Debris-flow Prone Area Research Articles

Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan

What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R2 = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas: A case study in the Hengduan Mountains

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas: A case study in the Hengduan Mountains

Methodology for assessing pipeline failure probability due to a debris flow in the near field

Aboveground transmission oil pipelines can cross debris flow-prone areas. Currently, there are no available methodologies to assess pipeline failure status with the different pipeline arrangements (location, direction, and segment lengths) and different operating conditions. For solving the research gap, this study proposes a novel methodology to simulate the cascade processes of debris flow propagation, the impact of debris flow on pipelines, and pipeline failure. With consideration of different pipeline arrangement and operating conditions. We introduce the polar coordinate system to set up locations and directions scenarios for the first time. Also, we use the 3-D debris flow simulation model (DebrisInterMixing solver in OpenFOAM) coupled with a modified pipeline mechanical model considering operating conditions for the first time. The proposed methodology shows the different trends of pipeline failure probability with the increase of pipeline segment length for the different pipeline locations and directions. The result shows, for the pipelines of 30° the tensile stress has a more moderate increase rate with the increase of pipeline segment length, and the pipeline failure probability keeps 0 at the 5-m location. At 5 m and 15 m locations, the failure probabilities of the pipelines of 60° and 90° start to increase when the segment length is 13–14 m, while for other pipelines the segment length is 17–19 m. The findings of this study can support the decisions of government authorities, stakeholders, and operators for risk assessment, prioritization of hazard mitigation measures and emergency planning, or concerning decisions regarding pipeline siting during the design, routing, construction, operation, and maintenance stage.

A multi-step hazard assessment for debris-flow prone areas influenced by hydroclimatic events

Hazard assessment studies are fundamental to identifying disaster-prone areas, especially in locations with high environmental and socioeconomic vulnerability. This study proposes a multi-step debris-flow hazard assessment, based on the combination of Logistic Regression (LR) analysis, numerical simulation and rainfall back-analysis. A landslide-prone area of 84 km2 is chosen as test-site, including 20 river catchments and one of the largest petrochemical plants in Latin America. Rainfall is the main influencing factor in debris-flow initiation, as highlighted by the LR analysis, followed by soil cover and slope. The analysis also indicated the catchments more susceptible to debris flows and the simulation results show that the average runout distance in these catchments is 470 m, with an average peak flow height of 5 m and a peak velocity of 23 m s−1. Debris flows are triggered by short duration (<48 h), high-intensity (>200 mm) precipitation, with return periods that vary from 3 to 10 years. Five levels of hazard (very low to very high) are, then, proposed for the study site, based mainly on 48-h accumulated rainfall and flow properties. Industrial and residential areas in the projected debris-flow route generally exhibit the highest overall hazard levels, as many were developed in the depositional area of debris flows and near fluvial courses, where associated floods and flash floods may occur. As pointed out by recent studies, an increase in the frequency of extreme precipitation events is projected in the Serra do Mar region and when the general short return period of the debris-flow triggering rainfall is considered (<10 years), large magnitude (>105 m3) debris flows are likely to occur in the near future.

A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples.

Ecosystem health assessment in debris flow-prone areas: A case study of Bailong River Basin in China

Ecosystem health assessment (EHA) is critical for the effective management of ecosystem services and evaluation of global environmental changes. Here, we explored the spatiotemporal patterns of EH in the Bailong River Basin in a debris flow-prone area from 2000 to 2019 using the VORS model. Net primary productivity, landscape index, land use land cover, water yield, and soil retention data were used to assess the vigor, organization, resilience, and services of the ecosystem. Moreover, using the spatial correlation method and hierarchical cluster analysis, the spatial clustering of and regional differences in EH were investigated in grid and catchment area. In the grid scale, the EH is optimal in the southwest but poor in the northeast. In most areas of the basin, the ecological health index (EHI) showed an increasing trend, and this pattern is expected to continue in the future from 2000 to 2019. The EHI showed significant global spatial correlation and local spatial positive correlation; however, the degree of clustering showed a gradual decline from 2000 to 2019. Based on the results of hierarchical clustering, the catchment area in the basin was divided into four clusters to elucidate regional differences in EH. Areas with high EHI were mainly located in the southern part of the basin, whereas those with low EHI were located in the east of the Wudu District and north of the Tanchang County. Overall, EHA is an important tool to obtain information on the state of the ecosystem, and its findings are fundamental for protecting and improving EH. Therefore, EHA should be incorporated in the decision-making framework for realising effective and appropriate ecosystem management.

Projection Pursuit Method Based on Connection Cloud Model for Assessment of Debris Flow Disasters

A rational evaluation of the danger of debris flow disasters at the regional scale is essential for developing effective disas-ter prevention measures and economic planning in debris flow-prone areas. A novel projection pursuit method based on the connection cloud model and fruit fly optimization algorithm is addressed to analyze the dangerous degree of debris flow disasters at the regional scale, considering the random and fuzzy uncertainties of the projection direction vector. In this method, the connection cloud model gen-erates the candidate projection directions around the latest optimization; these candidate projection direction vectors are screened based on set pair analysis to advance the convergence rate. Case studies and comparisons with other algorithms are further carried out to verify the validity and reliability of the proposed method. Results demonstrate that the proposed method does not require existing evaluation criteria compared to the conventional evaluation methods. It can describe the randomness and fuzziness of the projection direction vector and better find the structural characteristics of fuzzy indicators randomly distributed in the finite intervals with a quicker convergence rate.

Indices for exploring information in Lorentz curve of daily precipitation and their application in natural disaster risk assessment

The concentration index (CI) based on the Lorenz curve has been widely used to analyse the temporal structure of daily precipitation. However, two different Lorenz curves may have the same CI, while indicating different distribution information of precipitation. Previous studies have proposed Lorentz asymmetry coefficient, the value of which concentrated around 0.9 without significant distinction. Additional indicators are thus in need to investigate the complete information in Lorenz curves besides CI. In this study, six geometrical indices are defined to characterize a Lorenz curve. Together with the CI, any one of the proposed indices can help determine a unique Lorenz curve. An analysis of the daily precipitation over the Qinghai–Tibet Plateau derived five groups of meteorological stations with identical CIs, but completely different Lorenz curves. The correlation analysis between the CI and proposed indices indicated that there are two parameters independent of the CI, representing the contribution of heavy rain and the position of the transaction point, respectively, thus introducing relatively new information on precipitation beyond the CI. Within the stations with high CIs, these two indices were used to further identify areas in the Qinghai–Tibet Plateau at a high risk for natural disasters, such as rainstorms, debris flows, and aridity, the derived results were consistent with the debris flow prone areas in China designated by China Meteorological Administration. The proposed indices can not only help CI to describe the Lorentz curve of daily rainfall, but can also help identify areas at a high risk for natural disasters.

Check dam storage capacity calculation based on high-resolution topogrammetry: Case study of the Cutou Gully, Wenchuan County, China

Debris flows are a common natural trigger of disasters in mountainous areas, and check dams are standard structural measures for controlling debris flows. Despite their prevalence in debris flow-prone areas worldwide, the capacity of check dams is still calculated using empirical formulas, which lead to large calculation errors. This paper proposes a new method that uses GIS to calculating the design storage capacity of a check dam in the debris flow-prone Cutou Gully in Wenchuan County, China. Large-scale digital surface models derived from unmanned aerial vehicle imagery and ground surveys identify local topographic changes in the debris flow path and develop appropriate maintenance plans for check dams. The measured storage capacity of the check dam is determined by analyzing the DEM differences. This study uses the newly proposed method to calculate the design storage capacity of the check dam. The accuracy of the calculation results was evaluated using the checkpoint method, and the results showed that the design and measured siltation surface errors ranged from −1.16–2.96 m, with a root mean square error of 0.93 m. The design capacity of the check dam is 33.6× 104 m3, and the actual capacity is 36.7× 104 m3, with an absolute error of 3.1× 104 m3 and relative error of 8.6%. The results prove the validity of the proposed calculation method; moreover, this study shows that the new method is accurate, easy to operate, and highly efficient for visualizing the spatial distribution of the siltation depth behind the check dam. This work will help improve future engineering decisions, design strategies, and find optimal design solutions to minimize the risk of debris flow hazards.

Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms

Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes is still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms – grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning.

Assessment of Building Vulnerability Curve Subjected to Debris-Flow

In this study, the vulnerability curves for masonry and concrete frame buildings are assessed based on building fragility curves for the debris-flow caused by landslides on mountain slopes. The First-Order Second-Moment (FOSM) method is used to estimate the building fragility curve (expressed as probability of damage exceedance) subjected to debris-flow. In this method, the horizontal displacement of a building impacted by debris-flow and the statistics of resistance (i.e., building displacement) following four different damage states (i.e., slight, moderate, extensive, and complete) are utilized to estimate the building fragility curve. The building vulnerability curves (expressed as mean probability of damage) were evaluated based on the estimated building fragility curve and corresponding mean damage ratio for each damage state and were verified by calculating the root mean square error with datasets obtained from post-disaster damage assessment. In this study, the effects of structural material, type, and height on the building vulnerability curves were also studied. All vulnerability curves of buildings estimated in this study were fitted and databased using parameters of the log-normal cumulative distribution function and can be used to measure the performance of buildings in debris-flow prone areas as well as to provide information for risk and loss assessment.

Risk assessment of Sichuan–Tibet Highway susceptible to debris flows at Suotong Basin, southeastern Tibet

The planned Sichuan–Tibet Highway will pass through many debris flow-prone areas. Suotong catchment with a drainage area of 38.03 km2, a tributary of Parlung Zangbo River in the Bomi County of Tibet, has not experienced large-scale debris flows since 1991. Field survey and image interpretation show that glacial till in the catchment has volume of approximately 160 mm3 and is the primary source of loose materials for debris-flow reoccurrence. Once large glacial debris flows occur, they will cause harm to the highway. A comprehensive method including engineering geology, hydraulics, and computational mathematics is developed to quantitatively assess the debris-flow risk to the highway. The magnitude and peak discharge of glacial debris flows of a 100 year return period are calculated using empirical relationships in this region. The peak velocity at the outlet can reach 6.75 m/s, and the peak discharge is 6238 m3/s for such a return period. The Saint–Venant equations governing debris-flow movements are numerically solved by finite difference method. The simulated results are close to those by empirical methods. The hazard value at the alluvial fan is as high as 24.7 according to the numerical simulation. Two routing schemes (one goes through the tunnel and other pass the bridge) are compared with each other. Although the bridge scheme is cheaper than the tunnel scheme, the bridge’s section cannot fully discharge debris flows within the 100-year return period. Therefore, the tunnel scheme is safer and more strongly recommended than the bridge scheme.

A comparison of statistical and machine learning methods for debris flow susceptibility mapping

Debris flows destroys the facilities and seriously threatens human lives, especially in mountainous area. Susceptibility mapping is the key for hazard prevention. The aim of the present study is to compare the performance of three methods including Bayes discriminant analysis (BDA), logistic regression (LR) and random forest (RF) for debris flow susceptibility mapping from three aspects: applicability, analyticity and accuracy. Nyalam county, a debris flow-prone area, located in Southern Tibet, was selected as the study area. Firstly, the dataset containing 49 debris flow inventories and 16 conditioning factors was prepared. Subsequently, divided the dataset into two groups with a ratio of 70/30 for training and validation purposes, and repeated 5 times to obtain 5 different groups. Then, 16 factors were involved in the modeling of RF, of which 11 factors with low linear correlation were for BDA and LR. Finally, receiver operating characteristic curves, the area under curve (AUC) and contingency tables were applied to evaluated the accuracy performance of the 3 models. The prediction rates were 74.6–81.8%, 74.6–83.6% and 80–92.7%, for the BDA, LR and FR, while the AUC values of three models were 0.72–0.78, 0.82–0.92 and 0.90–0.99, respectively. Compare to LR an BDA, RF not only effectively process and preserved dataset without priori assumption and the obtained susceptibility zoning map and major factors were reasonable. The conclusion of the current study is useful for risk mitigation and land use planning in the study area and provide related references to other researches.

Driving forces of NPP change in debris flow prone area: A case study of a typical region in SW China

Net primary productivity (NPP) is one of the essential indicators of regional eco-environment security. In ecologically fragile areas, debris flow prone areas are extremely sensitive to human and natural impacts. In this study, our goal was to explore the main driving forces and their characteristics that caused changes in NPP. This was achieved by collecting and analyzing multi-source data of a typical region in SW China. A pixel-based spatial analysis method was conducted in this study to quantify the co-evolution characteristics between NPP and the main driving forces. Our results showed that the overall NPP in the region exhibits an increasing trend. The main driving forces are NDVI and population density, which can explain >70% of the environmental states. In addition, co-evolution characteristics were obvious; generally, NDVI had a positive effect and population density had a negative effect. However, extremely significant negative correlations between NPP and NDVI were found in 12.01% of the study area. Therefore, carrying out reasonable ecological restoration and applicable human activities in debris flow prone areas could ensure eco-environment security and sustainable development. This study contributes to improving our understanding of eco-environment evolution of critical zones and provides a scientific basis for regional eco-environment security construction and eco-treatment of debris flow.

Characterization of acceptable risk for debris flows in China: Comparison in debris-flow prone areas and nonprone areas

People's perception of debris-flow risk may be different in debris-flow prone and nonprone areas. Investigation of population risk consensus in different regions may provide a high-quality technical analysis for the acceptable risk criteria of debris-flow disasters. For this study, five indicators (disaster-affected toll, death toll, death rate, personal direct economic loss, and total direct economic loss) were designed to determine the acceptable risk level of debris-flow disasters for the public, and the demographic characteristics of the public affecting the acceptable risk of debris-flow disasters were identified using five factors (age, sex, educational level, occupation, and income). Questionnaire surveys were conducted in debris-flow prone and nonprone areas, respectively. A total of 823 valid samples were obtained, among them, 459 questionnaires were collected in debris-flow prone areas, and 364 questionnaires were collected in debris-flow nonprone areas. After statistical and comparative analyses, the results demonstrate that the acceptable criteria of disaster-affected toll was 100 persons, death toll was 1 person, death rate was 5 × 10−7/year, personal direct economic loss was 1000 CNY, and total direct economic loss was 1 × 105 CNY. Age and sex exhibited almost the same features for the acceptable risk levels of debris-flow disasters, whereas educational level, occupation, and income displayed discrepancies in both study areas, which resulted from imbalanced economic development, discrepant risk perception, and differing personal wealth.

Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China

Risk assessment lays a foundation for disaster risk reduction management, especially in relation to climate change. Intensified extreme weather and climate events driven by climate change may increase related disaster susceptibility. This may interact with exposed and vulnerable socioeconomic systems to aggravate the impacts and impede progress towards regional development. In this study, debris flow risk under climate change was assessed by an integrated debris flow mechanism model and an inclusive socioeconomic status evaluation. We implemented the method for a debris flow-prone area in the eastern part of the Qinghai-Tibet Plateau, China. Based on the analysis of three general circulation models (GCMs)—Beijing Climate Center Climate System Model version 1 (BCC_CSM), model for Interdisciplinary Research on Climate- Earth System, version 5 (MIROC5, and the Community Climate System Model version 4 (CCSM4)—the water–soil process model was applied to assess debris flow susceptibility. For the vulnerability evaluation, an index system established from the categories of bearing elements was analyzed by principle component analysis (PCA) methods. Our results showed that 432 to 1106 watersheds (accounting for 23% to 52% of the study area) were identified as debris-flow watersheds, although extreme rainfall would occur in most of the area from 2007 to 2060. The distributions of debris flow watersheds were concentrated in the north and transition zones of the study area. Additionally, the result of the index and PCA suggested that most areas had relatively low socioeconomic scores and such areas were considered as high-vulnerability human systems (accounts for 91%). Further analysis found that population density, road density, and gross domestic production made great contributions to vulnerability reduction. For practical mitigation strategies, we suggested that the enhancement of road density may be the most efficient risk reduction strategy.

Acceptability of debris-flow disasters and influential factors in a hazard prone area of northwestern China

Risk acceptability is an inevitable problem in human societies. In this case study of a debris-flow prone area in Zhouqu County of Gansu Province in northwestern China, on-site questionnaire surveys were administered and statistical analysis was conducted to evaluate public acceptance of debris-flow disasters and to identify the critical factors influencing their acceptability. The results indicate that local people's principal concern with regard to debris-flow-disaster consequences was disease occurrence, and their secondary concern was environmental destruction. Housing-related destruction represented the most common property-loss concern. The acceptability value of the distance between people's living or working place and debris-flow-disaster location (Distance) was 3.25 (moderate grade); the frequency of debris-flow occurrence in each community or village (Frequency) received an acceptability value of 1.43 (low grade); the warning time before debris-flow occurrence (Warning time) received an acceptability value of 3.18 (moderate grade); and people's willingness to pay for debris-flow-disaster insurance per year (Insurance premium) was reflected by an acceptability value of 3.33 (moderate grade). The significant factors affecting Distance acceptability were age, sex, and income; the significant factors affecting Frequency acceptability were sex, occupation, and income; the significant factors affecting Warning-time acceptability were educational level and income; and the significant factor influencing Insurance-premium acceptability was educational level. Income played the most influential role in respondents’ assessments of debris-flow-disaster acceptability. This study may provide insight into the reasons behind the courses of action people choose when faced with debris-flow disasters.

Analysis of the Correlation Between Strength and Fractal Dimension of Gravelly Soil in Debris-flow Source Areas

Particle size distribution of gravelly soil plays a crucial role in debris flow initiation. For better understanding the mechanism of debris flow formation, two crucial mechanical property parameters of the gravelly soil are required to be studied meticulously: hydraulic conductivity and strength. With the aim of measuring the composition of the gravelly soil, 182 soil samples were taken from debris flow prone areas. With the aid of a sieve test, the particle size distribution of the samples can be obtained and analyzed. Then fractal theory was employed to compute the fractal dimension of the soil samples. By analyzing the results of sieve test (particle size distribution curves) and the results of the fractal theory calculations, the relationship between fractal dimension and particle size distribution can be explored. The results illustrate that the particle compositions of the gravelly soil tends to remain uniform as the fractal dimension increases. Moreover, as the coarse particle content increases, the fractal dimension decreases. To better understand the formation mechanism of debris flows, direct shear tests were conducted. Subsequently the experimental results were analyzed. By analysis, the following conclusions can be drawn: the soil strength decreases as the fractal dimension increases, and for soils with lower moisture content and identical dry density, a linear relationship between fractal dimension and cohesion force was identified. Moreover, cohesion force and internal friction force both decrease as the fractal dimension increases, but the internal friction angle decreases slightly while the cohesion force decreases greatly. Therefore we concluded that soil strength decreased mainly due to the reduction in cohesion force.

도시내 산지의 토석류 위험구역 예측

As casualties are increased due to a landslide recently, it is emphasized that the division of a dangerous area from sediment disaster, specially in an urban area, is getting important. The aim of this study is to redefine how to delineate debris flow-prone area based on the hydrological micro-topography analysis techniques and to enhance the cognitive of residents on that risk in an urban mountainous area. As the results, movable sediment (Vec) is excessively larger than the erosional sediment (Ve), the range of runout distance is in order of using spread angle(L3), set lowest part of the debris flow-prone area(Lh), using stream length(L1), empirical formula(L2). But the maximum distance is in (L1)<(L2)<(Lh)<(L3). Therefore, the value of (L1), (L2) are proper in urban area, it was projected to be more proper that the value of (L 3 ) and (L h ) applicate in bigger watershed and lower development part. The method of hydrological microtopography analysis is likely to contribute to strengthening the activity of social disaster prevention by enabling the self-diagnostic determination of inhabitants on dwelling areas.

Experimental analysis on the impact force of viscous debris flow

AbstractA miniaturized flume experiment was carried out to measure impact forces of viscous debris flow. The flow depth (7.2–11.2 cm), velocity (2.4–5.2 m/s) and impact force were recorded during the experiment. The impact process of debris flow can be divided into three phases by analyzing the variation of impact signals and flow regime. The three phases are the sudden strong impact of the debris flow head, continuous dynamic pressure of the body and slight static pressure of the tail. The variation of impact process is consistent with the change in the flow regime. The head has strong–rapid impact pressure, which is shown as a turbulent‐type flow; the body approximates to steady laminar flow. Accordingly, the process of debris flows hitting structures was simplified to a triangle shape, ignoring the pressure of the tail. In order to study the distribution of the debris flow impact force at different depths and variation of the impact process over time, the impact signals of slurry and coarse particles were separated from the original signals using wavelet analysis. The slurry's dynamic pressure signal appears to be a smooth curve, and the peak pressure is 12–34 kPa when the debris flow head hits the sensors, which is about 1.54 ± 0.36 times the continuous dynamic pressure of the debris flow body. The limit of application of the empirical parameter α in the hydraulic formula was also noted. We introduced the power function relationship of α and the Froude number of debris flows, and proposed a universal model for calculating dynamic pressure. The impact pressure of large particles has the characteristic of randomness. The mean frequency of large particles impacting the sensor is 210 ± 50–287 ± 29 times per second, and it is 336 ± 114–490 ± 69 times per second for the debris flow head, which is greater than that in the debris flow body. Peak impact pressure of particles at different flow depths is 40–160 kPa, which is 3.2 ± 1.5 times the impact pressure of the slurry at the bottom of the flow, 3.1 ± 0.9 times the flow in the middle, and 3.3 ± 0.9 times the flow at the surface. The differences in impact frequency indicate that most of the large particles concentrate in the debris flow head, and the number of particles in the debris flow head increases with height. This research supports the study of solid–liquid two phase flow mechanisms, and helps engineering design and risk assessment in debris flow prone areas. Copyright © 2015 John Wiley &amp; Sons, Ltd.