Hydrological performance of gridded meteorological products in Peruvian Altiplano basins

ABSTRACT Gridded meteorological datasets represent a valuable alternative for hydrometeorological applications, especially in areas with limited ground observations. However, it is important to evaluate these datasets to quantify their accuracy, error, and bias in estimates. The objective of this re...

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Bibliographic Details
Main Authors: Efrain Lujano, Renny Daniel Diaz, Rene Lujano, Miguel Sanchez-Delgado, Apolinario Lujano
Format: Article
Language:English
Published: Associação Brasileira de Recursos Hídricos 2025-02-01
Series:Revista Brasileira de Recursos Hídricos
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Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100200&lng=en&tlng=en
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Summary:ABSTRACT Gridded meteorological datasets represent a valuable alternative for hydrometeorological applications, especially in areas with limited ground observations. However, it is important to evaluate these datasets to quantify their accuracy, error, and bias in estimates. The objective of this research was to assess the hydrological performance of gridded meteorological products in Peruvian Altiplano basins. Three evaluation approaches were employed: 1) pixel-to-point, 2) basin-averaged values, and 3) as forcings in hydrological modeling. Using precipitation (P) and potential evapotranspiration (PET) data from AgERA5, ERA5-Land, MERRA-2, and PERSIANN-CDR, comparisons were made with 33 local meteorological stations using statistical metrics such as correlation coefficient (CC), root mean square error (RMSE), and percentage bias (PBIAS). Additionally, the GR4J hydrological model was employed in four watersheds to assess model performance with different data combinations. Results showed that AgERA5 and ERA5-Land excelled in daily precipitation estimation, while MERRA-2 had the lowest PET bias. Using reference data for P and PET as model forcings yielded consistent results across basins. However, the performance declined when using gridded products for both P and PET, revealing significant limitations in replicating hydrological processes and emphasizing the need to enhance these products for areas with limited observational data.
ISSN:2318-0331