Abstract. We release two datasets that track the enhanced landsliding induced by the 2008 Mw 7.9 Wenchuan earthquake over a portion of the Longmen Mountains, at the eastern margin of the Tibetan Plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris and post-seismic landslides (new failures). It covers 471 km2 in the earthquake's epicentral area, from 2005 to 2018. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (∼17 000 km2), together with information on their triggering rainfall as recorded by a network of rain gauges. For some well-monitored events, we provide more detailed data on rainfall, discharge, flow depth and density. The datasets can be used to analyse, on various scales, the patterns of landsliding caused by the earthquake. They can be compared to inventories of landslides triggered by past or new earthquakes or by other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets can be accessed from https://doi.org/10.5281/zenodo.1405489. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.
Prediction of runout distance and deposit morphology is of great importance in hazard mitigation of geophysical flows, including viscoplastic mudflows. The major rheological parameters of mudflows, namely, yield stress and viscosity, are crucial factors in controlling the runout and deposition processes. However, the roles of the two parameters, especially in mudflows with high inertia, remain poorly understood and are not accounted for in runout scaling relations with source volume. Here we investigate the effects of flow rheology on runout scaling and deposit morphology using small‐scale laboratory experiments and three‐dimensional numerical simulations. We find that yield stress and viscosity both influence flow velocity gained during downslope propagation of mudflows, which is strongly correlated with the runout distance; the role of yield stress is more significant than viscosity. High yield stress and low viscosity lead to an elongated deposit, where longitudinal propagation is more significant than lateral spreading. In contrast, high viscosity promotes the dominance of lateral spreading of the deposit, while low yield stress and moderate viscosity produce an initial elongate deposit, followed by a secondary surge that spreads laterally near the head of the deposit. Following appropriate scaling relations for viscosity and yield stress, a general scaling function is proposed to incorporate flow properties in the well‐known correlation of runout distance and source volume. Our findings regarding the inertia effects and the roles of yield stress and viscosity enhance our understanding of mudflows, muddy debris flows, and other viscoplastic geophysical flows.
Abstract The frequency of snowmelt‐induced soil slope instabilities is increasing in some seasonally cold regions because of climate change. Reliable hazard assessment and risk mitigation of snowmelt‐induced landslides require physically‐based prediction models. However, existing models either apply only at the slope scale or assume precipitation as the sole landslide trigger. In doing so, they neglect the complexity and coupled nature of the thermo‐hydro‐mechanical processes leading to slope instability in seasonally cold regions (such as snow accumulation and melting, infiltration and surface runoff, soil saturation, pore water pressure buildup and dissipation). Here, we present a spatially distributed and sequentially coupled numerical model to simulate snowmelt‐induced slope instabilities at the catchment scale. The model accounts for temperature‐dependent changes in the soil hydraulic behavior related to changes in water state by means of a routine implemented in a geographic information system. We verified the performance of the model using a case study of spring snowmelt‐induced soil slope failures that occurred after the 2004 Mid‐Niigata earthquake in Japan. Considering limitations and simplifications, the model was able to predict the triggering condition, magnitude, and spatial distribution of the snowmelt‐induced landslides with a satisfactory degree of accuracy. We believe that the robustness and simplicity of our numerical approach make it suitable for implementation in early warning systems.
Abstract Rock‐ice avalanches in cold‐high mountainous regions exhibit remarkably high mobility, frequently resulting in catastrophic consequences. However, the systematic influence of ice on the mobility of rock‐ice avalanches remains poorly understood. This paper addresses this gap by conducting a comprehensive flume experiment in a temperature‐controlled room at −10°C, simulating rock‐ice avalanches and considering variations in rock‐ice particle size ratios and ice contents. Overall mobility and segregation patterns are quantified by analyzing deposition characteristics, while high‐speed cameras capture velocity and segregation features during motion. Our investigation reveals a notable rock‐ice segregation phenomenon that significantly impacts the mobility of the mixture. Building on insights from prior numerical experiments conducted under nearly‐no‐base‐slip conditions (Feng et al., 2023, https://doi.org/10.1029/2023jf007115 ), our results underscore that the particle segregation simultaneously influences both internal (bulk) and basal frictions, thereby producing different nonlinear impacts on the mobility of the rock‐ice flow. Additionally, an empirical formula is proposed to describe the evolution of the friction coefficient in cases with different rock‐ice particle size ratios and ice contents. These findings have significant implications for predicting runout and assessing the risk of rock‐ice avalanches.
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