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Assessment of extreme rainfall estimates from satellite-based: Regional analysis

  • Rayana Santos Araujo Palharini
  • , Daniel Alejandro Vila
  • , Daniele Torres Rodrigues
  • , Rodrigo Cassinelli Palharini
  • , Enrique Vieira Mattos
  • , George Ulguim Pedra

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

27 Citas (Scopus)

Resumen

Excessive rain may cause several problems for society. Understanding the behaviour of extreme rainfall and quantifying it in an assertive manner is important for whole society. The purpose of this work is to evaluate the ability of satellite precipitation products to detect the extreme rainfall over different regions of Brazil. The products evaluated in this investigation were from Frequent Rainfall Observations on GridS (FROGS) database. The results show that, each region of Brazil is characterized by extremes of rain with different intensities. The regions that presented the highest values are south and north regions of Brazil with values around 125.0 mm/day. In both regions, the GSMAP product (with rain gauges adjustments) have better performance, as shown in the metrics for the south and north regions where bias = −1.20 mm/day and −6.49 mm/day; r = 0.65 and 0.50; std = 10.15 mm/day and 10.63 mm/day; rmse = 9.58 mm/day and 13.16 mm/day respectively. On the other hand, the regions with the lowest intensities are the northeastern region, inland and coast, presented frequent extreme values of approximately 35.0 mm/day. At these regions, both versions of product 3B42RT v7.0 demonstrated a better performance, as demonstrated in the metrics for inland and coast northeastern regions, bias = 2.82 mm/day and −2.94 mm/day; r = 0.18 and 0.30; std = 8.53 mm/day and 6.97 mm/day; rmse = 14.75 mm/day and 7.03 mm/day, respectively. It is worth mentioning that the precipitation values found in this work do not necessarily cause disasters or generate impacts in the analyzed regions, they were considered extreme from a statistical point of view, considering the analyzed database.

Idioma originalInglés
Número de artículo100603
PublicaciónRemote Sensing Applications: Society and Environment
Volumen23
DOI
EstadoPublicada - ago 2021
Publicado de forma externa

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