Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina

Gridded data of precipitation are a valuable tool in scarce-observational data contexts. Validation through statistical analysis is essential for its use. This work aims to validate the Climate Hazards Infrared Precipitation with Stations (CHIRPS) database for the southwest of Buenos Aires province...

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Detalles Bibliográficos
Autores principales: Lambrecht, Yamila, Montico, Anabella, Picone, Natasha
Formato: Artículo revista
Lenguaje:Español
Publicado: Departamento de Geografía. Facultad de Humanidades. Universidad Nacional del Comahue 2024
Materias:
CHIRPS
gridded data
validation
precipitation
datos grillados
validación
precipitaciones
Acceso en línea:https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110
Aporte de:
Repositorio de Revistas Electrónicas REVELE (UNComahue) de Universidad Nacional del Comahue
id I22-R128-article-5110
record_format ojs
spelling I22-R128-article-51102024-12-31T02:13:38Z Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina Validación de precipitación estimada por CHIRPS en una región semiárida de Argentina Lambrecht, Yamila Montico, Anabella Picone, Natasha CHIRPS gridded data validation precipitation CHIRPS datos grillados validación precipitaciones Gridded data of precipitation are a valuable tool in scarce-observational data contexts. Validation through statistical analysis is essential for its use. This work aims to validate the Climate Hazards Infrared Precipitation with Stations (CHIRPS) database for the southwest of Buenos Aires province (1990-2020). This dataset has adequate spatio-temporal coverage to study rainfall variability. For validation purposes, Pearson's correlation coefficient (r-Pearson), mean absolute error (mae), root mean squared error (rmse) and percent bias (pbias) were applied in the R environment using the hydroGOF package. CHIRPS shows a correlation between 0.68 and 0.84 for observed data on both monthly and annual scales. It also tends to overestimate rainfall between 2 and 4%, except in the northwestern sector, where it underestimates between 4 and 11%. It is concluded that CHIRPS is applicable for rainfall variability studies in the analyzed region, where the lack of data is a recurrent problem, considering the spatial errors detected. Los datos grillados de precipitación son una herramienta valiosa en contextos de escasez de datos observacionales. Para su uso es fundamental la validación a través de análisis estadísticos. El objetivo del trabajo es validar la base de datos CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data, por sus siglas en inglés) para el suroeste de la provincia de Buenos Aires (1990-2020). Este conjunto de datos posee una adecuada cobertura espacio-temporal para estudiar la variabilidad de las precipitaciones. Para la validación, se aplicaron el coeficiente de correlación de Pearson (r-Pearson), el error absoluto medio (mae), el error cuadrático medio (rmse) y el porcentaje de sesgo (pbias), en entorno R utilizando el paquete hydroGOF. CHIRPS presentó correlaciones de entre 0.68 y 0.84 respecto a los datos observados, tanto a escala mensual como anual. Asimismo, se observó una tendencia a sobreestimar las precipitaciones entre 2 y 4%, excepto en el sector noroeste, donde se subestimaron entre 4 y 11%. Se concluye que CHIRPS es aplicable para estudios de variabilidad de las precipitaciones en la región analizada, donde la falta de datos se presenta como un problema recurrente, teniendo en cuenta los errores espaciales detectados. Departamento de Geografía. Facultad de Humanidades. Universidad Nacional del Comahue 2024-05-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html application/epub+zip https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110 ark:/s2313903x/ykja7shwa Boletín Geográfico; Vol. 46 No. PC (2024): Boletín Geográfico Boletin Geografico; Vol. 46 Núm. PC (2024): Boletín Geográfico 2313-903X 0326-1735 spa https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110/62241 https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110/62676 https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110/62243 Buenos Aires (province) Buenos Aires (provincia) Derechos de autor 2024 Boletin Geografico https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
institution Universidad Nacional del Comahue
institution_str I-22
repository_str R-128
container_title_str Repositorio de Revistas Electrónicas REVELE (UNComahue)
language Español
format Artículo revista
topic CHIRPS
gridded data
validation
precipitation
CHIRPS
datos grillados
validación
precipitaciones
spellingShingle CHIRPS
gridded data
validation
precipitation
CHIRPS
datos grillados
validación
precipitaciones
Lambrecht, Yamila
Montico, Anabella
Picone, Natasha
Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
topic_facet CHIRPS
gridded data
validation
precipitation
CHIRPS
datos grillados
validación
precipitaciones
author Lambrecht, Yamila
Montico, Anabella
Picone, Natasha
author_facet Lambrecht, Yamila
Montico, Anabella
Picone, Natasha
author_sort Lambrecht, Yamila
title Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
title_short Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
title_full Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
title_fullStr Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
title_full_unstemmed Validation of CHIRPS estimated precipitation in a semi-arid region of Argentina
title_sort validation of chirps estimated precipitation in a semi-arid region of argentina
description Gridded data of precipitation are a valuable tool in scarce-observational data contexts. Validation through statistical analysis is essential for its use. This work aims to validate the Climate Hazards Infrared Precipitation with Stations (CHIRPS) database for the southwest of Buenos Aires province (1990-2020). This dataset has adequate spatio-temporal coverage to study rainfall variability. For validation purposes, Pearson's correlation coefficient (r-Pearson), mean absolute error (mae), root mean squared error (rmse) and percent bias (pbias) were applied in the R environment using the hydroGOF package. CHIRPS shows a correlation between 0.68 and 0.84 for observed data on both monthly and annual scales. It also tends to overestimate rainfall between 2 and 4%, except in the northwestern sector, where it underestimates between 4 and 11%. It is concluded that CHIRPS is applicable for rainfall variability studies in the analyzed region, where the lack of data is a recurrent problem, considering the spatial errors detected.
publisher Departamento de Geografía. Facultad de Humanidades. Universidad Nacional del Comahue
publishDate 2024
url https://revele.uncoma.edu.ar/index.php/geografia/article/view/5110
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first_indexed 2024-08-12T23:05:21Z
last_indexed 2025-02-05T23:00:23Z
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