Due to their potentially long runout, debris flows are a major hazard and an important geomorphic process in mountainous environments. Understanding runout is therefore essential to minimize risk in the near‐term and interpret the pace and pattern of debris flow erosion and deposition over geomorphic timescales. Many debris flows occur in forested landscapes where they mobilize large volumes of large woody debris (LWD) in addition to sediment, but few studies have quantitatively documented the effects of LWD on runout. Here, we analyze recent and historic debris flows in southeast Alaska, a mountainous, forested system with minimal human alteration. Sixteen debris flows near Sitka triggered on August 18, 2015 or more recently had volumes of 80 to 25 000 m3and limited mobility compared to a global compilation of similarly‐sized debris flows. Their deposits inundated 31% of the planimetric area, and their runout lengths were 48% of that predicted by the global dataset. Depositional slopes were 6°–26°, and mobility index, defined as the ratio of horizontal runout to vertical elevation change, ranged from 1.2 to 3, further indicating low mobility. In the broader southeast Alaskan region consisting of Chichagof and Baranof Islands, remote sensing‐based analysis of 1061 historic debris flows showed that mobility index decreased from 2.3–2.5 to 1.4–1.8 as average forest age increased from 0 to 416 years. We therefore interpret that the presence of LWD within a debris flow and standing trees, stumps, and logs in the deposition zone inhibit runout, primarily through granular phenomena such as jamming due to force chains. Calibration of debris flow runout models should therefore incorporate the ecologic as well as geologic setting, and feedbacks between debris flows and vegetation likely control the transport of sediment and organic material through steep, forested catchments over geomorphic time. © 2020 John Wiley & Sons, Ltd.
Abstract. We detail a new prediction-oriented procedure aimed at volcanic hazardassessment based on geophysical mass flow models constrained withheterogeneous and poorly defined data. Our method relies on an itemizedapplication of the empirical falsification principle over an arbitrarily wideenvelope of possible input conditions. We thus provide a first step towards aobjective and partially automated experimental design construction. Inparticular, instead of fully calibrating model inputs on past observations,we create and explore more general requirements of consistency, and then weseparately use each piece of empirical data to remove those input values thatare not compatible with it. Hence, partial solutions are defined to the inverseproblem. This has several advantages compared to a traditionally posedinverse problem: (i) the potentially nonempty inverse images of partialsolutions of multiple possible forward models characterize the solutions tothe inverse problem; (ii) the partial solutions can provide hazard estimatesunder weaker constraints, potentially including extreme cases that areimportant for hazard analysis; (iii) if multiple models are applicable,specific performance scores against each piece of empirical information canbe calculated. We apply our procedure to the case study of the Atenquiquevolcaniclastic debris flow, which occurred on the flanks of Nevado de Colimavolcano (Mexico), 1955. We adopt and compare three depth-averaged modelscurrently implemented in the TITAN2D solver, available from https://vhub.org(Version 4.0.0 – last access: 23 June 2016). The associated inverse problemis not well-posed if approached in a traditional way. We show that our procedurecan extract valuable information for hazard assessment, allowing the explorationof the impact of synthetic flows that are similar to those that occurred in thepast but different in plausible ways. The implementation of multiple models isthus a crucial aspect of our approach, as they can allow the covering of otherplausible flows. We also observe that model selection is inherently linked tothe inversion problem.
more » « less- NSF-PAR ID:
- 10111637
- Date Published:
- Journal Name:
- Natural Hazards and Earth System Sciences
- Volume:
- 19
- Issue:
- 4
- ISSN:
- 1684-9981
- Page Range / eLocation ID:
- 791 to 820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Debris flows pose a significant hazard to communities in mountainous areas, and there is a continued need for methods to delineate hazard zones associated with debris-flow inundation. In certain situations, such as scenarios following wildfire, where there could be an abrupt increase in the likelihood and size of debris flows that necessitates a rapid hazard assessment, the computational demands of inundation models play a role in their utility. The inability to efficiently determine the downstream effects of anticipated debris-flow events remains a critical gap in our ability to understand, mitigate, and assess debris-flow hazards. To better understand the downstream effects of debris flows, we introduce a computationally efficient, reduced-complexity inundation model, which we refer to as the Progressive Debris-Flow routing and inundation model (ProDF). We calibrate ProDF against mapped inundation from five watersheds near Montecito, CA, that produced debris flows shortly after the 2017 Thomas Fire. ProDF reproduced 70% of mapped deposits across a 40 km 2 study area. While this study focuses on a series of post-wildfire debris flows, ProDF is not limited to simulating debris-flow inundation following wildfire and could be applied to any scenario where it is possible to estimate a debris-flow volume. However, given its ability to reproduce mapped debris-flow deposits downstream of the 2017 Thomas Fire burn scar, and the modest run time associated with a simulation over this 40 km 2 study area, results suggest ProDF may be particularly promising for post-wildfire hazard assessment applications.more » « less
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Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimization problems. However, both classical solvers and recent deep learning-based surrogate models are typically extremely computationally intensive, because of their local evolution: they need to update the state of each discretized cell at each time step during inference. Here we develop Latent Evolution of PDEs (LE-PDE), a simple, fast and scalable method to accelerate the simulation and inverse optimization of PDEs. LE-PDE learns a compact, global representation of the system and efficiently evolves it fully in the latent space with learned latent evolution models. LE-PDE achieves speedup by having a much smaller latent dimension to update during long rollout as compared to updating in the input space. We introduce new learning objectives to effectively learn such latent dynamics to ensure long-term stability. We further introduce techniques for speeding-up inverse optimization of boundary conditions for PDEs via backpropagation through time in latent space, and an annealing technique to address the non-differentiability and sparse interaction of boundary conditions. We test our method in a 1D benchmark of nonlinear PDEs, 2D Navier-Stokes flows into turbulent phase and an inverse optimization of boundary conditions in 2D Navier-Stokes flow. Compared to state-of-the-art deep learning-based surrogate models and other strong baselines, we demonstrate up to 128x reduction in the dimensions to update, and up to 15x improvement in speed, while achieving competitive accuracy.more » « less
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Debris flows are dense and fast-moving complex suspensions of soil and water that threaten lives and infrastructure. Assessing the hazard potential of debris flows requires predicting yield and flow behavior. Reported measurements of rheology for debris flow slurries are highly variable and sometimes contradictory due to heterogeneity in particle composition and volume fraction ( ϕ ) and also inconsistent measurement methods. Here we examine the composition and flow behavior of source materials that formed the postwildfire debris flows in Montecito, CA, in 2018, for a wide range of ϕ that encapsulates debris flow formation by overland flow. We find that shear viscosity and yield stress are controlled by the distance from jamming, Δ ϕ = ϕ m − ϕ , where the jamming fraction ϕ m is a material parameter that depends on grain size polydispersity and friction. By rescaling shear and viscous stresses to account for these effects, the data collapse onto a simple nondimensional flow curve indicative of a Bingham plastic (viscoplastic) fluid. Given the highly nonlinear dependence of rheology on Δ ϕ , our findings suggest that determining the jamming fraction for natural materials will significantly improve flow models for geophysical suspensions such as hyperconcentrated flows and debris flows.more » « less
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Abstract Post‐seismic debris flows are an important hazard following large earthquakes, propagating destruction downstream from hillslopes where coseismic landslides occur and extending damage for years after shaking stops. Data sets of post‐seismic debris flows are necessary to predict initiation and runout characteristics but are presently scarce. We used satellite imagery supplemented by field observations to compile an inventory of >1,000 debris flows associated with the 2015 Gorkha Earthquake in Nepal. We identified two distinct debris flow types: (1) Material from a coseismic landslide was remobilized in a steep channel during a later monsoon; and (2) a new post‐seismic hillslope failure occurred in saturated conditions and became fluidized and channelized. Runout distance was constrained by channel confluences and may be related to confluence geometry. Unstable landslide debris was largely flushed from steep channels during the first monsoon following the earthquake, and the rate of new hillslope failures tailed off over a few years.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/nhess-19-791-2019
