The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 505–508, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-505-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 505–508, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-505-2020

  06 Nov 2020

06 Nov 2020

RANKING OF DAILY SATELLITE-DERIVED PRECIPITATION EXTREMES FOR THE ORBIG PIPELINE IN RIO DE JANEIRO

I. C.F. Amaral, R. S. Libonati, and A. C. P. A. Palmeira I. C.F. Amaral et al.
  • Institute of Geosciences, Dept. of Meteorology, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Keywords: GPM, Precipitation extremes, Remote sensing, Rio de Janeiro, Pipeline

Abstract. The present work was motivated by the occurrence of vast damage caused by intense rainfalls in the state of Rio de Janeiro and the great importance of the oil pipelines for the economy by using remote sensing multisatellite dataset from the GPM 3-IMERG-HHE product from 06/2000 to 06/2019, along the ORBIG pipeline located between the municipalities of Angra dos Reis and Duque de Caxias, RJ. A statistical ranking method has been applied to classify extreme daily precipitation events over the region. An event is classified as extreme by considering the total affected area and its intensity, based on the daily normalized anomaly calculated from the climatology data. The results show that in cold front events the oil pipeline region is hit more spatially with high accumulations of daily precipitation. However, in thermal instability precipitation, despite affecting locally, it has also shown extreme precipitation events, highlighting that in the 10 largest cases there were no false alarms, according to records found in news reports and rainfall indexes. It was also noted that during summer time there were more extreme cases. In conclusion, this study served to indicate places and times of higher rainfall index regardless of whether the region has a dense population or not.