Abstract
Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments.
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References
Abbaszadeh Shahri A, Spross J, Johansson F, Larsson S (2019) Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena 183:104225
Baum RL, Godt JW (2010) Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7:259–272. https://doi.org/10.1007/s10346-009-0177-0
Carrara A, Guzzetti F, Cardinali M, Reichenbach P (1999) Use of GIS technology in the prediction and monitoring of landslide hazard. Nat Hazards 20:117–135. https://doi.org/10.1023/A:1008097111310
Casagli N, Catani F, Del Ventisette C, Luzi G (2010) Monitoring, prediction, and early warning using ground-based radar interferometry. Landslides 7:291–301. https://doi.org/10.1007/s10346-010-0215-y
Chen Y, Shi Y (2006) Basic characteristics of seismic landslides in loess area of Northwest China. J Seismol Res 29(3):276–280
Corominas J, van Westen C, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Cui Y, Xu C, Xu S et al (2020) Small-scale catastrophic landslides in loess areas of China: an example of the March 15, 2019, Zaoling landslide in Shanxi Province. Landslides 17:669–676. https://doi.org/10.1007/s10346-019-01322-4
Dahlquist MP, West AJ (2019) Initiation and runout of post-seismic debris flows: insights from the 2015 Gorkha earthquake. Geophys Res Lett 46:9658–9668. https://doi.org/10.1029/2019GL083548
de Oliveira GG, Ruiz LFC, Guasselli LA, Haetinger C (2019) Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil. Nat Hazards 99:1049–1073. https://doi.org/10.1007/s11069-019-03795-x
Dong Y, Jia J, Zhang M et al (2013) An analysis of the inducing effects of irrigation and the responses of loess landslides in Heifangtai area. Geol Bull China 32(6):893–898
Dou J, Yunus AP, Bui DT et al (2019) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides. https://doi.org/10.1007/s10346-019-01286-5
Fan X, Xu Q, Liu J et al (2019) Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China. Landslides 16:2445–2457. https://doi.org/10.1007/s10346-019-01269-6
Fan X, Yang F, Siva Subramanian S et al (2020) Prediction of a multi-hazard chain by an integrated numerical simulation approach: the Baige landslide, Jinsha River, China. Landslides 17:147–164. https://doi.org/10.1007/s10346-019-01313-5
Fannin J, Bowman E (2007) Debris flows - entrapment, deposition and travel distance. Geotech News 25(4):3–6
Fannin J, Bowman ET (2011) Knowledge guided empirical prediction. Geotech News 21
Ge D, Dai K, Guo Z, Li Z (2019) Early identification of serious geological hazards with integrated remote sensing technologies: thoughts and recommendations. Geomatics Inf Sci Wuhan Univ 44(7):949–956
Haddad B, Pastor M, Palacios D, Muñoz-Salinas E (2010) A SPH depth integrated model for Popocatépetl 2001 lahar (Mexico): Sensitivity analysis and runout simulation. Eng Geol 114:312–329. https://doi.org/10.1016/j.enggeo.2010.05.009
Hong H, Shahabi H, Shirzadi A et al (2019) Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural Hazards 96(1):173–212
Horton AJ, Hales TC, Ouyang C, Fan X (2019) Identifying post-earthquake debris flow hazard using Massflow. Engineering Geology 258:105134
Huang R, Li W (2014) Post-earthquake landsliding and long-term impacts in the Wenchuan earthquake area, China. Eng Geol 182:111–120. https://doi.org/10.1016/j.enggeo.2014.07.008
Hungr O, McDougall S (2009) Two numerical models for landslide dynamic analysis. Comput Geosci 35:978–992. https://doi.org/10.1016/j.cageo.2007.12.003
Intrieri E, Gigli G, Mugnai F et al (2012) Design and implementation of a landslide early warning system. Eng Geol 147–148:124–136. https://doi.org/10.1016/j.enggeo.2012.07.017
Intrieri E, Raspini F, Fumagalli A et al (2018) The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides 15:123–133. https://doi.org/10.1007/s10346-017-0915-7
Jaiswal P, Van Westen CJ, Jetten V (2011) Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India. Nat Hazards Earth Syst Sci 11:1723–1743. https://doi.org/10.5194/nhess-11-1723-2011
Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28. https://doi.org/10.1016/j.geomorph.2005.07.005
Li Y, Mo P (2019) Geomorphology a uni fi ed landslide classi fi cation system for loess slopes : a critical review. Geomorphology 340:67–83
Li P, Shen W, Hou X, Li T (2019) Numerical simulation of the propagation process of a rapid flow-like landslide considering bed entrainment: a case study. Eng Geol:263. https://doi.org/10.1016/j.enggeo.2019.105287
Liu D (1985) Loess and the environment, Science Pr. Science Press, Beijing
Liu Y, Zhang D, Wang G y et al (2019) Discrete element method-based prediction of areas prone to buried hill-controlled earth fissures. J Zhejiang Univ Sci A 20:794–803. https://doi.org/10.1631/jzus.A1900292
Lollino G, Manconi A, Clague J et al (2015) Engineering geology for society and territory – volume 1: climate change and engineering geology. Springer
Manfré LA, Hirata E, Silva JB et al (2012) An analysis of geospatial technologies for risk and natural disaster management. ISPRS Int J Geo-Information 1:166–185. https://doi.org/10.3390/ijgi1020166
Metternicht G, Hurni L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98:284–303. https://doi.org/10.1016/j.rse.2005.08.004
Monsieurs E, Jacobs L, Michellier C et al (2018) Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment. Landslides 15:2195–2209. https://doi.org/10.1007/s10346-018-1008-y
Ouyang C, He S, Xu Q et al (2013) A MacCormack-TVD finite difference method to simulate the mass flow in mountainous terrain with variable computational domain. Comput Geosci 52:1–10. https://doi.org/10.1016/j.cageo.2012.08.024
Ouyang C, He S, Tang C (2015a) Numerical analysis of dynamics of debris flow over erodible beds in Wenchuan earthquake-induced area. Eng Geol 194:62–72. https://doi.org/10.1016/j.enggeo.2014.07.012
Ouyang C, He S, Xu Q (2015b) MacCormack-TVD finite difference solution for dam break hydraulics over erodible sediment beds. J Hydraul Eng 141:1–9. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000986
Ouyang C, Zhou K, Xu Q et al (2017) Dynamic analysis and numerical modeling of the 2015 catastrophic landslide of the construction waste landfill at Guangming, Shenzhen, China. Landslides 14:705–718. https://doi.org/10.1007/s10346-016-0764-9
Ouyang C, An H, Zhou S et al (2019) Insights from the failure and dynamic characteristics of two sequential landslides at Baige village along the Jinsha River, China. Landslides 16:1397–1414. https://doi.org/10.1007/s10346-019-01177-9
Peng D (2018) Study on early recognition for potentially loess landslide——a case study at Heifangtai terrace, Gansu Province, China. Chengdu University of Technology
Peng J, Li X, Yan R, Ma X (2015) Failure modes classification and countermeasures of loess collapse in Northern Shaanxi Area. J Yangtze River Sci Res Inst 32(10):11–16
Peng D, Xu Q, Qi X et al (2016) Study on early recognition of loess landslides based on field investigation. Int J Geohazards Environ 2:35–52. https://doi.org/10.15273/ijge.2016.02.006
Peng D, Xu Q, Liu F et al (2018) Distribution and failure modes of the landslides in Heitai terrace, China. Eng Geol 236:97–110. https://doi.org/10.1016/j.enggeo.2017.09.016
Qi X, Xu Q, Liu F (2018a) Analysis of retrogressive loess flowslides in Heifangtai, China. Eng Geol 236:119–128. https://doi.org/10.1016/j.enggeo.2017.08.028
Qi X, Xu Q, Zhu X et al (2018b) Deformation characteristics and formation mechanism of static liquefaction No.8 loess landslide in Chenjia, Heifangtai, Gansu Province. Geol Sci Technol Inf 37(5):234–239
Sameen MI, Sarkar R, Pradhan B et al (2020) Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests. Comput Geosci:134. https://doi.org/10.1016/j.cageo.2019.104336
Sansare DA, Mhaske SY (2020) Natural hazard assessment and mapping using remote sensing and QGIS tools for Mumbai city, India. Nat Hazards. https://doi.org/10.1007/s11069-019-03852-5
Scaringi G, Fan X, Xu Q et al (2018) Some considerations on the use of numerical methods to simulate past landslides and possible new failures: the case of the recent Xinmo landslide (Sichuan, China). Landslides 15:1359–1375. https://doi.org/10.1007/s10346-018-0953-9
Shi J, Wu S, Shi L (2008) Remote sensing for landslide study:an overview. Geol Rev 54(4):505–514
Soga K, Alonso E, Yerro A et al (2018) Trends in large-deformation analysis of landslide mass movements with particular emphasis on the material point method. Geotechnique 68:457–458. https://doi.org/10.1680/jgeot.16.D.004
Strauch R, Istanbulluoglu E, Riedel J (2019) A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA. Nat Hazards Earth Syst Sci 19:2477–2495. https://doi.org/10.5194/nhess-19-2477-2019
Wang N, Zahng Z, Wang J (2013) Forecasting method of sliding distance on typical loess landslides. J Northwest Univ (Natural Sci Ed) 33(1):111–114. https://doi.org/10.1017/CBO9781107415324.004
Wei J, Zhao Z, Xu C, Wen Q (2019) Numerical investigation of landslide kinetics for the recent Mabian landslide (Sichuan, China). Landslides 16:2287–2298. https://doi.org/10.1007/s10346-019-01237-0
Wu W, He Q, Cheng J et al (1993) Development law of landslide in east part of Gansu province. Chinese J Geol Hazard Control 4(3):91–97
Xu Z, Lin Z, Zhang M (2007) Loess in china and loess landslides. Chinese J Rock Mech Eng 26:1297–1312
Xu L, Dai F, Tu X et al (2014) Landslides in a loess platform, North-West China. Landslides 11:993–1005. https://doi.org/10.1007/s10346-013-0445-x
Xu Q, Peng D, Qi X et al (2016) Dangchuan 2# landslide of April 29,2015 in Heifangtai area of Gansu province: characteristices and failure mechanism. J Eng Geol 24(2):167–180
Xu Q, Li H, He Y et al (2019) Comparison of data-driven models of loess landslide runout distance estimation. Bull Eng Geol Environ 78:1281–1294. https://doi.org/10.1007/s10064-017-1176-3
Yan T, Shen SL, Zhou AN, Chen J (2019) A brief report of Pingdi landslide (23 July 2019) in Guizhou province, China. Geosciences 9(9):368. https://doi.org/10.3390/geosciences9090368
Yang Z h, Lan H x, Gao X et al (2014) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75:2467–2487. https://doi.org/10.1007/s11069-014-1441-8 Landslides 3:149–158. 10.1007/s10346-005-0031-y
Zeng RQ, Meng XM, Zhang FY et al (2016) Characterizing hydrological processes on loess slopes using electrical resistivity tomography – a case study of the Heifangtai Terrace, Northwest China. J Hydrol 541:742–753. https://doi.org/10.1016/j.jhydrol.2016.07.033
Zhang M, Liu J (2010) Controlling factors of loess landslides in western China. Environ Earth Sci 59:1671–1680. https://doi.org/10.1007/s12665-009-0149-7
Zhang F, Wang G, Kamai T et al (2013) Undrained shear behavior of loess saturated with different concentrations of sodium chloride solution. Eng Geol 155:69–79. https://doi.org/10.1016/j.enggeo.2012.12.018
Zhu C, Xiao Q, Zhang W (2007) Study on the problem of agroecological geology induced by irrigation in Loess Plateau. J Anhui Agric Sci 35(35):11540–11541
Zhuang J q, Peng J b (2014) A coupled slope cutting—a prolonged rainfall-induced loess landslide: a 17 October 2011 case study. Bull Eng Geol Environ 73:997–1011. https://doi.org/10.1007/s10064-014-0645-1
Acknowledgments
We acknowledge that Prof. X.M. Meng from Lanzhou University and Prof. M.S. Zhang from Key laboratory for Geo-hazards in Loess Area, MLR/Xi'an Center of Geological Survey provided the terrain elevation data of HFT in 2001, 2010, and 2013.
Funding
This research is financially supported by the National Nature Science Foundation of China (Nos. 41630640, 41790445, and 41877254), and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 41521002).
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Highlights
• The volume occurrence probability was established based on time-volume information from a landslide database with 98 events.
• Over 400 groups of inversion cases of 11 loess landslides were evaluated to determine the optimal parameters for a numerical model.
• Classical probability models and interval estimates of uniform distribution function models are used to quantitatively determine the probability of occurrence.
• Typical cases are selected to verify the accuracy of the probability model and predict the spatial distribution probability of potential case failures.
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Zhou, Q., Xu, Q., Peng, D. et al. Quantitative spatial distribution model of site-specific loess landslides on the Heifangtai terrace, China. Landslides 18, 1163–1176 (2021). https://doi.org/10.1007/s10346-020-01551-y
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DOI: https://doi.org/10.1007/s10346-020-01551-y