Debris Flow Model Research Articles

An integrated hydrodynamic model for runoff-generated debris flows with novel formulation of bed erosion and deposition

Debris flow is one of the most common geohazards in mountainous regions, posing significant threats to people, property and infrastructure. Among different types of debris flows, runoff-generated debris flows are attributed to rain storms, which provide abundant runoff that entrain large quantities of bed material, resulting in the formation of a solid-liquid current known as a debris flow. One of the keys to effectively simulating runoff-generated debris flows is modelling the erosion-deposition process. The commonly used approach for formulating erosion and deposition, although constrained by physics, suffers from a singularity in the presence of vanishing velocity, which poses a major challenge for practical applications. It is also argued that the deposition rate cannot be represented by simply reversing the sign of the erosion rate. To address these two issues, we have developed a depth-averaged debris flow model with a novel method of calculating the erosion-deposition rate. We have demonstrated that the singularity is due to the non-linear erosion-deposition term but quickly disappears while the flow converges to the equilibrium that is defined by the classic Takahashi's formula. To resolve the non-linearity and avoid the singularity, an implicit method within a Godunov-type finite volume framework has been proposed. An additional parameter is introduced to differentiate the erosion rate from the deposition rate. The model is validated against several test cases, including a real-world debris flow event. Satisfactory results are obtained, demonstrating the model's simulation capability and potential for wider applications such as risk assessment and impact-based early warning.

Debris flow simulation and modeling of the 2021 flash flood hazard caused by a rock-ice avalanche in the Rishiganga River valley of Uttarakhand

Debris flow simulation and modeling of the 2021 flash flood hazard caused by a rock-ice avalanche in the Rishiganga River valley of Uttarakhand

Simulation and Prediction Algorithm for the Whole Process of Debris Flow Based on Multiple Data Integration

In order to solve the problems of large errors and low accuracy in debris-flow forecasting, the simulation and prediction algorithm for the whole process of debris flow based on multiple data integrations is studied. The middleware method is used to integrate multiple GIS data sets, and the GIS spatial database after multiple data integrations is used to provide the basis of data for the whole process simulation and prediction of debris flow. The spatial cellular simulation model of debris flow is built using the cellular automatic mechanism. The improved kernel principal component analysis method is used to reduce the dimension of debris-flow prediction index data. The reduced dimension index data is input into the support vector machine, and the support vector machine is used to output the prediction results of debris flow in the space cell simulation model of debris flow. Through the simulation visualization technology, the dynamic display of the simulation prediction of the whole process of debris flow is carried out. The experimental results show that the algorithm can realize the simulation of the whole process of debris-flow changes, that the prediction results of debris flow are close to the actual results, and that the error is less than 5%, which improves the prediction accuracy of debris flow and can be used as the auxiliary basis for relevant decision-making departments.

Geoinformatics Perspective of Landslide and Catastrophic Flash Floods in Dhauliganga, Uttarakhand, India

On 07 February 2021, around 10:30 hrs local time catastrophic flash flood occurred in the Dhauliganga River (a tributary of the Ganga River) near Rini village at 2000 m above MSL (mean sea level) (Chamoli District), which killed 79 people and about 125 people were missing. Part of the area belongs to Nanda Devi Biosphere Reserve, which is completely protected from human interventions. Further, on the Dhauliganga River, two run-of-river hydroelectric power Rishiganga Small Hydro (13.2 MW) at 1975 m above MSL and Tapovan Vishnugad (520 MW) at 1795 m above MSL projects were also severely damaged due to the devastating flash flood. More than 150 workers were also trapped in the under-construction power tunnel of the Tapovan Vishnugad project. Initial assessment on the day of the event suggested that there was a glacial burst. Later, it was evaluated through time series of high spatial resolution remote sensing images of various satellites that a large part of a north-facing triangular-shaped slope at 5540 m above MSL had failed, which was also supporting a small hanging glacier. This landslide and, consequently, massive debris flow into the Raunthi Gadhera initially blocked the flow of Dhauliganga near Rini village [at 2000 m above MSL (mean sea level)], which later failed around 10:30 hrs on 07 February 2021 and brought a catastrophic flash flood in the Dhauliganga river. Further, remote sensing images acquired around 10:33 hrs of 07 February 2021 revealed a large dust cloud which clearly unravels the sequence of events from a high-altitude landslide, collapse of a small hanging glacier, and snow avalanche to catastrophic flooding. Even after the catastrophic flash flood of 07 February 2021, an elongated lake was created due to the blocking of the flow of the Rishi Ganga River. For detailed analysis, the calculation of dimension, area and volume of the failed slope was done using the high-resolution satellite images and digital elevation model using the RAMMS modelling technique. The north-facing triangular shape had a base of about 660 m and 1100 m height and the estimated total volume calculated was 20 million cubic meters, including rocks, snow, and ice. The debris flow runout simulation of the event was performed using the RAMMS debris flow model to calculate flow depth, flow velocity and maximum pressure. Also, from high-resolution satellite images, the dimensions of the artificial Rini Lake were estimated to have a length of about 800 m, a width at the front of about 100 m and a depth of about 46m, including freshly deposited debris and silt of about 10 m. To calculate the volume of the lake, simulation of lake was done in ArcView software using digital elevation model, and it came out to be ~5 million cubic meters. The paper also emphasizes monitoring of such vulnerable areas based on high-resolution time series satellite images, which are available on a regular basis to avoid the loss of human lives in the future.

Comparative Analysis of Debris Flow Numerical Simulation Based on the Difference between the Resolution of Topographic Information and Grid Size

In this study, a comparative analysis was conducted for the debris flow that occurred in Gokseong-gun, Jeollanam-do, in August 2020, using the difference between the resolution of topographic information and grid size. High-resolution topographic information (0.03 m) was established through photogrammetry. For comparison, the low-resolution topographic information was obtained from the contour lines of the 1:5000 digital map. The topographic information was depicted as a grid, with resolutions of 1, 5, and 10 m, and analyzed by setting the simulation grid size to 1, 5, and 10 m. According to the topographic resolution, no significant difference was obtained in the numerical simulation results, when high-resolution topographic information from UAV photogrammetry was used, and the analysis was similar to the actual debris flow. Additionally, when the size of the analysis grid for the simulation was 10 m, the simulation was very similar to the actual debris-flow situation. For predicting a risk area using a debris-flow model, it is very important to reflect actual information, such as topography, geology, and rainfall; technical parameters, such as the grid size, are also important. Moreover, determining the optimal grid size by considering the conditions of the study area, such as the basin area, is necessary.

Prediction model of outburst debris flow triggered by landslide dam failure considering the erosion process

The formation of outburst debris flow is different from other types of debris flow as it is formed by the gradual erosion of the riverbed by the unsteady and rapid outburst flow triggered by landslide dam failure. However, a prediction model for outburst debris flow that comprehensively considers the erosion effect has not yet been established. In this paper, the shallow water equation is used to describe the motion of outburst flow. This study proposes a method for constructing a virtual unit and resetting the terrain variables to reproduce the wet and dry boundary. An erosion model considering the effect of hindered erosion is introduced. By combining the shallow water equations, a debris flow prediction model that considers the entire erosion process of outburst flow is thus proposed. The established model is verified by numerical experiments on the evolution of dam-break flow on a dry fixed bed and on a moveable bed; additionally, it is verified by physical experiments on the formation of outburst debris flow. Combined with the calculation results of erosion models with and without a hindered erosion effect, the erosion model without the hindered erosion effect overestimates the flow concentration in this paper. The simulation results are closer to the actual values when considering the effect of hindered erosion in the analysis of outburst debris flow formation. The proposed model in this paper can be used as a tool to predict the occurrence of outburst debris flow.

Geoinformatics-based investigation of slope failure and landslide damming of Chenab River, Lahaul-Spiti, Himachal Pradesh, India

A huge landslide blocked the flow of the Chenab River near Nalda village on the morning of 13th August 2021 in the Lahaul-Spiti district of Himachal Pradesh, which led to the flooding of several villages (Nalda, Jasrath, Tarang, etc.) in the Udaipur subdivision. The slope (∼200 ​m height, ∼220 ​m width, and approx. 30 ​m depth) on the left bank of the Chenab River failed, which brought colossal soil and debris. This resulted in the damming of the Chenab River near Leh Baring village, which lies upstream of Nalda Village Bridge and opposite to Junde village, creating a huge water reservoir that later started overflowing, posing a major threat to downstream villages. This caused damage to the houses located downstream, which were submerged, animals were also washed away, and a large part of agricultural land was also inundated. The event was observed and studied using satellite images of high-resolution obtained from Google Earth and Sentinel 2A. The Spatio-temporal satellite images were used to observe the scars developed in the region over the past few years along the river, which evidently show the early signatures of slope failure. To analyze the stability of the hill slope, the factor of safety of the hill was evaluated using SLOPE/W GeoStudio software. Slope map and drainage density map were also generated, showing the vulnerability of the hill slope. The debris flow runout simulation of the event was performed using the RAMMS debris flow model to calculate the volume of the landslide and the water reservoir formed due to damming of the Chenab River. The volume of the landslide (debris) due to slope failure was approx. 2.22 million m3 and the water reservoir volume was approximately 0.3 million m3. The significant factors accountable for the landslide i.e. the slope of the hill, geomorphology of the area, lithology (phyllites), presence of narrow and elongated valleys, the confluences of the river in the area, and continuous rainfall in the region during that period were evaluated. The paper also emphasizes monitoring of such vulnerable areas based on high-resolution time series satellite images, which are available on a regular basis to avoid the loss of human lives in the future.

Post-Wildfire Debris Flow and Large Woody Debris Transport Modeling from the North Complex Fire to Lake Oroville

The increase in wildfires across much of Western United States has a significant impact on the water quantity, water quality, and sediment and large woody debris transport (LWD) within the watershed of reservoirs. There is a need to understand the volume and fate of LWD transported by post-wildfire debris flows to the Lake Oroville Reservoir, north of Sacramento, California. Here, we combine debris flow modeling, hydrologic and hydraulic modeling, and large woody debris transport modeling to assess how much LWD is transported from medium and small watersheds to Lake Oroville. Debris flow modeling, triggered by a 50-year rainfall intensity, from 13 watersheds, transported 1073 pieces (1579.7 m3) of LWD to the mainstem river. Large woody debris transport modeling was performed for 1-, 2-, 5-, 25-, 50-, 100-, and 500-year flows. The transport ratio increased with discharge as expected. LWD is transported to the reservoir during a 2-year event with a transport ratio of 25% with no removal of LWD and 9% with removal of LWD greater than the cross-section width. The 500-year event produced transport ratios of 58% and 46% in our two sub scenarios.

Changes of hydro-meteorological trigger conditions for debris flows in a future alpine climate

Debris-flow activity is strongly controlled by hydro-meteorological trigger conditions, which are expected to change in a future climate. In this study we connect a regional hydro-meteorological susceptibility model for debris flows with climate projections until 2100 to assess changes of the frequency of critical trigger conditions for different trigger types (long-lasting rainfall, short-duration storm, snow-melt, rain-on-snow) in six regions in the Austrian Alps. We find limited annual changes of the number of days critical for debris-flow initiation when averaged over all regions, but distinct changes when separating between hydro-meteorological trigger types and study region. Changes become more evident at the monthly/seasonal scale, with a general trend of critical debris-flow trigger conditions earlier in the year. The outcomes of this study serve as a basis for the development of adaption strategies for future risk management.

Simulation of debris flow-barrier interaction using the smoothed particle hydrodynamics and coupled Eulerian Lagrangian methods

Debris flow events occur when fine soils and sediments are mixed with water, transporting coarse objects such as boulders and woody vegetation (debris) in a fast-moving down-slope flow. As such, debris flow can have catastrophic consequences for the environment, surrounding infrastructure and human life. The mitigation of damage caused by debris flow events is often addressed by rigid and flexible barriers placed in key locations to impede flow. The numerical simulation of interactions between large debris flow events and protective structures necessitates computational methods that model large deformation and flow behaviour as well as barrier performance. Although various numerical methods exist for debris flow modelling, the benefits of each method remain unclear when coupled with the analysis of protective structures. This research investigates debris flow-barrier interaction using two distinct numerical methods – the Coupled Eulerian-Lagrangian Method (CEL) and Smoothed Particle Hydrodynamics (SPH). The capabilities and limitations of each method are presented, highlighting the differences in computational cost. The results of simulations indicate preferable methods for modelling fluid-structure interaction for assessing the performance of debris flow-protective barrier structures. • The application of CEL and SPH methods in modelling of debris is investigated. • The capabilities and limitations of each method are studied. • Simulation of debris flow-boulders-barrier interactions is presented. • The effect of mesh and particle density is explained.

Numerical modelling of an alpine debris flow by considering bed entrainment

Numerical models have become a useful tool for predicting the potential risk caused by debris flows. Although a variety of numerical models have been proposed for the runout simulation of debris flows, the performances of these models in simulating specific events generally vary due to the difference in solving methods and the simulation of the entrainment/deposition processes. In this paper, two typical depth-averaged models have been used to analyze a well-documented debris-flow event that occurred in the Cancia basin on 23 July 2015. The simulations with and without bed entrainment are conducted to investigate the influence of this process on the runout behavior of the debris flow. Results show that the actual runout can be reproduced only by considering bed entrainment. If basal erosion is not taken into account, part of the debris mass deviates from the main path and both models predict unrealistic bank overflows not observed in the field. Moreover, the comparison between measured and simulated inundated areas shows that both models perform generally well in the terms of simulating the erosion-deposition pattern, although the DAN3D model predicts a greater lateral spreading and a thinner depositional thickness compared to Shen’s model. A simple numerical experiment obtains similar consequences and further illustrates the possible reasons that cause these differences.

Reconstruction of debris flow in the Gerkhozhan-Su river valley based on the chain modeling

The paper presents the reconstruction of the most catastrophic debris flows in the Gerkhozhan-Su river valley (North Caucasus, Russia) based on a chain of mathematical models. Transport-shift model was applied for 6 sections of debris flow formation or intense material increment, while for the rest the FLO-2D hydrodynamic model was used. A number of numerical experiments were carried out in the transport-shift model for a section of a debris flow origination site with a change in the parameters of a potential debris flow-forming soils, such as initial moisture content and density. According to the simulation results, with a rise in moisture content, the maximum debris flow discharge can increase by about 2.8 times. Modeling of debris flow in the Gerkhozhan-Su river valley was conducted for the case of low density debris flow. Based on the results, debris flow wave hydrographs were obtained at 12 sections. The results of modeling are in general agreement with the commonly accepted reconstructed pattern of the debris flow in 2000 and values obtained by videomaterials.

Advancing debris flow hazard and risk assessments using debris flow modeling and radar derived rainfall intensity data

Debris flow hazard and risk assessments are critical tools in mitigating and planning for these events. Existing debris flow hazard assessments can provide a rapid view of the likelihood of debris flows in recently burned watersheds and along stream segments within the watershed. Furthermore, debris flow volumes can be predicted for these watersheds and along the stream segments. Advances in modeling and remote-sensing data can add further value to the rapid assessments. Here, modeled debris flow volumes and a more detailed understanding of rainfall conditions highlight a need to reconcile debris flow probabilities and volumes using local conditions. Modeled debris flow volumes are consistently lower than even the lowest predicted volumes from empirical models used in the debris flow hazard assessments. Watershed probability and volume relations also over predict based on our probabilities derived from rainfall intensity from Multi-Radar/Multi-Sensor System 1-hr data. Probability and volume measures need to be further considered as the conservative measures of the rapid assessments have implication for risk analyses required for planning and management decisions, and ultimately for design and cost of mitigation to manage risk.

Numerical Simulation Reflecting Buildings in Area Damaged by Debris flow

More than 80% of average annual precipitation in South Korea occurs between June and September owing to heavy rainfall and typhoons in summer, and its land is vulnerable to mountain disasters (landslides and debris flow) as 63% of it is mountainous areas. In this study, an area damaged by debris flow in Wondeok-eup, Samcheok-si, Gangwon-do, Korea under the influence of Typhoon Mitag in 2019 was surveyed and numerical modeling was performed. Topographic data were created using the 5m grid DEM derived through the field survey data and GIS technique as well as the building data of the damaged area, and debris flow modeling was performed using the Hyper KANAKO model. A comparison with the inundation trace map showed that the simulation results based on topographic data that reflected buildings exhibited similar flow patterns and characteristics to the actual damage.

A dilatant two-phase debris flow model with erosion validated by full-scale field data from the Illgraben test station

We propose a dilatant, two-layer debris flow model validated by full scale density/saturation measurements obtained from the Swiss Illgraben test site. Like many existing models we suppose the debris flow consists of a matrix of solid particles (rocks, boulders) that is surrounded by muddy fluid. However, we split the muddy fluid into two fractions. One part, the inter-granular fluid, is bonded to the solid matrix and fills the void space between the solid particles. The combination of solid material and inter-granular fluid forms the first layer of the debris flow. The second part of the muddy fluid is not bonded to the solid matrix and can move independently from the first layer. This free fluid forms the second layer of the debris flow. During flow the rocky particulate material is sheared which induces dilatant motions that change the solid/fluid concentration of the first layer and then his density. As suggested by real data of Illgraben, the rheology used is not constant and uniform but a function of the flow composition/ density. The model is then compared to real debris flow data of Illgraben and tested on a real event in Ritigraben for which erosion data are available.

Debris Flow Hazard Mapping Along Linear Infrastructure: An Agent Based Model and GIS Approach

Often linear infrastructure, including rail, highways, and pipelines, span large geographic areas intersecting a variety of terrain, predisposing infrastructure to a higher likelihood of geohazard interaction. Debris flow models can be particularly advantageous in remote hazard and risk mapping along linear infrastructure as runout from susceptible slopes may extend considerable distance downslope to a receptor. In this sentiment, a method is developed using an agent-based model, DebrisFlow Predictor, in combination with geographic information software (GIS), to produce regional debris flow hazard and risk profiles along widespread corridors. Thousands of debris flows upslope of a receptor(s) are simulated in the model environment (i.e., model scenarios). Outputs of the modelled scenarios provide probabilistic spatial attributes of debris flow runout and depth across a digital elevation model at a 5m resolution. The outputs of multiple scenarios are mosaiced and corrected to in-situ temporal and spatial debris flow initiation conditions in GIS. The corrected scenario outputs provide a comprehensive hazard profile along the infrastructure alignment, that in turn can facilitate quantified vulnerability and risk calculations. Thousands of modelled debris flows throughout several physiographic regions of Canada and the United States of America, calibrated to local conditions, provide substantive support for a novel methodology to identify key hazard and risk locations to major linear infrastructure.

Debris-flow risk-to-life: Upper-bound preliminary screening

Where the potential for future debris-flow occurrence is unrecognised, developments can be unknowingly exposed to debris-flow impact, with corresponding risks to lives. Debris-flow modelling is unsuited to routine local office use, so a simple screening procedure is proposed to enable local officials to identify locations where debris flow risk-to-life may be unacceptable, and prioritise where expert modelling and risk analysis are most urgently required for risk-management decision-making . This procedure calculates catchment Melton ratio R from topographic data, uses a linear upper bound of field data relating R to the annual probability of debris-flow occurrence, and matches a model-based debris-flow risk-to-life analysis for Matata, New Zealand. Our data suggest that any development exposed to debris flows will require a detailed risk assessment to ensure that risk-to-life does not exceed acceptable levels.

Triggering-runout modelling of rainfall-triggered debris flows: A case study in the Campania region, Italy

Debris flows are unpredictable phenomena, listed among the hugest natural hazards, since they can cause important damages to humans and structures. Rainfall can trigger this type of movements, as it provokes the pore water pressure increasing, and so the soil strength reduction. The phenomenon modelling is a key aspect to predict and prevent damages. This article shows an approach for triggering and runout analysis: triggering is studied through an infinite slope stability model of rainfall-triggered shallow landslides, while runout is modelled using a depth-averaged numerical method, which replace the real heterogeneous flow with an equivalent homogeneous fluid. The work focuses the attention to events characterized by multiple triggering zones and releases converging on the same area, whose complexity is represented by the time- and space-distribution of the different flows. The proposed approach is applied to an event that hit part of the Campania region, Italy, in 1998, causing several damages. Two rheological laws are considered and compared for the analysis. The back-analysis allows the calibration of the rheological parameters and validation of the method. Results are discussed to identify the most suitable rheology for the benchmark event.

A regional early warning system for debris flows

In this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the drainage basins in which at least one debris-flow event was generated during the time period considered (2009-2019). The performances of the models obtained with different machine learning techniques were evaluated with the ROC analysis. The most suitable model was then experimentally implemented in the existing early warning system of the Aosta Valley Region. The model provides daily values of debris-flow probability (DFP) for individual basins, based on the input geo-morphological and hydro-meteorological variables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of the region.

A Simplex Multi-Phase Approach for Modelling Debris Flows in Smoothed-Terrain-Following Coordinate System

Herewith we present a multi-phase model for debris flows, of which the flow body is supposed to be composed of water, fine sediment (clay/silt) and grains. The rheology of debris flows varies due to the dynamical variation of the composition concentrations. In the present study the component of silt/clay is an individual phase, and its concentration plays a key role in determining the rheology of the interstitial fluid. Hence, there are three phases in the mixture, the grain phase, the clay phase and the water phase from the viewpoint of mass conservation. Only the grain phase and fluid phase are considered in the momentum conservation, since the clay is suspended in the fluid and the relative motion is negligible within the interstitial fluid. The grain constituent is treated as a frictional Coulomb-like continuum, and the viscosity of the interstitial depends on the clay concentration. The resultant models are given in a smoothed-terrain-following coordinate system, a compromise between the constraint of shallow curvature for the terrain-fitting coordinate system and retaining the high resolution of the topography. The numerical implementation is developed with the CUDA-library for GPU-high-performance computations. The feasibility and applicability will be presented by back calculation of a historical event.