EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analysis of remote sensing and in situ datasets to estimate spatial precipitation in high mountain areas: case study Cordillera Blanca, Peru

Edwin Loarte1,3,4, Katy Medina1,2, Eduardo Villavicencio2, Hairo León2, Waldo Lavado4,5, Antoine Rabatel6, Gerardo Jacome5, Johannes Hunink7, and Ernesto Lopez-Baeza3
Edwin Loarte et al.
  • 1National Institute for Research on Glaciers and Mountain Ecosystems (INAIGEM), Huaraz, Peru (
  • 2Faculty of Environmental Sciences (FCAM), Santiago Antunez de Mayolo National University (UNASAM), Huaraz, Peru
  • 3Climatology from Satellites Group, Earth Physics & Thermodynamics Department, Faculty of Physics, University of Valencia, 46100 Valencia, Spain
  • 4Universidad Nacional Agraria La Molina (UNALM), Lima, Perú
  • 5Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI), Lima, Perú
  • 6Univertsité Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France
  • 7FutureWater, Cartagena, Spain

The Santa River basin has a climatology that is characterized by strong spatial gradients in precipitation. The influence of topography becomes increasingly important when smaller time scales are considered and convective and orographic processes have a more profound influence. This makes its estimation complex and of relevance for research on precipitation estimation in high mountain environments.

This study focused on estimating precipitation for the Santa basin located north of the capital of Peru, assessing spatial patterns and temporal variation. Precipitation products were used at a daily temporal resolution obtained from remote sensing datasets, including CHIRPS, PERSIANN-CCS, GPM and PISCO, altitude and vegetation products as NDVI-BOKU and GDEM. Also ground-based precipitation data from weather stations were collected from 35 meteorological stations (2012 -2019).

The in situ precipitation data was reviewed, cleaned and quality-checked for processing. The following operations were applied to the raster data: Gaussian filter, resampling at 1km, temporal homogenization (monthly) by accumulating the precipitation products until obtaining the monthly values, and averaging. Afterwards, a linear regression model was built based in which various of the remote sensing datasets served as predictions. The model was validated using the mean square error and the coefficient of determination.

The developed regression model provides a better estimate of the precipitation than the individual precipitation dataset. Overall, the resulting model performs relatively low in the dry season (May-September) but improves considerably in the wet season (October-April), with correlations that go up to 0.95. The outcomes of this research can be used to improve the estimation of precipitation patterns in high mountain regions with complex orography.

How to cite: Loarte, E., Medina, K., Villavicencio, E., León, H., Lavado, W., Rabatel, A., Jacome, G., Hunink, J., and Lopez-Baeza, E.: Analysis of remote sensing and in situ datasets to estimate spatial precipitation in high mountain areas: case study Cordillera Blanca, Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8996,, 2021.