The turbidity behavior in an Amazon floodplain

Author:

Alcântara E.,Novo E.,Stech J.,Lorenzzetti J.,Barbosa C.,Assireu A.,Souza A.

Abstract

Abstract. The main objective of this study is to understand the turbidity behavior of an Amazon Floodplain Lake. Observations of turbidity provide quantitative information about water quality. However, the number of available in situ measurements for water quality determination is usually limited in time and space. Here, we present an analysis of the temporal and spatial variability using two approaches: (i) the first is based on wavelet analysis of a turbidity time series measured by an automatic monitoring system; (ii) the second is based on turbidity samples measured in different locations and then interpolated by an ordinary kriging algorithm. The space/time turbidity variability is clearly related to the Amazon River flood pulses in the floodplain. When the water level in the floodplain is rising or receding, the exchange between the Amazon River and the floodplain is the major driving force in turbidity variability. At high water level, the turbidity variability is controlled by the lake bathymetry. Finally, when the water level is low, the wind action and lake morphometry are the main causes of turbidity variability. The combined use of temporal and spatial data showed a great potential for understanding the turbidity behavior in a complex aquatic system, like the Amazon floodplain.

Publisher

Copernicus GmbH

Reference58 articles.

1. Alcântara, E. H.: Analysis of turbidity in Curuai floodplain through the integration of telemetric and MODIS/Terra image data (MSc. Dissertation), INPE: São José dos Campos, Brazil, 220 pp., 2006 (in Portuguese).

2. Alcântara, E. H., Stech, J. L., Novo, E. M. L. M., Shimabukuro, Y. E., and Barbosa, C. C. F.: Turbidity in the Amazon floodplain assessed through a spatial regression model applied to fraction images derived from MODIS/Terra, IEEE T. Geosci. Remote, 46, 2895–2905, 2008.

3. Alcântara, E. H., Barbosa, C. C. F., Stech, J. L., Novo, E. M. L. M., and Shimabukuro, Y. E.: Improving the spectral unmixing algorithm to map water turbidity distributions, Environ. Modell. Softw., 24, 1051–1061, 2009.

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