Landslide Dam Research Articles

Characteristics and mechanisms of large-scale old landslides and landslide dams in the Loess Plateau—A case study from Daning County, Shanxi Province, China

Giant landslides and dammed lakes occurred frequently during the prehistoric period; however, their mechanisms often remain an open issue because they are complex. This study used field investigations to observe four old landslides and three landslide dams in Daning County, Shanxi Province, China. Remote sensing images captured during different periods were used to compare the landforms of landslides, landslide dams, and their surrounding environments in detail. Subsequently, their shapes, scales, spatial distributions, and positional relationships were determined. The ages of 14C obtained from the paleolandslide dam lake (PDL) deposits were 3742 ± 95, 4115 ± 121, 6544 ± 91, 7814 ± 109 cal a BP. The 14C ages of the deposits under the sliding surfaces of the old landslides were 2748 ± 27, 4470 ± 54, and 8245 ± 79 cal a BP. Based on the actual physical geographical conditions and geomorphological characteristics of the region, combined with the above age data, that the following conclusions were made: (i) During the Holocene, from 2700 to 8200 years ago, heavy rainfall was the main reason for the occurrence of landslides and landslide dams in the region. (ii) Under the condition of heavy rainfall, the slopes with angles >60° in the Loess Plateau are prone to form landslide masses, and the size of the landslide is related to rainfall intensity. A landslide dam was easily formed when the sliding direction was perpendicular to the direction of the valley. It is difficult to form landslide dams when the sliding direction is almost consistent with the downward direction of the valley. (iii) The formation mechanism of old landslides shows that collapse or sliding may occur suddenly in loess geological masses under an external rainfall scenario. Moreover, the process of landslide dams is related to the geomorphic forms of the original valley.

Seismic and hydrological triggers for a complex cascading geohazard of the Tianmo Gully in the southeastern Tibetan Plateau

On 11th July 2018, a destructive rock-ice avalanche and subsequent glacier debris flow occurred in the Tianmo Gully in the southeastern Tibetan Plateau (SETP). However, the source area and triggering factors of this cascading geohazard event remained unclear. In this study, we combined satellite remote sensing, meteorological observations, numerical modeling, and post-event field investigation to comprehensively analyze its evolution processes and potential triggers. The remote sensing observations of terrain and landform changes suggest that the initial avalanche occurred on the southeastern flank of the glacier, releasing approximately 2.77 × 106 m3 of lithic and ice material. From our analysis, we suggest that the complex evolution process of this cascading geohazard event could be manifested as earthquake and hydrological triggers → an initial rock-ice avalanche → glacial debris flow → triggered landslides → landslide dam → dammed lake. Our results suggest that the 2017 Ms. 6.9 Nyingchi earthquake, the unusually high meltwater from snow and ice during the abnormally warm and dry summer in 2018, and the short-duration intense rainfall (22 mm on 10 July) recorded one day before the event are the three main factors for the catastrophic event. These factors caused the rock-ice avalanche in the source zone and subsequently cascaded glacial debris flow and shallow landslides. This study highlights the urgent need for regular monitoring of high-risk glaciers, including anomalous changes in temperature and precipitation, and accelerated movements of glaciers due to earthquakes, especially in the SETP, where climate warming will be expected to intensify occurrences of such cascading geohazard events in the future.

Controls on the regional distribution of landslide dams and implications for fluvial landform evolution in Bhutan and its surrounding area

Controls on the regional distribution of landslide dams and implications for fluvial landform evolution in Bhutan and its surrounding area

Asymmetric Bilateral Rupture of the 2022 Ms 6.8 Luding Earthquake on a Continental Transform Fault, Tibetan Border, China

Abstract The Xianshuihe (XSH) fault on the eastern boundary of the Tibetan plateau is one of the most active faults in the world. However, the Moxi fault, the southernmost segment of the XSH fault, remained seismically quiescent for over 230 yr, with a large slip rate and high locking degree. Thus, this region is considered to be a seismic gap capable of hosting earthquakes of Mw 7.0 or greater. In September 2022, the Ms 6.8 Luding earthquake occurred on the Moxi fault, resulting in destructive landslide damage, with 93 people dead and 25 missing. Using regional and global seismic recordings, we integrate the multiple-point-source analysis, finite-fault inversions, and backprojection imaging to investigate in detail the rupture process associated with the 2022 Luding, China, earthquake. Our results show that this event is characterized by an asymmetric bilateral rupture with three episodes: (1) an initial bilateral rupture propagation mainly toward north-northwest; (2) major rupture propagating toward south-southeast and up-dip direction; (3) south-southeast-rupture propagation speed accelerates to ∼2 km/s. Over 70% of the seismic moment is released at shallow depths <12 km thus explaining significant damaging effects. Furthermore, the seismic potential of the remaining locked portions of the XSH and Anninghe faults is still high and positive increases of the Coulomb stress on these faults due to the Luding earthquake might bring them closer to future failure.

A preliminary study of landslide dam failures induced by the combined influence of piping and overtopping

Recently, landslides occurred more frequently worldwide due to extreme climatic and geological disasters, many of which blocked the river in mountainous areas, posing a great threat to residents by inundation and outburst flood. The top two failure modes of landslide dams are overtopping and piping. Most of the current studies only investigated landslide dam failures caused purely by overtopping. However, landslide dams are normally composed of poorly graded and unconsolidated sediments, which are highly susceptible to piping. It is recorded that some landslide dams have experienced local piping before it overtopped. Under such circumstance, the dam erodibility and internal structure change accordingly, which further affect the subsequent overtopping and flood routing process. Therefore, the role of piping in landslide dam failures is crucial, which should be seriously considered and needed to be urgently studied. In this study, a preliminary investigation of landslide dam failures induced by the combined influence of piping and overtopping is carried out by laboratory experiments and numerical simulations. Results show that with increasing particle loss by piping, the soil void ratio increases while the friction angle decreases, leading to a more porous dam structure and a dramatically increased dam erodibility. The more particles have lost before overtopping, the larger the peak discharge is, and the thinner the hydrograph becomes, implying that the piping amplifies the outburst flood. It is also found that when the landslide dam has suffered a more serious piping before it overtopped, the peak flood discharge becomes larger and the peak arrival time advanced obviously, nevertheless, the flood attenuates more sharply along the river reach. The outcome of this research will enrich our understanding of landslide dam failures under the combined influence of piping and overtopping, which can provide scientific evidences for risk management and disaster mitigation of landslide dams.

Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey

The Eastern Black Sea Region is regarded as the most prone to landslides in Turkey due to its geological, geographical, and climatic characteristics. Landslides in this region inflict both fatalities and significant economic damage. The main objective of this study was to create landslide susceptibility maps (LSMs) using tree-based ensemble learning algorithms for the Ardeşen and Fındıklı districts of Rize Province, which is the second-most-prone province in terms of landslides within the Eastern Black Sea Region, after Trabzon. In the study, Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, and Extreme Gradient Boosting (XGBoost) were used as tree-based machine learning algorithms. Thus, comparing the prediction performances of these algorithms was established as the second aim of the study. For this purpose, 14 conditioning factors were used to create LMSs. The conditioning factors are: lithology, altitude, land cover, aspect, slope, slope length and steepness factor (LS-factor), plan and profile curvatures, tree cover density, topographic position index, topographic wetness index, distance to drainage, distance to roads, and distance to faults. The total data set, which includes landslide and non-landslide pixels, was split into two parts: training data set (70%) and validation data set (30%). The area under the receiver operating characteristic curve (AUC-ROC) method was used to evaluate the prediction performances of the models. The AUC values showed that the CatBoost (AUC = 0.988) had the highest prediction performance, followed by XGBoost (AUC = 0.987), RF (AUC = 0.985), and GBM (ACU = 0.975) algorithms. Although the AUC values of the models were close to each other, the CatBoost performed slightly better than the other models. These results showed that especially CatBoost and XGBoost models can be used to reduce landslide damages in the study area.

Research and practice of key technologies for landslide dam development and utilization—A case in Hongshiyan landslide Dam Water Conservancy Project

AbstractBased on the emergency rescue, the subsequent disposal, and the development and utilization projects of the Hongshiyan Landside Dam in Ludian, Yunnan, China, research has been conducted on key technical issues facing the development and utilization of landside dams, including the possibilty evaluation of development and utilization, structure analysis of wide gradation material, performance evaluation, investigation and design, dam seepage control, construction technology and equipment, and safe operation assessment. And innovative results has made in all seven aspects mentioned above, writing the history in this field. The achievements were directly applied to the development planning, investigation and design, construction, and operation and maintenance of the Hongshiyan Landside Dam, a comprehensive water conservancy project that integrates flood control, water supply, irrigation, and power generation, with significant comprehensive benefits.

Cyclic landslide-flood chains along a major mountain river

Landslides are a crucial Earth surface process in mountain landscapes, rapidly transporting large amounts of sediment into lower-elevation areas, and periodically negatively impacting people and their livelihoods. Most studies focus on their immediate, local-scale impacts, but few recognize their remote impacts in mountain environments. In particular, major landslides along rivers can block river channels to form lakes through backwater inundation, posing a flood hazard to downstream areas. In 2018, two landslides occurred along the Baige section of the Jinsha River, eastern Tibetan Plateau, forming two landslide lakes and subsequently, causing two flood events. In this study, we explore the impacts of these events on hillslopes along the river banks, upstream and downstream of the Baige site. Using small baseline subset Interferometric synthetic-aperture radar (SBAS InSAR), we find many active landslides along the Jinsha River and detect a sudden acceleration in these instabilities following the 2018 events. We attribute upstream instabilities to an increase in river width and depth, related to the remains of the landslide dam, while downstream slope instability is likely caused by undercutting along the outburst route. Modelling indicates that failure of these newly detected landslides may block the river again and potentially extend the hazard chain. This work demonstrates that landslides along major rivers can initiate new instabilities, and that even a short-lived landslide-dammed lake can have long-term impacts on stability of hillslopes in river valleys.

Surrogate Model Development for Slope Stability Analysis Using Machine Learning

In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and the regression problem of predicting the factor of safety (FOS) value. This study explored the implementation of machine learning to analyze slope stability using a comprehensive database of 880 homogenous slopes (266 unstable and 614 stable) based on a simulation model developed as a surrogate model. A classification model was developed to categorize slopes into three classes, including S (stable, FOS > 1.2), M (marginally stable, 1.0 ≤ FOS ≤ 1.2), and U (unstable, FOS < 1.0), and a regression model was used to predict the target FOS value. The results confirmed the efficiency of the developed classification model via testing, achieving an accuracy of 0.9222, with 96.2% accuracy for the U class, 55% for the M class, and 95.2% for the S class. When U and M are in the same class (i.e., the U + M class), the test accuracy is 0.9315, with 93.3% accuracy for the S class and 92.9% accuracy for the U + M class. The low accuracy level for class M led to minor inaccuracies, which can be attributed to a data imbalance. Additionally, the regression model was found to have a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value of 5.03 × 10−4, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.

Evaluation of post extreme floods in high mountain region: A case study of the Melamchi flood 2021 ​at the Koshi River Basin in Nepal

Extreme precipitation events and associated hazards are becoming more frequent and intense due to climate change. On June 2021, a devastating flash flood occurred in the Melamchi River in Nepal causing immense destruction to lives and property. The flood event was cascading in nature and associated with multiple driving factors. This study evaluates the post-extreme flood conditions of the Melamchi Flood 2021 (MF21). Rainfall, streamflow (discharge), and gauge height (water level) data of the nearest stations were analyzed. Since there was a lack of reliable instantaneous river discharge during the disastrous MF21, this study developed the flow rating curves at hydrometric stations. Post-extreme flood was evaluated considering upstream catchment hydrology, glacial lake outbursts, landslide damming, and the hydrodynamic propagations using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model and gauge-to-gauge (G2G) correlation. The results were then compared to field observations. The HEC-RAS model was simulated for several flood frequency periods in different return periods (namely 2, 5, 10, 25, 50, 100, 200, 500, and 1000 years). Results showed that the average 24 ​h gauge height and discharge at Nakote station (a few km downstream of landslide damming) are 15.88 ​m and 169.18 ​m3/s, in which the model-based highest frequency of 1-h extreme flood and return period can reach 716 ​m3/s in 1000 years. During MF21, the discharge of the Nakote region at the 10 ​min intervals was estimated to be with a maximum of 7162.10 ​m3/s, an average of 2207.13 ​m3/s and a minimum of 237.44 ​m3/s.Additionally, the outburst discharge from glacial lake at high elevation was estimated to be 295.14 ​m3/s contributing to downstream flooding. The model-based discharge during the past flood period was also estimated in 9 cross-section points of the river in which discharge at the damming area was 63.81 ​m3/s and downstream Dumredovan was 1206.58 ​m3/s. This research provides valuable insights to post-extreme flood evaluation and the approach can be used for other watersheds in evaluating post flood conditions.

Combining portable cone penetration test and electrical resistivity tomography to assess residual risks after shallow landslides: a case at the Hokkaido Eastern Iburi earthquake in 2018 in Japan

Landscapes disturbed by shallow landslides present several residual risks. For example, residual soils on sliding surfaces may lead to the re-occurrence of shallow landslides, and landslide deposits on valley floors may introduce multiple hazards associated with river blocking or landslide damming. Despite such risks, few studies have investigated the residual risks following shallow landslides, and thus, no methodology has been established for assessing such risks. In this study, we combined portable cone penetration test (PCPT) and electrical resistivity tomography (ERT) to investigate the post-event risks associated with a landslide scar resulting from the 2018 Hokkaido Eastern Iburi earthquake (HEIE) in Japan. The multi-point PCPT identified the interface between a weak volcanic soil layer and underlying weathered sedimentary rocks. This interface exhibited an abrupt spatial change in resistivity, ranging from 20 to 100 Ωm. The thickness of the residual soil layer was 0.3–3.2 m and that of the landslide deposit exceeded 2.0 m. An infinite-slope stability analysis was performed to predict the re-occurrence of shallow landslides due to a lower intensity earthquake than that of HEIE in this region. Considering the area affected by the HEIE and the physical properties of soil layers, our findings imply a widespread risk of re-occurrence of shallow landslides over an area of 20 km × 20 km. Although the areal coverage of the combined ERT/PCPT method needs to be extended, it is effective for assessing the residual risks associated with shallow landslides.

Investigating the important role of landslide dam geometry on breaching based on numerical experiments

The geometry of a landslide dam is one of the most important factors influencing its breaching process. Understanding how geometric parameters affect breaching is crucial for breach flood assessment and warning strategy development. In this study, numerical simulations were conducted by computational fluid dynamics method to systematically investigate the effect of the landslide dam geometry on the breaching process, with variables including the dam crest length, bottom length, upstream slope angle, downstream slope angle, and channel gradient. The simulation results were calibrated via laboratory tests with a satisfactory agreement. According to the erosion characteristics, the process of landslide dam overtopping failure could be divided into two stages, i.e., headward erosion and overall erosion, and the breach evolution, hydrodynamic characteristics, and erosion processes at the different stages were analyzed. Additionally, the findings indicated that the crest length, downstream slope, and channel gradient all significantly impacted the headward erosion process of landslide dams. At the overall erosion stage, the dam bottom length and maximum lake volume were the key influencing factors. Furthermore, we presented empirical equations to predict the time of headward erosion, time to the peak discharge, and peak discharge at the laboratory scale. Finally, the established empirical peak discharge prediction equations were extended to a dimensionless form, which are applicable to landslide dam failures of various scales. Our study successfully revealed the quantitative relationship between geometric parameters and breaching characteristics, which are beneficial for assessing hazard risk and developing mitigation strategies for landslide dam disasters.

An integral assessment of landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain

Landslides caused by rainfall are one of the most frequent causes of disasters in tropical countries and mountainous terrain and can block rivers generating landslide dams. This paper presents a methodology for the estimation of the obstruction of water streams generated by rainfall-induced shallow landslides. The spatial distribution of the landslide hazard was estimated in terms of the Factor of Safety (FoS) values using the deterministic method with physical basis SLIDE (Slope - Infiltration - Distributed Equilibrium). The rainfall regimes of the study area were estimated by means of a simple scaling Log Normal Model. Subsequently, the resulting areas with a high hazard level that could detach and reach the riverbed were identified as sources for the simulation of the debris flow runout using the Rapid Mass Movement Simulation model with its debris flow module, (i.e., RAMMS-DF), estimating zones of the riverbed that should be analyzed in detail. Finally, the effects of river channel obstructions generated after debris flow movement were analyzed by means of the Iber, a well-known, physically based 2D hydraulic model and their possible changes on the river hydraulic. In order to generate a workflow that allows the application of the SLIDE methodology and the preparation of inputs for the subsequent processes of debris flow propagation and hydraulic modeling of the river corridor of analysis, a Python-based toolbox was created. Our results highlight the changes in the fluvial dynamics in the corridor of the river of analysis after the landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain for the different return periods. In all cases, the material deposited in the river channel was sufficient to change the hydraulic regime of the river corridor, showing longer delay times in the transit of the flow, in addition to the decrease in the specific flow. This would imply a water shortage in the study basin of the hydroelectric project; however, in the scope of this project it is not possible to really determine the real effects that could be generated by this event.

Insights into the long-term stability of landslide dams on the eastern margin of the Tibetan Plateau, China–A case study of the Diexi area

Insights into the long-term stability of landslide dams on the eastern margin of the Tibetan Plateau, China–A case study of the Diexi area

Assessment of Landslide Risk Near Mountain Power Generation Facilities in Korea

Rising greenhouse gases fuel global warming, triggering disasters like droughts, floods, and landslides. Efforts to reduce emissions and combat climate change focus on renewable energy (wind, wave, hydrogen, and solar). However, rapid expansion of solar facilities in mountainous regions, coupled with unchecked logging, intensifies landslides and environmental pollution. Therefore, in this study, the risk of installed solar power generation facilities was evaluated based on the precipitation in a year and rainy season only, and topographical characteristics. The annual cumulative precipitation and rainy season were calculated for 2020, and the risk of landslides caused by rainfall was evaluated through correlation with the annual landslide area. As of 2020, more than 50% of landslides occurred in the areas having a slope range of 30 - 60% and the landslide damage area was also highly correlated with the precipitation during the rainy season (R2 = 0.74). Prior to the installation of solar power generation facilities in the steep mountainous area, more strict and appropriate verification must be done. Additionally, a systematic management plan of solar power generation facilities should be investigated in a permission process.Landslide risk of mountainous solar power generation facilities versus precipitation.

The comprehensive risk assessment of the Tangjiashan landslide dam incident, China.

Risk assessment for landslide dams is very important to avoid unanticipated landslide failure and calamity. Recognition of the risk of landslide dams associated with changing influencing factors is to identify the risk grade and provide early warning of oncoming failure, while quantitative risk analysis of landslide dams due to many influencing factors changing in spatiotemporal domain is currently lacking. We applied the model to analyze the risk level of the Tangjiashan landslide dam caused by the Wenchuan Ms 8.0 earthquake. The risk evaluation, obtained according to the analysis of the influencing factors located in the risk assessment grade criteria, clearly shows that the risk reaches a higher level at that moment. Our analysis shows that the risk level of landslide dams can be quantitatively analyzed with our assessment method. Our results suggest that the risk assessment system can be an effective measure to dynamically predict the risk level and provide a sufficient early warning of the oncoming hazard by analyzing the variables of influencing factors at different times.

Effects of soil properties and geomorphic parameters on the breach mechanisms of landslide dams and prediction of peak discharge

Effects of soil properties and geomorphic parameters on the breach mechanisms of landslide dams and prediction of peak discharge

The nurse-plant effect under the dislodgement stress of landslides

While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and tree-tree interactions have received much less research attention. The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot. Tree-tree interactions were examined by analysing the pre- and post-landslide spatial point patterns of large (DBH ≥ 20 cm) and small (1 cm ≤ DBH < 20 cm) tree cohorts. In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3.072 small trees survived. Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and missing (M) large-small tree paired cohorts (LM-SM). We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns. Post-landslide survival rates of trees increased monotonically with DBH. Large trees dislodged by landslides scarcely reduced small-tree survival. Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuals did not differ significantly from those without large trees nearby. Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuals clumped around LS individuals, whereas the virtual L34%-S13% spatial relationship was random. Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect. Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees.

최대 엔트로피 모형을 활용한 기후변화 시나리오 기반 산사태 취약성평가

Climate change has a significant impact on the occurrence of landslides, and as it intensifies, abnormal weather such as cold waves, heavy snowfall, torrential rains, and typhoons will become more frequent, leading to an increase in disasters. This study constructed a maximum entropy (MaxEnt) model using geospatial information and meteorological factors (using representative concentration pathways (RCP) 8.5 and RCP 4.5 scenarios) from landslide occurrence locations from 2010 to 2020 and simulated the national probability of landslide occurrence per year from 2021 to 2085. A vulnerability assessment was performed considering the government’s policy and budget, and the possible damage caused by landslides was quantitatively evaluated. The results of the impact assessment showed that the probability of landslide occurrence based on the RCP 8.5 scenario was 1.8 times higher in the 2030s, 1.7 times higher in the 2050s, and 7.2 times higher in the 2080s than that based on the RCP 4.5 scenario. In addition, passive and active mitigation, assuming a constant increasing rate of policy budget, showed a difference in reduction of landslide risk. Applying passive mitigation in the impact and vulnerability assessments reduced the risk of landslides by a minimum of 47.7% to a maximum of 52.6%. Active mitigation reduced the risk of landslide occurrence from a minimum of 63.6% to a maximum of 68.4%. The results of this study provide basic data for local governments to determine the priorities of landslide prevention projects. However, future research should consider landslide damage intensity and the physical limitations in spatialization of the adaptive capacity considered in the vulnerability evaluation.

Combining geophysics, remote sensing and numerical simulation to assess GLOFs: Case study of the Namulacuo Lake in the Southeastern Tibetan Plateau

The current highest glacial lake outburst floods (GLOFs) risk level is centered in the eastern Himalaya. GLOFs represent a serious threat to downstream inhabitants and ecological environment. In the context of climate warming on the Tibetan Plateau, such GLOFs will continue or even intensify in the future. Remote sensing and statistical methods are often used to diagnose glacial lakes with the highest outburst probability. These methods are efficient in large-scale glacial lake risk assessment but do not take into consideration the complexity of specific glacial lake dynamics and triggering factor uncertainty. Therefore, we explored a novel approach to integrate geophysics, remote sensing, and numerical simulation in glacial lake and GLOF disaster chain assessments. In particular, geophysical techniques are rarely applied to the exploration of glacial lakes. The Namulacuo Lake located in the southeastern Tibetan Plateau is considered as the experimental site. The current status of the lake, including landform construction and identifying potential triggering factors, was first investigated. Secondly, the outburst process and disaster chain effect were evaluated by numerical simulation based on the multi-phase modeling frame proposed by Pudasaini and Mergili (2019) implemented in the open source computational tool r.avaflow. The results allowed verifying that the Namulacuo Lake dam was a landslide dam with an obvious layered structure. Also, the piping-induced flood might have more severe consequences than the short-term ultra-high discharge flood caused by surge. The blocking event caused by a surge disappeared faster than that caused by piping. Therefore, this comprehensive diagnostic approach can assist GLOF researchers to increase their understanding of key challenges they are facing regarding GLOF mechanisms.