Climate variability and extreme drought in the upper Solimões River (western Amazon Basin): Understanding the exceptional 2010 drought

This work provides an initial overview of climate features and their related hydrological impacts during the recent extreme droughts (1995, 1998, 2005 and 2010) in the upper Solimes River (western Amazon), using comprehensive in situ discharge and rainfall datasets. The droughts are generally associated with positive SST anomalies in the tropical North Atlantic and weak trade winds and water vapor transport toward the upper Solimes, which, in association with increased subsidence over central and southern Amazon, explain the lack of rainfall and very low discharge values. But in 1998, toward the end of the 1997-98 El Nio event, the drought is more likely related to an anomalous divergence of water vapor in the western Amazon that is characteristic of a warm event in the Pacific. During the austral spring and winter of 2010, the most severe drought since the seventies has been registered in the upper Solimes. Its intensity and its length, when compared to the 2005 drought, can be explained by the addition of an El Nio in austral summer and a very warm episode in the Atlantic in boreal spring and summer. As in 2005, the lack of water in 2010 was more important in the southern tropical tributaries of the upper Solimes than in the northern ones

2015 IEEE international geoscience and remote sensing symposium (IGARSS) proceedings

The floods are an annual phenomenon on the Pacific coast of Ecuador and can become devastating during El Nino years, especially in the Guayas watershed (32,300 km(2)), the largest drainage basin on the South American western side of the Andes. In this study, we used ENVISAT ASAR GM SAR images with a spatial resolution of 1 km to map the flooded areas between 2004 and 2008 and study the spatio-temporal dynamics of floods in the Guayas Basin. Maximum of likelihood supervised classification performed on ASAR images acquired during four consecutive dry seasons allowed us to identify five classes of land cover consistent with land use map. From the four wet seasons, we computed standardized anomalies of backscattering coefficient to detect changes between dry and wet season and tested different thresholds to identify flooded areas