Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

• Fecha de publicación: 2024
• Institución: Universidad del Rosario
• Repositorio: Repositorio EdocUR - U. Rosario
• Idioma: eng
• OAI Identifier: oai:repository.urosario.edu.co:10336/44787
• Acceso en línea: https://doi.org/10.1016/j.jsames.2024.104898; https://repository.urosario.edu.co/handle/10336/44787
• Palabra clave: Performance metrics; Bias-correction; Climate variability; Gridded datasets
• License: Attribution-NonCommercial-NoDerivatives 4.0 International