Geology topics

Daniel D. Buscombe

Publications and source records attributed to Daniel D. Buscombe.

52 records · Page 3Linked to original sources

USGS2017

The sand dunes of the Colorado River, Grand Canyon, USA

The flow (Wright and Kaplinski, 2011), suspended sediment transport (Topping et al., 2000), sediment storage (Grams et al., 2013), and sedimentology of sandbars (Rubin et al., 1998) of the 250 miles of the Colorado River that run through Grand Canyon National Park have been well studied and described. However, there has been little systematic or synoptic description of the morphologies and sedimentology of the riverbed, where at least 80 percent of the active sand occurs (Grams et al., 2013). Here, we use high-resolution bathymetric and backscatter measurements collected with multibeam echosounder to comprehensively describe the morphology, sedimentology, and kinematics of sand dunes, and to estimate bedload sediment transport in certain reaches of the river.

Colorado River, Grand Canyon↗

USGS2017

Sand pulses and sand patches on the Colorado River in Grand Canyon

Alluvial sandbars occur in lateral recirculation zones (eddies) along the Colorado River in Grand Canyon National Park (Schmidt, 1990). Resource managers periodically release controlled floods from the upstream Glen Canyon Dam to rebuild these bars (Grams et al., 2015), which erode during fluctuating dam releases, and by hillslope runoff and wind deflation (Hazel et al., 2010). Because the dam blocks upstream sediment, episodic floods from tributaries provide the only supply to replace eroded sand; and much of this sand originates from a single tributary (Topping et al., 2000). Here, we present new evidence for the downstream translation of the sand component of these sediment inputs as discontinuous sand pulses. Improved understanding of the behaviour of these sand pulses may be used to adjust the timing, magnitude, and duration of controlled floods to maximize potential for deposition on sandbars in different segments of the 450 km-long Grand Canyon.

Colorado River, Grand Canyon↗

USGS2017

Linking fluvial and aeolian morphodynamics in the Grand Canyon, USA

In river valleys, fluvial and upland landscapes are intrinsically linked through sediment exchange between the active channel, near-channel fluvial deposits, and higher elevation upland deposits. During floods, sediment is transferred from channels to low-elevation nearchannel deposits [Schmidt and Rubin, 1995]. Particularly in dryland river valleys, subsequent aeolian reworking of these flood deposits redistributes sediment to higher elevation upland sites, thus maintaining naturallyoccurring aeolian landscapes [Draut, 2012].

Grand Canyon↗

USGS2016

Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques

Most techniques for estimating settling velocities of natural particles have been developed for siliciclastic sediments. Therefore, to understand how these techniques apply to bioclastic environments, measured settling velocities of bioclastic sedimentary deposits sampled from a nearshore fringing reef in Western Australia were compared with settling velocities calculated using results from several common grain-size analysis techniques (sieve, laser diffraction and image analysis) and established models. The effects of sediment density and shape were also examined using a range of density values and three different models of settling velocity. Sediment density was found to have a significant effect on calculated settling velocity, causing a range in normalized root-mean-square error of up to 28%, depending upon settling velocity model and grain-size method. Accounting for particle shape reduced errors in predicted settling velocity by 3% to 6% and removed any velocity-dependent bias, which is particularly important for the fastest settling fractions. When shape was accounted for and measured density was used, normalized root-mean-square errors were 4%, 10% and 18% for laser diffraction, sieve and image analysis, respectively. The results of this study show that established models of settling velocity that account for particle shape can be used to estimate settling velocity of irregularly shaped, sand-sized bioclastic sediments from sieve, laser diffraction, or image analysis-derived measures of grain size with a limited amount of error. Collectively, these findings will allow for grain-size data measured with different methods to be accurately converted to settling velocity for comparison. This will facilitate greater understanding of the hydraulic properties of bioclastic sediment which can help to increase our general knowledge of sediment dynamics in these environments.

Sedimentology↗

USGS2016

Bed texture mapping in large rivers using recreational-grade sidescan sonar

The size-distribution and spatial organization of bed sediment, or bed ‘texture’, is a fundamental attribute of natural channels and is one important component of the physical habitat of aquatic ecosystems. ‘Recreational-grade’ sidescan sonar systems now offer the possibility of imaging, and subsequently quantifying bed texture at high resolution with minimal cost, or logistical effort. We are investigating the possibility of using sidescan sonar sensors on commercially available ‘fishfinders’ for within-channel bed-sediment characterization of mixed sand-gravel riverbeds in a debris-fan dominated canyon river. We analyzed repeat substrate mapping of data collected before and after the November 2014 High Flow Experiment on the Colorado River in lower Marble Canyon, Arizona. The mapping analysis resulted in sufficient spatial coverage (e.g. reach) and resolutions (e.g. centrimetric) to inform studies of the effects of changing bed substrates on salmonid spawning on large rivers. From this preliminary study, we argue that the approach could become a tractable and cost-effective tool for aquatic scientists to rapidly obtain bed texture maps without specialized knowledge of hydroacoustics. Bed texture maps can be used as a physical input for models relating ecosystem responses to hydrologic management.

Conference Paper↗

USGS2016

Community for Data Integration 2015 annual report

The Community for Data Integration (CDI) continued to experience success in fiscal year 2015. The CDI community members have been sharing, learning, and collaborating through monthly forums, workshops, working groups, and funded projects. In fiscal year 2015, CDI coordinated 10 monthly forums with 16 different speakers from the U.S. Geological Survey and external partners; funded 11 collaborative projects; and hosted an in-person, four-day workshop, which attracted 168 (134 in-person and 34 remote) data practitioners, data providers, and data consumers from across the USGS, academia, industry, and other government agencies. The Citizen Science, Connected Devices, Data Management, Semantic Web, and Tech Stack Working Groups continued to accomplish great things in fiscal year 2015. These working groups were major stakeholders in planning the 2015 CDI Workshop; they continued developing solutions to pressing challenges, and they brought in speakers throughout the year for more focused presentations and discussions. Additionally, a new working group formed during the 2015 CDI Workshop—the Earth-Science Themes Working Group.

Open-File Report↗

USGS2015

Spatially explicit spectral analysis of point clouds and geospatial data

The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python , it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described, and its functionality illustrated with an example of a high-resolution bathymetric point cloud data collected with multibeam echosounder.

Computers & Geosciences↗

USGS2015

Evaluating unsupervised methods to size and classify suspended particles using digital in-line holography

Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing and classification of particles. These computational challenges are the subject of this paper, in which we draw upon data collected using a variety of holographic systems developed at Plymouth University, UK, from a significant range of particle types, sizes and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from over-segmentation of particles. A simple unsupervised particle classification system is developed, and is capable of successfully differentiating sand grains, bubbles and diatoms from within the surf-zone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations, and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments and oil droplets.

Journal of Atmospheric and Oceanic Technology↗

USGS2015

Use of flux and morphologic sediment budgets for sandbar monitoring on the Colorado River in Marble Canyon, Arizona

The magnitude and pfattern of streamflow and sediment supply of the Colorado River in Grand Canyon (Figure 1) has been affected by the existence and operations of Glen Canyon Dam since filling of Lake Powell Reservoir began in March 1963. In the subsequent 30 years, fine sediment was scoured from the downstream channel (Topping et al., 2000; Grams et al., 2007), resulting in a decline in the number and size of sandbars in the eastern half of Grand Canyon National Park (Wright et al., 2005; Schmidt et al., 2004). The Glen Canyon Dam Adaptive Management Program (GCDAMP) administered by the U.S. Department of Interior oversees efforts to manage the Colorado River ecosystem downstream from Glen Canyon Dam. One of the goals of the GCDAMP is to maintain and increase the number and size of sandbars in this context of a limited sand supply. Management actions to benefit sandbars have included curtailment of daily streamflow fluctuations, which occur for hydropower generation, and implementation of controlled floods, also called high-flow experiments. Studies of controlled floods, defined as intentional releases that exceed the maximum discharge capacity of the Glen Canyon Dam powerplant, implemented between 1996 and 2008, have demonstrated that these events cause increases in sandbar size throughout Marble and Grand Canyons (Hazel et al., 2010; Schmidt and Grams, 2011; Mueller et al., 2014), although the magnitude of response is spatially variable (Hazel et al., 1999; 2010). Controlled floods may build some sandbars at the expense of erosion of sand from other, upstream, sandbars (Schmidt, 1999). To increase the frequency and effectiveness of sandbar building, the U.S. Department of Interior adopted a “high-flow experimental protocol” to implement controlled floods regularly under conditions of enriched sand supply (U.S. Department of Interior, 2012). Because the supply of sand available to build sandbars has been substantially reduced by Glen Canyon Dam (Topping et al., 2000) and depends entirely on infrequent tributary floods, monitoring of both sandbars and gross sand storage (the sand budget) is required to evaluate whether the high-flow protocol is having the intended effect of increasing sandbar size without progressively depleting sand from the system. There are many challenges associated with monitoring sand storage and active sand deposits in a river system as large and complex as the 450-km segment of the Colorado River between Glen Canyon Dam and Lake Mead. Previous studies have demonstrated the temporal variation in sand storage associated with sand-supply limitation (Topping et al., 2000) and the spatial variability in the amount of sand stored in eddies and the channel associated with channel hydraulics (Grams et al., 2013). In this study, we report on companion measurements of sand flux and morphologic change to quantify, for the first time, the relation between changes in sand mass balance, changes in within-channel sand storage, and changes in sandbars comprehensively for a 50-km river segment of the Colorado River in lower Marble Canyon within Grand Canyon National Park. We show that, when measured over the scale of a 50-km river segment, these complementary measurements of the sand budget agree within measurement uncertainty and provide a rare opportunity to integrate the temporally rich sand-flux record with the spatially rich morphologic measurements. Both methods show that sediment was evacuated from lower Marble Canyon over the 3-year study period. The flux-based budget shows the timing of changes in storage relative to dam-release patterns, while the morphologic measurements depict the spatial distribution of erosion and deposition among different depositional settings.

Arizona↗

USGS2015

Large river bed sediment characterization with low-cost sidecan sonar: Case studies from two setting in the Colorado (Arizona) and Penobscot (Maine) Rivers

Mapping subaqueous riverbed sediment grain size across channels and in nearshore areas typically used by fish and benthic invertebrates is difficult where and when the water flow is too swift or deep to wade yet impractical to access with large boats and instruments. Fluvial characteristics can further constrain sampling options, particularly where flow depth, water column turbidity or channel bottom structure prohibit use of aerial or bottom deployed imaging platforms. Here we discuss considerations in the use of sidescan sonar for riverbed sediment classification using examples from two large rivers, the Colorado River below Glen Canyon Dam in Arizona and the Upper Penobscot River in northern Maine (Figure 3). These case studies represent two fluvial systems that differ in recent history, physiography, sediment transport, and fluvial morphologies. The bed of the Colorado River in Glen Canyon National Recreation Area is predominantly graveled with extensive mats of submerged vegetation, and ephemeral surficial sand deposits exist below major tributaries. The bed is imaged periodically to assess the importance of substrate type and variability on rainbow trout spawning and juvenile rearing habitats and controls on aquatic invertebrate population dynamics. The Colorado River bed further below the dam in Grand Canyon National Park is highly dynamic. Tributary inputs of sand, gravel and boulders are spatially variable, and hydraulics of individual pools and eddies vary considerably in space and in response to varying dam operations, including experimental controlled flood releases to rebuild eroding sandbars. The bed encompasses the full range of noncohesive sediments, deposited in complicated spatial patterns. The mobile portion of the Penobscot River is generally more uniform, and consists predominantly of embedded gravels interspersed between bedrock outcrops with small isolated sand patches in sections with modest or low gradients. Patches of large cobbles, boulders and bedrock outcrops are present in the lower reaches of the river near locations of two recent dam removal projects but are of limited extent below the "head of tide" on the river. Aggregations of coarse materials often correspond to locations with abrupt bed elevation drops in the Upper Penobscot River.

Arizona, Maine↗

USGS2015

Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar

Characterizing the large-scale sedimentary make-up of heterogeneous riverbeds (Nelson et al., 2014), which consist of a patchwork of sediment types over small scales (less than one to several tens of meters) (Dietrich and Smith, 1984) requires high resolution measurements of sediment grain size. Capturing such variability with conventional physical (e.g. grabs, cores, and dredges) or underwater photographic sampling (Rubin et al., 2007; Buscombe et al., 2014a) would be prohibitively costly and time-consuming. However, characterizing bed sediments using high-frequency (several hundred kilohertz) acoustic backscatter from swath-mapping systems has the potential to provide near complete coverage of the bed (Brown and Blondel, 2009; Brown et al., 2011; Snellen et al., 2013), at resolutions down to a few centimeters, which photographic sampling could not practically achieve within the same time and with the same positional accuracy. In shallow water, the physics of high frequency scattering of sound are relatively poorly understood, therefore acoustic sediment classification are almost always statistical (Snellen et al., 2013). Many such methods proposed to date are designed for characterizing large areas of seabed (Brown and Blondel, 2009; Brown et al., 2011) at relatively poor resolution (tens of meters to several hundred meters) and therefore rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds. In response to this need, Buscombe et al. (2014b, 2014c) developed a new statistical method for acoustic sediment classification based on spectral analysis of backscatter. This method is both continuous in coverage and of sufficient resolution (order meter or less) to characterize sediment variability on patchy riverbeds. Here, we apply these methods to multibeam echosounder (MBES) data collected from the bed of the Colorado River in Marble and Grand Canyons. Sediment dynamics on the Colorado River in Grand Canyon National Park have been studied for several decades (e.g. Howard and Dolan, 1981; Rubin et al., 2002). Particular focus has been given to sandbars in large eddies downstream of tributary debris fans (Schmidt, 1990) because they are considered valuable resources by stakeholders and managers. Due to the severe limitations in sand supply imposed by Glen Canyon Dam (Howard and Dolan, 1981; Topping et al., 2000; Hazel et al., 2006), understanding the effectiveness of sandbar management practices, such as controlled floods (Rubin et al. 2002; Topping et al., 2006; Hazel et al., 2010), and the long-term fate of sand in Grand Canyon over decadal timescales, requires construction of accurate sand budgets, which involves detailed monitoring of influx, efflux and changes in sand storage (Topping et al., 2000; Topping et al., 2010; Grams et al., 2013) and assessments of uncertainties in sand-budget calculations (Grams et al., 2013). In order to estimate the sand budget, it is necessary to estimate what component of observed morphological changes is sand and what component is coarser. Grams et al. (2013) classified sand and coarse substrates using topographic roughness derived from digital elevation models, but the classification skill was estimated to be only 60-70%. In addition, sand bedforms had to be delineated manually, and validation was based on grain-size observations with positional uncertainties up to tens of meters. Because the morphology of the Colorado riverbed in Grand Canyon is mapped - to a large extent - using MBES (Kaplinski et al., 2009), the primary motivation for the present study is to examine how uncertainties in sand budgets can be constrained by producing maps of surface sediment types using the completely automated methods of Buscombe et al (2014b, 2014c) based on statistical analysis of MBES acoustic backscatter.

Arizona↗

USGS2015

Using oblique digital photography for alluvial sandbar monitoring and low-cost change detection

The maintenance of alluvial sandbars is a longstanding management interest along the Colorado River in Grand Canyon. Resource managers are interested in both the long-term trend in sandbar condition and the short-term response to management actions, such as intentional controlled floods released from Glen Canyon Dam. Long-term monitoring is accomplished at a range of scales, by a combination of annual topographic survey at selected sites, daily collection of images from those sites using novel, autonomously operating, digital camera systems (hereafter referred to as 'remote cameras'), and quadrennial remote sensing of sandbars canyonwide. In this paper, we present results from the remote camera images for daily changes in sandbar topography.

Conference Paper↗

USGS2014

Characterizing riverbed sediment using high-frequency acoustics 1: spectral properties of scattering

Bed-sediment classification using high-frequency hydro-acoustic instruments is challenging when sediments are spatially heterogeneous, which is often the case in rivers. The use of acoustic backscatter to classify sediments is an attractive alternative to analysis of topography because it is potentially sensitive to grain-scale roughness. Here, a new method is presented which uses high-frequency acoustic backscatter from multibeam sonar to classify heterogeneous riverbed sediments by type (sand, gravel,rock) continuously in space and at small spatial resolution. In this, the first of a pair of papers that examine the scattering signatures from a heterogeneous riverbed, methods are presented to construct spatially explicit maps of spectral properties from geo-referenced point clouds of geometrically and radiometrically corrected echoes. Backscatter power spectra are computed to produce scale and amplitude metrics that collectively characterize the length scales of stochastic measures of riverbed scattering, termed ‘stochastic geometries’. Backscatter aggregated over small spatial scales have spectra that obey a power-law. This apparently self-affine behavior could instead arise from morphological- and grain-scale roughnesses over multiple overlapping scales, or riverbed scattering being transitional between Rayleigh and geometric regimes. Relationships exist between stochastic geometries of backscatter and areas of rough and smooth sediments. However, no one parameter can uniquely characterize a particular substrate, nor definitively separate the relative contributions of roughness and acoustic impedance (hardness). Combinations of spectral quantities do, however, have the potential to delineate riverbed sediment patchiness, in a data-driven approach comparing backscatter with bed-sediment observations (which is the subject of part two of this manuscript).

Journal of Geophysical Research F: Earth Surface↗

USGS2014

Characterizing riverbed sediment using high-frequency acoustics 2: scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

In this, the second of a pair of papers on the statistical signatures of riverbed sediment in high-frequency acoustic backscatter, spatially explicit maps of the stochastic geometries (length- and amplitude-scales) of backscatter are related to patches of riverbed surfaces composed of known sediment types, as determined by geo-referenced underwater video observations. Statistics of backscatter magnitudes alone are found to be poor discriminators between sediment types. However, the variance of the power spectrum, and the intercept and slope from a power-law spectral form (termed the spectral strength and exponent, respectively) successfully discriminate between sediment types. A decision-tree approach was able to classify spatially heterogeneous patches of homogeneous sands, gravels (and sand-gravel mixtures), and cobbles/boulders with 95, 88, and 91% accuracy, respectively. Application to sites outside the calibration, and surveys made at calibration sites at different times, were plausible based on observations from underwater video. Analysis of decision trees built with different training data sets suggested that the spectral exponent was consistently the most important variable in the classification. In the absence of theory concerning how spatially variable sediment surfaces scatter high-frequency sound, the primary advantage of this data-driven approach to classify bed sediment over alternatives is that spectral methods have well understood properties and make no assumptions about the distributional form of the fluctuating component of backscatter over small spatial scales.

Arizona↗

USGS2012

Advances in the simulation and automated measurement of well-sorted granular material: 2. Direct measures of particle properties

1. In this, the second of a pair of papers on the structure of well-sorted natural granular material (sediment), new methods are described for automated measurements from images of sediment, of: 1) particle-size standard deviation (arithmetic sorting) with and without apparent void fraction; and 2) mean particle size in material with void fraction. A variety of simulations of granular material are used for testing purposes, in addition to images of natural sediment. Simulations are also used to establish that the effects on automated particle sizing of grains visible through the interstices of the grains at the very surface of a granular material continue to a depth of approximately 4 grain diameters and that this is independent of mean particle size. Ensemble root-mean squared error between observed and estimated arithmetic sorting coefficients for 262 images of natural silts, sands and gravels (drawn from 8 populations) is 31%, which reduces to 27% if adjusted for bias (slope correction between observed and estimated values). These methods allow non-intrusive and fully automated measurements of surfaces of unconsolidated granular material. With no tunable parameters or empirically derived coefficients, they should be broadly universal in appropriate applications. However, empirical corrections may need to be applied for the most accurate results. Finally, analytical formulas are derived for the one-step pore-particle transition probability matrix, estimated from the image's autocorrelogram, from which void fraction of a section of granular material can be estimated directly. This model gives excellent predictions of bulk void fraction yet imperfect predictions of pore-particle transitions.

Journal of Geophysical Research F: Earth Surface↗

USGS2010

An automated and universal method for measuring mean grain size from a digital image of sediment

Existing methods for estimating mean grain size of sediment in an image require either complicated sequences of image processing (filtering, edge detection, segmentation, etc.) or statistical procedures involving calibration. We present a new approach which uses Fourier methods to calculate grain size directly from the image without requiring calibration. Based on analysis of over 450 images, we found the accuracy to be within approximately 16% across the full range from silt to pebbles. Accuracy is comparable to, or better than, existing digital methods. The new method, in conjunction with recent advances in technology for taking appropriate images of sediment in a range of natural environments, promises to revolutionize the logistics and speed at which grain-size data may be obtained from the field.

Conference Paper↗