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Amazon floodplain lakes particle size characterization based on time-series of bio-optical in-situ and Remote Sensing Dataset.

Grant number: 16/09953-9
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): September 01, 2016
Effective date (End): August 31, 2018
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:Cláudio Clemente Faria Barbosa
Grantee:Lino Augusto Sander de Carvalho
Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovação (Brasil). São José dos Campos , SP, Brazil


Amazon aquatic systems have been investigated throughout the years and there is a great deal of knowledge on hydrological processes and aquatic ecology at local level (Huszar et al. 1997 Bonnet et al. 2008, Rudorff et al. 2014a/b) and at large scale (Forsberg et al. 1988, Junk and Piedade, 1997, Wilson et al. 2007). However there is a lack of knowledge on the Optical Properties (OPs) of floodplain lakes, which are mainly driven by the concentration and composition of Optical Active Components (OAC). Those components are directly affected the Amazon River Floodpulse and may vary along the hydrological year bringing changes in the euphotic zone depth, lake thermal structure, lake primary productivity, among other processes. Sander de Carvalho (2016) investigated both, Inherent and Apparent Optical Properties, (IOPs and AOPs), at a cardinal example of thousands of turbid floodplain lakes of the Amazon basin (Curuai Lake), in four different phases of the hydrological year and, on that basis, this proposal seeks to incorporate datasets acquired since September 2012 (nine phases) to assess the viability of using optical properties to increase the understanding of spatial and temporal variability of particle size distribution in the Curuai floodplain lakes. The three main steps will be focused on a (i) data quality assessment of the current dataset; (ii) temporal-spatial analysis of IOPs, AOPs and biogeochemical parameters within the hydrological cycle; (iii) derive an algorithm to retrieve IOPs and biogeochemical variables from Remote Sensing Landsat Images. The study will give continuation where previous research projects (FAPESP: 2002/00785-3, 2003/00785-3, 2004/1406-5, 2007/07867-9, 2008/07537-1, and 2011/23594-8; CNPq: 477885/2007-1, 304929/2007-7, and 550373/2010-1; Rede Temática GEOMA, FAPESP 2011/23594-8, Clim-Fabian (IRD) and BNDES-Fundo Amazonia-INPE) to provide suitable analysis and inputs for future hydrological/biogeochemical models.

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