@article{ParraHormazabalMohrKorup2021,
  author    = {Parra Hormaz{\´a}bal, Eric and Mohr, Christian Heinrich and Korup, Oliver},
  title     = {Predicting Patagonian landslides},
  series = {Geophysical research letters : GRL / American Geophysical Union},
  volume    = {48},
  journal   = {Geophysical research letters : GRL / American Geophysical Union},
  number    = {23},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2021GL095224},
  pages     = {10},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{GoetzKohrsParraHormazabaletal.2021,
  author    = {Goetz, Jason and Kohrs, Robin and Parra Hormaz{\´a}bal, Eric and Bustos Morales, Manuel and Araneda Riquelme, Mar{\´i}a Bel{\´e}n and Henr{\´i}quez Ruiz, Cristian and Brenning, Alexander},
  title     = {Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling},
  series = {Natural hazards and earth system sciences : NHESS / European Geophysical Society},
  volume    = {21},
  journal   = {Natural hazards and earth system sciences : NHESS / European Geophysical Society},
  number    = {8},
  publisher = {European Geophysical Society},
  address   = {Katlenburg-Lindau},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-21-2543-2021},
  pages     = {2543 -- 2562},
  year      = {2021},
  abstract  = {Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random-walk and Perla et al.'s (PCM) two-parameter friction model components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial crossvalidation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. >= 80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.},
  language  = {en}
}