Quantitative Assessment of Multi-Scenario High-Elevation and Long-Runout Debris Flow Hazard and Risk: A Case Study of Cuojiu Valley, South-eastern Qinghai-Tibet Plateau

Quantitative Assessment of Multi-Scenario High-Elevation and Long-Runout Debris Flow Hazard and Risk: A Case Study of Cuojiu Valley, South-eastern Qinghai-Tibet Plateau | 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 Quantitative Assessment of Multi-Scenario High-Elevation and Long-Runout Debris Flow Hazard and Risk: A Case Study of Cuojiu Valley, South-eastern Qinghai-Tibet Plateau Tanfang ZHU, Tao WANG, Shuai ZHANG, Peng XIN, Xinfu XING This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4324036/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract In recent years, the impacts of climate change have significantly increased the susceptibility southeastern Tibet to various geological hazards, characterized by high-elevation and long-runout geological events. These hazards pose significant long-term implications for the development and maintenance of critical railways in the vicinity. Consequently, the implementation of an effective quantitative assessment method for geological hazards becomes paramount for disaster prevention and mitigation. This study introduces a novel method integrating remote sensing, drone-based oblique photogrammetry, and onsite field investigation for effectively identifying geological hazards, and presents a risk quantification technique tailored for high mountain regions under varied rainfall possibilities. By applying this innovative approach, a comprehensive investigation was conducted to assess the characteristics and impacts of rainfall-induced debris flow in the Cuojiu Valley, southeastern Tibet, under varying rainfall probabilities. The study examines the effects of these debris flow on the regional railway, based on the maximum accumulated thickness and the highest affected height triggered by rainfall. The analysis revealed that severe rainfall events act as triggers for these hazardous occurrences. Importantly, the study highlights that the safety of critical railways in the region is compromised by the identified debris flow risk in the Cuojiu Valley during extreme rainfall events. This study's novelty lies in identifying the distribution of geological hazard sources through the proposed method and conducting a quantitative assessment of multi-scenario high-elevation and long-runout debris flows in the Cuojiu Valley. This provides valuable insights for preventing geological hazards in high-elevation valleys. south-eastern Qinghai-Tibet plateau high-elevation and long-runout debris flow risk assessment FLO-2D 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 1 Introduction In recent years, climate change has had a profound impact on the stability of mountainous regions, resulting in a noticeable increase in both the frequency and magnitude of geological hazards (Clague 2000 ; Scherler et al. 2011 ; Kraaijenbrink et al. 2017 ). The assessment of geological hazard risk is a fundamental task in the mitigation and prevention of regional hazards (Ward et al. 2020 ; Tan et al. 2021 ). Such assessments play a pivotal role in understanding the potential implications of geological hazards and serve as the basis for the implementation of effective mitigation strategies. The southeastern region of Tibet is characterized by intricate topography, active tectonic movements, intense ground surface erosion, and abundant precipitation (Gong et al. 2022 ; Li et al. 2022 ; Yao et al. 2022 ). Statistical data from the past 70 years indicates that this region has experienced multiple significant geological hazards, particularly in areas such as the Yigong River, Palongzangbu River, and Yarlung Tsangpo River (Shang et al. 2003 ; Chen et al. 2016 ; Zhang et al. 2022 ). These catastrophic geological events, occurring at high elevations, have resulted in substantial economic losses and human casualties. Previous studies on geological hazard risk assessment in Tibet (Richardson and Reynolds 2000 ; Lu and Cai 2019 ) have demonstrated notable progress and have proven beneficial for engineering purposes. However, the rugged terrain, challenging transportation, and dense vegetation in the southeastern region of Tibet present challenges for traditional manual investigation methods in quantitatively analyzing and efficiently mitigating geological disasters. Furthermore, the comprehensive identification of ice and rock mass movements in high altitude mountainous regions through satellite remote sensing images represents a significant research challenge (Jiang et al. 2022 ). Consequently, the task of identifying potential instabilities in geological hazard sources, characterizing their spatial distribution, and quantitatively assessing the reservoir of these sources remains intricate in high-altitude mountainous locales. In particular, the significant railway project traversing the Cuojiu Valley in southeastern Tibet has the potential to have long-term implications due to geological hazards. These hazards can be triggered by a number of factors including ice-dammed lake breaches, ice and snow melt, heavy rainfall, and rockfalls (Li et al. 2022 ; Peng et al. 2022 ). Conventional hazard assessment methods are inadequate for addressing the complex construction demands of the railway over the long term. Consequently, there is an urgent need for a comprehensive study to gain a deep understanding of the multi-scenario high-elevation and long-runout geological hazards, and their potential impact on the railway project. This research endeavor is dedicated to unraveling the intricacies associated with understanding the mechanisms and evaluating the risks posed by high-elevation and long-runout geological hazards within mountainous terrains. The study introduces an innovative methodology for identifying geological hazards by integrating optical remote sensing, drone-based oblique photogrammetry and onsite field investigation. Based on the results of geological hazard identification, numerical simulations were conducted to quantitatively analyze the hazards of rainfall events with different return periods. This paper utilizes the Cuojiu Valley in Tibet as a case study. The first section introduces the regional geological background of the study area. The second section outlines the collection of remote sensing data and numerical simulation methods. The third section conducts an analysis of geological hazard sources and activity. In the fourth section, numerical simulations of potential debris flow risk are completed, followed by analysis and discussion. 2 Regional geological background The study area is located in the Cuojiu Valley, a high-altitude mountainous valley in southeastern Tibet, China. It is located at the western boundary of the eastern tectonic knot within the Qinghai-Tibet Plateau (Fig. 1 ), which is renowned as one of the world's largest canyon regions (Bian et al. 2004 ; Bracciali et al. 2016 ). This area is characterized by intense internal and external dynamic activities, significant variations in topographic elevation, diverse geomorphological types, and complex loose material sources (Burbank et al. 2003 ; Wang et al. 2021 ). The convergence of various risk sources has led to the occurrence of numerous geological hazards in this region (Delaney and Evans 2015 ; Yang et al. 2023 ). The study area is situated within a plateau temperate semi-humid climate zone, characterized by an average annual temperature of 9.6°C, with a maximum temperature of 30.9°C and a minimum temperature of -11.9°C. The annual precipitation reaches 1276.0 mm, with rainfall during the May to September period accounting for approximately 90% of the yearly total. The precipitation in the study area demonstrates an increasing trend, and there are significant seasonal variations in rainfall that provide favorable conditions for the occurrence of high-level geological hazards. The area is situated within a medium-intensity seismic zone within the southern part of the Tibetan Plateau and has witnessed multiple significant earthquakes throughout its history. Statistical analysis indicates that over the past 50 years, the study area has experienced nine strong earthquakes with a magnitude of 6 or higher, along with approximately 40 earthquakes ranging from magnitudes 4.7 to 5.9. Frequent felt earthquakes have been observed. The accumulation of collapsed materials triggered by earthquakes has the potential to induce secondary geological disasters by obstructing river channels. Cuojiu Valley is situated to the west of Dongjiu Village, Lulang Town, Nyingchi City, Tibet, approximately 6 km away from National Highway G318 (Fig. 2 ). The valley encompasses an extensive expanse, spanning 33.46 km2, with elevations ranging from 3,057 m at its entrance to an impressive 4,760 m at its highest point. The primary channel extends for approximately 10.5 km, with approximately 20 tributaries on either side. An assessment of the morphological and geomorphological characteristics of the Cuojiu Valley has led to the systematic categorization of the area into three distinct sectors: the origin zone, the conveyance zone, and the deposition zone. Of particular interest are the slope decline ratios observed in these discrete areas, which have been measured at 164.03‰, 120.06‰, and 92.5‰, respectively. The cross-sectional profile of the Cuojiu Valley is presented in Fig. 3 . 3 Data and method 3.1 Basic data After completing the collection of multi-phase and multi-source satellite image data of the Cuojiu Valley, the study of hazard risk sources and their dynamic change characteristics in the study area will be carried out (Table 1 ). By analyzing the general characteristics of the Cuojiu Valley watershed and its spatial relationship with the ongoing construction of the important railway, a delineation of the remote sensing interpretation zone for Cuojiu Valley was carried out (Fig. 4 ). This involved utilizing ArcGIS for basin extraction and analysis to redefine the boundaries of the remote sensing interpretation zone for Cuojiu Valley. Leveraging multi-temporal remote sensing data, a comprehensive dynamic remote sensing interpretation was conducted to delve into various aspects of the Cuojiu Valley watershed, including its geological background and geological hazards. Table 1 Sources of satellite remote sensing data Data Number Data Time Data Source Data Resolution Remarks 1 2012.03.31 Historical Remote Sensing Imagery Panchromatic 2.0m Fusion data 2 2014.12.28 GF-1 Satellite Panchromatic 2.0m, Multispectral 8.0m Single scene 3 2017.12.04 Historical Remote Sensing Imagery Panchromatic 2.0m Fusion data 4 2018.11.26 Landsat-8 Panchromatic 15.0m, Multispectral 30.0m Single scene 5 2019.12.31 GF-2 Satellite Panchromatic 0.8m, Multispectral 3.2m Two scenes 6 2020.12.09 GF-1 Satellite Panchromatic 2.0m, Multispectral 8.0m Two scenes 7 2021.01.17 GF-7 Satellite Panchromatic 2.0m, Multispectral 8.0m Single scene 3.2 Simulation method FLO-2D, as a lumped hydrology and hydraulic model, plays a crucial role in the research of geological hazards. Its effectiveness has been acknowledged by the Federal Emergency Management Agency (FEMA) (Peng and Lu 2013 ; Erena et al. 2018 ). The software leverages the non-Newtonian fluid model and central finite difference scheme to solve the governing equations of debris flow movement, enabling precise numerical quantification of the flow process, deposition extent, and identification of hazardous areas (Fallas Salazar and Rojas González 2021 ). These capabilities make FLO-2D an indispensable tool in the field of debris flow research and provide valuable insights for mitigating potential disasters. However, it is crucial to consider specific restrictions and assumptions during the calculation process due to theoretical model limitations. These include assuming a static hydrostatic pressure distribution for the fluid, maintaining consistent parameters within grid cells, adopting the shallow water wave model, assuming fixed and constant flow within the finite difference time step, neglecting channel erosion phenomenon, disregarding jumping and oscillation during the flow process, and ignoring the destructive effects of debris flow on engineering structures. 4 Geological hazards sources and activity analysis 4.1 Identification by optical radar images In order to comprehend the varying patterns of geological hazards within the interior of the Cuojiu Valley area under distinct triggering factors, as well as to understand the dynamic processes of risk evolution, a comprehensive and iterative procedure encompassing field reconnaissance, preliminary interpretation, ground verification, and detailed interpretation was undertaken. The undertaken endeavors comprised a 5 km linear geological survey, drone-based oblique photography covering 16 km 2 , remote sensing interpretation over 36 km 2 . This systematic process facilitated the identification of potential hazardous deformations, demarcation of latent material source zones within Cuojiu Valley, and the quantitative assessment of geological hazard susceptibility. In the initial stages of the research, careful consideration was given to the geological context of the hazard-prone setting and the frequent instances of geological hazards within the study area. This led to the establishment of distinct categories of remote sensing interpretation markers. This categorization was achieved through comprehensive field reconnaissance and the compilation of relevant data from various sources. Employing the established remote sensing interpretation markers for geological hazards within the study region, geological hazards and their contextual geological settings were accurately identified on remote sensing imagery, culminating in the creation of a preliminary remote sensing interpretation map (Fig. 5 ). 4.2 Verification by drone-based oblique photogrammetry Based on the preliminary remote sensing interpretation map, this paper subsequently conducted verification through drone-based oblique photogrammetry. The area designated for drone-based aerial photography presents challenges such as high elevation, inadequate transportation infrastructure, and limited signal coverage, which elevate the risks and complexities associated with drone-based aerial photography operations. Consequently, leveraging the outcomes of optical remote sensing interpretation, drone-based photography surveys were predominantly carried out in the source region of Cuojiu Valley, characterized by a heightened concentration of hazard distribution. The results obtained from drone-based oblique photogrammetry serve a dual function: validating optical remote sensing interpretation and furnishing comprehensive route data for subsequent on-site surveys. The area covered by drone-based oblique photogrammetry in the source region of the Cuojiu Valley is depicted in Fig. 6 . The approach covered an aerial area of approximately 20 km 2 with a precision range of 5 to 8 cm. The initiative comprised 24 drone flights conducted at altitudes between 350 and 500 m above ground level. The drones were flown at a consistent speed of 12 m/s, with an 80% overlap in flight paths to ensure comprehensive data capture. A total of approximately 30,000 original aerial images were obtained, with minimal impact from cloud and snow cover, accounting for less than 1% of the acquired images. Following the multi-perspective bundle adjustment and dense matching processing of drone-based oblique photography data, a three-dimensional model was successfully constructed. Drawing from the data acquired through drone-based oblique photography surveys, a model of the Cuojiu Valley's source area was derived (Fig. 7 ). The three-dimensional geological images obtained through drone photography provide comprehensive terrain information, facilitating the identification of alterations in topography and distinctive landform features. Subsequently, based on the drone-based oblique photography model of the Cuojiu Valley's source area, the remote sensing interpretation results were verified and further analyzed. This process also aided in determining the optimal route for on-site field investigation. Consequently, these images enable a more precise analysis of potential geological hazard risks, which is essential for disaster prevention and mitigation efforts in geologically vulnerable areas. 4.3 Verification by onsite field investigation Based on the outcomes of the initial interpretation phase, categories that raised questions during the interpretation process were subjected to on-site verification (Fig. 8 ) to further validate the accuracy of remote sensing interpretation markers and interpretation results. In the study area, Location a is characterized by proximal alluvial deposits, Location b represents a landslide-prone area in the high-elevation middle of the valley, Location c exhibits glacial deposits at the valley margin, and Location d features a glacial lake at the top of the valley. The outcomes of the field geological survey hold substantial significance as they serve as a primary foundation for identifying the sources of geological hazards and conducting analyses of disaster occurrences. Through comprehensive field survey, it has been observed that there are three principal origins of quaternary alluvial sediments, colluvial sediments, and glacial moraines, extending from the historical debris flow accumulation fan at the mouth of the gully to the glacial lake and the terminal moraine dam. The presence of dense vegetation cover in the gully's vicinity does not exhibit distinct indications of geological hazards. The field investigation revealed the development of nearly 20 tributaries on both sides of the main channel of the gully. Within the watershed, various sources of material, such as collapses, landslides, debris flow, glacial deposits, and glacial lakes, are prevalent, alongside diverse hydraulic conditions. The terrain predominantly consists of forests, shrubs, grasslands, steep rock faces, and eroded mountainous areas. There is a potential risk of large-scale debris flow disasters occurring in this area. After concluding the phase of ground verification, precise adjustments and refinements were meticulously incorporated into the remote sensing interpretation indicators, which were originally devised to evaluate geological hazards in the region. This crucial stage not only facilitated a comprehensive understanding of various categories of loose materials, their spatial dispersion, quantities, and distinct deformation patterns, but also laid the groundwork for an exhaustive and holistic interpretation of geological hazards (Fig. 9 ). The total provenance reserves amount to approximately 3.75 × 10 7 m 3 , comprising 31 adverse geological bodies. These include 14 collapses, 12 glacial lakes, 3 moraines, 1 landslide, and 1 debris flow accumulation body. The collapse source area covers an approximate area of 0.77 km 2 , while the collapse accumulation body occupies around 0.37 km 2 , primarily distributed in the high and steep slopes of the source area. The glacial lakes span an area of approximately 0.10 km 2 , predominantly found in the source area. The three moraines cover areas of 0.14 m 2 , 0.09 m 2 , and 0.03 m 2 , respectively, with a combined total area of approximately 0.26 m 2 , also concentrated in the source area. The landslide area measures approximately 0.30 km 2 , located in the primary ditch of the Valley, near the source of the ditch. Lastly, the debris flow accumulation body spans an area of roughly 0.65 km 2 , taking on a fan-shaped appearance extending directly to the mouth of the ditch. Drawing upon an analysis of temperature, precipitation, and source characteristics, three distinct categories of high-elevation and long-runout hazards have been identified within the Cuojiu Valley: glacial lake outburst flows, freeze-thaw flows/debris flow, and rainstorm debris flow (Fig. 10 ). Among these potential hazards, rainstorm-induced debris flow exhibits a relatively heightened probability, whereas the occurrence of freeze-thaw flows, or debris flow is deemed to be exceptionally unlikely. 5 Potential debris flow risk 5.1 Simulation of potential debris flow A multi-phase remote sensing interpretation, drone-enabled oblique photogrammetry, and comprehensive field surveys were employed to locate the potential unstable sources within the Cuojiu Valley, paving the way to systematically assess these sources across a spectrum of rainfall probabilities. The utilization of FLO-2D modeling facilitated this analysis, providing a robust understanding of motion patterns within the valley. The velocity distribution of debris flows under various rainfall probabilities is depicted in Fig. 11 . The rainfall intensity under varied return periods refers to the adjacent watershed rainfall data(Wu et al. 2020 ). For a rainfall probability of P = 10% (10-year-return-period), the primary source consists of historical alluvial sediments in the gully, with estimated reserves of approximately 1.92 × 10 7 m 3 . When the rainfall probability is P = 2% (50-year-return-period), the provenance includes high collapses, glacial moraines, and historical alluvial sediments in the strongly deformed area on the right bank of the gully. The estimated reserves of these materials are approximately 2.30 × 10 7 m 3 . Furthermore, when the probability of rainfall decreases to P = 1% (100-year-return-period), the provenance encompasses landslides on both sides of the gully, glacial moraines on both sides, and historical alluvial sediments at the gully's front, with estimated reserves of approximately 3.75 × 10 7 m 3 . The gravity of debris flow and clean water flow under different rainfall probabilities in the Cuojiu Valley was calculated based on regional experiences. The calculations were made using the catchment area and the supply of solid matter per unit area. The probabilities considered were P = 1% (213.7 m 3 /s), P = 2% (171.0 m 3 /s), and P = 10% (128.7 m 3 /s), respectively. The clear water flow was calculated, as was the debris flow rates, considering the increase coefficient of debris flow, clogging coefficient, volume expansion factor, and volume concentration of debris flow. These calculations were carried out under three different rainfall probabilities for P = 1% (562.4 m 3 /s), P = 2% (438.4 m 3 /s), and P = 10% (305.3 m 3 /s), respectively. Subsequently, the FLO-2D software platform was employed to simulate the debris flow hazard under different rainfall probabilities in the Cuojiu Valley. For a rainfall probability of 10%, the maximum velocity of the debris flow was determined to be 4.8 m/s, with a maximum accumulation thickness of 2.6 m. The debris flow accumulation fan had a length of 735.25 m. a width of 567.9 m, and an accumulation range of 1757 m 2 , which was evenly distributed within the historical debris flow accumulation fan. This accounted for 27.1% of the area. In the event rainfall with 2% probability, the maximum velocity of the debris flow increased to 6.0 m/s, with a maximum accumulation thickness of 6.1 m. The accumulation fan was observed to have a length of 1043.28 m, a width of 754.02 m, and an accumulation range of 3600 m 2 . It was estimated that the area of the historical accumulation fan of debris flow contributed to approximately 54.7%. Finally, under a rainfall with 1% probability, the maximum velocity of the debris flow reached 7.6 m/s, accompanied by a maximum accumulation thickness of 10.7 m. The dimensions of the debris flow accumulation fan were 1400.1 m in length, 1468.8 m in width, and 6705 m 2 in area. The historical accumulation fan comprised approximately 81.7% of the total area. 5.2 Impact on engineering The influences of debris flow on important railway under different rainfall probabilities are summarized in Table 2 . The depiction of debris flow accumulation thickness under various rainfall probabilities is depicted in Fig. 12 . Table 2 The effects of debris flow on railway across varied rainfall intensities Rainfall intensity Influence height /m Minimum clearance of bridges /m Distance between alluvial fans and train station /m 10-year-return-period 17.6 10.4 137.5 50-year-return-period 21.1 6.9 91.9 100-year-return-period 25.7 2.3 31.6 The results derived from the FLO-2D numerical simulation indicate that during a rainfall event with a return period of 10 years, the debris flow accumulation fan body maintains a minimum distance of 137.5 m from the station, with the highest elevation of debris impact reaching 17.6 m, which is significantly below the level of the bridge floor. Similarly, in the case of a 50-year return period rainfall, the distance between the debris flow accumulation fan body and the station decreases to 91.9 m, while the peak of debris influence reaches a height of 21.1 m, still below the bridge floor. Furthermore, with a decrease in rainfall probability to a 100-year return period, the separation between the debris flow accumulation fan body and the station further diminishes to 31.6 m, resulting in a peak debris impact elevation of 25.7 m, which is slightly below the bridge floor level. It is important to note that erosion caused by debris flow in the Cuojiu Valley mainly occurs on the left bank. Based on the utilization of FLO-2D numerical simulation, it is observed that for a rainfall frequency of P = 1%, the maximum accumulation thickness of the debris flow-induced disaster reaches 10.7 m, with a corresponding maximum impact height of 25.7 m. However, the accumulation area does not extend to the railway station and the railway station bridge. Consequently, the analysis suggests that the flow depth of potential high debris flow hazards in the Cuojiu Valley does not directly impact the railway station and the station bridge. Nonetheless, it is crucial to remain attentive to the impact of debris flow on the bridge piers and implement targeted reinforcement and preventive measures accordingly. 5.3 Corresponding engineering measures To address the challenges posed by high debris flow occurrences in the Cuojiu Valley and bolster the safety of the railway, a comprehensive set of refined engineering strategies is proposed for geological risk prevention and control: (1) Establishment of a monitoring system: The implementation of a robust geological hazards monitoring system is imperative. This system will serve as an early warning mechanism, enabling the timely identification of geological hazard risks and facilitating the prompt execution of preventive measures. Moreover, it will play a pivotal role in meticulously documenting the characteristics of geological hazards, conducting in-depth analyses of influencing factors, and forecasting the potential development trajectories of such hazards. (2) Implementation of protective measures: A proactive approach involves the installation of protective piers above the bridge pier and the construction of a grid dam upstream of the section bridge. These structural enhancements aim to fortify the infrastructure against the impact of debris flow events. By erecting additional barriers and redirecting the flow away from vulnerable areas such as the bridge, these measures seek to effectively reduce the peak flow of debris, thereby mitigating potential damage and ensuring the structural integrity of the railway. (3) Adoption of strategic management practices: Ensuring the safe disposal of project waste slag through a meticulous site selection process is paramount. By adhering to stringent safety protocols and selecting suitable disposal sites, the risk of triggering artificial debris flow or exacerbating existing hazards can be significantly minimized. This proactive management approach not only safeguards against potential environmental repercussions but also mitigates the likelihood of exacerbating geological risks in the region. Through the diligent implementation of these multifaceted engineering measures, the adverse impact of debris flow occurrences will be effectively mitigated, thereby substantially enhancing the overall safety and resilience of the railway infrastructure in the Cuojiu Valley. 6 Discussion In the challenging terrain of high cold-altitude mountainous regions, the integration of remote sensing techniques for comprehensive identification of ice and rock masses remains a formidable task. Present technologies excel in either ice or rock identification individually, yet struggle to effectively address both simultaneously, posing a complex challenge for accurate identification. To advance in this field, research endeavors should concentrate on integrated method of optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation to bolster the accuracy and efficiency of ice and rock mass identification. Once the distribution of sources is discerned through remote sensing, it becomes paramount to analyze how these sources interact under varying probability scenarios. High cold-altitude mountainous regions are susceptible to a range of probability scenarios, including extreme weather events, glacier surges, and rockfall incidents. To gain deeper insights into these interactions, an integrated approach optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation with numerical modeling is essential. This holistic approach offers invaluable insights into the underlying mechanisms governing sediment mobility in these rugged terrains. The establishment of monitoring and early warning systems in high cold-altitude mountainous regions is a critical facet of effective risk management. Such systems play a pivotal role in averting the accumulation and exacerbation of geological hazard risks. Presently, monitoring stations have predominantly been deployed in areas like the Cuojiu Valley for rainfall monitoring, with successful real-time transmission of monitoring data achieved. By refining these strategies and leveraging technological advancements, we can significantly enhance our understanding of geological hazards in high cold-altitude mountainous regions and develop robust mitigation measures to safeguard both lives and infrastructure. 7 Conclusions The paper introduced a novel method to identify the geological hazards by integrating optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation. Based on the identified geological hazards, a multi-scenario risk quantification technique under the varied rainfall probabilities was conducted. The main conclusions are as follows: (1) The geological hazards can be precisely identified through the integrated method of optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation. In Cuojiu Valley, the possible geological hazards are identified in the source area by the optical remote sensing, the precise locations are verified by drone-based oblique photogrammetry, and the types of geological hazards are confirmed by onsite field investigation, respectively. (2) The significant presence of adverse geological formations heightens the risk of rainstorm-induced debris flows in Cuojiu Valley, potentially endangering existing infrastructure. A detailed analysis reveals that the minimum distance between the debris flow accumulation area and the railway station is 31.6 meters. In response to the analysis results, various geological disaster risk prevention and control measures have been proposed. Combined with various methods, long-term dynamic monitoring offers crucial insights into disaster prevention strategies in high mountain regions, serving as invaluable guidelines for disaster mitigation. Moreover, these insights contribute to facilitating sustainable development for engineering projects in such areas. Declarations Acknowledgement This research was supported by the National Science Foundation of China (Grant No.42077276), the Geological Survey Project (No.DD20221738), and the China Scholarship Council (CSC). Compliance with ethical standards Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Bian Q-T, Li D-H, Pospelov I, et al (2004) Age, geochemistry and tectonic setting of Buqingshan ophiolites, North Qinghai-Tibet Plateau, China. Journal of Asian Earth Sciences 23:577–596. https://doi.org/10.1016/j.jseaes.2003.09.003 Bracciali L, Parrish RR, Najman Y, et al (2016) Plio-Pleistocene exhumation of the eastern Himalayan syntaxis and its domal ‘pop-up.’ Earth-Science Reviews 160:350–385. https://doi.org/10.1016/j.earscirev.2016.07.010 Burbank DW, Blythe AE, Putkonen J, et al (2003) Decoupling of erosion and precipitation in the Himalayas. Nature 426:652–655. https://doi.org/10.1038/nature02187 Chen R, Zhou S, Li Y, Deng Y (2016) Glacial geomorphology of the Parlung Zangbo Valley, southeastern Tibetan Plateau. Journal of Maps 12:716–724. https://doi.org/10.1080/17445647.2015.1069765 Clague J (2000) A review of catastrophic drainage of moraine-dammed lakes in British Columbia. Quaternary Science Reviews 19:1763–1783. https://doi.org/10.1016/S0277-3791(00)00090-1 Delaney KB, Evans SG (2015) The 2000 Yigong landslide (Tibetan Plateau), rockslide-dammed lake and outburst flood: Review, remote sensing analysis, and process modelling. Geomorphology 246:377–393. https://doi.org/10.1016/j.geomorph.2015.06.020 Erena SH, Worku H, De Paola F (2018) Flood hazard mapping using FLO-2D and local management strategies of Dire Dawa city, Ethiopia. Journal of Hydrology: Regional Studies 19:224–239. https://doi.org/10.1016/j.ejrh.2018.09.005 Fallas Salazar S, Rojas González AM (2021) Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto. Hydrology 8:122. https://doi.org/10.3390/hydrology8030122 Gong Y, Yao A, Li Y, et al (2022) Classification and distribution of large-scale high-position landslides in southeastern edge of the Qinghai–Tibet Plateau, China. Environ Earth Sci 81:311. https://doi.org/10.1007/s12665-022-10433-6 Jiang W, Xi J, Li Z, et al (2022) Deep Learning for Landslide Detection and Segmentation in High-Resolution Optical Images along the Sichuan-Tibet Transportation Corridor. Remote Sensing 14:5490. https://doi.org/10.3390/rs14215490 Kraaijenbrink PDA, Bierkens MFP, Lutz AF, Immerzeel WW (2017) Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549:257–260. https://doi.org/10.1038/nature23878 Li Y, Chen J, Zhou F, et al (2022) Stability evaluation and potential damage of a giant paleo-landslide deposit at the East Himalayan Tectonic Junction on the Southeastern margin of the Qinghai–Tibet Plateau. Nat Hazards 111:2117–2140. https://doi.org/10.1007/s11069-021-05132-7 Lu C, Cai C (2019) Challenges and Countermeasures for Construction Safety during the Sichuan–Tibet Railway Project. Engineering 5:833–838. https://doi.org/10.1016/j.eng.2019.06.007 Peng D, Zhang L, Jiang R, et al (2022) Initiation mechanisms and dynamics of a debris flow originated from debris-ice mixture slope failure in southeast Tibet, China. Engineering Geology 307:106783. https://doi.org/10.1016/j.enggeo.2022.106783 Peng S-H, Lu S-C (2013) FLO-2D simulation of mudflow caused by large landslide due to extremely heavy rainfall in southeastern Taiwan during Typhoon Morakot. J Mt Sci 10:207–218. https://doi.org/10.1007/s11629-013-2510-2 Richardson SD, Reynolds JM (2000) An overview of glacial hazards in the Himalayas. Quaternary International 65–66:31–47. https://doi.org/10.1016/S1040-6182(99)00035-X Scherler D, Bookhagen B, Strecker MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nature Geosci 4:156–159. https://doi.org/10.1038/ngeo1068 Shang Y, Yang Z, Li L, et al (2003) A super-large landslide in Tibet in 2000: background, occurrence, disaster, and origin. Geomorphology 54:225–243. https://doi.org/10.1016/S0169-555X(02)00358-6 Tan Q, Bai M, Zhou P, et al (2021) RETRACTED: Geological hazard risk assessment of line landslide based on remotely sensed data and GIS. Measurement 169:108370. https://doi.org/10.1016/j.measurement.2020.108370 Wang H, Wang P, Hu G, et al (2021) An Early Holocene river blockage event on the western boundary of the Namche Barwa Syntaxis, southeastern Tibetan Plateau. Geomorphology 395:107990. https://doi.org/10.1016/j.geomorph.2021.107990 Ward PJ, Blauhut V, Bloemendaal N, et al (2020) Review article: Natural hazard risk assessments at the global scale. Nat Hazards Earth Syst Sci 20:1069–1096. https://doi.org/10.5194/nhess-20-1069-2020 Wu G, Zhao R, Ma Z, Shi C (2020) The hourly precipitation intensity and frequency in the Yarlung Zangbo river basin in China during last decade. Meteorol Atmos Phys 132:899–907. https://doi.org/10.1007/s00703-020-00730-9 Yang Z, Pang B, Dong W, et al (2023) Hydromechanical coupling mechanism and an early warning method for paraglacial debris flows triggered by infiltration: Insights from field monitoring in Tianmo gully, Tibetan Plateau. Nat Hazards 117:3287–3305. https://doi.org/10.1007/s11069-023-05987-y Yao J, Lan H, Li L, et al (2022) Characteristics of a rapid landsliding area along Jinsha River revealed by multi-temporal remote sensing and its risks to Sichuan-Tibet railway. Landslides 19:703–718. https://doi.org/10.1007/s10346-021-01790-7 Zhang T, Yin Y, Li B, et al (2022) Characteristics and dynamic analysis of the October 2018 long-runout disaster chains in the Yarlung Zangbo River downstream, Tibet, China. Nat Hazards 113:1563–1582. https://doi.org/10.1007/s11069-022-05358-z Cite Share Download PDF Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Minor revisions 06 Jun, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers invited by journal 29 Apr, 2024 Editor assigned by journal 27 Apr, 2024 First submitted to journal 25 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4324036","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296775938,"identity":"3d18a1ee-ab31-4fcf-8264-ed0bf6fe8cca","order_by":0,"name":"Tanfang ZHU","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tanfang","middleName":"","lastName":"ZHU","suffix":""},{"id":296775940,"identity":"3386d1d7-9f51-467b-adbd-6dc051e50b92","order_by":1,"name":"Tao WANG","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"WANG","suffix":""},{"id":296775942,"identity":"4b5bdb8b-884c-489e-9ec9-db6f4c1676a1","order_by":2,"name":"Shuai ZHANG","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"ZHANG","suffix":""},{"id":296775943,"identity":"5d006107-5dcd-4909-9a7a-3bbe11b2f9a4","order_by":3,"name":"Peng XIN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFACxoYDIIqfmfngA9K0SLazJRuQZpnBeR4zAeJU3khuPFy4406e8WEGMwaGGptoglokew42HJ555lmx2WGGtAcMx9JyGwhp4WdvbDjM23Y4cdthhuMGjA2HCWthY2aEaNnczNgmQZQWuC0bmJnZiNMC9gtv27NiicNszAYJxPjF4Eb648+8bXfy+PvPf3zwocaGsBYoOJAAphKIVI6kZRSMglEwCkYBNgAANVlDbMKXY1kAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-5059-9916","institution":"Chinese Academy of Geological Sciences Institute of Geomechanics","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"XIN","suffix":""},{"id":296775944,"identity":"9d639715-6119-4e64-9d4f-a560d607f809","order_by":4,"name":"Xinfu XING","email":"","orcid":"","institution":"PowerChina Chengdu Engineering Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Xinfu","middleName":"","lastName":"XING","suffix":""}],"badges":[],"createdAt":"2024-04-25 12:09:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4324036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4324036/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-024-06853-1","type":"published","date":"2024-08-14T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56039665,"identity":"70d05f0a-147a-4c62-8ad1-9a73d9270411","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation of the study area\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/ee9bffcca70c86e07aae7d79.png"},{"id":56040922,"identity":"6a434547-adfd-41cb-8c1d-6446e21f709c","added_by":"auto","created_at":"2024-05-07 19:20:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336463,"visible":true,"origin":"","legend":"\u003cp\u003eThe watershed range of Cuojiu Valley\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/62105ac5ef4cb998dc591d61.png"},{"id":56040921,"identity":"6a5132ea-d687-4ca9-9869-6eaf328caacd","added_by":"auto","created_at":"2024-05-07 19:20:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78525,"visible":true,"origin":"","legend":"\u003cp\u003eGeological cross-section map of profile A-A'\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/97ffd198ec2187dd4d3a01c1.png"},{"id":56040923,"identity":"90e0eb03-7436-488a-8954-c6269c8b521e","added_by":"auto","created_at":"2024-05-07 19:20:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":345492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnalysis area of satellite optical images\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/99ebb97e6b5a7cf713158b95.png"},{"id":56039668,"identity":"bf3d395d-5718-4e26-829b-a442d0e30404","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":194972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInterpretation result of remote sensing\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/648ce951fab5549c4b79af6c.png"},{"id":56039669,"identity":"776de007-3423-4b83-a829-ef2fbf88119e","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":353860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe coverage of drone-based oblique photogrammetry in source area of Cuojiu Valley\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/7a18ed85ffa14c509bd8e5b8.png"},{"id":56039674,"identity":"eeace16d-a1e2-4706-8ac6-15292b0f28ca","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":170371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDrone-based oblique photography model of the source area in Cuojiu Valley\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/c0439241f8434d14153ec5f8.png"},{"id":56039672,"identity":"68db50ec-4a82-4667-bfb9-f6d2e993abf1","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":494398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOnsite field investigation of Cuojiu Valley\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/04e0f043cb7e52dfaba11124.png"},{"id":56039673,"identity":"84e95557-9e42-4b21-9295-8817f84bdeba","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":375716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Geological hazards identification of Cuojiu Valley\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/d3f3b60ea77e298ec69cdc39.png"},{"id":56039671,"identity":"a0d0d8b9-9224-46d0-ba8b-6d103aeedeb5","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":28506,"visible":true,"origin":"","legend":"\u003cp\u003eFormation model of geological hazards in Cuojiu Valley\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/c9127dbf01ec07ac8bb222eb.png"},{"id":56039676,"identity":"bf11d3ee-58d3-4f1a-9bd1-73d7bf6ec320","added_by":"auto","created_at":"2024-05-07 19:12:56","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":326224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVelocity distribution of debris flow across varied rainfall intensities\u003c/em\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/9a4be96ccdd2384b98796729.png"},{"id":56040924,"identity":"439ae6b4-5ee0-41dd-a21c-0c23e38278a3","added_by":"auto","created_at":"2024-05-07 19:20:56","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":320717,"visible":true,"origin":"","legend":"\u003cp\u003eDeposit thickness distribution of debris flow across varied rainfall intensities\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/f728a1763c98cfa167c1d164.png"},{"id":63071011,"identity":"9598c7e5-3ef4-48e2-b338-41d15af3392c","added_by":"auto","created_at":"2024-08-22 20:02:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4270713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4324036/v1/e12afad9-9513-4a2b-ab9f-1dd940d56266.pdf"}],"financialInterests":"","formattedTitle":"Quantitative Assessment of Multi-Scenario High-Elevation and Long-Runout Debris Flow Hazard and Risk: A Case Study of Cuojiu Valley, South-eastern Qinghai-Tibet Plateau","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent years, climate change has had a profound impact on the stability of mountainous regions, resulting in a noticeable increase in both the frequency and magnitude of geological hazards (Clague \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Scherler et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kraaijenbrink et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The assessment of geological hazard risk is a fundamental task in the mitigation and prevention of regional hazards (Ward et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tan et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such assessments play a pivotal role in understanding the potential implications of geological hazards and serve as the basis for the implementation of effective mitigation strategies.\u003c/p\u003e \u003cp\u003eThe southeastern region of Tibet is characterized by intricate topography, active tectonic movements, intense ground surface erosion, and abundant precipitation (Gong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yao et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Statistical data from the past 70 years indicates that this region has experienced multiple significant geological hazards, particularly in areas such as the Yigong River, Palongzangbu River, and Yarlung Tsangpo River (Shang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These catastrophic geological events, occurring at high elevations, have resulted in substantial economic losses and human casualties.\u003c/p\u003e \u003cp\u003ePrevious studies on geological hazard risk assessment in Tibet (Richardson and Reynolds \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lu and Cai \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have demonstrated notable progress and have proven beneficial for engineering purposes. However, the rugged terrain, challenging transportation, and dense vegetation in the southeastern region of Tibet present challenges for traditional manual investigation methods in quantitatively analyzing and efficiently mitigating geological disasters. Furthermore, the comprehensive identification of ice and rock mass movements in high altitude mountainous regions through satellite remote sensing images represents a significant research challenge (Jiang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the task of identifying potential instabilities in geological hazard sources, characterizing their spatial distribution, and quantitatively assessing the reservoir of these sources remains intricate in high-altitude mountainous locales.\u003c/p\u003e \u003cp\u003eIn particular, the significant railway project traversing the Cuojiu Valley in southeastern Tibet has the potential to have long-term implications due to geological hazards. These hazards can be triggered by a number of factors including ice-dammed lake breaches, ice and snow melt, heavy rainfall, and rockfalls (Li et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conventional hazard assessment methods are inadequate for addressing the complex construction demands of the railway over the long term. Consequently, there is an urgent need for a comprehensive study to gain a deep understanding of the multi-scenario high-elevation and long-runout geological hazards, and their potential impact on the railway project.\u003c/p\u003e \u003cp\u003eThis research endeavor is dedicated to unraveling the intricacies associated with understanding the mechanisms and evaluating the risks posed by high-elevation and long-runout geological hazards within mountainous terrains. The study introduces an innovative methodology for identifying geological hazards by integrating optical remote sensing, drone-based oblique photogrammetry and onsite field investigation. Based on the results of geological hazard identification, numerical simulations were conducted to quantitatively analyze the hazards of rainfall events with different return periods.\u003c/p\u003e \u003cp\u003eThis paper utilizes the Cuojiu Valley in Tibet as a case study. The first section introduces the regional geological background of the study area. The second section outlines the collection of remote sensing data and numerical simulation methods. The third section conducts an analysis of geological hazard sources and activity. In the fourth section, numerical simulations of potential debris flow risk are completed, followed by analysis and discussion.\u003c/p\u003e"},{"header":"2 Regional geological background","content":"\u003cp\u003eThe study area is located in the Cuojiu Valley, a high-altitude mountainous valley in southeastern Tibet, China. It is located at the western boundary of the eastern tectonic knot within the Qinghai-Tibet Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is renowned as one of the world's largest canyon regions (Bian et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bracciali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This area is characterized by intense internal and external dynamic activities, significant variations in topographic elevation, diverse geomorphological types, and complex loose material sources (Burbank et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The convergence of various risk sources has led to the occurrence of numerous geological hazards in this region (Delaney and Evans \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study area is situated within a plateau temperate semi-humid climate zone, characterized by an average annual temperature of 9.6\u0026deg;C, with a maximum temperature of 30.9\u0026deg;C and a minimum temperature of -11.9\u0026deg;C. The annual precipitation reaches 1276.0 mm, with rainfall during the May to September period accounting for approximately 90% of the yearly total. The precipitation in the study area demonstrates an increasing trend, and there are significant seasonal variations in rainfall that provide favorable conditions for the occurrence of high-level geological hazards. The area is situated within a medium-intensity seismic zone within the southern part of the Tibetan Plateau and has witnessed multiple significant earthquakes throughout its history. Statistical analysis indicates that over the past 50 years, the study area has experienced nine strong earthquakes with a magnitude of 6 or higher, along with approximately 40 earthquakes ranging from magnitudes 4.7 to 5.9. Frequent felt earthquakes have been observed. The accumulation of collapsed materials triggered by earthquakes has the potential to induce secondary geological disasters by obstructing river channels.\u003c/p\u003e \u003cp\u003eCuojiu Valley is situated to the west of Dongjiu Village, Lulang Town, Nyingchi City, Tibet, approximately 6 km away from National Highway G318 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The valley encompasses an extensive expanse, spanning 33.46 km2, with elevations ranging from 3,057 m at its entrance to an impressive 4,760 m at its highest point. The primary channel extends for approximately 10.5 km, with approximately 20 tributaries on either side.\u003c/p\u003e \u003cp\u003eAn assessment of the morphological and geomorphological characteristics of the Cuojiu Valley has led to the systematic categorization of the area into three distinct sectors: the origin zone, the conveyance zone, and the deposition zone. Of particular interest are the slope decline ratios observed in these discrete areas, which have been measured at 164.03\u0026permil;, 120.06\u0026permil;, and 92.5\u0026permil;, respectively. The cross-sectional profile of the Cuojiu Valley is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e"},{"header":"3 Data and method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic data\u003c/h2\u003e \u003cp\u003eAfter completing the collection of multi-phase and multi-source satellite image data of the Cuojiu Valley, the study of hazard risk sources and their dynamic change characteristics in the study area will be carried out (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By analyzing the general characteristics of the Cuojiu Valley watershed and its spatial relationship with the ongoing construction of the important railway, a delineation of the remote sensing interpretation zone for Cuojiu Valley was carried out (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This involved utilizing ArcGIS for basin extraction and analysis to redefine the boundaries of the remote sensing interpretation zone for Cuojiu Valley. Leveraging multi-temporal remote sensing data, a comprehensive dynamic remote sensing interpretation was conducted to delve into various aspects of the Cuojiu Valley watershed, including its geological background and geological hazards.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSources of satellite remote sensing data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012.03.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistorical Remote\u003c/p\u003e \u003cp\u003eSensing Imagery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 2.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFusion data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014.12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGF-1 Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 2.0m,\u003c/p\u003e \u003cp\u003eMultispectral 8.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSingle scene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017.12.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistorical Remote\u003c/p\u003e \u003cp\u003eSensing Imagery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 2.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFusion data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018.11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandsat-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 15.0m,\u003c/p\u003e \u003cp\u003eMultispectral 30.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSingle scene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019.12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGF-2 Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 0.8m,\u003c/p\u003e \u003cp\u003eMultispectral 3.2m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTwo scenes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020.12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGF-1 Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 2.0m,\u003c/p\u003e \u003cp\u003eMultispectral 8.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTwo scenes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021.01.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGF-7 Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanchromatic 2.0m,\u003c/p\u003e \u003cp\u003eMultispectral 8.0m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSingle scene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Simulation method\u003c/h2\u003e \u003cp\u003eFLO-2D, as a lumped hydrology and hydraulic model, plays a crucial role in the research of geological hazards. Its effectiveness has been acknowledged by the Federal Emergency Management Agency (FEMA) (Peng and Lu \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Erena et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe software leverages the non-Newtonian fluid model and central finite difference scheme to solve the governing equations of debris flow movement, enabling precise numerical quantification of the flow process, deposition extent, and identification of hazardous areas (Fallas Salazar and Rojas Gonz\u0026aacute;lez \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These capabilities make FLO-2D an indispensable tool in the field of debris flow research and provide valuable insights for mitigating potential disasters.\u003c/p\u003e \u003cp\u003eHowever, it is crucial to consider specific restrictions and assumptions during the calculation process due to theoretical model limitations. These include assuming a static hydrostatic pressure distribution for the fluid, maintaining consistent parameters within grid cells, adopting the shallow water wave model, assuming fixed and constant flow within the finite difference time step, neglecting channel erosion phenomenon, disregarding jumping and oscillation during the flow process, and ignoring the destructive effects of debris flow on engineering structures.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Geological hazards sources and activity analysis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Identification by optical radar images\u003c/h2\u003e \u003cp\u003eIn order to comprehend the varying patterns of geological hazards within the interior of the Cuojiu Valley area under distinct triggering factors, as well as to understand the dynamic processes of risk evolution, a comprehensive and iterative procedure encompassing field reconnaissance, preliminary interpretation, ground verification, and detailed interpretation was undertaken. The undertaken endeavors comprised a 5 km linear geological survey, drone-based oblique photography covering 16 km\u003csup\u003e2\u003c/sup\u003e, remote sensing interpretation over 36 km\u003csup\u003e2\u003c/sup\u003e. This systematic process facilitated the identification of potential hazardous deformations, demarcation of latent material source zones within Cuojiu Valley, and the quantitative assessment of geological hazard susceptibility.\u003c/p\u003e \u003cp\u003eIn the initial stages of the research, careful consideration was given to the geological context of the hazard-prone setting and the frequent instances of geological hazards within the study area. This led to the establishment of distinct categories of remote sensing interpretation markers. This categorization was achieved through comprehensive field reconnaissance and the compilation of relevant data from various sources. Employing the established remote sensing interpretation markers for geological hazards within the study region, geological hazards and their contextual geological settings were accurately identified on remote sensing imagery, culminating in the creation of a preliminary remote sensing interpretation map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Verification by drone-based oblique photogrammetry\u003c/h2\u003e \u003cp\u003eBased on the preliminary remote sensing interpretation map, this paper subsequently conducted verification through drone-based oblique photogrammetry. The area designated for drone-based aerial photography presents challenges such as high elevation, inadequate transportation infrastructure, and limited signal coverage, which elevate the risks and complexities associated with drone-based aerial photography operations. Consequently, leveraging the outcomes of optical remote sensing interpretation, drone-based photography surveys were predominantly carried out in the source region of Cuojiu Valley, characterized by a heightened concentration of hazard distribution. The results obtained from drone-based oblique photogrammetry serve a dual function: validating optical remote sensing interpretation and furnishing comprehensive route data for subsequent on-site surveys.\u003c/p\u003e \u003cp\u003eThe area covered by drone-based oblique photogrammetry in the source region of the Cuojiu Valley is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The approach covered an aerial area of approximately 20 km\u003csup\u003e2\u003c/sup\u003e with a precision range of 5 to 8 cm. The initiative comprised 24 drone flights conducted at altitudes between 350 and 500 m above ground level. The drones were flown at a consistent speed of 12 m/s, with an 80% overlap in flight paths to ensure comprehensive data capture. A total of approximately 30,000 original aerial images were obtained, with minimal impact from cloud and snow cover, accounting for less than 1% of the acquired images.\u003c/p\u003e \u003cp\u003eFollowing the multi-perspective bundle adjustment and dense matching processing of drone-based oblique photography data, a three-dimensional model was successfully constructed. Drawing from the data acquired through drone-based oblique photography surveys, a model of the Cuojiu Valley's source area was derived (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The three-dimensional geological images obtained through drone photography provide comprehensive terrain information, facilitating the identification of alterations in topography and distinctive landform features.\u003c/p\u003e \u003cp\u003eSubsequently, based on the drone-based oblique photography model of the Cuojiu Valley's source area, the remote sensing interpretation results were verified and further analyzed. This process also aided in determining the optimal route for on-site field investigation. Consequently, these images enable a more precise analysis of potential geological hazard risks, which is essential for disaster prevention and mitigation efforts in geologically vulnerable areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Verification by onsite field investigation\u003c/h2\u003e \u003cp\u003eBased on the outcomes of the initial interpretation phase, categories that raised questions during the interpretation process were subjected to on-site verification (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) to further validate the accuracy of remote sensing interpretation markers and interpretation results. In the study area, Location a is characterized by proximal alluvial deposits, Location b represents a landslide-prone area in the high-elevation middle of the valley, Location c exhibits glacial deposits at the valley margin, and Location d features a glacial lake at the top of the valley.\u003c/p\u003e \u003cp\u003eThe outcomes of the field geological survey hold substantial significance as they serve as a primary foundation for identifying the sources of geological hazards and conducting analyses of disaster occurrences. Through comprehensive field survey, it has been observed that there are three principal origins of quaternary alluvial sediments, colluvial sediments, and glacial moraines, extending from the historical debris flow accumulation fan at the mouth of the gully to the glacial lake and the terminal moraine dam. The presence of dense vegetation cover in the gully's vicinity does not exhibit distinct indications of geological hazards.\u003c/p\u003e \u003cp\u003eThe field investigation revealed the development of nearly 20 tributaries on both sides of the main channel of the gully. Within the watershed, various sources of material, such as collapses, landslides, debris flow, glacial deposits, and glacial lakes, are prevalent, alongside diverse hydraulic conditions. The terrain predominantly consists of forests, shrubs, grasslands, steep rock faces, and eroded mountainous areas. There is a potential risk of large-scale debris flow disasters occurring in this area.\u003c/p\u003e \u003cp\u003eAfter concluding the phase of ground verification, precise adjustments and refinements were meticulously incorporated into the remote sensing interpretation indicators, which were originally devised to evaluate geological hazards in the region. This crucial stage not only facilitated a comprehensive understanding of various categories of loose materials, their spatial dispersion, quantities, and distinct deformation patterns, but also laid the groundwork for an exhaustive and holistic interpretation of geological hazards (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The total provenance reserves amount to approximately 3.75 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e, comprising 31 adverse geological bodies. These include 14 collapses, 12 glacial lakes, 3 moraines, 1 landslide, and 1 debris flow accumulation body. The collapse source area covers an approximate area of 0.77 km\u003csup\u003e2\u003c/sup\u003e, while the collapse accumulation body occupies around 0.37 km\u003csup\u003e2\u003c/sup\u003e, primarily distributed in the high and steep slopes of the source area. The glacial lakes span an area of approximately 0.10 km\u003csup\u003e2\u003c/sup\u003e, predominantly found in the source area. The three moraines cover areas of 0.14 m\u003csup\u003e2\u003c/sup\u003e, 0.09 m\u003csup\u003e2\u003c/sup\u003e, and 0.03 m\u003csup\u003e2\u003c/sup\u003e, respectively, with a combined total area of approximately 0.26 m\u003csup\u003e2\u003c/sup\u003e, also concentrated in the source area. The landslide area measures approximately 0.30 km\u003csup\u003e2\u003c/sup\u003e, located in the primary ditch of the Valley, near the source of the ditch. Lastly, the debris flow accumulation body spans an area of roughly 0.65 km\u003csup\u003e2\u003c/sup\u003e, taking on a fan-shaped appearance extending directly to the mouth of the ditch.\u003c/p\u003e \u003cp\u003eDrawing upon an analysis of temperature, precipitation, and source characteristics, three distinct categories of high-elevation and long-runout hazards have been identified within the Cuojiu Valley: glacial lake outburst flows, freeze-thaw flows/debris flow, and rainstorm debris flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Among these potential hazards, rainstorm-induced debris flow exhibits a relatively heightened probability, whereas the occurrence of freeze-thaw flows, or debris flow is deemed to be exceptionally unlikely.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Potential debris flow risk","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Simulation of potential debris flow\u003c/h2\u003e \u003cp\u003eA multi-phase remote sensing interpretation, drone-enabled oblique photogrammetry, and comprehensive field surveys were employed to locate the potential unstable sources within the Cuojiu Valley, paving the way to systematically assess these sources across a spectrum of rainfall probabilities. The utilization of FLO-2D modeling facilitated this analysis, providing a robust understanding of motion patterns within the valley.\u003c/p\u003e \u003cp\u003eThe velocity distribution of debris flows under various rainfall probabilities is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The rainfall intensity under varied return periods refers to the adjacent watershed rainfall data(Wu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For a rainfall probability of P\u0026thinsp;=\u0026thinsp;10% (10-year-return-period), the primary source consists of historical alluvial sediments in the gully, with estimated reserves of approximately 1.92 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e. When the rainfall probability is P\u0026thinsp;=\u0026thinsp;2% (50-year-return-period), the provenance includes high collapses, glacial moraines, and historical alluvial sediments in the strongly deformed area on the right bank of the gully. The estimated reserves of these materials are approximately 2.30 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e. Furthermore, when the probability of rainfall decreases to P\u0026thinsp;=\u0026thinsp;1% (100-year-return-period), the provenance encompasses landslides on both sides of the gully, glacial moraines on both sides, and historical alluvial sediments at the gully's front, with estimated reserves of approximately 3.75 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e. The gravity of debris flow and clean water flow under different rainfall probabilities in the Cuojiu Valley was calculated based on regional experiences. The calculations were made using the catchment area and the supply of solid matter per unit area. The probabilities considered were P\u0026thinsp;=\u0026thinsp;1% (213.7 m\u003csup\u003e3\u003c/sup\u003e/s), P\u0026thinsp;=\u0026thinsp;2% (171.0 m\u003csup\u003e3\u003c/sup\u003e/s), and P\u0026thinsp;=\u0026thinsp;10% (128.7 m\u003csup\u003e3\u003c/sup\u003e/s), respectively. The clear water flow was calculated, as was the debris flow rates, considering the increase coefficient of debris flow, clogging coefficient, volume expansion factor, and volume concentration of debris flow. These calculations were carried out under three different rainfall probabilities for P\u0026thinsp;=\u0026thinsp;1% (562.4 m\u003csup\u003e3\u003c/sup\u003e/s), P\u0026thinsp;=\u0026thinsp;2% (438.4 m\u003csup\u003e3\u003c/sup\u003e/s), and P\u0026thinsp;=\u0026thinsp;10% (305.3 m\u003csup\u003e3\u003c/sup\u003e/s), respectively.\u003c/p\u003e \u003cp\u003eSubsequently, the FLO-2D software platform was employed to simulate the debris flow hazard under different rainfall probabilities in the Cuojiu Valley. For a rainfall probability of 10%, the maximum velocity of the debris flow was determined to be 4.8 m/s, with a maximum accumulation thickness of 2.6 m. The debris flow accumulation fan had a length of 735.25 m. a width of 567.9 m, and an accumulation range of 1757 m\u003csup\u003e2\u003c/sup\u003e, which was evenly distributed within the historical debris flow accumulation fan. This accounted for 27.1% of the area. In the event rainfall with 2% probability, the maximum velocity of the debris flow increased to 6.0 m/s, with a maximum accumulation thickness of 6.1 m. The accumulation fan was observed to have a length of 1043.28 m, a width of 754.02 m, and an accumulation range of 3600 m\u003csup\u003e2\u003c/sup\u003e. It was estimated that the area of the historical accumulation fan of debris flow contributed to approximately 54.7%. Finally, under a rainfall with 1% probability, the maximum velocity of the debris flow reached 7.6 m/s, accompanied by a maximum accumulation thickness of 10.7 m. The dimensions of the debris flow accumulation fan were 1400.1 m in length, 1468.8 m in width, and 6705 m\u003csup\u003e2\u003c/sup\u003e in area. The historical accumulation fan comprised approximately 81.7% of the total area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Impact on engineering\u003c/h2\u003e \u003cp\u003eThe influences of debris flow on important railway under different rainfall probabilities are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The depiction of debris flow accumulation thickness under various rainfall probabilities is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effects of debris flow on railway across varied rainfall intensities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluence height\u003c/p\u003e \u003cp\u003e/m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum clearance\u003c/p\u003e \u003cp\u003eof bridges /m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance between alluvial\u003c/p\u003e \u003cp\u003efans and train station /m\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-year-return-period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50-year-return-period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100-year-return-period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results derived from the FLO-2D numerical simulation indicate that during a rainfall event with a return period of 10 years, the debris flow accumulation fan body maintains a minimum distance of 137.5 m from the station, with the highest elevation of debris impact reaching 17.6 m, which is significantly below the level of the bridge floor. Similarly, in the case of a 50-year return period rainfall, the distance between the debris flow accumulation fan body and the station decreases to 91.9 m, while the peak of debris influence reaches a height of 21.1 m, still below the bridge floor. Furthermore, with a decrease in rainfall probability to a 100-year return period, the separation between the debris flow accumulation fan body and the station further diminishes to 31.6 m, resulting in a peak debris impact elevation of 25.7 m, which is slightly below the bridge floor level. It is important to note that erosion caused by debris flow in the Cuojiu Valley mainly occurs on the left bank.\u003c/p\u003e \u003cp\u003eBased on the utilization of FLO-2D numerical simulation, it is observed that for a rainfall frequency of P\u0026thinsp;=\u0026thinsp;1%, the maximum accumulation thickness of the debris flow-induced disaster reaches 10.7 m, with a corresponding maximum impact height of 25.7 m. However, the accumulation area does not extend to the railway station and the railway station bridge. Consequently, the analysis suggests that the flow depth of potential high debris flow hazards in the Cuojiu Valley does not directly impact the railway station and the station bridge. Nonetheless, it is crucial to remain attentive to the impact of debris flow on the bridge piers and implement targeted reinforcement and preventive measures accordingly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Corresponding engineering measures\u003c/h2\u003e \u003cp\u003eTo address the challenges posed by high debris flow occurrences in the Cuojiu Valley and bolster the safety of the railway, a comprehensive set of refined engineering strategies is proposed for geological risk prevention and control:\u003c/p\u003e \u003cp\u003e(1) Establishment of a monitoring system: The implementation of a robust geological hazards monitoring system is imperative. This system will serve as an early warning mechanism, enabling the timely identification of geological hazard risks and facilitating the prompt execution of preventive measures. Moreover, it will play a pivotal role in meticulously documenting the characteristics of geological hazards, conducting in-depth analyses of influencing factors, and forecasting the potential development trajectories of such hazards.\u003c/p\u003e \u003cp\u003e(2) Implementation of protective measures: A proactive approach involves the installation of protective piers above the bridge pier and the construction of a grid dam upstream of the section bridge. These structural enhancements aim to fortify the infrastructure against the impact of debris flow events. By erecting additional barriers and redirecting the flow away from vulnerable areas such as the bridge, these measures seek to effectively reduce the peak flow of debris, thereby mitigating potential damage and ensuring the structural integrity of the railway.\u003c/p\u003e \u003cp\u003e(3) Adoption of strategic management practices: Ensuring the safe disposal of project waste slag through a meticulous site selection process is paramount. By adhering to stringent safety protocols and selecting suitable disposal sites, the risk of triggering artificial debris flow or exacerbating existing hazards can be significantly minimized. This proactive management approach not only safeguards against potential environmental repercussions but also mitigates the likelihood of exacerbating geological risks in the region.\u003c/p\u003e \u003cp\u003eThrough the diligent implementation of these multifaceted engineering measures, the adverse impact of debris flow occurrences will be effectively mitigated, thereby substantially enhancing the overall safety and resilience of the railway infrastructure in the Cuojiu Valley.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eIn the challenging terrain of high cold-altitude mountainous regions, the integration of remote sensing techniques for comprehensive identification of ice and rock masses remains a formidable task. Present technologies excel in either ice or rock identification individually, yet struggle to effectively address both simultaneously, posing a complex challenge for accurate identification. To advance in this field, research endeavors should concentrate on integrated method of optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation to bolster the accuracy and efficiency of ice and rock mass identification.\u003c/p\u003e \u003cp\u003eOnce the distribution of sources is discerned through remote sensing, it becomes paramount to analyze how these sources interact under varying probability scenarios. High cold-altitude mountainous regions are susceptible to a range of probability scenarios, including extreme weather events, glacier surges, and rockfall incidents. To gain deeper insights into these interactions, an integrated approach optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation with numerical modeling is essential. This holistic approach offers invaluable insights into the underlying mechanisms governing sediment mobility in these rugged terrains.\u003c/p\u003e \u003cp\u003eThe establishment of monitoring and early warning systems in high cold-altitude mountainous regions is a critical facet of effective risk management. Such systems play a pivotal role in averting the accumulation and exacerbation of geological hazard risks. Presently, monitoring stations have predominantly been deployed in areas like the Cuojiu Valley for rainfall monitoring, with successful real-time transmission of monitoring data achieved.\u003c/p\u003e \u003cp\u003eBy refining these strategies and leveraging technological advancements, we can significantly enhance our understanding of geological hazards in high cold-altitude mountainous regions and develop robust mitigation measures to safeguard both lives and infrastructure.\u003c/p\u003e"},{"header":"7 Conclusions","content":"\u003cp\u003eThe paper introduced a novel method to identify the geological hazards by integrating optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation. Based on the identified geological hazards, a multi-scenario risk quantification technique under the varied rainfall probabilities was conducted. The main conclusions are as follows:\u003c/p\u003e \u003cp\u003e(1) The geological hazards can be precisely identified through the integrated method of optical remote sensing, drone-based oblique photogrammetry, and onsite field investigation. In Cuojiu Valley, the possible geological hazards are identified in the source area by the optical remote sensing, the precise locations are verified by drone-based oblique photogrammetry, and the types of geological hazards are confirmed by onsite field investigation, respectively.\u003c/p\u003e \u003cp\u003e(2) The significant presence of adverse geological formations heightens the risk of rainstorm-induced debris flows in Cuojiu Valley, potentially endangering existing infrastructure. A detailed analysis reveals that the minimum distance between the debris flow accumulation area and the railway station is 31.6 meters. In response to the analysis results, various geological disaster risk prevention and control measures have been proposed.\u003c/p\u003e \u003cp\u003eCombined with various methods, long-term dynamic monitoring offers crucial insights into disaster prevention strategies in high mountain regions, serving as invaluable guidelines for disaster mitigation. Moreover, these insights contribute to facilitating sustainable development for engineering projects in such areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Science Foundation of China (Grant No.42077276), the Geological Survey Project (No.DD20221738), and the China Scholarship Council (CSC).\u003c/p\u003e\n\u003cp\u003eCompliance with ethical standards\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBian Q-T, Li D-H, Pospelov I, et al (2004) Age, geochemistry and tectonic setting of Buqingshan ophiolites, North Qinghai-Tibet Plateau, China. Journal of Asian Earth Sciences 23:577\u0026ndash;596. https://doi.org/10.1016/j.jseaes.2003.09.003\u003c/li\u003e\n \u003cli\u003eBracciali L, Parrish RR, Najman Y, et al (2016) Plio-Pleistocene exhumation of the eastern Himalayan syntaxis and its domal \u0026lsquo;pop-up.\u0026rsquo; Earth-Science Reviews 160:350\u0026ndash;385. https://doi.org/10.1016/j.earscirev.2016.07.010\u003c/li\u003e\n \u003cli\u003eBurbank DW, Blythe AE, Putkonen J, et al (2003) Decoupling of erosion and precipitation in the Himalayas. Nature 426:652\u0026ndash;655. https://doi.org/10.1038/nature02187\u003c/li\u003e\n \u003cli\u003eChen R, Zhou S, Li Y, Deng Y (2016) Glacial geomorphology of the Parlung Zangbo Valley, southeastern Tibetan Plateau. Journal of Maps 12:716\u0026ndash;724. https://doi.org/10.1080/17445647.2015.1069765\u003c/li\u003e\n \u003cli\u003eClague J (2000) A review of catastrophic drainage of moraine-dammed lakes in British Columbia. Quaternary Science Reviews 19:1763\u0026ndash;1783. https://doi.org/10.1016/S0277-3791(00)00090-1\u003c/li\u003e\n \u003cli\u003eDelaney KB, Evans SG (2015) The 2000 Yigong landslide (Tibetan Plateau), rockslide-dammed lake and outburst flood: Review, remote sensing analysis, and process modelling. Geomorphology 246:377\u0026ndash;393. https://doi.org/10.1016/j.geomorph.2015.06.020\u003c/li\u003e\n \u003cli\u003eErena SH, Worku H, De Paola F (2018) Flood hazard mapping using FLO-2D and local management strategies of Dire Dawa city, Ethiopia. Journal of Hydrology: Regional Studies 19:224\u0026ndash;239. https://doi.org/10.1016/j.ejrh.2018.09.005\u003c/li\u003e\n \u003cli\u003eFallas Salazar S, Rojas Gonz\u0026aacute;lez AM (2021) Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto. Hydrology 8:122. https://doi.org/10.3390/hydrology8030122\u003c/li\u003e\n \u003cli\u003eGong Y, Yao A, Li Y, et al (2022) Classification and distribution of large-scale high-position landslides in southeastern edge of the Qinghai\u0026ndash;Tibet Plateau, China. Environ Earth Sci 81:311. https://doi.org/10.1007/s12665-022-10433-6\u003c/li\u003e\n \u003cli\u003eJiang W, Xi J, Li Z, et al (2022) Deep Learning for Landslide Detection and Segmentation in High-Resolution Optical Images along the Sichuan-Tibet Transportation Corridor. Remote Sensing 14:5490. https://doi.org/10.3390/rs14215490\u003c/li\u003e\n \u003cli\u003eKraaijenbrink PDA, Bierkens MFP, Lutz AF, Immerzeel WW (2017) Impact of a global temperature rise of 1.5 degrees Celsius on Asia\u0026rsquo;s glaciers. Nature 549:257\u0026ndash;260. https://doi.org/10.1038/nature23878\u003c/li\u003e\n \u003cli\u003eLi Y, Chen J, Zhou F, et al (2022) Stability evaluation and potential damage of a giant paleo-landslide deposit at the East Himalayan Tectonic Junction on the Southeastern margin of the Qinghai\u0026ndash;Tibet Plateau. Nat Hazards 111:2117\u0026ndash;2140. https://doi.org/10.1007/s11069-021-05132-7\u003c/li\u003e\n \u003cli\u003eLu C, Cai C (2019) Challenges and Countermeasures for Construction Safety during the Sichuan\u0026ndash;Tibet Railway Project. Engineering 5:833\u0026ndash;838. https://doi.org/10.1016/j.eng.2019.06.007\u003c/li\u003e\n \u003cli\u003ePeng D, Zhang L, Jiang R, et al (2022) Initiation mechanisms and dynamics of a debris flow originated from debris-ice mixture slope failure in southeast Tibet, China. Engineering Geology 307:106783. https://doi.org/10.1016/j.enggeo.2022.106783\u003c/li\u003e\n \u003cli\u003ePeng S-H, Lu S-C (2013) FLO-2D simulation of mudflow caused by large landslide due to extremely heavy rainfall in southeastern Taiwan during Typhoon Morakot. J Mt Sci 10:207\u0026ndash;218. https://doi.org/10.1007/s11629-013-2510-2\u003c/li\u003e\n \u003cli\u003eRichardson SD, Reynolds JM (2000) An overview of glacial hazards in the Himalayas. Quaternary International 65\u0026ndash;66:31\u0026ndash;47. https://doi.org/10.1016/S1040-6182(99)00035-X\u003c/li\u003e\n \u003cli\u003eScherler D, Bookhagen B, Strecker MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nature Geosci 4:156\u0026ndash;159. https://doi.org/10.1038/ngeo1068\u003c/li\u003e\n \u003cli\u003eShang Y, Yang Z, Li L, et al (2003) A super-large landslide in Tibet in 2000: background, occurrence, disaster, and origin. Geomorphology 54:225\u0026ndash;243. https://doi.org/10.1016/S0169-555X(02)00358-6\u003c/li\u003e\n \u003cli\u003eTan Q, Bai M, Zhou P, et al (2021) RETRACTED: Geological hazard risk assessment of line landslide based on remotely sensed data and GIS. Measurement 169:108370. https://doi.org/10.1016/j.measurement.2020.108370\u003c/li\u003e\n \u003cli\u003eWang H, Wang P, Hu G, et al (2021) An Early Holocene river blockage event on the western boundary of the Namche Barwa Syntaxis, southeastern Tibetan Plateau. Geomorphology 395:107990. https://doi.org/10.1016/j.geomorph.2021.107990\u003c/li\u003e\n \u003cli\u003eWard PJ, Blauhut V, Bloemendaal N, et al (2020) Review article: Natural hazard risk assessments at the global scale. Nat Hazards Earth Syst Sci 20:1069\u0026ndash;1096. https://doi.org/10.5194/nhess-20-1069-2020\u003c/li\u003e\n \u003cli\u003eWu G, Zhao R, Ma Z, Shi C (2020) The hourly precipitation intensity and frequency in the Yarlung Zangbo river basin in China during last decade. Meteorol Atmos Phys 132:899\u0026ndash;907. https://doi.org/10.1007/s00703-020-00730-9\u003c/li\u003e\n \u003cli\u003eYang Z, Pang B, Dong W, et al (2023) Hydromechanical coupling mechanism and an early warning method for paraglacial debris flows triggered by infiltration: Insights from field monitoring in Tianmo gully, Tibetan Plateau. Nat Hazards 117:3287\u0026ndash;3305. https://doi.org/10.1007/s11069-023-05987-y\u003c/li\u003e\n \u003cli\u003eYao J, Lan H, Li L, et al (2022) Characteristics of a rapid landsliding area along Jinsha River revealed by multi-temporal remote sensing and its risks to Sichuan-Tibet railway. Landslides 19:703\u0026ndash;718. https://doi.org/10.1007/s10346-021-01790-7\u003c/li\u003e\n \u003cli\u003eZhang T, Yin Y, Li B, et al (2022) Characteristics and dynamic analysis of the October 2018 long-runout disaster chains in the Yarlung Zangbo River downstream, Tibet, China. Nat Hazards 113:1563\u0026ndash;1582. https://doi.org/10.1007/s11069-022-05358-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"south-eastern Qinghai-Tibet plateau, high-elevation and long-runout debris flow, risk assessment, FLO-2D","lastPublishedDoi":"10.21203/rs.3.rs-4324036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4324036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, the impacts of climate change have significantly increased the susceptibility southeastern Tibet to various geological hazards, characterized by high-elevation and long-runout geological events. These hazards pose significant long-term implications for the development and maintenance of critical railways in the vicinity. Consequently, the implementation of an effective quantitative assessment method for geological hazards becomes paramount for disaster prevention and mitigation. This study introduces a novel method integrating remote sensing, drone-based oblique photogrammetry, and onsite field investigation for effectively identifying geological hazards, and presents a risk quantification technique tailored for high mountain regions under varied rainfall possibilities. By applying this innovative approach, a comprehensive investigation was conducted to assess the characteristics and impacts of rainfall-induced debris flow in the Cuojiu Valley, southeastern Tibet, under varying rainfall probabilities. The study examines the effects of these debris flow on the regional railway, based on the maximum accumulated thickness and the highest affected height triggered by rainfall. The analysis revealed that severe rainfall events act as triggers for these hazardous occurrences. Importantly, the study highlights that the safety of critical railways in the region is compromised by the identified debris flow risk in the Cuojiu Valley during extreme rainfall events. This study's novelty lies in identifying the distribution of geological hazard sources through the proposed method and conducting a quantitative assessment of multi-scenario high-elevation and long-runout debris flows in the Cuojiu Valley. This provides valuable insights for preventing geological hazards in high-elevation valleys.\u003c/p\u003e","manuscriptTitle":"Quantitative Assessment of Multi-Scenario High-Elevation and Long-Runout Debris Flow Hazard and Risk: A Case Study of Cuojiu Valley, South-eastern Qinghai-Tibet Plateau","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 19:12:50","doi":"10.21203/rs.3.rs-4324036/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2024-06-06T04:18:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-29T13:54:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-29T13:41:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-27T04:22:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-04-26T03:43:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"913059ab-4452-485e-95c2-c84c0b11ef58","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:30:46+00:00","versionOfRecord":{"articleIdentity":"rs-4324036","link":"https://doi.org/10.1007/s11069-024-06853-1","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2024-08-14 15:57:11","publishedOnDateReadable":"August 14th, 2024"},"versionCreatedAt":"2024-05-07 19:12:50","video":"","vorDoi":"10.1007/s11069-024-06853-1","vorDoiUrl":"https://doi.org/10.1007/s11069-024-06853-1","workflowStages":[]},"version":"v1","identity":"rs-4324036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4324036","identity":"rs-4324036","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}