Assessment and mapping of slope stability based on slope units: A case study in Yan’an, China Jianqi Zhuang1,2,3, Jianbing Peng1,∗, Yonglong Xu1, Qiang Xu3, Xinghua Zhu1 and Wei Li1 1College of Geological Engineering and Surveying of Changan University/Key Laboratory of Western Mineral Resources and Geological Engineering, Xi’an 710054, China. 2Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. 3State Key Laboratory of Geohazard Prevention and Geoenvrionment Protection/Collaborative Innovation Center of Geological Prevention, Chengdu 610041, China. ∗Corresponding author. e-mail: [email protected] Precipitation frequently triggers shallow landslides in the Loess Plateau of Shaanxi, China, resulting in loss of life, damage to gas and oil routes, and destruction of transport infrastructure and farmland. To assess the possibility of shallow landslides at different precipitation levels, a method to draw slope units and steepest slope profiles based on ARCtools and a new method for calculating slope stability are proposed. The methods were implemented in a case study conducted in Yan’an, north-west China. High resolution DEM (Digital Elevation Model) images, soil parameters from in-situ laboratory measurements and maximum depths of precipitation infiltration were used as input parameters in the method. Next, DEM and reverse DEM were employed to map 2146 slope units in the study area, based on which the steepest profiles of the slope units were constructed. Combining analysis of the water content of loess, strength of the sliding surface, its response to precipitation and the infinite slope stability equation, a new equation to calculate infinite slope stability is proposed to assess shallow landslide stability. The slope unit stability was calculated using the equation at 10-, 20-, 50- and 100-year return periods of antecedent effective precipitation. The number of slope units experiencing failure increased in response to increasing effective antecedent rainfall. These results were validated based on the occurrence of landslides in recent decades. Finally, the applicability and limitations of the model are discussed. 1. Introduction 2005; Zhuang et al. 2014; Wang et al. 2015). Loess is characterised by the presence of macro- Shallow landslides tend to be widely distributed pores, vertical joints, loose texture and sensiti- in landslide-prone areas. They typically involve a vity to water, which makes it prone to erosion and small volume of debris flowing at high velocity and forming landslides (Dijkstra et al. 1995; Derbyshire with high impact energy, and represent one of the et al. 2000; Xu et al. 2007; Zhang et al. 2009; most destructive geo-hazards in many parts of the Zhang and Liu 2010; Zhu 2012). One-third of the world which result in damaging roads and buildings, landslides occurring annually in China occur in loss of life and bringing environmental problems Loess Plateaus, resulting in loss of life, damage (Baeza and Corominas 2001; Gomez and Kavzoglu to gas and oil pipelines, destruction of transport Keywords. Shallow landslide; infinite slope stability equation; return period precipitation; assessment; slope unit. J. Earth Syst. Sci., DOI 10.1007/s12040-016-0741-7, 125, No. 7, October 2016, pp. 1439–1450 c Indian Academy of Sciences 1439 1440 Jianqi Zhuang et al. infrastructure and reduction in the area of arable Lee 2010; Sarkar et al. 2010), logistic regression land (Li et al. 2007, 2013a; Xu et al. 2009; Wang models (Bai et al. 2010; Akgun 2012; Sarkar et al. et al. 2014). 2012; Paul´ın et al. 2014), etc. However, statisti- In China, loess covers a total area of approx- cal models are largely dependent on the quality imately 631,000 km2,or4.4%ofthetotalland of data, as well as on the equations used. As an area (Liu 1985). Most of the loess deposits are alternative, many researchers have focused on the concentrated in the Loess Plateau, which has an mechanisms of landslides to assist in forecasting area of approximately 430,000 km2. Although the them based on quantitative assessments. Several Loess Plateau is situated in a semi-arid region with physical models have been proposed, including about 500 mm of precipitation per year, most of steady hydrology (SHALSTAB and SINMAP) the rainfall is during the monsoon season from (Montgomery and Dietrich 1994; Pack et al. 1999), June to September. Frequent storms during this quasi-steady hydrology (dSLAM, IDSSM) (Wu and season trigger many shallow landslides every year. Sidle 1995; Dhakal and Sidle 2003) and transient These landslides occasionally mobilize into poten- hydrology (TRIGRS) (Baum et al. 2008; Baum tially destructive debris flows, resulting in catas- and Godt 2010), etc. However, all these mod- trophic damage (Peng et al. 2015). For example, els assessed landslides based on grids rather than in July 2013, a prolonged heavy rainfall occurred on slope units. Landslides are more accurately in Yan’an region (on the Loess Plateau) in north- mapped based on slope units, rather than on grids. west China with the rainfall exceeding 100-year Although spatial landslide hazard mapping is averages, in terms of both total cumulative pre- very important for risk assessment of landslides in cipitation and maximum rainfall intensity, resulted the Loess Plateau, there has been little research in over a thousand shallow loess landslides. These on landslide initiation mechanisms in this area. In landslides resulted in 27 deaths and significant this study, we assumed that shallow landslides in damage to road infrastructure (Wang et al. 2015). the study area occur as a result of heavy rain- A similar event occurred in September 2011 in fall. By combining an infinite-slope stability model Xi’an and triggered many shallow loess landslides, with the assumption that loess strength decreases resulting in 32 deaths and significant damage to with increasing water content, concurrently with buildings and infrastructures (Zhuang and Peng the inclusion of rainfall, we derived a physical 2014). The Loess Plateau is one of the major model to assess the hazard of shallow landslides oil and gas exploitation areas in China, with a based on slope units. The model was used to assess high urban population density and concentration the hazard of shallow landslides at the precipita- of buildings and roads near slopes. Therefore, for tion with return periods of 10, 20, 50 and 100 future land-use planning, it is important to develop years. Finally, these assessments based on slope an effective method to assess the hazards of shallow units were compared with those from grids in the landslides in this region. study area. Extensive research has been conducted on rainfall events that trigger landslides, and several early warning predictive models and hazard/ 2. Physical setting susceptibility models have been proposed and implemented at various areas to predict rainfall- The city of Yan’an is located in the central part of induced landslides and/or identify potentially the Yan River Basin; this river is the first tributary unstable areas. Examples include empirical rain- of the Yellow River. Yan’an is located near the geo- fall threshold methods (Caine 1980; De Vita et al. graphical center of the Loess Plateau (36◦3521N, 1998; Godt et al. 2006; Guzzetti et al. 2007, 2008; 109◦297E, figure 1) and is one of the major cities Baum and Godt 2010; Gupte et al. 2013; Zhuang in the Loess Plateau with its high population et al. 2014; Singh et al. 2016) and probabilistic density, but with limited land area; consequently, methods based on historical rainfall records. The it faces significant threats from landslides. statistical methods or probabilistic methods used The topography of the Loess Plateau is to evaluate landslide susceptibility or hazards are characterised by alternating hills and gullies, of associated with the factors that affect landslide elevations between 800 and 1400 m. The slope occurrences and determine hazard zones. In order angle is steep in lower slope section and gentle to create a landslide hazard zone map, many sta- in upper slope section, with an average of 23.5◦. tistical methods have been applied, such as fuzzy The stratigraphic unit in the area comprises prima- set methods (Gorsevski et al. 2003; Ercanoglu and rily Quaternary strata (Zhang and Liu 2010). Qua- Gokceoglu 2004; Tangestani 2004; Muthu et al. ternary loess spans the entire area of the Malan 2008; Singh et al. 2012, 2013), artificial neural deposits (deposited in Late Pleistocene, upper network (ANN) methods (Gomez and Kavzoglu 0–2 m thick, which have mostly disappeared due 2005; Yesilnacar and Topal 2005; Pradhan and to erosion), Lishi deposits (deposited in Middle Assessment and mapping of slope stability based on slope units 1441 (a) (b) A Beijing Yan’an First terrace Yellow River Xi’an Second- Yangtze River A’ terrace Third- terrace 1150 B Baota Mountain (c) 1100 A-A’ Site 1 1050 1000 Elevation (m) 950 B’ 900 0 500 1,000 1,499 1,999 2,499 2,999 3,499 3,998 4,498 4,998 5,498 5,998 6,497 6,997 7,497 Distance (m) 1200 (d) 1150 B-B’ 1100 C 1050 Elevation (m) 1000 950 900 0 499 998 1,497 1,996 2,495 2,994 3,493 3,992 4,491 Distance (m) C’ 1300 (e) 1250 C-C’ 1200 1150 1100 1050 Elevation (m) 1000 950 900 0 499 998 1,497 1,997 2,496 2,995 3,494 3,993 4,492 4,992 Distance (m) Figure 1. The physical setting of the study area. (a) Location in China; (b) the physical setting; (c) cross section of A–A’; (d) cross section of B–B’; and (e) cross section of C–C’. Pleistocene, 40–60 m thick, which cover nearly the are dependent on DEM resolution (Claessens et al. entire area of the plateau), and a few Wucheng loess 2005).