Description
Climate is a powerful driver of agricultural and natural systems, and spatial climate data sets in digital form are currently in great demand. This is especially true in the Arequipa department of Peru, a region with low seasonal precipitation, significant topographic variability, and high demand for water use in highly managed water systems. This dataset contains 30-year average annual and average annual monthly maps of cumulative precipitation, and average, minimum and maximum daily air temperature from January 1, 1988 to December 31, 2017. It was created using weather station data from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and National Ocean and Atmospheric Administration’s (NOAA) Global Summary of the Day (GSOD). Topography data, used as covariates, came from ALOS World 3D DEM. Weather data went through extensive preprocessing for removal of implausible values, data gap filling, and inhomogeneity detection. Maps were created using a Regression Thin-Plate Splines (RTPS) method that made use of Polynomial Regression Models specifically developed for the region.
A manuscript entitled “Terrain sensitive climate maps for Arequipa region, Peru,” is under development and will bring more information about the development of this dataset.
This dataset was developed by the Center for Sustainable Watershed Management of the Arequipa Nexus Institute (https://www.purdue.edu/discoverypark/arequipa-nexus/en/index.php)
Cite this work
Researchers should cite this work as follows:
- Andre Geraldo de Lima Moraes; Laura C Bowling; Carlos Renzo Zeballos Velarde; Keith A Cherkauer (2021). Arequipa Climate Maps – Normals. (Version 2.0). Purdue University Research Repository. doi:10.4231/JNBK-ZK34
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Notes
ACM-N version 2 expanded it's spatial coverage to all watersheds that are at least partially contained in the Arequipa department and run to the pacific ocean; made improvements to data quality checking, data gap-filling, and inhomogeneity detection processes applied to the source data; introduced a new regression to represent precipitation spatial trends that helps compensate for the weather station bias at higher elevations; substituted Ordinary kriging for Thin-Plate Splines for Interpolation of residues.