Debris Flow Risk Research Articles

Pure risk premium rating of debris flows based on a dynamic run-out model: a case study in Anzhou, China

Debris flow risk is growing with the current increases in landscape exploitation and extreme precipitation events associated with global warming. Insurance is an efficient approach to cope with disasters. Based on the quantitative assessment, the debris flow hazard was obtained by a dynamic run-out model (FLO-2D), and the pure risk premiums of debris flow disaster were rating in a high-risk region (Anzhou) in Sichuan, China. Simulation results indicated that disastrous debris flows would reoccur every 5 years on average under the contemporary climatic and geological conditions of the study area. The mountain region experienced a wider area affected by debris flows than plains and hills; however, its area affected by low debris flow depth (d < 0.1 m) accounts for a larger proportion (56.69% and 22.60%, respectively). The probability of sub-basins with basin slopes of 5°–10° experiencing serious debris flows was pretty close to the probability of sub-basins with slopes of 25°–35°, which are considered to be susceptible to trigger debris flows. Furthermore, for relatively flat sub-basins (slopes < 25°), the part closer to source was more susceptible to higher flow depth. Risk assessment results showed that the significant spatial difference between the distribution of debris flow risk and hazard largely linked to the distribution of at-risk element (buildings). The pure risk premium of Anzhou was rated as 0.69–25.48‰ depending on the proportion of at-risk buildings to insured buildings, which highlights the need to encourage or require more low-risk and secure buildings to participate in insurance schemes to share the debris flow risk of the entire region.

Risk Assessment of Highway in the Upper Reaches of Minjiang River under the Stress of Debris Flow

The southwest mountainous area is a frequent debris flow disaster area in China, which poses a serious threat to the regional roads and greatly affects the normal traffic operation and the safety of residents’ lives and property. The debris flow risk assessment of highway can quantify the threat degree of debris flow to the roads. In this paper, from the perspective of villages and towns, taking the upper reaches of Minjiang River as the research area, four factors including road network density, highway disaster resistance capacity, population density and highway cost are selected, and the weight is obtained by using entropy weight method, and the debris flow vulnerability evaluation results of highway are obtained by weighted calculation. Four indexes of debris flow density, shape factor, relative height difference and annual rainfall are selected to evaluate the debris flow hazard of highway by using the information method. Based on the vulnerability and hazard evaluation results, the risk of highway debris flow is evaluated, and the results are classified and discussed. The results show that: the risk of debris flow on the upper reaches of Minjiang River is relatively low, the overall spatial distribution shows a trend of high in the East and low in the west, and the overall risk of township roads in Wenchuan county is the highest; The risk of debris flow is generally high within -10 to 30 km from the central fault zone of Longmenshan.

Characteristics and risk analysis of debris flow movement in a strong earthquake area - Sichuan earthquake stricken area as an example

It is very likely that debris flow will break out in a strong earthquake area under the stimulation of a large-scale rainfall, and its risk needs to be analysed. This study analysed the debris flow in a single gully in Wenchuan, introduced the natural environment, geological conditions, and travel conditions and movement characteristics of debris flow in Wenchuan, and then analysed the risk of debris flow using two methods. It was found from the results of a risk calculation based on evaluation factors that the risk of the study area was 0.76, which was high. It was found from the results of a risk calculation based on intensity that the debris flow in the study area had XL level risk, which needed long-term control and monitoring. The research results verify the high risk of debris flow in a strong earthquake area and provide some theoretical bases for debris flow control in that area.

Spatial Pattern and Response of Landslide and Debris Flow Risks in China-Pakistan Economic Corridor

Spatial Pattern and Response of Landslide and Debris Flow Risks in China-Pakistan Economic Corridor

Debris Flow Risk Assessment Method Based on Combination Weight of Probability Analysis

Risk assessment of debris flow is conducted by multicriteria decisions. Based on the shortcomings of the existing methods in determining the weight of assessment factors, this paper proposes a new approach to conduct a risk assessment of debris flow. This new approach regards the weight of factors as a uniform random variable, whose bounds could be determined by the equal weight method, maximal deviation method, and entropy method. The results of this new approach are obtained by Monte Carlo simulation. According to the risk of 72 debris flows collected in Beichuan, Sichuan, China, this new approach proves convergent. It is suggested that the minimum sample amount of Monte Carlo simulation should be 63095. The result also demonstrates that sorted results with different weights of factors vary a lot, so it is not convincing to sort samples with a specific weight.

Debris-flows, mudflows and submerged tree drifting in the plains

Traditionally, it is believed that debris-flows are formed only in mountainous areas, and their occurrence in flat areas is impossible. These ideas about the conditions for the development and spread of debris-flow processes are reflected in the Russian Technical Regulations that determine the composition of engineering surveys for construction. In these Regulations, debris-flow and potentially debris-flow areas are identified only in mountainous areas, while flat and hilly areas are shown as non-debris-flow. Debris-flow risks are not calculated for flat and hilly areas both in Russia and in other countries. The vulnerability of objects, structures and territories to the impact of debris-flows is not taken into account either in the development of urban planning documentation, or in the design of objects and structures, or in the development of recreational areas. When designing hydraulic engineering and other structures, the loads created by water flows with a density of up to 1000 kg/m3 are calculated, and not by debris-flows, mudflows and suspended streams with a density of 1100-2000 kg/m3. The results of the research show that the presence, even in local areas, of slopes exceeding (depending on the type of rock) 5°-10°, creates the possibility of incoherent debris-flows (suspended streams) on lowland small rivers. For example, the number of such small rivers in the Novgorod region is 20%. The presence of even local sections with slopes of more than 100‰ on flat rivers creates conditions for the formation of debris-flows and mudflows. Debris-flows and mudflows are formed on plains not only in ravines, but also in the beds of permanent and temporary small watercourses and in erosion furrows on the slopes of hills and river terraces. The density of debris-flows and mudflows of small volumes formed in erosion cuts on the slopes of hills and river terraces can reach 2000 kg/m3, and sediment flows passing through the beds of small rivers-1100-1200 kg/m3. Submerged tree drifting on small watercourses in its composition are not water streams, and the incoherent debris flows.

An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

In South Korea, the risk of debris-flow is relatively high due to the country's vast mountainous topographical features and intense continuous rainfall during the summer. Debris-flows can result in the loss of human life and severe property damage, which can be made worse due to the poor spatiotemporal predictability of such hazards. Therefore, it is essential to research the preemptive prediction and mitigation of debris-flow hazards. For this purpose, this study developed an ANN model to predict the debris-flow volume based on 63 historical events. By considering the morphology, rainfall, and geology characteristics of the studied area in central South Korea, the data of 15 debris-flow predisposing factors were obtained. Among these data, four predisposing factors (watershed area, channel length, watershed relief, and rainfall data) were selected based on Pearson's correlation analysis to check for significant correlations with the debris-flow volume. To determine the best performing ANN model, a validation testing was carried out involving ten-fold cross-validation with MSE and R2 using both training and validation datasets, which were randomly split into a 7:3 ratio. The model performance validation results showed that an ANN model with two hidden neurons (4×2×1 architecture) had the highest R2 value (0.828) and the lowest MSE (0.022). In addition, in a comparative study with other existing regression models, the ANN model showed better results in terms of adjusted R2 value (0.911) using all datasets. Furthermore, 94% of the observed debris-flow volumes from the ANN model were within 1:2 and 2:1 lines of the predicted volumes. The results of this study have shown the potentiality of the developed ANN model to be a useful resource for decision-making and designing barriers in areas prone to debris-flows in South Korea.

Characteristics and risk analysis of debris flow movement in strong earthquake area-taking Sichuan earthquake stricken area as an example

It is very likely that debris flow will break out in a strong earthquake area under the stimulation of a large-scale rainfall, and its risk needs to be analysed. This study analysed the debris flow in a single gully in Wenchuan, introduced the natural environment, geological conditions, and travel conditions and movement characteristics of debris flow in Wenchuan, and then analysed the risk of debris flow using two methods. It was found from the results of a risk calculation based on evaluation factors that the risk of the study area was 0.76, which was high. It was found from the results of a risk calculation based on intensity that the debris flow in the study area had XL level risk, which needed long-term control and monitoring. The research results verify the high risk of debris flow in a strong earthquake area and provide some theoretical bases for debris flow control in that area.

Debris Flow Risk Assessment in Wudongde Reservoir Area Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation

The outbreak of debris flow is a threat to human life and property, especially so in reservoir areas. Hence, it is critical to evaluate the risk of debris flow in the reservoir area. In this study, data were obtained through remote-sensing imagery interpretation and field survey. By using analytic hierarchy process, determined the weight of each evaluation factor was determined. Fuzzy comprehensive evaluation was applied for the debris flow risk assessment of Wudongde reservoir in China. The evaluation results show that the reservoir area has 20 mildly dangerous mudslides, 200 moderately dangerous mudslides, and 18 severely dangerous mudslides; only one debris flow ditch is extremely dangerous. Thus, the risk of debris flow in Wudongde reservoir area is relatively small and will not have a great impact on the construction and operation of hydropower stations here.

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.

Debris flow disaster risk analysis and modeling via numerical simulation and land use assessment

To mitigate the risk of debris flows, quantitative risk assessments for different land use types are quite significant. However, with respect to debris flows, no sound relationship has so far been established between risks and different land use types. This study developed a method of debris flow risk assessment by incorporating numerical simulations with land use types. First, the key debris flow intensities for different return periods, including movement velocities and maximum flow depths, were identified via numerical simulations. Second, a debris flow hazard classification model based on a combination of the debris flow intensity and the return period was established to assess debris flow hazard degree. Then, the land use distribution within the inundated areas was itemized via the interpretation of remote sensing and field surveys, and the debris flow vulnerability was determined by the degree of functional damage or the cost of recovery of land uses caused under a given hazard. Finally, the potential debris flow risk zones were identified and mapped by combining debris flow hazard and vulnerability. The methodology was applied and verified in a small debris flow watershed in China. The proposed methodology can be performed effectively to conduct debris flow risk analysis and can be widely employed for debris flow mitigation.

Risk perception and management of debris flow hazards in the upper salween valley region: Implications for disaster risk reduction in marginalized mountain communities

Debris flows in mountain environments are among the most destructive natural hazards often leading to loss of human lives and economic damage. Understanding the public perception of debris flow hazards and associated adaptation measures generates theoretical and practical basis for disaster risk reduction in mountain environments. Based on household questionnaires and key informant interviews, we determined risk perception and management of debris flow events in the Upper Salween Valley Region, China, and implications for disaster risk reduction in marginalized mountain communities. Our findings demonstrated that farmers perceived heavy and frequent rainfall as a primary natural cause for debris flow events, and road construction as a major anthropogenic factor. In addition, farmers perceived a big threat and high occurrence probability of debris flow hazards. Farmers obtained information from television programs and local government to enhance their awareness of debris flow risks; they preferred living away from debris flow gullies and relying on help from neighbors to reduce risks. The government employed emergency cards to improve farmers’ emergency response capabilities, and manual early-warning systems to ensure risk management. This research indicates a long-term interaction between mountain communities and debris flow hazards, and a development perspective which integrates the development of disaster risk reduction strategies into rural development programs. Therefore, risk reduction of debris flows in marginalized mountain communities will benefit from the integration of farmer-based measures into government-led programs, and the implementation of a comprehensive rural development scheme.

Assessment of Debris Flow Risk Factors Based on Meta-Analysis—Cases Study of Northwest and Southwest China

Debris flow is a type of special torrent containing numerous solid materials. With many types of factors affecting debris flow, there is no reliable basis for the selection of risk factors for debris flow risk assessment. Therefore, to study the factors affecting debris flow, exploring a reliable method for assessing the relative importance of these factors is a significant endeavor in debris flow prevention and control work. In this research, debris flow risk assessment was combined with meta-analysis to analyze quantitatively the relative importance of risk factors of debris flow in northwest and southwest China. The final relative importance of each factor in northwest China is as follows, maximum relative height difference &gt; slope of main channel &gt; maximum daily precipitation &gt; ratio of longitudinal slope &gt; drainage area &gt; length of main channel. In addition, in southwest China, maximum relative height difference &gt; maximum daily precipitation &gt; slope of main channel &gt; ratio of longitudinal slope &gt; length of main channel &gt; drainage area. The meta-analysis results were accurate, which can provide a reliable basis for the selection of debris flow risk factors in debris flow risk assessment. Furthermore, it provides strong support for the application of meta-analysis in risk assessment of other geological hazards.

A hybrid machine-learning model to estimate potential debris-flow volumes

Empirical-statistical models of debris-flow are challenging to implement in environments where sedimentary and hydrologic triggering processes change through time, such as after a large earthquake. The flexible and adaptive statistical methods provided by machine learning algorithms may improve the quality of debris flow predictions where triggering conditions and the nature of sediment that can bulk flows varies with time. We developed a hybrid machine-learning model of future debris-flow volumes using a dataset of measured debris-flow volumes from 60 catchments that generated post-Wenchuan Earthquake (Mw 7.9) debris flows. We input topographic variables (catchment area, topographic relief, channel length, distance from seismic fault, and average channel gradient) and the total volume of co-seismic landslide debris into the PSO-ELM_AdaBoost machine-learning model, created by combining Extreme learning machine (ELM), particle swarm optimization (PSO) and adaptive boosting machine learning algorithm (AdaBoost). The model was trained and tested using post-2008 Mw 7.9 Wenchuan Earthquake debris flows, then applied to understand potential volumes of post-earthquake debris flows associated with other regional earthquakes (2013 Mw 6.6 Lushan Earthquake, 2010 Mw 6.9 Yushu Earthquake). We compared the PSO-ELM_Adaboost method with different machine learning methods, including back-propagation neural network (BPNN), support vector machine (SVM), ELM, PSO-ELM. The Comparative analysis demonstrated that the PSO-ELM_Adaboost method has a higher statistical validity and prediction accuracy with a mean absolute percentage error (MAPE) less than 0.10. The prediction accuracy of debris-flow volumes trigged by other earthquakes decreases to 0.11–0.16 (absolute percentage error), suggesting that once calibrated for a region this method can be applied to other regional earthquakes. This model may be useful for engineering design to mitigate the risk of large post-earthquake debris flows.

Numerical Modeling of Debris Flows Induced by Dam-Break Using the Smoothed Particle Hydrodynamics (SPH) Method

Dam-break flows may change into debris flows if certain conditions are satisfied, such as abundant loose material and steep slope. These debris flows are typically characterized by high density and can generate strong impact forces. Due to the complexity of the materials that they are made of, it has always been very challenging to numerically simulate these phenomena and accurately reproduce experimentally debris flows’ processes. Therefore, to fill this gap, the formation-movement processes of debris flows induced by dam-break were simulated numerically, modifying the existing smoothed particle hydrodynamics (SPH) method. By comparing the shape and the velocity of dam break debris flows under different configurations, it was found that when simulating the initiation process, the number of particles in the upstream section is overestimated while the number of particles in the downstream area is underestimated. Furthermore, the formation process of dam-break debris flow was simulated by three models which consider different combinations of the viscous force, the drag force and the virtual mass force. The method taking into account all these three kinds of interface forces produced the most accurate outcome for the numerical simulation of the formation process of dam-break debris flow. Finally, it was found that under different interface force models, the particle velocity distribution did not change significantly. However, the direction of the particle force changed, which is due to the fact that the SPH model considers generalized virtual mass forces, better replicating real case scenarios. The modalities of dam failures have significant impacts on the formation and development of debris flows. Therefore, the results of this study will help authorities to select safe sites for future rehabilitation and relocation projects and can also be used as an important basis for debris flow risk management. Future research will be necessary to understand more complex scenarios to investigate mechanisms of domino dam-failures and their effects on debris flows propagation.

A coupled human and landscape conceptual model of risk and resilience in Swiss Alpine communities

Disasters induced by natural hazards or extreme events consist of interacting human and natural components. While progress has been made to mitigate and adapt to natural hazards, much of the existing research lacks interdisciplinary approaches that equally consider both natural and social processes. More importantly, this lack of integration between approaches remains a major challenge in developing disaster risk management plans for communities. In this study, we made a first attempt to develop a conceptual model of a coupled human-landscape system in Swiss Alpine communities. The conceptual model contains a system dynamics (e.g. interaction, feedbacks) component to reproduce community level, socio-economic developments and shocks that include economic crises leading to unemployment, depopulation and diminished community revenue. Additionally, the conceptual model contains climate, hydrology, and geomorphic components that are sources of natural hazards such as floods and debris flows. Feedbacks between the socio-economic and biophysical systems permit adaptation to flood and debris flow risks by implementing spatially explicit mitigation options including flood defenses and land cover changes. Here we justify the components, scales, and feedbacks present in the conceptual model and provide guidance on how to operationalize the conceptual model to assess risk and community resilience as well as determine which shocks overcome the buffering capacity of Swiss Alpine communities.

AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China

Debris flow is a major geohazard in mountainous regions and poses a significant threat to life and property. The damage caused by debris flows have increased with the expansion of human settlements and activity into the mountainous regions of China. In regards to risks from debris flows, previously unrecognized low-frequency debris flow catchments constitute an especially significant threat. According to our investigation, only about 500 catchments have debris flow records in >2000 catchments of Bailong River basin. The main purpose of this paper is to introduce a new methodology using Artificial Intelligence (AI) that can simultaneously input parameters related to geomorphological conditions and material conditions to better distinguish low-frequency debris flow catchments (LFDs) from medium-high frequency debris flow catchments (MHFDs). A total of 449 prototypical debris flow catchments, 15 parameters, and 9 commonly used learning machines were used to build identification models. Debris flow catchments are divided into 4 cases (LO1-LO4) based on different sample ratios of LFDs and MHFDs, which are input into each classifier one by one. Based on model evaluation, the CHAID model in the case LO2 performs best, which only uses five parameters (formation lithology index, land use index, vegetation coverage index, drainage density and landslide density index) to predict LFDs. The results indicate that LFDs are mainly distributed in areas with less landslide distribution and better vegetation coverage compared with MHFDs. However, the distribution of LFDs is concentrated on FLI (formation lithology index) =4, which is the weak lithology area. The tree classifier seems to be better at classifying fluvial processes. The model developed in this paper can help us quickly find LFDs in similar areas, and help to assess the risk of debris flows.

Risk Assessment of Debris Flow in Ya’an City Based on BP Neural Network

Since the occurrence of debris flow hazards is related to many factors, this paper selects five evaluation indicators: rainfall, lithology level, slope gradient, undulation degree and distance from faultages based on the three scopes of geomorphology, meteorology and geology. In this paper, the data extracted from the occurrence point of historical debris flow disaster is used, and BP neural network is used for training, then the weights of evaluation indicators are obtained. Finally, the GIS software is used to superimpose and analyze these indicators, and the risk of debris flow in Ya’an city is divided into five levels, thus the danger distribution map of debris flow in Ya’an city is obtained. And the map is compared with historical debris flow disaster occurrence points, the results show that, three districts of Tiancheng County, Lushan County and Baoxing County have the highest overall risk; the overall risk of Yingjing County, Yucheng District and Shimian County is high; and the overall risk of Hanyuan County and Mingshan District is relatively low. The evaluation results reflect the actual situation of the spatial distribution of debris flow in Ya’an city, which can provide a scientific basis for the development of reasonable flood control and disaster against planning in Ya’an city.

The assessment of quantitative risk to road users from debris flow

BackgroundA methodology for the quantitative risk assessment (QRA) of the impact of debris flow in a road environment has been developed and applied to two sites that are typically subject to high frequency-low magnitude and low frequency-high magnitude events. The methodology considers the probability of an event of a typical size, and the conditional probabilities of a vehicle being affected, given an event, and of damage (fatality) occurring given that the vehicle is affected. Scenarios covering a vehicle being hit by a debris flow and of a vehicle hitting a debris flow are considered. The computed Personal Individual Risk (PIR) is used to calculate worst case fatality probabilities for commuters and logistics truck drivers. The overall risk to society is expressed both by the annual probability of fatality amongst all road users, the Potential Loss of Life (PLL), and using the F-N diagram and is used to demonstrate the effect of a programme of management and mitigation works on the societal risk at one of the sites. The authors believe that this is the first full, formal quantitative risk assessment for debris flow risk to road users.ResultsThe PIR for a single trip through the sites ranges between 1.147E-10 for the low frequency-high magnitude site and 1.583E-09 for the high frequency-low magnitude site. These figures increase to 1.248E-07 and 1.922E-06, respectively, when more frequent travellers are considered. The PLL for the two sites ranges between 2.616E-04 for the low frequency-high magnitude site and 4.083E-03 for the high frequency-low magnitude site. The F-N diagrams illustrate the Broadly Acceptable level of risk at the low frequency-high magnitude site and the partially Unacceptable level of risk at the high frequency-low magnitude site. The risk at the high frequency-low magnitude site is reduced to ALARP levels when management and mitigation measures extant as of October 2014 are considered.ConclusionsThe QRA proves an effective technique for understanding, comparing and articulating the differences in levels of risk and the temporal changes in risk at a given site as a result of landslide risk reduction activities.

Gradation and Rheological Characteristics of Glacial Debris Flow along the Kangding–Linzhi Section of Sichuan–Tibet Railway

To date, most research on the characteristics of glacial debris flow along the Sichuan–Tibet railway has focused on numerical simulations and remote sensing, resulting in a lack of direct experimental data from debris flow samples. Therefore, in the present study, a field investigation was conducted along the Sichuan–Tibet railway, and 55 samples of glacial debris flow deposits were systematically analyzed to determine their grading and rheological properties. This is the first systematic experimental study on glacial debris flow deposits. The results showed that the proportion of coarse particles was high and the proportion of fine particles was low in the glacial debris flow along the Sichuan–Tibet railway. The average gravel and sand contents were 37.8% and 58%, respectively, and the average contents of silt and clay were 3.7% and 0.47%, respectively. The average fractal dimension was 2.1507, which is much greater than that of viscous debris flow. Under the same gravity and shear rate, the stress of typical glacial debris flow was significantly less than that of viscous debris flow, whereas the variability of the stress was more significant. These findings will contribute to revealing the movement rules and disaster risk of glacier debris flow along the Sichuan–Tibet railway and have considerable theoretical and practical significance for ensuring safety during both its construction and later operation.