Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R² value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R² of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

The global transition toward low-carbon energy and advanced technologies has intensified demands for rare earth elements (REEs), while available data remain fragmented across government and academic sources. To address this gap, this study compiles and standardizes a publicly accessible geochemical database of REE concentrations (among other variables) across Peru, motivated by the need for consolidated, evidence-based resource assessment. The dataset integrates over 30,000 records from national agencies and university repositories, classified by sample source (stream sediments, rocks and minerals, deep and surface soils, tailings, and industrial materials). This structure enhances comparability and interpretation across geological and environmental contexts. By providing a centralized, high-resolution dataset for an undercharacterized mineral province, this work offers a resource for exploration, policy development, and sustainable management amid growing global demand and strategic interest in critical minerals.

The Colca Valley has historically been subject to a variety of geological hazards such as landslides, rock falls, and debris flows. On June 18, 2020, a rotational landslide occurred near the community of Achoma in the Colca Valley. The event destroyed agricultural land, impacting the finances of many families, and the displaced material crossed the Colca River and created a dam that increased the risk of flooding for the towns upstream. Because of the potential for future movement at this site or similar landslides nearby in the future, it is important to understand the potential triggers and therefore future management strategies for the area. Field mapping, computer modeling of slope stability, records of rainfall and agricultural activity, and observations of leakage from a water conveyance structure upgradient were used to test various groundwater and infiltration scenarios and identify the critical groundwater conditions that affected the landslide. These scenarios include three possible contributions to the triggering of the landslide: rainfall, local irrigation, or leakage from the water conveyance system. The modeling results indicate that the cause was most likely a combination of factors: weak lacustrine materials within the landslide mass, irrigation activity in the area, high antecedent rainfall, and an incremental increase in the water table that may be related to leakage of a tunnel section of the water conveyance project.