A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China
Abstract
:1. Introduction
2. Methodology
2.1. Preliminary Investigation
2.2. Data Source
3. Rescue Effort
4. Analysis and Discussions
4.1. Rainfall
4.2. Recommendations
5. Conclusions
- A large landslide of approximately 2 million m3 of soil and rock occurred at Pingdi village, resulting in 42 deaths and 9 missing persons (as of July 28) being reported. In the preliminary investigation, the vertical distance between the highest point and toe of the landslide and sliding trajectory along the sliding surface of the landslide were approximately 500 and 1400 m, respectively.
- Rainfall was considered as the most important causing factor for this landslide. The continuous heavy rainfall significantly deteriorated the stormwater of the sliding interface. The shear stresses due to the deterioration of the sliding interface acted along the shear band, resulting in a reduced shear strength available at the shear band. This in turn caused a displacement of the soil and rock above, thereby triggering this massive landslide.
- The government should strengthen the ability of warning and communication to inform villagers in time. In addition, the villagers should be educated regarding rescue during natural disasters and practice evacuation drills regularly. In addition, more risk prediction methods should be applied to prevent geological disasters.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Xu, Y.S.; Shen, S.L.; Lai, Y.; Zhou, A.N. Design of sponge city: Lessons learnt from an ancient drainage system in Ganzhou, China. J. Hydrol. 2018, 563, 900–908. [Google Scholar] [CrossRef]
- Xu, Y.S.; Yan, X.X.; Shen, S.L.; Zhou, A.N. Experimental investigation on the blocking of groundwater seepage from a waterproof curtain during pumped dewatering in an excavation. Hydrogeol. J 2019, 1–14. [Google Scholar] [CrossRef]
- Lyu, H.M.; Wang, G.F.; Cheng, W.C.; Shen, S.L. Tornado hazards on June 23rd in Jiangsu Province, China: preliminary investigation and analysis. Nat. Hazards 2017, 85, 597–604. [Google Scholar] [CrossRef]
- Ministry of Emergency Management of the People’s Republic of China (MEM). Ministry of Emergency Management of the People’s Republic of China and the National Disaster Reduction Commission have released basic information on natural disasters in 2018. Available online: http://www.mem.gov.cn/xw/zhsgxx/201901/t20190108_242580.shtml (accessed on 8 January 2019). (In Chinese)
- Bezak, N.; Auflič, M.J.; Mikoš, M. Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides. Landslides 2019, 16, 1273–1283. [Google Scholar] [CrossRef] [Green Version]
- Meng, W.; Xu, Y.; Cheng, W.C.; Arulrajah, A. Landslide event on 24 June in Sichuan Province, China: Preliminary investigation and analysis. Geosciences 2018, 8, 39. [Google Scholar] [CrossRef]
- Bogaard, T.; Greco, R. Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: Proposing hydro-meteorological thresholds. Nat. Hazards Earth Syst. Sci. 2018, 18, 31–39. [Google Scholar] [CrossRef]
- National Bureau of Statistics (NBS). China statistical yearbook 2018; National Bureau of Statistics: Beijing, China, 2018. Available online: http://www.stats.gov.cn/tjsj/ndsj/2018/indexch.htm (accessed on 22 June 2019). (In Chinese)
- Cong, W.G.; Pan, M.; Li, T.F.; Wu, Z.X.; Lü, G.X.; Lo, G.X. Key research on landslide and debris flow hazard zonation based on GIS. Dixue Qianyuan/ Earth Sci. Front. 2006, 13, 185–190. [Google Scholar]
- Liang, S.; Yang, X. Landslide hazard assessment based on GIS: A case study of a hydropower station area in China. In Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, Shanghai, China, 21–22 December 2008; pp. 155–158. [Google Scholar]
- Lyu, H.M.; Sun, W.J.; Shen, S.L.; Arulrajah, A. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci. Total. Environ. 2018, 626, 1012–1025. [Google Scholar] [CrossRef]
- Lyu, H.-M.; Shen, J.S.; Arulrajah, A. assessment of geohazards and preventative countermeasures using ahp incorporated with GIS in Lanzhou, China. Sustainability 2018, 10, 304. [Google Scholar] [CrossRef]
- Peng, J.; Peng, F.-L. A GIS-based evaluation method of underground space resources for urban spatial planning: Part 1 methodology. Tunn. Undergr. Space Technol. 2018, 74, 82–95. [Google Scholar] [CrossRef]
- Qiao, Y.K.; Peng, F.L.; Wang, Y. Monetary valuation of urban underground space: A critical issue for the decision-making of urban underground space development. Land Use Policy 2017, 69, 12–24. [Google Scholar] [CrossRef]
- Qiao, Y.K.; Peng, F.L.; Sabri, S.; Rajabifard, A. low carbon effects of urban underground space. Sustain. Cities Soc. 2019, 45, 451–459. [Google Scholar] [CrossRef]
- Caracciolo, D.; Arnone, E.; Conti, F.L.; Noto, L.V. Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds. Environ. Earth Sci. 2017, 76, 222. [Google Scholar] [CrossRef]
- Segoni, S.; Piciullo, L.; Gariano, S.L. A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 2018, 15, 1483–1501. [Google Scholar] [CrossRef]
- Liu, X.X.; Shen, S.L.; Zhou, A.N.; Xu, Y.S. Evaluation of foam conditioning effect on groundwater inflow at tunnel cutting face. Int. J. Numer. Anal. Methods Geomech. 2019, 43, 463–481. [Google Scholar] [CrossRef]
- Wang, X.W.; Yang, T.L.; Xu, Y.S.; Shen, S.L. Evaluation of optimized depth of waterproof curtain to mitigate negative impacts during dewatering. J. Hydrol. 2019, 577, 123969. [Google Scholar] [CrossRef]
- Wu, Y.X.; Lyu, H.M.; Han, J.; Shen, S.L. Dewatering-induced building settlement around a deep excavation in soft deposit in Tianjin, China. J. Geotech. Geoenviron. Eng. 2019, 145, 05019003. [Google Scholar] [CrossRef]
- She, X.N. Comprehensive comments on the present situation of monitoring collapse and landslide. J. Railw. Eng. Soc. 2007, 5, 6–11. (In Chinese) [Google Scholar]
- Ministry of Emergency Management of the People’s Republic of China (MEM). Opinions of the General Office of the State Council on Strengthening Meteorological Disaster Monitoring, Early Warning and Information Release. Available online: http://www.mem.gov.cn/fw/flfgbz/fg/gwywj/201107/t20110714_232750.shtml (accessed on 14 July 2011). (In Chinese)
- Ministry of Emergency Management of the People’s Republic of China (MEM). Emergency Response Law of the People’s Republic of China. (In Chinese). Available online: http://www.mem.gov.cn/fw/flfgbz/fl/200709/t20070924_232540.shtml (accessed on 24 September 2007).
- Lin, C.N. Earthquake Disaster Prevention for Life; Iwanami Shoten: Tokyo, Japan, 2003. (In Japanese) [Google Scholar]
- Wu, L.H.; Bao, S. Construction of Emergency Management System of Earthquake Disasters in Japan. J. Inst. Disaster Prev. 2017, 19, 54–63. (In Chinese) [Google Scholar]
- Wang, L.-J.; Guo, M.; Sawada, K.; Lin, J.; Zhang, J. Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 2015, 135, 271–282. [Google Scholar] [CrossRef]
- Shahabi, H.; Khezri, S.; Bin Ahmad, B.; Hashim, M. Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 2014, 115, 55–70. [Google Scholar] [CrossRef]
- Yalcin, A.; Yalçın, A. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena 2008, 72, 1–12. [Google Scholar] [CrossRef]
- Zhu, Z.; Wang, X.C.; Zhang, P.; He, M.C. A landslide prediction model based on load-unload response ratio theory and its application. DYNA-ingeniería e industria 2019, 94, 304–312. [Google Scholar] [CrossRef]
- Yang, B.; Yin, K.; Lacasse, S.; Liu, Z. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides 2019, 16, 677–694. [Google Scholar] [CrossRef]
- Anderson, S.A. Remote Sensing Applications for Landslides, Slopes and Embankments. Geo-Congress 2013, III, 2204–2223. [Google Scholar]
- Czikhardt, R.; Papco, J.; Bakon, M.; Liscak, P.; Ondrejka, P.; Zlocha, M. Ground stability monitoring of undermined and landslide prone areas by means of sentinel-1 multi-temporal InSAR, case study from Slovakia. Geosciences 2017, 7, 87. [Google Scholar] [CrossRef]
- Liu, J.; Mason, P.J.; Bryant, E.C. Regional assessment of geohazard recovery eight years after the Mw 7.9 Wenchuan earthquake: a remote-sensing investigation of the Beichuan region. Int. J. Remote Sens 2018, 39, 1671–1695. [Google Scholar] [CrossRef]
- Lyu, H.M.; Sun, W.J.; Shen, S.L.; Zhou, A.N. Risk assessment using a new consulting process in fuzzy AHP. J. Constr. Eng. Manag. 2019. In press. [Google Scholar] [CrossRef]
- Lyu, H.M.; Shen, S.L.; Zhou, A.N.; Zhou, W.H. Flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach. Sustain. Cities Soc. 2019, 50, 1012–1025. [Google Scholar] [CrossRef]
- Lyu, H.M.; Shen, S.L.; Zhou, A.N.; Yang, J. Perspectives for flood risk assessment and management for mega-city metro system. Tunn. Undergr. Space Technol. 2019, 84, 31–44. [Google Scholar] [CrossRef]
- Zhang, N.; Shen, S.L.; Zhou, A.N.; Xu, Y.S. Investigation on performance of neural network using quadratic relative error cost function. IEEE Access 2019, 7, 106642–106652. [Google Scholar] [CrossRef]
- Baum, R.L.; Godt, J.W. Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 2010, 7, 259–272. [Google Scholar] [CrossRef]
- Segoni, S.; Rosi, A.; Fanti, R.; Gallucci, A.; Monni, A.; Casagli, N. A regional-scale landslide warning system based on 20 years of operational experience. Water 2018, 10, 1297. [Google Scholar] [CrossRef]
- Piciullo, L.; Calvello, M.; Cepeda, J.M. Territorial early warning systems for rainfall-induced landslides. Earth-Sci. Rev. 2018, 179, 228–247. [Google Scholar] [CrossRef]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yan, T.; Shen, S.-L.; Zhou, A.-N.; Chen, J. A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China. Geosciences 2019, 9, 368. https://doi.org/10.3390/geosciences9090368
Yan T, Shen S-L, Zhou A-N, Chen J. A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China. Geosciences. 2019; 9(9):368. https://doi.org/10.3390/geosciences9090368
Chicago/Turabian StyleYan, Tao, Shui-Long Shen, An-Nan Zhou, and Jun Chen. 2019. "A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China" Geosciences 9, no. 9: 368. https://doi.org/10.3390/geosciences9090368