Remote monitoring of volumetric discharge employing bathymetry determined from surface turbulence metrics

Johnson, Erika; Cowen, Edwin A.

doi:10.1002/2015wr017736

Cited by 38 publications

(66 citation statements)

References 43 publications

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“…Strictly speaking, one must start by solving the heat budget equation (see [17,18]), which involves in situ measurements of heat flux boundary conditions, and then face the directional ambiguity of converting a scalar gradient into a surface velocity vector field. A more feasible quantitative method for analyzing infrared imagery involves turbulent length scale analysis, which can lead to useful quantities such as the depth of the flow and its dissipation rate [19,20]. Furthermore, an even more straightforward use of feature-rich infrared imagery is by treating it as a passive tracer and applying Pattern Tracking Velocimetry principles, i.e., tracking image deformations to infer velocity vectors.…”

Section: Infrared Methodsmentioning

confidence: 99%

Airborne Remote Sensing of the Upper Ocean Turbulence during CASPER-East

et al. 2018

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This study takes on the challenge of resolving upper ocean surface currents with a suite of airborne remote sensing methodologies, simultaneously imaging the ocean surface in visible, infrared, and microwave bands. A series of flights were conducted over an air-sea interaction supersite established 63 km offshore by a large multi-platform CASPER-East experiment. The supersite was equipped with a range of in situ instruments resolving air-sea interface and underwater properties, of which a bottom-mounted acoustic Doppler current profiler was used extensively in this paper for the purposes of airborne current retrieval validation and interpretation. A series of water-tracing dye releases took place in coordination with aircraft overpasses, enabling dye plume velocimetry over 100 m to 10 km spatial scales. Similar scales were resolved by a Multichannel Synthetic Aperture Radar, which resolved a swath of instantaneous surface velocities (wave and current) with 10 m resolution and 5 cm/s accuracy. Details of the skin temperature variability imprinted by the upper ocean turbulence were revealed in 1-14,000 m range of spatial scales by a mid-wave infrared camera. Combined, these methodologies provide a unique insight into the complex spatial structure of the upper ocean turbulence on a previously under-resolved range of spatial scales from meters to kilometers. However, much attention in this paper is dedicated to quantifying and understanding uncertainties and ambiguities associated with these remote sensing methodologies, especially regarding the smallest resolvable turbulent scales and reference depths of retrieved currents.Keywords: airborne remote sensing; ocean surface currents; upper ocean turbulence BackgroundCapabilities for spatial measurements of ocean surface turbulence are highly desirable for a wide variety of needs ranging from basic studies of upper ocean mixing processes to data assimilation into high-resolution coastal and ocean circulation models. On the global scale, presently available observations of the ocean currents are provided by satellite altimeters, which are limited to mesoscales and resolve only the geostrophic component of the surface current. Much anticipated launches of the

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Section: Infrared Methodsmentioning

confidence: 99%

Airborne Remote Sensing of the Upper Ocean Turbulence during CASPER-East

et al. 2018

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“…A series of experiments were conducted in a recirculating, wide‐open channel flume, housed in the DeFrees Hydraulics Laboratory at Cornell University and described in detail in Johnson and Cowen [] and Johnson []. The surface velocity field was measured with Large‐Scale Particle Image Velocimetry (LSPIV) and the in situ water column velocity and turbulence where characterized with Acoustic Doppler Velocimetry (ADV).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…The gravel was added in a strip that was ∼0.90 m wide and spanned

0.05 < y / B < 0.5

(where B is the channel width equal to 2 m). It ran from the beginning of the test section to well past the location where the measurements were made (12 m total length), allowing sufficient distance for the resulting flow to develop fully (see Figure 4 in Johnson and Cowen []). The gravel was leveled by hand before the experiments were run.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Three experimental cases were completed for which a PVC false‐bottom section was added to the channel (cross section shown in Figure 9a) to create an asymmetric compound channel (see Figure 3 in Johnson and Cowen []). The maximum height of the added PVC section is 0.16 m. The height of the section decreases linearly over an 0.80 m span, creating bathymetry that varies in the lateral but not the streamwise direction (Figure 9a).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…A brief overview of the experimental setup is described here; details on the experimental setup and image analysis procedure can be found in Johnson and Cowen [] and Johnson []. The FOV (∼2.03 m × 1.93 m) was imaged with an IMPERX BobCat GEV camera.…”

Section: Experimental Methodsmentioning

confidence: 99%

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Estimating bed shear stress from remotely measured surface turbulent dissipation fields in open channel flows

Johnson

Cowen

2017

Water Resources Research

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Synoptic information on bed shear stress is necessary in predicting the transport of sediments and environmental contaminants in rivers and open channels. Existing methods of estimating bed shear stress typically involve measuring vertical profiles of streamwise velocity or Reynolds stress and taking advantage of the logarithmic or the constant stress region, respectively, to determine friction velocity and subsequently, bed shear stress. While effective, these methods yield local measurements of bed shear stress only. Direct measurements of bed shear stress can also be obtained through measurements with a drag plate. However, this method yields average shear stress information over the area of the plate and is impractical for large‐scale implementation in the field. Here we present a method capable of providing continuous synoptic measurements of bed shear stress over a large field‐of‐view. A series of Large‐Scale Particle Image Velocimetry (LSPIV) and Acoustic Doppler Velocimetry (ADV) measurements were made in a variety of flows generated in a wide‐open channel facility. Turbulent dissipation is calculated on the free surface from the 2‐D LSPIV results and is correlated with near‐surface ADV measurements of turbulent dissipation in the water column. The ADV results are consistent with the Nezu (1977) established relationship for the vertical variation of turbulent dissipation in the water column. Knowledge of the correlation between free‐surface and near‐surface dissipation values coupled with Nezu's (1977) relationship allow a robust and accurate estimate of friction velocity to be made and subsequently, shear stress at the bed can be estimated.

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Remote determination of the velocity index and mean streamwise velocity profiles

Johnson

Cowen

2017

Water Resources Research

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When determining volumetric discharge from surface measurements of currents in a river or open channel, the velocity index is typically used to convert surface velocities to depth‐averaged velocities. The velocity index is given by, k=Ub/Usurf, where Ub is the depth‐averaged velocity and Usurf is the local surface velocity. The USGS (United States Geological Survey) standard value for this coefficient, k = 0.85, was determined from a series of laboratory experiments and has been widely used in the field and in laboratory measurements of volumetric discharge despite evidence that the velocity index is site‐specific. Numerous studies have documented that the velocity index varies with Reynolds number, flow depth, and relative bed roughness and with the presence of secondary flows. A remote method of determining depth‐averaged velocity and hence the velocity index is developed here. The technique leverages the findings of Johnson and Cowen (2017) and permits remote determination of the velocity power‐law exponent thereby, enabling remote prediction of the vertical structure of the mean streamwise velocity, the depth‐averaged velocity, and the velocity index.

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Remote monitoring of volumetric discharge employing bathymetry determined from surface turbulence metrics

Cited by 38 publications

References 43 publications

Airborne Remote Sensing of the Upper Ocean Turbulence during CASPER-East

Airborne Remote Sensing of the Upper Ocean Turbulence during CASPER-East

Estimating bed shear stress from remotely measured surface turbulent dissipation fields in open channel flows

Remote determination of the velocity index and mean streamwise velocity profiles

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