From rock collapse to slide movement: a case study of the 2004 Zuojiaying Landslide

From rock collapse to slide movement: a case study of the 2004 Zuojiaying Landslide | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From rock collapse to slide movement: a case study of the 2004 Zuojiaying Landslide Muhammad Bilal, Xueyong Xu, Haoshan Zhang, Aiguo Xing, Ye Zhou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4039450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The mining process instigates the development of subsurface goaf, resulting in surface deformation and subsidence. The deformation and failure characteristics of the landslides in mining regions present a challenge to the detection and analysis of the initiation and progression mechanisms. There has been a significant focus on comprehending the complex nature and destruction associated with such geohazards, which are becoming more common and typical in the southwestern region of China. The present study showcases a compelling example of the 2004 Zuojiaying landslide in Guizhou, China, where rainfall and long-term mining activity triggered a rock collapse that transformed into a sliding event, causing 44 fatalities. A thorough field and aerial survey are conducted for the rock collapse-transformed sliding mass, further supplemented with a dynamic modeling approach to simulate the runout, entrainment, and accumulation processes of the landslide. Our work presents a comprehensive analysis and discussion of the geological condition, potential triggers, collapse mechanism, erosion characteristics, and the 3D runout evolution of the Zuojiaying landslide. Furthermore, the present study explores the significant influence of entrainment and erosion on landslide volume, mobility, and deposition, particularly in the transition from rock collapse to sliding. We have identified and outlined the stages of runout progression and landslide transformation by analyzing the entrainment over the entire runout. This study aims to highlight the significance of developing a thorough understanding of such landslide hazards in areas with comparable geological characteristics to improve geohazard management. Zuojiaying rock collapse landslide transformation runout modeling dynamic characteristics entrainment effect field investigation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction The main source of natural slope failure in gently inclined layered structures is typically localized collapse deformation, hence reducing the likelihood of significant geological disasters. Nevertheless, in the mining region, the gently inclined layered slopes are susceptible to collapse and damage due to the combined influence of deformation and rainfall within the goaf. Consequently, the overlying rock mass experiences deformation and loosening, resulting in a significant decrease in slope stability. The landslides generated by mining activities are a prevalent geological hazard, where the formation of joints can give rise to structures exhibiting unfavorable features that divide the rock and soil, significantly impacting the stability of the slope and accelerating the disintegration and fragmentation of the rock and soil during the process of failure. In recent years, increased mining activities have been attributed to a notable rise in the occurrence of mining-induced landslides. This, in turn, inflicts severe human casualties and property damage (Dash et al., 2016; Froude and Petley, 2018). Some recent studies on the formation mechanism of mining-induced landslides have been performed by (Li et al., 2023; Tang et al., 2019; Yan et al., 2023; Yang et al., 2022). While the combination of InSAR and offset tracking method has also been used to monitor the mining-induced landslides and collect displacement data (Chen et al., 2021; Wang et al., 2020; Wasowski and Pisano, 2020). Whereas the Discrete Element Method has shown good performance in modeling mining-induced landslides (Li et al., 2017; Li et al., 2022; Li et al., 2012; Zhang et al., 2015; Feng et al., 2012). Currently, existing research has provided evidence indicating that the collision, fragmentation, and disintegration of sliding masses exert a substantial influence on the kinematics and extent of damage caused by landslides (Ruiz et al., 2017; Zhao and Crosta, 2018). Furthermore, the process of impact shoveling and entrainment is an important process observed during a landslide runout movement. These processes can significantly multiply the volume and movement of landslides, hence stimulating their catastrophic nature (Hungr et al., 2005; Mergili et al., 2018; Mergili et al., 2020). The thickness of the erodible layer, material properties, and slope angle affect the mobility of a dry granular landslide (Edwards et al., 2021; Mangeney et al., 2007; Pudasaini and Krautblatter, 2021). McDougall and Hungr, (2005) state that the runout zone consists of bedrock and loose materials such as bed sediments. As a result, the entrained material can amplify the volume of a landslide, which is directly linked to the landslide energy and thus has a significant impact on its mobility (Evans et al., 2009; Liu et al., 2019; Theule et al., 2015; Zhou et al., 2015). For example, as observed in the Yigong rock avalanche and the Wenjia Valley rock avalanche. For a landslide with significant volume change, it is essential to consider the effects of erosion and entrainment while analyzing the sliding process. The advance of computer-based modeling allows us to analyze landslides and their dynamic processes efficiently and quickly. Numerous computational models have been developed to examine landslides dynamically, with a specific focus on their movement characteristics (Denlinger and Iverson, 2004; Hungr, 1995; Hungr and Evans, 2004; Pudasaini and Hutter, 2003; McDougall and Hungr, 2005; Pastor et al., 2014; Savage and Hutter, 1989; Wang et al., 2004). In recent years, modeling tools have been utilized to analyze the landslide mobility, volume, and runout by including the erosion-entrainment aspect (Liu et al., 2019; Wang et al., 2019; Zhou et al., 2020). Dietrich and Krautblatter (2019) argue that erosion and entrainment are complex processes, and the landslide forces are frequently underestimated, which consequently leads to inaccuracies in predicting entrainment behavior. In their recent study, Pudasaini and Krautblatter (2021) thoroughly examined the mechanisms of erosion and entrainment and developed a quantitative model to measure the mobility shown by erosive landslides. The current investigation employs the 2004 Zuojiaying landslide as a case study (herein referred to as ZJL), which involved a mass of approximately 36,000 m 3 that rushed downhill. The presence of colluvium on the lower part of the cliff resulted in the transformation of the rock collapse into a rock slide, creating an accumulation region that extended over a distance of more than 250 m. The unique landform formation in Guizhou province results in the occurrence of landslides, particularly rock collapses and rock avalanches, predominantly during periods of moderate to heavy rainfall within the mining-affected region. Such cases are particularly being observed in the central and southwestern areas of China. Typical documented cases in recent decades include the Jiguanling rock collapse in Wulong in 1994, the Jiweishan rock avalanche in 2009, the Nayong rock avalanche in 2017, the Baiwu rock avalanche in 2020, the Jianshanying landslide in 2020, the Baiyan rock avalanche in 2022, and the recurring Kaiyang rockfalls (Chen et al., 2023; Chen et al., 2021; Huang and Li, 2011; Lai et al., 2023; Li et al., 2023; Yan et al., 2023; Zheng et al., 2015). These landslides were observed in regions that exhibit distinct characteristics, including specific geological conditions and the presence of mining activities in the surrounding environment. The examination of the relationship between joints and the collapse evolution due to the underlying goaf can have a significant impact on slope stability. The present study analyzes the 2004 Zuojiaying landslide, which occurred in a steep layered structure over a coal mine goaf in Guizhou Province. The research involves thorough fieldwork to establish a geological model using numerical modeling. The study focuses on analyzing the runout characteristics of the Zuojiaying landslide at various stages, as well as examining the erosion and subsequent mobility of the landslide and the respective deposit distribution. We aim to examine the characteristics of a region affected by mining and rainfall events that caused failure and runout transformation of the rock mass, and subsequent deposit accumulation. Furthermore, we attempt to identify the landslide evolution process and runout region that transforms a rock collapse into a rock slide. Investigating the instability and understanding the characteristics of landslide mobility, evolution, and accumulation in wide valley landforms will provide valuable insights for implementing effective disaster prevention measures. 2. Case Study 2.1 Geology: The landslide area belongs to a low to medium elevation mountain area which is high in the northeast and low in the southwest. The geographic location of the study area is 26°42'49" N and 105°14'10" E (Fig. 1). A cliff rock belt with a north-northeast strike and a height of 30-200 meters is formed in the area. The study area resembles a slide-like topography with a steep uphill slope and a gradual downhill slope. The strata in the area, from new to old, are sequentially Quaternary, Triassic, and Permian Stacked strata are described in Table 1. The Quaternary loose deposit (Q) is the cover layer with loose structure, and developed pores, containing a relatively lower water rate. While the Triassic Feixianguan Formation (T 1 f) bedrock is divided into 3 sections and mostly comprises the vertical cliff from mountain base to top. The terrain slope ranges from 10 ° to 50 °, with some areas exceeding 70 °. The Longtan Formation (P 3 l) of the Upper Permian System is distributed on the moderate lower part of the slope. The Zuojiaying Village is located in the southeast on the lower part slope, 300 m away from the cliff. The overall rock formation is 330°∠ 22°, which is a gently inclined rock formation. Large structural faults are not developed in the area, and the regional stability is good. Only two sets of joints are developed and distributed in the upper and middle parts of the steep slope (T 1 f 2 ) with an occurrence of J 1 ranging from 315°∠ 85°, while the occurrence of J 2 190°∠ 87°. Due to excessive coal mining, two goaves have been formed in the study area. The research area experiences a moderate climate, characterized by the absence of extreme temperatures during both hot and cold seasons. The region has a subtropical monsoon climate having an average annual temperature of 13.6 °C and an average annual rainfall of 1243.5 mm (Fig. 2). According to the statistics, the study region had the lowest amount of rainfall in December. Additionally, the cumulative rainfall data from the last 14 days indicates sporadic and light rainfall leading up to the event. The cumulative rainfall in the 14 days before the occurrence of ZJL was roughly 18 mm, while the monthly rainfall reached a maximum of 32 mm. 2.2 Collapse Process: A significant collapse of a columnar rock block took place on December 3, 2004, at Zoujiaying Village, located in the Zongling Township of Guizhou, China. The Zuojiaying collapse process may be categorized as the long-term effect of mining and differential weathering, which caused the slow development of joints and fissures, ultimately resulting in the formation of a fractured rock mass. Due to ongoing mining activities and rainfall, a crack formed on the upper part of the cliff that gradually expanded. Prior to failure, the crack had a width of around 0.5 meters. According to the reports, there were many precursor movements, such as sporadic rock falls, in the days leading up to the main rock collapse. The most significant indication was a rock collapse of 100 m3 that occurred 12 hours before the Zoujiaying Rock collapse. The rapid downward movement of the rock mass along the steep slope led to substantial erosion and the entrainment of the loose soil, ultimately resulting in the creation of a rock slide that tragically claimed the lives of 44 individuals. The existence of a small hill at the base of the precipice resulted in the separation of the sliding motion into two distinct paths, namely the left and right branches, as depicted in Fig. 3a. The left branch was more devastating, as it covered a wider distance and inflicted widespread damage upon the village. The ZJL had an approximate total runout of ~450 meters with an average thickness of 3 meters. 2.3 Landslide Zoning Based on the field investigation, deposit accumulation, and structural condition of the source area, the landslide area is divided into four zones based on failure (Zone A), runout (Zone B), and deposition (Zone C and D).as shown in Fig. 3a. Zone A include the source area is situated within the T 1 f 2 formation at an elevation of 2200 m (above sea level). The primary composition of the source material was mostly limestone, forming block-shaped rock masses. The slope inclined approximately 80° (Fig. 4). The dimensions of the source region were roughly 50 m in height, and 50 m in breadth, with an average thickness of 5 m. The estimated initial volume of the landslide is 11,000 m 3 . Following the sliding event, a series of fractures appeared near the source area, defining a deforming mass with an outward dipping orientation. These cracks gradually expanded due to mining activities and rainfall, and caused small rock collapses, as depicted in Figs. 3b-c. Thus forming a hazardous rock zone area. Zone B comprises a transport area situated at an elevation of ~2150-1990 m. The sliding mass exerted increased scraping forces as it moved downward on the steep slope of the transport area. The upper portion of Zone B exhibits a comparatively firm stratum in contrast to the loose bed sediment (agricultural land) at the base of the cliff, which serves as an appropriate erodible material for landslide volume expansion (Figs 3d-e). The rock collapse transforms into a rock slide of considerable magnitude during the descent phase. After entering the deposition zone, the sliding mass encounters a small hillslope (Fig. 3f) and subsequently splits into two diverging branches, namely the left and right branches. Zone C includes the left branch formed by the southwest deflection of the landslide mass. A significant amount of the sliding mass was deposited in this particular branch and caused damage to Zoujiaying Village. The mass accumulated in the left branch has a volume of 18600 m 3 and traveled along the direction of 213°. The average deposit thickness in the Zuojiaying landslide is 3 m, while the maximum thickness along the main branch is above 3 m. The deposit in this branch has been depleted over time, as shown in Fig. 3g. While, zone D is the right branch formed when the sliding mass is deflected towards the southeast direction. It has an estimated deposit volume of 9400 m 3 and a sliding direction of 120°. The deposits along the right branch had an average thickness of 2-3 m, with a local maximum of 3 m. 2.4 Potential Triggers: During the field investigation, we have outlined that the rock mass near the slope surface is fragmented and has become unstable over the period under the action of differential weathering (Fig. 3h). Although the hard upper rock (limestone) and soft lower part (mudstone and siltstone) provides favorable lithological conditions for joints and cracks development due to different strength and softening parameters. The differing weather resistance of the slope lithology and the steep terrain offer suitable settings for rainfall infiltration, leading to the formation and extension of cracks in the rock mass. However, the overall rock strata appear to be stable, and the presence of two sets of joints J1 and J2 have cut the rock stratum in the source area into a block (Figs. 5a-b). Therefore, based on the joints and stratum data, we conducted a stereographic projection analysis as shown in Fig. 5c. The intersecting lines of all combinations indicate that the regulating structural planes are the junction of the slope surface (P) and joint (J1) as well as the slope surface and joint (J2), which are oriented towards the outer side of the slope. The mining activities in the bottom section of the slope have substantially altercated the stress distribution, leading to the deformation and crack formation in the overlying strata. The goaf formation has a profound effect on subsidence and results in surface crack widening. Furthermore, the mining-induced rock deformation in combination with natural external factors such as excessive rainfall will ultimately result in the destabilization and subsequent occurrences of such phenomena in the region (Fig. 3c), thus presenting a substantial hazard. 3. Method 3.1 Digital Model: The numerical modeling requires an accurate Digital Elevation Model to reproduce the actual event, and our studies use an integrated approach involving field investigation and an Uncrewed Aerial Vehicle (UAV) survey to obtain such a model. The UAV effectively manages a course overlap above 90% and a side overlap surpassing 80% in the context of aerial photography. It further utilizes the captured images to build a three-dimensional point cloud, employing triangulation techniques to construct a highly accurate digital surface model (DSM), as shown in Fig. 6. The DSM is further used to obtain a 3D elevation model for numerical simulation and other applications. While, the sliding body model is constructed by conducting a comparative analysis between the historical terrain, and geomorphology data before and after the occurrence of the landslide event. This modeling technique involves the utilization of Boolean intersection operations. Based on a comprehensive analysis conducted through field investigation, it has been determined that the rock body of the slide source location mostly exhibits the presence of two distinct groups of joints as shown in Fig. 5. 3.2 RAMMS The Rapid Mass Movement Systems (RAMMS) module Debris flow, aimed at examining the particulate flow behaviors of rock debris has been utilized in this research to model the rapid movement of rock debris. We used an un-channelized debris flow setting that employs a block release approach and only needs the initial depth of the sliding material. This method incorporates a three-dimensional terrain analysis to assess the runout motion from the start, using depth-averaged equations to estimate the flow's runout height and velocity along the slope. Additionally, the model adopts the Voellmy-fluid flow model for friction, initially introduced by Salm et al., (1990) and Salm (1993), to simulate the frictional characteristics of flowing debris. The model uses the following basic expressions: where frictional resistance ( S ), and includes terms for solid phase resistance , the normal force N . The density of the material is denoted as ρ , the gravitational acceleration as g, the flow velocity as u , and ξ signifies the resistance due to turbulent flow. The normal force N can be further defined as: In eq. (2), h denotes the height of the flow, and g is the acceleration due to gravity, and slope angle . This software incorporates a friction model that is meticulously calibrated, as necessary, to yield precise outcomes, supported by extensively documented case studies. The Debris flow module is equipped with an erosion feature, making it capable of modeling sediment erosion within the entrainment zone. This feature makes it suitable for application in cases with high sediment volume as a result of entrainment (Frank et al., 2015). Field studies have highlighted the correlation between rapid erosion rates and increased flow intensity (Berger et al., 2011), which in turn, elevates the erosion depth, a finding supported by research from Schürch et al., (2011) and Frank et al., (2015). However, it's noted that sediment erosion may not always accompany minor debris flows. Therefore, the RAMMS calculates the basal shear stress independently within each grid cell to determine the maximum possible erosion depth. The parameters used in RAMMS are summarized in Table 2, with values obtained through back analysis and literature review, ensuring the model's capability to accurately reproduce the landslide event. 4. Results Following the sudden collapse and disintegration of the steeply positioned rock mass, the collapse quickly transitioned into a rapid downhill sliding movement, traveling about 430 m in 105 seconds. Figure 7 illustrates the evolution of the Zuojiaying landslide runout movement, showing the initiation together with the progressive deposition of sliding mass during the runout duration. The sliding mass traveled a maximum distance of 430 m with a maximum velocity of approximately 55.95 m/s. The total runout duration of the landslide from initiation to final deposition is 105 seconds. The entire runout process of sliding mass can be divided into four stages. Stage I initiates from t= 5s and ends when the sliding material reaches the slope break (t= 15 s), as shown in Figs. 7a-c. Stage II lasts between 15 to 25 seconds, and during this stage, the runout propagates, and the front end of the sliding mass gradually expands and scrapes material from the surface (Figs. 7d-e). The rock mass during this stage travels on a gentle slope and encounters a ridge that divides the material into two branches (Figs. 7f-g). In stage III (25-75 s), the sliding mass deflects and starts moving in the southwest and southeast direction (Figs. 7h-k). The runout evolution during this phase indicates that during the subsequent accumulation process of the sliding mass in the left and right branches. In the last stage (75-105 s), the sliding mass continues to accumulate in the left and right branches (Fig. 7l). The final volume increased to 36,000 cubic meters with a maximum deposit thickness of 7 m. 4.1 Sensitivity Analysis Sensitivity analysis is performed to investigate the impact of erosion on uncertainty in the numerical modeling of landslides. The role of slope and surface erosion can either impede or accelerate the landslide motion. The findings of the sensitivity analysis demonstrate that altering erosion parameters to match various on-site circumstances has a notable impact on runout distance and deposit depth (refer to Fig. 8). High landslide mobility can cause more scraping on the slope surface, especially when combined with certain substrate conditions in the transport region. The runout distance is notably affected if the eroded material density and placement condition (such as normal or wet/loose) is changed. The sensitivity analysis demonstrates that erosion is a crucial factor in accurately reconstructing the landslide using numerical modeling. According to the simulated results of ZJL runout and deposition, using a denser material in the transport area can lead to a roughly 200% rise in the entrained volume. While, a denser material under loose or wet placed conditions can triple the entrained volume, leading to a considerable increase in deposit thickness and runout. The verification of modeling results for the landslide runout simulation was conducted by comparative analysis of the simulated results, field investigation, and incident report as shown in Table 3. 4.2 Simulated Velocity Figure 9 depicts the velocity evolution during the progression of the landslide. The results obtained from the dynamic study conducted using RAMMS indicate the highest value along the steep slope, reaching its maximum value of 36.5 m/s. Throughout the collapse movement (0-180 m phase), the maximum velocity ranged from 30 to 36 m/s, gradually decreasing as the mass descended into the valley. Voellmy (1955) states that the frictional resistance on the sliding surface base remains constant during the initial increase. The energy and velocity of the landslide gradually reduced due to a transition from a steep to a more gradual slope, as well as due to the impacts shoveling process. As the materials slid further downhill, their velocity showed a gradual fluctuation, as a result of the amplified entrainment effect indicating a transition from collapse movement to sliding movement. Furthermore, after a deflection from the hillslope, the sliding mass velocity dropped. The velocity continued to experience significant fluctuations and approached zero as it reached the far end of the deposition zone. Overall, the velocity simulated by RAMMS shows a gradual decrease after the distance of 180 m, implying that as the erosion and entrainment effect amplified, the velocity decreased. To validate the simulated velocity of the sliding mass, the change in landslide velocity along the runout path was determined using Scheidegger (1973) first-order estimates. The sliding velocity is estimated using the expression where H represents the vertical distance from the highest point of the source area, L denotes the horizontal distance, μ characterizes the constant sliding resistance, and g represents gravity. Scheidegger’s first-order velocity estimation method mostly provides a rough estimate by utilizing a constant coefficient of sliding friction (μ) to simplify the assessment. The maximum vertical displacement and horizontal runout are 340 and 450 meters, respectively. The value 0.756 denotes the coefficient of sliding friction, μ. The velocity estimation along the main sliding direction of the landslide runout is illustrated in Figure 9, while Fig. 3a shows the spatial coordinates of locations A to D. Both simulated and estimated velocities are higher along the steep slope due to the increased vertical drop across a limited horizontal distance. The estimated velocity shows a gradual decrease as vertical descent decreases with the increase in horizontal distance. As a result, the calculated velocity falls until it reaches zero. The estimated maximum velocity at point A is 37 m/s, higher than the simulated value of 34 m/s due to the high initial velocities induced by the collapse on steep slopes. The maximum simulated velocity is at Point B (36.5 m/s), and the decrease onwards is most probably from the high erosion-entrainment process and landslide transformation. The simulated and estimated velocities at Point C (before deflection from hillslope) are 24 m/s and 39 m/s, respectively. Whereas both estimated and simulated velocities show an agreement near the end of runout (Point D). Nevertheless, the overall velocity trend simulated shows good agreement with the data derived from Scheidegger (1973). The simulated behavior shows a similar velocity trend along the steep slope, however, after entering the valley, the simulated velocity shows a decreasing trend due to the entrainment and landslide transformation mechanism. Moreover, the simulated velocity shows an abrupt decrease in velocity after deflection from the hillslope, corresponding to the field observation. While the first-order velocity approximation provides a general reference, thus the estimated velocity does not account for such change in behavior, which explains the difference in results. 4.3 Simulated erosion and flow depth Figure 10 displays the outcomes of the simulation for the dynamic flow depth of the ZJL, as well as the erosion depth of the basal surface at various time intervals. The varying thicknesses of the landslide flow height and erosion of the base surface are represented using distinct colors. The initial landslide in the source region has a maximum depth of 5 meters. At first, the landslide gained speed because of the sharp angle of the slope after its collapse. During the descent of the collapsed mass in the steep stretch of the runout, the simulated mass achieved a peak velocity of 55 m/s. The flow height was about 24 m, and the depth of erosion reached 1 m (Fig. 10a). The simulation demonstrates that the erosion level of the basal surface is most pronounced in the region affected by the high-speed impact movement of the collapsing rock mass, resulting in an impact-shovel zone. At this stage, the rock mass experiences a peak flow and erosion depth of 4.5 m and 2.4 m, respectively (Fig. 10b). The bottom section of the cliff features a mild slope consisting of loose bed sediments. The collapsed rock mass decelerates and transforms into a sliding mass. In this phase, the erosion depth is consistent around 2 meters, whereas the flow height is greater than 5 meters (Fig. 10c). As the distance increases, the landslide mass experiences a decrease in flow and erosion depth at the front edge along the main sliding direction. Both the highest measured flow and erosion depth are recorded at the rear section of the sliding mass, measuring 5.5 m and 2.3 m, respectively (Fig. 10d). At t= 75 s, the sliding mass is observed moving towards the far end of the deposit area, leading to an augmentation in the thickness of the sliding mass. Specifically, the front edge experiences an increase to 3 meters, while the middle section rises to 4 meters (as illustrated in Figure 10e). The maximum erosion in the middle section has been increased to 1.8 meters. The erosion of the basal surface in the deposit region diminishes as the movement velocity decreases due to friction resistance, especially at the far end of the accumulation area (Fig. 10f). The overall temporal changes in the volume and moving momentum of the landslide body are presented in Fig. 11. The runout process is separated into four stages based on the position of the sliding mass along the runout path. The initial phase occurs during the time interval of 0–15 seconds, during which the entire collapse mass moves along the steep slope surface. The rock mass travels uninterrupted in a downhill trajectory and the energy possessed by the collapsed mass is maximum. In this stage, the collapsed material accelerates, erodes, and entrains continually, resulting in a gradual increase in landslide volume. The second stage occurs over 15-25 seconds when the collapsed mass enters the valley and transitions into a sliding mass while traveling on the gentle slope surface of the valley. As a result, more energy is dissipated during the erosion and entrainment process. Since the collapse mass is in a more gentle slope area, more potential energy is converted into kinetic energy, hence moving momentum shows a gradual decrease, although the erosion and entrainment process is continuous. The third stage occurs throughout the time range of 25–75 seconds. Most of the sliding mass has entered the valley and is divided into two branches. The sliding volumes increase and the sliding mass undergoes steady deceleration, and eventually, its energy diminishes due to the continuous erosion process and the influence of frictional resistance. After 75 seconds, the entrainment effect reduces and the moving momentum reaches a minimum value, thus resulting in a slow gradual accumulation of the sliding mass. A field investigation was conducted to obtain the geological characteristics of Zuojiaying Village. However, there is a slight difference between the width of the simulated flow region and the actual landslide. The calculated volume of the accumulated mass is about 36,000 m 3 , which closely matches the estimated volume. The calculated maximum depth of erosion on the bed surface is around 2.4 meters, moreover, the erosion is concentrated in the slide transformation zone, as illustrated in Fig. 12. The landslide movement is assessed by incorporating the erosion and entrainment effect to investigate the impact of entrainment on the travel distance, velocity, and volume of a sliding mass. Overall, the results demonstrate a strong correlation between the simulated deposit area, volume, velocity, and erosion extent of the landslide and the data obtained from the field investigation. 5. Discussion Landslides are one of the major geological disasters in China, besides human factors, such as underground mining, rainfall is also one of the most common causes. In the rainy season, the rainwater seeps into the rock–soil mass, increases its quality and the downslide strength, and further softens, and erodes the rock–soil mass to reduce its shear strength. Meanwhile, the continuous rainfall leads to the alternation of dry and wet rock–soil mass, which will cause a large number of cracks, thus accelerating the landslide initiation. The rainfall also catalyzes saturation of the soil in the transport and deposition areas, which plays a crucial role in landslide volume expansion and mobility. The mechanism of rock movement transformation into a sliding mass, especially in mining-affected regions, is complex and is controlled by various factors, including (1) high and steep terrain, (2) good surface slide conditions, (3) weak interlayers, vertical fissures, and/or dissolution fissures, (4) the subsidence and deformation from goaf, and (5) extensive rainfall (Grenon et al., 2017), (6) easily erodible soil in the transport and depositional area. The Zuojiaying landslide almost had all of the above conditions. Based on the results of the field survey, UAV photography, and RAMMS simulation, the evolution of the Zuojiaying landslide can be described as the emergence of joints and cracks due to rainfall and mining leading to the development of a fractured columnar block. In the long-term geological evolution process, two steeply dipping orthogonal structural joints were formed in the mountain. After long-term moderate precipitation, the interface of the structural joints became a good seepage channel. As a result, the strength of the rock mass at the top of the mountain continuously deteriorated, and these structural planes became the potential controlling boundaries of the landslide. Previously, some researchers have shown that for completely fractured sliding masses, the particles at the bottom are violently mixed due to frequent collisions, which can serve as a lubricating layer to reduce the overall effective friction of landslide debris flow and promote its remote movement. This article studies the process of landslide movement and runout transformation of the sliding body. 5.1 Role of Entrainment Figures 7 and 11 display the simulated motion of the sliding mass. The collapse, which began with a volume of 11,000 m 3 , experienced a sudden collapse and traveled downhill by a distance of 180 m. It subsequently eroded the underlying slope surface as a result of impact scouring. Consequently, the collapsed material's volume increased to 15,000 m 3 . After entering the valley the slope became gradual, and substantial erosion of the bed sediments occurred, where the eroded depth was constantly above 2 meters, with lateral expansion of sliding material. Upon its descent along the valley, the sliding mass was redirected by a hillslope located at an approximate distance of 250 m, as a result, the landslide mass was divided into two branches. The velocity reduced as the energy dissipated due to the impact deflection from the hillslope and continuous material erosion. After deflection, the main mass of the landslide traveled towards the Zuojiaying Village in the southwest direction and deposited along the path. At t=75s, the landslide volume expanded up to 31,000 cubic meters. In the depositional stage (75s-105s), the erosion rate gradually dissipated and became zero as the runout distance reached its limit. The combined amount of simulated entrainment during this stage achieved an approximate volume of 5000 m 3 , the volume change is smaller, yet it is 50% of its original volume (refer to Fig. 11). The impact of entrainment on the movement of landslides is also investigated, and depositional comparison is provided in Fig. 13. Erosion and entrainment have a crucial role in determining the distance traveled by a landslide, the runout length, runout duration, and the depth of the deposit. For ZJL, the maximum deposit thickness and the runout distance without entrainment can reach up to 4 meters and 380 meters, respectively. The Fig. 13 illustrates the changes in the landslide velocity and momentum as it moves throughout the runout phase. These figures are used to compare the mobility of the two different scenarios. In general, the simulation results show comparable characteristics for both entrainment and without entrainment. The velocity profiles for both situations exhibit significant similarity, with the highest velocity observed around the edge of the rupture surface. However, in the scenario without entrainment (Fig. 13a), the velocity is somewhat greater due to the lack of energy dissipation in the erosion entrainment process. Nevertheless, the affected region is reduced due to the consistent volume of the mass that collapsed. In addition, according to the principle of momentum conservation, the occurrence of entrainment in ZJL results in a further decrease in energy as the movement of the rock changes into a sliding motion by carrying along a significant amount of loose substrate materials that produce a greater deposit depth. It is important to acknowledge that a higher level of entrainment will result in increased disparities in the deposit distribution (refer to Figure 13b), overall velocity, and consequently the momentum of the landslide. Such volume expansion leads to an extension of the impacted zone, posing a threat to the population and infrastructure in hilly areas. 5.2 Landslide mobility and evolution process Based on our analysis, we infer that entrainment is a continuous, influencing, and notable process of the ZJL runout. The landslide body slides on the steep slope and has a forceful impact on the substrate material (bedrock). While the entrained material in the valley area is predominantly colluvium (bed sediments composed of silty clay with gravel). Therefore, the sliding mass is composed of both bedrock and the sediments on the sliding surface. Thus, understanding the evolution of entrainment is necessary, for analyzing the effect of entrainment on the landslide. Landslides commonly accompany high-speed movement, and the equivalent friction coefficient (H/L), the ratio of vertical displacement to horizontal displacement is used to determine the mobility. A smaller value indicates greater energy and travel mobility. For Zuojiaying, the incident angle along the main branch is 37°, which indicates a comparatively higher mobility. With a relative elevation difference of 340 m, the Zuojiaying landslide traveled a horizontal distance of 450 m. However, the runout and deposit characteristics of the Zuojiaying landslide show multiple signs that its initiation and movement speed reach high speed during the initial phase of the runout, which is closely related to the terrain conditions. The evolution model can be divided into four phases presented as follows (Fig. 14): Initiation: In the first stage (initiation), the rock block fails and undergoes a rapid collapse. The block disintegrates into relatively smaller fragments and is subjected to volumetric expansion. At this stage, the entrainment-induced change in the velocity and volume of the landslide is small, and the high initial runout velocity is mostly due to the steep slope. Fig. 14a shows the rock block at rest position. Rock Collapse Movement: The collapsed rock mass travels a distance of 180 m from initiation along the steep slope (Impact Shovel Zone), and its characteristics change regarding velocity and volume. A considerable volume of substrate material was scraped in the impact shovel zone under the action of impact and scour, and mixed with the rock mass during the collapse process, which substantially affected the movement characteristics of the landslide. During this stage, the basal shear stress caused by the landslide on the eroded bed surface increases due to the process of impact scouring (McDougall and Hungr, 2005), indicating that the landslide's erosion capability is approaching its maximum. The basal frictional angle is the key variable that affects the movement of the landslide and is calculated by the ratio of the vertical descent H to the horizontal extent L. Since vertical drop varies throughout the runout, therefore, greater velocities are recorded on the steeper while moderate to low velocities exist on the gentle slope section of the runout path. As a result, the speed and volume of the landslide experienced a substantial increase due to the combined effects of continued crushing and transportation of substrate material, as well as movement along the steep slope (Fig. 14b). Rock Slide Transformation: During this phase, the rock mass collides with the slope break and transforms into a sliding mass that traverses along the gentle valley region ranging from 180 meters to 350 meters. The entrainment material at this stage mostly originates from the erosion of the valley floor caused by the forceful impact of the rock mass, leading to an increase in the landslide volume and runout distance. Plowing is the main process at this stage that causes erosion and displacement of material at the base of the steep hill, which is entrained by the sliding mass (McDougall and Hungr, 2005). The density and the material condition on the sliding surface influence the runout. In ZJL, the area present at the bottom of the cliff and the deposition area was predominantly agricultural land, hence the loose soil might contain water within, which could easily provide a saturated or partially saturated condition due to the farming process and sporadic rainfall in the study area. According to Sassa, (1998) and Xu et al., (2012), such soils can be subject to liquefaction under undrained loading conditions, which allows the landslide to produce a long-runout motion. Once the failed rock mass dropped from the source area and slid downwards with high energy, the loose bed sediments in the valley (undrained conditions) were easily crushed, plowed, and entrained by the rock mass causing liquefaction of the shear band under the sliding rock mass. Thus reducing the ground resistance of the sliding mass and resulting in the long-runout movement. The landslide displaced the upper layer of loose sediment down the slope, removing the surface material to a comparatively significant depth. The slope gradually became less steep as the landslide runout increases, resulting in an energy drop leading to a reduction in velocity. The erosion and entrainment depth was also decreased, however, the landslide volume further increased (Fig. 14b). Propagation and Deposition: After traveling a distance over the valley floor, the landslide propagation experiences a substantial dampening effect (Kean et al., 2015), resulting in reduced scraping of surface materials and the initiation of landslide deposition. Hence, the depth of erosion and erosion rate decreases as the runout increases and the landslide deposition starts. In the case of ZJL, the landslide mass was deflected by a small hillslope located at a distance of 250 m from the release point. The landslide was split into two branches and propagated further, causing a rapid drop in velocity due to increased frictional resistance and ultimately deposited (Fig. 14c). 6. Conclusion The present study thoroughly examines the complex dynamics of the 2004 Zuojiaying landslide, highlighting the confluence of mining activities, rainfall events, and the unique geomorphological characteristics of the region as crucial elements contributing to its occurrence. We extensively analyzed the initiation, collapse mechanism, entrainment process, and their subsequent impact through a robust combination of field investigations, unmanned aerial vehicle (UAV) surveys, and RAMMS to map the landslide transformation, volume increase, propagation, and runout behavior. Our work emphasizes the important role of erosion and mass entrainment in transforming rock collapse into a rock slide, increasing the landslide mobility and magnitude. These factors have been shown to have considerable influence on the overall risk profile of such geohazards. Furthermore, the sensitivity study for ZJL reveals that erosion has a substantial impact on landslide modeling, specifically influencing the runout distance and deposit depth. These effects vary depending on the material density and condition. The complex interactions influencing the landslide movement and evolution illustrate the crucial impact of entrainment on altering the landslide's volume and behavior as it moves downhill. We utilized an equivalent friction coefficient and performed a thorough phase-wise analysis from initiation to rock collapse movement and subsequent transformation and deposition. This study provides a complete understanding of the mobility and evolution of landslides. Moreover, this study underlines the potential limitations of the study, including the challenges in accurately quantifying the extent of material entrainment and the need for more comprehensive models that can better simulate the complex interactions between geological and hydrological factors in landslide-prone areas. By providing a holistic view of the landslide phenomenon, our study promotes the need for an integrated risk assessment approach, combining geological analysis, accurate numerical simulations, and advanced remote sensing techniques, to foster a broad understanding of such landslide hazards in regions with similar geological setting. Declarations The authors declare no competing interest. References Berger C, McArdell BW, Schlunegger F (2011) Direct measurement of channel erosion by debris flows, Illgraben, Switzerland. Journal of Geophysical Research: Earth Surface 116(F1). 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Environmental Earth Sciences 79(16):396. https://doi.org/10.1007/s12665-020-09141-w Tables Table 1 Lithological description of the study area Formation Layer Thickness Description Quaternary loose deposit Q 4 pl It is present in the form of a loose cover layer on top and base of the mountain. Triassic Feixianguan Formation T 1 f 3 ~30-50 m The composition consists of mudstone, sandstone, and argillaceous siltstone, with traces of weathered and loose rock mass forming the quaternary loose deposits. The weathered rock mass is found on the mountain slope. T 1 f 2 50 m It is composed of thin to medium-thick bedded grayish limestone, situated in the steep section of the mountain. The source area is located in this formation. T 1 f 1 ~150 m Predominantly of lithic material composed of gray medium-thick mudstone, siltstone, and argillaceous siltstone with silty mudstone interlayers. It is found in the steeply inclined region and the upper section of the base of the slope. Longtan Formation Upper Permian System P 3 l Gray medium thick-bedded mudstone with sandstone interlayers distributed along the base and lower part of the slope. The coal seams are present in this layer. Table 2 RAMMS modeling input parameters Symbol Value Description V i 11,000 m 3 Release volume V f 36,000 m 3 Final volume H 50 m Source Area Height W 50 m Source Area Width T 5 m Source Area Thickness r 2500 kg/m 3 Sliding mass density μ 0.3 Friction Co-efficient ξ 1000 m/s 2 Turbulence Co-efficient Table 3 Comparison of field investigation and simulated results Method Landslide Runout Landslide Duration Maximum Runout Velocity Left Branch Length Right Branch Field Investigation 450 m ~120 s (reported) 43 m/s (Scheidegger, 1973) 200 m 100 m Numerical Simulation ~430 m 105 s 36.5 m/s 180 m ~90 m Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4039450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277443023,"identity":"9da1a263-96b2-488f-ab70-e87a9bb7cca7","order_by":0,"name":"Muhammad Bilal","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Bilal","suffix":""},{"id":277443024,"identity":"93b1e966-4175-4a04-a559-6d16c3435393","order_by":1,"name":"Xueyong Xu","email":"","orcid":"","institution":"North Information Control Research Academy Group CO., LTD","correspondingAuthor":false,"prefix":"","firstName":"Xueyong","middleName":"","lastName":"Xu","suffix":""},{"id":277443025,"identity":"a35c66d5-fca8-4472-b091-249435621e78","order_by":2,"name":"Haoshan Zhang","email":"","orcid":"","institution":"North Information Control Research Academy Group CO., LTD","correspondingAuthor":false,"prefix":"","firstName":"Haoshan","middleName":"","lastName":"Zhang","suffix":""},{"id":277443027,"identity":"fdd56233-29d9-493f-aa75-1f5cedb8f018","order_by":3,"name":"Aiguo Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZhBhAMTsDWCKgeEA0Vp4DjAYHCBKCxxIJEBVE9JicJz38IsfBbWJ/ZLPHxR/bGOQ47uRwPi5AI8WyWa+NMseg+OJM2fnGBgcbGMwlryRwCw9A48WfmYeMwMeg2OJG27nMIC0JG64kcDGzINHC1DWzPAPSMvN4w9AWuoJagHaYvyYx6AGaDgD2GEJBoS0SDbzmDHLGBwwntkD9MuZcxKGM888bJbGp8Xg/Bnjj2/+1Mn2sx9/ZlBRZiPPdzz54Gd8WkDekWBgOAxmAGMUyGZgbMCvARj/HxgY6sCMB4SUjoJRMApGwcgEAN3OTSL76EmaAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Aiguo","middleName":"","lastName":"Xing","suffix":""},{"id":277443029,"identity":"dc25a10f-a891-4fb3-9162-f5fa654ea882","order_by":4,"name":"Ye Zhou","email":"","orcid":"","institution":"North Information Control Research Academy Group CO., LTD","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhou","suffix":""},{"id":277443030,"identity":"f10f748d-ae03-4577-ad74-fd353323a0b5","order_by":5,"name":"Yu Zhuang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhuang","suffix":""},{"id":277443033,"identity":"42e9673b-49b3-41e9-bb38-175ba16c1779","order_by":6,"name":"Junyi He","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-03-08 08:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4039450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4039450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52542121,"identity":"7d096125-4d18-45dd-a9dd-9a62f1603994","added_by":"auto","created_at":"2024-03-12 17:37:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4035142,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Elevation Map for Guizhou Province showing the location of the study area\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/fbe7bb3548f7864799334f9c.png"},{"id":52542110,"identity":"bfa69c5f-58c2-4f87-a07d-cf2e55bcb3f6","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211334,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly (left inset), daily, and cumulative rainfall data for the study area\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/c52ad933be3f43b242ac805e.png"},{"id":52542114,"identity":"7a568940-b3af-4195-bf9e-f5940cf067d2","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9688458,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Aerial image of the study area showing the landslide boundary and location of key features. (b) Massive crack near the source area (c) Source area for the rock collapses after ZJL. (d) Impact shoveling area along the steep slope. (e) Agricultural lands in the deposit area. (f) Hillslope along the runout path. (g) Image showing the area of depleted deposit of the ZJL. (h) Cracks and fractured mass on the rear slope surface of the ZJL source area.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/9dd5a3b6caa5a8aa8e049317.png"},{"id":52542107,"identity":"90c6a9ff-caf5-4bf8-b9c8-c750d31806c8","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":735365,"visible":true,"origin":"","legend":"\u003cp\u003eEngineering geology profile of ZJL along the section line I-Iˊ\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/f706196a583feabc52ead93b.png"},{"id":52542117,"identity":"e98709ed-04e6-4724-8d55-68c8078c3f1b","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3635839,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Image of the source area showing the joint's orientation. (b) Aerial image showing the joint's orientation in the study area. (c) Stereographic projection analysis of the ZJL source area.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/a25e714c1037454d8b60da30.png"},{"id":52542113,"identity":"5925174d-7ec3-4d72-9c4f-5f2b2e65b111","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7209695,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Surface Model (DSM) showing elevation gradient in the study area.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/faa7417be18031be9dd116db.png"},{"id":52542112,"identity":"393b7d6d-da24-44e6-96ff-96a13d8ef91b","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9023484,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series imagery representing the runout evolution of ZJL\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/60a45b12a60cb414d15fcc7d.png"},{"id":52542111,"identity":"131b58e1-a663-4987-bcb9-11f6dd9396b2","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6289593,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of ZJL based on density and placement condition of the material in the transport area\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/9a00d56ec9e0c62d39f789a1.png"},{"id":52542671,"identity":"b97f22ff-cb09-4ef5-be38-bfa8ddf87762","added_by":"auto","created_at":"2024-03-12 17:45:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":403680,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated velocity with first-order velocity approximation for ZJL\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/4772fdd67e5b49ebfa76d20a.png"},{"id":52542108,"identity":"4880310e-f46e-403d-ab01-29c358bb31e6","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":317816,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series analysis of the runout flow and subsequent erosion along the runout path I-Iˊ\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/f0472359c67bca1da36666c5.png"},{"id":52542115,"identity":"26689afa-b292-4d42-8a39-7285f345bb75","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":336105,"visible":true,"origin":"","legend":"\u003cp\u003eEntrainment and final volume behavior with the moving momentum of ZJL\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/4e42c898d21798b91cdca103.png"},{"id":52542118,"identity":"f2680a3f-ff77-473b-9e5f-5e8ca46d3e15","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":4071125,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum erosion as a result of ZJL\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/2f40e0c4c691fdf655c26947.png"},{"id":52542119,"identity":"1571882e-f096-44cf-9177-71f174b8fee3","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":3748933,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of simulation results with different scenarios. (a) No erosion consideration. (b) Including erosion.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/98d78f3bb1e2d5537c123cc9.png"},{"id":52542116,"identity":"53919eae-cdd3-449f-ba37-7502fe2910c8","added_by":"auto","created_at":"2024-03-12 17:37:46","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":585767,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the landslide evolution process. (a) Initial position. (b) Rock collapse movement process. (c) Rock slide formation process. (d) Propagation and deposition\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/9fce066367917d4abfffa0ff.png"},{"id":81091898,"identity":"665fe5c6-6ee6-40c3-a61b-5b744db8e694","added_by":"auto","created_at":"2025-04-22 07:17:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":63917253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4039450/v1/7d6b9023-5c30-4bf8-9245-939af6be3613.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From rock collapse to slide movement: a case study of the 2004 Zuojiaying Landslide","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe main source of natural slope failure in gently inclined layered structures is typically localized collapse deformation, hence reducing the likelihood of significant geological disasters. Nevertheless, in the mining region, the gently inclined layered slopes are susceptible to collapse and damage due to the combined influence of deformation and rainfall within the goaf. Consequently, the overlying rock mass experiences deformation and loosening, resulting in a significant decrease in slope stability. The landslides generated by mining activities are a prevalent geological hazard, where the formation of joints can give rise to structures exhibiting unfavorable features that divide the rock and soil, significantly impacting the stability of the slope and accelerating the disintegration and fragmentation of the rock and soil during the process of failure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, increased mining activities have been attributed to a notable rise in the occurrence of mining-induced landslides. This, in turn, inflicts severe human casualties and property damage\u0026nbsp;(Dash et al., 2016; Froude and Petley, 2018). Some recent studies on the formation mechanism of mining-induced landslides have been performed by\u0026nbsp;(Li et al., 2023; Tang et al., 2019; Yan et al., 2023; Yang et al., 2022). While the combination of InSAR and offset tracking method has also been used to monitor the mining-induced landslides and collect displacement data\u0026nbsp;(Chen et al., 2021; Wang et al., 2020; Wasowski and Pisano, 2020). Whereas the Discrete Element Method has shown good performance in modeling mining-induced landslides\u0026nbsp;(Li et al., 2017; Li et al., 2022; Li et al., 2012; Zhang et al., 2015; Feng et al., 2012). Currently, existing research has provided evidence indicating that the collision, fragmentation, and disintegration of sliding masses exert a substantial influence on the kinematics and extent of damage caused by landslides\u0026nbsp;(Ruiz et al., 2017; Zhao and Crosta, 2018).\u003c/p\u003e\n\u003cp\u003eFurthermore, the process of impact shoveling and entrainment is an important process observed during a landslide runout movement.\u0026nbsp;These processes can significantly multiply the volume and movement of landslides, hence stimulating their catastrophic nature\u0026nbsp;(Hungr et al., 2005; Mergili et al., 2018; Mergili et al., 2020).\u0026nbsp;The thickness of the erodible layer, material properties, and slope angle affect the mobility of a dry granular landslide\u0026nbsp;(Edwards et al., 2021; Mangeney et al., 2007; Pudasaini and Krautblatter, 2021).\u0026nbsp;McDougall and Hungr, (2005)\u0026nbsp;state that the runout zone consists of bedrock and loose materials such as bed sediments. As a result, the entrained material can amplify the volume of a landslide, which is directly linked to the landslide energy and thus has a significant impact on its mobility\u0026nbsp;(Evans et al., 2009; Liu et al., 2019; Theule et al., 2015; Zhou et al., 2015). For example, as observed in the Yigong rock avalanche and the Wenjia Valley rock avalanche. For a landslide with significant volume change, it is essential to consider the effects of erosion and entrainment while analyzing the sliding process.\u003c/p\u003e\n\u003cp\u003eThe advance of computer-based modeling allows us to analyze landslides and their dynamic processes efficiently and quickly. Numerous computational models have been developed to examine landslides dynamically, with a specific focus on their movement characteristics\u0026nbsp;(Denlinger and Iverson, 2004; Hungr, 1995; Hungr and Evans, 2004; Pudasaini and Hutter, 2003; McDougall and Hungr, 2005; Pastor et al., 2014; Savage and Hutter, 1989; Wang et al., 2004).\u0026nbsp;In recent years, modeling tools have been utilized to analyze the landslide mobility, volume, and runout by including the erosion-entrainment aspect\u0026nbsp;(Liu et al., 2019; Wang et al., 2019; Zhou et al., 2020).\u0026nbsp;Dietrich and Krautblatter (2019)\u0026nbsp;argue that erosion and entrainment are complex processes, and the landslide forces are frequently underestimated, which consequently leads to inaccuracies in predicting entrainment behavior.\u0026nbsp;In their recent study,\u0026nbsp;Pudasaini and Krautblatter (2021)\u0026nbsp;thoroughly examined the mechanisms of erosion and entrainment and developed a quantitative model to measure the mobility shown by erosive landslides.\u003c/p\u003e\n\u003cp\u003eThe current investigation employs the 2004 Zuojiaying landslide as a case study (herein referred to as ZJL), which involved a mass of approximately 36,000 m\u003csup\u003e3\u003c/sup\u003e that rushed downhill. The presence of colluvium on the lower part of the cliff resulted in the transformation of the rock collapse into a rock slide, creating an accumulation region that extended over a distance of more than 250 m. The unique landform formation in Guizhou province results in the occurrence of landslides, particularly rock collapses and rock avalanches, predominantly during periods of moderate to heavy rainfall within the mining-affected region. Such cases are particularly being observed in the central and southwestern areas of China. Typical documented cases in recent decades include the Jiguanling rock collapse in Wulong in 1994, the Jiweishan rock avalanche in 2009, the Nayong rock avalanche in 2017, the Baiwu rock avalanche in 2020, the Jianshanying landslide in 2020, the Baiyan rock avalanche in 2022, and the recurring Kaiyang rockfalls\u0026nbsp;(Chen et al., 2023; Chen et al., 2021; Huang and Li, 2011; Lai et al., 2023; Li et al., 2023; Yan et al., 2023; Zheng et al., 2015). These landslides were observed in regions that exhibit distinct characteristics, including specific geological conditions and the presence of mining activities in the surrounding environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe examination of the relationship between joints and the collapse evolution due to the underlying goaf can have a significant impact on slope stability. The present study analyzes the 2004 Zuojiaying landslide, which occurred in a steep layered structure over a coal mine goaf in Guizhou Province. The research involves thorough fieldwork to establish a geological model using numerical modeling. The study focuses on analyzing the runout characteristics of the Zuojiaying landslide at various stages, as well as examining the erosion and subsequent mobility of the landslide and the respective deposit distribution. We aim to examine the characteristics of a region affected by mining and rainfall events that caused failure and runout transformation of the rock mass, and subsequent deposit accumulation. Furthermore, we attempt to identify the landslide evolution process and runout region that transforms a rock collapse into a rock slide. Investigating the instability and understanding the characteristics of landslide mobility, evolution, and accumulation in wide valley landforms will provide valuable insights for implementing effective disaster prevention measures.\u003c/p\u003e"},{"header":"2. Case Study","content":"\u003cp\u003e\u003cstrong\u003e2.1 Geology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe landslide area belongs to a low to medium elevation mountain area which is high in the northeast and low in the southwest. The geographic location of the study area is 26\u0026deg;42\u0026apos;49\u0026quot; N and 105\u0026deg;14\u0026apos;10\u0026quot; E (Fig. 1). A cliff rock belt with a north-northeast strike and a height of 30-200 meters is formed in the area. The study area resembles a slide-like topography with a steep uphill slope and a gradual downhill slope. The strata in the area, from new to old, are sequentially Quaternary, Triassic, and Permian Stacked strata are described in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Quaternary loose deposit (Q) is the cover layer with loose structure, and developed pores, containing a relatively lower water rate. While the Triassic Feixianguan Formation (T\u003csub\u003e1\u003c/sub\u003ef) bedrock is divided into 3 sections and mostly comprises the vertical cliff from mountain base to top. The terrain slope ranges from 10 \u0026deg; to 50 \u0026deg;, with some areas exceeding 70 \u0026deg;. The Longtan Formation (P\u003csub\u003e3\u003c/sub\u003el) of the Upper Permian System is distributed on the moderate lower part of the slope. The Zuojiaying Village is located in the southeast on the lower part slope, 300 m away from the cliff. The overall rock formation is 330\u0026deg;\u0026ang;\u0026nbsp;22\u0026deg;, which is a gently inclined rock formation. Large structural faults are not developed in the area, and the regional stability is good. Only two sets of joints are developed and distributed in the upper and middle parts of the steep slope (T\u003csub\u003e1\u003c/sub\u003ef\u003csup\u003e2\u003c/sup\u003e) with an occurrence of J\u003csub\u003e1\u003c/sub\u003e ranging from 315\u0026deg;\u0026ang;\u0026nbsp;85\u0026deg;, while the occurrence of J\u003csub\u003e2\u003c/sub\u003e 190\u0026deg;\u0026ang;\u0026nbsp;87\u0026deg;. Due to excessive coal mining, two goaves have been formed in the study area.\u003c/p\u003e\n\u003cp\u003eThe research area experiences a moderate climate, characterized by the absence of extreme temperatures during both hot and cold seasons. The region has a subtropical monsoon climate having an average annual temperature of 13.6 \u0026deg;C and an average annual rainfall of 1243.5 mm (Fig. 2). According to the statistics, the study region had the lowest amount of rainfall in December. Additionally, the cumulative rainfall data from the last 14 days indicates sporadic and light rainfall leading up to the event. The cumulative rainfall in the 14 days before the occurrence of ZJL was roughly 18 mm, while the monthly rainfall reached a maximum of 32 mm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Collapse Process:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant collapse of a columnar rock block took place on December 3, 2004, at Zoujiaying Village, located in the Zongling Township of Guizhou, China. The Zuojiaying collapse process may be categorized as the long-term effect of mining and differential weathering, which caused the slow development of joints and fissures, ultimately resulting in the formation of a fractured rock mass. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to ongoing mining activities and rainfall, a crack formed on the upper part of the cliff that gradually expanded. Prior to failure, the crack had a width of around 0.5 meters. According to the reports, there were many precursor movements, such as sporadic rock falls, in the days leading up to the main rock collapse. The most significant indication was a rock collapse of 100 m3 that occurred 12 hours before the Zoujiaying Rock collapse. The rapid downward movement of the rock mass along the steep slope led to substantial erosion and the entrainment of the loose soil, ultimately resulting in the creation of a rock slide that tragically claimed the lives of 44 individuals. The existence of a small hill at the base of the precipice resulted in the separation of the sliding motion into two distinct paths, namely the left and right branches, as depicted in Fig. 3a. The left branch was more devastating, as it covered a wider distance and inflicted widespread damage upon the village. The ZJL had an approximate total runout of ~450 meters with an average thickness of 3 meters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Landslide Zoning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the field investigation, deposit accumulation, and structural condition of the source area, the landslide area is divided into four zones based on failure (Zone A), runout (Zone B), and deposition (Zone C and D).as shown in Fig. 3a. Zone A include the source area is situated within the T\u003csub\u003e1\u003c/sub\u003ef\u003csup\u003e2\u003c/sup\u003e formation at an elevation of 2200 m (above sea level). The primary composition of the source material was mostly limestone, forming block-shaped rock masses. The slope inclined approximately 80\u0026deg; (Fig. 4). The dimensions of the source region were roughly 50 m in height, and 50 m in breadth, with an average thickness of 5 m. The estimated initial volume of the landslide is 11,000 m\u003csup\u003e3\u003c/sup\u003e. Following the sliding event, a series of fractures appeared near the source area, defining a deforming mass with an outward dipping orientation. These cracks gradually expanded due to mining activities and rainfall, and caused small rock collapses, as depicted in Figs. 3b-c. Thus forming a hazardous rock zone area. Zone B comprises a transport area situated at an elevation of ~2150-1990 m. The sliding mass exerted increased scraping forces as it moved downward on the steep slope of the transport area. The upper portion of Zone B exhibits a comparatively firm stratum in contrast to the loose bed sediment (agricultural land) at the base of the cliff, which serves as an appropriate erodible material for landslide volume expansion (Figs 3d-e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rock collapse transforms into a rock slide of considerable magnitude during the descent phase. After entering the deposition zone, the sliding mass encounters a small hillslope (Fig. 3f) and subsequently splits into two diverging branches, namely the left and right branches. Zone C includes the left branch formed by the southwest deflection of the landslide mass. A significant amount of the sliding mass was deposited in this particular branch and caused damage to Zoujiaying Village. The mass accumulated in the left branch has a volume of 18600 m\u003csup\u003e3\u003c/sup\u003e and traveled along the direction of 213\u0026deg;. The average deposit thickness in the Zuojiaying landslide is 3 m, while the maximum thickness along the main branch is above 3 m.\u0026nbsp;The deposit in this branch has been depleted over time, as shown in Fig. 3g. While, zone D is the right branch formed when the sliding mass is deflected towards the southeast direction. It has an estimated deposit volume of 9400 m\u003csup\u003e3\u003c/sup\u003e and a sliding direction of 120\u0026deg;. The deposits along the right branch had an average thickness of 2-3 m, with a local maximum of 3 m.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Potential Triggers:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the field investigation, we have outlined that the rock mass near the slope surface is fragmented and has become unstable over the period under the action of differential weathering (Fig. 3h). Although the hard upper rock (limestone) and soft lower part (mudstone and siltstone) provides favorable lithological conditions for joints and cracks development due to different strength and softening parameters. The differing weather resistance of the slope lithology and the steep terrain offer suitable settings for rainfall infiltration, leading to the formation and extension of cracks in the rock mass. However, the overall rock strata appear to be stable, and the presence of two sets of joints J1 and J2 have cut the rock stratum in the source area into a block (Figs. 5a-b). Therefore, based on the joints and stratum data, we conducted a stereographic projection analysis as shown in Fig. 5c. The intersecting lines of all combinations indicate that the regulating structural planes are the junction of the slope surface (P) and joint (J1) as well as the slope surface and joint (J2), which are oriented towards the outer side of the slope. The mining activities in the bottom section of the slope have substantially altercated the stress distribution, leading to the deformation and crack formation in the overlying strata. The goaf formation has a profound effect on subsidence and results in surface crack widening. Furthermore, the mining-induced rock deformation in combination with natural external factors such as excessive rainfall will ultimately result in the destabilization and subsequent occurrences of such phenomena in the region (Fig. 3c), thus presenting a substantial hazard.\u003c/p\u003e"},{"header":"3.\tMethod","content":"\u003cp\u003e\u003cstrong\u003e3.1 Digital Model:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe numerical modeling requires an accurate Digital Elevation Model to reproduce the actual event, and our studies use an integrated approach involving field investigation and an Uncrewed Aerial Vehicle (UAV) survey to obtain such a model. The UAV effectively manages a course overlap above 90% and a side overlap surpassing 80% in the context of aerial photography. It further utilizes the captured images to build a three-dimensional point cloud, employing triangulation techniques to construct a highly accurate digital surface model (DSM), as shown in Fig. 6. The DSM is further used to obtain a 3D elevation model for numerical simulation and other applications. While, the sliding body model is constructed by conducting a comparative analysis between the historical terrain, and geomorphology data before and after the occurrence of the landslide event. This modeling technique involves the utilization of Boolean intersection operations. Based on a comprehensive analysis conducted through field investigation, it has been determined that the rock body of the slide source location mostly exhibits the presence of two distinct groups of joints as shown in Fig. 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 RAMMS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Rapid Mass Movement Systems (RAMMS) module Debris flow, aimed at examining the particulate flow behaviors of rock debris has been utilized in this research to model the rapid movement of rock debris. We used an un-channelized debris flow setting that employs a block release approach and only needs the initial depth of the sliding material. This method incorporates a three-dimensional terrain analysis to assess the runout motion from the start, using depth-averaged equations to estimate the flow\u0026apos;s runout height and velocity along the slope. Additionally, the model adopts the Voellmy-fluid flow model for friction, initially introduced by\u0026nbsp;Salm et al., (1990)\u0026nbsp;and\u0026nbsp;Salm (1993), to simulate the frictional characteristics of flowing debris. The model uses the following basic expressions:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere frictional resistance (\u003cem\u003eS\u003c/em\u003e), and includes terms for solid phase resistance \u0026nbsp;, the normal force \u003cem\u003eN\u003c/em\u003e. The density of the material is denoted as \u003cem\u003e\u0026rho;\u003c/em\u003e, the gravitational acceleration as \u003cem\u003eg,\u003c/em\u003e the flow velocity as \u003cem\u003eu\u003c/em\u003e, and \u0026xi; signifies the resistance due to turbulent flow. The normal force N can be further defined as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn eq. (2), h denotes the height of the flow, and g is the acceleration due to gravity, and slope angle . This software incorporates a friction model that is meticulously calibrated, as necessary, to yield precise outcomes, supported by extensively documented case studies. The Debris flow module is equipped with an erosion feature, making it capable of modeling sediment erosion within the entrainment zone. This feature makes it suitable for application in cases with high sediment volume as a result of entrainment (Frank et al., 2015). Field studies have highlighted the correlation between rapid erosion rates and increased flow intensity (Berger et al., 2011), which in turn, elevates the erosion depth, a finding supported by research from Sch\u0026uuml;rch et al., (2011) and Frank et al., (2015). However, it\u0026apos;s noted that sediment erosion may not always accompany minor debris flows. Therefore, the RAMMS calculates the basal shear stress independently within each grid cell to determine the maximum possible erosion depth. The parameters used in RAMMS are summarized in Table 2, with values obtained through back analysis and literature review, ensuring the model\u0026apos;s capability to accurately reproduce the landslide event.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eFollowing the sudden collapse and disintegration of the steeply positioned rock mass, the collapse quickly transitioned into a rapid downhill sliding movement, traveling about 430 m in 105 seconds. Figure 7 illustrates the evolution of the Zuojiaying landslide runout movement, showing the initiation together with the progressive deposition of sliding mass during the runout duration. The sliding mass traveled a maximum distance of 430 m with a maximum velocity of approximately 55.95 m/s. The total runout duration of the landslide from initiation to final deposition is 105 seconds. The entire runout process of sliding mass can be divided into four stages. Stage I initiates from t= 5s and ends when the sliding material reaches the slope break (t= 15 s), as shown in Figs. 7a-c. Stage II lasts between 15 to 25 seconds, and during this stage, the runout propagates, and the front end of the sliding mass gradually expands and scrapes material from the surface (Figs. 7d-e). The rock mass during this stage travels on a gentle slope and encounters a ridge that divides the material into two branches (Figs. 7f-g). In stage III (25-75 s), the sliding mass deflects and starts moving in the southwest and southeast direction (Figs. 7h-k). The runout evolution during this phase indicates that during the subsequent accumulation process of the sliding mass in the left and right branches. In the last stage (75-105 s), the sliding mass continues to accumulate in the left and right branches (Fig. 7l). The final volume increased to 36,000 cubic meters with a maximum deposit thickness of 7 m.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.1 Sensitivity Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSensitivity analysis is performed to investigate the impact of erosion on uncertainty in the numerical modeling of landslides. The role of slope and surface erosion can either impede or accelerate the landslide motion. The findings of the sensitivity analysis demonstrate that\u0026nbsp;altering erosion parameters to match various on-site circumstances has a notable impact on runout distance and deposit depth (refer to Fig. 8).\u0026nbsp;High landslide mobility can cause more scraping on the slope surface, especially when combined with certain substrate conditions in the transport region. The runout distance is notably affected if the eroded material density and placement condition (such as normal or wet/loose) is changed. The sensitivity analysis demonstrates that erosion is a crucial factor in accurately reconstructing the landslide using numerical modeling. According to the simulated results of ZJL runout and deposition,\u0026nbsp;using a denser material in the transport area can lead to a roughly 200% rise in the entrained volume. While,\u0026nbsp;a denser material under loose or wet placed conditions can triple the entrained volume, leading to a considerable increase in deposit thickness and runout.\u0026nbsp;The verification of modeling results for the landslide runout simulation was conducted by comparative analysis of the simulated results, field investigation, and incident report as shown in Table 3.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.2 Simulated\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVelocity\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFigure 9 depicts the velocity evolution during the progression of the landslide. The results obtained from the dynamic study conducted using RAMMS indicate the highest value along the steep slope, reaching its maximum value of 36.5 m/s. Throughout the collapse movement (0-180 m phase), the maximum velocity ranged from 30 to 36 m/s, gradually decreasing as the mass descended into the valley. Voellmy (1955) states that the frictional resistance on the sliding surface base remains constant during the initial increase. The energy and velocity of the landslide gradually reduced due to a transition from a steep to a more gradual slope, as well as due to the impacts shoveling process. As the materials slid further downhill, their velocity showed a gradual fluctuation, as a result of the amplified entrainment effect indicating a transition from collapse movement to sliding movement. Furthermore, after a deflection from the hillslope, the sliding mass velocity dropped. The velocity continued to experience significant fluctuations and approached zero as it reached the far end of the deposition zone. Overall, the velocity simulated by RAMMS shows a gradual decrease after the distance of 180 m, implying that as the erosion and entrainment effect amplified, the velocity decreased.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo validate the simulated velocity of the sliding mass, the change in landslide velocity along the runout path was determined using Scheidegger (1973) first-order estimates. The sliding velocity is estimated using the expression \u003cimg src=\"data:image/png;base64,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\" style=\"width: 125px;\"\u003e\u0026nbsp;where H represents the vertical distance from the highest point of the source area, L denotes the horizontal distance, \u0026mu; characterizes the constant sliding resistance, and g represents gravity. Scheidegger\u0026rsquo;s first-order velocity estimation method mostly provides a rough estimate by utilizing a constant coefficient of sliding friction (\u0026mu;) to simplify the assessment. The maximum vertical displacement and horizontal runout are 340 and 450 meters, respectively. The value 0.756 denotes the coefficient of sliding friction, \u0026mu;. The velocity estimation along the main sliding direction of the landslide runout is illustrated in Figure 9, while Fig. 3a shows the spatial coordinates of locations A to D. Both simulated and estimated velocities are higher along the steep slope due to the increased vertical drop across a limited horizontal distance. The estimated velocity shows a gradual decrease as vertical descent decreases with the increase in horizontal distance. As a result, the calculated velocity falls until it reaches zero. The estimated maximum velocity at point A is 37 m/s, higher than the simulated value of 34 m/s due to the high initial velocities induced by the collapse on steep slopes. The maximum simulated velocity is at Point B (36.5 m/s), and the decrease onwards is most probably from the high erosion-entrainment process and landslide transformation. The simulated and estimated velocities at Point C (before deflection from hillslope) are 24 m/s and 39 m/s, respectively. Whereas both estimated and simulated velocities show an agreement near the end of runout (Point D). Nevertheless, the overall velocity trend simulated shows good agreement with the data derived from Scheidegger (1973). The simulated behavior shows a similar velocity trend along the steep slope, however, after entering the valley, the simulated velocity shows a decreasing trend due to the entrainment and landslide transformation mechanism. Moreover, the simulated velocity shows an abrupt decrease in velocity after deflection from the hillslope, corresponding to the field observation. While the first-order velocity approximation provides a general reference, thus the estimated velocity does not account for such change in behavior, which explains the difference in results.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.3 Simulated erosion and flow depth\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFigure 10 displays the outcomes of the simulation for the dynamic flow depth of the ZJL, as well as the erosion depth of the basal surface at various time intervals. The varying thicknesses of the landslide flow height and erosion of the base surface are represented using distinct colors. The initial landslide in the source region has a maximum depth of 5 meters. At first, the landslide gained speed because of the sharp angle of the slope after its collapse. During the descent of the collapsed mass in the steep stretch of the runout, the simulated mass achieved a peak velocity of 55 m/s. The flow height was about 24 m, and the depth of erosion reached 1 m (Fig. 10a). The simulation demonstrates that the erosion level of the basal surface is most pronounced in the region affected by the high-speed impact movement of the collapsing rock mass, resulting in an impact-shovel zone. At this stage, the rock mass experiences a peak flow and erosion depth of 4.5 m and 2.4 m, respectively (Fig. 10b). The bottom section of the cliff features a mild slope consisting of loose bed sediments. The collapsed rock mass decelerates\u0026nbsp;and transforms into a sliding mass. In this phase, the erosion depth is consistent around 2 meters, whereas the flow height is greater than 5 meters (Fig. 10c). As the distance increases, the landslide mass experiences a decrease in flow and erosion depth at the front edge along the main sliding direction. Both the highest measured flow and erosion depth are recorded at the rear section of the sliding mass, measuring 5.5 m and 2.3 m, respectively (Fig. 10d). At t= 75 s, the sliding mass is observed moving towards the far end of the deposit area, leading to an augmentation in the thickness of the sliding mass. Specifically, the front edge experiences an increase to 3 meters, while the middle section rises to 4 meters (as illustrated in Figure 10e). The maximum erosion in the middle section has been increased to 1.8 meters. The erosion of the basal surface in the deposit region diminishes as the movement velocity decreases due to friction resistance, especially at the far end of the accumulation area (Fig. 10f). The overall temporal changes in the volume and moving momentum of the landslide body are presented in Fig.\u0026nbsp;11. The runout process is separated into four stages based on the position of the sliding mass along the runout path. The initial phase occurs during the time interval of 0\u0026ndash;15 seconds, during which the entire collapse mass moves along the steep slope surface. The rock mass travels uninterrupted in a downhill trajectory and the\u0026nbsp;energy possessed by the collapsed mass is maximum. In this stage, the collapsed material accelerates, erodes, and entrains continually, resulting in a gradual increase in\u0026nbsp;landslide volume. The second stage occurs over\u0026nbsp;15-25 seconds\u0026nbsp;when the collapsed mass enters the valley and transitions into\u0026nbsp;a sliding mass while traveling on the gentle slope surface of the valley. As a result, more energy is dissipated during the erosion and entrainment process. Since the collapse mass is in a more gentle slope area, more potential energy is converted into kinetic energy, hence moving momentum shows a gradual decrease, although the erosion and entrainment process is continuous. The third stage occurs throughout the time range of 25\u0026ndash;75 seconds. Most\u0026nbsp;of the sliding mass has entered the valley and is divided into two branches. The sliding volumes increase and the sliding mass undergoes steady deceleration, and eventually, its energy diminishes due to the continuous erosion process and the influence of frictional resistance. After\u0026nbsp;75 seconds, the entrainment effect reduces and the moving momentum reaches a minimum value, thus resulting in a slow gradual accumulation of the sliding mass.\u003c/p\u003e\n\u003cp\u003eA field investigation was conducted to obtain the geological characteristics of Zuojiaying Village.\u0026nbsp;However, there is a slight difference between the width of the simulated flow region and the actual landslide. The calculated volume of the accumulated mass is about 36,000 m\u003csup\u003e3\u003c/sup\u003e, which closely matches the estimated volume. The calculated maximum depth of erosion on the bed surface is around 2.4 meters, moreover, the erosion is concentrated in the slide transformation zone, as illustrated in Fig. 12. The landslide movement is assessed by incorporating the erosion and entrainment effect to investigate the impact of entrainment on the travel distance, velocity, and volume of a sliding mass. Overall, the results demonstrate a strong correlation between the simulated deposit area, volume, velocity, and erosion extent of the landslide and the data obtained from the field investigation.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eLandslides are one of the major geological disasters in China, besides human factors, such as underground mining,\u003cbr\u003e\u0026nbsp;rainfall is also one of the most common causes. In the rainy season, the rainwater seeps into the rock\u0026ndash;soil mass, increases its quality and the downslide strength, and further softens, and erodes the rock\u0026ndash;soil mass to reduce its shear strength. Meanwhile, the continuous rainfall leads to the alternation of dry and wet rock\u0026ndash;soil mass, which will cause a large number of cracks, thus accelerating the landslide initiation. The rainfall also catalyzes saturation of the soil in the transport and deposition areas, which plays a crucial role in landslide volume expansion and mobility. The mechanism of rock movement transformation into a sliding mass, especially in mining-affected regions, is complex and is controlled by various factors, including (1) high and steep terrain, (2) good surface slide conditions, (3) weak interlayers, vertical fissures, and/or dissolution fissures, (4) the subsidence and deformation from goaf, and (5) extensive rainfall\u0026nbsp;(Grenon et al., 2017), (6) easily erodible soil in the transport and depositional area. The Zuojiaying landslide almost had all of the above conditions. Based on the results of the field survey, UAV photography, and RAMMS simulation, the evolution of the Zuojiaying landslide can be described as the emergence of joints and cracks due to rainfall and mining leading to the development of a fractured columnar block. In the long-term geological evolution process, two steeply dipping orthogonal structural joints were formed in the mountain. After long-term moderate precipitation, the interface of the structural joints became a good seepage channel. As a result, the strength of the rock mass at the top of the mountain continuously deteriorated, and these structural planes became the potential controlling boundaries of the landslide.\u0026nbsp;Previously, some researchers have shown that for completely fractured sliding masses, the particles at the bottom are violently mixed due to frequent collisions, which can serve as a lubricating layer to reduce the overall effective friction of landslide debris flow and promote its remote movement. This article studies the process of landslide movement and runout transformation of the sliding body.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e5.1 Role of Entrainment\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFigures\u0026nbsp;7\u0026nbsp;and 11 display the simulated motion of the sliding mass. The collapse, which began with a volume of 11,000 m\u003csup\u003e3\u003c/sup\u003e, experienced a sudden collapse and traveled downhill by a distance of 180 m. It subsequently eroded the underlying slope surface as a result of impact scouring. Consequently, the collapsed material\u0026apos;s volume\u0026nbsp;increased to 15,000 m\u003csup\u003e3\u003c/sup\u003e. After entering the valley the slope became gradual, and substantial erosion of the bed sediments occurred, where the eroded depth was constantly above 2 meters, with lateral expansion of sliding material. Upon its descent along the valley, the sliding mass was redirected by a hillslope located at an approximate distance of 250 m, as a result, the landslide mass was divided into two branches. The velocity reduced\u0026nbsp;as the energy dissipated due to the impact deflection from the hillslope and continuous material erosion. After deflection, the main mass of the landslide traveled towards the Zuojiaying Village in the southwest direction and deposited along the path. At t=75s, the landslide volume expanded up to 31,000 cubic meters. In the depositional stage (75s-105s), the erosion rate gradually dissipated and became zero as the runout distance reached its limit. The combined amount of simulated entrainment during this stage achieved an approximate volume of 5000 m\u003csup\u003e3\u003c/sup\u003e, the volume change\u0026nbsp;is smaller, yet it is 50% of its original volume (refer to Fig. 11).\u003c/p\u003e\n\u003cp\u003eThe impact of entrainment on the movement of landslides is also investigated, and depositional comparison is provided in Fig. 13. Erosion and entrainment have a crucial role in determining the distance traveled by a landslide, the runout length, runout duration, and the depth of the deposit. For ZJL, the maximum deposit thickness and the runout distance without entrainment can reach up to 4 meters and 380 meters, respectively. The Fig. 13 illustrates the changes in the landslide velocity and momentum as it moves throughout the runout phase. These figures are used to compare the mobility of the two different scenarios. In general, the simulation results show comparable characteristics for both entrainment and without entrainment. The velocity profiles for both situations exhibit significant similarity, with the highest velocity observed around the edge of the rupture surface. However, in the scenario without entrainment (Fig. 13a), the velocity is somewhat greater due to the lack of energy dissipation in the erosion entrainment process. Nevertheless, the affected region is reduced due to the consistent volume of the mass that collapsed. In addition, according to the principle of momentum conservation, the occurrence of entrainment in ZJL results in a further decrease in energy as the movement of the rock changes into a sliding motion by carrying along a significant amount of loose substrate materials that produce a greater deposit depth. It is important to acknowledge that a higher level of entrainment will result in increased disparities in the deposit distribution (refer to Figure 13b), overall velocity, and consequently the momentum of the landslide.\u0026nbsp;Such volume expansion leads to an extension of the impacted zone, posing a threat to the population and infrastructure in hilly areas.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e5.2 Landslide mobility and evolution process\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eBased on our analysis, we infer that\u0026nbsp;entrainment is a continuous, influencing, and notable process of the ZJL runout. The landslide body slides on the steep slope and has a forceful impact on the substrate material (bedrock). While the entrained material in the valley area is predominantly colluvium (bed sediments composed of silty clay with gravel). Therefore, the sliding mass is composed of both bedrock and the sediments on the sliding surface.\u0026nbsp;Thus, understanding the evolution of entrainment is necessary, for analyzing the effect of entrainment on the landslide.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLandslides commonly accompany high-speed movement, and the equivalent friction coefficient (H/L), the ratio of vertical displacement to horizontal displacement is used to determine the mobility. A smaller value indicates greater energy and travel mobility. For Zuojiaying, the incident angle along the main branch is 37\u0026deg;, which indicates a comparatively higher mobility. With a relative elevation difference of 340 m, the Zuojiaying landslide traveled a horizontal distance of 450 m. However, the runout and deposit characteristics of the Zuojiaying landslide show multiple signs that its initiation and movement speed reach high speed during the initial phase of the runout, which is closely related to the terrain conditions. The evolution model can be divided into four phases presented as follows (Fig. 14):\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cstrong\u003eInitiation:\u003c/strong\u003e In the first stage (initiation), the rock block fails and undergoes a rapid collapse. The block disintegrates into relatively smaller fragments and is subjected to volumetric expansion. At this stage, the entrainment-induced change in the velocity and volume of the landslide is small, and the high initial runout velocity is mostly due to the steep slope. Fig. 14a shows the rock block at rest position.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRock Collapse Movement:\u003c/strong\u003e The collapsed rock mass travels a distance of 180 m from initiation along the steep slope (Impact Shovel Zone), and its characteristics change regarding velocity and volume. A considerable volume of substrate material was scraped in the impact shovel zone under the action of impact and scour, and mixed with the rock mass during the collapse process, which substantially affected the movement characteristics of the landslide. During this stage, the basal shear stress caused by the landslide on the eroded bed surface increases due to the process of impact scouring\u0026nbsp;(McDougall and Hungr, 2005), indicating that the landslide\u0026apos;s erosion capability is approaching its maximum. The basal frictional angle is the key variable that affects the movement of the landslide and is calculated by the ratio of the vertical descent H to the horizontal extent L. Since vertical drop varies throughout the runout, therefore, greater velocities are recorded on the steeper while moderate to low velocities exist on the gentle slope section of the runout path. As a result, the speed and volume of the landslide experienced a substantial increase due to the combined effects of continued crushing and transportation of substrate material, as well as movement along the steep slope (Fig. 14b).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRock Slide Transformation:\u003c/strong\u003e During this phase, the rock mass collides with the slope break and transforms into a sliding mass that traverses along the gentle valley region ranging from 180 meters to 350 meters. The entrainment material at this stage mostly originates from the erosion of the valley floor caused by the forceful impact of the rock mass, leading to an increase in the landslide volume and runout distance. Plowing is the main process at this stage that causes erosion and displacement of material at the base of the steep hill, which is entrained by the sliding mass\u0026nbsp;(McDougall and Hungr, 2005). The density and the material condition on the sliding surface influence the runout. In ZJL, the area present at the bottom of the cliff and the deposition area was predominantly agricultural land, hence the loose soil might contain water within, which could easily provide a saturated or partially saturated condition due to the farming process and sporadic rainfall in the study area. According to\u0026nbsp;Sassa, (1998)\u0026nbsp;and\u0026nbsp;Xu et al., (2012), such soils can be subject to liquefaction under undrained loading conditions, which allows the landslide to produce a long-runout motion. Once the failed rock mass dropped from the source area and slid downwards with high energy, the loose bed sediments in the valley (undrained conditions) were easily crushed, plowed, and entrained by the rock mass causing liquefaction of the shear band under the sliding rock mass. Thus reducing the ground resistance of the sliding mass and resulting in the long-runout movement.\u0026nbsp;The landslide displaced the upper layer of loose sediment down the slope, removing the surface material to a comparatively significant depth. The slope gradually became less steep as the landslide runout increases, resulting in an energy drop leading to a reduction in velocity. The erosion and entrainment depth was also decreased, however, the landslide volume further increased\u0026nbsp;(Fig.\u0026nbsp;14b).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePropagation and Deposition:\u003c/strong\u003e After traveling a distance over the valley floor, the landslide propagation experiences a substantial dampening effect (Kean et al., 2015), resulting in reduced scraping of surface materials and the initiation of landslide deposition. Hence, the depth of erosion and erosion rate decreases as the runout increases and the landslide deposition starts. In the case of ZJL, the landslide mass was deflected by a small hillslope located at a distance of 250 m from the release point. The landslide was split into two branches and propagated further, causing a rapid drop in velocity due to increased frictional resistance and ultimately deposited (Fig. 14c).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe present study thoroughly examines the complex dynamics of the 2004 Zuojiaying landslide, highlighting the confluence of mining activities, rainfall events, and the unique\u0026nbsp;geomorphological\u0026nbsp;characteristics of the region\u0026nbsp;as crucial elements contributing to its occurrence. We extensively analyzed the initiation, collapse mechanism, entrainment process, and their subsequent impact through a robust combination of field investigations, unmanned aerial vehicle (UAV) surveys, and RAMMS to map the landslide transformation, volume increase, propagation, and runout behavior.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur work emphasizes the important role of erosion and mass entrainment in transforming rock collapse into a rock slide,\u0026nbsp;increasing the\u0026nbsp;landslide mobility and magnitude. These factors have been shown to have considerable influence on the overall risk profile of such geohazards. Furthermore,\u0026nbsp;the sensitivity study for ZJL reveals that erosion has a substantial impact on landslide modeling, specifically influencing the runout distance and deposit depth. These effects vary depending on the material density and condition.\u003c/p\u003e\n\u003cp\u003eThe complex interactions influencing the landslide movement and evolution\u0026nbsp;illustrate the crucial impact of entrainment on altering the landslide\u0026apos;s volume and behavior as it moves downhill. We utilized an equivalent friction coefficient and performed a thorough phase-wise analysis from initiation to rock collapse movement and subsequent transformation and deposition. This study provides a complete understanding of the mobility and evolution of landslides.\u003c/p\u003e\n\u003cp\u003eMoreover, this study underlines the potential limitations of the study, including the challenges in accurately quantifying the extent of material entrainment and the need for more comprehensive models that can better simulate the complex interactions between geological and hydrological factors in landslide-prone areas. By providing a holistic view of the landslide phenomenon, our study promotes the need for an integrated risk assessment approach, combining geological analysis, accurate numerical simulations, and advanced remote sensing techniques, to foster a broad understanding of such landslide hazards in regions with similar geological setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerger C, McArdell BW, Schlunegger F (2011) Direct measurement of channel erosion by debris flows, Illgraben, Switzerland. Journal of Geophysical Research: Earth Surface 116(F1).\u003c/li\u003e\n\u003cli\u003eChen H, Zhao C, Li B, Gao Y, Chen L, Liu D (2023) Monitoring spatiotemporal evolution of Kaiyang landslides induced by phosphate mining using distributed scatterers InSAR technique. Landslides 20(3):695-706. https://doi.org/10.1007/s10346-022-01986-5\u003c/li\u003e\n\u003cli\u003eChen L, Zhao C, Li B, He K, Ren C, Liu X, Liu D (2021. Deformation monitoring and failure mode research of mining-induced Jianshanying landslide in karst mountain area, China with ALOS/PALSAR-2 images. Landslides 18(8):2739-2750. https://doi.org/10.1007/s10346-021-01678-6\u003c/li\u003e\n\u003cli\u003eDash AK, Bhattacharjee RM, Paul PS (2016) Lessons learnt from Indian inundation disasters: An analysis of case studies. International Journal of Disaster Risk Reduction 20:93-102. https://doi.org/https://doi.org/10.1016/j.ijdrr.2016.10.013\u003c/li\u003e\n\u003cli\u003eDenlinger RP, Iverson RM (2004) Granular avalanches across irregular three-dimensional terrain: 1. Theory and computation. Journal of Geophysical Research: Earth Surface 109(F1). https://doi.org/https://doi.org/10.1029/2003JF000085\u003c/li\u003e\n\u003cli\u003eDietrich A, Krautblatter M (2019) Deciphering controls for debris-flow erosion derived from a LiDAR-recorded extreme event and a calibrated numerical model (Ro\u0026szlig;bichelbach, Germany): Controls for debris-flow erosion. Earth Surface Processes and Landforms 44. https://doi.org/10.1002/esp.4578\u003c/li\u003e\n\u003cli\u003eEdwards A, Viroulet S, Johnson C, and Gray J (2021) Erosion-deposition dynamics and long distance propagation of granular avalanches. Journal of Fluid Mechanics 915:A9. https://doi.org/10.1017/jfm.2021.34\u003c/li\u003e\n\u003cli\u003eEvans SG, Bishop NF, Fidel Smoll L, Valderrama Murillo P, Delaney KB, Oliver-Smith A (2009) A re-examination of the mechanism and human impact of catastrophic mass flows originating on Nevado Huascar\u0026aacute;n, Cordillera Blanca, Peru in 1962 and 1970. 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Canadian Geotechnical Journal, 42:1437-1448.\u003c/li\u003e\n\u003cli\u003eMergili M, Emmer A, Juřicov\u0026aacute; A, Cochachin A, Fischer JT, Huggel C, and Pudasaini SP (2018) How well can we simulate complex hydro-geomorphic process chains? The 2012 multi-lake outburst flood in the Santa Cruz Valley (Cordillera Blanca, Per\u0026uacute;). Earth Surface Process and Landform 43(7):1373-1389. https://doi.org/10.1002/esp.4318\u003c/li\u003e\n\u003cli\u003eMergili M, Jaboyedoff M, Pullarello J, Pudasaini SP (2020) Back calculation of the 2017 Piz Cengalo\u0026ndash;Bondo landslide cascade with r.avaflow: what we can do and what we can learn. Natural Hazards and Earth System Science 20(2):505-520. https://doi.org/10.5194/nhess-20-505-2020\u003c/li\u003e\n\u003cli\u003ePastor M, Blanc T, Haddad Akni B, Petrone S, Sanchez M, Drempetic V, Issler D, Crosta G, Cascini L, Sorbino G, Cuomo S (2014) Application of a SPH depth-integrated model to landslide run-out analysis. 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Journal of Geophysical Research: Solid Earth, 123(11):9914-9932. https://doi.org/https://doi.org/10.1029/2018JB016378\u003c/li\u003e\n\u003cli\u003eZhen F, Yueping Y, Bin L, Ming Z (2012) Mechanism analysis of apparent dip landslide of Jiweishan in Wulong,Chongqing. Rock and Soil Mechanics 33:2704-2712.\u003c/li\u003e\n\u003cli\u003eZheng D, Frost JD, Huang RQ, Liu FZ (2015) Failure process and modes of rockfall induced by underground mining: A case study of Kaiyang Phosphorite Mine rockfalls. Engineering Geology 197:145-157. https://doi.org/https://doi.org/10.1016/j.enggeo.2015.08.011\u003c/li\u003e\n\u003cli\u003eZhou JW, Cui P, Hao MH (2015) Comprehensive analyses of the initiation and entrainment processes of the 2000 Yigong catastrophic landslide in Tibet, China. Landslides 13. https://doi.org/10.1007/s10346-014-0553-2\u003c/li\u003e\n\u003cli\u003eZhou J, Xu H, Yang H, Liu X (2020). Effects of entrainment phenomenon on the rapid and long-runout movement of landslides in Wenjia gully, Sichuan, China. Environmental Earth Sciences 79(16):396. https://doi.org/10.1007/s12665-020-09141-w\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Lithological description of the study area\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eFormation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003eLayer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003eThickness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eQuaternary loose deposit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Q\u003csub\u003e4\u003c/sub\u003epl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003eIt is present in the form of a loose cover layer on top and base of the mountain.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTriassic Feixianguan Formation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003ef\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e~30-50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003eThe composition consists of mudstone, sandstone, and argillaceous siltstone, with traces of weathered and loose rock mass forming the quaternary loose deposits. The weathered rock mass is found on the mountain slope. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.38961038961039%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.883116883116884%\" valign=\"top\"\u003e\n \u003cp\u003e50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.72727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eIt is composed of thin to medium-thick bedded grayish limestone, situated in the steep section of the mountain. The source area is located in this formation.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.38961038961039%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003ef\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.883116883116884%\" valign=\"top\"\u003e\n \u003cp\u003e~150 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.72727272727273%\" valign=\"top\"\u003e\n \u003cp\u003ePredominantly of lithic material composed of gray medium-thick mudstone, siltstone, and argillaceous siltstone with silty mudstone interlayers. It is found in the steeply inclined region and the upper section of the base of the slope.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eLongtan Formation Upper Permian System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e3\u003c/sub\u003el\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003eGray medium thick-bedded mudstone with sandstone interlayers distributed along the base and lower part of the slope. The coal seams are present in this layer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e RAMMS modeling input parameters\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"73%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSymbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Description\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.0204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eV\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e11,000 m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRelease volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eV\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e36,000 m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFinal volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSource Area Height\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSource Area Width\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSource Area Thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2500 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSliding mass density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026mu;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFriction Co-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026xi;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1000 m/s\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTurbulence Co-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Comparison of field investigation and simulated results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.54901960784314%\" valign=\"top\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.803921568627452%\" valign=\"top\"\u003e\n \u003cp\u003eLandslide \u0026nbsp;Runout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" valign=\"top\"\u003e\n \u003cp\u003eLandslide \u0026nbsp;Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.568627450980394%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum Runout \u0026nbsp;Velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Branch\u003c/p\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.745098039215685%\" valign=\"top\"\u003e\n \u003cp\u003eRight Branch\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.54901960784314%\" valign=\"top\"\u003e\n \u003cp\u003eField Investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.803921568627452%\" valign=\"top\"\u003e\n \u003cp\u003e450 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" valign=\"top\"\u003e\n \u003cp\u003e~120 s (reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.568627450980394%\" valign=\"top\"\u003e\n \u003cp\u003e43 m/s\u003c/p\u003e\n \u003cp\u003e(Scheidegger, 1973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" valign=\"top\"\u003e\n \u003cp\u003e200 m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.745098039215685%\" valign=\"top\"\u003e\n \u003cp\u003e100 m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.54901960784314%\" valign=\"top\"\u003e\n \u003cp\u003eNumerical Simulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.803921568627452%\" valign=\"top\"\u003e\n \u003cp\u003e~430 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" valign=\"top\"\u003e\n \u003cp\u003e105 s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.568627450980394%\" valign=\"top\"\u003e\n \u003cp\u003e36.5 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" valign=\"top\"\u003e\n \u003cp\u003e180 m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.745098039215685%\" valign=\"top\"\u003e\n \u003cp\u003e~90 m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zuojiaying rock collapse, landslide transformation, runout modeling, dynamic characteristics, entrainment effect, field investigation","lastPublishedDoi":"10.21203/rs.3.rs-4039450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4039450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The mining process instigates the development of subsurface goaf, resulting in surface deformation and subsidence. The deformation and failure characteristics of the landslides in mining regions present a challenge to the detection and analysis of the initiation and progression mechanisms. There has been a significant focus on comprehending the complex nature and destruction associated with such geohazards, which are becoming more common and typical in the southwestern region of China. The present study showcases a compelling example of the 2004 Zuojiaying landslide in Guizhou, China, where rainfall and long-term mining activity triggered a rock collapse that transformed into a sliding event, causing 44 fatalities. A thorough field and aerial survey are conducted for the rock collapse-transformed sliding mass, further supplemented with a dynamic modeling approach to simulate the runout, entrainment, and accumulation processes of the landslide. Our work presents a comprehensive analysis and discussion of the geological condition, potential triggers, collapse mechanism, erosion characteristics, and the 3D runout evolution of the Zuojiaying landslide. Furthermore, the present study explores the significant influence of entrainment and erosion on landslide volume, mobility, and deposition, particularly in the transition from rock collapse to sliding. We have identified and outlined the stages of runout progression and landslide transformation by analyzing the entrainment over the entire runout. This study aims to highlight the significance of developing a thorough understanding of such landslide hazards in areas with comparable geological characteristics to improve geohazard management.","manuscriptTitle":"From rock collapse to slide movement: a case study of the 2004 Zuojiaying Landslide","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 17:37:41","doi":"10.21203/rs.3.rs-4039450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75ac2c7d-93e0-4633-a979-a02d0a561741","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-22T07:09:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-12 17:37:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4039450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4039450","identity":"rs-4039450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}