ALBERT

Description: Individual, large thrusting earthquakes can cause hundreds to thousands of years of exhumation in a geologically instantaneous moment through landslide generation. The bedrock landslides generated are important weathering agents through the conversion of bedrock into mobile regolith. Despite this, orogen-scale records of surface uplift and exhumation, whether sedimentary or geochemical, contain little to no evidence of individual large earthquakes. We examine how earthquakes and landslides influence exhumation and surface uplift rates with a zero-dimensional numerical model, supported by observations from the 2008 Mw 7.9 Wenchuan earthquake. We also simulate the concentration of cosmogenic radionuclides within the model domain, so we can examine the timescales over which earthquake-driven changes in exhumation can be measured. Our model uses empirically constrained relationships between seismic energy release, weathering, and landsliding volumes to show that large earthquakes generate the most surface uplift, despite causing lowering of the bedrock surface. Our model suggests that when earthquakes are the dominant rock uplift process in an orogen, rapid surface uplift can occur when regolith, which limits bedrock weathering, is preserved on the mountain range. After a large earthquake, there is a lowering in concentrations of 10Be in regolith leaving the orogen, but the concentrations return to the long-term average within 103 years. The timescale of the seismically induced cosmogenic nuclide concentration signal is shorter than the averaging time of most thermochronometers (〉103 years). However, our model suggests that the short-term stochastic feedbacks between weathering and exhumation produce measurable increases in cosmogenically measured exhumation rates which can be linked to earthquakes.

Description: 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.