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3 results for “Cordillera Blanca”
High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru
<p>Supplementary materials for the article "High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru"</p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
Surface elevation (2012-2020) and ice thickness (2014-2020) datasets measured at Artesonraju Glacier, Cordillera Blanca, Perú
<p>We present a set of data of interpreted ice thickness and ice surface elevation of Artesonraju glacier.<br> The ice thickness was obtained by means of Ground Penetrating Radar (GPR) which was measured by Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM) and Autoridad Nacional de Agua (ANA). The years of ice thickness records include 2013, 2014, 2015, 2017, 2018, and 2020. On the other hand, the surface elevation points were obtained by means of automated total stations and mass balance stakes, integrally measured by ANA. The years of measurements are 2012, 2014, 2015, 2017, 2018, 2019, and 2020.<br> The results from GPR data show a maximum depth of 235±18 m and a decreasing mean depth of ranging from 134±18 m in 2013 to 110±18 m in 2020. Additionally, we estimate a mean ice thickness change rate of 4.2±3.2 m yr<sup>-1</sup> between 2012 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of -3.2±0.2 m yr<sup>-1</sup>, and hence, confirming a negative glacier mass balance. The datasets can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF risk assessment.</p>
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