Geology topics

John W, Fulton

Publications and source records attributed to John W, Fulton.

11 recordsLinked to original sources

USGS2025

Computing discharge using the entropy-based probability concept

This report describes the techniques and methods for computing the mean-channel velocity and discharge using the entropy-based probability concept (probability concept). The method is an alternative to or augments standard streamgaging methods adopted by the U.S. Geological Survey (USGS). Although sensor technology for measuring the mean velocity and discharge has advanced, standard streamgaging and computational methods have remained relatively unchanged since the USGS established its first streamgage at the Rio Grande at Embudo, New Mexico in 1889. Standard streamgaging methods rely on integrating velocities and depths measured at multiple verticals at a channel cross section (standard cross section) to compute a discharge. The probability concept computes discharge at a single vertical (y-axis) using the ratio of the mean-channel velocity (mean velocity) and maximum velocity, the measured maximum velocity, and the area as a function of stage at the standard cross section. Proper siting and operation and maintenance are required. If siting is conducted appropriately, the probability concept parameters and the y-axis stationing will be similar for different streamflow conditions. The timing of operation and maintenance visits should be based on hydrologic and meteorologic occurrences and seasonality and should capture low, medium, high, and opportunistic streamflow conditions. Advantages of the probability concept are the capacity to (1) compute discharge time series immediately after streamgage siting, (2) compute discharge for complex streamflow conditions that cannot be quantified by stage-discharge methods, (3) augment time-series data where gaps exist, and (4) integrate with surface velocity sensors such as Doppler velocity radars and cameras, which are not subject to damage caused by ice, debris, and flood flows. Potential sources of bias in discharge derived from the probability concept include (1) rain, (2) wind, and (3) geomorphologic and hydraulic instabilities. Recommendations to address these biases are provided. This report guides users through the steps to parameterize the probability concept, process field data, and compute the mean velocity and discharge using the probability concept.

Techniques and Methods↗

USGS2025

Mobile radar provides insights into hydrologic responses in burn areas

Background Wildfires often occur in mountainous terrain, regions that pose substantial challenges to operational meteorological and hydrologic observing networks. Aims A mobile, post-fire hydrometeorological observatory comprising remote-sensing and in situ instrumentation was developed and deployed in a burnt area to provide unique insights into rainfall-induced post-fire hazards. Methods Mobile radar-based rainfall estimates were produced throughout the burn area at 75-m resolution and compared with rain gauge accumulations and basin response variables. Key results The mobile radar was capable of resolving details in intra-basin rain fields as well as detecting storms approaching the burn area with accuracy equivalent to rain gauges. Runoff responses were complex and dependent on spatiotemporal patterns and magnitude of rainfall intensity over the burn area. Conclusions The complement of the mobile radar with the near-field, non-contact instruments measuring the hydrologic response provided valuable information in regions that are difficult to access and are not routinely monitored by conventional observing networks. Implications Post-fire observatories equipped with mobile radars deployed on burn areas provide real-time data, early alerting capabilities and visualizations to potentially guide impact-based decision support for local authorities.

Colorado↗

USGS2024

Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins

The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 ( MTBS, 2020 ). Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m 3 /s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity ( c k = 2.619 m e t e r s p e r s e c o n d , m / s ± 0.556 p e r c e n t "> 𝑐 𝑘 = 2.619 𝑚𝑒𝑡𝑒𝑟𝑠 𝑝𝑒𝑟 𝑠𝑒𝑐𝑜𝑛𝑑, m/s ± 0.556 𝑝𝑒𝑟𝑐𝑒𝑛𝑡) and dynamic celerity ( c d = 3.533 m / s ± 0.181 p e r c e n t a n d t h e i r u n c e r t a i n t i e s "> 𝑐 𝑑 = 3.533 m/s ± 0.181 𝑝𝑒𝑟𝑐𝑒𝑛𝑡) and their uncertainties were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data. Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage. The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.

Colorado↗

USGS2023

Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times ( twt ) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m −3 ). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Colorado↗

USGS2022

The applicability of time-integrated unit stream power for estimating bridge pier scour using noncontact methods in a gravel-bed river

In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas.

Colorado↗

USGS2022

Snow depth retrieval with an autonomous UAV-mounted software-defined radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600-2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as ρ = 0.9 demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with in situ measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.

Colorado↗

USGS2021

Uncertainty in remote sensing of streams using noncontact radars

Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.

Arizona, Colorado, Oklahoma, Texas↗

USGS2020

Near-field remote sensing of surface velocity and river discharge using radars and the probability concept at 10 USGS streamgages

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m 3 /s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m 3 /s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

Alaska, Colorado, Montana, New Mexico, North Dakot↗

USGS2020

Fundamental hydraulics of cross sections in natural rivers: Preliminary analysis of a large data set of acoustic doppler flow measurements

We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models.

Water Resource Research↗

USGS2020

Predicting the floods that follow the flames

No abstract available.

Bulletin of the American Meteorological Society↗

USGS2018

Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska

A methodology based on general hydraulic relations for rivers has been developed to estimate the discharge (flow rate) of rivers using satellite remote sensing observations. The estimates of discharge, flow depth, and flow velocity are derived from remotely observed water surface area, water surface slope, and water surface height, and demonstrated for two reaches of the Yukon River in Alaska, at Eagle (reach length 34.7 km) and near Stevens Village (reach length 38.3 km). The method is based on fundamental equations of hydraulic flow resistance in rivers, including the Manning equation and the Prandtl-von Karman universal velocity distribution equation. The method employs some new hydraulic relations to help define flow resistance and height of the zero flow boundary in the channel. Estimates are made both with and without calibration. The water surface area of the river reach is measured by using a provisional version of the U.S. Geological Survey (USGS) Landsat based product named Dynamic Surface Water Extent (DSWE). The water surface height and slope measurements require a self-consistent datum, and are derived from observations from the Jason-2 satellite altimeter mission. At both reach locations, the Jason-2 radar altimeter non-winter heights consistently tracked the stage recorded at USGS streamgages with a standard deviation of differences (error) during the non-winter periods of less than 7%. Part of the error may be due to differences in the gage and altimeter crossing locations with respect to the range of stage change and the response to changes in discharge at the upstream and downstream locations. For the non-winter periods, the radar derived slope estimates (mean = 0.0003) were constant over the mission lifetime, and in agreement with previously measured USGS water surface slopes and slopes determined from USGS topographic maps. The accuracy of the mean of the uncalibrated daily estimates of discharge varied between reaches, ranging from 13% near Stevens Village (N = 90) to −21% at Eagle (N = 246) based on the absolute error, and 5% to −6% based on the error of the log of the estimates. Calibrating to the mean of USGS daily discharge estimates from the streamflow rating for the same period of record at each streamgage resulted in mean absolute errors ranging from 1% to 2%, and log errors ranging from 1% or less. The error pattern of the estimates shows that without calibration, even though the mean is well simulated, the high and low end values over the range of estimates may have significant bias.

Alaska↗