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Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China
Journal Pre-proof Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China
Shibiao Bai, Ping Lu, Benni Thiebes PII:
S0013-7952(19)30199-1
DOI:
https://doi.org/10.1016/j.enggeo.2020.105518
Reference:
ENGEO 105518
To appear in:
Engineering Geology
Received date:
30 January 2019
Revised date:
29 January 2020
Accepted date:
6 February 2020
Please cite this article as: S. Bai, P. Lu and B. Thiebes, Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China, Engineering Geology (2019), https://doi.org/10.1016/j.enggeo.2020.105518
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© 2019 Published by Elsevier.
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Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China Shibiao Bai1 , Ping Lu2* , Benni Thiebes 3 1
College of Marine Sciences and Engineering, Jiangsu Center for Collaborative Innovation in
Geographical Information Resource Development and Application, Nanjing Normal University, Wenyuan Road 1, Nanjing 210023, China. [email protected] 2
College of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai
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200092, China. [email protected]
German Committee for Disaster Reduction, Kaiser-Friedrich-Str. 13, 53113 Bonn, Germany.
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Kaiser-Friedrich-Str. 13, 53113 Bonn, Germany. [email protected]
Ping Lu
Tel: +86 21 6598 3911
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Fax: +86 21 6598 5123
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Email: [email protected]
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*Corresponding Author
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Journal Pre-proof Abstract The Upper Minjiang catchment is located at the eastern margin of the Tibetan Plateau and is frequently affected by landslides. The two main triggering factors in the region are rainfall and high magnitude seismic shocks, such as the 2008 Wenchuan earthquake. In this paper, we present a comparison study of pre-conditioning factors that drive the spatial occurrence of landslides in this high elevation region. We used a multi-temporal landslide inventory that differentiates between rainfall and earthquake triggers based on the analyses of satellite imagery, aerial photos, and field investigations. Our investigation revealed that the strongest influences on landslides are lithology and topographic factors. The difference between these two triggering factors can also be observed with respect to the relative slope pos itions : rainfall-triggered lands lides are more frequently found at lower slopes, while seismic-induced landslides are more evenly distributed on very steep slope sections. In particular, the steep and deeply incised river terraces in this region are
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prone to landslides due to their rapid formation. This study presents the first analysis that differentiates two main triggering factors in order to understand the influence of rainfall and earthquakes on landslide occurrences in this region. Our results can also aid in the sustainable
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management of performance-based slope engineering and landslide risk reduction by reporting the
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differences regarding the spatial distribution of rainfall- and seismic-triggered slope failures. Consequently, these results can be used by local and regional decision-makers for risk management and spatial planning.
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Keywords: Landslides; Rainfall; Earthquake; Upper Minjiang catchment
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1. Introduction
Landslide processes represent one of the major types of natural hazards around the world (Nadim
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et al., 2006; Petley, 2012; Guzzetti et al., 2012; Froude and Petley, 2018). They may pose great risk to loss of human lives and may additionally bring substantial damage to infrastructure. The annual death
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toll of non-seismic events, i.e. primarily rainfall-t riggered landslides, was estimated by Pet ley (2012) to be as high as 1600, with the highest losses occurring in East and Southeast Asia. In China, more than 20% o f all direct economic losses caused by natural hazards are attributed to geological hazards (Li et al., 2004). In particu lar, landslides are considered the most frequent type of geological hazard in mountainous areas of Ch ina and account for 86% of all events (Jin et al. , 2007). Global climate change will cause landslides to occur even more frequently (Dehn et al., 2000; Cro zier, 2010; Huggel et al., 2012; Gariano and Guzzetti, 2016; Haque et al., 2019). Earthquakes are an important triggering factor for landslides and may init iate thousands of events and cause a chain of geological hazards in a short period of time (Keefer, 1984; Yin, 2009; Ch igira et al., 2010; Qi et al., 2010; Fan et al., 2019). In the case of the 2008 Wenchuan Earthquake (Mw 7.9), more than 15,000 landslides were triggered, covering an area of about 31,686.12 km2 , and caused the deaths of more than 20,000 people (Huang and Li, 2009; Yin et al., 2009; Chigara et al., 2010; Qi et al., 2010). Additionally, 257 large dammed lakes were identified along the fault rupture zone, posing a great risk of sudden failure of these landslide dams (Cui et al., 2009). Moreover, damage to infrastructure, such as mountain tunnels , has also been produced by these events (Shen et al., 2014). Yin (2009) estimated the annual losses caused by landslides triggered by the Wenchuan earthquake as about 10 billion China Yuan (CNY). With regards to the number and volu me of triggered landslides, 2
Journal Pre-proof the Wenchuan earthquake is considered the largest event in the past 100 years (Fan et al., 2018). Identifying and understanding multi-hazard scenarios and consequences is , therefore, essential for risk assessment after such a catastrophic earthquake (Zhang et al., 2014). For earthquake-related geohazard risk mitigation purposes in the future, it is also crucial to analyse and summarize landslide characteristics from such large events, to avoid or mitigate harm to human life and property loss. Similar to the 2008 Wenchuan earthquake, the 1999 Ch i-Chi earthquake also triggered nu merous coseismic landslides. It was found that after this earthquake in 1999, intensive rainfall the following years appeared to have triggered more landslides than the earthquake itself (Lin et al., 2006; Chen et al., 2013). Chang et al. (2007) co mpared the characteristics of rain fall- and earthquake-triggered landslides for the Ch i-Chi earthquake and concluded that rainfall-t riggered landslides had a tendency to occur close to rivers, wh ile earthquake-triggered landslides dominantly took place close to ridges. In addition, Chang et al. (2007) revealed that the earthquake still in fluenced the spatial distribution of
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rainfall-triggered landslides six years after the event. The impact of rainfall on landslides has also been
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investigated by Li et al. (2004), who reported that approximately 90% of all landslides and debris flows in China are caused by rainfall. Using a model test study, Lu et al. (2015) also confirmed that rainfall is
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a triggering factor for slope failure. In terms of the Wenchuan earthquake, it was also found that heavy rainfall after the earthquake triggered a large number of landslides (Tang et al., 2011). However, a complete analysis on the characteristics of rainfall- and earthquake-triggered landslides was not
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accomplished. Considering that the Wenchuan earthquake contributed to several chains of geological hazards (Fan et al., 2019), it is , therefore, necessary to compare the characteristics of rainfall- and
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earthquake-triggered landslides in order to develop a detailed understanding of the impact of hazard chains and to identify a more effect ive hazard mit igation strategy. In terms of perfo rmance-based slope engineering, a clear understanding of different landslide triggering factors occurrences would also
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greatly benefit the decision-making process used for landslide hazard and risk mitigation. Landslide occurrences in an earthquake region typically fo llo w a function of direct or indirect
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natural conditions and human factors including topography, lithology, tectonics, geomorphology, land cover, infiltration and runoff, etc. (Kamp et al., 2008), both of which are generally recognized as
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pre-conditioning factors and greatly contribute to the spatial susceptibility of landslides (Glade and Cro zier, 2005). In this study, we investigate the pre-conditioning factors contributing to the occurrence of landslides in the Upper Minjiang catchment, wh ich was strongly affected by the 2008 Wenchuan earthquake. A landslide inventory was prepared and analysed with reference to different pre-conditioning factors. In order to differentiate the impact of rainfall and earthquake as mentioned above, the landslide inventory was consequently split into rainfall- and earthquake-triggered landslides for separate evaluation. The novelty of this study is to help understand the influence of rainfall and earthquake on landslide occurrence and provide fundamental evidence for investigating the chain of geological hazards after a catastrophic earthquake. The outcomes of this work are also expected to support sustainable management for performance-based slope engineering and improve landslide risk management and spatial planning practices in this region.
2. Study area The study area, the Upper M injiang catchment (Fig. 1), is seriously affected by landslide processes, as a consequence of the 2008 Wenchuan earthquake. It is located along the eastern marg in of the north-south trending Tibetan Plateau and covers a total area of appro ximately 22,000 km2 . To the 3
Journal Pre-proof northeast, southeast, southwest, and northwest, there are the M inshan, Longmenshan, and Qionglai mountain ranges as well as the Zoige Plateau, respectively (Fig. 1b). Th is area, similar to most regions in Qinghai-Tibetan Plateau, is experiencing dynamic environ mental conditions and climate change (Lu
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et al., 2020).
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Journal Pre-proof Fig. 1. The location of the study area. (a) The location of Tibetan Plateau. (b) The eastern margin of the Tibetan Plateau and the Long menshan Mountain. (c) The upper Min jiang catch ment. (d) to (i) are transects of different sections of the Minjiang River with their terraces: (d ) Doujitai basin, (e) Zhangla basin, (f) Mao xian basin, (g) Wenchuan and (h) Zipingpu. T1 -T5 are the terrace sequences in different locations. In the (d) and (e) transects , Q1w is from the Wenjiasi group of the Lower Pleistocene, Q2g is fro m the Guanyinshan group of the Middle Pleistocene, and Q3f is fro m the Feijiba group of the Upper Pleistocene. In (g) and (h), ‘1’ represents sandy gravel and ‘2’ represents a gravel layer with sabulous clay. Terrace sequences are based on Yang et al. (2003) and Li et al. (2005).
The Minshan uplift is responsible for the tectonic uplift in the northern section of the study area, while the southern part is dominated by the Qionglai and Longmenshan mountains, and the Chengdu Plain (Fig. 1b). The river along the uplift flows in a southern direction and diverts to a
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southwest direction after passing the Jiuding Mountain (Fig. 1c). The river is located at elevations above 3000 m a.s.l and is marked by a mean gradient of approximately 8%. Several tributaries, e.g.
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the Heishui and the Zagunao rivers (Fig. 1c), can be found in the western area of the main stream. Headwater erosion is the dominant effect in their respective valleys. The two highest mountains in
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the region, i.e. the Xuebaoding and Jiuding mountains, reach maximum heights of 5588 m and 4984 m a.s.l., located in the Minshan and Central Longmenshan mountains, respectively (Fig. 1c). The transects of different sections of Minjiang river terraces is illustrated in Figs. 1d to 1h with the terrace sequences derived from Yang et al. (2003) and Li et al. (2005).
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The area is strongly influenced by the 500 km long and north-east trending Longmenshan
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thrust belt (Fig. 1b). It includes eastern, central, and western margin faults (Tang and Han, 1993). The eastern margin fault trends on a bearing of 35° to 45° and dips 50° to 70° to the north-west. The central fault, also known as the Yingxiu–Beichuan fault (Fig. 2), trends 35° to 45° and dips in a north-western direction. The uplift rate of the hanging wall is approximately 0.6 to 1 mm/year,
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with a right-slip rate of 1 mm/year since Pleistocene times (Deng et al., 1994; Ma et al., 2005; Li et al., 2006; Densmore et al., 2007). The western margin fault comprises a northern and southern
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sector. The northern sector is the Qingchuan fault (Fig. 2) and is characterised by a right-slip, trending 60° to 70° with a northwest dip. The southern sector is the Maowen fault (Fig. 2), which trends 25° to 45° and dips to the northwest direction. It has an average right-slip rate since the Pleistocene of 0.8 to 1.4 mm/year and a hanging wall uplift rate of 0.5 mm/year during the Holocene (Tang and Han, 1993; Ma et al., 2005).
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3. Data and Methods
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Fig. 2. Lithology and faults in the Upper Minjiang catchment.
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3.1 Rainfall-triggered landslides
A geohazard survey project was carried out by the Ministry of Land and Resources of China to map landslides, subsidence, ground fissures, and sinkholes across mountainous areas in western China between 1999 and 2008. Landslide hazard investigation and mapping activities were accomplished on a 1:100,000 scale covering 600 cities and counties. Moreover, monitoring and early warning systems were established in 1640 counties (Zhou and Yao, 2009). In parallel to these activities, a database of rainfall-triggered lands lides was established to document the 446 rainfall-triggered landslides that occurred before 2008 in the Upper Minjiang catchment. According to the Chinese landslide classification algorithm, which primarily focuses on material composition (Liu and Yan, 2002), the processes reported for the Upper Minjiang catchment fall into the categories of colluvial landslides, loess landslides, loess -mudstone landslides, and bedrock landslides. According to the international landslide classification presented by Cruden and Varnes (1996), the landslides can be classified as deep-seated earth and rock slides.
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3.2 Landslides triggered by the 2008 Wenchuan earthquake Following the main shocks of the Wenchuan earthquake in 2008, a reconnaissance mission was employed by the Ministry of Land and Resources to investigate earthquake damage and the geological conditions in the affected area. The aim was to provide data and expert opinions on seismogenic-hazards for decision-making on immediate emergency measures and long term reconstruction activities. These activities included extensive field work and analyses of remote sensing data (Huang and Li, 2008; Tang et al., 2009), by utilizing information from, for instance, Advanced Land Observing Satellite (ALOS), Satellite Pour l’Observation de la Terre 5 (SPOT-5), and aerial photographs.
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The investigation also prepared an inventory of earthquake-triggered landslides including their major geomorphological features such as visible scarps, hummocky topography, and
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landslide dams. Landslide types inc lude rock debris falls, rock avalanches, and debris slides according to the international classification (Cruden and Varnes, 1996). In total, the post-earthquake inventory lists 3,081 landslides in the Minjiang catchment (Qin
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et al., 2009). However, all investigations focused on the inhabited areas and it is, thus, likely that landslides in unpopulated high mountain areas may be missing from the database. The spatial
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distribution of lands lides triggered by rainfall and by the 2008 Wenchuan earthquake is
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demonstrated in Fig. 3.
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Fig. 3. The Spatial distribution of landslides triggered by rainfall and the 2008 Wenchuan earthquake.
3.3 Landslide pre-conditioning factors The occurrence of landslides is driven by a variety of environmental conditions, such as geomorphology, topography, lithology, precipitation, tectonics, infiltration and groundwater conditions, land cover, as well as human impact such as construction activity (Kamp et al., 2008). These are frequently referred to as pre-conditioning factors (Glade and Crozier, 2005). To investigate the influences of these factors on the presence and absence of landslides in the Upper Minjiang catchment, a consistent database of environmental factors was constructed with a spatial 8
Journal Pre-proof resolution of 90 m. Based on the digital terrain model (DTM), topographic factors such as altitude, slope angles, aspect, as well as additional geomorphometric factors, were derived. The sediment transport capacity index (length-slope factor, LS, Moore and Burch, 1986; Moore et al., 1991) was calculated using the following equation: m
As sinβ LS = m 1 22.13 0.0896
n
(1)
where As is the specific catchment area (m2 /m) and β is the slope angle (in degrees). Following Moore and Wilson (1992), the values of m and n are defined as 0.4 and 1.3, respectively.
SPI=As × tanβ
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(2)
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The stream power index (SPI) was used to express the intensity of erosive power of a river system based on the specific catchment area (As) and the slope angle (Moore et al., 1991):
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where A s is the specific catchment area (m /m) and β is the slope angle in degrees. As the third geomorphic factor, the topographic wetness index (TWI) was utilised. It describes the saturation of a given area based on the drainage area above (Moore et al., 1991):
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As TWI ln tanβ
(3)
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where A s is the specific catchment area (m /m) and β is the slope angle in degrees.
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As for additional data sources, the available lithological data that depicts strata from the Silurian to the Quaternary was produced to show the lithology of the region in 15 classes, which were primarily defined according to geotechnical and physical-mechanical characteristics. A map of lithology and faults at a scale of 1:200,000 is shown in Fig. 2. The classification of land use was
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carried out using pre-processed Landsat TM 5 images. The analysed imagery was further processed by applying a supervised classification using ERDAS (Earth Resource Data Analys is
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System) software. Field surveys were used to validate the land use map. The land use map at 1:100,000 is presented in Fig. 4. In addition, a map of rivers at 1:50,000 was produced from DTM and is shown in Fig. 5.
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Fig. 4. The land use map for the Upper Minjiang catchment.
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Fig. 5. The map of rivers in the Upper Minjiang catchment.
4. Correlation analysis of landslide pre-conditioning factors We used landslide proportion (LP), wh ich refers to the percentage of landslide numbers accounting for each class of the pre-conditioning factors, as the main statistical variable. Particular
concern was given to the variation between the spatial distribution of landslides and the pre-conditioning factors as estimated from LP.
4.1 Topography and elevation Based on elevation, we divided the upper Minjiang catchment into four sections. Fig. 6 depicts the percentage of landslides triggered by both rainfall and the 2008 Wenchuan earthquake. 11
Journal Pre-proof Interestingly, no rainfall-triggered landslide has been recorded in the northern section around Songpan; however, a number of earthquake-triggered lands lides on elevations above 3000 m a.s.l. were mapped (Fig. 6a). In the sections located between Songpan and Wenchuan, approximately half of all rainfall-triggered lands lides can be found between 1300 and 1900 m a.s.l.; similarly, slightly more than half (i.e. 51,4%) of all earthquake-triggered landslides are located between 1400 m and 2100 m a.s.l. (Fig. 6b). In general, landslides tend to be distributed in areas with consistent terrace heights (Fig. 1f and Fig. 1g). In the third section, which stretches from Wenchuan to Dujiangyan, 85.5% of the rainfall-triggered landslides are located at elevations between 1000 and 1900 m a.s.l.; landslides triggered by the seismic shocks of the earthquake have a more even distribution (Fig. 6c). In the last section, i.e. the Zagunao basin, 64.3% of the earthquake-triggered landslides were mapped at elevations between 1800 and 2400 m a.s.l., corresponding to the height of the terraces in this basin. Rainfall-triggered lands lides seem to
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cluster on elevations ranging from 1400 to 2600 m a.s.l. (Fig. 6d).
Fig. 6. Histogram of landslide proportion (LP) versus elevation in different sections of the study area: (a) Songpan, (b) Songpan to Wenchuan, (c) Wenchuan to Dujiangyan, and (d) the Zagunao basin.
Following Densmore (2000), the probability distribution of landslides with slopes steeper than 40° were analysed. To the end, the ridge lines were calculated from the DTM. Moreover, the distance between landslides and rivers normalized by slope length were computed. Herein, a value of 1 represents the crest and a value of 0 corresponds to the toe of the slope (Densmore, 2000). Approximately two thirds of the rainfall-triggered landslides show distance to river values lower than 0.2; and the number of landslides steadily decreases with higher values (Fig. 7). For seismic-induced landslides, only 42% of landslides show distance to river values below 0.2, and 12
Journal Pre-proof more landslides tend to occur closer to ridges (Fig. 7).
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Fig. 7. Distance to rivers for landslides with rainfall and seismic triggers. The impact of slope angle on the occurrence of landslides was also analysed (Fig. 8a). In
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general, the number of landslides increases with slope angles until approximately 26° to 42° , after which steeper slopes then correspond to a decreasing number of landslides. The proportion of landslides in the slope angle bracket of 26° and 42° is approximately even for landslides triggered
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by rainfall and earthquakes, and was assessed to be 32.1% and 30%, respectively. Aspect was found to affect the presence of earthquake- and rainfall-triggered landslides
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differently. For landslides induced by seismic shaking, northeastern, eastern, southeastern, and southern directions were found to be the most common. Rainfall-triggered landslides, however, are more commonly present in slopes facing south, southwest, west, and northwest (Fig. 8b). However, as all earthquake-triggered landslides were derived from the 2008 Wenchuan earthquake,
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this distribution might represent the conditions of this very heavy earthquake and might not be common to all seismic events. Analyses of the LS index showed that the majority of landslides, i.e. 94% of rainfall-triggered and 97% of earthquake-triggered landslides, are below the value of 500 (Fig. 8c).
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For the SPI analyses, 61% and 57% of earthquake and rainfall-triggered lands lides have been calculated to have index values below 500, respectively (Fig. 8d). For TWI, high proportions (i.e. 88% of earthquake-triggered and 82.4% of rainfall-triggered landslides) were found to be present for areas with TWI values below 100 (Fig. 8e).
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Fig. 8. Influence of (a) slope, (b) aspect, (c) LS, (d) stream power index (SPI), (e) topographic wetness
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index (TWI), (f) lithological units, (g) distances to faults , (h) distances to rivers, and (g) land use on
4.2 Lithology
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the presence of landslides.
The analyses showed that the six most landslide prone lithological units include Sands tone, Siltstone interbedded with Phyllite, Granitic rocks, Limestone and Sandstone, Limestone and Dolomite intercalated with Phyllite, and Amphibolite, which combined to represent more than 80% of all landslides (Fig. 8f).
4.3 Distance to faults Not surprisingly, the number of landslides decreases with greater distance from geological faults. This trend was observed for both earthquake- and rainfall-triggered lands lides, probably indicating a general trend of lowered slope stability due to the effects of s eismic events. In total, approximately 43% and 36% of all earthquake and rainfall-triggered lands lides are located within a 2 km radius of mapped faults, respectively (Fig. 8g).
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Journal Pre-proof 4.4 Distance to rivers Rivers also drive the presence of landslides in the study area and the number of landslides decreases with increased distance to a channel. This effect is more prominent with rainfall-triggered lands lides, 86% of which are closer than 600 m to rivers; for earthquake-induced landslides, the percentage is approximately 65% (Fig. 8h).
4.5 Land use Agricultural areas are most affected by landslides, reaching 64% and 53% for earthquake-induced and rainfall-triggered landslides, respectively. For woodlands, the numbers decrease to 27% and 32%, respectively. Only 10% and 15% of earthquake-triggered and rainfall-triggered landslides, respectively, occurred for other land use classes. However, this result
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may be altered by the present land use patterns, as agricultural lands are currently the land use type majority in this area.
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5. Discussion
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The landslide inventory available for this study was prepared as point-based and was manually mapped from satellite imagery, aerial photos, and field investigations. It only presents landslide locations by points and, therefore, information about the spatial extent of landslides could not be investigated. Future investigations should address this shortcoming and add further in-depth analyses on the spatial distribution of lands lide pre-conditioning factors with respect to landslide area and volume. In order to increase the mapping efficiency and accuracy, further
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improvement may focus on a polygon-based inventory that can be automatically mapped or detected using remote sensing approaches. Promising approaches may concentrate on object-oriented change detection (Lu et al., 2011), random forest (Stumpf and Kerle, 2011), level
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set method (Li et al., 2016a), Markov random field (Li et al., 2016b; Lu et al., 2019a), and Persistent Scatterer Interferometry (Lu et al., 2014a, b, 2019b). This research shows that rainfall-triggered landslides can be comparable to earthquake landslides at regional scale. However, in this study, the rainfall-triggered landslides were mapped before 2008. Thus, those post-seismic landslides triggered by heavy precipitation after the 2008 Wenchuan earthquake were not analyzed. The fact that heavy rainfall may activate slope failures directly after the earthquake was also not taken into consideration. Marc et al. (2015) have pointed out that heavy rainfall may potentially form post-seismic landslides at a relatively high rate due to the over-steepened slopes in the crowns of co-seismic landslides. In addition, a strong earthquake in mountainous areas such as the Wenchuan earthquake can bring further chains of geological hazards for decades (Fan et al., 2018). Therefore, it would be worthwhile for our future works to collect complete rainfall-triggered landslide datasets after the Wenchuan earthquake for a better understanding of long-term post-seismic effects on rainfall and earthquake-triggered landslides. Finally, further studies on landslide triggering conditions in the Minjiang catchment should also assist in developing thresholds and landslide forecasting for creating early warning systems.
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Journal Pre-proof 6. Conclusion In this study, the influence of pre-conditioning factors on landslide occurrences was investigated for the Upper Minjiang catchment, China. In this region, both strong rainfall events and heavy seismic shocks, such as the 2008 Wenchuan earthquake, were considered as the dominant triggering factors for slope failures. This research aimed to contribute to a better understanding of the differences between seismic and rainfall-triggered lands lides. We used landslide proportion (LP), which refers to the percentage of landslide numbers for each class of the pre-conditioning factors as the main statistical variable. We then investigated the LP for lithological units, land use, topography, sediment trans port capacity index (length-slope factor, LS), topographic wetness index (TWI), stream power index (SPI), d istance to faults, and distance to rivers. The results
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highlight the major influence of lithology and topography on the distribution of landslides triggered by rainfall and the 2008 Wenchuan earthquake. In addition, the results indicate that faults are the driving force that influences the presence of landslides in the Upper Minjiang catchment as a triggering factor of both rainfall and earthquakes. Also, seismic induced landslides can be observed more frequently in relatively flat and very steep locations. The strong incision by
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the steep mountain rivers resulted in fluvial terraces in the Minjiang catchment and the earthquake-triggered lands lide distributions were consistent with the height of these terraces. Rainfall-triggered lands lides tend to occur on the lower sections of slopes, while earthquake-triggered lands lides are more evenly distributed. The comparison between the spatial
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distribution and topographic context of rainfall-triggered and coseismic lands lides in the same study area could be a useful attempt to understand the relationship between cascading hazards and
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pre-conditioning factors. Moreover, in terms of performance-based slope engineering, our results can help improve slope management practices in the region and assist practitioners and
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decision-makers to better prepare for future landslide events.
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Acknowledgements This study was supported by the National Key R&D Program of China (2017YFA0603100), National Natural Science Foundation of China (41941017, 41671413, 41877522), the Jiangsu provincial key R&D Program (Social Development) (BE2015704, BE2019776), the Opening Fund of Key Laboratory of Mountain Surface Process and Hazards of Chinese Academy of Sciences (KLMHESP-17-07) and the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0205).
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Journal Pre-proof References Chang, K. T., Chiang, S. H., Hsu, M. L. 2007. Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology 89(3-4), 335-347. Chen J. C., Jan, C. D., Huang, W. S. 2013. Characteristics of rainfall triggering of debris flo ws in the Chenyulan watershed, Taiwan. Nat. Hazards Earth Syst. Sci. 13, 1015-1023. Chig ira, M ., Wu, X., Inokuchi, T., Wang, G. 2010. Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China. Geomorphology 118, 225–238. Cro zier, M.J., 2010. Deciphering the effect of climate change on landslide activity: a rev iew. Geo morph
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Journal Pre-proof mapping. IEEE Geoscience and remote sensing letters8(4), 701-705. Lu, P., Catani, F., Tofan i, V., Casagli, N., 2014a. Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry. Landslides 11(4), 685-696. Lu, P., Bai, S., Casagli, N., 2014b. Investigating spatial patterns of persistent scatterer interfero metry point targets and landslide occurrences in the Arno River Basin. Remote Sensing 6(8), 6817-6843. Lu, P., Wu, H., Qiao, G., Li, W., Scaioni, M., Feng, T. et al., 2015. Model test study on monitoring dynamic p rocess of slope failure through spatial sensor network. Environmental earth sciences 74(4), 3315-3332. Lu, P., Qin, Y., Li, Z., Mondini, A. C., Casagli, N., 2019a. Landslide mapping fro m mu lti-sensor data
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after earthquakes. Geology 43(10), 883-886. Moore, I.D., Burch, G.J., 1986. Sediment transport capacity of sheet and rill flow: application of unit stream pow theory. Water Resources Research 22, 1350-1360. Moore, I.D., Grayson, R.B., Ladson, A.R., 1991. Digital terrain modeling: areview of hydrological, geomorphological, and biological applications. Hydrological Processes 5, 3–30. Moore, L.D., Wilson, J.P., 1992. Length-slope factors for the revised universal soil loss equation: simplified method for estimation. Journal of Soil and Water Conservation 47, 423-428. Nadim, F., Kjekstad, O., Peduzzi, P., Hero ld, C., Jaedicke, C., 2006. Global landslide and avalanche hotspots. Landslides 3, 159–173. Petley D., 2012. Global patterns of loss of life from landslides. Geology 40(10), 927–930. Qin X.W., Yang J.Z., Zhang Z., Huang H., Yu D. Q., Chen Y.P., Zhang G., Gu Y.Q., 2009. Remote sensing emergency survey in Wenchuan earthquake area. The Science Press of China (in Chinese). 19
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Zhang, L. M., Zhang, S., Huang, R. Q. 2014. Multi-hazard scenarios and consequences in Beichuan, China: the first five years after the 2008 Wenchuan earthquake. Engineering Geology 180, 4-20. Zhou, P., Yao, L., 2009. Landslide hazard early warning system in China and future trends. In: Culshaw, M. G., Reeves, H. J., Jefferson, I. &Sp ink, T. W. (eds). Engineering Geology for To morrow’s Cit ies. Geological Society, London, Engineering Geology Special Publications, 22 on CD-ROM insert, Paper 805.
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Highlights - Spatial distribution of landslides correlated to pre-conditioning factors - Rainfall- and earthquake-triggered landslides were separately mapped and compared. - Major influence of lithology and topography on landslide distribution identified
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- LP variations found between rainfall and seismic triggers
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Author Statement
Shibiao Bai: Conceptualization, Methodology, Software Ping Lu: Supervision, Writing- Original draft preparation, Validation.
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Benni Thiebes: Writing- Reviewing and Editing.
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Shibiao Bai, Ping Lu, Benni Thiebes PII:
S0013-7952(19)30199-1
DOI:
https://doi.org/10.1016/j.enggeo.2020.105518
Reference:
ENGEO 105518
To appear in:
Engineering Geology
Received date:
30 January 2019
Revised date:
29 January 2020
Accepted date:
6 February 2020
Please cite this article as: S. Bai, P. Lu and B. Thiebes, Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China, Engineering Geology (2019), https://doi.org/10.1016/j.enggeo.2020.105518
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© 2019 Published by Elsevier.
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Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China Shibiao Bai1 , Ping Lu2* , Benni Thiebes 3 1
College of Marine Sciences and Engineering, Jiangsu Center for Collaborative Innovation in
Geographical Information Resource Development and Application, Nanjing Normal University, Wenyuan Road 1, Nanjing 210023, China. [email protected] 2
College of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai
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200092, China. [email protected]
German Committee for Disaster Reduction, Kaiser-Friedrich-Str. 13, 53113 Bonn, Germany.
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Kaiser-Friedrich-Str. 13, 53113 Bonn, Germany. [email protected]
Ping Lu
Tel: +86 21 6598 3911
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Fax: +86 21 6598 5123
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Email: [email protected]
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*Corresponding Author
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Journal Pre-proof Abstract The Upper Minjiang catchment is located at the eastern margin of the Tibetan Plateau and is frequently affected by landslides. The two main triggering factors in the region are rainfall and high magnitude seismic shocks, such as the 2008 Wenchuan earthquake. In this paper, we present a comparison study of pre-conditioning factors that drive the spatial occurrence of landslides in this high elevation region. We used a multi-temporal landslide inventory that differentiates between rainfall and earthquake triggers based on the analyses of satellite imagery, aerial photos, and field investigations. Our investigation revealed that the strongest influences on landslides are lithology and topographic factors. The difference between these two triggering factors can also be observed with respect to the relative slope pos itions : rainfall-triggered lands lides are more frequently found at lower slopes, while seismic-induced landslides are more evenly distributed on very steep slope sections. In particular, the steep and deeply incised river terraces in this region are
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prone to landslides due to their rapid formation. This study presents the first analysis that differentiates two main triggering factors in order to understand the influence of rainfall and earthquakes on landslide occurrences in this region. Our results can also aid in the sustainable
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management of performance-based slope engineering and landslide risk reduction by reporting the
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differences regarding the spatial distribution of rainfall- and seismic-triggered slope failures. Consequently, these results can be used by local and regional decision-makers for risk management and spatial planning.
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Keywords: Landslides; Rainfall; Earthquake; Upper Minjiang catchment
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1. Introduction
Landslide processes represent one of the major types of natural hazards around the world (Nadim
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et al., 2006; Petley, 2012; Guzzetti et al., 2012; Froude and Petley, 2018). They may pose great risk to loss of human lives and may additionally bring substantial damage to infrastructure. The annual death
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toll of non-seismic events, i.e. primarily rainfall-t riggered landslides, was estimated by Pet ley (2012) to be as high as 1600, with the highest losses occurring in East and Southeast Asia. In China, more than 20% o f all direct economic losses caused by natural hazards are attributed to geological hazards (Li et al., 2004). In particu lar, landslides are considered the most frequent type of geological hazard in mountainous areas of Ch ina and account for 86% of all events (Jin et al. , 2007). Global climate change will cause landslides to occur even more frequently (Dehn et al., 2000; Cro zier, 2010; Huggel et al., 2012; Gariano and Guzzetti, 2016; Haque et al., 2019). Earthquakes are an important triggering factor for landslides and may init iate thousands of events and cause a chain of geological hazards in a short period of time (Keefer, 1984; Yin, 2009; Ch igira et al., 2010; Qi et al., 2010; Fan et al., 2019). In the case of the 2008 Wenchuan Earthquake (Mw 7.9), more than 15,000 landslides were triggered, covering an area of about 31,686.12 km2 , and caused the deaths of more than 20,000 people (Huang and Li, 2009; Yin et al., 2009; Chigara et al., 2010; Qi et al., 2010). Additionally, 257 large dammed lakes were identified along the fault rupture zone, posing a great risk of sudden failure of these landslide dams (Cui et al., 2009). Moreover, damage to infrastructure, such as mountain tunnels , has also been produced by these events (Shen et al., 2014). Yin (2009) estimated the annual losses caused by landslides triggered by the Wenchuan earthquake as about 10 billion China Yuan (CNY). With regards to the number and volu me of triggered landslides, 2
Journal Pre-proof the Wenchuan earthquake is considered the largest event in the past 100 years (Fan et al., 2018). Identifying and understanding multi-hazard scenarios and consequences is , therefore, essential for risk assessment after such a catastrophic earthquake (Zhang et al., 2014). For earthquake-related geohazard risk mitigation purposes in the future, it is also crucial to analyse and summarize landslide characteristics from such large events, to avoid or mitigate harm to human life and property loss. Similar to the 2008 Wenchuan earthquake, the 1999 Ch i-Chi earthquake also triggered nu merous coseismic landslides. It was found that after this earthquake in 1999, intensive rainfall the following years appeared to have triggered more landslides than the earthquake itself (Lin et al., 2006; Chen et al., 2013). Chang et al. (2007) co mpared the characteristics of rain fall- and earthquake-triggered landslides for the Ch i-Chi earthquake and concluded that rainfall-t riggered landslides had a tendency to occur close to rivers, wh ile earthquake-triggered landslides dominantly took place close to ridges. In addition, Chang et al. (2007) revealed that the earthquake still in fluenced the spatial distribution of
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rainfall-triggered landslides six years after the event. The impact of rainfall on landslides has also been
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investigated by Li et al. (2004), who reported that approximately 90% of all landslides and debris flows in China are caused by rainfall. Using a model test study, Lu et al. (2015) also confirmed that rainfall is
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a triggering factor for slope failure. In terms of the Wenchuan earthquake, it was also found that heavy rainfall after the earthquake triggered a large number of landslides (Tang et al., 2011). However, a complete analysis on the characteristics of rainfall- and earthquake-triggered landslides was not
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accomplished. Considering that the Wenchuan earthquake contributed to several chains of geological hazards (Fan et al., 2019), it is , therefore, necessary to compare the characteristics of rainfall- and
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earthquake-triggered landslides in order to develop a detailed understanding of the impact of hazard chains and to identify a more effect ive hazard mit igation strategy. In terms of perfo rmance-based slope engineering, a clear understanding of different landslide triggering factors occurrences would also
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greatly benefit the decision-making process used for landslide hazard and risk mitigation. Landslide occurrences in an earthquake region typically fo llo w a function of direct or indirect
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natural conditions and human factors including topography, lithology, tectonics, geomorphology, land cover, infiltration and runoff, etc. (Kamp et al., 2008), both of which are generally recognized as
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pre-conditioning factors and greatly contribute to the spatial susceptibility of landslides (Glade and Cro zier, 2005). In this study, we investigate the pre-conditioning factors contributing to the occurrence of landslides in the Upper Minjiang catchment, wh ich was strongly affected by the 2008 Wenchuan earthquake. A landslide inventory was prepared and analysed with reference to different pre-conditioning factors. In order to differentiate the impact of rainfall and earthquake as mentioned above, the landslide inventory was consequently split into rainfall- and earthquake-triggered landslides for separate evaluation. The novelty of this study is to help understand the influence of rainfall and earthquake on landslide occurrence and provide fundamental evidence for investigating the chain of geological hazards after a catastrophic earthquake. The outcomes of this work are also expected to support sustainable management for performance-based slope engineering and improve landslide risk management and spatial planning practices in this region.
2. Study area The study area, the Upper M injiang catchment (Fig. 1), is seriously affected by landslide processes, as a consequence of the 2008 Wenchuan earthquake. It is located along the eastern marg in of the north-south trending Tibetan Plateau and covers a total area of appro ximately 22,000 km2 . To the 3
Journal Pre-proof northeast, southeast, southwest, and northwest, there are the M inshan, Longmenshan, and Qionglai mountain ranges as well as the Zoige Plateau, respectively (Fig. 1b). Th is area, similar to most regions in Qinghai-Tibetan Plateau, is experiencing dynamic environ mental conditions and climate change (Lu
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et al., 2020).
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Journal Pre-proof Fig. 1. The location of the study area. (a) The location of Tibetan Plateau. (b) The eastern margin of the Tibetan Plateau and the Long menshan Mountain. (c) The upper Min jiang catch ment. (d) to (i) are transects of different sections of the Minjiang River with their terraces: (d ) Doujitai basin, (e) Zhangla basin, (f) Mao xian basin, (g) Wenchuan and (h) Zipingpu. T1 -T5 are the terrace sequences in different locations. In the (d) and (e) transects , Q1w is from the Wenjiasi group of the Lower Pleistocene, Q2g is fro m the Guanyinshan group of the Middle Pleistocene, and Q3f is fro m the Feijiba group of the Upper Pleistocene. In (g) and (h), ‘1’ represents sandy gravel and ‘2’ represents a gravel layer with sabulous clay. Terrace sequences are based on Yang et al. (2003) and Li et al. (2005).
The Minshan uplift is responsible for the tectonic uplift in the northern section of the study area, while the southern part is dominated by the Qionglai and Longmenshan mountains, and the Chengdu Plain (Fig. 1b). The river along the uplift flows in a southern direction and diverts to a
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southwest direction after passing the Jiuding Mountain (Fig. 1c). The river is located at elevations above 3000 m a.s.l and is marked by a mean gradient of approximately 8%. Several tributaries, e.g.
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the Heishui and the Zagunao rivers (Fig. 1c), can be found in the western area of the main stream. Headwater erosion is the dominant effect in their respective valleys. The two highest mountains in
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the region, i.e. the Xuebaoding and Jiuding mountains, reach maximum heights of 5588 m and 4984 m a.s.l., located in the Minshan and Central Longmenshan mountains, respectively (Fig. 1c). The transects of different sections of Minjiang river terraces is illustrated in Figs. 1d to 1h with the terrace sequences derived from Yang et al. (2003) and Li et al. (2005).
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The area is strongly influenced by the 500 km long and north-east trending Longmenshan
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thrust belt (Fig. 1b). It includes eastern, central, and western margin faults (Tang and Han, 1993). The eastern margin fault trends on a bearing of 35° to 45° and dips 50° to 70° to the north-west. The central fault, also known as the Yingxiu–Beichuan fault (Fig. 2), trends 35° to 45° and dips in a north-western direction. The uplift rate of the hanging wall is approximately 0.6 to 1 mm/year,
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with a right-slip rate of 1 mm/year since Pleistocene times (Deng et al., 1994; Ma et al., 2005; Li et al., 2006; Densmore et al., 2007). The western margin fault comprises a northern and southern
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sector. The northern sector is the Qingchuan fault (Fig. 2) and is characterised by a right-slip, trending 60° to 70° with a northwest dip. The southern sector is the Maowen fault (Fig. 2), which trends 25° to 45° and dips to the northwest direction. It has an average right-slip rate since the Pleistocene of 0.8 to 1.4 mm/year and a hanging wall uplift rate of 0.5 mm/year during the Holocene (Tang and Han, 1993; Ma et al., 2005).
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3. Data and Methods
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Fig. 2. Lithology and faults in the Upper Minjiang catchment.
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3.1 Rainfall-triggered landslides
A geohazard survey project was carried out by the Ministry of Land and Resources of China to map landslides, subsidence, ground fissures, and sinkholes across mountainous areas in western China between 1999 and 2008. Landslide hazard investigation and mapping activities were accomplished on a 1:100,000 scale covering 600 cities and counties. Moreover, monitoring and early warning systems were established in 1640 counties (Zhou and Yao, 2009). In parallel to these activities, a database of rainfall-triggered lands lides was established to document the 446 rainfall-triggered landslides that occurred before 2008 in the Upper Minjiang catchment. According to the Chinese landslide classification algorithm, which primarily focuses on material composition (Liu and Yan, 2002), the processes reported for the Upper Minjiang catchment fall into the categories of colluvial landslides, loess landslides, loess -mudstone landslides, and bedrock landslides. According to the international landslide classification presented by Cruden and Varnes (1996), the landslides can be classified as deep-seated earth and rock slides.
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3.2 Landslides triggered by the 2008 Wenchuan earthquake Following the main shocks of the Wenchuan earthquake in 2008, a reconnaissance mission was employed by the Ministry of Land and Resources to investigate earthquake damage and the geological conditions in the affected area. The aim was to provide data and expert opinions on seismogenic-hazards for decision-making on immediate emergency measures and long term reconstruction activities. These activities included extensive field work and analyses of remote sensing data (Huang and Li, 2008; Tang et al., 2009), by utilizing information from, for instance, Advanced Land Observing Satellite (ALOS), Satellite Pour l’Observation de la Terre 5 (SPOT-5), and aerial photographs.
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The investigation also prepared an inventory of earthquake-triggered landslides including their major geomorphological features such as visible scarps, hummocky topography, and
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landslide dams. Landslide types inc lude rock debris falls, rock avalanches, and debris slides according to the international classification (Cruden and Varnes, 1996). In total, the post-earthquake inventory lists 3,081 landslides in the Minjiang catchment (Qin
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et al., 2009). However, all investigations focused on the inhabited areas and it is, thus, likely that landslides in unpopulated high mountain areas may be missing from the database. The spatial
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distribution of lands lides triggered by rainfall and by the 2008 Wenchuan earthquake is
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demonstrated in Fig. 3.
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Fig. 3. The Spatial distribution of landslides triggered by rainfall and the 2008 Wenchuan earthquake.
3.3 Landslide pre-conditioning factors The occurrence of landslides is driven by a variety of environmental conditions, such as geomorphology, topography, lithology, precipitation, tectonics, infiltration and groundwater conditions, land cover, as well as human impact such as construction activity (Kamp et al., 2008). These are frequently referred to as pre-conditioning factors (Glade and Crozier, 2005). To investigate the influences of these factors on the presence and absence of landslides in the Upper Minjiang catchment, a consistent database of environmental factors was constructed with a spatial 8
Journal Pre-proof resolution of 90 m. Based on the digital terrain model (DTM), topographic factors such as altitude, slope angles, aspect, as well as additional geomorphometric factors, were derived. The sediment transport capacity index (length-slope factor, LS, Moore and Burch, 1986; Moore et al., 1991) was calculated using the following equation: m
As sinβ LS = m 1 22.13 0.0896
n
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where As is the specific catchment area (m2 /m) and β is the slope angle (in degrees). Following Moore and Wilson (1992), the values of m and n are defined as 0.4 and 1.3, respectively.
SPI=As × tanβ
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The stream power index (SPI) was used to express the intensity of erosive power of a river system based on the specific catchment area (As) and the slope angle (Moore et al., 1991):
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where A s is the specific catchment area (m /m) and β is the slope angle in degrees. As the third geomorphic factor, the topographic wetness index (TWI) was utilised. It describes the saturation of a given area based on the drainage area above (Moore et al., 1991):
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where A s is the specific catchment area (m /m) and β is the slope angle in degrees.
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As for additional data sources, the available lithological data that depicts strata from the Silurian to the Quaternary was produced to show the lithology of the region in 15 classes, which were primarily defined according to geotechnical and physical-mechanical characteristics. A map of lithology and faults at a scale of 1:200,000 is shown in Fig. 2. The classification of land use was
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carried out using pre-processed Landsat TM 5 images. The analysed imagery was further processed by applying a supervised classification using ERDAS (Earth Resource Data Analys is
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System) software. Field surveys were used to validate the land use map. The land use map at 1:100,000 is presented in Fig. 4. In addition, a map of rivers at 1:50,000 was produced from DTM and is shown in Fig. 5.
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Fig. 4. The land use map for the Upper Minjiang catchment.
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Fig. 5. The map of rivers in the Upper Minjiang catchment.
4. Correlation analysis of landslide pre-conditioning factors We used landslide proportion (LP), wh ich refers to the percentage of landslide numbers accounting for each class of the pre-conditioning factors, as the main statistical variable. Particular
concern was given to the variation between the spatial distribution of landslides and the pre-conditioning factors as estimated from LP.
4.1 Topography and elevation Based on elevation, we divided the upper Minjiang catchment into four sections. Fig. 6 depicts the percentage of landslides triggered by both rainfall and the 2008 Wenchuan earthquake. 11
Journal Pre-proof Interestingly, no rainfall-triggered landslide has been recorded in the northern section around Songpan; however, a number of earthquake-triggered lands lides on elevations above 3000 m a.s.l. were mapped (Fig. 6a). In the sections located between Songpan and Wenchuan, approximately half of all rainfall-triggered lands lides can be found between 1300 and 1900 m a.s.l.; similarly, slightly more than half (i.e. 51,4%) of all earthquake-triggered landslides are located between 1400 m and 2100 m a.s.l. (Fig. 6b). In general, landslides tend to be distributed in areas with consistent terrace heights (Fig. 1f and Fig. 1g). In the third section, which stretches from Wenchuan to Dujiangyan, 85.5% of the rainfall-triggered landslides are located at elevations between 1000 and 1900 m a.s.l.; landslides triggered by the seismic shocks of the earthquake have a more even distribution (Fig. 6c). In the last section, i.e. the Zagunao basin, 64.3% of the earthquake-triggered landslides were mapped at elevations between 1800 and 2400 m a.s.l., corresponding to the height of the terraces in this basin. Rainfall-triggered lands lides seem to
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cluster on elevations ranging from 1400 to 2600 m a.s.l. (Fig. 6d).
Fig. 6. Histogram of landslide proportion (LP) versus elevation in different sections of the study area: (a) Songpan, (b) Songpan to Wenchuan, (c) Wenchuan to Dujiangyan, and (d) the Zagunao basin.
Following Densmore (2000), the probability distribution of landslides with slopes steeper than 40° were analysed. To the end, the ridge lines were calculated from the DTM. Moreover, the distance between landslides and rivers normalized by slope length were computed. Herein, a value of 1 represents the crest and a value of 0 corresponds to the toe of the slope (Densmore, 2000). Approximately two thirds of the rainfall-triggered landslides show distance to river values lower than 0.2; and the number of landslides steadily decreases with higher values (Fig. 7). For seismic-induced landslides, only 42% of landslides show distance to river values below 0.2, and 12
Journal Pre-proof more landslides tend to occur closer to ridges (Fig. 7).
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Fig. 7. Distance to rivers for landslides with rainfall and seismic triggers. The impact of slope angle on the occurrence of landslides was also analysed (Fig. 8a). In
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general, the number of landslides increases with slope angles until approximately 26° to 42° , after which steeper slopes then correspond to a decreasing number of landslides. The proportion of landslides in the slope angle bracket of 26° and 42° is approximately even for landslides triggered
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by rainfall and earthquakes, and was assessed to be 32.1% and 30%, respectively. Aspect was found to affect the presence of earthquake- and rainfall-triggered landslides
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differently. For landslides induced by seismic shaking, northeastern, eastern, southeastern, and southern directions were found to be the most common. Rainfall-triggered landslides, however, are more commonly present in slopes facing south, southwest, west, and northwest (Fig. 8b). However, as all earthquake-triggered landslides were derived from the 2008 Wenchuan earthquake,
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this distribution might represent the conditions of this very heavy earthquake and might not be common to all seismic events. Analyses of the LS index showed that the majority of landslides, i.e. 94% of rainfall-triggered and 97% of earthquake-triggered landslides, are below the value of 500 (Fig. 8c).
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For the SPI analyses, 61% and 57% of earthquake and rainfall-triggered lands lides have been calculated to have index values below 500, respectively (Fig. 8d). For TWI, high proportions (i.e. 88% of earthquake-triggered and 82.4% of rainfall-triggered landslides) were found to be present for areas with TWI values below 100 (Fig. 8e).
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Fig. 8. Influence of (a) slope, (b) aspect, (c) LS, (d) stream power index (SPI), (e) topographic wetness
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index (TWI), (f) lithological units, (g) distances to faults , (h) distances to rivers, and (g) land use on
4.2 Lithology
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the presence of landslides.
The analyses showed that the six most landslide prone lithological units include Sands tone, Siltstone interbedded with Phyllite, Granitic rocks, Limestone and Sandstone, Limestone and Dolomite intercalated with Phyllite, and Amphibolite, which combined to represent more than 80% of all landslides (Fig. 8f).
4.3 Distance to faults Not surprisingly, the number of landslides decreases with greater distance from geological faults. This trend was observed for both earthquake- and rainfall-triggered lands lides, probably indicating a general trend of lowered slope stability due to the effects of s eismic events. In total, approximately 43% and 36% of all earthquake and rainfall-triggered lands lides are located within a 2 km radius of mapped faults, respectively (Fig. 8g).
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Journal Pre-proof 4.4 Distance to rivers Rivers also drive the presence of landslides in the study area and the number of landslides decreases with increased distance to a channel. This effect is more prominent with rainfall-triggered lands lides, 86% of which are closer than 600 m to rivers; for earthquake-induced landslides, the percentage is approximately 65% (Fig. 8h).
4.5 Land use Agricultural areas are most affected by landslides, reaching 64% and 53% for earthquake-induced and rainfall-triggered landslides, respectively. For woodlands, the numbers decrease to 27% and 32%, respectively. Only 10% and 15% of earthquake-triggered and rainfall-triggered landslides, respectively, occurred for other land use classes. However, this result
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may be altered by the present land use patterns, as agricultural lands are currently the land use type majority in this area.
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5. Discussion
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The landslide inventory available for this study was prepared as point-based and was manually mapped from satellite imagery, aerial photos, and field investigations. It only presents landslide locations by points and, therefore, information about the spatial extent of landslides could not be investigated. Future investigations should address this shortcoming and add further in-depth analyses on the spatial distribution of lands lide pre-conditioning factors with respect to landslide area and volume. In order to increase the mapping efficiency and accuracy, further
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improvement may focus on a polygon-based inventory that can be automatically mapped or detected using remote sensing approaches. Promising approaches may concentrate on object-oriented change detection (Lu et al., 2011), random forest (Stumpf and Kerle, 2011), level
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set method (Li et al., 2016a), Markov random field (Li et al., 2016b; Lu et al., 2019a), and Persistent Scatterer Interferometry (Lu et al., 2014a, b, 2019b). This research shows that rainfall-triggered landslides can be comparable to earthquake landslides at regional scale. However, in this study, the rainfall-triggered landslides were mapped before 2008. Thus, those post-seismic landslides triggered by heavy precipitation after the 2008 Wenchuan earthquake were not analyzed. The fact that heavy rainfall may activate slope failures directly after the earthquake was also not taken into consideration. Marc et al. (2015) have pointed out that heavy rainfall may potentially form post-seismic landslides at a relatively high rate due to the over-steepened slopes in the crowns of co-seismic landslides. In addition, a strong earthquake in mountainous areas such as the Wenchuan earthquake can bring further chains of geological hazards for decades (Fan et al., 2018). Therefore, it would be worthwhile for our future works to collect complete rainfall-triggered landslide datasets after the Wenchuan earthquake for a better understanding of long-term post-seismic effects on rainfall and earthquake-triggered landslides. Finally, further studies on landslide triggering conditions in the Minjiang catchment should also assist in developing thresholds and landslide forecasting for creating early warning systems.
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Journal Pre-proof 6. Conclusion In this study, the influence of pre-conditioning factors on landslide occurrences was investigated for the Upper Minjiang catchment, China. In this region, both strong rainfall events and heavy seismic shocks, such as the 2008 Wenchuan earthquake, were considered as the dominant triggering factors for slope failures. This research aimed to contribute to a better understanding of the differences between seismic and rainfall-triggered lands lides. We used landslide proportion (LP), which refers to the percentage of landslide numbers for each class of the pre-conditioning factors as the main statistical variable. We then investigated the LP for lithological units, land use, topography, sediment trans port capacity index (length-slope factor, LS), topographic wetness index (TWI), stream power index (SPI), d istance to faults, and distance to rivers. The results
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highlight the major influence of lithology and topography on the distribution of landslides triggered by rainfall and the 2008 Wenchuan earthquake. In addition, the results indicate that faults are the driving force that influences the presence of landslides in the Upper Minjiang catchment as a triggering factor of both rainfall and earthquakes. Also, seismic induced landslides can be observed more frequently in relatively flat and very steep locations. The strong incision by
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the steep mountain rivers resulted in fluvial terraces in the Minjiang catchment and the earthquake-triggered lands lide distributions were consistent with the height of these terraces. Rainfall-triggered lands lides tend to occur on the lower sections of slopes, while earthquake-triggered lands lides are more evenly distributed. The comparison between the spatial
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distribution and topographic context of rainfall-triggered and coseismic lands lides in the same study area could be a useful attempt to understand the relationship between cascading hazards and
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pre-conditioning factors. Moreover, in terms of performance-based slope engineering, our results can help improve slope management practices in the region and assist practitioners and
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decision-makers to better prepare for future landslide events.
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Acknowledgements This study was supported by the National Key R&D Program of China (2017YFA0603100), National Natural Science Foundation of China (41941017, 41671413, 41877522), the Jiangsu provincial key R&D Program (Social Development) (BE2015704, BE2019776), the Opening Fund of Key Laboratory of Mountain Surface Process and Hazards of Chinese Academy of Sciences (KLMHESP-17-07) and the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0205).
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Highlights - Spatial distribution of landslides correlated to pre-conditioning factors - Rainfall- and earthquake-triggered landslides were separately mapped and compared. - Major influence of lithology and topography on landslide distribution identified
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- LP variations found between rainfall and seismic triggers
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Author Statement
Shibiao Bai: Conceptualization, Methodology, Software Ping Lu: Supervision, Writing- Original draft preparation, Validation.
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Benni Thiebes: Writing- Reviewing and Editing.
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