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The Effect of Algal Blooms on Carbon Emissions in Western Lake Erie: An Integration of Remote Sensing and Eddy Covariance Measurements.

Authors :
Zutao Ouyang
Changliang Shao
Housen Chu
Becker, Richard
Bridgeman, Thomas
Stepien, Carol A.
John, Ranjeet
Jiquan Chen
Source :
Remote Sensing; Jan2017, Vol. 9 Issue 1, p44, 19p
Publication Year :
2017

Abstract

Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO<subscript>2</subscript> sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO<subscript>2</subscript> (F<subscript>CO2</subscript>) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on F<subscript>CO2</subscript> in the western basin of Lake Erie--a large lake infamous for these blooms. Three years of long-term EC data (2012-2014) at two sites were analyzed. We found that at both sites: (1) daily F<subscript>CO2</subscript> significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly F<subscript>CO2</subscript> was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of F<subscript>CO2</subscript> and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
9
Issue :
1
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
120987906
Full Text :
https://doi.org/10.3390/rs9010044