The evaluation of potential landslides in mountain areas is a very complex process. Currently, event understanding is scarce due to information limitations. Identifying the whole chain of events is not a straightforward task, and the impacts of mass-wasting processes depend on the conditions downstream of the origin. In this paper, we present an example that illustrates the complexities in the evaluation of the chain of events that may lead to a natural disaster. On 16 December 2017, a landslide occurred in the Yelcho mountain range (southern Chile). In that event, 7 million m³ of rocks and soil fell on the Yelcho glacier, depositing 2 million m³ on the glacier terminal, and the rest continued downstream, triggering a mudflow that hit Villa Santa Lucía in Chilean Patagonia and killing 22 people. The complex event was anticipated in the region by the National Geological and Mining Survey (Sernageomin in Spanish). However, the effects of the terrain characteristics along the run-out area were more significant than anticipated. In this work, we evaluate the conditions that enabled the mudflow that hit Villa Santa Lucía. We used the information generated by Sernageomin's professionals after the mudflow. We carried out geotechnical tests to characterize the soil. We simulated the mudflow using two hydrodynamic programs (r.avaflow and Flo-2D) that can handle the rheology of the water–soil mixture. Our results indicate that the soil is classified as volcanic pumices. This type of soil can be susceptible to the collapse of the structure when subjected to shearing (molding), flowing as a viscous liquid. From the numerical modeling, we concluded that r.avaflow performs better than Flo-2D. The mudflow was satisfactorily simulated using a water content in the mixture ranging from 30 % to 40 %, which would have required a source of about 3 million m³ of water. Coupling the simulations and the soil tests that we performed, we estimated that in the area scoured by the mudflow, there were probably around 2 800 000 m³ of water within the soil. Therefore, the conditions of the valley were crucial to enhancing the impacts of the landslide. This result is relevant because it highlights the importance of evaluating the complete chain of events to map hazards. We suggest that in future hazard mapping, geotechnical studies in combination with hydrodynamic simulation should be included, in particular when human lives are at risk.

Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.

The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.

Slow movement processes correspond to slow to extremely slow deformation in slopes. Slow movement is often known to be the cause of severe damage to structures and infrastructures. In the Northern Biobío hillside NBH, many slow movements were not previously identified, which led to building damage. To evaluate their triggering factors, we use two complementary techniques: interferometry of synthetic aperture radar InSAR and electrical resistivity tomography ERT. We observed deformation patterns using SAR data derived from PALSAR-1 during a year that exhibited extreme rainfall 2006. We established that reactivation of slow movements occurs during extreme rainfall periods, which can be observed through deformation patterns. ERT inversion results reveal a formation of silt and clay layers. Our results indicate the existence of slow movement processes close to densely populated areas. An interpretation of InSAR and ERT results showed that during extreme rain events, soil saturation occurs. The silt layer loads the hillslope and the clay layer acts as a lubricant below the silt.

Debris flow generation in volcanic zones in the southern Andes has not been widely studied, despite the enormous economic and infrastructure damage that these events can generate. The present work contributes to the understanding of these dynamics based on a study of the 2017 Petrohué debris flow event from two complementary points of view. First, a comprehensive field survey allowed us to determine that a rockfall initiated the debris flow due to an intense rainfall event. The rockfall lithology corresponds to lava blocks and autobrecciated lavas, predominantly over 1500 m a.s.l. Second, the process was numerically modelled and constrained by in situ data collection and geomorphological mapping. The event was studied by back analysis using the height of flow measured on Route CH-255 with errors of 5 %. Debris flow volume has a high sensitivity with the initial water content in the block fall zone, ranging from 4.7×10⁵ up to 5.5×10⁵ m³, depending on the digital elevation model (DEM) used. Therefore, debris flow showed that the zone is controlled by the initial water content available previous to the block fall. Moreover, our field data suggest that future debris flows events can take place, removing material from the volcanic edifice. We conclude that similar events could occur in the future and that it is necessary to increase the mapping of zones with autobrecciated lava close to the volcano summit. The study contributes to the understanding of debris flows in the southern Andes since the Osorno volcano shares similar features with other stratovolcanoes in the region.