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Using CDOM optical properties for estimating DOC concentrations and pCO2 in the Lower Amazon River
Optics Express
|August 17, 2018
Summary
Coloured dissolved organic matter (CDOM) can estimate dissolved organic carbon (DOC) and carbon dioxide (pCO2) in the Amazon River. CDOM
Area of Science:
- Environmental Science
- Geochemistry
- Remote Sensing
Background:
- Coloured dissolved organic matter (CDOM) significantly influences freshwater absorption budgets.
- CDOM serves as a proxy for dissolved organic carbon (DOC) and carbon dioxide (pCO2) but riverine studies are limited, especially in tropical regions.
- Understanding spatial-seasonal variability of CDOM, DOC, and pCO2 is crucial for carbon cycling in large river systems.
Purpose of the Study:
- To document the spatial-seasonal variability of CDOM, DOC, and pCO2 in the Lower Amazon River.
- To assess the potential of CDOM absorption coefficient (aCDOM(412)) for estimating DOC concentration and pCO2.
- To investigate the influence of discharge and water temperature on these relationships.
Main Methods:
- Field sampling along the Lower Amazon River and its tributaries.
- Measurement of CDOM, DOC, and pCO2 concentrations.
- Statistical analysis to determine relationships between variables, including linear regression models.
Main Results:
- Distinct differences in dissolved organic matter quality were observed between clearwater tributaries and the Amazon River mainstream.
- Linear relationships between DOC and CDOM were found when analyzed separately for the mainstream and tributaries, but not during high discharge periods.
- A strong positive linear relationship was identified between aCDOM(412) and pCO2, which was further improved by including water temperature in the model.
Conclusions:
- CDOM optical properties can be used to estimate both DOC and pCO2 in the Lower Amazon River.
- The relationship between CDOM and DOC is influenced by discharge and water source (mainstream vs. tributary).
- CDOM-based estimation of pCO2 is robust across different discharge conditions and water types, with temperature as a key factor.
- This approach offers potential for monitoring carbon dynamics in large river systems using remote sensing data.
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