Runout Model Research Articles

Physically based probabilistic prediction of regional rainfall-induced shallow landslides: from initiation to runout analysis

ABSTRACT In this study, a physically based method is proposed to estimate the exceedance probability of the intensity measure of the regional rainfall-induced shallow landslides. This method consists of a digital elevation model, an initiation model, a runout model and a probabilistic analysis model. In the suggested method, a study area is discretised into a grid of cells, and the stability of each cell can be evaluated through the initiation model. Then, the runout behaviour of the unstable cells can be simulated by the runout model. Finally, the exceedance probability of the intensity measures of each cell can be estimated through Monte Carlo simulation considering the uncertainties in the initiation model and runout model. The proposed method is applied to analyse the rainfall-induced landslides in a study area in the southwest of China, which can provide a reasonably accurate prediction of the impact zones of the landslides. The suggested method provides a useful tool to probabilistically predict the impacted area of the rainfall-induced landslides at a regional scale.

Construction of a Joint Newmark–Runout Model for Seismic Landslide Risk Identification: A Case Study in the Eastern Tibetan Plateau

The key to seismic landslide risk identification resides in the accurate evaluation of seismic landslide hazards. The traditional evaluation models for seismic landslide hazard seldom consider the landslide dynamic runout process, leading to an underestimation of seismic landslide hazard. Therefore, a joint Newmark–Runout model based on landslide dynamic runout is proposed. According to the evaluation results of static seismic landslide hazard, the landslide source points can be extracted, and the landslide dynamic runout process is simulated to obtain the dynamic seismic landslide hazard. Finally, the static and dynamic seismic landslide hazards are fused to obtain an optimized seismic landslide hazard. In September 2022, a strong Ms6.8 earthquake occurred in the eastern Tibetan Plateau, triggering thousands of landslides. Taking the 2022 Luding earthquake-induced landslide as a sample, the function relationship between seismic slope displacement and landslide occurrence probability is statistically modeled, which partly improves the traditional Newmark model. The optimized seismic landslide hazard evaluation of the Luding earthquake area is conducted, and then, the seismic landslide risk identification is completed by taking roads and buildings as hazard-affected bodies. The results show that the length of the roads facing very high and high seismic landslide risks are 3.36 km and 15.66 km, respectively, and the buildings on the Moxi platform near the epicenter are less vulnerable to seismic landslides. The research findings can furnish critical scientific and technological support for swift earthquake relief operations.

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.

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.

The dynamic threat from landslides following large continental earthquakes.

Earthquake-triggered landslides show three important characteristics: they are often responsible for a considerable proportion of the damage sustained during mountain region earthquakes, they are non-randomly distributed across space, and they continue to evolve in the years after the earthquake. Despite this, planning for future earthquakes rarely takes into consideration either landslides or their evolution with time. Here we couple a unique timeseries of mapped landslides between 2014-2020 across the area of Nepal impacted by the 2015 Mw 7.8 Gorkha earthquake and a numerical landslide runout model overlain with building locations to examine how the distributions of both evolving landslide hazard and exposure intersect to generate a dynamic threat to buildings. The threat from landslide runout is shown to change in predictable ways after the earthquake, becoming more pronounced at mid- and lower-hillslope positions and remaining in the landscape for multiple years. Using the positions of our mapped landslides as a starting point, we can identify a priori the locations of 78% of buildings that were subsequently impacted by landslide debris. We show that landslide exposure and hazard vary from negligible to high, in relative terms, over lateral distances of as little as 10s of m. Our findings hold important implications for guiding reconstruction and for taking steps to reduce the risks from future earthquakes.

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.

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

AbstractIncreases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris‐flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris‐flow runout model based on the Herschel‐Bulkley (HB) rheology (HEC‐RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall‐runoff simulations, and a debris‐flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris‐flow hazards before a fire begins and provides guidance for future applications of single‐phase rheological models when assessing PFDF hazards.

Debris‐flow entrainment modelling under climate change: Considering antecedent moisture conditions along the flow path

AbstractDebris‐flow volumes can increase along their flow path by entraining sediment stored in the channel bed and banks, thus also increasing hazard potential. Theoretical considerations, laboratory experiments and field investigations all indicate that the saturation conditions of the sediment along the flow path can greatly influence the amount of sediment entrained. However, this process is usually not considered for practical applications. This study aims to close this gap by combining runout and hydrological models into a predictive framework that is calibrated and tested using unique observations of sediment erosion and debris‐flow properties available at a Swiss debris‐flow observation station (Illgraben). To this end, hourly water input to the erodible channel is predicted using a simple, process‐based hydrological model, and the resulting water saturation level in the upper sediment layer of the channel is modelled based on a Hortonian infiltration concept. Debris‐flow entrainment is then predicted using the RAMMS debris‐flow runout model. We find a strong correlation between the modelled saturation level of the sediment on the flow path and the channel‐bed erodibility for single‐surge debris‐flow events with distinct fronts, indicating that the modelled water content is a good predictor for erosion simulated in RAMMS. Debris‐flow properties with more complex flow behaviour (e.g., multiple surges or roll waves) are not as well predicted using this procedure, indicating that more physically complete models are necessary. Finally, we demonstrate how this modelling framework can be used for climate change impact assessment and show that earlier snowmelt may shift the peak of the debris‐flow season to earlier in the year. Our novel modelling framework provides a plausible approach to reproduce saturation‐dependent entrainment and thus better constrain event volumes for current and future hazard assessment.

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.

Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps

Abstract. Debris flows are important processes for the assessment of natural hazards due to their damage potential. To assess the impact of a potential debris flow, parameters such as the flow velocity, flow depth, maximum discharge, and volume are of great importance. This study uses data from the Illgraben observation station in the central Alps of Switzerland to explore the relationships between these flow parameters and the debris flow dynamics. To this end, we simulated previous debris flow events with the RAMMS::Debrisflow (Rapid Mass Movement Simulation::Debrisflow) runout model, which is based on a numerical solution of the shallow water equations for granular flows using the Voellmy friction relation. Here, the events were modelled in an effort to explore possible controls on the friction parameters μ and ξ, which describe the Coulomb friction and the turbulent friction, respectively, in the model. Additionally, sediment samples from levee deposits were analysed for their grain size distributions (14 events) and their mineralogical properties (4 events) to explore if the properties of the fine-grained matrix have an influence on the debris flow dynamics. Finally, field data from various debris flows such as the flow velocities and depths were statistically compared with the grain size distributions, the mineralogical properties, and the simulation results to identify the key variables controlling the kinematics of these flows. The simulation results point to several ideal solutions, which depend on the Coulomb and turbulent friction parameters (μ and ξ, respectively). In addition, the modelling results show that the Coulomb and turbulent frictions of a flow are related to the Froude number if the flow velocity is < 6–7 m s−1. It is also shown that the fine-sediment grain size or clay-particle mineralogy of a flow neither correlates with the flow's velocity and depth, nor can it be used to quantify the friction in the Voellmy friction relation. This suggests that the frictional behaviour of a flow may be controlled by other properties such as the friction generated by the partially fluidised coarse granular sediment. Yet, the flow properties are well-correlated with the flow volume, from which most other parameters can be derived, which is consistent with common engineering practice.

Forest damage and its braking effect on the extreme snow avalanche on Mt. Nodanishoji, Japan, in 2021

ABSTRACT An extreme snow avalanche occurred on Mt. Nodanishoji, Japan, in 2021. This avalanche was the second largest ever documented in Japan and destroyed many trees and structures. Despite the increasing interest in avalanche disaster mitigation effects of forests, there are limited opportunities to obtain actual data sets on avalanches and forests. In this study, we reconstructed the avalanche velocity based on the cedar forest damage and simulated the avalanche flow using the dynamical run-out model Titan2D to reveal the avalanche movement and ascertain the braking effect of the forest on the avalanche. We successfully simulated the whole movement of the extreme avalanche with a horizontal runout distance of 2,800 m from the starting zone at an altitude of 1,700 m to the run-out zone considering the effect of the forest by increasing the frictional parameters in the model. Whereas in the case of no forest, the avalanche spread beyond its actual reach, considering the forest, the run-out distance decreased and was consistent with the observed reach. Comparing the results with and without forests, we ascertained the distinct braking effect of the forest on the extreme avalanche and quantify it in terms of a locally increased friction angle.

A landscape scale model to predict post-fire debris flow impact zones

Post-fire debris flows pose a significant hazard in mountainous regions, but accurate assessment of the associated risk at large scales, particularly in the context of mega-fires, is limited. Although numerous models exist, few are functional at the landscape scale, and none integrate assessments of downstream effects with the likelihood of flow initiation to define both the frequency and magnitude of potential flows. The aim of this work, therefore, was to develop a post-fire debris flow model that could characterise both the occurrence frequency and associated runout of debris flow events and which could be rapidly applied to fire events at the landscape scale. We achieve this using a model integration approach that included the development of a reduced-complexity debris flow runout model, which was calibrated and independently tested for its application at large scales with a dataset of 1377 individual post-fire flows across south-eastern Australia. This model was then integrated with previously published methods determining debris flow source areas and likelihood of initiation specific to our study area. The developed model is intentionally designed for rapid response hazard management. Therefore, it predominantly incorporates only the fundamental, lowest parameter form drivers of debris flow initiation and runout termination, thus minimising computational complexity while ensuring prediction accuracy sufficient for hazard assessment even at application scales as large as mega-fires. Accounting for debris flows initiated from single headwaters and cases where flows simultaneously converge from multiple initiation points, our integrated model effectively characterises debris flow hazard across a geomorphologically diverse landscape encompassing >200,000km2 (capturing ~640,000 potential debris flow initiation locations across Victoria, Australia). Model testing using an independent validation dataset resulted in debris flow runout predictions with root mean square error (RMSE) of 176 and 308 m for non-convergent and convergent flows respectively. Crucially, these predictions can be computed rapidly for large (up to landscape scale) fire events with minimal input requirements and the use of a remotely-sensed observation dataset for domain calibration. As such, the model provides a critical means of addressing the capacity gap in assessments of post-fire debris flow hazard by efficiently integrating measures of occurrence frequency and runout for functional application at large scales. This new work empowers land managers to rapidly predict the hazard arising from post-fire erosion processes and represents an important applied step towards better understanding and mitigating the impacts of wildfire-induced debris flows.

Analysis of Uncertainty and Sensitivity in Tailings Dam Breach-Runout Numerical Modelling

AbstractTailings dam breaches (TDBs) and subsequent flows can pose significant risk to public safety, the environment, and the economy. Numerical runout models are used to simulate potential tailings flows and understand their downstream impacts. Due to the complex nature of the breach-runout processes, the mobility and downstream impacts of these types of failures are highly uncertain. We applied the first-order second-moment (FOSM) methodology to a database of 11 back-analyzed historical tailings flows to evaluate uncertainties in TDB runout modelling and conducted a sensitivity analysis to identify key factors contributing to the variability of the HEC-RAS model output, including at different locations along the runout path. The results indicate that prioritizing resources toward advancements in estimating the values of primary contributors to the sensitivity of the selected model outputs is necessary for more reliable model results. We found that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. However, the primary contributors to the sensitivity of the model outputs varied depending on the case study; therefore, the selection of appropriate rheological models and consideration of site-specific conditions are crucial for accurate predictions. The study proposes and demonstrates the FOSM methodology as an approximate probabilistic approach to model-based tailings flow runout prediction, which can help improve the accuracy of risk assessments and emergency response plans.

Human-centered avalanche susceptibility mapping (H-CASM): shifting the cartographic emphasis of backcountry avalanche susceptibility maps

ABSTRACT With backcountry winter travel increasing in popularity, the importance of accessible avalanche safety information is crucial. Widely accessible avalanche backcountry maps use slope shading to show where avalanches are likely to start. This paper presents a new methodology called Human-Centered Avalanche Susceptibility Mapping (H-CASM), which shifts the cartographic emphasis of backcountry avalanche maps from the hazard of an avalanche starting in a specific location to risk to a human traveling in the terrain. Utilizing ArcGIS, RAMMS avalanche runout model, and Python, we developed H-CASM as an open-source code to incorporate possible avalanche runouts, terrain traps, and connected slopes while trying to reduce instances of false certainty in visualizations. To provide a more comprehensive representation of susceptible terrain, we integrated aspect, distance to ridges, and vector ruggedness in addition to slope for start zone susceptibility using high-resolution one-meter Digital Elevation Models (DEMs). Avoiding the use of discrete susceptibility categories, we applied continuous gradient shading reducing false certainty in the final visualization. This proof of concept sets a new precedent for the future of backcountry avalanche susceptibility maps.

Back-analysis of the paraglacial slope failure at Grewingk Glacier and Lake, Alaska

The relationship between rock-slope failure and glacier retreat is complex, and paraglacial failures often lack clearly identified triggers. To better understand the role of glacier retreat in rock-slope failures, we analysed the processes that led to the October 1967 Grewingk landslide in Kachemak Bay State Park on the Kenai Peninsula, Southcentral Alaska. The rock material collapsed onto the glacier toe and into its pro-glacial lake and produced a tsunami wave that swept the outwash plain. On the day of the failure, rainfall and snowmelt were well within normal ranges, and seismic records show no significant shaking. Three years prior to the 1967 failure, the slope withstood the second largest earthquake ever recorded (Great Alaskan earthquake, MW 9.2). We reassessed the volume of the failure by differencing pre- and post-digital terrain models and found a value of 20–24 × 106 m3, which is four times smaller than a previous estimate. The back analysis of the Grewingk landslide is based on remote sensing data and field measurements including aerial satellite image analysis, detailed surveying and understanding of the structural geology, a kinematic analysis, and runout modelling. Our research provides an example of a major paraglacial failure that lacks an obvious trigger and points to several geological factors and changing environmental conditions that likely promote such failures. This study further indicates that the Grewingk landslide, pre-conditioned by the geometry of faults and joints, may have reached a critical stability state due to internal processes and the potential combined effects of seismic activity and glacier retreat prior to the collapse.

Mass movement hazard & global climate change: Physical-deterministic modelling study of the rest and be thankful pass, Scotland UK

Mass movements frequently affect infrastructure corridors, particularly so in mountainous areas. The A83 at the Rest and be Thankful pass (Scotland, UK) is a prime example of a road experiencing frequent closure due to precipitation-triggered landslides and debris flows. The closures result in long (>90 km) diversions and severe economic losses (>£60 million). To ensure persistence of transport services, future engineered interventions and maintenance schedules must anticipate the potential consequences of climate change that affects the frequency and magnitude threshold precipitation events that are capable of triggering mass movement. In turn this requires a better understanding of the hydrological implications of climate change on precipitation-triggered landslides and debris flows. The area is prone to mass movements due to steep topography, the glacial history, reworked deposits of mud and siltstone, and landslide-triggering precipitation. The objective here is to assess the severity of future events, i.e. the coming decades, was assessed by means of an event-based runout model for selected events that were identified using a present-day precipitation threshold applied to hydro-meteorological forecasts for this part of the UK. A convex hull method was applied to these forecasts to create a comprehensive selection of modelled events and a much reduced computational load. This selection is considered to be representative for the largest variation within the forecasts.Results show that 61 mm of daily precipitation will likely trigger mass movement. This threshold reduces by 0.15 mm for every millimetre accounted for in an antecedent precipitation index calculated based on 8 antecedent days. On the basis of the hydro-meteorological forecasts it can be concluded that the frequency of these trigger conditions gradually declines, as seven out of the eleven RCM (Regional Climate Model) configurations show longer recurrence intervals. This corresponds with the decline in the modelled future precipitation. However, it is observed that, for scenarios with lower average daily precipitation and antecedent precipitation indices, event severity is likely to increase as intense daily precipitation follows drier antecedent conditions. For scenarios with higher average daily precipitation and antecedent precipitation indices, persistent wetting appears to expand source areas and increases material entrainment. The simulations show that the main source areas, situated between 300 and 500 metre elevation, extend upslope when the antecedent precipitation regime has peak precipitation values in the latter stages of the 8 antecedent days.

Time-resolved triggering and runout analysis of rainfall-induced shallow landslides

AbstractRainfall-induced shallow landslides often turn into flows. These phenomena occur worldwide and pose severe hazard to infrastructure and human lives on mountainous areas. Risk assessment, and the design of mitigation measures, can both be informed by back-analysis of previous events. However, shallow instabilities are frequently spread over a large area, with the generated flows occurring in sequences, or surges. Conventionally, back-analysis exercises tackle the problem by simulating runout as a single event, with all surges happening simultaneously. This simplification has repercussions that have not been explored in the literature so far, and whose impact in hazard assessment practice is unclear. Therefore, a novel time-resolving procedure is proposed in this paper, which can for the first time be applied to resolve instability sequences of arbitrary duration. The methodology discretizes the event, detecting instabilities at equally spaced time intervals as a function of rainfall. Thanks to this, the post-failure behaviour of each surge can be tracked by a runout model, with a separate simulation performed every time a new instability is detected. The methodology robustness is tested on two documented case studies. The results reveal that, under some conditions, the time-resolving procedure can lead to significantly different results in terms of runout path, flooded area, and flow heights. This leads to criticism on how back-analysis is conventionally applied, prompting for a review of historical cases.

Topology optimisation for robust design of large composite structures

The manufacturing of composite structures can lead to material defects. Some of these defects, due to their small size, can go undetected by current non-destructive testing techniques; especially in very large and geometrically complex components. Therefore, it is of paramount importance to design composite components to resist defects by accounting for them as early as possible. Topology optimisation is a methodology whereby complex features, such as cracks and debonds, can be accounted for in the design. Therefore, this work proposes a new approach for the design of very large composite structures. This new approach is enabled by a solid shell element formulation with boundary tracking capabilities (through the floating node and the level-set methods) and optimisation capabilities (through design sensitivity analysis of an energy release rate objective function). Furthermore, this work proposes a new algorithm and modelling workflow that has the ability to handle large multi-scale models. The proposed methodology shows advantages as the ability to track the topology explicitly, and the ability to increase defect tolerance of the design. This works shows the new methodology is capable of handling complex structures, such as a multi-scale stringer run-out model from an aircraft wing-box, while a kissing bond defect is present.

Engineering geological investigation and runout modelling of the disastrous Taliye landslide, Maharashtra, India of 22 July 2021

Engineering geological investigation and runout modelling of the disastrous Taliye landslide, Maharashtra, India of 22 July 2021