Search

Search Results

• 1. [Article] A Multi-Class Suspended Particulate Matter Calibration for Bottom Boundary Layers

  We present a multi-class suspended particulate matter (SPM) calibration for use with in situ water sample SPM concentration data and backscatter (or transmission) from one or more acoustic and optical ...

  Citation × Citation Citation Abstract Copy Title: A Multi-Class Suspended Particulate Matter Calibration for Bottom Boundary Layers Author: Orton, Philip M., Jay, David A., Wilson, D. J. Year: 2003 We present a multi-class suspended particulate matter (SPM) calibration for use with in situ water sample SPM concentration data and backscatter (or transmission) from one or more acoustic and optical sensors. The output of this calibration is high-resolution SPM concentration data in several discrete settling velocity (Ws) classes. Separately for each sensor, the calibration involves three steps: (1) a calibration of backscatter to total SPM concentration; (2) a decomposition of resulting concentration data into several Wsclasses, utilizing a dynamical vertical SPM profile model; and (3) a sensor bias calibration where water sample concentration data are used to correct for the sediment size-dependence of sensor response. Multiple sensors can be incorporated to improve the results, as step 3 can objectively choose the best sensor for monitoring each Ws-class. The calibration is generally applicable in wave-current boundary layers, though this paper is focused on currents only. We demonstrate the method using data from greater-ebb SPM export events in the Fraser River estuary, when salt has washed out beyond the river entrance, surface currents are strong (3+ m s??) and turbulent mixing is intense. The resulting concentration estimates show good agreement with in situ particle-size observations near the bed, but discrepancies increasing with height above the bed, likely due to violation of model assumptions in the outer part of the water column. An uncertainty analysis indicates that the standard deviation in concentration estimates is 32-48%, primarily due to poor near-bed acoustic data coverage and uncertainty in water sample concentration data. We conclude by discussing planned improvements for strongly advective and aggregate-dominated systems. Title: A Multi-Class Suspended Particulate Matter Calibration for Bottom Boundary Layers Author: Orton, Philip M., Jay, David A., Wilson, D. J. Year: 2003 We present a multi-class suspended particulate matter (SPM) calibration for use with in situ water sample SPM concentration data and backscatter (or transmission) from one or more acoustic and optical sensors. The output of this calibration is high-resolution SPM concentration data in several discrete settling velocity (Ws) classes. Separately for each sensor, the calibration involves three steps: (1) a calibration of backscatter to total SPM concentration; (2) a decomposition of resulting concentration data into several Wsclasses, utilizing a dynamical vertical SPM profile model; and (3) a sensor bias calibration where water sample concentration data are used to correct for the sediment size-dependence of sensor response. Multiple sensors can be incorporated to improve the results, as step 3 can objectively choose the best sensor for monitoring each Ws-class. The calibration is generally applicable in wave-current boundary layers, though this paper is focused on currents only. We demonstrate the method using data from greater-ebb SPM export events in the Fraser River estuary, when salt has washed out beyond the river entrance, surface currents are strong (3+ m s??) and turbulent mixing is intense. The resulting concentration estimates show good agreement with in situ particle-size observations near the bed, but discrepancies increasing with height above the bed, likely due to violation of model assumptions in the outer part of the water column. An uncertainty analysis indicates that the standard deviation in concentration estimates is 32-48%, primarily due to poor near-bed acoustic data coverage and uncertainty in water sample concentration data. We conclude by discussing planned improvements for strongly advective and aggregate-dominated systems. Close