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Debris-flow grain-size distributions (GSDs) control runout length and mobility. Wide, bimodal GSDs and those containing a higher proportion of silt and clay have been shown experimentally to increase runout length. However, the relationship between grain size and mobility has not been well established in field conditions. Here, we compared the grain-size characteristics of two debris flows with considerably different runout lengths (1.5 km vs. 8 km) to understand the role of grain size in governing runout. The two debris flows were triggered in the same rainfall event from coseismic landslide debris generated in the 2008 Wen-chuan earthquake in catchments with similar lithology and topography. We compared the deposited GSDs and their spatial patterns using our rare, three-dimensional GSD datasets. Surprisingly, the proportions of each size fraction deposited by the two flows were statistically indistinguishable. The spatial pattern of grain size differed between the two flows, with evidence of inverse grading only preserved in the smaller deposit. From these observations, we can infer that the GSDs of both flows were determined by the coseismic landslide source material, and that there was little difference in the GSDs of material entrained as the flows bulked. The contrasting spatial distributions of grains indicated that different internal processes were dominant within the two flows. These findings demonstrate that where GSDs are dominated by coarse grains and are governed by similar source conditions, grain size plays a lesser role relative to sediment supply and hydrology in controlling the runout length of large catastrophic post-earthquake debris flows. [ABSTRACT FROM AUTHOR]

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Strong earthquakes in mountainous areas can trigger a large number of landslides that generate deposits of loose and unconsolidated debris across the landscape. These deposits can be easily remobilised by rainfalls, with their movement frequently evolving into catastrophic debris flows and avalanches. This has been the fate of many of the 200,000 co-seismic deposits generated by the 2008 Mw 7.9 Wenchuan earthquake in Sichuan, China. Here we present one of the first studies on the post-seismic patterns of landsliding through a detailed multi-temporal inventory that covers a large portion of the epicentral area (462.5 km2). We quantify changes of size-frequency distribution, active volumes and type of movement. We analyse the possible factors controlling landslide activity and we discuss the significance of mapping uncertainties. We observe that the total number of active landslides decreased with time significantly (from 9189 in 2008 to 221 in 2015), and that post-seismic remobilisations soon after the earthquake (2008-2011) occurred stochastically with respect to the size of the co-seismic deposits. Subsequently (2013-2015), landslide rates remained higher in larger deposits than in smaller ones, particularly in proximity to the drainage network, with channelised flows becoming comparatively more frequent than hillslope slides. However, most of the co-seismic debris remained along the hillslopes and are largely stabilised, urging to rethink the way we believe that seismic activity affects the erosion patterns in mountain ranges. [ABSTRACT FROM AUTHOR]

We developed a generalized model to describe and predict the spatial distribution of earthquake-induced landslides, based on a regression analysis of 9 co-seismic landslide inventories from different earthquakes and regions. Our model expresses the absolute spatial probability of landslides as a function of peak ground acceleration and hillslope gradient, based on data from global topographic and seismic ground motion datasets. The output from our model predicts probabilities for landslides triggered in sedimentary, meta-sedimentary, igneous and volcanic lithology, and is applicable to shallow continental earthquakes of moment magnitude range 6.2 to 7.9, and depths between 10 and 21 km. To obtain absolute probability predictions, we use only landslide source areas as input data, and explicitly estimate and correct for known incompleteness in input datasets, through a novel Monte Carlo approach. We estimate the uncertainty of these predictions, through extensive testing of the performance of the model, when making out-of-sample predictions for all 9 earthquakes. Our model is notably simpler than others developed to predict spatial probability of landsliding, as we have only included variables that could be constrained consistently at the global-scale, and eliminated those that did not influence landslide probability in a consistent manner across all earthquakes in our dataset. The model outputs also provide a baseline to further investigate spatial and temporal sources of unexplained variability in co-seismic landslide distributions. Using freely available topographic and ground motion data, we suggest that our model can be applied more widely, to provide landslide predictions for earthquakes with no landslide data. [ABSTRACT FROM AUTHOR]

In steep soil-mantled landscapes, the initiation of shallow landslides is strongly controlled by the distribution of vegetation, whose roots reinforce the soil. The magnitude of root reinforcement depends on the number, diameter distribution, orientation and the mechanical properties of roots that cross potential failure planes. Understanding how these properties vary in space and time in forests remains a significant challenge. Here we test the hypothesis that spatio-temporal variations in root reinforcement along a hillslope occur as a function of topographic soil moisture gradients. To test this hypothesis we compared root reinforcement measurements from relatively dry, divergent noses to relatively wet, convergent hollows in the southern Appalachian Mountains, North Carolina, USA. Our initial results showed that root reinforcement decreased in areas of higher soil moisture because the tensile strength of roots decreased. A post hoc laboratory experiment further demonstrated that root tensile strength decreased as root moisture content increased. This effect is consistent with other experiments on stem woods showing that increased water content in the cell wall decreases tensile strength. Our experimental data demonstrated that roots can adjust to changes in the external root moisture conditions within hours, suggesting that root moisture content will change over the timescale of large storm events (hours-days). We assessed the effects of the dynamic changes in root tensile strength to the magnitude of apparent cohesion within the infinite slope stability model. Slopes can be considerably less stable when precipitation-driven increases in saturated soil depth both increase pore pressures and decrease root reinforcement. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]