Debris-flow Volume Research Articles

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Abstract. Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I15, the model generally under-forecasted the inundation area. The observed peak I15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I15 than observed. Using these I15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

Scraping Effect of Dam-Overtopping Debris Flow—A Case Study of Chutou Gully ‘8.20’ in Miansi Town, Wenchuan County

ABSTRACT After the May 12 earthquake in Wenchuan, numerous debris flow disasters occurred due to heavy rainfall. After the earthquake, disaster prevention and mitigation projects, such as the sand dam, played an essential role in safety. However, when the outflow of solid material exceeds the designed storage capacity of the sand dam, the debris flow overtopping the dam intensifies the scraping effect on the source material of the gully channel. This paper takes the ”8.20” debris flow in the Chutou gully as the research object. A transport mode of the debris flow after dam overtopping is studied through comprehensive field investigation, theoretical analysis, and indoor model tests. A quantitative evaluation model is established for the scraping depth and scraping volume of dam-overtopping debris flow. The study results showed that once the debris flow overtops a dam, the loose source carried by the channel is involved in the entire migration process, enlarging the fluid scale and intensifying the damage along the channel. The results agree with the measurements and observations, with maximum relative errors of 5.81% and 14.70%, respectively. This shows model validity. Furthermore, the test results show that the debris flow scarping phenomenon in the γ=[1.4 t/m3, 1.6 t/m3] interval is the most significant. Overall, this study will help improve future engineering decisions, design strategies, and finding the best design solutions to minimize the risk of dam-overtopping debris flow disasters.

Evaluation of debris-flow building damage forecasts

Abstract. Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

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

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

Preliminary Investigation on the Kinetic Characteristics of the Glacial Debris Flows in Tianmo Valley, Tibet Plateau, China

AbstractGlacial debris flows occurring on the Tibetan Plateau consistently result in significant property damage and loss of human life. A comprehensive field investigation was conducted in Tianmo valley along the Sichuan‐Tibet Highway to reveal the dynamics of a debris flow that occurred on 11 July 2018. Furthermore, a depth‐averaged multiphase debris flow model was proposed and employed to reconstruct the characteristics of the debris flow. The model derivation, implementation, evaluation, and application were presented to demonstrate its performance. The Voellmy model was chosen because it adequately accounts for both basal frictional effects and the entrainment phenomenon. The entrainment processes, the ice melting, and the lubrication effect, were also taken into consideration. Based on the numerical results combined with field investigation data, the kinetic characteristics of the glacial debris flow were analyzed. The Tianmo valley has a small area, but the volume and erosion rate of debris flows were much larger than that of two‐phase debris flows in the same location due to ice melting. The simulation results demonstrated that the glacial debris reached a peak velocity of 20 m/s. Additionally, the volume of the debris flow increased by 50% due to the erosion over a short runout distance of approximately 4,000 m. This increase was a result of the high velocity and abundant entrainment sources on the slope. This study aims to improve understanding of the high velocity and destructive potential of debris flows in the Tianmo valley.

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

A reliable economic risk map is critical for effective debris-flow mitigation. However, the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application. To estimate the economic risks caused by future debris flows, a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map. We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year. The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact, temporal probability, and annual susceptibility. We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities. Simultaneously, a Poisson model was applied for temporal probabilities, while the determination of annual probability of spatial impact relied on statistical results. Additionally, four major elements at risk were selected for the generation of an economic loss map: roads, vegetation-covered land, residential buildings, and farmland. The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values. Therefore, we proposed a physical vulnerability matrix for residential buildings, factoring in impact pressure on buildings and their horizontal distance and vertical distance to debris-flow channels. In this context, an ensemble model (XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings. The results show that residential buildings occupy 76.7% of the total economic risk, while road-covered areas contribute approximately 6.85%. Vegetation-covered land and farmland collectively represent 16.45% of the entire risk. These findings can provide a scientific support for the effective mitigation of future debris flows.

Copula-Based Probabilistic Hazard Assessment Model for Debris Flow Considering the Uncertainties of Multiple Influencing Factors

This paper proposes a probabilistic hazard assessment model for debris flows considering the uncertainties of multiple influencing factors based on copula approaches. Fifty-nine rainfall-induced debris flows occurred between 2001 and 2009 in Taiwan are taken as an illustrative example to validate the proposed approaches. A copula-based probabilistic model is developed to model the joint probability distribution of debris-flow volume V and its influencing factors (e.g., rainfall intensity, RI and landslide area, AL). The developed model is then used to make probabilistic prediction of debris-flow volume for a specific hazard level, and compared with the empirical approaches. The proposed probabilistic model is also used to develop the exceedance probability charts of quantities for a specific debris-flow basin. Results show that the developed V–RI–AL probabilistic model can provide reasonable estimates of debris-flow volume in Taiwan for a specific probability level of 0.94, and show better predictive performance than the empirical relationships by using an independent debris-flow dataset in Taiwan for validation. The developed multivariate joint probabilistic model can also provide the exceedance probability of debris flows through considering the uncertainties of debris flow and its influencing factors, providing a preliminary reference for hazard assessment of the debris flows.

Assessment of check dams and afforestation in mitigating debris flows based on dendrogeomorphic reconstructions, field surveys and semi-empirical models

Check dams and afforestation are two common measures for debris-flow mitigation; however, there is a lack of methods allowing quantitative evaluation of check dam and afforestation effects on debris flow motion and deposition. Laogan Gully is an active debris-flow system with a large landslide on the right hillslope, currently representing the main source of debris flows in the catchment. Several debris flows have occurred since 1985 which led to the construction of check dams and the plantation of silver wattle (Acacia dealbata) trees between 1991 and 1993. These measures significantly reduced the volume of debris flows reaching downstream portions of the system. At the same time, large amounts of sediment were trapped on the gully bed and thereby increased the base level of the torrent, which again tended to stabilize the large landslide on the right hillslope. This paper analyzes sedimentation of debris-flow material on the gully bed and on the fan using 12 trial pits behind the check dams and 6 incised geological sections on the fan. The timing and extent of debris flow motion and deposition was reconstructed using scars and growth suppression events in the annual growth ring series of 306 Acacia trees trapped in the newly formed deposits. The gravitational potential energy of the sediment of the large landslide is calculated according to the change in the erosion base level behind the check dams. On the basis of these results, we constructed a semi-empirical vegetation coverage–landslide energy–debris flow volume model to quantitatively evaluate the effects of the check dams and afforestation on debris-flow mitigation. The model exhibited predictive capabilities for estimating the volume of debris flow generated by the reactivation of landslides. Check dams are found to play a major role in debris-flow mitigation in the early stages, whereas the Acacia trees play an increasingly important role once the check dams are filled. The partial resurgence of the landslide caused a sudden increase in the gravitational potential energy of the sediment source, which in turn affected the effectiveness of the debris-flow mitigation measures.

Enhanced debris flow activities in the subtropical mountainous catchment of South China during early Marine Isotope Stage 3

Geomorphic processes and their responses to climatic change in subtropical mountainous catchments are not yet well understood. In this study, a unique sequence of alluvial sediments (>33 m thick) from the Nanxu karst valley, Guilin, South China, was investigated to characterize the nature of erosion–deposition processes in humid, subtropical catchments and decipher any climatic constraints on the earth surface dynamics and landscape evolution. Erosional outcrops show that the Nanxu valley fill sequence mainly comprises m-scale sheets of matrix-supported gravel (a debris flow facies), which, in the distal part of the valley, are intercalated with beds of fine-grained sediments (facies of horizontally interbedded sand and mud or massive mud). The fine-grained sediments were likely deposited in a subaqueous environment, which was probably formed when the karst sinkhole was contemporarily blocked by a large volume of debris flows. Optically stimulated luminescence (OSL) as well as radiocarbon dating constrained a chorology framework of ca. 62–42 ka for the valley fill sequence. Comparisons of the sedimentation phase with local and regional climate proxy records revealed that enhanced debris flow activities and sediment aggradation occurred under generally warm and wet but unstable climatic conditions during early Marine Isotope Stage 3. Under these climatic conditions, there was likely a subtropical landscape with high vegetation cover in the catchment at the time of sedimentation, as supported by our palynological data. Thus, it is suspected that enhanced debris flow activities were triggered by the recurrence of extreme monsoonal rainfall events. Since ∼ 42 ka, the stream-channel process dominated the valley, and the valley fill was deeply dissected, which might be related to changes in the overall intensity and internal variability of monsoonal rainfall over a period. These findings identify the role of high-intensity monsoonal precipitation in controlling geomorphological activities in humid, subtropical landscapes of South China.

The influence of large woody debris on post-wildfire debris flow sediment storage

Abstract. Debris flows transport large quantities of water and granular material, such as sediment and wood, and this mixture can have devastating effects on life and infrastructure. The proportion of large woody debris (LWD) incorporated into debris flows can be enhanced in forested areas recently burned by wildfire because wood recruitment into channels accelerates in burned forests. In this study, using four small watersheds in the Gila National Forest, New Mexico, which burned in the 2020 Tadpole Fire, we explored new approaches to estimate debris flow velocity based on LWD characteristics and the role of LWD in debris flow volume retention. To understand debris flow volume model predictions, we examined two models for debris flow volume estimation: (1) the current volume prediction model used in US Geological Survey debris flow hazard assessments and (2) a regional model developed to predict the sediment yield associated with debris-laden flows. We found that the regional model better matched the magnitude of the observed sediment at the terminal fan, indicating the utility of regionally calibrated parameters for debris flow volume prediction. However, large wood created sediment storage upstream from the terminal fan, and this volume was of the same magnitude as the total debris flow volume stored at the terminal fans. Using field and lidar data we found that sediment retention by LWD is largely controlled by channel reach slope and a ratio of LWD length to channel width between 0.25 and 1. Finally, we demonstrated a method for estimating debris flow velocity based on estimates of the critical velocity required to break wood, which can be used in future field studies to estimate minimum debris flow velocity values.

Debris Flow Run-Out Prediction Based on the Shallow-Water Flow Numerical Model—A Case Study of Xulong Gully

Here we present a method for predicting debris flow run-out based on a numerical model for shallow water flows, using a case study conducted on Xulong Gully, a proposed dam site for a hydropower station in the upper reaches of the Jinsha River. A field investigation and remote sensing interpretation methods were used to develop a comprehensive evaluation of debris flow zones and calculate the potential provenance volume in the Xulong Gully. Particle-size analysis was conducted on the early debris flow fan in the Xulong Gully to determine the rheological properties of the debris flow materials. A numerical model for shallow flows was constructed using the finite volume method to verify fluid motion across complex terrain and explore the debris flow run-out range with various provenance volumes. The model showed that for a total debris flow volume of less than two million m3, the debris flow impact area would remain within the Xulong Gully. However, if the total debris flow volume is more than two million m3, the debris flow would flush out into the Jinsha River, blocking a portion of the river. If all the provenance in the Xulong Gully were flushed out, the maximum flow velocity of the generated debris flow would be 11 m/s and the thickness of the debris flow at the Xulong Gully estuary would be about 28.8 m. The debris flow would completely block 470 m of the Jinsha River.

Spatial and temporal evolution of long-term debris flow activity and the dynamic influence of condition factors in the Wenchuan earthquake-affected area, Sichuan, China

In recent decades, there have been several extreme earthquakes around the world, with the 2008 Wenchuan earthquake resulting in the highest number of landslides and substantial deposition of loose material on steep terrain and deep gullies, thereby promoting post-earthquake debris flows. Our monitoring indicates that debris flows have become long-term threats for over a decade after the earthquake, and it is crucial to determine the long-term spatial and temporal changes of debris flows and the evolving influence of condition factors in the earthquake-affected area. Thus, we established a database of 1628 debris flow events occurring between 2000 and 2020 within the 31,627 km2 area affected by the Wenchuan earthquake. The database included information on the date of occurrence, GNSS (Global Navigation Satellite System) coordinate at the gully outlet, event washout volume, and scale level obtained from various sources. Post-seismic debris flow activity witnessed a significant increase, followed by oscillation with rainfall and gradual weakening. Using the average washout volume for each scale in different periods, we applied a grid calculation method to characterize the debris flow intensity (DFI) using the total debris flow volume of cells in different epochs to study the spatial and temporal evolution of debris flow activity. Thus, based on the DFI calculation, we introduced the certainty factor and Geodetector to determine the dynamic evolution of condition factors. Our results indicate that lithology, rainfall, and slope degree primarily controlled debris flows before the earthquake. However, coseismic landslides became the main promoting factor after the earthquake. As loose materials were consumed, hydrodynamic and topographic factors began to play a more significant role in controlling debris flows. During this process, the requirements for rainfall, slope degree, and terrain relief amplitude increased over time. Based on the annual volume of debris flows from 2000 to 2020, we developed a preliminary conceptual model, which indicated that post-earthquake debris flow activity can be categorized into three phases: active (2008–2013), unstable (2014–2033), and recession (2034–2045), with the debris flow activity anticipated to return to pre-earthquake levels by 2045.

A Physically Based Model for the Sequential Evolution Analysis of Rainfall‐Induced Shallow Landslides in a Catchment

AbstractPredicting the occurrence time, volume, distribution and run‐out length of shallow landslides is critical for assessing the volume of debris flow in a catchment. Recent studies suggest that the landslides hydro‐mechanical triggering (LHT) model based on threshold‐based mechanical interactions can adequately predict rainfall‐induced landslides. However, this model assumes that the soil‐rock interface is the only sliding surface and cannot dynamically determine a sliding surface above the soil‐rock interface. Therefore, by calculating the wetting front depth in the shallow soil layer and using the limit analysis method, the most likely sliding surface can be dynamically calculated to improve the model. Through linking the Soil and Water Assessment Tool (SWAT, USDA) model and the depth‐resolved LHT model (D‐LHT), a framework for predicting shallow landslides in a catchment was proposed in this work. This framework considers the effects of antecedent rainfall, the mechanical reinforcement of roots, and the spatial distribution of soil properties on slopes. The D‐LHT model was applied to the Jiangjia gully in Yunnan, China, to adequately predicted the occurrence time, scale and spatial distribution of shallow landslides. In addition, the present and antecedent rainfall effects on the shallow landslide volume were analyzed. The results showed that the average soil moisture content threshold was determined by antecedent and same‐day rainfall, which affect the timing and volume of the shallow landslides. This study provides a new method for predicting shallow landslides. The stability and simplicity of the model make it suitable for early warning systems.

Two-phase MPM modelling of debris flow impact against dual rigid barriers

Multiple barriers are usually installed to effectively mitigate large-volume debris flows. Existing multiple-barrier design recommendations ignore the effects of solid–fluid interaction and may cause uncertainties in estimating the flow impact force on subsequent barriers. In this study, a fully coupled, two-layer, two-phase material point method with an incompressible fluid phase is implemented and validated using the experimental results of dry sand, water and sand–water mixture flows impacting on rigid barriers. Numerical parametric study is carried out using the validated model to investigate the effects of Froude number (Fr) and barrier spacing on second barrier impact force. A debris flow volume of 500 m3 is modelled, with the Fr ranging from 2 to 6; these are values relevant to the gentle and steep terrains in Hong Kong. Simulation results show the importance of changes in fluidisation ratio, which is the ratio of basal pore fluid pressure and total stress. The debris flow, after impacting the first barrier, overflows and lands on the slope between the two barriers. The fluidisation ratio of debris flow after landing increases by up to 30%, leading to an increase of impact velocity at the second barrier by up to 80%. Consequently, the impact force of debris flows on the second barrier is underestimated by up to 50% compared with existing design guidelines. This implies that neglecting the fluidisation ratio may lead to non-conservative design of multiple barriers.

The effect of sediment density parameter values on the debris flow velocity parameters

Debris flow is a phenomenon where the material from the eruption is carried away by the flow of water due to rain in the upstream area of the river and usually crashes into watersheds around the volcano. The potential for large debris flows can be caused by high rainfall and sediment deposits that occur during debris flows. Rainfall and sediment carried by water flow can affect the volume and velocity of debris flows. A debris flow simulation was carried out to anticipate casualties and damage using SIMLAR V2.1 by modifying rain intensity and the density of sediment carried by the debris flow. Based on the result, we can obtain the results of the velocity, volume, and area affected by the debris flow. The flow velocity values obtained for testing with rainfall intensity of 162 mm with a density of sediment 1370 kg/m3, 2340 kg/m3, 2635 kg/m3, and 2820 kg/m3, respectively, were 2.72 m/s, 2.16 m/s, 2.50 m/s, and 2.57 m/s. The volume values with the same intensity and density of sediment are 186,381 m3, 311,116 m3, 274,030 m3, and 294,987 m3. Based on the test results, it can be concluded that sediment density significantly affects the velocity and volume of the debris flow. Where sediment density is inversely proportional to the flow velocity, the higher the sediment density, the slower the flow velocity, while the volume is directly proportional to the sediment density. The higher the density of the sediment, the higher the volume.

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.

Constraining post-fire debris-flow volumes in the southwestern United States

Debris flows pose a serious threat to human life and infrastructure in downstream areas following wildfire. This underscores the necessity for having a hazard assessment framework in place that can be used to estimate the impacts of post-wildfire debris flows. Current hazard assessments in the western United States (USA) use empirical models to assess the volume of potential post-wildfire debris flows. Volume models provide information regarding the magnitude and potential downstream impacts of debris flows. In this study, we gathered post-wildfire debris-flow volume data from 54 watersheds across the states of Arizona (AZ) and New Mexico (NM), USA, and compared these data to the output of a widely used empirical post-wildfire debris-flow volume model. Results show that the volume model, which was developed using data from the Transverse Ranges of southern California (CA), tends to overestimate observed volumes from AZ and NM, sometimes by several orders of magnitude. This disparity may be explained by regional differences between southern CA and AZ and NM, including differences in sediment supply. However, we found a power- law relationship between debris-flow volume and watershed area that can be used to put first-order constraints on debris-flow volume in AZ and NM.

Evaluation of a method to calculate debris-flow volume based on observations of flow depth

The volume of a debris flow is a critical parameter in hazard analysis, yet accurate estimates of volume are often unavailable due to mixing with larger rivers at the downstream end of alluvial fans. We describe a method to calculate the volume of debris flows using flow depth data collected at a check dam, using a Manning friction relation to describe the velocity of the debris flow as a function of flow depth, and the geometry of the channel cross section. The method is evaluated using a published data set from the USGS debris-flow flume where event volume and stage information have been accurately measured, and results in volume estimates either somewhat smaller than or up to 50% larger than observed volumes. We further demonstrate the method to single-surge and a multiple-surge debris flows observed at Illgraben.

The influence of particle size on the spread distance and angle of friction of granular materials

To produce a hazard map and thus provide mitigation measures against natural catastrophic events such as landslides, avalanches, and debris flows, it is necessary to determine the spread distance of such flows. There is a well-documented relationship between the angle of friction and the debris flow volume, allowing one to determine the possible distance a debris flow can travel. However, the effect of mean particle size (d50) and the sorting coefficient (Sc) on the final spread distance (Df) has received mere attention. In this study, a mini conical-shaped mould was used to measure the final spread distance (Df) and angle of friction (αf) for various dry granular material samples both in air and in water. Experimental results indicated that a granular material sample with a smaller d50 travel a longer distance compared to a sample with a higher d50. However, a sample with a smaller d50 results in a smaller angle of friction than the one with a higher d50. Conversely, a well-sorted sample has a smaller spread distance and larger angle of friction. Results also indicated that the spread distance in air is slightly larger and the angle of friction is slightly smaller than in water.

Using integrated growth to delineate debris-flow inundation

Debris-flow volume is fundamental to mobility, yet many debris flows change volume as they travel. Growth can occur through diverse processes such as channel-bed entrainment, bank failures, aggregation of landslides, and coalescence of multiple flows. Integrating growth, either over upslope area or stream length, combines the effects of these growth processes and requires specification of only the growth zone extent and a growth factor. To delineate potential debris-flow inundation, we implement integrated growth factors and simple volume-area relations in a new USGS software package, Grfin Tools. We present two examples of forecasting debris-flow inundation – one using an area growth factor in Puerto Rico and another using a channel-length growth factor in Oregon, USA. The use of growth zones and growth factors enables scenario-based hazard assessments for geomorphic settings with debris-flow growth.