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Dense tree stands and high wind speeds characterize the temperate rainforests of southern Chilean Patagonia, where landslides frequently strip hillslopes of soils, rock, and biomass. Assuming that wind loads on trees promote slope instability, we explore the role of forest cover and wind speed in predicting landslides with a hierarchical Bayesian logistic regression. We find that higher crown openness and wind speeds credibly predict higher probabilities of detecting landslides regardless of topographic location, though much better in low-order channels and on midslope locations than on open slopes. Wind speed has less predictive power in areas that were impacted by tephra fall from recent volcanic eruptions, while the influence of forest cover in terms of crown openness remains. <br /> Plain Language Summary Chilean Patagonia hosts some of Earth's largest swaths of temperate rainforests, where frequent landslides erode soil, rock, and vegetation. We explore the role of forest cover and wind disturbances in promoting such landslides with a model that predicts from crown openness and wind speed the probability of detecting landslide terrain. We find that both forest cover and wind speed play important, yet previously underappreciated, roles in this context, especially when grouped by landform types and previous volcanic disturbance, which may override the comparable modest control of wind on landslides. Our study is the first of its kind in one of the windiest spots on Earth and encourages a more discerning approach to landslide prediction.
Detecting whether and how river discharge responds to strong earthquake shaking can be time-consuming and prone to operator bias when checking hydrographs from hundreds of gauging stations. We use Bayesian piecewise regression models to show that up to a fifth of all gauging stations across Chile had their largest change in daily streamflow trend on the day of the M-w 8.8 Maule earthquake in 2010. These stations cluster distinctly in the near field though the number of detected streamflow changes varies with model complexity and length of time window considered. Credible seismic streamflow changes at several stations were the highest detectable in eight months, with an increased variance of discharge surpassing the variance of discharge following rainstorms. We conclude that Bayesian piecewise regression sheds new and unbiased insights on the duration, trend, and variance of streamflow response to strong earthquakes, and on how this response compares to that following rainstorms.