Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico
Accurate precipitation data is essential for any hydrometeorological study, particularly for calibration and simulation of hydrological models. In this paper, we evaluate the precipitation of two different reanalysis products (the ERA5 and GLDAS), and two satellite-based precipitation products (TRMM...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | Universidad Nacional Autónoma de México |
| Repositorio: | Redalyc-UNAM |
| OAI Identifier: | oai:redalyc.org:56572300005 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=56572300005 https://www.redalyc.org/journal/565/56572300005/ https://www.redalyc.org/journal/565/56572300005/html/ https://www.redalyc.org/journal/565/56572300005/56572300005.epub https://www.redalyc.org/journal/565/56572300005/movil |
| Access Level: | acceso abierto |
| Palabra clave: | Ciencias de la Tierra satellite reanalysis Precipitation assessment |
| Sumario: | Accurate precipitation data is essential for any hydrometeorological study, particularly for calibration and simulation of hydrological models. In this paper, we evaluate the precipitation of two different reanalysis products (the ERA5 and GLDAS), and two satellite-based precipitation products (TRMM 3B42 and CHIRPS) over the La Sierra river basins in Southern Mexico, on regional and daily time scales, from 2008 to 2010. We compare the collocated gridded precipitation data against in-situ precipitation measurements in each gauge station, as well as the mean areal precipitation (MAP) over the catchments in the study area for the different products. The Pearson correlation coefficient, the root mean square error, and the multiplicative bias metrics suggest that CHIRPS and ERA5 are the highest quality precipitation products over the study area. CHIRPS performs better on the grid to point comparison, estimating better precipitation events from 10-50 mm, above 100 mm, and for all the values without threshold. ERA5 does better for precipitation from 0-10 and 50-100 mm. These two datasets also have better performance on representing the spatial rainfall variability according to the mean annual precipitation and MAP analysis, showing statistical values close to each other. |
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