Debris Flow Gullies Research Articles

Development characteristics and hazard analysis of debris flow along the Emei to Mianning section of the Chengdu–Kunming Railway

Geological disasters in the Emei to Mianning section of the Chengdu–Kunming Railway located in southwestern China occur frequently, and many debris flow gullies are distributed along the line. Debris flows often cause damage to the railway and highway that threatens the safe operation of the original line and the double-track line. Along the railway, the debris flow outbreak risks are analyzed for 53 debris flow gullies using field survey, remote sensing interpretation, and numerical simulation. Their developmental characteristics were judged according to the code. The results are used for numerical simulation of debris flow to further determine the movement characteristics and the accumulation range of debris flow. The results are combined with high-definition satellite images to analyze the form of the railway passing from the mouth of the ditch and the simulation results to determine the impact on the safe operation of the Chengdu–Kunming Railway when the debris flow erupts. Finally, disaster prevention and mitigation projects are proposed, and the prevention and control effects of the proposed works are validated.

Characteristics of debris flow development in Daxilada watershed and its hazard analysis on Lexi Expressway

The occurrence of frequent debris flow catastrophes in the mountainous regions of southwest China has necessitated the inclusion of debris flow disaster analysis and prevention as an essential component in the planning and construction of mountainous roadways. Daxilada watershed is located in the south of Mabian Yi Zuzizhixian, Leshan City, Sichuan Province, and the proposed Leshan-Xichang Expressway (Lexi Expressway) will pass through the upper reaches of Daxilada watershed. It is essential to consider that the presence of debris flows within the Daxilada watershed could have detrimental effects on the construction and functionality of the proposed Luoshanxi Bridge. This study examined the Daxilada watershed as a case study and analyzed the factors contributing to debris flow formation in the area. This analysis was based on field investigations, remote sensing interpretation, and experimental analysis. Additionally, the study utilized the Massflow software to simulate debris flow movement. It integrated the simulation results to determine the potential hazards the Daxilada Gully debris flow posed to the line project. This study found that Daxilada Gully meets debris flow formation conditions. The simulation results demonstrated that during the debris flow activity, there would be a maximum deposition depth of 2.1 m in the proposed Engineering Agency, which may lead to siltation and blocking disaster of the line project. Concerning the parameters related to the debris flow with a frequency of one in a hundred years, in conjunction with the outcomes obtained from numerical simulation, it would provide the design basis of the cross-flow cross-section of the proposed bridge. In a quantitative analysis of the blockage situation in the gully, debris flow deposits have the potential to cause damage to the line project. Debris flow deposits block the gully, but the risk of blockage is small. The study results have specific reference values for the debris flow prevention and control project of Lexi Expressway and offer valuable insights for the prevention and mitigation of similar disasters in relative projects.

Experimental and numerical investigation of debris flow interception using multiple stepped flexible barriers

Flexible barriers are a type of environmentally friendly and easily constructed retaining structure. Owing to the deformability and limited strength of the flexible barrier material, these barriers can only be used in small- or medium-sized debris-flow gullies. In this study, we proposed an improved layout, namely the multiple stepped flexible barriers, to address the limitations of traditional flexible barriers in the application of debris-flow control. We used experimental and numerical methods to investigate the separation effect of the multiple stepped flexible barriers and their stress and strain distributions in solid aggradation after reaching its maximum storage capacity. The results demonstrated that the ability of the multiple stepped flexible barriers to separate large particles gradually decreases with increasing debris-flow density, which implies that the debris-flow regulation ability of the multiple stepped flexible barriers decreases with increasing debris-flow density. When the debris-flow density exceeds 2200 kg/m3, the multiple stepped flexible barriers will completely lose their ability to reduce the density of debris flows. In addition, the thickness of the solid aggradation initially increases and then subsequently decreases upstream; the thickness of the solid aggradation increases with increasing debris-flow density. The results obtained from numerical simulation demonstrated that there would be relatively large shear stress and shear strain near the bottom of each flexible barrier. When the longitudinal spacing of the flexible barriers increases to 1.6 times their height, the internal stress of the upper aggradation layer that was transferred to the lower layer gradually weakens or disappears. Under this condition, the soil pressure of the flexible barrier is relatively small. The results of this study provide guidance for the design and construction of multiple stepped flexible barriers in wide debris-flow gullies.

Evaluation of highway debris flow hazard based on geomorphic evolution theory coupled with material response rate

Assessments of highway feasibility frequently lack the detailed data and geological information necessary to conduct hazard evaluations of debris flows. This study discusses the processes of debris flow development when regional rainfall meets the critical level required for debris flow initiation. It utilizes geomorphic evolution theory and establishes a regional risk assessment matrix for debris flow by combining information about gullies and source sensitivity. Considering the location relationship between the highway and debris flow gullies, a rapid evaluation method for debris flow risk assessment along the G318 highway in Sichuan Province is proposed by modifying the judgment matrix. The four debris flow gullies constructed during the upgrading project in Yajiang County, stretching from the west of the city to the Shearer Bay section, were analyzed via examples. The results show that, among the four selected debris flow gullies, two had medium hazard levels, and two had high hazard levels. The validation results are consistent with the actual results, implying that the evaluation method used in this study is accurate and feasible. This method is suitable for the rapid evaluation of debris flow disaster hazards in the feasibility assessment stage of a highway because it relies on readily available data sources, and the evaluation results are fast and convenient. The highway passes through four debris flow gullies, which directly impact the alignment of this particular section of the geological route and the engineering layout. Based on current specifications, the maximum impact range of a one-time debris flow under the given frequency conditions was calculated using the "rainfall method." The results showed that the maximum impact ranges of a debris flow, occurring once in 100 years, for four gullies would be 9.08 m, 9.09 m, 10.86 m, and 10.08 m. The safe clearance heights of bridges over the four gullies should be 14.58 m, 14.59 m, 16.36 m, and 16.3 m. Additionally, the safety clearance width for all gullies should be 5.0 m.

Assessment of debris flow risk in Mentougou District, Beijing, based on runout of potential debris flow

Debris flow is a prevalent geological disaster in Beijing, particularly in the Mentougou District, situated in the mountainous southwestern region of Beijing City. The past irrational exploitation of minerals has led to a substantial accumulation of slag, providing abundant loose materials for the formation of debris flow in the study area. Different from the traditional method of using hazard and vulnerability to carry out debris flow risk assessment, this paper proposes a debris flow risk assessment method based on (a) the possibility of debris flow occurrence, (b) the value of the disaster-affected object, and (c) the destructiveness of the debris flow. The possibility of 41 gully debris flows occurring was determined through theoretical calculations. Using the SFLOW model, the runout of the potential debris flow was simulated. On this basis, an investigation of the value of the disaster-affected objects was carried out. At the same time, according to the simulated movement information of debris flow, its destructiveness was determined. According to the results of the occurrence possibility of the debris flow, its destructiveness, and the value of the disaster-affected object, the risk levels of 41 potential debris flow gullies in the Mentougou District were obtained. The research results can guide the warning systems for debris flows in the Mentougou District of Beijing.

Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization algorithm.

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.

Geological hazards susceptibility evaluation based on multi‐year spatial–temporal evolution of assessment factors in Luding area, Sichuan Province, China

More than 50 earthquakes occurred in the Luding region, including a 6.8‐magnitude earthquake on September 5, 2022, which induced huge amounts of geological disasters. As the main triggered factors for the geological disasters, the vegetation, precipitation and temperature show regular changes on the time axis. Thus, this paper based on the spatial–temporal evolution of the precipitation, temperature and normalized difference vegetation index (NDVI), focuses on the susceptibility of the Luding area using the weighted information content method (ICM) and analytic hierarchy process (AHP) for the post‐earthquake regional planning and reconstruction. In the Luding area, it was found that there were 18 debris flow gullies, and 470 geological disasters scattered in the debris flow gullies. The NDVI of the study area has been declining in recent years, with only about 12% of the NDVI increasing from 2015 to 2020. From 2011 to 2015, the annual cumulative precipitation has been dropping overall, while the July average temperature has increasing quadratic functions after decreasing trend. Furthermore, the studied area's geological hazards susceptibility was classified into five categories, with extremely low susceptibility accounting for 5.82%, low susceptibility accounting for 13.61%, moderate susceptibility accounting for 38.54%, high susceptibility accounting for 39.96% and extremely high susceptibility accounting for 2.08%. The geological disasters in moderate, high and extremely high susceptibility accounted for approximately 95.08%, and the covered area of geological disasters in moderate, high and extremely high susceptibility accounted for 88.06%. Through the receiver operating characteristic (ROC) curve of the model verification found the AUC = 0.743. The geological hazards susceptibility study results show that the approach is useful for the analysis and prevention of geological disasters.

Research on Spatial Distribution Pattern of Stability Inter-Controlled Factors of Fine-Grained Sediments in Debris Flow Gullies—A Case Study

Studies on the stability inter-controlled factors of fine-grained sediments in debris flow gullies play an important role in predicting the scale and danger of debris flows. However, up to the present, few studies have been carried out on the spatial distribution pattern and causes of stability inter-controlled factors of fine-grained sediments in debris flow gullies, leading to difficulty in finding the dangerous section of debris flow gullies to be monitored and controlled to reduce disaster losses. Therefore, the objective of this paper is to analyze the spatial distribution pattern and causes of stability inter-controlled factors (grain size, permeability coefficient, shear strength, and porosity), taking the Beichuan Debris Flow Gully, China, as a case. After collecting soil samples in the field, we carried out experiments to measure the stability inter-controlled factors and, from these, the results show that (1) fine-grained sediments in this case are mainly silty loams, which are stable under non-heavy rains; (2) the grain size of silty loams is mainly concentrated between 10 and 20 μm, with a spatial distribution pattern of fine in the middle and coarse at both ends; (3) the permeability coefficient of silty loams is concentrated between 1.15 and 2.17 m/d, with a spatial distribution pattern of high in the middle and low at both ends; (4) the average cohesion of silty loams is mainly concentrated between 20 and 30 kPa, with a spatial distribution pattern of low in the middle and high at both ends; and (5) the internal friction angle of silty loams is concentrated between 18.98 and 21.8°, with a spatial distribution pattern of high in the middle and low at both ends. The main reasons for these spatial distribution patterns are analyzed from three aspects of shear strength, water flow velocity, and terrain, which can provide a scientific basis for the prediction of debris flow disasters in such areas.

WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway

WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway

Accurate and Intelligent Early Warning Method of Debris Flow Formation Based on IGWO-LSTM Algorithm

To improve the accuracy of debris flow forecasts and serve as disaster prevention and mitigation, an accurate and intelligent early warning method of debris flow initiation based on the IGWO-LSTM algorithm is proposed. First, the entropy method is employed to screen the early warning indicators. Then, the improved grey wolf algorithm (IGWO) is obtained by optimizing the grey wolf algorithm by combining elite reverse learning and adaptive convergence factors. Finally, the IGWO-LSTM algorithm is obtained by using IGWO to improve the total connection layer weight and bias parameters of LSTM, which takes the screened early warning indicators as input and outputs the early warning results of the debris flow formation risk level. In comparison with the methods introduced in earlier studies, the results demonstrate that the proposed method achieves superior outcomes in terms of assessing a single warning of multiple debris flow gullies, a multi-year warning of a single debris flow gully, and a multi-year warning of multiple debris flow gullies. The mean absolute error and root mean square error of the early warning results of the ANN model and PEEM method show low values, while the early warning hit rate shows high values, surpassing 90%. Also, the other two methods developed in the previous studies show low values of the early warning coverage rate, reaching 90% at most. Moreover, the triggered traffic model and MLPG method show high values in the early warning coverage rate, exceeding 90%, and low values in the early warning hit rate of less than 90%, and the average absolute error and root mean square error are high. On the other hand, the results of the proposed method show that the overall early warning hit rate is higher than 95%, the coverage rate is close to 100%, and the error is less than 1.5. Thus, the comprehensive analysis results show that the proposed method has better performance and higher reliability than other studied methods.

Responses of biodiversity to microhabitat heterogeneity in debris flow gullies: Assessing the impact of hydrological disturbance

Rivers play a vital role in the maintenance of the biosphere and human society, since they participate in the global water cycle and provide varied habitats to support biodiversity. Microhabitat heterogeneity is regarded as a key factor driving biodiversity and it plays an active ecological role in different types of mountain rivers. Whether river microhabitat heterogeneity exhibits the same ecological patterns across hydrological periods remains unclear. Here, we analyzed the changes in macroinvertebrate community composition, functional traits, and multi-faceted α-diversity in five debris flow gullies in the Xiaojiang River Basin (southwestern China) between two different hydrological periods. We explored the responses of biodiversity to river microhabitat heterogeneity and its driving factors before and after hydrological disturbance. The results indicated that river microhabitat heterogeneity and three facets of macroinvertebrate α-diversity decreased after hydrological disturbance, with macroinvertebrate state traits becoming more unbalanced. Macroinvertebrate taxonomic diversity increased with increasing river microhabitat heterogeneity across hydrological periods, and this pattern was more prominent before hydrological disturbance. A high correlation emerged between macroinvertebrate phylogenetic diversity and river microhabitat heterogeneity only before hydrological disturbance. Hydrogeomorphic parameters prominently affected macroinvertebrate communities before hydrological disturbance. Water environmental parameters worked together with hydrogeomorphic parameters to shape macroinvertebrate communities in hydrologically disturbed debris flow gullies, indicating a reduced ecological role of river microhabitat heterogeneity. The ecological health of debris flow gullies can be improved by increasing vegetation coverage on river bank slopes to increase slope stability and mitigate hydrological disturbances, as well as placing large rocks into river channels to enhance riverbed stability and create habitats for more biological groups.

Effect of initial water content on soil failure mechanism of loess mudflow disasters

The frequency of mudflow disasters induced by rainfall in the Loess Plateau is increasing with the occurrence of global warming. The initial water content is one of the basic properties of soil, which affects the initiation of loess mudflow. In this work, the field study of the debris flow gullies in Yan’an City, Shaanxi Province, China, was conducted, and the main factors that induce gully loess mudflow were summarized. Based on the investigation results, a flume model was designed to carry out flume tests with different initial soil water contents. The experimental results demonstrate the following. (1) Different initial soil water contents lead to different soil failure models. The damage of soil by water flow when the soil water content is in the range of 0−5% is mainly gully erosion; that within the range of 10−15% is mainly rill surface erosion; that within the range of 20−25% is mainly dam breach failure. (2) When the water content of loess is equal to or less than 5% or equal to or greater than 20%, soil can promote the formation of loess mudflow, and the destruction of soil is more likely to cause mudflow disasters. In contrast, when the water content is within 10−15%, loess mudflow is not easily produced. The research results of the initial water content provide not only theoretical support for the study of loess mudflow disasters, but also a reference for the prevention and control of loess mudflow disasters in the Loess Plateau.

Evolution of debris flow activities in the epicentral area, 10 years after the 2008 Wenchuan earthquake

The occurrence of debris flows after the earthquake not only damaged infrastructure but also caused large numbers of casualties. Analyzing the evolution of debris flows after the Wenchuan earthquake is significant for understanding the long-term impacts of the earthquake on the subsequent debris flow activities. This study made a continuous track of mass movements of loose deposits in all debris flow gullies from 2008 to 2018 along Provincial Road 303 (PR303) after the Wenchuan earthquake according to remote sensing images interpretation and field investigations. Four aspects, i.e. vegetation recovery, source materials, runout characteristics, and human activities were analysed to describe the evolution laws based on the 10-year inventory of debris flows. The loose deposits on the hillslope were found to be gradually transported into channels. A declining trend of mass transport rate from 2010 (24.5%) to 2018 (6.5%) was found. The occurrence frequency, magnitude, and mobility of debris flows were found to decrease. Recovery of vegetation made the loose deposits stable, whereas human activities of excavation of debris flows deposits promoted the occurrence of landslides. Three stages were divided for debris flows activities: the active period (2010–2014), the unstable period (2014–2017), and the recession period (after 2017). The decreasing trend of debris flows in the future is predicted.

Debris Flow Gully Classification and Susceptibility Assessment Model Construction

The location of debris flow occurring in a gully determines the observable differences in its formation, evolution and effects. In this piece of research, we propose a new method for debris flow gully classification based on the locations of the debris flows occurring in the gullies. It is termed the three-section method (TSM). It includes eight different types of gullies with different digital identifications (IDs) and susceptibility degrees (SDs). By taking the Jiangjia Gully (JJG), in Yunnan, China, as a case study site, the main gully and the sub-gullies at different levels were identified using a hydrological analysis method. Then, the gullies were divided into different types using the new classification TSM. The results show that there are seven different types of debris flow gullies in the JJG. The number of different types varied greatly in gullies at different levels. In particular, the topological diagram of debris flow gullies was drawn after simplifying the shape of the gullies, and it was a good way to understand the characteristics of debris flow gullies. Finally, the relationships were explored between the hypsometric integrals (HIs), surface exposures (SEs) and susceptibility degrees (SDs), and a new calculation model construction method for determining the degree of debris flow susceptibility was proposed. This model, using the above method, can not only be used to calculate the SDs of debris flows in the gullies, but can also be instrumental in pointing out the approximate locations of the debris flow commonly and easily occurring in the gullies. We hope that our research can provide a new concept for the assessment of debris flow susceptibility.

Method on Early Identification of Low-Frequency Debris Flow Gullies along the Highways in the Chuanxi Plateau

Low-frequency debris flows are characterized by strong concealment, high potential danger, and difficulty achieving an early warning; hence early identification of low-frequency debris flow gullies is crucial to mitigation. Here, an identification system for low-frequency debris flow gullies along the traffic arteries in the Chuanxi Plateau is proposed based on the identification and stability calculation of colluvium deposits in a hollow region (CDH) and the quantitative roundness analysis for the stones in a deposit fan. At first, for the watershed without a deposit fan, the CDH is identified and analyzed using the geomorphologic change point detection method combined with high-precision remote sensing images and field investigation. The watershed can be identified as a low-frequency debris flow gully with the safety factors (Fs) of all CDHs greater than 1. Then, the roundness of stones in the deposit fan is quantitatively analyzed. The watershed can also be identified as a low-frequency debris flow gully with the average roundness of the stones ranging from 0.30 to 0.41. Lastly, the identification system was tested and verified based on another ten watersheds along three traffic arteries. It shows that the method proposed in this paper has good applicability and high accuracy. Here we try to achieve the accurate early identification of low-frequency debris flow gullies by combining remote sensing interpretation and field investigation, which can provide theoretical support for predicting and mitigating debris flows in mountainous areas.

Comparison of Machine Learning and Traditional Statistical Methods in Debris Flow Susceptibility Assessment: A Case Study of Changping District, Beijing

As a common geological hazard, debris flow is widely distributed around the world. Meanwhile, due to the influence of many factors such as geology, geomorphology and climate, the occurrence frequency and main inducing factors are different in different places. Therefore, the evaluation of debris flow sensitivity can provide a very important theoretical basis for disaster prevention and control. In this research, 43 debris flow gullies in Changping District, Beijing were cataloged and studied through field surveys and the 3S technology (GIS (Geography Information Systems), GPS (Global Positioning Systems), RS (Remote Sensing)). Eleven factors, including elevation, slope, plane curvature, profile curvature, roundness, geomorphic information entropy, TWI, SPI, TCI, NDVI and rainfall, were selected to establish a comprehensive evaluation index system. The watershed unit is directly related to the development and activities of debris flow, which can fully reflect the geomorphic and geological environment of debris flow. Therefore, the watershed unit was selected as the basic mapping unit to establish four evaluation models, namely ACA–PCA–FR (Analytic Hierarchy Process–Principal Component Analysis–Frequency Ratio), FR (Frequency Ratio), SVM (Support Vector Machines) and LR (Logistic Regression). In other words, this research evaluates debris flow susceptibility by comparingit with two traditional weight methods (ACA–PCA–FR and FR) and two machine learning methods (SVM and LR). The results show that the SVM evaluation model is superior to the other three models, and thevalueofthe area under the receiver-operating characteristic curve (AUC) is 0.889 from the receiver operating characteristic curve (ROC). It verifies that the SVM model has strong adaptability to small sample data. The study was divided into five regions, which were very low, low, moderate, high and very high, accounting for 22.31%, 25.04%, 17.66%, 18.85% and 16.14% of the total study area, respectively, by SVM model. The results obtained in this researchagree with the actual survey results, and can provide theoretical help for disaster prevention and reduction projects.

Risk assessment of debris flow disaster based on the cloud model-Probability fusion method.

This paper proposes a new debris flow risk assessment method based on the Monte Carlo Simulation and an Improved Cloud Model. The new method tests the consistency of coupling weights according to the characteristics of the Cloud Model firstly, so as to determine the weight boundary of each evaluation index. Considering the uncertain characteristics of weights, the Monte Carlo Simulation is used to converge the weights in a minimal fuzzy interval, then the final weight value of each evaluation index is obtained. Finally, a hierarchical comprehensive cloud is established by the Improving Cloud Model, which is used to input the comprehensive expectation composed of weights to obtain the risk level of debris flow. Through statistical analysis, this paper selects Debris flow scale (X1), Basin area (X2), Drainage density (X3), Basin relative relief (X4), Main channel length (X5), Maximum rainfall (X6) as evaluation indexes. A total of 20 debris flow gullies were selected as study cases (8 debris flow gullies as model test, 12 debris flow gullies in reservoir area as example study). The comparison of the final evaluation results with those of other methods shows that the method proposed in this paper is a more reliable evaluation method for debris flow prevention and control.

Debris flow characteristics of the compound channels with vegetated floodplains

Compound cross-sections with vegetated floodplains are a common type of cross-section in debris-flow gullies. Floodplain vegetation participates in large-scale debris flow events and regulates debris-flow discharge. Extensive research has been conducted on the water flow characteristics of compound rivers. However, few studies have investigated the debris flow characteristics of compound channels in mountainous areas, particularly those of debris flow and flash flood inundation areas with vegetation. This study discusses the section characteristics of debris flow gullies with vegetated floodplains, gully evolution processes, and their influence on debris flow. The results show that the compound debris flow gully with a vegetated floodplain is formed in the gully from the mature stage to the old-mature stage. The compound sections are developed in flow areas with a gentle slope, which can be bilateral floodplain, unilateral floodplain, and multi-main gully floodplain types. Owing to the vegetation of the floodplain, the roughness of the channel increases, which makes the beach roughness coefficient much larger than that for the main channel. In the integrated Manning coefficient method, the error in resolving the flow velocity and discharge is large and cannot reflect the difference in velocities of the floodplain and main channel, therefore the sectional splitting method is most applicable. Influencing debris flow movement, limiting channel migration, and retaining debris flow to the main channel were the main contributions of the riparian forest zone.

Discrimination of debris flow in narrow-steep type and wide-gentle type gullies in Wenchuan meizoseismal area

The debris flow disasters in the Wenchuan meizoseismal area are dominantly triggered by the gully-type debris flow. Research on its classification method can be of great theoretical value and practical significance for developing targeted prevention measures. The current empirical classification method has some disadvantages, such as inconsistent discrimination criteria and poor practicability. In this paper, in order to overcome these drawbacks, the topography, rainfall, and source characteristics data of 176 gully-type debris flows in the Wenchuan “5.12” meizoseismal area since 2008 were collected and divided into the narrow-steep, transitional, and wide-gentle types based on field investigation. The narrow-steep type gullies are mainly concentrated in small catchments with severe erosion. In contrast, the wide-gentle type gullies are often characterized by big catchments, gentle vertical slopes, and debris flows movement dominated by deposition. An empirical discrimination method for debris flow gullies is proposed based on the characters of the gullies in the meizoseismal area, and a mathematical discrimination model named Gully Geomorphology Index (GGI) is also constructed. The results from existing cases indicated that both methods were accurate to discriminate between the narrow-steep and wide-gentle debris flow gullies. According to the empirical discrimination method, among the 176 channel-type debris flows, the numbers of narrow-steep, transitional, and wide-gentle channel types are 105 (59.66%), 12 (6.82%), and 59 (33.52%), respectively. While for the GGI method, the value 0.05 and 0.10 were defined as the threshold of the three types, and the distribution of the results is 104 (59.09%) for the narrow-steep type, 16 (9.09%) for the transitional type, and 56 (31.82%) for the narrow-steep type, which can better classify the transitional type gullies and is more practical. We hope that the discrimination methods proposed in this paper will help better understand the disaster-causing mechanism and improve the prevention measures of debris flow in the Wenchuan meizoseismal area.

Prediction of the Periglacial Debris Flow in Southeast Tibet Based on Imbalanced Small Sample Data

Using data sourced from 15 periglacial debris flow gullies in the Parlung Zangbo Basin of southeast Tibet, the importance of 26 potential indicators to the development of debris flows was analyzed quantitatively. Three machine learning approaches combined with the borderline resampling technique were introduced for predicting debris flow occurrences, and several scenarios were tested and compared. The results indicated that temperature and precipitation, as well as vegetation coverage, were closely related to the development of periglacial debris flow in the study area. Based on seven selected indicators, the Random Forest-based model, with its weighted recall rate and Area Under the ROC Curve (AUC) greater than 0.76 and 0.77, respectively, performed the best in predicting debris flow events. Scenario tests indicated that the resampling was necessary to the improvement of model performance in the context of data scarcity. The new understandings obtained may enrich existing knowledge of the effects of main factors on periglacial debris flow development, and the modeling method could be promoted as a prediction scheme of regional precipitation-related debris flow for further research.