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Landslide damage along Araniko highway and Pasang Lhamu highway and regional assessment of landslide hazard related to the Gorkha, Nepal earthquake of 25 April 2015

Landslide damage along Araniko highway and Pasang Lhamu highway and regional assessment of... Background: The Gorkha, Nepal Mw 7.8 earthquake of 25 April 2015 triggered a large number of coseismic landslides in a broad area. Two highways, Araniko Highway and Pasang Lhamu Highway, that connect Tibet of China and Nepal, were affected seriously by these landslides. The purpose of this study was to investigate the landslide damage along the two highways, construct a detailed and complete inventory of coseismic landslides in the 5-km buffer area of the Araniko Highway, and perform a regional assessment of landslide hazard in the affected area. Findings: Based on visual interpretation of high-resolution satellite images, field investigations, and GIS technology, we investigated the coseismic landslides along the Araniko Highway and Pasang Lhamu Highway. A detailed point- based inventory of coseismic landslides was constructed and spatial distributions of the landslides were analyzed. Correlations between the landslides and five controlling factors, i.e. elevation, slope angle, slope aspect, lithology, and seismic intensity, were illustrated statistically which permitted to assess landslides hazard in a larger rectangle area. Conclusions: We examined the coseismic landslides of the 2015 Gorkha earthquake that blocked or damaged the Araniko Highway (117.3 km) and Pasang Lhamu Highway (139.3 km) in Nepal. Results show 35 coseismic landslides damaged the Araniko Highway along a total length 1,415 m. The total volume of them was estimated to be 0.37 million m . We delineated 89 coseismic landslides that damaged the Pasang Lhamu Highway, where the total length of the damaged or buried roads is about 2,842 m and the total volume of the 89 landslides is about 1.47 million m .In the 5-km buffer area along the Araniko Highway, we mapped 3,005 landslides caused by the Gorkha earthquake. The -2 landslide number density of the study area is 2.925 km . The places with elevations 2,000-2,500 m have the highest landslide concentration. Landslide number density values increase with the slope angle. The slope aspects E and SE correspond to the highest concentrations of coseismic landslides. The underlying bedrock of Precambrian rocks-1 (Pc1) registered the largest landslide number density. The area of seismic intensity IX has a much higher LND value than that of the intensity VIII. We used the weigh index method to perform landslide hazard assessment in the 5-km buffer area on either side of the highway, which shows a success ratio of 85.9%. This method has been applied to a larger area mainly encompassing Rasuwa and Sindhupalchok counties of Nepal. Keywords: Gorkha earthquake, Coseismic landslides, Field investigation, Visual interpretation, Landslide hazard assessment * Correspondence: xuchong@ies.ac.cn Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, 1# Huayanli, Chaoyang District, PO Box 9803, Beijing 100029, China Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 2 of 17 Introduction Hashash et al. 2015; Sun and Yan 2015; Kargel et al. The 25 April 2015 Gorkha, Nepal Mw 7.8 earthquake 2016; Lacroix 2016; Sharma et al. 2016). Until now, how- caused more than 8,800 fatalities and enormous eco- ever, little work focuses on the landslides that damaged nomic losses. It also triggered a large number of coseis- these two highways. Although the materials of the mic landslides, mainly shallow and disrupted landslides coseismic landslides blocking the two main roads have and a few deep-seated landslides, some of which buried been cleaned up in time, some new landslides were trig- villages, roads, and valleys (Hashash et al. 2015; Moss et gered by aftershocks or strong rainfalls, resulting in fur- al. 2015; Dahal 2016; Gnyawali et al. 2016; Martha et al. ther damage. Therefore, identifying the landslides 2016; Wang et al. 2016; Xu et al. 2016a). The affected destroying the roads and assessment of landslide hazard areas include Central Nepal and Gyirong and Nielamu is very important for prevention and mitigation of future counties of southern Tibet, China. The coseismic land- geologic hazard around these two roads. In this work, slides seriously damaged two highways, Pasang Lhamu we firstly identified the coseismic landslides that Highway and Araniko Highway, connecting China and destroyed the Lhamu Highway and Araniko Highway Nepal. After the event, several research teams carried using field investigation and visual interpretation of sat- out field investigations of seismic damages and ellite images. Then we constructed a detailed inventory earthquake-triggered landslides (Collins and Jibson 2015; map containing 3,005 individual coseismic landslides in Fig. 1 Shaded topographic relief map showing the study area (big black box) and two highways Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 3 of 17 the buffer area of 5 km to the Araniko Highway. Next, Avouac et al. 2015; Hashash et al. 2015; Parameswaran et correlations between the 3,005 landslides and five land- al. 2015; Duputel et al. 2016; Elliott et al. 2016). The slide controlling factors were analyzed. Finally, we per- earthquake-affected area is mainly in the east to the epicen- formed landslide hazard assessment for a larger area ter (28.23°N, 84.731°E), likely associated with the eastward affected by the Gorkha earthquake using the weigh index rupturing directivity (Wang and Fialko 2015; Koketsu et al. (WI) method. 2016), from which we selected is a rectangular area as the study area, which has a length of 113 km in east- Data and methods west direction and width of 92 km in north-south dir- The study area ection (Fig. 1), covering 10,396 km .Fromnorth to Despite its large magnitude, the Gorkha earthquake did not south, the elevation of the study area generally de- produce visible ruptures on the surface, which was confined clines from 7,975 m to 387 m, i.e. more than 7,500 m to the subsurface at depths 10–15 km (Angster et al. 2015; elevation drop in an about 100 km-wide zone. The Fig. 2 Maps showing controlling factors of coseismic landslides of the study area. a Elevation. b Slope angle. c Aspect. d Lithology (sources are mentioned in the text) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 4 of 17 area encompasses the Rasuwa and Sindhupalchok high-quality landslide inventories (Xu 2015). As a sup- counties of Nepal (Fig. 1). The Araniko Highway plement and verification of results from visual interpret- passes through Sindhupalchok county and the Pasang ation, we carried out several days of field investigation Lhamu Highway passes through Rasuwa county, re- mainly along the Pasang Lhamu Highway and Araniko spectively. Based on the seismic intensity map released Highway. by the China Earthquake Administration (www.cea.- gov.cn), most of the study area lies in the IX intensity Spatial distribution and hazard assessment of landslides zone, and part in VIII and VII intensity zones (Fig. 1). The Gorkha, Nepal earthquake affected a very large area about tens of thousands of square kilometers. Immediately Data after the quake, it was difficult to construct a detailed and The satellite images for landslide interpretation are complete landslide inventory throughout the affected area. from the Google Earth (GE) platform. After the earth- Fortunately, spatial distribution of the partial affected area quake occurred, several organizations have imple- can represent the overall spatial patterns of landslides mented specialized tasks to obtain post-earthquake under some conditions (Lee et al. 2008; Xu et al. 2013a). satellite images. Some of the images with very high Therefore, we selected a 5-km buffer area on either side of resolution (1 m or better) are available on the Google the Araniko Highway to construct a detailed landslide in- Earth platform. In addition, pre-earthquake images ventory. Although we prepared a polygon-based inventory with high qualityand resolution in theareaarealso of landslides that directly damaged Araniko Highway and available on the GE platform. These images allow re- Pasang Lhamu Highway, we chose point-based inventory searchers to map co-seismic landslides conveniently of coseismic landslides and landslide number density and accurately. The regional DEM for analyzing cor- (LND, defined as the number of landslides per square relations between topography and coseismic landslides kilometers (Xu et al. 2013b) to conduct analysis of the were derived from SRTM DEM in 3-arc-second reso- spatial distribution and hazard assessment of landslides. lution (Fig. 2a). The slope angle map (Fig. 2b) and as- The reasons include: (1) The precise source area of a land- pect map (Fig. 2c) were derived from the regional slide is very difficult to be distinguished from the whole DEM on the GIS platform. The geologic map (Fig. 2d) landslide area because the boundaries of the source area, of the study area was clipped and revised from movement area, and accumulation area of the landslide “World Geologic Maps” on the USGS Website are usually in the subsurface, thus cannot be exactly delin- (www.usgs.gov). eated, which perhaps reduce the objectiveness of land- slides hazard assessment. (2) Preparation of a point-based Methods landslide inventory is relatively time-saving, permitting to Landslide identification carry out a quick regional assessment of earthquake- In this study, we used two methods to identify triggered landslides. Five controlling factors, including ele- landslides, i.e. visual interpretation of pre- and post- vation, slope angle, slope aspect, lithology, and seismic in- earthquake satellite images and field investigation. Com- tensity were taken into account for a statistical analysis. puter screen-based visual interpretation of satellite Currently, many statistical methods are available for land- images is the most widely used method for earthquake- slide hazard assessment (Xu et al. 2012; Feng et al. 2016; triggered landslide mapping which permits to prepare Pathak 2016; Tsangaratos and Ilia 2016), among which the 500m (a) (b) Fig. 3 Coseismic landslides at the Araniko Highway. a Satellite image of 4 May 2015. b Field photo of 14 June 2015 (by Chong Xu, view to south). The solid and dotted arrows in (a) and (b) show the same places, respectively Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 5 of 17 300m (a) (b) Fig. 4 Rockfalls on the upper slope of an inspection station of Nepal. a Satellite image of 4 May 2015. b Field photo of 14 June 2015 (by Chong Xu, view to northwest). The solid and dotted arrows in (a) and (b) indicate the same places, respectively bivariate statistical analysis method has been widely used Findings, results and analysis in various areas because it is time-saving and does not Landslides on satellite images need complex calculations (Xu et al. 2013b). In this study, In this section, we present several groups of comparisons a weight index (WI) model was employed to perform of satellite images and field photos of coseismic landslides landslide susceptibility mapping in the 5-km buffer area to illustrate the excellent capacity of detecting coseismic aforementioned. This WI method is based on a bivariate landslides on high-resolution satellite images. The satellite statistical analysis based on calculating landslide number images used in this study are from the GE platform col- density (LND). In this method, the weigh value of each lected in early May, 2015. The red solid arrow on Fig. 3 factor class is defined as the natural logarithm of the LND shows a coherent landslide (27.87°N, 85.911°E) with clear in the class divided by the LND of the whole area (Sarkar exposed bedrocks in the landslide source area and partly et al. 2008; Yalcin 2008; Xu et al. 2013b): damaged vegetation stayed at its deposit area. The red dotted line defines several shallow, disrupted landslides along the Araniko Highway road. Due to the high reso- WI ¼ lnðÞ LND =LND i i ð1Þ lution and quality of the satellite image, the locations and ¼ lnðÞ ðÞ LN =Area =ðÞ LN=Area i i boundaries of the landslides can be mapped correctly and conveniently on the ortho images. where WI is the weight of the factor-class i, LND is the Figure 4 shows two rockfalls (27.927°N, 85.932°E) i i landslide number intensity within the area of the ith fac- occurred on the upper slope at an inspection station of tor class, and LND is the landslide number intensity in Nepal, which originated from nearby the ridge of the re- the whole area. In this study, the value of LND is 3,005/ verse slope and accumulated into two conical heaps with 2 -2 1,027.4 km = 2.925 km . two narrow runout paths. The broken accumulate 200m (a) (b) Fig. 5 A series of shallow, disrupted landslides blocking the Pasang Lhamu Highway. a Satellite image of 3 May 2015. b Field photo of 15 June 2015 (by Chong Xu, view to southeast) showing the road was blocked by a secondary landslide caused by a heavy rainfall. The red solid arrow in (a)shows the location of field photo in (b) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 6 of 17 600m (a) (b) Fig. 6 An area characterized by high density of coseismic landslides. a Satellite image taken on 3 May 2015. b Field photo taken on 15 June 2015 (by Chong Xu, view to west). The solid and dotted arrows denote the same places, respectively materials are dangerous for the structures on the toe of almost vertical. Small rockfalls or falling stones are more the slope. Despite different expressions of the rockfalls on susceptible than large deep-seated landslides on such a the image and field photos due to the image stretching reverse slope. caused by steep topography, the rockfalls can be easily After the main shock, a series of aftershocks and rain- identified on the satellite image with the aid of field inves- falls struck the affected area and caused more landslides. tigations. They have short runout distances on the image, For example, the satellite image of 3 May 2015 (Fig. 5a) whereas the actual runout distances of them are likely lon- shows quite a few shallow, disrupted landslides (located at ger. This is because the slope of the rockfalls occurrence is 28.064°N, 85.225°W) that occurred in weathering layers 200m 300m (a) (b) 200m 100m (c) (d) Fig. 7 Damaged sites along Araniko Highway by coseismic landslides have been seen on satellite images. The detailed information of the landslides and associated damages on the road is listed in Table 1. a the No. 1, 2, and 3 landslides, (b) the No. 21, 22, and 23 landslides, (c) the No. 24, 25, 26, and 27 landslides, and (d) the No. 31, 32, 33, and 34 landslides along Araniko Highway. All the satellite images were acquired on 4 May, 2015 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 7 of 17 and blocked the Pasang Lhamu Highway. Fig. 5b shows materials related to the main shock and subsequent trig- the road was blocked by a secondary landslide caused by a gers blocking the roads have been cleared up or were be- heavy rainfall in the area. Information from local residents ing cleared away in time to keep the traffic flowing. suggests that the landslide accumulation material that The satellite image (Fig. 6a) shows an area with high blocked the road was not triggered by the main shock, but density of coseismic landslides, dominated by shallow, by a strong rainfall a few days before. All the landslide disrupted landslides. The red solid arrows wherein Table 1 Information of damage along the Araniko Highway and associated coseismic landslides 2 3 No. Longitude (°) Latitude (°) Length of road damaged (m) Area (m ) Estimated volume (m ) 1 85.98343 27.9868 45 29131 50000 2 85.98196 27.98562 52 6180 12000 3 85.9791 27.98369 35 22953 60000 4 85.96235 27.97215 16 3648 6000 5 85.96186 27.97166 27 3756 7000 6 85.96482 27.96926 29 8383 20000 7 85.96511 27.96912 30 ditto ditto 8 85.95799 27.96591 10 435 400 9 85.93064 27.91744 156 31816 20000 10 85.92627 27.91352 19 4233 8000 11 85.9227 27.90877 5 972 1000 12 85.92241 27.90828 27 2271 3000 13 85.9222 27.9079 33 2630 4000 14 85.91496 27.8927 10 15890 30000 15 85.91307 27.88253 30 1739 2000 16 85.90599 27.87934 46 12025 30000 17 85.90493 27.87892 12 268 200 18 85.90185 27.87782 115 6224 10000 19 85.89957 27.87686 49 4245 6000 20 85.89595 27.87602 15 303 200 21 85.88781 27.87385 95 9237 20000 22 85.88341 27.87226 25 608 500 23 85.88309 27.87113 20 3583 5000 24 85.88062 27.85115 43 3805 5000 25 85.88092 27.85034 94 7461 15000 26 85.88045 27.84828 54 9367 20000 27 85.88063 27.84779 42 7061 15000 28 85.87291 27.82729 57 3399 5000 29 85.87352 27.82707 43 3094 5000 30 85.89329 27.8036 15 1250 1500 31 85.89425 27.80022 30 949 1000 32 85.89475 27.80006 13 522 300 33 85.89492 27.79994 7 937 1000 34 85.89613 27.7986 52 1971 2500 35 85.88417 27.77117 1 628 500 36 85.77906 27.73006 63 3778 5000 Total 1,415 214,751 372,100 No. 6 and No. 7 landslide-damaged sections were caused by one landslide Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 8 of 17 Table 2 Information of damaged along the Pasang Lhamu Table 2 Information of damaged along the Pasang Lhamu Highway and associated coseismic landslides Highway and associated coseismic landslides (Continued) No. Longitude Latitude Length of road Area Estimated 44 85.27853 28.09454 15 169 100 2 3 (°) (°) damaged (m) (m ) volume (m ) 45 85.2867 28.10178 9 95 100 1 85.24384 27.82563 12 166 100 46 85.28784 28.104 7 109 40000 2 85.23569 27.82943 14 613 500 47 85.2877 28.10459 104 15961 3000 3 85.20104 27.83575 14 275 200 48 85.28754 28.1055 30 2082 3000 4 85.13849 27.8637 17 628 500 49 85.28715 28.10675 14 577 500 5 85.18591 27.98131 18 311 200 50 85.31217 28.10857 19 1711 2000 6 85.1884 27.98278 14 197 100 51 85.31202 28.10873 22 1306 1500 7 85.18866 27.98288 16 209 100 52 85.31183 28.11008 60 10024 250000 8 85.18777 27.9828 50 1144 1200 53 85.31135 28.11061 49 4482 8000 9 85.21029 28.00465 16 336 300 54 85.31117 28.11098 17 1550 2000 10 85.2181 28.01873 9 177 100 55 85.29239 28.11159 24 555 500 11 85.22025 28.02089 21 553 400 56 85.31072 28.11175 41 3888 6000 12 85.22087 28.02175 28 2766 4000 57 85.31043 28.11217 55 7463 15000 13 85.22311 28.02457 40 32568 80000 58 85.29454 28.11298 6 168 100 14 85.22349 28.02528 48 17673 50000 59 85.30909 28.11914 11 117 100 15 85.22334 28.02617 39 10247 20000 60 85.30815 28.12234 14 5918 12000 16 85.22309 28.02716 45 9266 20000 61 85.30685 28.12406 4 357 300 17 85.22299 28.02766 18 10071 20000 62 85.34242 28.17233 25 957 1000 18 85.21933 28.0394 14 199 100 63 85.34248 28.17269 31 4223 8000 19 85.22441 28.04657 14 227 100 64 85.34254 28.17314 14 1996 2000 20 85.22547 28.04732 7 171 100 65 85.3426 28.17358 63 21402 60000 21 85.22722 28.04826 24 805 800 66 85.34237 28.178 18 1374 2000 22 85.22892 28.05021 22 279 200 67 85.34225 28.17896 11 695 800 23 85.22869 28.05054 14 103 100 68 85.3439 28.18229 14 197 100 24 85.22923 28.05373 5 271 200 69 85.34455 28.18387 17 4005 6000 25 85.22867 28.05858 19 11564 5000 70 85.34617 28.18621 30 33718 100000 26 85.22811 28.06002 53 3790 5000 71 85.34634 28.18678 57 32800 100000 27 85.22798 28.061 67 7609 15000 72 85.34741 28.18903 16 565 500 28 85.22674 28.0633 57 5222 10000 73 85.34761 28.18932 24 8214 10000 29 85.22643 28.06371 19 1815 2000 74 85.34883 28.19127 59 18536 30000 30 85.22562 28.0644 28 9168 20000 75 85.34972 28.19332 14 445 300 31 85.22542 28.06454 15 3018 4000 76 85.35092 28.19643 19 398 300 32 85.22515 28.06481 14 525 300 77 85.35147 28.19703 41 4677 80000 33 85.22494 28.06501 21 2679 3000 78 85.35182 28.19742 27 3538 5000 34 85.22447 28.06545 31 5012 5000 79 85.35224 28.19803 13 1716 1000 35 85.22374 28.06637 36 3662 5000 80 85.35285 28.19931 19 3032 3000 36 85.22535 28.06689 25 2876 4000 81 85.35292 28.19967 12 1805 1000 37 85.22884 28.06806 59 11267 20000 82 85.35304 28.20057 43 2852 5000 38 85.23007 28.06827 99 20358 60000 83 85.35425 28.20403 41 1693 2500 39 85.23904 28.07238 209 54607 200000 84 85.35507 28.20837 38 4976 10000 40 85.25552 28.0772 16 920 800 85 85.35804 28.2197 102 13969 40000 41 85.2558 28.07737 11 152 100 86 85.36041 28.22164 19 3995 5000 42 85.25023 28.07813 13 500 300 87 85.36106 28.22261 107 14279 30000 43 85.277 28.09335 18 241 100 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 9 of 17 Table 2 Information of damaged along the Pasang Lhamu (27.678835°N, 85.349647°E) southeast to Kathmandu Highway and associated coseismic landslides (Continued) was selected as the target to investigate the damage of 88 85.36698 28.25649 44 2549 4000 coseismic landslides on the road. This section of Araniko Highway is about 117.3 km long. Based on visual inter- 89 85.37802 28.27514 92 21169 60000 pretation of high-resolution satellite images and field in- Total 2,842 500,552 1,470,600 vestigations, we delineated 35 coseismic landslides damaging the Araniko Highway at 36 places. The longest indicate a large rock slide located at 28.079°N, 85.194°W. section of the road damaged is about 156 m long which It occurred at the lower part of the slope and blocked the was buried by a landslide at 27.91744°N, 85.93064°E. valley. However, they did not create a lake because of the Considering the previous correlations between area and small area of the catchment upstream. The red dotted ar- volume of individual landslides (Larsen et al. 2010; Xu et rows show a shallow, disrupted landslide originated from al. 2016b) and field investigations, the total volume of the a ridge (located at 28.08°N, 85.208°W). Most of the land- 35 coseismic landslides was estimated to be about 0.37 slides shown in Fig. 6a are distributed along the rivers, million m . Figure 7 shows the Araniko Highway damaged likely associated with river incision or loose deluvium with by coseismic landslides on satellite images at four places. high landslide susceptibility. Table 1 lists the detailed information on the 35 coseismic landslides and hazards on the road they caused. Landslide damage to the two roads The Pasang Lhamu Highway connects Kathmandu, The Pasang Lhamu Highway and Araniko Highway are Nepal and Gyirong County. The length of the section two most important roads connecting Nepal and China. between the point (27.735268°N, 85.305939°E) northwest The Araniko Highway links Kathmandu, Nepal and to Kathmandu and the point (28.278972°N, 85.377904°E) Nielamu County, China. In this study, the section of the China-Nepal border is about 139.3 km. Visual interpret- Araniko Highway between the place (27.987262°N, ation of satellite images and field investigations allowed 85.982552°E) nearby Zhangmu Port and the location us to delineate 89 coseismic landslides that damaged the N N 100m 200m (a) (b) N N 200m 100m (c) (d) Fig. 8 Damaged sites along Pasang Lhamu Highway by coseismic landslides on satellite images. The detailed information of the landslides and associated damages on the road is listed in Table 2. a the No. 11 and 12 landslides, (b) the No. 28~36 landslides, (c) the No. 59, 60, and 61 landslides, (d) the No. 62, 63, 64, and 65 landslides along Pasang Lhamu Highway. All the satellite images were acquired on 3 May, 2015 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 10 of 17 Fig. 9 Distribution map of coseismic landslides in the 5-km buffer area on either side the Araniko Highway 600 15 Class area LND 400 10 200 5 0 0 610-1000 1000-1500 1500-2000 2000-2500 2500-3000 3000-4750 Fig. 10 Relationships between elevation class (horizontal axis), its area (vertical axis on left) and landslide number density (LND, vertical axis on right) Class area (km ) −2 LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 11 of 17 250 15 Class area LND 0 0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 >40 Fig. 11 Same as Fig. 10 but for slope angle (horizontal axis, unit: degree) Pasang Lhamu Highway. The total length of damaged or Therefore, we estimated the Gorkha quake triggered at buried roads is about 2,842 m, of which the longest sec- least 30,000 landslides. Several other teams have released tion is about 209 m long, caused by a landslide located coseismic landslides related to the Gorkha quake. For at 27.91744°N, 85.93064°E. The total volume of the 89 example, Kargel (Kargel et al. 2016) only mapped 4,312 landslides was estimated to be 1.47 million m . Figure 8 coseismic and postseismic landslides. A team from British shows satellite images of coseismic landslides along the Geological Survey et al. (British Geological Survey et al. Pasang Lhamu Highway. Table 2 shows the detailed in- 2015) identified about 5,600 coseismic landslides as poly- formation on the 89 coseismic landslides and their haz- lines marking the location and movement path from head ards on the road. to toe of a landslide. Therefore, there might be false nega- tive errors (omissions) in these released inventories of Landslide inventory along the Araniko highway landslides triggered by the Gorkha quake. On either side of the 117.3 km-long Araniko Highway, we constructed a 5-km buffer region to construct a detailed Spatial distribution of landslides along the Araniko and complete point-based coseismic landslide inventory. highway The area of this buffer region is 1,027.4 km .Individual As a common index to reflect landslide abundance, coseismic landslides were mapped as points at the central landslide number density was employed as the index to of the landslide. Consequently, we mapped 3,005 coseismic measure spatial distribution of the 3,005 landslides in landslides in the area (Fig. 9), and calculated the landslide the 5-km buffer area of the Araniko Highway. In this 2 -2 number density to be 3,005/1,027.4 km = 2.925 km .The study, five controlling factors including elevation, slope spatial distribution of the coseismic landslides along the angle, slope aspect, lithology, and seismic intensity were Araniko Highway is quite uneven. Most of the landslides selected to analyze their correlations with the landslides occurred in the mountainous areas to the north, where the (Figs. 10, 11, 12, 13 and 14). The DEM of the area was landslide inventory is complete and detailed, i.e. small land- derived from SRTM in 3 arc-second, which permitted to slides are included. The buffer area only accounts for less determine the elevations of the buffer area vary from 10% than the primary affected area of the main shock. The 610 m to 4,750 m. The study area was divided into six buffer area is approximately normal to the strike of the classes based on 500 m of elevation intervals, i.e. 610– seismogenic structure (EW trending). Usually the seismic 1000 m, 1000–1500 m, 1500–2000 m, 2000–2500 m, landslide density along the causative fault is uniform. 2500–3000 m, and 3000–4750 m (Fig. 10). The 250 15 Class area LND 0 0 Flat N NE E SE S SW W NW Fig. 12 Same as Fig. 10 but for slope aspects (horizontal axis) 2 2 Class area (km ) Class area (km ) −2 −2 LND(km ) LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 12 of 17 500 10 Class area 400 8 LND 300 6 200 4 100 2 0 0 Ti Pc1 Pc2 Pc3 Mi Fig. 13 Same as Fig. 10 but for lithology (horizontal axis) elevations of most of the area (814.97 km , 79.3% of the northwest (NW). Fig. 12 shows the correlations between total) are lower than 2,000 m. The class 1000–1500 m the slope aspect, area of its classes and landslide number occupies the largest area, which is 433 km , accounting density. It is clear that the slope aspects E and SE cor- for 42.1% of the total. The class 2,000-2,500 m registered respond to the two largest LND values, which are -2 -2 -2 the largest LND value, which is 10.3 km . The landslide 4.87 km and 4.58 km , respectively. This is perhaps re- number density values gradually decrease at the eleva- lated to the movement direction of the hanging wall of tions higher than 2,500 m and lower than 2,000 m. the seismogenic fault or the propagation direction of Slope angle is an important controlling factor of seismic wave (Shen et al. 2016). The study area is located coseismic landslides. In this study, the slope angle of the east of the epicenter of the Gorkha main shock, and thus buffer area ranges from 0° to 74°, which was divided into the propagating direction of seismic waves is eastward. 9 classes with an interval of 5°. Majority of the area During the Gorkha earthquake, the hanging wall of the (780.7 km , 76% of the total) has slope angles lower than fault, where the buffer area is located, moved toward 30°. As shown in Fig. 11, the landslide number density in- south and probably generated inertia effect to the south. creases with the growing slope angle. The class >40° cor- In addition, the slopes of southward aspect in the area -2 responds to the largest LND value, which is 12.92 km .In are easily exposed to sunlight and rainfall, thus leading addition, the LND curve shows a concave form, implying to widespread weathering layers and high susceptibility the LND increases with the slope angle gradually. This to seismic landslides. suggests a very strong control of the slope angle on the The Gorkha earthquake affected area can be divided coseismic landslides. Such a situation is also common in into a series of east-west trending major tectonic regions other earthquake events (Gorum et al. 2014; Xu et al. by three major active fault zones, including MFT, MBT, 2014; Xu et al. 2015; Tian et al. 2016). and MCT (Le Fort 1975; Nakata 1989; Upreti 1999; Slope aspects (or facing directions) can affect the pat- Wesnousky et al. 1999; Mukherjee 2015). Based on the tern of coseismic landslides because slopes with different geologic map of South Asia, the study area has five clas- aspects have varied responses to the movement direc- ses of lithology (rock types) generally from north to tions of blocks or the propagating direction of seismic south, i.e. Tertiary igneous rocks (Ti), Precambrian waves. The study area has nine classes of slope aspects, rocks-1 (Pc1), Precambrian rocks-2 (Pc2), Precambrian i.e. flat, north (N), northeast (NE), east (E), southeast rocks-3 (Pc3), and Mesozoic intrusive rocks (Mi). (SE), south (S), southwest (SW), west (W), and Figure 13 shows the correlations between lithology, its 1000 5 Class area 800 4 LND 600 3 400 2 200 1 0 0 VIII IX Fig. 14 Same as Fig. 10 but for seismic intensity (horizontal axis) Class area (km ) Class area (km ) −2 −2 LND(km ) LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 13 of 17 class area and landslide number density. The three LHI ¼ WI þ WI þ WI Elevation Slope angle Slope aspect groups of Precambrian rocks cover most of the area, þ WI þ WI ð2Þ Lithology Seismic intensity -2 which is 942.8 km , occupying 91.8% of the total. The lithology class Pc1 registered the largest landslide num- The WI values indicate the relative importance of each -2 ber density, which is 8.32 km . factor to landslide hazard. Positive WI values mean the Seismic intensity and peak ground accumulation factor-class area is prone to coseismic landslides, (PGA) are two common proxies representing the degree Table 3 Weight index values of various classes of five of seismic energy and often used to explore the effect of controlling factors earthquakes on landslides. The PGA distribution map Factor Class area Landslide number LND WI released by USGS (www.usgs.gov) is rather irregular in Elevation the 5-km buffer area of the Araniko Highway because 610–1000 m 155.94 159 1.02 -1.05 the buffer area is relatively small and there are perhaps significant errors generated by numerical simulation and 1000–1500 m 433 438 1.01 -1.06 limited stations. Therefore, we preferred to analyze the 1500–2000 m 226.03 1000 4.42 0.41 correlation between coseismic landslides and seismic in- 2000–2500 m 95.24 981 10.3 1.26 tensity in this study. The seismic intensity map of the 2500–3000 m 58.83 327 5.56 0.64 Gorkha earthquake was produced by China Earthquake 3000–4750 m 58.39 100 1.71 -0.54 Administration (CEA) (Fig. 1). Only VIII and IX inten- Slope angle sity zones appear in the study area, which have the land- -2 -2 slide number density values 0.32 km and 3.5 km , 0–5° 139.07 15 0.11 -3.3 respectively (Fig. 14). Despite merely two data points, 5–10° 86.75 42 0.48 -1.8 these data show a positive correlation with the coseismic 10–15° 96.61 71 0.73 -1.38 landslides, i.e. the place with larger seismic intensity has 15–20° 141.86 144 1.02 -1.06 a higher landslide number density. 20–25° 163.49 290 1.77 -0.5 25–30° 152.92 458 3.00 0.02 Landslide hazard assessment In the aforementioned 5-km buffer area of the Araniko 30–35° 115.59 641 5.55 0.64 Highway, the WI vales were calculated to each class of all 35–40° 76.65 640 8.35 1.05 the five controlling factors, respectively. Then, the weighted 40–74° 54.5 704 12.92 1.49 thematic maps of the five factors were produced and were Slope aspect superposed to derive a landslide hazard index (LHI) map: Flat 16.63 6 0.36 -2.09 N 120.07 300 2.50 -0.16 NE 131.27 459 3.50 0.18 E 112.63 549 4.87 0.51 SE 119.02 545 4.58 0.45 S 121.36 305 2.51 -0.15 SW 158.51 295 1.86 -0.45 W 127.22 312 2.45 -0.18 NW 120.72 234 1.94 -0.41 Lithology Ti 29.34 101 3.44 0.16 Pc1 308.10 2564 8.32 1.05 Pc2 288.82 324 1.12 -0.96 Pc3 345.91 16 0.05 -4.15 0 20406080 100 Mi 55.26 0 0 -14.89 Cumulative of area% Seismic intensity Fig. 15 The area under the curve representing the success ratio of VIII 185.60 60 0.32 -2.2 the landslide hazard assessment. Area % means the percentage of IX 841.84 2945 3.5 0.18 area to the study area for each factor class. Landslide number % Lithology type Mi registered no landslide. In order to avoid ln(0) in calculating means the percentage of landslide number in a factor class to the WI value, we assigned the LND value of the class Mi with 0.000001, i.e. a small total landslide number enough value, and the WI value of the class is -14.89 Cumulative of landslide number% Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 14 of 17 whereas negative WI values represent the opposite. WI that 10% of the area with the highest landslide hazard values close to zero mean moderate probabilities of oc- index could cover 1,514 landslides, about 50.4% of the currence of coseismic landslides. Results show the LHI total. Likewise, 20 and 30% of the area with the highest of the area is in the range from -23.546 to 4.48. In order landslide hazard index can account for 2,207 and 2,645 to examine the validity of the model, the 3,005 coseismic landslides, about 73.4 and 88% of the total, respectively. landslides aforementioned were employed to compare We applied the WI values in Table 3 to a larger area, the known landslides with the landslide hazard index i.e. the rectangle in Fig. 1, to construct a landslide haz- map. By referring to a common method, the regional ards map. For the areas with factor-attribute values be- area was categorized into 100 classes with a same area yond the ranges of the 5-km buffer area, they are by the LHI value and the percentages of landslide num- classified into the classes that are closest to them. The ber in each class were calculated. Then, a correlation elevation ranges from 291 m to 7968 m in the area. The curve between cumulative area percentages and cumula- area with elevation less than 610 m was classified into tive percentage of landslide number from high to low the class 610–1000 m and the area with elevation higher LHI in a descending order was drawn (Fig. 15). It shows than 4,750 m was classified into the class 3000–4750 m. the area under the curve (AUC) is as much as 85.9%, i.e. The range of slope angle of the rectangle area is 0–81.7°, a quite satisfactory success ratio. The curve also reveals therefore, the range of 74°–81.7° was merged into the Fig. 16 Landslide hazard map for a part of the Gorkha earthquake region Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 15 of 17 class 40–74°. The northern part of the rectangle area rivers and seismogenic fault. (2) The weight value outcrop several other lithology types, such as Quaternary method is a bivariate method, interactions among factors perennial ice and snow, Quaternary sediments, Neogene cannot be considered; and (3) WI values were calculated granite, Triassic metamorphic and sedimentary rocks, based on the 3,005 landslides in the 5-km buffer area and Cretaceous sedimentary rocks. The northern rock along the Araniko Highway. Of course, it is inferior to group located at the northern part of the study area was use of a complete inventory of landslides throughout the not subdivided further because of limited geologic infor- earthquake-affected area. These limitations are expected mation there. The WI values of these lithology types to be improved in future research. were assigned the values same as the type Tertiary igne- ous rocks (Ti). The rectangle area includes three seismic Conclusions intensity zones, i.e. VII, VIII, and IX (Fig. 1). The seismic Based on high-resolution satellite images, field investiga- intensity VII is out of the range of the 5-km buffer area tion, and GIS technology, we examined the coseismic and its WI value was calculated by linear extrapolation, landslides of the 2015 Gorhka, Nelpal earthquake that which is -4.58. Subsequently, we constructed the LHI blocked or damaged the Araniko Highway (117.3 km) map of the rectangle area. We divided the map into four and Pasang Lhamu Highway (139.3 km) in Nepal. Re- classes, i.e. very low, low, high, and very high, based on sults show 35 coseismic landslides damaged the Araniko three breakpoints of the index values, i.e. -3, -1, 1, and 3. Highway with a total length of the sections of the dam- Figure 16 shows the derived landslide hazard map of the aged road about 1,415 m. The total volume of these 35 study area. The high zone and very high zone show a coseismic landslides was estimated to be about 0.37 NWW-SEE directed distribution, which is coincident million m . We delineated 89 coseismic landslides that with the seismogenic fault and earthquake damage area. damaged the Pasang Lhamu Highway. The total length Figure 17 shows a three-dimensional view on the land- of the damaged or buried roads is about 2,842 m. The slide hazard map. We overlaid the hazard map with 50% total volume of these 89 landslides was estimated to be transparency on satellite images of the Google Earth 1.47 million m . In the 5-km buffer area on either side platform. However, several limitations of this result of the Araniko Highway, we mapped 3,005 landslides should be noted, including (1) Only five common factors caused by the Gorkha earthquake. The landslide number -2 were considered, while there should more factors can density of the buffer area is 2.925 km . Correlations be- affect the occurrence of the coseismic landslides, such as tween the landslides and five controlling factors were Fig. 17 A printing screen showing a three-dimensional perspective on the landslide hazard map. View to north Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 16 of 17 analyzed based on the bivariate method. The results British Geological Survey, Earthquakes without Frontiers, Durham University. 2015. 2015 Nepal Earthquakes mapped landslide intensity (Revision 4.0 - 19 June show the elevation class 2,000–2,500 m has the highest 2015). https://data.hdx.rwlabs.org/group/nepal-earthquake. landslide concentration. The landslide number density Collins, B.D., and R.W. Jibson. 2015. Assessment of existing and potential landslide value increases with the slope angle. The slope aspects E hazards resulting from the April 25, 2015 Gorkha, Nepal earthquake sequence. US Geological Survey.. https://pubs.er.usgs.gov/publication/ofr20151142. and SE correspond to the highest concentrations of doi:10.3133/ofr20151142. coseismic landslides. The underlying bedrock of Precam- Dahal, R.K. 2016. Initiatives for rockfall hazard mitigation in Nepal. Bulletin of Nepal brian rocks-1 (Pc1) registered the largest landslide num- Geological Society 33: 51–56. Duputel, Z., J. Vergne, L. Rivera, G. Wittlinger, V. Farra, and G. Hetényi. 2016. The ber density. The area with seismic intensity IX has a 2015 Gorkha earthquake: a large event illuminating the main Himalayan much higher LND value than the area of the intensity thrust fault. Geophysical Research Letters 43(6): 2517–2525. doi:10.1002/ VIII. We used the weigh index method to perform land- 2016GL068083. Elliott, J.R., R. Jolivet, P.J. González, J.-P. Avouac, J. Hollingsworth, M.P. Searle, and slide hazard assessment in the 5-km buffer area. Result V.L. Stevens. 2016. Himalayan megathrust geometry and relation to shows the success ratio as high as 85.9%. In addition, we topography revealed by the Gorkha earthquake. Nature Geoscience 9: 174– prepared a landslide hazard assessment map for a larger 180. doi:10.1038/ngeo2623. Feng, H., A. Zhou, J. Yu, X. Tang, J. Zheng, X. Chen, and S. You. 2016. A area encompassing Rasuwa and Sindhupalchok counties comparative study on plum-rain-triggered landslide susceptibility assessment of Nepal. It indicates the areas most likely to be prone models in West Zhejiang Province. Earth Science 41(3): 403–415. to coseismic landslides, which would be helpful for con- Gnyawali, K.R., S. Maka, B.R. Adhikari, D. Chamlagain, S. Duwal, and A.R. Dhungana. 2016. Spatial implications of earthquake induced landslides triggered by the structing a more detailed and complete coseismic land- April 25 Gorkha earthquake Mw 7.8: preliminary analysis and findings. In slide inventory map throughout the earthquake-affected International conference on earthquake engineering and post disastor area subsequently. The result is also helpful predict the reconstruction planning 24 – 26 April, 2016, Bhaktapur, Nepal,50–58. locations of landslides triggered by subsequent events, e.g. Gorum, T., O. Korup, C.J. van Westen, M. van der Meijde, C. Xu, and F.D. van der Meer. 2014. Why so few? Landslides triggered by the 2002 Denali strong aftershocks and rainfalls, which would be a scien- earthquake, Alaska. Quaternary Science Reviews 95: 80–94. doi:10.1016/j. tific reference for restoration, reconstruction, and land- quascirev.2014.04.032. slide reduction and mitigation in the Gorkha earthquake- Hashash, Y.M.A., B. Tiwari, R.E.S. Moss, D. Asimaki, K.B. Clahan, D.S. Kieffer, D.S. Dreger, A. Macdonald, C.M. Madugo, H.B. Mason, M. Pehlivan, D. Rayamajhi, I. affected area. Acharya, and B. Adhikari. 2015. Geotechnical field reconnaissance: Gorkha (Nepal) earthquake of April 25, 2015 and related shaking sequence. In Acknowledgments Geotechnical extreme event reconnaisance GEER association report No. GEER- This research was supported by the National Natural Science Foundation of 040.. https://works.bepress.com/rmoss/47: 250 pages. China (41661144037, 41472202, 41472264) and the Special Project for China Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, Bevington A, Fielding EJ, Fujita Earthquake Research (201508024). We are very grateful to the comments of K, Geertsema M, Miles ES, Steiner J, Anderson E, Bajracharya S, Bawden GW, two anonymous reviewers and the help of Ranjan Kumar Dahal (Editor). Breashears DF, Byers A, Collins B, Dhital MR, Donnellan A, Evans TL, Geai ML, Glasscoe MT, Green D, Gurung DR, Heijenk R, Hilborn A, Hudnut K, Huyck C, Immerzeel WW, Jiang L, Jibson R, Kääb A, Khanal NR, Kirschbaum D, Competing interests Kraaijenbrink PDA, Lamsal D, Liu S, Lv M, McKinney D, Nahirnick NK, Nan Z, The authors declare that they have no competing interests. Ojha S, Olsenholler J, Painter TH, Pleasants M, Pratima KC, Qi Y, Raup BH, Regmi D, Rounce DR, Sakai A, Shangguan D, Shea JM, Shrestha AB, Shukla A, Authors’ contributions Stumm D, van der Kooij M, Voss K, Wang X, Weihs B, Wolfe D, Wu L, Yao X, CX and YT mapped landslides on GE images. CX and GL participated the Yoder MR, Young N. 2016. Geomorphic and geologic controls of geohazards field investigations. CX, BZ, and HR provided the regional geologic and tectonic induced by Nepal’s 2015 Gorkha earthquake. Science 351(6269): aac8353. data. CX drafted the manuscript. All the authors reviewed and approved the doi:10.1126/science.aac8353 manuscript. Koketsu, K., H. Miyake, Y. Guo, H. Kobayashi, T. Masuda, S. Davuluri, M. Bhattarai, L. B. Adhikari, and S.N. Sapkota. 2016. Widespread ground motion distribution caused by rupture directivity during the 2015 Gorkha, Nepal earthquake. Publisher’sNote Scientific Reports 6: 28536. doi:10.1038/srep28536. Springer Nature remains neutral with regard to jurisdictional claims in Lacroix, P. 2016. Landslides triggered by the Gorkha earthquake in the Langtang published maps and institutional affiliations. valley, volumes and initiation processes. Earth, Planets and Space 68(1): 46. doi:10.1186/s40623-016-0423-3. Author details Larsen, I.J., D.R. Montgomery, and O. Korup. 2010. Landslide erosion controlled by Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China hillslope material. Nature Geoscience 3(4): 247–251. doi:10.1038/ngeo776. Earthquake Administration, 1# Huayanli, Chaoyang District, PO Box 9803, Le Fort, P. 1975. Himalayas: the collided range. Present knowledge of the Beijing 100029, China. CCCC Highway Consultants Co., Ltd., Beijing 100088, continental arc. American Journal of Science 275-A: 1–44. China. Lee, C.T., C.C. Huang, J.F. Lee, K.L. Pan, M.L. Lin, and J.J. Dong. 2008. Statistical approach to earthquake-induced landslide susceptibility. Engineering Geology Received: 9 August 2016 Accepted: 30 March 2017 100(1-2): 43–58. doi:10.1016/j.enggeo.2008.03.004. Martha TR, Roy P, Mazumdar R, Govindharaj KB, Kumar KV. 2016. Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides. doi:10.1007/s10346-016-0763-x References Angster, S., E.J. Fielding, S. Wesnousky, I. Pierce, D. Chamlagain, D. Gautam, B.N. Moss, R.E.S., E.M. Thompson, D.S. Kieffer, B. Tiwari, Y.M.A. Hashash, I. Acharya, B.R. Upreti, Y. Kumahara, and T. Nakata. 2015. Field reconnaissance after the 25 Adhikari, D. Asimaki, K.B. Clahan, and B.D. Collins. 2015. Geotechnical effects April 2015 M 7.8 Gorkha earthquake. Seismological Research Letters 86(6): of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks. 1506–1513. doi:10.1785/0220150135. Seismological Research Letters 86(6): 1514–1523. doi:10.1785/0220150158. Avouac, J.-P., L. Meng, S. Wei, T. Wang, and J.-P. Ampuero. 2015. Lower edge of Mukherjee, S. 2015. A review on out-of-sequence deformation in the locked main Himalayan Thrust unzipped by the 2015 Gorkha earthquake. Himalaya. Geological Society, London, Special Publications 412(S): 67–109. Nature Geoscience 8: 701–711. doi:10.1038/ngeo2518. doi:10.1144/SP412.13. Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 17 of 17 Nakata, T. 1989. Active faults of the Himalaya of India and Nepal. Geological Xu C, Xu X, Shen L, Yao Q, Tan X, Kang W, Ma S, Wu X, Cai J, Gao M, Li K. 2016b. Society of America Special Papers 232: 243–264. doi:10.1130/SPE232-p243. Optimized volume models of earthquake-triggered landslides. Scientific Parameswaran, R.M., T. Natarajan, K. Rajendran, C.P. Rajendran, R. Mallick, M. Reports 6: 29797. doi:10.1038/srep29797. Wood, and H.C. Lekhak. 2015. Seismotectonics of the April-May 2015 Nepal Yalcin, A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy earthquakes: An assessment based on the aftershock patterns, surface effects process and bivariate statistics in Ardesen (Turkey): Comparisons of results and and deformational characteristics. Journal of Asian Earth Sciences 111: 161– confirmations. Catena 72(1): 1–12. doi:10.1016/j.catena.2007.01.003. 174. doi:10.1016/j.jseaes.2015.07.030. Pathak, D. 2016. Knowledge based landslide susceptibility mapping in the Himalayas. Geoenvironmental Disasters 3(1): 8. doi:10.1186/s40677-016-0042-0. Sarkar, S., D.P. Kanungo, A.K. Patra, and P. Kumar. 2008. GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science 5(1): 52–62. doi:10.1007/s11629-008-0052-9. Sharma, K., L. Deng, and C.C. Noguez. 2016. Field investigation on the performance of building structures during the April 25, 2015, Gorkha earthquake in Nepal. Engineering Structures 121: 61–74. doi:10.1016/j. engstruct.2016.04.043. Shen, L., C. Xu, and L. Liu. 2016. Interaction among controlling factors for landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. Frontiers of Earth Science 10(2): 264–273. doi:10.1007/s11707-015-0517-4. Sun, B., and P. Yan. 2015. Damage characteristics and seismic capacity of buildings during Nepal Ms 8.1 earthquake. Earthquake Engineering and Engineering Vibration 14(3): 571–578. doi:10.1007/s11803-015-0046-x. Tian, Y., C. Xu, X. Xu, and J. Chen. 2016. Detailed inventory mapping and spatial analyses to landslides induced by the 2013 Ms 6.6 Minxian earthquake of China. Journal of Earth Science 27(6): 1016–1026. doi:10.1007/s12583-016-0905-z. Tsangaratos, P., and I. Ilia. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena 145: 164–179. doi:10.1016/j.catena.2016.06.004. Upreti, B.N. 1999. An overview of the stratigraphy and tectonics of the Nepal Himalaya. Journal of Asian Earth Sciences 17(5-6): 577–606. doi:10.1016/S1367- 9120(99)00047-4. Wang, K., and Y. Fialko. 2015. Slip model of the 2015 Mw 7.8 Gorkha (Nepal) earthquake from inversions of ALOS‐2 and GPS data. Geophysical Research Letters 42(18): 7452–7458. doi:10.1002/2015GL065201. Wang, F., M. Miyajima, R. Dahal, M. Timilsina, T. Li, M. Fujiu, Y. Kuwada, and Q. Zhao. 2016. Effects of topographic and geological features on building damage caused by 2015.4.25 Mw7.8 Gorkha earthquake in Nepal: a preliminary investigation report. Geoenvironmental Disasters 3(1): 7. doi:10.1186/s40677-016-0040-2. Wesnousky, S.G., S. Kumar, R. Mohindra, and V. Thakur. 1999. Uplift and convergence along the Himalayan Frontal Thrust of India. Tectonics 18(6): 967–976. doi:10.1029/1999TC900026. Xu, C. 2015. Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geoscience Frontiers 6(6): 825–836. doi:10.1016/j.gsf.2014.03.004. Xu, C., X. Xu, F. Dai, and A.K. Saraf. 2012. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences 46: 317–329. doi:10.1016/j.cageo.2012.01.002. Xu C, Xu X, Dai F, Wu Z, He H, Wu X, Xu S, Shi F. 2013a. Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Natural Hazards 68(2): 883-900. doi:10.1007/s11069-013-0661-7. Xu C, Xu X, Yao Q, Wang Y. 2013b. GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Submit your manuscript to a Wenchuan earthquake, China. Quarterly Journal of Engineering Geology and journal and benefi t from: Hydrogeology 46(2): 221-236. doi:10.1144/qjegh2012-006. Xu, C., X. Xu, X. Yao, and F. Dai. 2014. Three (nearly) complete inventories of 7 Convenient online submission landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of 7 Rigorous peer review China and their spatial distribution statistical analysis. Landslides 11(3): 441– 461. doi:10.1007/s10346-013-0404-6. 7 Immediate publication on acceptance Xu, C., X. Xu, and J.B.H. Shyu. 2015. Database and spatial distribution of landslides 7 Open access: articles freely available online triggered by the Lushan, China Mw 6.6 earthquake of 20 April 2013. 7 High visibility within the fi eld Geomorphology 248: 77–92. doi:10.1016/j.geomorph.2015.07.002. 7 Retaining the copyright to your article Xu C, Xu X, Tian Y, Shen L, Yao Q, Huang X, Ma J, Chen X, Ma S. 2016a. Two comparable earthquakes produced greatly different coseismic landslides: The 2015 Gorkha, Nepal and 2008 Wenchuan, China events. Journal of Earth Submit your next manuscript at 7 springeropen.com Science 27(6): 1008-1015. doi:10.1007/s12583-016-0684-6. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoenvironmental Disasters Springer Journals

Landslide damage along Araniko highway and Pasang Lhamu highway and regional assessment of landslide hazard related to the Gorkha, Nepal earthquake of 25 April 2015

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Environment; Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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10.1186/s40677-017-0078-9
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Abstract

Background: The Gorkha, Nepal Mw 7.8 earthquake of 25 April 2015 triggered a large number of coseismic landslides in a broad area. Two highways, Araniko Highway and Pasang Lhamu Highway, that connect Tibet of China and Nepal, were affected seriously by these landslides. The purpose of this study was to investigate the landslide damage along the two highways, construct a detailed and complete inventory of coseismic landslides in the 5-km buffer area of the Araniko Highway, and perform a regional assessment of landslide hazard in the affected area. Findings: Based on visual interpretation of high-resolution satellite images, field investigations, and GIS technology, we investigated the coseismic landslides along the Araniko Highway and Pasang Lhamu Highway. A detailed point- based inventory of coseismic landslides was constructed and spatial distributions of the landslides were analyzed. Correlations between the landslides and five controlling factors, i.e. elevation, slope angle, slope aspect, lithology, and seismic intensity, were illustrated statistically which permitted to assess landslides hazard in a larger rectangle area. Conclusions: We examined the coseismic landslides of the 2015 Gorkha earthquake that blocked or damaged the Araniko Highway (117.3 km) and Pasang Lhamu Highway (139.3 km) in Nepal. Results show 35 coseismic landslides damaged the Araniko Highway along a total length 1,415 m. The total volume of them was estimated to be 0.37 million m . We delineated 89 coseismic landslides that damaged the Pasang Lhamu Highway, where the total length of the damaged or buried roads is about 2,842 m and the total volume of the 89 landslides is about 1.47 million m .In the 5-km buffer area along the Araniko Highway, we mapped 3,005 landslides caused by the Gorkha earthquake. The -2 landslide number density of the study area is 2.925 km . The places with elevations 2,000-2,500 m have the highest landslide concentration. Landslide number density values increase with the slope angle. The slope aspects E and SE correspond to the highest concentrations of coseismic landslides. The underlying bedrock of Precambrian rocks-1 (Pc1) registered the largest landslide number density. The area of seismic intensity IX has a much higher LND value than that of the intensity VIII. We used the weigh index method to perform landslide hazard assessment in the 5-km buffer area on either side of the highway, which shows a success ratio of 85.9%. This method has been applied to a larger area mainly encompassing Rasuwa and Sindhupalchok counties of Nepal. Keywords: Gorkha earthquake, Coseismic landslides, Field investigation, Visual interpretation, Landslide hazard assessment * Correspondence: xuchong@ies.ac.cn Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, 1# Huayanli, Chaoyang District, PO Box 9803, Beijing 100029, China Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 2 of 17 Introduction Hashash et al. 2015; Sun and Yan 2015; Kargel et al. The 25 April 2015 Gorkha, Nepal Mw 7.8 earthquake 2016; Lacroix 2016; Sharma et al. 2016). Until now, how- caused more than 8,800 fatalities and enormous eco- ever, little work focuses on the landslides that damaged nomic losses. It also triggered a large number of coseis- these two highways. Although the materials of the mic landslides, mainly shallow and disrupted landslides coseismic landslides blocking the two main roads have and a few deep-seated landslides, some of which buried been cleaned up in time, some new landslides were trig- villages, roads, and valleys (Hashash et al. 2015; Moss et gered by aftershocks or strong rainfalls, resulting in fur- al. 2015; Dahal 2016; Gnyawali et al. 2016; Martha et al. ther damage. Therefore, identifying the landslides 2016; Wang et al. 2016; Xu et al. 2016a). The affected destroying the roads and assessment of landslide hazard areas include Central Nepal and Gyirong and Nielamu is very important for prevention and mitigation of future counties of southern Tibet, China. The coseismic land- geologic hazard around these two roads. In this work, slides seriously damaged two highways, Pasang Lhamu we firstly identified the coseismic landslides that Highway and Araniko Highway, connecting China and destroyed the Lhamu Highway and Araniko Highway Nepal. After the event, several research teams carried using field investigation and visual interpretation of sat- out field investigations of seismic damages and ellite images. Then we constructed a detailed inventory earthquake-triggered landslides (Collins and Jibson 2015; map containing 3,005 individual coseismic landslides in Fig. 1 Shaded topographic relief map showing the study area (big black box) and two highways Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 3 of 17 the buffer area of 5 km to the Araniko Highway. Next, Avouac et al. 2015; Hashash et al. 2015; Parameswaran et correlations between the 3,005 landslides and five land- al. 2015; Duputel et al. 2016; Elliott et al. 2016). The slide controlling factors were analyzed. Finally, we per- earthquake-affected area is mainly in the east to the epicen- formed landslide hazard assessment for a larger area ter (28.23°N, 84.731°E), likely associated with the eastward affected by the Gorkha earthquake using the weigh index rupturing directivity (Wang and Fialko 2015; Koketsu et al. (WI) method. 2016), from which we selected is a rectangular area as the study area, which has a length of 113 km in east- Data and methods west direction and width of 92 km in north-south dir- The study area ection (Fig. 1), covering 10,396 km .Fromnorth to Despite its large magnitude, the Gorkha earthquake did not south, the elevation of the study area generally de- produce visible ruptures on the surface, which was confined clines from 7,975 m to 387 m, i.e. more than 7,500 m to the subsurface at depths 10–15 km (Angster et al. 2015; elevation drop in an about 100 km-wide zone. The Fig. 2 Maps showing controlling factors of coseismic landslides of the study area. a Elevation. b Slope angle. c Aspect. d Lithology (sources are mentioned in the text) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 4 of 17 area encompasses the Rasuwa and Sindhupalchok high-quality landslide inventories (Xu 2015). As a sup- counties of Nepal (Fig. 1). The Araniko Highway plement and verification of results from visual interpret- passes through Sindhupalchok county and the Pasang ation, we carried out several days of field investigation Lhamu Highway passes through Rasuwa county, re- mainly along the Pasang Lhamu Highway and Araniko spectively. Based on the seismic intensity map released Highway. by the China Earthquake Administration (www.cea.- gov.cn), most of the study area lies in the IX intensity Spatial distribution and hazard assessment of landslides zone, and part in VIII and VII intensity zones (Fig. 1). The Gorkha, Nepal earthquake affected a very large area about tens of thousands of square kilometers. Immediately Data after the quake, it was difficult to construct a detailed and The satellite images for landslide interpretation are complete landslide inventory throughout the affected area. from the Google Earth (GE) platform. After the earth- Fortunately, spatial distribution of the partial affected area quake occurred, several organizations have imple- can represent the overall spatial patterns of landslides mented specialized tasks to obtain post-earthquake under some conditions (Lee et al. 2008; Xu et al. 2013a). satellite images. Some of the images with very high Therefore, we selected a 5-km buffer area on either side of resolution (1 m or better) are available on the Google the Araniko Highway to construct a detailed landslide in- Earth platform. In addition, pre-earthquake images ventory. Although we prepared a polygon-based inventory with high qualityand resolution in theareaarealso of landslides that directly damaged Araniko Highway and available on the GE platform. These images allow re- Pasang Lhamu Highway, we chose point-based inventory searchers to map co-seismic landslides conveniently of coseismic landslides and landslide number density and accurately. The regional DEM for analyzing cor- (LND, defined as the number of landslides per square relations between topography and coseismic landslides kilometers (Xu et al. 2013b) to conduct analysis of the were derived from SRTM DEM in 3-arc-second reso- spatial distribution and hazard assessment of landslides. lution (Fig. 2a). The slope angle map (Fig. 2b) and as- The reasons include: (1) The precise source area of a land- pect map (Fig. 2c) were derived from the regional slide is very difficult to be distinguished from the whole DEM on the GIS platform. The geologic map (Fig. 2d) landslide area because the boundaries of the source area, of the study area was clipped and revised from movement area, and accumulation area of the landslide “World Geologic Maps” on the USGS Website are usually in the subsurface, thus cannot be exactly delin- (www.usgs.gov). eated, which perhaps reduce the objectiveness of land- slides hazard assessment. (2) Preparation of a point-based Methods landslide inventory is relatively time-saving, permitting to Landslide identification carry out a quick regional assessment of earthquake- In this study, we used two methods to identify triggered landslides. Five controlling factors, including ele- landslides, i.e. visual interpretation of pre- and post- vation, slope angle, slope aspect, lithology, and seismic in- earthquake satellite images and field investigation. Com- tensity were taken into account for a statistical analysis. puter screen-based visual interpretation of satellite Currently, many statistical methods are available for land- images is the most widely used method for earthquake- slide hazard assessment (Xu et al. 2012; Feng et al. 2016; triggered landslide mapping which permits to prepare Pathak 2016; Tsangaratos and Ilia 2016), among which the 500m (a) (b) Fig. 3 Coseismic landslides at the Araniko Highway. a Satellite image of 4 May 2015. b Field photo of 14 June 2015 (by Chong Xu, view to south). The solid and dotted arrows in (a) and (b) show the same places, respectively Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 5 of 17 300m (a) (b) Fig. 4 Rockfalls on the upper slope of an inspection station of Nepal. a Satellite image of 4 May 2015. b Field photo of 14 June 2015 (by Chong Xu, view to northwest). The solid and dotted arrows in (a) and (b) indicate the same places, respectively bivariate statistical analysis method has been widely used Findings, results and analysis in various areas because it is time-saving and does not Landslides on satellite images need complex calculations (Xu et al. 2013b). In this study, In this section, we present several groups of comparisons a weight index (WI) model was employed to perform of satellite images and field photos of coseismic landslides landslide susceptibility mapping in the 5-km buffer area to illustrate the excellent capacity of detecting coseismic aforementioned. This WI method is based on a bivariate landslides on high-resolution satellite images. The satellite statistical analysis based on calculating landslide number images used in this study are from the GE platform col- density (LND). In this method, the weigh value of each lected in early May, 2015. The red solid arrow on Fig. 3 factor class is defined as the natural logarithm of the LND shows a coherent landslide (27.87°N, 85.911°E) with clear in the class divided by the LND of the whole area (Sarkar exposed bedrocks in the landslide source area and partly et al. 2008; Yalcin 2008; Xu et al. 2013b): damaged vegetation stayed at its deposit area. The red dotted line defines several shallow, disrupted landslides along the Araniko Highway road. Due to the high reso- WI ¼ lnðÞ LND =LND i i ð1Þ lution and quality of the satellite image, the locations and ¼ lnðÞ ðÞ LN =Area =ðÞ LN=Area i i boundaries of the landslides can be mapped correctly and conveniently on the ortho images. where WI is the weight of the factor-class i, LND is the Figure 4 shows two rockfalls (27.927°N, 85.932°E) i i landslide number intensity within the area of the ith fac- occurred on the upper slope at an inspection station of tor class, and LND is the landslide number intensity in Nepal, which originated from nearby the ridge of the re- the whole area. In this study, the value of LND is 3,005/ verse slope and accumulated into two conical heaps with 2 -2 1,027.4 km = 2.925 km . two narrow runout paths. The broken accumulate 200m (a) (b) Fig. 5 A series of shallow, disrupted landslides blocking the Pasang Lhamu Highway. a Satellite image of 3 May 2015. b Field photo of 15 June 2015 (by Chong Xu, view to southeast) showing the road was blocked by a secondary landslide caused by a heavy rainfall. The red solid arrow in (a)shows the location of field photo in (b) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 6 of 17 600m (a) (b) Fig. 6 An area characterized by high density of coseismic landslides. a Satellite image taken on 3 May 2015. b Field photo taken on 15 June 2015 (by Chong Xu, view to west). The solid and dotted arrows denote the same places, respectively materials are dangerous for the structures on the toe of almost vertical. Small rockfalls or falling stones are more the slope. Despite different expressions of the rockfalls on susceptible than large deep-seated landslides on such a the image and field photos due to the image stretching reverse slope. caused by steep topography, the rockfalls can be easily After the main shock, a series of aftershocks and rain- identified on the satellite image with the aid of field inves- falls struck the affected area and caused more landslides. tigations. They have short runout distances on the image, For example, the satellite image of 3 May 2015 (Fig. 5a) whereas the actual runout distances of them are likely lon- shows quite a few shallow, disrupted landslides (located at ger. This is because the slope of the rockfalls occurrence is 28.064°N, 85.225°W) that occurred in weathering layers 200m 300m (a) (b) 200m 100m (c) (d) Fig. 7 Damaged sites along Araniko Highway by coseismic landslides have been seen on satellite images. The detailed information of the landslides and associated damages on the road is listed in Table 1. a the No. 1, 2, and 3 landslides, (b) the No. 21, 22, and 23 landslides, (c) the No. 24, 25, 26, and 27 landslides, and (d) the No. 31, 32, 33, and 34 landslides along Araniko Highway. All the satellite images were acquired on 4 May, 2015 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 7 of 17 and blocked the Pasang Lhamu Highway. Fig. 5b shows materials related to the main shock and subsequent trig- the road was blocked by a secondary landslide caused by a gers blocking the roads have been cleared up or were be- heavy rainfall in the area. Information from local residents ing cleared away in time to keep the traffic flowing. suggests that the landslide accumulation material that The satellite image (Fig. 6a) shows an area with high blocked the road was not triggered by the main shock, but density of coseismic landslides, dominated by shallow, by a strong rainfall a few days before. All the landslide disrupted landslides. The red solid arrows wherein Table 1 Information of damage along the Araniko Highway and associated coseismic landslides 2 3 No. Longitude (°) Latitude (°) Length of road damaged (m) Area (m ) Estimated volume (m ) 1 85.98343 27.9868 45 29131 50000 2 85.98196 27.98562 52 6180 12000 3 85.9791 27.98369 35 22953 60000 4 85.96235 27.97215 16 3648 6000 5 85.96186 27.97166 27 3756 7000 6 85.96482 27.96926 29 8383 20000 7 85.96511 27.96912 30 ditto ditto 8 85.95799 27.96591 10 435 400 9 85.93064 27.91744 156 31816 20000 10 85.92627 27.91352 19 4233 8000 11 85.9227 27.90877 5 972 1000 12 85.92241 27.90828 27 2271 3000 13 85.9222 27.9079 33 2630 4000 14 85.91496 27.8927 10 15890 30000 15 85.91307 27.88253 30 1739 2000 16 85.90599 27.87934 46 12025 30000 17 85.90493 27.87892 12 268 200 18 85.90185 27.87782 115 6224 10000 19 85.89957 27.87686 49 4245 6000 20 85.89595 27.87602 15 303 200 21 85.88781 27.87385 95 9237 20000 22 85.88341 27.87226 25 608 500 23 85.88309 27.87113 20 3583 5000 24 85.88062 27.85115 43 3805 5000 25 85.88092 27.85034 94 7461 15000 26 85.88045 27.84828 54 9367 20000 27 85.88063 27.84779 42 7061 15000 28 85.87291 27.82729 57 3399 5000 29 85.87352 27.82707 43 3094 5000 30 85.89329 27.8036 15 1250 1500 31 85.89425 27.80022 30 949 1000 32 85.89475 27.80006 13 522 300 33 85.89492 27.79994 7 937 1000 34 85.89613 27.7986 52 1971 2500 35 85.88417 27.77117 1 628 500 36 85.77906 27.73006 63 3778 5000 Total 1,415 214,751 372,100 No. 6 and No. 7 landslide-damaged sections were caused by one landslide Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 8 of 17 Table 2 Information of damaged along the Pasang Lhamu Table 2 Information of damaged along the Pasang Lhamu Highway and associated coseismic landslides Highway and associated coseismic landslides (Continued) No. Longitude Latitude Length of road Area Estimated 44 85.27853 28.09454 15 169 100 2 3 (°) (°) damaged (m) (m ) volume (m ) 45 85.2867 28.10178 9 95 100 1 85.24384 27.82563 12 166 100 46 85.28784 28.104 7 109 40000 2 85.23569 27.82943 14 613 500 47 85.2877 28.10459 104 15961 3000 3 85.20104 27.83575 14 275 200 48 85.28754 28.1055 30 2082 3000 4 85.13849 27.8637 17 628 500 49 85.28715 28.10675 14 577 500 5 85.18591 27.98131 18 311 200 50 85.31217 28.10857 19 1711 2000 6 85.1884 27.98278 14 197 100 51 85.31202 28.10873 22 1306 1500 7 85.18866 27.98288 16 209 100 52 85.31183 28.11008 60 10024 250000 8 85.18777 27.9828 50 1144 1200 53 85.31135 28.11061 49 4482 8000 9 85.21029 28.00465 16 336 300 54 85.31117 28.11098 17 1550 2000 10 85.2181 28.01873 9 177 100 55 85.29239 28.11159 24 555 500 11 85.22025 28.02089 21 553 400 56 85.31072 28.11175 41 3888 6000 12 85.22087 28.02175 28 2766 4000 57 85.31043 28.11217 55 7463 15000 13 85.22311 28.02457 40 32568 80000 58 85.29454 28.11298 6 168 100 14 85.22349 28.02528 48 17673 50000 59 85.30909 28.11914 11 117 100 15 85.22334 28.02617 39 10247 20000 60 85.30815 28.12234 14 5918 12000 16 85.22309 28.02716 45 9266 20000 61 85.30685 28.12406 4 357 300 17 85.22299 28.02766 18 10071 20000 62 85.34242 28.17233 25 957 1000 18 85.21933 28.0394 14 199 100 63 85.34248 28.17269 31 4223 8000 19 85.22441 28.04657 14 227 100 64 85.34254 28.17314 14 1996 2000 20 85.22547 28.04732 7 171 100 65 85.3426 28.17358 63 21402 60000 21 85.22722 28.04826 24 805 800 66 85.34237 28.178 18 1374 2000 22 85.22892 28.05021 22 279 200 67 85.34225 28.17896 11 695 800 23 85.22869 28.05054 14 103 100 68 85.3439 28.18229 14 197 100 24 85.22923 28.05373 5 271 200 69 85.34455 28.18387 17 4005 6000 25 85.22867 28.05858 19 11564 5000 70 85.34617 28.18621 30 33718 100000 26 85.22811 28.06002 53 3790 5000 71 85.34634 28.18678 57 32800 100000 27 85.22798 28.061 67 7609 15000 72 85.34741 28.18903 16 565 500 28 85.22674 28.0633 57 5222 10000 73 85.34761 28.18932 24 8214 10000 29 85.22643 28.06371 19 1815 2000 74 85.34883 28.19127 59 18536 30000 30 85.22562 28.0644 28 9168 20000 75 85.34972 28.19332 14 445 300 31 85.22542 28.06454 15 3018 4000 76 85.35092 28.19643 19 398 300 32 85.22515 28.06481 14 525 300 77 85.35147 28.19703 41 4677 80000 33 85.22494 28.06501 21 2679 3000 78 85.35182 28.19742 27 3538 5000 34 85.22447 28.06545 31 5012 5000 79 85.35224 28.19803 13 1716 1000 35 85.22374 28.06637 36 3662 5000 80 85.35285 28.19931 19 3032 3000 36 85.22535 28.06689 25 2876 4000 81 85.35292 28.19967 12 1805 1000 37 85.22884 28.06806 59 11267 20000 82 85.35304 28.20057 43 2852 5000 38 85.23007 28.06827 99 20358 60000 83 85.35425 28.20403 41 1693 2500 39 85.23904 28.07238 209 54607 200000 84 85.35507 28.20837 38 4976 10000 40 85.25552 28.0772 16 920 800 85 85.35804 28.2197 102 13969 40000 41 85.2558 28.07737 11 152 100 86 85.36041 28.22164 19 3995 5000 42 85.25023 28.07813 13 500 300 87 85.36106 28.22261 107 14279 30000 43 85.277 28.09335 18 241 100 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 9 of 17 Table 2 Information of damaged along the Pasang Lhamu (27.678835°N, 85.349647°E) southeast to Kathmandu Highway and associated coseismic landslides (Continued) was selected as the target to investigate the damage of 88 85.36698 28.25649 44 2549 4000 coseismic landslides on the road. This section of Araniko Highway is about 117.3 km long. Based on visual inter- 89 85.37802 28.27514 92 21169 60000 pretation of high-resolution satellite images and field in- Total 2,842 500,552 1,470,600 vestigations, we delineated 35 coseismic landslides damaging the Araniko Highway at 36 places. The longest indicate a large rock slide located at 28.079°N, 85.194°W. section of the road damaged is about 156 m long which It occurred at the lower part of the slope and blocked the was buried by a landslide at 27.91744°N, 85.93064°E. valley. However, they did not create a lake because of the Considering the previous correlations between area and small area of the catchment upstream. The red dotted ar- volume of individual landslides (Larsen et al. 2010; Xu et rows show a shallow, disrupted landslide originated from al. 2016b) and field investigations, the total volume of the a ridge (located at 28.08°N, 85.208°W). Most of the land- 35 coseismic landslides was estimated to be about 0.37 slides shown in Fig. 6a are distributed along the rivers, million m . Figure 7 shows the Araniko Highway damaged likely associated with river incision or loose deluvium with by coseismic landslides on satellite images at four places. high landslide susceptibility. Table 1 lists the detailed information on the 35 coseismic landslides and hazards on the road they caused. Landslide damage to the two roads The Pasang Lhamu Highway connects Kathmandu, The Pasang Lhamu Highway and Araniko Highway are Nepal and Gyirong County. The length of the section two most important roads connecting Nepal and China. between the point (27.735268°N, 85.305939°E) northwest The Araniko Highway links Kathmandu, Nepal and to Kathmandu and the point (28.278972°N, 85.377904°E) Nielamu County, China. In this study, the section of the China-Nepal border is about 139.3 km. Visual interpret- Araniko Highway between the place (27.987262°N, ation of satellite images and field investigations allowed 85.982552°E) nearby Zhangmu Port and the location us to delineate 89 coseismic landslides that damaged the N N 100m 200m (a) (b) N N 200m 100m (c) (d) Fig. 8 Damaged sites along Pasang Lhamu Highway by coseismic landslides on satellite images. The detailed information of the landslides and associated damages on the road is listed in Table 2. a the No. 11 and 12 landslides, (b) the No. 28~36 landslides, (c) the No. 59, 60, and 61 landslides, (d) the No. 62, 63, 64, and 65 landslides along Pasang Lhamu Highway. All the satellite images were acquired on 3 May, 2015 Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 10 of 17 Fig. 9 Distribution map of coseismic landslides in the 5-km buffer area on either side the Araniko Highway 600 15 Class area LND 400 10 200 5 0 0 610-1000 1000-1500 1500-2000 2000-2500 2500-3000 3000-4750 Fig. 10 Relationships between elevation class (horizontal axis), its area (vertical axis on left) and landslide number density (LND, vertical axis on right) Class area (km ) −2 LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 11 of 17 250 15 Class area LND 0 0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 >40 Fig. 11 Same as Fig. 10 but for slope angle (horizontal axis, unit: degree) Pasang Lhamu Highway. The total length of damaged or Therefore, we estimated the Gorkha quake triggered at buried roads is about 2,842 m, of which the longest sec- least 30,000 landslides. Several other teams have released tion is about 209 m long, caused by a landslide located coseismic landslides related to the Gorkha quake. For at 27.91744°N, 85.93064°E. The total volume of the 89 example, Kargel (Kargel et al. 2016) only mapped 4,312 landslides was estimated to be 1.47 million m . Figure 8 coseismic and postseismic landslides. A team from British shows satellite images of coseismic landslides along the Geological Survey et al. (British Geological Survey et al. Pasang Lhamu Highway. Table 2 shows the detailed in- 2015) identified about 5,600 coseismic landslides as poly- formation on the 89 coseismic landslides and their haz- lines marking the location and movement path from head ards on the road. to toe of a landslide. Therefore, there might be false nega- tive errors (omissions) in these released inventories of Landslide inventory along the Araniko highway landslides triggered by the Gorkha quake. On either side of the 117.3 km-long Araniko Highway, we constructed a 5-km buffer region to construct a detailed Spatial distribution of landslides along the Araniko and complete point-based coseismic landslide inventory. highway The area of this buffer region is 1,027.4 km .Individual As a common index to reflect landslide abundance, coseismic landslides were mapped as points at the central landslide number density was employed as the index to of the landslide. Consequently, we mapped 3,005 coseismic measure spatial distribution of the 3,005 landslides in landslides in the area (Fig. 9), and calculated the landslide the 5-km buffer area of the Araniko Highway. In this 2 -2 number density to be 3,005/1,027.4 km = 2.925 km .The study, five controlling factors including elevation, slope spatial distribution of the coseismic landslides along the angle, slope aspect, lithology, and seismic intensity were Araniko Highway is quite uneven. Most of the landslides selected to analyze their correlations with the landslides occurred in the mountainous areas to the north, where the (Figs. 10, 11, 12, 13 and 14). The DEM of the area was landslide inventory is complete and detailed, i.e. small land- derived from SRTM in 3 arc-second, which permitted to slides are included. The buffer area only accounts for less determine the elevations of the buffer area vary from 10% than the primary affected area of the main shock. The 610 m to 4,750 m. The study area was divided into six buffer area is approximately normal to the strike of the classes based on 500 m of elevation intervals, i.e. 610– seismogenic structure (EW trending). Usually the seismic 1000 m, 1000–1500 m, 1500–2000 m, 2000–2500 m, landslide density along the causative fault is uniform. 2500–3000 m, and 3000–4750 m (Fig. 10). The 250 15 Class area LND 0 0 Flat N NE E SE S SW W NW Fig. 12 Same as Fig. 10 but for slope aspects (horizontal axis) 2 2 Class area (km ) Class area (km ) −2 −2 LND(km ) LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 12 of 17 500 10 Class area 400 8 LND 300 6 200 4 100 2 0 0 Ti Pc1 Pc2 Pc3 Mi Fig. 13 Same as Fig. 10 but for lithology (horizontal axis) elevations of most of the area (814.97 km , 79.3% of the northwest (NW). Fig. 12 shows the correlations between total) are lower than 2,000 m. The class 1000–1500 m the slope aspect, area of its classes and landslide number occupies the largest area, which is 433 km , accounting density. It is clear that the slope aspects E and SE cor- for 42.1% of the total. The class 2,000-2,500 m registered respond to the two largest LND values, which are -2 -2 -2 the largest LND value, which is 10.3 km . The landslide 4.87 km and 4.58 km , respectively. This is perhaps re- number density values gradually decrease at the eleva- lated to the movement direction of the hanging wall of tions higher than 2,500 m and lower than 2,000 m. the seismogenic fault or the propagation direction of Slope angle is an important controlling factor of seismic wave (Shen et al. 2016). The study area is located coseismic landslides. In this study, the slope angle of the east of the epicenter of the Gorkha main shock, and thus buffer area ranges from 0° to 74°, which was divided into the propagating direction of seismic waves is eastward. 9 classes with an interval of 5°. Majority of the area During the Gorkha earthquake, the hanging wall of the (780.7 km , 76% of the total) has slope angles lower than fault, where the buffer area is located, moved toward 30°. As shown in Fig. 11, the landslide number density in- south and probably generated inertia effect to the south. creases with the growing slope angle. The class >40° cor- In addition, the slopes of southward aspect in the area -2 responds to the largest LND value, which is 12.92 km .In are easily exposed to sunlight and rainfall, thus leading addition, the LND curve shows a concave form, implying to widespread weathering layers and high susceptibility the LND increases with the slope angle gradually. This to seismic landslides. suggests a very strong control of the slope angle on the The Gorkha earthquake affected area can be divided coseismic landslides. Such a situation is also common in into a series of east-west trending major tectonic regions other earthquake events (Gorum et al. 2014; Xu et al. by three major active fault zones, including MFT, MBT, 2014; Xu et al. 2015; Tian et al. 2016). and MCT (Le Fort 1975; Nakata 1989; Upreti 1999; Slope aspects (or facing directions) can affect the pat- Wesnousky et al. 1999; Mukherjee 2015). Based on the tern of coseismic landslides because slopes with different geologic map of South Asia, the study area has five clas- aspects have varied responses to the movement direc- ses of lithology (rock types) generally from north to tions of blocks or the propagating direction of seismic south, i.e. Tertiary igneous rocks (Ti), Precambrian waves. The study area has nine classes of slope aspects, rocks-1 (Pc1), Precambrian rocks-2 (Pc2), Precambrian i.e. flat, north (N), northeast (NE), east (E), southeast rocks-3 (Pc3), and Mesozoic intrusive rocks (Mi). (SE), south (S), southwest (SW), west (W), and Figure 13 shows the correlations between lithology, its 1000 5 Class area 800 4 LND 600 3 400 2 200 1 0 0 VIII IX Fig. 14 Same as Fig. 10 but for seismic intensity (horizontal axis) Class area (km ) Class area (km ) −2 −2 LND(km ) LND(km ) Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 13 of 17 class area and landslide number density. The three LHI ¼ WI þ WI þ WI Elevation Slope angle Slope aspect groups of Precambrian rocks cover most of the area, þ WI þ WI ð2Þ Lithology Seismic intensity -2 which is 942.8 km , occupying 91.8% of the total. The lithology class Pc1 registered the largest landslide num- The WI values indicate the relative importance of each -2 ber density, which is 8.32 km . factor to landslide hazard. Positive WI values mean the Seismic intensity and peak ground accumulation factor-class area is prone to coseismic landslides, (PGA) are two common proxies representing the degree Table 3 Weight index values of various classes of five of seismic energy and often used to explore the effect of controlling factors earthquakes on landslides. The PGA distribution map Factor Class area Landslide number LND WI released by USGS (www.usgs.gov) is rather irregular in Elevation the 5-km buffer area of the Araniko Highway because 610–1000 m 155.94 159 1.02 -1.05 the buffer area is relatively small and there are perhaps significant errors generated by numerical simulation and 1000–1500 m 433 438 1.01 -1.06 limited stations. Therefore, we preferred to analyze the 1500–2000 m 226.03 1000 4.42 0.41 correlation between coseismic landslides and seismic in- 2000–2500 m 95.24 981 10.3 1.26 tensity in this study. The seismic intensity map of the 2500–3000 m 58.83 327 5.56 0.64 Gorkha earthquake was produced by China Earthquake 3000–4750 m 58.39 100 1.71 -0.54 Administration (CEA) (Fig. 1). Only VIII and IX inten- Slope angle sity zones appear in the study area, which have the land- -2 -2 slide number density values 0.32 km and 3.5 km , 0–5° 139.07 15 0.11 -3.3 respectively (Fig. 14). Despite merely two data points, 5–10° 86.75 42 0.48 -1.8 these data show a positive correlation with the coseismic 10–15° 96.61 71 0.73 -1.38 landslides, i.e. the place with larger seismic intensity has 15–20° 141.86 144 1.02 -1.06 a higher landslide number density. 20–25° 163.49 290 1.77 -0.5 25–30° 152.92 458 3.00 0.02 Landslide hazard assessment In the aforementioned 5-km buffer area of the Araniko 30–35° 115.59 641 5.55 0.64 Highway, the WI vales were calculated to each class of all 35–40° 76.65 640 8.35 1.05 the five controlling factors, respectively. Then, the weighted 40–74° 54.5 704 12.92 1.49 thematic maps of the five factors were produced and were Slope aspect superposed to derive a landslide hazard index (LHI) map: Flat 16.63 6 0.36 -2.09 N 120.07 300 2.50 -0.16 NE 131.27 459 3.50 0.18 E 112.63 549 4.87 0.51 SE 119.02 545 4.58 0.45 S 121.36 305 2.51 -0.15 SW 158.51 295 1.86 -0.45 W 127.22 312 2.45 -0.18 NW 120.72 234 1.94 -0.41 Lithology Ti 29.34 101 3.44 0.16 Pc1 308.10 2564 8.32 1.05 Pc2 288.82 324 1.12 -0.96 Pc3 345.91 16 0.05 -4.15 0 20406080 100 Mi 55.26 0 0 -14.89 Cumulative of area% Seismic intensity Fig. 15 The area under the curve representing the success ratio of VIII 185.60 60 0.32 -2.2 the landslide hazard assessment. Area % means the percentage of IX 841.84 2945 3.5 0.18 area to the study area for each factor class. Landslide number % Lithology type Mi registered no landslide. In order to avoid ln(0) in calculating means the percentage of landslide number in a factor class to the WI value, we assigned the LND value of the class Mi with 0.000001, i.e. a small total landslide number enough value, and the WI value of the class is -14.89 Cumulative of landslide number% Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 14 of 17 whereas negative WI values represent the opposite. WI that 10% of the area with the highest landslide hazard values close to zero mean moderate probabilities of oc- index could cover 1,514 landslides, about 50.4% of the currence of coseismic landslides. Results show the LHI total. Likewise, 20 and 30% of the area with the highest of the area is in the range from -23.546 to 4.48. In order landslide hazard index can account for 2,207 and 2,645 to examine the validity of the model, the 3,005 coseismic landslides, about 73.4 and 88% of the total, respectively. landslides aforementioned were employed to compare We applied the WI values in Table 3 to a larger area, the known landslides with the landslide hazard index i.e. the rectangle in Fig. 1, to construct a landslide haz- map. By referring to a common method, the regional ards map. For the areas with factor-attribute values be- area was categorized into 100 classes with a same area yond the ranges of the 5-km buffer area, they are by the LHI value and the percentages of landslide num- classified into the classes that are closest to them. The ber in each class were calculated. Then, a correlation elevation ranges from 291 m to 7968 m in the area. The curve between cumulative area percentages and cumula- area with elevation less than 610 m was classified into tive percentage of landslide number from high to low the class 610–1000 m and the area with elevation higher LHI in a descending order was drawn (Fig. 15). It shows than 4,750 m was classified into the class 3000–4750 m. the area under the curve (AUC) is as much as 85.9%, i.e. The range of slope angle of the rectangle area is 0–81.7°, a quite satisfactory success ratio. The curve also reveals therefore, the range of 74°–81.7° was merged into the Fig. 16 Landslide hazard map for a part of the Gorkha earthquake region Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 15 of 17 class 40–74°. The northern part of the rectangle area rivers and seismogenic fault. (2) The weight value outcrop several other lithology types, such as Quaternary method is a bivariate method, interactions among factors perennial ice and snow, Quaternary sediments, Neogene cannot be considered; and (3) WI values were calculated granite, Triassic metamorphic and sedimentary rocks, based on the 3,005 landslides in the 5-km buffer area and Cretaceous sedimentary rocks. The northern rock along the Araniko Highway. Of course, it is inferior to group located at the northern part of the study area was use of a complete inventory of landslides throughout the not subdivided further because of limited geologic infor- earthquake-affected area. These limitations are expected mation there. The WI values of these lithology types to be improved in future research. were assigned the values same as the type Tertiary igne- ous rocks (Ti). The rectangle area includes three seismic Conclusions intensity zones, i.e. VII, VIII, and IX (Fig. 1). The seismic Based on high-resolution satellite images, field investiga- intensity VII is out of the range of the 5-km buffer area tion, and GIS technology, we examined the coseismic and its WI value was calculated by linear extrapolation, landslides of the 2015 Gorhka, Nelpal earthquake that which is -4.58. Subsequently, we constructed the LHI blocked or damaged the Araniko Highway (117.3 km) map of the rectangle area. We divided the map into four and Pasang Lhamu Highway (139.3 km) in Nepal. Re- classes, i.e. very low, low, high, and very high, based on sults show 35 coseismic landslides damaged the Araniko three breakpoints of the index values, i.e. -3, -1, 1, and 3. Highway with a total length of the sections of the dam- Figure 16 shows the derived landslide hazard map of the aged road about 1,415 m. The total volume of these 35 study area. The high zone and very high zone show a coseismic landslides was estimated to be about 0.37 NWW-SEE directed distribution, which is coincident million m . We delineated 89 coseismic landslides that with the seismogenic fault and earthquake damage area. damaged the Pasang Lhamu Highway. The total length Figure 17 shows a three-dimensional view on the land- of the damaged or buried roads is about 2,842 m. The slide hazard map. We overlaid the hazard map with 50% total volume of these 89 landslides was estimated to be transparency on satellite images of the Google Earth 1.47 million m . In the 5-km buffer area on either side platform. However, several limitations of this result of the Araniko Highway, we mapped 3,005 landslides should be noted, including (1) Only five common factors caused by the Gorkha earthquake. The landslide number -2 were considered, while there should more factors can density of the buffer area is 2.925 km . Correlations be- affect the occurrence of the coseismic landslides, such as tween the landslides and five controlling factors were Fig. 17 A printing screen showing a three-dimensional perspective on the landslide hazard map. View to north Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 16 of 17 analyzed based on the bivariate method. The results British Geological Survey, Earthquakes without Frontiers, Durham University. 2015. 2015 Nepal Earthquakes mapped landslide intensity (Revision 4.0 - 19 June show the elevation class 2,000–2,500 m has the highest 2015). https://data.hdx.rwlabs.org/group/nepal-earthquake. landslide concentration. The landslide number density Collins, B.D., and R.W. Jibson. 2015. Assessment of existing and potential landslide value increases with the slope angle. The slope aspects E hazards resulting from the April 25, 2015 Gorkha, Nepal earthquake sequence. US Geological Survey.. https://pubs.er.usgs.gov/publication/ofr20151142. and SE correspond to the highest concentrations of doi:10.3133/ofr20151142. coseismic landslides. The underlying bedrock of Precam- Dahal, R.K. 2016. Initiatives for rockfall hazard mitigation in Nepal. Bulletin of Nepal brian rocks-1 (Pc1) registered the largest landslide num- Geological Society 33: 51–56. Duputel, Z., J. Vergne, L. Rivera, G. Wittlinger, V. Farra, and G. Hetényi. 2016. The ber density. The area with seismic intensity IX has a 2015 Gorkha earthquake: a large event illuminating the main Himalayan much higher LND value than the area of the intensity thrust fault. Geophysical Research Letters 43(6): 2517–2525. doi:10.1002/ VIII. We used the weigh index method to perform land- 2016GL068083. Elliott, J.R., R. Jolivet, P.J. González, J.-P. Avouac, J. Hollingsworth, M.P. Searle, and slide hazard assessment in the 5-km buffer area. Result V.L. Stevens. 2016. Himalayan megathrust geometry and relation to shows the success ratio as high as 85.9%. In addition, we topography revealed by the Gorkha earthquake. Nature Geoscience 9: 174– prepared a landslide hazard assessment map for a larger 180. doi:10.1038/ngeo2623. Feng, H., A. Zhou, J. Yu, X. Tang, J. Zheng, X. Chen, and S. You. 2016. A area encompassing Rasuwa and Sindhupalchok counties comparative study on plum-rain-triggered landslide susceptibility assessment of Nepal. It indicates the areas most likely to be prone models in West Zhejiang Province. Earth Science 41(3): 403–415. to coseismic landslides, which would be helpful for con- Gnyawali, K.R., S. Maka, B.R. Adhikari, D. Chamlagain, S. Duwal, and A.R. Dhungana. 2016. Spatial implications of earthquake induced landslides triggered by the structing a more detailed and complete coseismic land- April 25 Gorkha earthquake Mw 7.8: preliminary analysis and findings. In slide inventory map throughout the earthquake-affected International conference on earthquake engineering and post disastor area subsequently. The result is also helpful predict the reconstruction planning 24 – 26 April, 2016, Bhaktapur, Nepal,50–58. locations of landslides triggered by subsequent events, e.g. Gorum, T., O. Korup, C.J. van Westen, M. van der Meijde, C. Xu, and F.D. van der Meer. 2014. Why so few? Landslides triggered by the 2002 Denali strong aftershocks and rainfalls, which would be a scien- earthquake, Alaska. Quaternary Science Reviews 95: 80–94. doi:10.1016/j. tific reference for restoration, reconstruction, and land- quascirev.2014.04.032. slide reduction and mitigation in the Gorkha earthquake- Hashash, Y.M.A., B. Tiwari, R.E.S. Moss, D. Asimaki, K.B. Clahan, D.S. Kieffer, D.S. Dreger, A. Macdonald, C.M. Madugo, H.B. Mason, M. Pehlivan, D. Rayamajhi, I. affected area. Acharya, and B. Adhikari. 2015. Geotechnical field reconnaissance: Gorkha (Nepal) earthquake of April 25, 2015 and related shaking sequence. In Acknowledgments Geotechnical extreme event reconnaisance GEER association report No. GEER- This research was supported by the National Natural Science Foundation of 040.. https://works.bepress.com/rmoss/47: 250 pages. China (41661144037, 41472202, 41472264) and the Special Project for China Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, Bevington A, Fielding EJ, Fujita Earthquake Research (201508024). We are very grateful to the comments of K, Geertsema M, Miles ES, Steiner J, Anderson E, Bajracharya S, Bawden GW, two anonymous reviewers and the help of Ranjan Kumar Dahal (Editor). Breashears DF, Byers A, Collins B, Dhital MR, Donnellan A, Evans TL, Geai ML, Glasscoe MT, Green D, Gurung DR, Heijenk R, Hilborn A, Hudnut K, Huyck C, Immerzeel WW, Jiang L, Jibson R, Kääb A, Khanal NR, Kirschbaum D, Competing interests Kraaijenbrink PDA, Lamsal D, Liu S, Lv M, McKinney D, Nahirnick NK, Nan Z, The authors declare that they have no competing interests. Ojha S, Olsenholler J, Painter TH, Pleasants M, Pratima KC, Qi Y, Raup BH, Regmi D, Rounce DR, Sakai A, Shangguan D, Shea JM, Shrestha AB, Shukla A, Authors’ contributions Stumm D, van der Kooij M, Voss K, Wang X, Weihs B, Wolfe D, Wu L, Yao X, CX and YT mapped landslides on GE images. CX and GL participated the Yoder MR, Young N. 2016. Geomorphic and geologic controls of geohazards field investigations. CX, BZ, and HR provided the regional geologic and tectonic induced by Nepal’s 2015 Gorkha earthquake. Science 351(6269): aac8353. data. CX drafted the manuscript. All the authors reviewed and approved the doi:10.1126/science.aac8353 manuscript. Koketsu, K., H. Miyake, Y. Guo, H. Kobayashi, T. Masuda, S. Davuluri, M. Bhattarai, L. B. Adhikari, and S.N. Sapkota. 2016. Widespread ground motion distribution caused by rupture directivity during the 2015 Gorkha, Nepal earthquake. Publisher’sNote Scientific Reports 6: 28536. doi:10.1038/srep28536. Springer Nature remains neutral with regard to jurisdictional claims in Lacroix, P. 2016. Landslides triggered by the Gorkha earthquake in the Langtang published maps and institutional affiliations. valley, volumes and initiation processes. Earth, Planets and Space 68(1): 46. doi:10.1186/s40623-016-0423-3. Author details Larsen, I.J., D.R. Montgomery, and O. Korup. 2010. Landslide erosion controlled by Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China hillslope material. Nature Geoscience 3(4): 247–251. doi:10.1038/ngeo776. Earthquake Administration, 1# Huayanli, Chaoyang District, PO Box 9803, Le Fort, P. 1975. Himalayas: the collided range. Present knowledge of the Beijing 100029, China. CCCC Highway Consultants Co., Ltd., Beijing 100088, continental arc. American Journal of Science 275-A: 1–44. China. Lee, C.T., C.C. Huang, J.F. Lee, K.L. Pan, M.L. Lin, and J.J. Dong. 2008. Statistical approach to earthquake-induced landslide susceptibility. Engineering Geology Received: 9 August 2016 Accepted: 30 March 2017 100(1-2): 43–58. doi:10.1016/j.enggeo.2008.03.004. Martha TR, Roy P, Mazumdar R, Govindharaj KB, Kumar KV. 2016. Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides. doi:10.1007/s10346-016-0763-x References Angster, S., E.J. Fielding, S. Wesnousky, I. Pierce, D. Chamlagain, D. Gautam, B.N. Moss, R.E.S., E.M. Thompson, D.S. Kieffer, B. Tiwari, Y.M.A. Hashash, I. Acharya, B.R. Upreti, Y. Kumahara, and T. Nakata. 2015. Field reconnaissance after the 25 Adhikari, D. Asimaki, K.B. Clahan, and B.D. Collins. 2015. Geotechnical effects April 2015 M 7.8 Gorkha earthquake. Seismological Research Letters 86(6): of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks. 1506–1513. doi:10.1785/0220150135. Seismological Research Letters 86(6): 1514–1523. doi:10.1785/0220150158. Avouac, J.-P., L. Meng, S. Wei, T. Wang, and J.-P. Ampuero. 2015. Lower edge of Mukherjee, S. 2015. A review on out-of-sequence deformation in the locked main Himalayan Thrust unzipped by the 2015 Gorkha earthquake. Himalaya. Geological Society, London, Special Publications 412(S): 67–109. Nature Geoscience 8: 701–711. doi:10.1038/ngeo2518. doi:10.1144/SP412.13. Xu et al. Geoenvironmental Disasters (2017) 4:14 Page 17 of 17 Nakata, T. 1989. Active faults of the Himalaya of India and Nepal. Geological Xu C, Xu X, Shen L, Yao Q, Tan X, Kang W, Ma S, Wu X, Cai J, Gao M, Li K. 2016b. Society of America Special Papers 232: 243–264. doi:10.1130/SPE232-p243. Optimized volume models of earthquake-triggered landslides. Scientific Parameswaran, R.M., T. Natarajan, K. Rajendran, C.P. Rajendran, R. Mallick, M. Reports 6: 29797. doi:10.1038/srep29797. Wood, and H.C. Lekhak. 2015. Seismotectonics of the April-May 2015 Nepal Yalcin, A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy earthquakes: An assessment based on the aftershock patterns, surface effects process and bivariate statistics in Ardesen (Turkey): Comparisons of results and and deformational characteristics. Journal of Asian Earth Sciences 111: 161– confirmations. Catena 72(1): 1–12. doi:10.1016/j.catena.2007.01.003. 174. doi:10.1016/j.jseaes.2015.07.030. Pathak, D. 2016. Knowledge based landslide susceptibility mapping in the Himalayas. Geoenvironmental Disasters 3(1): 8. doi:10.1186/s40677-016-0042-0. Sarkar, S., D.P. Kanungo, A.K. Patra, and P. Kumar. 2008. GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science 5(1): 52–62. doi:10.1007/s11629-008-0052-9. Sharma, K., L. Deng, and C.C. Noguez. 2016. Field investigation on the performance of building structures during the April 25, 2015, Gorkha earthquake in Nepal. Engineering Structures 121: 61–74. doi:10.1016/j. engstruct.2016.04.043. Shen, L., C. Xu, and L. Liu. 2016. Interaction among controlling factors for landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. Frontiers of Earth Science 10(2): 264–273. doi:10.1007/s11707-015-0517-4. Sun, B., and P. Yan. 2015. Damage characteristics and seismic capacity of buildings during Nepal Ms 8.1 earthquake. Earthquake Engineering and Engineering Vibration 14(3): 571–578. doi:10.1007/s11803-015-0046-x. Tian, Y., C. Xu, X. Xu, and J. Chen. 2016. Detailed inventory mapping and spatial analyses to landslides induced by the 2013 Ms 6.6 Minxian earthquake of China. Journal of Earth Science 27(6): 1016–1026. doi:10.1007/s12583-016-0905-z. Tsangaratos, P., and I. Ilia. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena 145: 164–179. doi:10.1016/j.catena.2016.06.004. Upreti, B.N. 1999. An overview of the stratigraphy and tectonics of the Nepal Himalaya. Journal of Asian Earth Sciences 17(5-6): 577–606. doi:10.1016/S1367- 9120(99)00047-4. Wang, K., and Y. Fialko. 2015. Slip model of the 2015 Mw 7.8 Gorkha (Nepal) earthquake from inversions of ALOS‐2 and GPS data. Geophysical Research Letters 42(18): 7452–7458. doi:10.1002/2015GL065201. Wang, F., M. Miyajima, R. Dahal, M. Timilsina, T. Li, M. Fujiu, Y. Kuwada, and Q. Zhao. 2016. Effects of topographic and geological features on building damage caused by 2015.4.25 Mw7.8 Gorkha earthquake in Nepal: a preliminary investigation report. Geoenvironmental Disasters 3(1): 7. doi:10.1186/s40677-016-0040-2. Wesnousky, S.G., S. Kumar, R. Mohindra, and V. Thakur. 1999. Uplift and convergence along the Himalayan Frontal Thrust of India. Tectonics 18(6): 967–976. doi:10.1029/1999TC900026. Xu, C. 2015. Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geoscience Frontiers 6(6): 825–836. doi:10.1016/j.gsf.2014.03.004. Xu, C., X. Xu, F. Dai, and A.K. Saraf. 2012. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences 46: 317–329. doi:10.1016/j.cageo.2012.01.002. Xu C, Xu X, Dai F, Wu Z, He H, Wu X, Xu S, Shi F. 2013a. Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Natural Hazards 68(2): 883-900. doi:10.1007/s11069-013-0661-7. Xu C, Xu X, Yao Q, Wang Y. 2013b. GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Submit your manuscript to a Wenchuan earthquake, China. Quarterly Journal of Engineering Geology and journal and benefi t from: Hydrogeology 46(2): 221-236. doi:10.1144/qjegh2012-006. Xu, C., X. Xu, X. Yao, and F. Dai. 2014. Three (nearly) complete inventories of 7 Convenient online submission landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of 7 Rigorous peer review China and their spatial distribution statistical analysis. Landslides 11(3): 441– 461. doi:10.1007/s10346-013-0404-6. 7 Immediate publication on acceptance Xu, C., X. Xu, and J.B.H. Shyu. 2015. Database and spatial distribution of landslides 7 Open access: articles freely available online triggered by the Lushan, China Mw 6.6 earthquake of 20 April 2013. 7 High visibility within the fi eld Geomorphology 248: 77–92. doi:10.1016/j.geomorph.2015.07.002. 7 Retaining the copyright to your article Xu C, Xu X, Tian Y, Shen L, Yao Q, Huang X, Ma J, Chen X, Ma S. 2016a. Two comparable earthquakes produced greatly different coseismic landslides: The 2015 Gorkha, Nepal and 2008 Wenchuan, China events. Journal of Earth Submit your next manuscript at 7 springeropen.com Science 27(6): 1008-1015. doi:10.1007/s12583-016-0684-6.

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