Por: Edwin Ocas Ramírez, Universidad Nacional de Cajamarca y Dánica Salazar Ildefonso, Minera Boroo Misquichilca.AbstracHigh-altitude mining operations in Peru face significant limitations in the direct monitoring of climatic variables, particularly precipitation. This study presents a real-world application at a mining operation in Quiruvilca, northern Peruvian Andes, where different approaches for the spatial estimation of precipitation were evaluated using historical records from six rainfall stations collected between 2020 and 2024. Three methods widely recognized in the scientific literature (Thiessen polygons, Inverse Distance Weighting (IDW), and Ordinary Kriging) were compared with the arithmetic mean method, the conventional approach currently used at the operation. In addition to the comparative analysis, the study highlights the potential of Ordinary Kriging as a foundation for more advanced geostatistical applications, such as Kriging with external drift or co-kriging, which would allow the incorporation of auxiliary variables such as elevation and slope. Statistical validation was performed using the root mean square error (RMSE), bias, and coefficient of determination (R²), supported by spatial analysis tools within a GIS environment. Beyond the technical comparison, this study represents a practical and cost-effective application that provides immediate environmental value to the mining operation by enabling predictive water resource management in areas lacking direct monitoring. Although initially validated for the spatial estimation of precipitation, the proposed model has the potential to be extended to the geostatistical analysis of hydrogeological variables, including piezometric levels and surface runoff within watersheds. This transforms the precipitation model into an enabling technical platform from which future key environmental studies can be developed, optimizing resource utilization and strengthening the mining unit's internal capabilities.
