Influence of high Andean grasslands on landslide reduction in Peru

• Autores: Cerna-Cueva, Albert Franco, Uriarte-Barraza, Katherin Lourdes, Lobatón-Tarazona, Grecia Isabel, Saenz-Corrales, Wensty, Aguirre-Escalante, Casiano, Coaguila-Rodriguez, Peter, Reategui-Inga, Manuel
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Nacional de Trujillo
• Repositorio: Revistas - Universidad Nacional de Trujillo
• Lenguaje: inglés
• OAI Identifier: oai:ojs.revistas.unitru.edu.pe:article/5415
• Enlace del recurso: https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/5415
• Nivel de acceso: acceso abierto
• Materia: High Andean grasslands; landslide; machine learning; ecosystem services; climate change

Agricultural and urban expansion has caused considerable degradation of ecosystems. In the case of Peruvian high Andean grasslands, it was reported that between 2000 and 2009, this ecosystem was reduced by 7%. The limited or no protection they receive is partly due to the fact that the benefits of ecosystem services are not widely known. This research aims to establish and predict the influence of high Andean grasslands on the annual occurrence of landslides. To do so, we identified occurrences of landslides, falls, huaycos, avalanches, and alluviums in high Andean grasslands. We also examined urban areas and agricultural zones of Peru for the period from 2003 to 2016. Subsequently, we extracted data on precipitation, temperature, slopes, soil types, and geographical variables. This data was used to train a machine learning model. The results show that 96% of landslides occurred in human-intervened areas, and only 4% in high Andean grasslands. Precipitation and slope thresholds for landslide occurrence are higher in high Andean grasslands compared to agricultural and urban areas. The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. They had coefficients of determination of R² = 0.80, 0.80, 0.66, and 0.64, respectively. Predictions show that if agricultural or urban areas are established in wet or dry puna grasslands, the average number of occurrences multiplies. The multiplier factors are 2.1 and 7.08, the number of deaths by 2.8 and 10.49, the number of houses destroyed by 2.4 and 7.51, and the number of roads destroyed by 2.2 and 7.37, respectively. The study demonstrates that conserving high Andean grasslands significantly reduces landslides compared to urban or agricultural areas.