Are there IOPs that are better for estimating total suspended matter in coastal waters? - PowerPoint PPT Presentation

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Are there IOPs that are better for estimating total suspended matter in coastal waters? PowerPoint Presentation
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Are there IOPs that are better for estimating total suspended matter in coastal waters?

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Are there IOPs that are better for estimating total suspended matter in coastal waters?
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Are there IOPs that are better for estimating total suspended matter in coastal waters?

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  1. Are there IOPs that are better for estimating total suspended matter in coastal waters? E. Boss & L. Taylor, University of Maine An analysis of the Alliance of Coastal Technology (ACT) turbidity data set. • The ACT data set is comprised of: • 8 sites. • 7 instruments spanning: • 1 – transmissometer • SeaTech • 2– back-scatter • WETLabs, Aquatec • 3– side-scatter • InSitu, McVan, YSI

  2. An example:

  3. Hypothesis: Beam attenuation at a red wavelength (or particulate scattering at a non-absorbing wavelength) should be the best predictor of TSS. Because beam attenuation is least sensitive of all these IOPs to absorption peaks, composition, shape, and internal structure (particularly for particles >> wavelength). Note: provides a sense of how well we can expect to do from Rrs. Data taken next to sensor.

  4. Data set: • Note: • Beam attenuation is limited to 4hours after periodic cleaning. • Some other sensors had anti bio fouling devices. • All personnel was trained together on how to do the measurements. Number of matchup data: For the whole data set: 21 co-located measurements with all instruments. 96 co-located measurements with at least one instrument of each method. If we limit ourselves to data within first 5 days of deployment: 13 co-located measurements with all instruments. 36 co-located measurements with at least one instrument of each method. For comparison, Babin et al., 2003, had 220 matchups of TSM with bp(555).

  5. Statistical test of hypothesis: • Compare IOPs only when the same TSS data are available (100 data points). • Analyze the data distribution (is it normal? lognormal?). Normalizing the data and log-transforming them provide non-dimensional and scaled variables for which linear-correlation analysis is more suited.

  6. Statistical test of hypothesis: • Take into account uncertainties associated with the data. • Uncertainties: in TSS (std of triplicates) and in optical measurements (based on calibration data and temperature tests). •  Conduct linear correlation analysis between log-normalized data {log[data/median(data)} using a Monte-Carlo procedure to obtain uncertainty in the correlation coefficient.

  7. cp*=0.5m2/gr Data: cp: bbp: cp*=0.25m2/gr bsp:

  8. When we look at data sets comprising all instruments, or first five days without HI, we find correlations to be high (>0.95) and insignificantly different across methods. • Question: is it worth measuring with more than one method? • Answer: YES!!!, at least bb and beam-c. • Provides redundancy in case of failure. • Provides additional information: Change consistent with change in index of refraction

  9. Summary: • Analysis suggest NO significant differences between methods when using more stringent criterion and no CaCO3 sediments. • Backscattering/TSS ratio seem less sensitive to CaCO3 then beam-c. • If possible, it is advisable to use different methods to estimate TSS. Beside redundancy, the additional information can provide information on composition.