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Remote Sens. 2017, 9(9), 889; doi:10.3390/rs9090889

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

1
French National Center for Scientific Research (CNRS), Université Rennes 2, UMR LETG, Place du Recteur Henri Le Moal, 354043 Rennes Cedex, France
2
CNRS, Université de Nantes, UMR LETG, Campus du Tertre BP 81227, 44312 Nantes Cedex 3, France
*
Author to whom correspondence should be addressed.
Received: 22 May 2017 / Revised: 17 August 2017 / Accepted: 22 August 2017 / Published: 27 August 2017
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Abstract

Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) is a relatively new product (released in 2013) but that contains data since 1983, thus enabling long-term rainfall analysis. In this work, we used three decades (1983–2014) of PERSIANN-CDR daily rainfall data to characterize precipitation patterns in the southern part of the Amazon basin, which has been drastically impacted in recent decades by anthropogenic activities that exacerbate the spatio-temporal variability of rainfall regimes. We computed metrics for the rainy season (onset date, demise date and duration) on a pixel-to-pixel basis for each year in the time series. We identified significant trends toward a shortening of the rainy season in the southern Amazon, mainly linked to earlier demise dates. This work thus contributes to monitoring possible signs of climate change in the region and to assessing uncertainties in rainfall trends and their potential impacts on human activities and natural ecosystems. View Full-Text
Keywords: PERSIANN-CDR; Amazon; rainy season; validation; onset; demise PERSIANN-CDR; Amazon; rainy season; validation; onset; demise
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Arvor, D.; Funatsu, B.M.; Michot, V.; Dubreuil, V. Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends. Remote Sens. 2017, 9, 889.

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