Light absorption properties of CDOM in the Changjiang (Yangtze) estuarine and coastal waters: An alternative approach for DOC estimation
Field measurements of CDOM absorption properties and DOC concentrations were collected in the Changjiang estuarine and coastal waters from 2011 to 2013. CDOM absorption coefficient at 355 nm (ag (355)) was found to be inversely correlated with salinity, with Pearson's coefficients r of -0.901 and -0.826 for summer and winter observations, respectively. Analysis results of the relationships between salinity and CDOM optical properties (i.e., absorption coefficient and spectral slope) suggested that terrigenous inputs dominated CDOM sources in the Changjiang estuary, but the proportion of terrigenous CDOM declined with increasing salinity. The level of CDOM in the Changjiang estuary was lower compared to some of the major estuaries in the world, which could be attributed to several controlling factors such as vegetation cover in the drainage basin, the origin of recharged streams and high sediment load in the Changjiang estuary. We further evaluated the relationships between CDOM and DOC and their mixing behavior among world's major estuaries. An empirical model was finally developed to estimate DOC concentration from ag (355) and spectral slope S275-295 using a non-linear regression. This empirical relationship was calibrated using the Cal dataset, and was validated with the Val dataset, resulting in an acceptable error with the R2 of 0.746, the RMSE of 20.99 mmol/L and the rMAD of 14.46%.
Fig. 1. Location of sampling stations in the Changjiang estuarine and coastal waters. Samples were collected from five cruises in summer (July 2011, July 2012 and August 2013) and winter (February and March of 2012).
Fig. 7. Calibration and validation for DOC estimation model using the Cal and Val datasets. (a) A non-linear relationship (black line) is used to fit a*g(335) and S275-295 using the Cal dataset. A referential relationship from Fichot and Benner (2012) is also provided (dash line). (b) Validation of the developed model using the Val dataset.
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