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The objective of the research is to develop a global land use / land cover map (LULC) of the Apurímac Region, from ESA Sentinel-2 images with a resolution of 10 m. to predict 10 soil type classes throughout the year in order to generate a representative snapshot of 2020. The methodology used in the analysis is the machine learning model, for the classification it was based on Artificial Intelligence (AI). For the processing, 6 bands of Sentinel-2 surface reflectance data were used: visible blue, green, red, near-infrared and two short-wave infrared bands, to create the final map, the model is run on multiple dates of images throughout the year on the Google Earth Engine (GEE) platform. The results of the study determine the total area is 2 111 415.29 ha, where the water represents 9 392.84 ha. (0.44%), on the other hand, snow/ice occupies 227.89 ha, representing 0.01%, while cultivated land occupies an area of 34 408.09 ha, (1.63%), bushes/shrubs occupy most of 1 740 486.69 ha, which represents 82.435% of the total area. [ABSTRACT FROM AUTHOR]

Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers. [ABSTRACT FROM AUTHOR]