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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/129304
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dc.contributor.authorCurtarelli, M. P.-
dc.contributor.authorOgashawara, I.-
dc.contributor.authorAlcantara, E. H.-
dc.contributor.authorStech, J. L.-
dc.date.accessioned2015-10-21T20:48:06Z-
dc.date.accessioned2016-10-25T21:08:52Z-
dc.date.available2015-10-21T20:48:06Z-
dc.date.available2016-10-25T21:08:52Z-
dc.date.issued2015-02-01-
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0034425714002260-
dc.identifier.citationRemote Sensing Of Environment. New York: Elsevier Science Inc, v. 157, p. 185-198, 2015.-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/11449/129304-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/129304-
dc.description.abstractThe goal of this study was to determine the extent to which mixing and stratification processes influence the horizontal surface phytoplankton distribution in a tropical hydroelectric reservoir in Brazil. We used a synergistic approach that combines remote sensing bio-optical empirical and three-dimensional hydrodynamic modeling to represent phytoplankton dynamics. Seasonal differences were evaluated by simulating the bio-optical and the three-dimensional hydrodynamic models for two periods: the austral summer and winter of 2009. The three-dimensional hydrodynamic model simulations showed that the water column was completely mixed during winter; the water column remained stratified during summer. We also noticed a permanent thermocline during the summer between 15 and 25 m in the reservoir. In both seasons, the surface current was wind driven and preferentially directed eastward. The bio-optical model showed that the horizontal surface phytoplankton distribution, as indicated by chlorophyll-a concentration, was heterogeneous during winter and homogenous during summer. Moreover, higher concentrations were observed by the empirical bio-optical model during winter (the highest mean areal concentration was 24 mu g L-1) than in summer (2.1 mu g L-1). This difference was explained by short timescale events, such as cold front passages, which contributed to the proliferation of phytoplankton in winter by enhancing vertical mixing in the water column. Lake number analysis showed that upwelling events were the most common mechanisms driving the mixing process during winter (L-N < 1 82% of the time). Combining remote sensing and three-dimensional hydrodynamic modeling makes it possible to more fully analyze the dynamics of the horizontal surface phytoplankton distribution under different meteorological conditions. The two modeling techniques can be used in a complementary manner and serve as an essential tool for the environmental monitoring of aquatic systems. (C) 2014 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipINCT for Climate Change-
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
dc.format.extent185-198-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectRemote sensingen
dc.subjectBio-optical modelen
dc.subjectHydrodynamic modelingen
dc.subjectHorizontal surface phytoplankton distributionen
dc.subjectMixing regimesen
dc.titleCoupling remote sensing bio-optical and three-dimensional hydrodynamic modeling to study the phytoplankton dynamics in a tropical hydroelectric reservoiren
dc.typeoutro-
dc.contributor.institutionNatl Inst Space Res-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationNatl Inst Space Res, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP, Brazil-
dc.description.affiliationSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, Brazil-
dc.description.affiliationUnespSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, Brazil-
dc.description.sponsorshipIdCNPq: 5737797/2008-
dc.description.sponsorshipIdFAPESP: 2008/57719-9-
dc.description.sponsorshipIdFAPESP: 2007/08103-2-
dc.description.sponsorshipIdCNPq: 161233/2013-9-
dc.identifier.doihttp://dx.doi.org/10.1016/j.rse.2014.06.013-
dc.identifier.wosWOS:000348257100016-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofRemote Sensing Of Environment-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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