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Brandon Overstreet

Publications and source records attributed to Brandon Overstreet.

11 recordsLinked to original sources
USGS2026

Evaluation of submersible pressure transducers for streamflow monitoring in small streams

Compact streamgages requiring minimal infrastructure and equipped with submersible pressure transducers (PTs) are increasingly used to monitor small streams, yet disparate implementations obscure their accuracy under real-world conditions. This study isolated instrumentation-derived uncertainty in stage monitoring by co-locating various combinations of commercial vented and unvented PTs with seven U.S. Geological Survey (USGS) reference streamgages on small streams. Multi-year PT stage records from compact streamgages, collected and corrected following USGS protocols, were compared to concurrent reference observations. Vented PTs demonstrated an average measurement uncertainty of ±0.005 m and mean absolute percent error (MAPE) of ±0.2%. Unvented PTs exhibited higher uncertainty, averaging ±0.009 m and ±0.4% MAPE. Although both sensor types had stage errors up to ±0.3 m, 95% of vented PT and unvented PT observations were within 0.01 and 0.02 m of reference stage, respectively. Analysis of additional unvented PTs revealed stage errors of up to ±1.2% when using barometric sensors within 15 km of the in-water sensor. Propagation of stage error to discharge using reference rating models resulted in cumulative discharge MAPEs of ±4.5% for vented and ±5.5% for unvented PTs. These findings highlight PTs as practical alternatives to reference instrumentation when deployed with standardized procedures, potentially expanding access to reliable streamflow data.

conterminous United States↗
USGS2026

Quantifying Chinook Salmon habitat across flow regimes: High resolution modeling in Oregon’s Willamette River basin

The Santiam and McKenzie River basins of western Oregon provide critical habitat for federally listed Upper Willamette River Spring Chinook salmon ( Oncorhynchus tshawytscha ). Streamflows in each basin are regulated in part by high-head multi-purpose dams; however, quantitative linkages between regulated streamflows and downstream habitat for juvenile and adult Chinook salmon remain limited. This study integrates high-resolution topo-bathymetric lidar, two-dimensional hydraulic models, and novel detailed sediment distribution models to evaluate habitat across a range of flows for over 400 river km. Results suggest that habitat quantity for two juvenile life stages (fry and parr) differs in response to streamflow; fry habitat appears to be relatively insensitive to streamflow, except in the downstream-most reaches of each river, while parr habitat generally increases from low to moderate flows, yet decreases at higher flows. Spawning habitat is largely inversely related to streamflow and is primarily limited by suitable substrate availability, likely reflecting sediment retention effects of upstream dams. Additionally, streamflow thresholds were identified where redd desiccation risk increases significantly with declining streamflow. These findings provide managers with spatially explicit tools to evaluate flow management tradeoffs during critical rearing and spawning periods and provide insights into habitat dynamics in large, regulated rivers. This work demonstrates how high-resolution modeling can support quantification of habitat at the landscape level.

Oregon↗
USGS2025

High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon

The distribution of river-bed grain sizes plays a foundational role in river morphology and ecology. River-bed grain size is a key driver of channel form and process, and has first order effects on aquatic macroinvertebrate assemblages, fish nesting, and biogeochemical processes. Despite this importance, tools to spatially quantify grain-size distributions, particularly submerged grain-size distributions, are lacking. Efforts to address this knowledge gap include developing optical and sonographic tools, however, these approaches have limitations, especially in shallow rivers and over large spatial extents. This study quantifies submerged grain size at high resolution (1 m 2 ) across 260 km of geomorphically diverse river corridors in the Santiam River Basin, Oregon, by pairing bathymetric Lidar point clouds with georeferenced pebble counts. Results suggest that derivatives of Lidar point clouds are able to accurately estimate measured median grain size across seven of the eight river reaches investigated, including reaches above and below high-head dams. Spatial analysis of predicted grain-sizes in the context of Chinook salmon spawning habitat suggests that suitable size sediment patches in the upper, unregulated reaches the study basin is typically small and unorganized. In contrast, the larger rivers downstream of high-head dams typically have larger areas of suitable spawning gravels. This method may be useful for quantification of fish and macroinvertebrates habitats, surface grain-size metrics for sediment transport models, and monitoring of natural and anthropogenic changes in river systems.

Oregon↗
USGS2025

Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry

Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon ( Oncorhynchus tshawytscha ) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index ( α ) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.

Oregon↗
USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗
USGS2024

Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R 2 ( p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.

Willamette River basin, Delaware River basin↗
USGS2019

Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations

The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

Oregon↗
USGS2018

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↗
USGS2017

A framework for modeling connections between hydraulics, water surface roughness, and surface reflectance in open channel flows

This paper introduces a framework for examining connections between the flow field, the texture of the air-water interface, and the reflectance of the water surface and thus evaluating the potential to infer hydraulic information from remotely sensed observations of surface reflectance. We used a spatial correlation model describing water surface topography to illustrate the application of our framework. Nondimensional relations between model parameters and flow intensity were established based on a prior flume study. Expressing the model in the spatial frequency domain allowed us to use an efficient Fourier transform-based algorithm for simulating water surfaces. Realizations for both flume and field settings had water surface slope distributions positively correlated with velocity and water surface roughness. However, most surface facets were gently sloped and thus unlikely to yield strong specular reflections; the model exaggerated the extent of water surface features, leading to underestimation of facet slopes. A ray tracing algorithm indicated that reflectance was greatest when solar and view zenith angles were equal and the sensor scanned toward the Sun to capture specular reflections of the solar beam. Reflected energy was concentrated in a small portion of the sky, but rougher water surfaces reflected rays into a broader range of directions. Our framework facilitates flight planning to avoid surface-reflected radiance while mapping other river attributes, or to maximize this component to exploit relationships between hydraulics and surface reflectance. This initial analysis also highlighted the need for improved models of water surface topography in natural rivers.

Journal of Geophysical Research F: Earth Surface↗
USGS2017

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↗