Debris Flow Events Research Articles

The Application of Numerical Simulation in Debris Flow Disaster Early Warning: A Case Study of Shiyang Gully, China

This study explores the application of numerical simulation in debris flow disaster early warning, using the Shiyang Gully in China as a case study. Using both the HEC-HMS and FLO-2D, the 18 June 2017 debris flow event was reconstructed to analyze the impacts of cumulative rainfall, rainfall intensity, and rainfall range on debris flow hazards. Simulation results showed that cumulative rainfall exceeding 90 mm or rainfall intensity surpassing 200 mm/8 h significantly increases debris flow depth, impact force, and affected areas, leading to severe structural damage. Expanding the rainfall range to the entire basin further amplifies disaster risks, increasing both inundation depth and exposed elements. Based on these findings, a four-tier debris flow early warning system was developed: (1) blue (IV) warning for cumulative rainfall of up to and including 20 mm or intensity of 200 mm/24 h, indicating preparation and monitoring; (2) yellow (III) warning for rainfall exceeding 20 mm but below 60 mm, requiring enhanced inspections and safety measures; (3) orange (II) warning for rainfall between 60 and 90 mm or intensity of 200 mm/12 h, necessitating immediate evacuation preparations; and (4) red (I) warning for rainfall over 90 mm or intensity of 200 mm/8 h, demanding full evacuation and emergency responses. This study demonstrates the value of numerical simulation in refining early warning systems by integrating multi-scenario analyses of rainfall parameters. The proposed system offers scientific and practical insights for enhancing debris flow disaster management, particularly in small, high-risk watersheds, providing a framework for cross-regional disaster mitigation strategies.

Evolutionary characteristics of mass movement in two adjacent debris flow gullies at the Great Bend of the Yarlung Zangbo River

Debris flows have occurred in two adjacent gullies, namely Sedongpu Gully (SDP) and Zelongnong Gully (ZLN), since the 1950s. The most recent event occurred in ZLN in 2020, causing damage to human communities and infrastructure. Despite their close geographical proximity to the Great Bend of the Yarlung Zangbo River, SDP has experienced more frequent and severe events compared to ZLN. This paper aims to compare the evolutionary characteristics of mass movement between these two gullies. A comprehensive review of 12 debris flow events in both gullies from 1950 to 2020 was conducted. A novel method was proposed and verified for quantifying the debris flow mass movement by integrating InSAR, multi-DSMs, optical images, and field investigations. Comparative analysis revealed distinct evolutionary stages between the gullies, with SDP exhibiting mixed hillslope and channelized debris flows, whereas ZLN primarily experienced channelized debris flows. SDP witnessed a decrease in rainfall and temperature triggering debris flows, contrasting with ZLN where both factors increased. These explained the smaller frequency and magnitude of debris flows in ZLN. Overall, this study emphasizes the need for tailored risk prevention and control measures that reflect the specific dynamics of each gully.

Research on the Heavy Rainstorm–Flash Flood–Debris Flow Disaster Chain: A Case Study of the “Haihe River ‘23·7’ Regional Flood”

Over the past decades, China has experienced severe compound natural disasters, such as extreme rainfalls, which have led to significant losses. In response to the challenges posed by the lack of a clear investigation process and inadequate comprehensiveness in evaluating the natural disaster chains, this study proposes a comprehensive retrospective simulation strategy for emergency investigation and simulation of heavy rainstorm–flash flood–debris flow chain disasters at the county–town level. The primary aim is to avert potential new chain disasters and alleviate subsequent disasters. This study combines emergency investigation efforts with hydrodynamic models to digitally simulate and analyze compound chain disasters triggered by an extreme rainfall event in the Haihe River regional area, specifically Gaoyakou Valley, Liucun Town, Changping District, Beijing, in July 2023, along with potential new disasters in adjacent regions. The findings indicate that the heavy rainstorm chain disaster on “7.29” resulted from a complex interplay of interrelated natural phenomena, including flash floods, debris flows, urban floodings, and river overflows. Hantai Village has experienced flash flood and debris flow events at different times in this area. Should the rainfall volume experienced in Liucun Town be replicated in the Ming Tombs Town area, approximately 6.2 km2 of land would be inundated, leading to damages to 458 residences and impacting around 240 ha of agricultural land. The anticipated release of floodwater from the reservoir would lead to significant impacts on downstream residents and roads. Our research can improve the efficacy of emergency investigations and assessments, which in turn can help with the management and reduction of disaster risks at the grassroots level.

Debris Flow Debris Flow Detection by Combinations of LVP Sensors and Wires

Various kinds of sensors for debris flows detection have been proposed such as wire sensor, acceleration sensor, and so on. In Europe, a geophone that is based on a vibration meter is usually used though the applicability is not confirmed for debris flow detection in Japan. A wire sensor is still currently used for debris flow detection in Japan, because of its easy maintenance and measurement principle of disconnected wires. However, there is a drawback in that debris flow cannot be detected until manual maintenance is performed after the wires are disconnected. Sakura-jima is in southwest of Japan. Debris flows occur by rainfall and ash fall after eruption. Many debris flows occur and transport sediment by debris flow events. The number of debris flow occurrences is defined by the number of disconnected wires from a wire sensor, and three wires are set vertically at the height of 60 cm, 120 cm, 180 cm from the bed, respectively, to know magnitude of debris flow height. A LVP sensor has been developed and installed there for continuous detection of debris flows and modified based on technical information obtained by maintenance after debris flow events. The sensor consists of load cell (L), acceleration meter due to vibration (V) and pressure meter (P). The sensor is mainly for debris flow detection, though weight of debris flows on the bed is attempted to be measured using a small box with loadcell. Present studies introduce some examples of debris flow detections using the LVP and emphasize usage of the LVP sensor in combination with wires.

Kinematics of Post-Wildfire Debris Flow Initiation Mechanism: Impact of Hydrophobic Layer Spatial Variability and Particle Size

Post-wildfire debris flow events have been more frequent due to climate change and increased wildfire adversity. Changes in non-cohesive soil hydrophobicity, burn, and vegetation removal during wildfires dramatically enhance slopes' vulnerability to rainfall-induced failure in the arid Southwest USA and similar areas worldwide. This paper contributes to a better understanding the hydrophobic sand slope failure mechanism using experiments and theory. In total, 36 indoor raining experiments use different configurations of fine, medium, and coarse hydrophobic sands subjected to various rain intensities on different slope inclinations. Specific findings contribute to understanding how the spatial variability of the post-wildfire hydrophobic layer, concerning depth and the sand grain size variations, affects the slope failure mechanisms. Clarifying slope failure mechanisms in different layouts is crucial for understanding the initiation and further spatiotemporal debris flow evolution. Results indicate that the surficial hydrophobic layer fails because erosion develops simultaneously from many small failure patches. Erosion pattern, time to failure, water, and sediment overflow temporal dynamics correlate to the dominant sand grain size. Furthermore, seepage-induced shallow infinite slope occurs when the hydrophobic sand layer develops below the surface. Finally, this study identifies the fine sand as the most vulnerable to failure in both configurations.

Predictive modeling of river blockage severity from debris flows: Integrating statistical and machine learning approaches with insights from Sichuan Province, China

River blockages caused by debris flows pose serious threats to the environment and infrastructure. This study introduces the river blockage index (RBI) as a key measure of river blockage severity. We collected data from 60 debris flow events to build a comprehensive dataset. To enhance model robustness and accuracy, we optimized variable selection using multicollinearity analysis and the Akaike information criterion (AIC). Four statistical models were developed, including multiple linear, logarithmic, power, and exponential regressions. We also constructed models based on machine learning algorithms, including random forests and gradient boosting decision trees, and tested them using 5-fold cross-validation. After confirming that the training dataset met linear statistical assumptions, we built robust regression models. We tested the significance of the regression equations and coefficients using F-tests and t-tests. Hyperparameters of the machine learning algorithms were optimized through Bayesian methods. Model performance was evaluated using metrics such as R2, adjusted R2, mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE). Results show that the most important factors influencing RBI are catchment area (A) and the discharge ratio between the debris flow and the main river (Q). Among the statistical models, the logarithmic and power models performed best due to their simplicity and efficiency. The random forest model demonstrated the highest predictive accuracy and stability overall. By combining statistical methods with machine learning, we improved prediction accuracy and provided practical guidance for disaster prevention strategies. This approach overcomes the limitations of numerical simulations and experimental studies, offering a more flexible and efficient method for RBI prediction. Future work will extend these findings to other geological settings to further enhance model adaptability and performance.

Trench investigation to quantify debris flow activity for landslide hazard mapping in populated areas: Lessons learned from Gol, southern Norway

AbstractWe here describe the results of stratigraphic and sedimentological examinations of debris flow deposits at Breidokk, Gol, southern Norway. The deposits are situated at the valley floor, below a steep slope with three large and several smaller debris flow channels incised into the thick till cover. The study area is populated and with abundant infrastructure such as roads, public and private buildings and other types of infrastructure, including underground water pipes and cables. Six, 10–15 m long and 1–3 m deep trenches were dug out with an excavator and examined. The sediments in the trenches consist of moraine‐, glaciofluvial/fluvial‐ and debris flow deposits. The latter consist of matrix supported, unsorted, massive beds from 1 cm to more than 1 m in thickness, with clasts up to 80 cm in diameter. A total of 16 post glacial debris flow beds are identified in five of the six trenches, representing a minimum of eight individual debris flow events. This is probably an underestimation of the debris flow activity through postglacial times as the location of the trenches was in large determined by infrastructure and were not optimally placed for mapping all debris flow deposits in the area. Also, correlation between trenches proved difficult. A total of 37 radiocarbon ages of buried soil and other organic material situated above and below debris flow deposits, together with the sedimentological and stratigraphical interpretation, show that debris flow activity has prevailed throughout the Holocene, also within the last 1000 years. A possible increase in activity within the last 3–4000 years BP has been noted. This is important knowledge to aid in the interpretation of the Quaternary history of the area but also to determine the hazard zones.

Debris flow simulations for hazard, vulnerability and risk assessment in the Karakorum mountain ranges, northern Pakistan

The Karakorum mountainous ranges in northern Pakistan have frequently witnessed devastating debris flows with damaging impacts on surrounding communities and infrastructure. Debris flow runout modeling and hazard assessment are crucial to develop and implement effective mitigation and adaptation measures. In this study, debris flow runout modeling using multiple scenarios of slope failures is carried out for hazard and risk assessments in the Karimabad watershed of the Hunza Valley in northern Pakistan. A 3D numerical simulation tool RAMMS was used to estimate the hazard intensity parameters (runout distance, flow velocity and height) along the debris flow track, based on the Voellmy model. To calibrate the model inputs, a back analysis of the Haiderabad stream debris flow event of July 26, 2022 has been conducted based on the estimated initial volume and the known runout distance. The calibrated values of the dry friction coefficient μ = 0.07 and turbulent coefficient ξ = 550 m/s2 were utilized for the three potential release areas. Different initial volumes were considered, and the results were combined to determine the debris flow runout distance and other intensity parameters for each scenario. The failure of three selected release areas could potentially block or divert the river Hunza. The integration of RAMMS parameters mainly flow velocity and height into hazard mapping assists in the demarcation of the area and elements at risk which are exposed to the potential impacts of debris flows. The derived debris flow hazard maps show that the road network including the KKH is exposed to debris flows. The impact of the debris flows on the surrounding communities and infrastructure is carried out through the vulnerability assessment and combined with the hazard assessment to derive the risk. Due to the intensive incision in the channels as the path for debris flow, the settlements and populations are less vulnerable to debris flow, however, poses a risk to the communication network and KKH. The study should assist the concerned agencies in developing and implementing debris flow risk mitigation strategies.

Expecting the expected – learning from the past to provide forward scenarios through geomorphic change detection, monitoring and modeling

This paper tells the story of a landslide, its origins, its activity and the actions undertaken to mitigate the risks that it poses. The Rotolon landslide is a large Deep-seated Gravitational Slope Deformation (DGSD), located in the Eastern Italian Alps, whose unremitting movements provide a sediment supply for large-scale debris flow events. It has been active since at least 1789, threatening the valley where the thermal baths town of Recoaro Terme is located. In 2010, a major reactivation of the landslide channelled 320,000 m3 of material towards the town causing significant concern, the evacuation of the exposed population and threatening several buildings. A few days after the emergency, the personnel of the Research Institute for Geo-Hydrological Protection of the Italian Research Council (IRPI-CNR) deployed a monitoring system consisting of an automatic total station, several extensometers and web cameras to monitor the evolution of the unstable slope and set up an early warning and alarm system equipped with sirens to warn the local population. Subsequently, after the emergency phase, the 2010 event was studied through multi-temporal LiDAR Digital Terrain Models (DTMs) and modeling. The authors have continued monitoring and actively studying this landslide ever since. In 2020, a new LiDAR survey of the area allowed assessment of the sediment dynamics within the catchment through the comparison with the post-2010 event LiDAR DTM. Based on DEM of difference maps, new insight into the behaviour of the catchment emerged, which appears to be influenced both by natural processes and anthropic activities. The authors were able to assess the amount of sediment that could be stored in the channel without causing overflooding and to calculate the expected damage to the built environment should another event occur. Furthermore, the considerable amount of data collected by the monitoring system throughout the years has allowed identification of two active sectors of the DGSD that may be prone to detachment, and through numerical modeling, future hazard scenarios were produced. Based on these outcomes, a second-tier targeted monitoring campaign was implemented, consisting of a network of four permanent GNSS receivers, additional topographic benchmarks and web cameras, and a new early warning protocol, in an ever-updating cycle of monitoring, modeling and mitigation.

Evaluating the thresholds for predicting post-earthquake debris flows: Comparison of meteorological, hydro-meteorological and critical discharge approaches

Post-earthquake debris flows pose significant hazards in mountainous regions following large seismic events. Evaluating the thresholds for predicting the occurrence of these flows is crucial. However, the absenting of comparison for different predicting methods hampers progress in improving and updating predictions for debris flows. In this study, based on on-site measurements of post-earthquake debris flows in an active catchment during the first year following the 2022 Luding Ms6.8 earthquake, 30 debris-flow events were identified and observed. We established and compared three distinct methods—namely, the meteorological approach, the hydro-meteorological approach, and the critical discharge approach for predicting the occurrence of post-earthquake debris flows. Additionally, we introduced a factor called absolute energy to improve the accuracy of the traditional meteorological approach. Absolute energy is defined as the sum of squared values within a time series. Our findings indicate that the hydro-meteorological model outperforms others in predicting post-earthquake debris flows, whereas the meteorological approaches especially the intensity–duration (I–D) thresholds exhibit suboptimal performance. Furthermore, the updated meteorological model incorporating absolute energy demonstrates improved predictive capability compared to traditional meteorological approaches like intensity–duration (I–D) thresholds. We argue that this comparative analysis will aid in selecting the suitable method for predicting post-earthquake debris flows.

Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin

Abstract Gravity flow sand body is an important reservoir for deep water deposition. The in-depth study of its formation mechanism and oil enrichment law can provide important guidance for oil and gas exploration in deep water areas. In this article, taking the deep-water gravity flow sand body of the Triassic Chang 63 sub-member in the Huaqing area as an example, the identification characteristics, formation mechanism, and oil-bearing characteristics of the gravity flow sand body are systematically discussed. The results show that sandy debris flow, turbidite flow, and mixed event flow present different superposition combination modes on vertical gravity flow deposition. The lithofacies of the gravity flow sandstone complex are mainly affected by lake level fluctuation, provenance supply, and hydrodynamic conditions. According to the formation conditions, sedimentary types. and distribution characteristics of deep water gravity flow sand bodies, a three-dimensional sedimentary mode of deep water gravity flow sand bodies is established. It is found that the physical and oil-bearing properties of the sandy debris flow sand body are better than those of the turbidity flow sand body. The reason is that the proportion of debris in the sand body of sandy debris flow is high, and the content of debris particles is generally greater than 70% according to the statistics of thin sections, which is conducive to the formation of pores. Second, sandy debris flow belongs to block transport, and the mixing of lithology is not sufficient in the process of block flow transport, so some pores will be preserved. Finally, gravity flows, which are dense at the beginning, become less dense later as detrital sediments unload, allowing them to be transported to distant regions. Therefore, the monolayer thickness of the sandy debris flow sand body is large (generally greater than 0.5 m), and the particle size of the debris ranges from 0.03 to 0.3 mm. Finally, the physical and oil-bearing properties of the sandy clastic flow sand body are better than those of the turbidity flow sand body.

The sources and selectivity characteristics of organic carbon transported by debris flow events in a mountainous catchment, Southwest China

The sources and selectivity characteristics of organic carbon transported by debris flow events in a mountainous catchment, Southwest China

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

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

Cascading Landslide: Kinematic and Finite Element Method Analysis through Remote Sensing Techniques

Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated.

Identifying the Characteristics and Implications of Post-Earthquake Debris Flow Events Based on Multi-Source Remote Sensing Images

Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris flow events, as well as to explore their long-term development and impact on internal and external channels. Using multi-source remote sensing images from four perspectives, hillslope, channel, accumulation fan, and their relationship with the mainstream, we reconstructed a debris flow event dataset from 2008 to 2020, explored a method for identifying these events, and analyzed their impacts on channels and accumulation fans in Mozi Gully affected by the Wenchuan earthquake. Loose matter was predominantly found in areas proximate to the channel and at lower elevations during debris flow events. Alterations in channel width, accumulation fans downstream, and their potential to obstruct rivers proved to be vital for identifying the large scale of debris flow event. Finally, we encapsulated the evolution patterns and constraints of post-earthquake debris flows. Determination in frequency and scale could offer valuable supplementary data for scenario hypothesis parameters in post-earthquake disaster engineering prevention and control.

Topography-based and vectorized algorithm for extracting physical quantities in 3D-SPH form and its application in debris-flow entrainment modeling

Extraction of physical quantities such as flow depth and velocities, is one of the major purposes of geophysical flow numerical modeling and critical for estimating consequent impact forces and sediment entrainment. It is simple in nature for mesh-based models but presenting challenges in three-dimensional smoothed particle hydrodynamics (SPH) schemes. The difficulties lie in the substantial number of particles and their uneven spatial-temporal distribution, particularly over complex topography. Inspired by our previous surface cell (SC) -based approach, we propose a novel topography-based and vectorized algorithm that significantly enhances the ability to extract physical quantities over complex topography. In the proposed algorithm, geomorphologic characteristics are mathematically represented by topographical normal vectors. The correlations of physical quantities with distinct coordinate descriptions are established through the vectorization concept, ultimately leading to effective extraction of physical quantities in SPH form over complex topography. This algorithm provides an important tool to incorporate topography-linked physical models within discretized frameworks. To validate its effectiveness, we employed the algorithm to integrate the debris-flow entrainment law with our previous HBP-SPH model, utilizing the 2010 Yohutagawa debris-flow event in Japan as a case study. The results demonstrate a good agreement between the numerical simulation and on-site observation. Discussion regarding the applicability and limitation of the algorithm concludes the paper.

Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range

Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range

Big Disaster from Small Watershed: Insights into the Failure and Disaster-Causing Mechanism of a Debris Flow on 25 September 2021 in Tianquan, China

The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km2) in Tianquan County, Ya’an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts.

Analysis of Debris Flow Protective Barriers Using the Coupled Eulerian Lagrangian Method

Protective structures play a vital role in mitigating the risks associated with debris flows, yet assessing their performance poses crucial challenges for their real-world effectiveness. This study proposes a comprehensive procedure for evaluating the performance of protective structures exposed to impacts from media transported by large debris flow events. The method combines numerical modelling with site conditions for existing structures along the Hobart Rivulet in Tasmania, Australia. The Coupled Eulerian Lagrangian (CEL) model was validated by comparing simulation results with experimental data, demonstrating high agreement. Utilising three-dimensional modelling of debris flow–boulder interactions over the Hobart Rivulet terrain, boulder velocities were estimated for subsequent finite element analyses. Importantly, a model of interaction between boulders and I-beam posts was established, facilitating a comparative assessment of five distinct I-beam barrier systems defined as Type A to E, which are currently in use at the site. Simulation results reveal larger boulders display a slower increase in their velocities over the 3D terrain. Introducing a key metric, the failure ratio, enable a mechanism for comparative assessments of these barrier systems. Notably, the Type E barriers demonstrate superior performance due to fewer weak points within the structure. The combined CEL and FE assessments allow for multiple aspects of the interactions between debris flows, boulders, and structures to be considered, including structural failure and deformability, to enhance the understanding of debris flow risk mitigation in Tasmania.

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.