Citas bibligráficas
Arias, A., Núñez, N. (2024). Generación de un mapa de predicción espacial de retroceso glaciar a partir de mapas de vulnerabilidad en la cordillera central, Perú [Tesis, Universidad de Ingeniería y Tecnología]. https://hdl.handle.net/20.500.12815/389
Arias, A., Núñez, N. Generación de un mapa de predicción espacial de retroceso glaciar a partir de mapas de vulnerabilidad en la cordillera central, Perú [Tesis]. PE: Universidad de Ingeniería y Tecnología; 2024. https://hdl.handle.net/20.500.12815/389
@misc{renati/230561,
title = "Generación de un mapa de predicción espacial de retroceso glaciar a partir de mapas de vulnerabilidad en la cordillera central, Perú",
author = "Núñez Jorge, Nicolás Gabriel",
publisher = "Universidad de Ingeniería y Tecnología",
year = "2024"
}
Increase in average global temperature over the last decades has caused an accelerated retreat of tropical glaciers. Peruvian population settled in the Andes live in dependence on the water services provided by mountains and glaciers. The present study aims to generate a glacier melt projection map in the Peruvian Central Cordillera based on vulnerability maps over the 1990-2021 period. Seven satellite images were selected to determine the change in glacier coverage based on normalized indexes. Subsequently, seven parametric maps consisting of terrain and climate characteristics were assimilated into a vulnerability analysis based on the Frequency Index (FR) and the Shannon Entropy Index model, allowing to identify most susceptible areas to glacial retreat. The results show that the most important criteria for the southern and northern glacial study areas are: atmospheric temperature, surface temperature, elevation, precipitation, aspect, orientation, slope and flow direction. The validation results showed the set of parameters from the vulnerability map which is the most accurate in terms of projecting melting areas and was used to produce a spatial projection map for the 2021-2055 period. Since 2021, a glacier loss in the range of 84% to 98% is reached by 2050s.
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