthquakes. Geology 43(10):883–886. https://doi.org/10.1130/G36961.110
Nakamura H, Tsuchiya S, Inoue K, Ishikawa Y (2000) Sabo against earthquakes. Kokon Shoin, Tokyo, pp 190–220 (in Japanese)
Owen LA, Kamp U, Khattak GA, Harp EL, Keefer DK, Bauer MA (2008) Landslides triggered by the 8 October 2005 Kashmir earthquake. Geomorphology 94(1–2):1–9. https://doi.org/10.1016/j.geo-morph.2007.04.007
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Saba SB, van der Meijde M, van der Werff H (2010) Spatiotemporal landslide detection for the 2005 Kashmir earthquake region. Geomorphology 124(1–2):17–25. https://doi.org/10.1016/j.geo-morph.2010.07.026
Samia J, Temme A, Bregt A, Wallinga J, Guzzetti F, Ardizzone F (2020) Dynamic path-dependent landslide susceptibility modelling. Nat Hazard 20(1):271–285. https://doi.org/10.5194/nhess-20-271-2020
Sato HP, Hasegawa H, Fujiwara S, Tobita M, Koarai M, Une H, Iwahashi J (2007) Interpretation of landslide distribution triggered by the 2005 Northern Pakistan earthquake using SPOT 5 imagery. Landslides 4:113–122. https://doi.org/10.1007/s10346-006-0069-5
Shafique M (2020) Spatial and temporal evolution of co-seismic landslides after the 2005 Kashmir earthquake. Geomorphology 362:107228. https://doi.org/10.1016/j.geomorph.2020.107228
Shafique M, van der Meijde M, Kerle N, van der Meer F (2011) Impact of DEM source and resolution on topographic seismic amplification. Int J Appl Earth Obs Geoinf 13(3):420–427. https://doi.org/10.1016/j.jag.2010.09.005
Shao X, Ma S, Xu C, Zhang P, Wen B, Tian Y, Zhou Q, Cui Y (2019) Planet image-based inventorying and machine learning-based susceptibility map** for the landslides triggered by the 2018 Mw6.6 Tomakomai, Japan earthquake. Remote Sens 11:978. https://doi.org/10.3390/rs11080978
Tang C, Van Westen CJV, Tanyas H, Jetten VG (2016) Analysing post-earthquake landslide activity using multi-temporal landslide inventories near the epicentral area of the 2008 Wenchuan earthquake. Nat Hazards Earth Syst Sci 16:2641–2655. https://doi.org/10.5194/nhess-16-2641-2016
Huang C, Hu QJ, Li MY (2024) Scra** effect of dam-overtop** debris flow—A case study of Chutou Gully ‘8.20’ in Miansi Town, Wenchuan County. J Earthq Eng 1–19. https://doi.org/10.1080/13632469.2024.2343781
Yi YN, Zhang ZJ, Zhang WC, Xu Q, Deng C, Li QL (2019) GIS-based earthquake-triggered-landslide susceptibility map** with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China. Nat Hazard 19(9):1973–1988. https://doi.org/10.5194/nhess-19-1973-2019
Yi YN, Zhang ZJ, Zhang WC, Jia HH, Zhang JQ (2020) Landslide susceptibility map** using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. CATENA 195:104851. https://doi.org/10.1016/j.catena.2020.104851
Zang M, Qi S, Zou Y, Sheng Z, Zamora BS (2020) An improved method of newmark analysis for map** hazards of coseismic landslides. Nat Hazard 20(3):713–726. https://doi.org/10.5194/nhess-20-713-2020
Zhang M, Cao XL, Peng L, Niu RQ (2016) Landslide susceptibility map** based on global and local LR models in three Gorges reservoir area, China. Environ Earth Sci 75:1–11. https://doi.org/10.1007/s12665-016-5764-5
Zhang M, Seyler BC, Di BF, Wang Y, Tang Y (2021) Impact of earthquakes on natural area-driven tourism: case study of China’s Jiuzhaigou National Scenic Spot. Int J Disaster Risk Reduct 58:102216. https://doi.org/10.1016/j.ijdrr.2021.102216
Zhang YY, Huang C, Huang C, Li MY (2022) Spatio-temporal evolution characteristics of typical debris flow sources after an earthquake. Landslides 19(9):2263–2275. https://doi.org/10.1007/s10346-022-01883-x
Zhao B, Wang YS, Luo YH, Li J, Zhang X, Shen T (2018) Landslides and dam damage resulting from the Jiuzhaigou earthquake (8 August 2017), Sichuan, China. R Soc Open Sci 5(3):171418. https://doi.org/10.1098/rsos.171418
Acknowledgements
We are thankful to Professor You-yi Zhang for his constructive comments, Mr. Cheng-zhuang Gu for his remote sensing image data, and Mr. **ao-bing Ye for his technical support for data post-processing. We would like to thank MogoEdit (https://www.mogoedit.com) for its English editing during the preparation of this manuscript.
Funding
This research was financially supported by the National Natural Science Foundation of China (Grant No. U23A202277), National Natural Science Foundation of China (Grant No. 52178357), 2020 Tianfu Technology Elite Project, Tianfu 10000 Talents Program of Sichuan Province (No. 658), and the Science and Technology Innovation Base (platform) Project of Education Department of Sichuan Province (Grant No. 22CXTD0087).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Huang, C., Hu, Q., Cai, Q. et al. Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area. Bull Eng Geol Environ 83, 242 (2024). https://doi.org/10.1007/s10064-024-03724-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10064-024-03724-8