Measurement and computation of streamflow: Volume 1, Measurement of stage and discharge

Rantz, S.E.

doi:10.3133/wsp2175_vol1

Cited by 106 publications

(9 citation statements)

References 2 publications

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“…Streamflow measurements were made using a top-setting wading rod and a YSI Sontek FlowTracker ® acoustic Doppler velocimeter. Measurements were taken according to standard USGS procedures (Rantz, 1982;Turnipseed and Sauer, 2010).…”

Section: Field Methodsmentioning

confidence: 99%

Trace metal and nutrient loads from groundwater seepage into the South Fork Coeur d’Alene River near Smelterville, northern Idaho, 2017

Zinsser¹

2019

Scientific Investigations Report

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For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

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Section: Field Methodsmentioning

confidence: 99%

Trace metal and nutrient loads from groundwater seepage into the South Fork Coeur d’Alene River near Smelterville, northern Idaho, 2017

Zinsser¹

2019

Scientific Investigations Report

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“…To enable comparison of the surface velocities inferred by PIV and the ADCP data, we used a velocity index approach to convert surface velocities to depth-averaged velocities. In open channel flows, the depth-averaged velocity is typically 0.85 of the velocity at the water surface, assuming a logarithmic velocity profile [16,37,45]. We applied this velocity index to the PIV magnitudes before comparing them to the depth-averaged velocities reported by the ADCP.…”

Section: Comparison Of Remotely Sensed Hydraulic Quantities With Fielmentioning

confidence: 99%

“…Typically, this conversion is achieved by multiplying the surface velocity by a velocity index < 1 and this approach brought the PIV-based surface velocities for Blue River cross section 1 to within the error bars of the ADCP data collected along this transect (Figure 5a). We used a value of 0.85, the most common velocity index e.g., [45], but a lower value would have provided better agreement between the PIV-and ADCP-based velocities. Using a velocity index of 0.85 overestimated the depth-averaged velocity in this case, resulting in a positive intercept term for the regression equation in Figure 5b that indicated a positive bias of the PIV-based velocities relative to the ADCP measurements.…”

Section: Uncertainties Associated With Thermal Piv-based Velocity Estmentioning

confidence: 99%

sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

Kinzel

Legleiter

2019

Remote Sensing

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This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R 2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R 2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R 2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and <1% at the second.

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“…In 2010 and 2011, discharge was measured throughout the field season at CS1 through CS5 using a Sontek FlowTracker ADV (Xylem, Inc. San Diego, CA, USA), two section wading rod for the ADV, Sontek data logger, and field surveying tapes. While detailed flow data were collected at all five permanent CS, CS1 was used to determine the stage-discharge relationship for Elton Creek per Rantz (1982). The discharge stage relationship for Elton Creek was used to determine all discharges (Q) and velocities while metabolic experiments were in place.…”

Section: Characterization Of Reach Hydraulics and Geomorphologymentioning

confidence: 99%

Metabolic Variance: A Metric to Detect Shifts in Stream Ecosystem Function as a Result of Stream Restoration

Blersch

Atkinson

2019

J American Water Resour Assoc

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The restoration of lotic ecosystems as currently practiced is constrained by a limited understanding of the emergence of stream ecosystem functions and their linkages to perceived ecosystem services valued by society. An investigation was made into the connections between ecosystem function and hydraulic structure of a stream impacted by gravel mining operations in western New York. Stream ecosystem metabolic parameters were measured using two‐point dissolved oxygen diurnals on multiple reaches of the same stream, and were correlated with geomorphic and hydraulic descriptors of the same reaches. Results showed ecosystem metabolism at the reach scale varied as a function of geomorphic condition, where higher primary production and community respiration (CR) were observed for reaches observed to be unstable vs. those that were stable or restored. The metabolic variance of a stream ecosystem, proposed as a measure of the degree to which a desired ecological functional goal has been achieved post‐restoration, is determined as the rate of change of the ratio of primary production to CR. Shifts in the P/R ratio were evident in reaches post‐restoration, presumably as a result of changes in hydraulic structure, supporting the utility of the ratio by describing a direct link between instream hydraulic conditions and ecosystem function.

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Measurement and computation of streamflow: Volume 1, Measurement of stage and discharge

Cited by 106 publications

References 2 publications

Trace metal and nutrient loads from groundwater seepage into the South Fork Coeur d’Alene River near Smelterville, northern Idaho, 2017

Trace metal and nutrient loads from groundwater seepage into the South Fork Coeur d’Alene River near Smelterville, northern Idaho, 2017

sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

Metabolic Variance: A Metric to Detect Shifts in Stream Ecosystem Function as a Result of Stream Restoration

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