Evaluation of Satellite-Derived Products for the Daily Average and Extreme Rainfall in the Mearim River Drainage Basin (Maranhão, Brazil)
Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to...
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Autores principales: | , , , , |
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Formato: | article |
Lenguaje: | EN |
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MDPI AG
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/ec4a8877569d42fbba93e2abfc829869 |
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Sumario: | Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to evaluate CHIRPS version 2, MSWEP version 2, and PERSIANN-CDR, compared to gridBR, as daily mean and extreme inputs represented on a monthly scale and their respective seasonal trends of rainfall in the Mearim River Drainage Basin (MDB), Maranhão state, Brazil. Estimates of errors were calculated (relative error, pbias; root mean square error, RMSE, and Willmott concordance index, d), and the chances of precipitation were estimated by remote sensing (RES). In addition, trends in precipitation were estimated by the two-sample Mann–Kendall test. Given the overall performance, the best products for estimating monthly mean daily rainfall in the MDB are CHIRPS and PERSIANN-CDR, especially for rainy months (December to May). For daily extremes on the monthly scale, the best RES is PERSIANN-CDR. There is no general agreement between gridBR and RES methods for the trend signal, even a nonsignificant one, much less a significant one. The use of MSWEP for the MDB region is discouraged by this study because it overestimates monthly averages and extremes. Finally, studies of this kind in drainage basins are essential to improve the information generated for managing territories and developing regionalized climate and hydrological models. |
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