USGS ScienceSearch

Geology topics

Paul J. Kinzel

Publications and source records attributed to Paul J. Kinzel.

At least 37 records · Page 2Linked to original sources

Field evaluation of a compact, polarizing topo‐bathymetric lidar across a range of river conditions

This paper summarizes field trials to evaluate the performance of a prototype compact topo‐bathymetric lidar sensor for surveying rivers. The sensor uses a novel polarization technique to distinguish between laser returns from the water surface and streambed and its size and weight permit deployment from a small unmanned aerial system (sUAS) or a boat. Field testing was designed to identify the range of operational conditions under which the sensor can provide accurate information on river depths. For accuracy assessment, conventional, field‐based depth measurements were collected by wading and sonar. Additionally, optical properties of the rivers were measured in situ. Wading and lidar bathymetry comparisons in relatively shallow channels yielded observed versus predicted (OP) regression R 2 values ranging from 0.60 to 0.97. A comparison between sonar and lidar bathymetry in a deeper river resulted in an OP R 2 of 0.72. Absorption and attenuation coefficients at the 532 nm wavelength of the lidar were recorded in the field and the highest values of these inherent optical properties were at sites with the highest turbidity and highest concentrations of colored dissolved organic matter, chlorophyll, and suspended sediment. At these sites, which included both sand and gravel/cobble beds, the point density of riverbed returns was not uniform, with areas of sparse coverage occurring primarily in deeper water. However, submerged objects and slopes could be resolved in the lidar point clouds.

Arizona, Colorado

Improving remotely sensed river bathymetry by image-averaging

Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R 2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.

Water Resources Research

Inferring surface flow velocities in sediment-laden Alaskan rivers from optical image sequences acquired from a helicopter

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0 . 9 "> R 2 > 0 . 9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

Alaska

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics

sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar

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.

Colorado

Remote sensing of river flow in Alaska—New technology to improve safety and expand coverage of USGS streamgaging

The U.S. Geological Survey monitors water level (water surface elevation relative to an arbitrary datum) and measures streamflow in Alaska rivers to compute and compile river flow records for use by water resource planners, engineers, and land managers to design infrastructure, manage floodplains, and protect life, property, and aquatic resources. Alaska has over 800,000 miles of rivers including the Yukon River, the third longest river in the United States. These rivers are home to rare and important ecosystems and are used for recreation, hydropower generation, commercial fishing, and transportation. River flow measurements are essential for wise and safe development and use of Alaska rivers.

Alaska

Near-field remote sensing of Alaskan Rivers

The U.S. Geological Survey (USGS) Geomorphology and Sediment Transport Laboratory (GSTL), in collaboration with the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), acquired remotely sensed data from several Alaskan rivers in 2017 and 2018 with the goal of developing a methodology for measuring streamflow from a helicopter. CRREL operates a custom airborne lidar system that can be deployed in a helicopter-based pod (HeliPod). Data were collected with the HeliPod near existing USGS streamflow information stations on the Knik, Matanuska, Chena, and Salcha Rivers in both 2017 and 2018. Sites on the Tanana and Snow Rivers were added in 2018. In 2018, the HeliPod was modified to accommodate both a thermal infrared and a visible camera. The cameras were integrated with the flight management software to simultaneously acquire imagery with lidar. The Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) in the HeliPod were used to compute trajectories with precise position and orientation information needed for image orthorectification. The HeliPod sensors provide data for measuring river channel characteristics. Lidar can map the elevation of the water surface and thus be used to measure water-surface slopes and return intensity can be used to delineate the extent of the wetted river channel. Various approaches are currently being evaluated to estimate surface flow velocity from visible and thermal image time series. In this paper, we examine and compare water-surface elevation returns and slopes derived from the HeliPod lidar and found good agreement with measurements made using conventional field-based techniques.

Alaska

New methods for predicting and measuring dispersion in rivers

To develop a better predictive tool for dispersion in rivers over a range of temporal and spatial scales, our group has developed a simple Lagrangian model that is applicable for a wide range of coordinate systems and flow modeling methodologies. The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and temporal discretization, and turbulence closures. As the model is based on a discrete non-interacting particle approach, parallelization is straightforward, such that simulations with large numbers of particles are tractable. Results from the approach are compared to dispersion measurements made with conventional Rhodamine WT dye experiment in which typical at-a-point sensors are employed to determine concentration. The model performs well, but spatial resolution for experiments over large and or complex river flows was inadequate for model testing. To address this issue, we explored the idea of measuring spatial concentrations in river flows using hyperspectral remote sensing. Experiments both for idealized channels and real rivers show that this technique is viable and can provide high levels of spatial detail in concentration measurements with quantitatively accurate concentrations.

Conference Paper

Sampling strategies to improve passive optical remote sensing of river bathymetry

Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth ($d_{max}$) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that $d_{max}$ was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of $d_{max}$ consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than $d_{max}$, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Remote Sensing

Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California

Vast sections of the Sacramento River have been listed as critical habitat by the National Marine Fisheries Service for green sturgeon spawning ( Acipenser medirostris ), yet spawning is known to occur at only a few specific locations. This study reveals the range of physical habitat variables selected by adult green sturgeon during their spawning period. We integrated fine-scale fish positions, physical habitat characteristics, discharge, bathymetry, and simulated velocity and depth using a 2-dimensional hydraulic model (FaSTMECH). The objective was to create habitat suitability curves for depth, velocity, and substrate type within three known spawning locations over two years. An overall cumulative habitat suitability score was calculated that averaged the depth, velocity, and substrate scores over all fish, sites, and years. A weighted usable area (WUA) index was calculated throughout the sampling periods for each of the three sites. Cumulative results indicate that the microhabitat characteristics most preferred by green sturgeon in these three spawning locations were velocities between 1.0-1.1 m/s, depths of 8-9 m, and gravel and sand substrate. This study provides guidance for those who may in the future want to increase spawning habitat for green sturgeon within the Sacramento River.

California

Physical characteristics of the lower San Joaquin River, California, in relation to white sturgeon spawning habitat, 2011–14

The U.S. Fish and Wildlife Service confirmed that white sturgeon ( Acipenser transmontanus ) recently spawned in the lower San Joaquin River, California. Decreases in the San Francisco Bay estuary white sturgeon population have led to an increased effort to understand their migration behavior and habitat preferences. The preferred spawning habitat of other white sturgeon (for example, those in the Columbia and Klamath Rivers) is thought to be areas that have high water velocity, deep pools, and coarse bed material. Coarse bed material (pebbles and cobbles), in particular, is important for the survival of white sturgeon eggs and larvae. Knowledge of the physical characteristics of the lower San Joaquin River can be used to preserve sturgeon spawning habitat and lead to management decisions that could help increase the San Francisco Bay estuary white sturgeon population. Between 2011 and 2014, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, assessed selected reaches and tributaries of the lower river in relation to sturgeon spawning habitat by (1) describing selected spawning reaches in terms of habitat-related physical characteristics (such as water depth and velocity, channel slope, and bed material) of the lower San Joaquin River between its confluences with the Stanislaus and Merced Rivers, (2) describing variations in these physical characteristics during wet and dry years, and (3) identifying potential reasons for these variations. The lower San Joaquin River was divided into five study reaches. Although data were collected from all study reaches, three subreaches where the USFWS collected viable eggs at multiple sites in 2011–12 from Orestimba Creek to Sturgeon Bend were of special interest. Water depth and velocity were measured using two different approaches—channel cross sections and longitudinal profiles—and data were collected using an acoustic Doppler current profiler. During the first year of data collection (water year 2011), runoff was greatest, and gaged streamflow, measured as discharge, peaked at 875 cubic meters per second in the lower San Joaquin River. Also during that year, water velocity was generally between 0.6 and 0.9 meters per second, and depth was typically between 2.5 and 4.5 meters, but water depth exceeded 6 meters in several pools. Water year 2011 was classified as a “ wet ” year. Later water years were classified as either “ dry ” (water year 2012) or “ critical ” (water years 2013 and 2014). During the drier years, water was shallower, and velocities were slower. The streambed aggraded in several areas during the study. At Sturgeon Bend, for example, which had the deepest pool measured in 2011 (maximum depth was 14 meters), about 8 meters of sediment was deposited by 2014. The bed of the lower San Joaquin River was predominately sand, except in areas downstream from the mouth of Del Puerto Creek. A large amount of sand, gravel, and cobble was deposited at the mouth of Del Puerto Creek, and in the 9.5 kilometers downstream from the mouth of Del Puerto Creek, we encountered several gravel bars and patches of gravel-size (8–64 millimeters) bed material. Del Puerto and Orestimba Creeks drain from the Coast Ranges on the west side of the river. Only small quantities of gravel-size bed material were observed in the reach downstream from Orestimba Creek, indicating Orestimba Creek does not deliver much coarse sediment to the lower San Joaquin River. Del Puerto Creek appeared to be the primary source of gravels suitable for white sturgeon spawning in the lower San Joaquin River, and thus, it is important for the long-term spawning success of sturgeon in the San Joaquin River.

California

Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016

To better characterize the water supply capacity of Clear Creek Reservoir, Chaffee County, Colorado, the U.S. Geological Survey, in cooperation with the Pueblo Board of Water Works and Colorado Mountain College, carried out a bathymetry survey of Clear Creek Reservoir. A bathymetry map of the reservoir is presented here with the elevation-surface area and the elevation-volume relations. The bathymetry survey was carried out June 6–9, 2016, using a man-operated boat-mounted, multibeam echo sounder integrated with a Global Positioning System and a terrestrial survey using real-time kinematic Global Navigation Satellite Systems. The two collected datasets were merged and imported into geographic information system software. The equipment and methods used in this study allowed water-resource managers to maintain typical reservoir operations, eliminating the need to empty the reservoir to carry out the survey.

Colorado

Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information

Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured in situ and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of in situ measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.

Journal of Hydrology

Effect of sediment supply and flow rate on the initiation and topographic evolution of sandbars in laboratory and numerical channels

The evolution of barforms from a bed of uniform sediment and changes in sediment storage were measured in a laboratory flume and simulated numerically. Flume experiments were conducted with several upstream sediment supplies and flow conditions. For the sediment supply rates (no upstream supply, equilibrium supply, and 133, 166, and 200 percent of the equilibrium supply) and flow rates examined, the plane bed tended to evolve into mid-channel bars early in the runs ~15 minutes. As the flume experiments progressed, the bed transitioned to a lower mode configuration of alternate bars or a single-thread meandering thalweg. Increasing the upstream sediment supply to 133 percent or more of the equilibrium rate, increased the height and volume of deposited sediment relative to experiments conducted at the equilibrium rate and those experiments without sediment supply. Experiments conducted at flow rates of 0.5 and 1.0 L/s without sediment supply demonstrated that an increase in flow corresponded to a greater volume of erosion. A coupled two-dimensional flow and sediment transport model, Nays2DH, was used to simulate the evolution of bed topography for three sediment supply rates. We compared the morphodynamics and sediment storage predicted by Nays2DH for two initial bed conditions: one set of calculations used a plane bed with a small upstream perturbation as the initial bed condition, and the other set used the bed topography measured 15 minutes after the start of the flume run. Whereas initializing the model with measured flume topography provided a somewhat better analog to the final evolved morphology, predictions of sedimentation were not substantially improved over simulations using the plane bed as the initial condition.

Conference Paper

Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge

This paper describes a project combining field studies and analyses directed at providing an assessment of the accuracy of remotely sensed methods for determining river characteristics such as velocity and discharge. In particular, we describe a remote sensing method for surface velocities using mid-wave thermal camera videography combined with image analysis. One of the critical problems in this work is determining a method for relating remotely measured water-surface velocities to vertically averaged velocities through a velocity index. We explore three similarity profiles that allow a relationship between surface and vertically averaged velocity to be found either using empirical results or simple roughness-to-depth ratios. To test the approaches we compare them in a situation where vertical structure is known over most of the flow depth through ADCP measurements. By determining best-fit profiles through the ADCP profiles, average values of the velocity index are found for the cross-sections where measurement were made. By comparing these to the predicted velocity indices from the three similarity profiles, we find that, although the differences between the various similarity profiles are substantial, they are smaller than differences associated with local nonuniformity and nonhydrostatic flow. Nevertheless, the velocity indices are accurate to about +/-5%, meaning that remotely sensed vertically averaged velocities can be computed to well within the current accuracy standard for such values when used for river gaging.

Conference Paper

Remote measurement of surface-water velocity using infrared videography and PIV: a proof-of-concept for Alaskan rivers

Thermal cameras with high sensitivity to medium and long wavelengths can resolve features at the surface of flowing water arising from turbulent mixing. Images acquired by these cameras can be processed with particle image velocimetry (PIV) to compute surface velocities based on the displacement of thermal features as they advect with the flow. We conducted a series of field measurements to test this methodology for remote sensing of surface velocities in rivers. We positioned an infrared video camera at multiple stations across bridges that spanned five rivers in Alaska. Simultaneous non-contact measurements of surface velocity were collected with a radar gun. In situ velocity profiles were collected with Acoustic Doppler Current Profilers (ADCP). Infrared image time series were collected at a frequency of 10Hz for a one-minute duration at a number of stations spaced across each bridge. Commercial PIV software used a cross-correlation algorithm to calculate pixel displacements between successive frames, which were then scaled to produce surface velocities. A blanking distance below the ADCP prevents a direct measurement of the surface velocity. However, we estimated surface velocity from the ADCP measurements using a program that normalizes each ADCP transect and combines those normalized transects to compute a mean measurement profile. The program can fit a power law to the profile and in so doing provides a velocity index, the ratio between the depth-averaged and surface velocity. For the rivers in this study, the velocity index ranged from 0.82 – 0.92. Average radar and extrapolated ADCP surface velocities were in good agreement with average infrared PIV calculations.

Conference Paper

Unsteady flow in natural compound channel: Experiment and simulation

Phragmites Australis, or common reed, is an invasive plant species that has spread along channels of the Platte River (Nebraska, USA), adversely altering the biogeomorphology of the system. Of particular interest have been the impacts on riparian habitat, specifically the reduction of suitable areas for crane roosting and shorebird nesting. A program for managing and removing these and other invasive plant species has been in place to mitigate these effects. Spraying and mechanical removal of phragmites has been the established practice, but there is interest in evaluating other potential forms of maintenance, in particular the potential of uprooting seedlings using high velocity/high shear flows. For that purpose, in 2010 a high flow experiment was conducted on a return channel to the Platte River that feeds from the Thirty Mile Canal near Brady, Nebraska. As part of the experiment, a 250-meter long reach of the channel was monitored and data were collected for the set-up, testing, and evaluation of numerical models. This article presents a description of the experimental conditions, data-collection program, and application of an unsteady, depth-averaged numerical model to reproduce the event.

Nebraska

Field and laboratory determination of water-surface elevation and velocity using noncontact measurements

Noncontact methods for measuring water-surface elevation and velocity in laboratory flumes and rivers are presented with examples. Water-surface elevations are measured using an array of acoustic transducers in the laboratory and using laser scanning in field situations. Water-surface velocities are based on using particle image velocimetry or other machine vision techniques on infrared video of the water surface. Using spatial and temporal averaging, results from these methods provide information that can be used to develop estimates of discharge for flows over known bathymetry. Making such estimates requires relating water-surface velocities to vertically averaged velocities; the methods here use standard relations. To examine where these relations break down, laboratory data for flows over simple bumps of three amplitudes are evaluated. As anticipated, discharges determined from surface information can have large errors where nonhydrostatic effects are large. In addition to investigating and characterizing this potential error in estimating discharge, a simple method for correction of the issue is presented. With a simple correction based on bed gradient along the flow direction, remotely sensed estimates of discharge appear to be viable.

Conference Paper