USGS ScienceSearch

Geology topics

Joel T. Groten

Publications and source records attributed to Joel T. Groten.

At least 19 recordsLinked to original sources

The electric vehicle “EV”-crane—A next-generation system for fluvial sediment and water-quality sampling using an electric cart with integrated crane and accessories

The U.S. Geological Survey (USGS), with support from the Federal Interagency Sedimentation Project (FISP), developed the electric vehicle (EV)-crane, a modified electric cart equipped with a rigid swivel-mounted crane and integrated accessory holders. The EV-crane was developed to improve the efficiency and safety of fluvial sediment and water-quality sampling from bridges. USGS field staff have relied on foldable cranes on a four-wheeled base or nonstandardized platforms, such as utility terrain vehicle (UTV) mounted cranes, which highlight the need for a safer, standardized, and more adaptable solution. This redesigned platform replaces the foldable cranes on a four-wheeled base with a compact, powered system that reduces physical strain, enhances operator safety, and streamlines field operations on bridges. Dedicated holders for samplers, a controller, batteries, counterweights, and a field computer or tablet create a versatile, organized workspace that supports a wide range of sampling and heavy-lift applications. The EV-crane described here is version 1 of the prototype. Evaluation of the design is ongoing, and the findings summarized here are from initial qualitative user feedback and early quantitative testing. Potential plans may include additional testing and refinement to inform subsequent design iterations.

Open-File Report

The US XPD-24–200—An isokinetic suspended-sediment and water-quality collapsible-bag sampler with point- or depth-integrating sampling capabilities

Accurate collection of fluvial suspended‑sediment and water‑quality samples is essential for understanding transport processes, evaluating river health, quantifying loads, and supporting regulatory and management decisions. Since 1939, the Federal Interagency Sedimentation Project (FISP) has led the development and standardization of sediment‑sampling equipment and methods across Federal agencies. Although substantial advancements have been made, the most recent point‑integrating sampler developed in 2006 has notable limitations, including insufficient sample volume for low‑concentration analyses and a mechanically complex pressure‑equalization system required for a rigid-bottle sample container. To address these limitations, FISP collaborated with Carnet Technology to design, fabricate, and test a new collapsible‑bag sampler capable of collecting larger sample volumes and operating as either a point‑integrating or depth‑integrating sampler. The resulting sampler, the US XPD‑24‑200, was evaluated through controlled tow‑tank tests and field testing in a lake environment. Results indicate that the US XPD‑24‑200 can collect a 5.5-fold increase in maximum sample volume than a pre-existing point sampler developed in 2006. The US XPD‑24‑200 performs isokinetically within the expected intake efficiency (IE) range under most conditions. Approximately 73 percent of IEs fell within the target range of 0.9–1.1. A mild bias was observed with IE values outside this range that were predominantly lower than 0.9 and occurred most frequently at the lower test velocities, whereas only 2 percent of IEs exceeded 1.1, primarily at the highest velocities tested. Tow‑tank tests produced tightly clustered IE values with low variability, demonstrating stable performance under controlled conditions. Lake towing tests exhibited more variability, reflecting natural environmental influences such as turbulence, temperature gradients, and unsteady inflow. Collectively, the results demonstrate that the US XPD‑24‑200 provides reliable IEs comparable to existing samplers while offering increased sample volume, reduced mechanical complexity, and enhanced operational flexibility.

Open-File Report

Beach nourishment response and recent morphological evolution of Minnesota Point, Lake Superior

Beach nourishments are a popular nature-based alternative to armoring for shoreline erosion mitigation, but nourishments have been criticized due to their environmental impacts and uncertain sustainability. Monitoring is often nonexistent or insufficient to constrain nourishment longevity and inform the renourishment interval required to maintain shoreline protection. This study uses a combination of topobathymetric surveys, high-resolution satellite-derived shorelines, and coastal engineering analyses to investigate the recent evolution of Minnesota Point and the fate of three beach nourishments constructed adjacent to littoral barriers. We use semi-empirical formulations for sediment compatibility, wave runup, and longshore sediment transport to inform the observed nourishment behavior. Minnesota Point experienced widespread foredune retreat averaging 7±2.8 m from 2009–2019 and 130,000 (70,000–140,000) m 3 of sediment was eroded during this interval. The 2019 nourishment at the Superior Entry was rapidly eroded by strong storms, losing >80% of the added beach width by the following spring. The 2020 and 2021 nourishments at the Duluth Entry retained >80% of the nourishment material at the time of the last topobathymetric survey in the fall of 2022, and satellite-derived shorelines indicate that the beach remained 10 m wider than pre-nourishment conditions at the end of 2023. Modeled longshore transport rates over the period 2009–2022 averaged 11,400 m 3 yr −1 northwestward at the Superior Entry, nearly 3x greater than the 4000 m 3 yr −1 southeastward transport modeled at the Duluth Entry. These observations show that differences in shoreline orientation, littoral sediment supply, and grain size compatibility can lead to contrasting beach nourishment longevities, and this study provides additional measurements of Minnesota Point’s long-term morphological change which can help inform coastal resiliency efforts.

Minnesota, Wisconsin

Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023

The geologic history and anthropogenic modifications of Minnesota’s Wild Rice River have caused major morphological adjustments, which induce erosion and excess fluvial sediment transport. The excess sediment deposits in the lower Wild Rice River, exacerbating flooding. To help mitigate these problems, the Wild Rice Watershed District has future plans to implement a river restoration on the lower Wild Rice River. The Wild Rice Watershed District collaborated with the U.S. Geological Survey to measure and analyze sediment transport along the Wild Rice River’s Main and South Branches to assess any potential changes in sediment transport among sites and time periods. Time differencing results indicated that all suspended-sediment constituents showed a significant difference between the two sampling periods at one South Branch site but not at the Main Branch site. Piecewise regression analysis better matched the suspended-sediment constituents transport process at most sites by differentiating no relation between suspended-sediment constituents at lower streamflows and a positive relation at higher streamflows at most Wild Rice River sites. Five of the sites showed elevated sediment transport with increasing streamflow. In contrast, the site farthest downstream showed a negative relation with increasing streamflow, indicating that that the lower Wild Rice River is supply limited and deposition is likely occurring upstream and (or) near the site. Overall, the uncertainty in results indicates the complexity of sediment transport in a river when using streamflow as the sole explanatory variable and suggests a need for multisite, multiyear, and multifaceted data.

Minnesota

Using machine learning in Minnesota’s StreamStats to predict fluvial sediment

A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.

Minnesota

A novel suspended-sediment sampling method: Depth-Integrated Grab (DIG)

Measuring suspended sediment in fluvial systems is critical to understanding and managing water resources. Sampling suspended sediment has been the primary means of understanding fluvial suspended sediment. Specialized samplers, sampling methods, and laboratory methods developed by select U.S. Federal Agencies are more representative of river and stream conditions than commonly used grab sampling and total suspended solids (TSS) laboratory methods but are not widely used because they are expensive, time consuming, and not required as part of water quality standards in the United States. A new suspended-sediment sampling method called a depth-integrated grab (DIG) was developed by combining certain elements from both grab and depth-integrating sampling methods and suspended-sediment concentration (SSC) laboratory methods. The goal of the DIG method was to provide more accurate results than Grab-TSS while being easier and cheaper to sample than specialized samplers and methods. Approximately 50 paired comparison samples were collected at 9 sites in Minnesota from 2018 through 2019. Results showed no significant difference between the DIG and specialized sampling methods and a significant difference between both methods and the Grab-TSS method. The DIG-SSC provided an improved alternative to the Grab-TSS method, but additional research and testing is important to evaluate if this method is appropriate in different conditions than were observed in this study.

Minnesota

How machine learning can improve predictions and provide insight into fluvial sediment transport in Minnesota

Understanding fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. However, fluvial sediment transport is difficult to understand because of the multitude of factors controlling the potential sources, delivery, mechanics, and storage of sediment in aquatic systems. While physical fluvial sediment samples are an integral part of developing solutions for these environmental concerns, samples cannot be collected at every river and time of interest. Therefore, accurate and cost-effective estimates of sediment loading are needed to manage riverine sediment transport at a multitude of scales (Ellison et al. 2016); also needed are methods to estimate sediment transport at sites where little or no physical samples have been collected (Gray & Simes 2008). The application of machine learning (ML) approaches to estimate sediment transport has grown over the past two decades (Afan et al. 2016). ML used in sediment transport research has shown multiple benefits over traditional approaches, such as increased prediction accuracy, the ability to learn complex linear and non-linear relations amongst the dataset and providing the ability to interpret these complex relations with important features used in the model (Cisty et al. 2021; Francke et al. 2008; Khan et al. 2021; Zounemat-Kermani et al. 2020; Cutler et al. 2007).

Minnesota

Sand- and gravel-trapping efficiencies derived for four types of pressure-difference bedload samplers

Bedload-trapping efficiencies (coefficients) were derived for four types of pressure-difference bedload samplers at the St. Anthony Falls Laboratory, University of Minnesota during the first two phases of flume experiments in January-March, 2006, referred to as “StreamLab06.” The bedload-sampler research component was part of a series of community-led, large-scale laboratory experiments performed under the auspices of the National Center for Earth-surface Dynamics (Marr and others, 2010; Singh and others, 2013; Gray and others, 2010, 2019, 2021). A bedload-trapping coefficient is the ratio of the mass of bedload – sediment transported by rolling, sliding, or skipping in close contact with the riverbed – collected by the deployed sampler, to the mass of bedload that would have passed through the width of the sample section at the same time but in the absence of the sampler (Hubbell, 1964). A trapping coefficient of 1.0 would mean the mass of every particle-size fraction of sediment in the collected sample is in the same proportion as those in transport. For the 2006 experiments, a Helley-Smith (intake-nozzle width of 76.2 millimeter [mm] and height of 76.2 mm), BLH-84 (76.2 mm × 76.2 mm), Elwha (203 mm × 102 mm) and Toutle River-2 (TR-2; 305 mm × 152 mm) were repeatedly deployed by a hand-held rod with a stabilizing tether line in the main flume. Six combinations of bedload sampler types and bed compositions were tested: The BLH-84, Elwha, and Helley-Smith samplers were deployed on a sand bed (d50 = 1.0 mm) during five steady flows ranging from 2.0-3.6 cubic meters per second (m3/s). The BLH-84, Elwha, and TR-2 samplers were deployed on a gravel bed (d50 = 11.2 mm) at four steady flows ranging from 4.0-5.5 m3/s. Bedload samples collected manually as part of 37 trials – each associated with a unique combination of a bedload sampler type, steady-flow rate, and bed composition – and associated ancillary data were used to calculate 2,030 instantaneous, at-a-point bedload-transport rates (1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.). Five contiguous weigh drums embedded in a slot spanning the width of the flume independently and continuously weighed captured bedload on approximately 1.1-second intervals. Approximately 3.8-million individual weigh-drum time-series measurements were recorded during the bedload sampler experiments (Groten and Gray, 2021; Gray and others, 2021).

Conference Paper

Comparing empirical sediment transport modeling approaches in Michigan rivers

Excess or limited fluvial sediment transport can contribute to and exacerbate many environmental issues including nutrient loading, aquatic habitat degradation, flooding, channel navigation dredging, dam operation, and stream degradation or aggradation. However, fluvial sediment transport is difficult and expensive to comprehensively characterize because it can vary substantially both temporally and spatially. Having better estimates of fluvial sediment transport is important for understanding and solving these environmental issues when it is not possible to collect fluvial sediment samples. Different modeling approaches can be used to help estimate suspended sediment when sampling data are limited or unavailable. This study compared dimensionless sediment rating curves (DSRCs) developed in Pagosa Springs Colorado, Minnesota, and Michigan to determine if these DSRCs were suitable to make predictions of suspended sediment for Michigan rivers. Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs. Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs.

Michigan

State of the science and decision support for measuring suspended sediment with acoustic instrumentation

Acoustic instrumentation can be used to provide time-series and discrete estimates of suspended-sediment concentration, load, and sediment particle sizes in fluvial systems, which are essential for creating informed solutions to many sediment-related environmental, engineering, and land management concerns. Historically, scientists have developed relations between suspended sediment characteristics and other parameters, most commonly streamflow, to estimate sediment information when physical sediment samples cannot be collected. Approaches using streamflow can have substantial accuracy limitations because of hysteresis effects, giving rise to the use of more direct surrogate approaches such as acoustic methods. Interagency efforts in recent years have advanced the testing, methods development, operational guidelines, and training on acoustic methods for measuring suspended sediment. Scientists interested in using these methods are faced with many decisions on the type of application and deployment: horizontal profiling, vertical profiling, or point acoustic instruments; single or multifrequency instruments; continuous or discrete sediment measurements; and fixed or mobile instrument deployments. To promote cost-effective, accurate, and high-resolution fluvial sediment data for the Nation, the interagency Sediment Acoustic Leadership Team (SALT) develops technical guidance and training for using acoustic instruments to measure aquatic sediment. Even though acoustic instrumentation has been used successfully to measure suspended-sediment characteristics throughout the world, some deployments have been unsuccessful because of limited technical guidance and selection of an inappropriate method. To guide decisions on method selection, the SALT has compiled the state of the science for the main types of acoustics-based suspended-sediment measurement methods in development, testing, and use, and has created a flowchart to guide method selection.

Conference Paper

Using machine learning to improve predictions and provide insight into fluvial sediment transport

A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.

Minnesota

Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019

The Rice Creek Watershed District (RCWD) cooperated with the U.S. Geological Survey to establish a 10-year suspended sediment and bedload monitoring and streamflow modeling study to evaluate the effects of two restored meander sections on middle Rice Creek in Arden Hills, Minnesota. The RCWD goals of this stream restoration were to reduce water quality impairments, improve aquatic habitat, and reduce associated costs of dredging a sedimentation pond. During the study there were several factors that introduced uncertainty in the sampling results; however, the sampling results indicated there was an increase in the post-stream restoration sediment data because of higher streamflows during the post-stream than the pre-stream restoration monitoring period. The negative relation between suspended fines and streamflow was explained by a reduction in the supply of fines with increasing streamflows. The positive relation among suspended sand, bedload, and streamflow was because of those constituents having a functional relation with the hydraulic properties of flow and a consistent supply of sand. Two-dimensional flow modeling simulations indicated the downstream restored section had less shear stress, more pools, and could access the floodplain at a lower streamflow than the original channel. Overall, the uncertainty of the sampling results indicates the complexity of sediment transport in a river and suggests a need for multisite, multifaceted, multiyear data, and tools to simulate those data to effectively evaluate river restorations.

Minnesota

Virtual training prepared for the former Afghanistan Ministry of Energy and Water—Streamgaging, fluvial sediment sampling, bathymetry, and streamflow and sediment modeling

The U.S. Geological Survey (USGS) created a virtual training series for the Afghanistan Ministry of Energy and Water (MEW), now known as the National Water Affairs Regulation Authority (NWARA), to provide critical hydrological training as an alternative to an in-person training. The USGS was scheduled to provide in-person surface-water training for NWARA during 2020; however, travel was halted because of the Coronavirus disease 2019 (COVID–19) pandemic. The virtual training consisted of prerecorded and live presentations that were scheduled during 4 weeks in August 2021. However, the training was halted after the second week due to the collapse of the Afghan Government. Fortunately, the prerecorded presentations and training materials were delivered before the trainings were halted, so they can be viewed or shared by the participants in the future. A benefit to having produced prerecorded trainings is that USGS can leverage or adapt the trainings for nongovernmental organizations (NGOs) involved in humanitarian water relief efforts in Afghanistan or can be used for other international training efforts.

Fact Sheet

The use of continuous sediment-transport measurements to improve sand-load estimates in a large sand-bedded river: The Lower Chippewa River, WI

Accurately determining sediment loads is necessary for managing river environments but is difficult because multiple processes can lead to large discharge-independent changes in sediment transport. Thus, estimations of sediment load using discharge–sediment rating curves fit to sparse or historical sediment-transport measurements can be inaccurate, necessitating alternative approaches to reduce uncertainty. Continuous sediment-transport measurements reduce uncertainty because they can be used to detect discharge-independent changes in transport and are therefore unaffected by hysteresis. We used largely continuous approaches to measure sand transport in the lower Chippewa River, a large sand-supplying tributary to the Mississippi River. We used side-looking acoustic-Doppler profilers to continuously measure suspended-sand concentration, and bedform-tracking techniques to episodically measure bedload transport. Bedload transport was then continuously estimated using a discharge-dependent ratio of bedload to suspended-sand transport. This approach allowed determination of sand loads that were not estimated based only on water discharge. Our continuous suspended-sand measurements show that hysteresis between discharge and suspended-sand concentration occurs during most floods. Quasi-continuous bed-elevation measurements using a scour monitor show that lags between discharge and dune geometric adjustment is also common, causing hysteresis between discharge and bedload transport during floods. Furthermore, comparisons of our measurements with historical sediment-transport measurements indicate large discharge-independent declines in both suspended-sand and bedload transport since the 1980s. These findings indicate that sand transport is a non-stationary function of water discharge over timescales ranging from within individual floods to decades. Consequently, although our continuous-measurement approach yields only a ~20–30% improvement over rating-curve estimates of sand load over multi-year periods, our approach yields up to a factor-of-five improvement in sand-load estimates over the shorter, i.e., within a flood, timescales over which the largest discharge-independent changes in sand transport occur.

Wisconsin

Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers

Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota. Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials. Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume. Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients. Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler. This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.

Open-File Report

Estimation of suspended sediment at a discontinued streamgage on the lower Minnesota River at Fort Snelling State Park, Minnesota

In the spring of 2019, ice sheets transported down-stream during a large streamflow rise event in the lower Minnesota River destroyed an index-velocity streamgage at the Minnesota River at Fort Snelling State Park, Minnesota (U.S. Geological Survey station 05330920; hereafter referred to as “Ft. Snelling”). The streamgage previously used an acoustic Doppler velocity meter to provide instantaneous streamflow and suspended-sedimentation concentration (SSC) data in backwater conditions caused by the confluence with the Mississippi River. In response, the U.S. Geological Survey cooperated with the U.S. Army Corps of Engineers and Lower Minnesota River Watershed District to develop linear regression models that estimate SSCs and suspended-sand concentrations (sand) at the destroyed streamgage using streamflow data from an upstream site Minnesota River near Jordan, Minn. (U.S. Geological Survey station 05330000, hereafter referred to as “Jordan”). Simple linear regression models were developed for selected positions on the streamflow hydrograph to estimate SSC and sand at Ft. Snelling from the streamflow at Jordan. Statistically significant models could not be developed for estimating SSC at low streamflows and sand at high streamflows. Models developed to estimate sand were more uncertain than models used to estimate SSC, and models using streamflow to predict SSC and sand were more uncertain than models using acoustic backscatter to predict SSC. Annual loads of SSC and sand estimated from these models show the dynamic nature of sediment transport and storage in this section of the lower Minnesota River. These models and the associated ancillary data can help with management decisions that are crucial in managing aquatic habitat, supporting power production, and commercial navigation.

Minnesota

Performance of bedload sediment transport formulas applied to the Lower Minnesota River

Despite limitations in reproducing complex bedload sediment transport processes in rivers, formulas have been preferred over collection and analysis of field data due to the high cost and time-consuming nature of bedload discharge measurements. However, the performance of such formulas depends on the hydraulic and sedimentological conditions they attempt to describe. The availability of field measurements provides a unique opportunity to test bedload transport formulas to better guide formula selection. Hydraulic parameters and bedload discharge data from the Lower Minnesota River and two of its tributaries were used to evaluate nine bedload transport formulas using three different indices. The bedload data for the different sites were collected by the United States Geological Survey (USGS) from 2011 through 2014, with bed material varying from very coarse to medium sand. The formulas calculated higher bedload rates than were measured due to a combination of site-specific physical characteristics, including the presence of bed forms (dunes), and sampling uncertainties. Because of the lack of reproducibility of the tested formulas, five power functions, based on the relation between the specific unit power (independent hydraulic variable) and the USGS measured data (dependent variable), were derived as provisional equations to estimate the bedload discharge on the Lower Minnesota River and tributaries.

Minnesota

Using acoustic Doppler velocity meters to estimate suspended sediment along the lower Minnesota and Mississippi Rivers

Lake Pepin is the largest naturally formed lake on the Mississippi River and has complex management needs to satisfy economic, environmental, and cultural demands. Lake Pepin is filling in with sediment at a rapid rate compared to conditions before settlement by European immigrants and intense agricultural cultivation. Accordingly, the Minnesota Pollution Control Agency has developed aggressive plans to prioritize sediment sources, understand transport mechanisms, and implement large-scale strategies to reduce sedimentation in Lake Pepin. The Minnesota River is the primary sediment source to Lake Pepin, and reductions in sediment loading from the Minnesota River are needed to reduce sedimentation in Lake Pepin. Current loading estimates were calculated from grab sampling and total suspended solids laboratory methods that greatly underestimate the actual concentrations in the rivers when compared to U.S. Geological Survey width and depth integrated sampling and laboratory methods for determining suspended-sediment concentration (SSC). Therefore, the U.S. Geological Survey, with funding from the Environment and Natural Resources Trust Fund and in cooperation with the U.S. Army Corps of Engineers, Lower Minnesota River Watershed District, Minnesota Pollution Control Agency, and Minnesota Department of Natural Resources, collected SSCs and acoustic backscatter data from acoustic Doppler velocity meters over a 2-year period at nine sites. The purpose of the study was to improve understanding of sediment-transport processes and increase accuracy of estimating SSCs and suspended-sediment loads for the lower Minnesota River and the Mississippi River compared to traditional measures. The study results indicated that acoustic backscatter worked well in estimating SSCs at sites not regulated by locks, dams, and lakes. The results also confirmed previous studies that determined most of the suspended-sediment loading into the Mississippi River is from the Minnesota River and the largest sediment sink is Lake Pepin. Suspended-sediment loading from site to site and year to year was often variable when compared to streamflow, which has been traditionally used to estimate SSC. As a result, this study demonstrates the value in having high temporal and spatial resolution of continuous sediment monitoring from acoustic devices to help manage the sources of sediment into Lake Pepin.

Minnesota River, Mississippi River