2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges

McDougall, Scott

doi:10.1139/cgj-2016-0104

Cited by 180 publications

(130 citation statements)

References 86 publications

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“…• if banks of the deterministic channel were dampened out in DEM realizations, flow material tends to spread out along channel cross section direction and travel distance would be shorter; • if the relatively high elevation area that holds back flow material was dampened out, flow material tends to travel further; • increasing topographic roughness leads to higher simulated momentum losses and shorter travel distance as pointed out by McDougall [10]. The black outline plotted on the hazard maps represents the deterministic hazard area (see figure 7 (a)).…”

Section: Probabilistic Hazard Mapsmentioning

confidence: 96%

“…[8,9], yet are typically not feasible for practical hazard mitigation purposes. Detailed reviews of computational run-out models for rapid, flow-like landslide models have been published by McDougall [10] and Pastor et al [11]. An indispensable input to any of these computational landslide run-out models is data that represents the terrain in which the slide is likely to occur.…”

Section: Introductionmentioning

confidence: 99%

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Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Zhao¹,

Kowalski²

2020

Preprint

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Topography representing digital elevation models (DEMs) are essential inputs for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide-run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as further topographic characteristics and the DEM error's variability. We further find that in the absence of systematic bias in the DEM, a performant root mean square error based unconditional stochastic simulation yields similar results than a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability of the DEM error, which leads to an increase of the potential hazard area as well as extreme values of dynamic flow properties.

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Section: Probabilistic Hazard Mapsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Zhao¹,

Kowalski²

2020

Preprint

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“…The centroid of the debris-flow cluster is computed using Equation (6). The boundary of a debris-flow cluster can be defined as a set of debris-flow particle locations that indicate the extent of the cluster.…”

Section: Integration Of Debris-flow Particles Into a Debris-flow Clustermentioning

confidence: 99%

Hazard Assessment Based on the Combination of DAN3D and Machine Learning Method for Planning Closed-Type Barriers against Debris-Flow

Cheon

Lee

2020

Water

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If a slope located near a densely populated region is susceptible to debris-flow hazards, barriers are used as a mitigation method by placing them in flow channels; i.e., flowpaths. Selecting the location and the design of a barrier requires hazard assessment to determine the width, volume, and impact pressure of debris-flow at the moment of collision. DAN3D (Three-Dimensional Dynamic Analysis), a 3D numerical model for simulating debris-flow, has been widely used to perform hazard assessment; however, solely using DAN3D would be both insufficient and inefficient in finding the optimal barrier location. Therefore, the present study developed a framework that interprets the results from DAN3D simulation without considering any barriers. Then, the framework generates hazard assessment maps showing the impact parameters of debris-flow along the flowpath by various algorithms and machine learning methods, such as the k-means clustering algorithm, and also computes the width of the debris-flow, which is not explicitly calculated in DAN3D. A case study of the debris-flow at Umyeon mountain, Korea, in 2011, was used to generate hazard assessment maps. The maps were demonstrated to be a tool to quickly compute the impact parameters for conceptual barrier design with the aim of finding potential barrier locations.

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“…Both empirical and numerical approaches are used to simulate landslide runout (McDougall, 2017). Salzmann et al (2004) integrated optical remote sensing with GIS-based analysis of a DEM to assess ice avalanche hazard potential.…”

Section: Landslide and Avalanche Runout Analysismentioning

confidence: 99%

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

et al. 2019

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Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward toward improved characterization of evolving hazards and risk across HMA.

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2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges

Cited by 180 publications

References 86 publications

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Hazard Assessment Based on the Combination of DAN3D and Machine Learning Method for Planning Closed-Type Barriers against Debris-Flow

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

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