USGS Science⌕ Search

USGS · sir20165002

Sediment loads and transport at constructed chutes along the Missouri River - Upper Hamburg Chute near Nebraska City, Nebraska, and Kansas Chute near Peru, Nebraska

Abstract

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored suspended sediment within constructed Missouri River chutes during March through October 2012. Chutes were constructed at selected river bends by the U.S. Army Corps of Engineers to help mitigate aquatic habitat lost through the creation and maintenance of the navigation channel on the Missouri River. The restoration and development of chutes is one method for creating shallow-water habitat within the Missouri River to meet requirements established by the amended 2000 Biological Opinion. Understanding geomorphic channel-evolution processes and sediment transport is important for the design of chutes, monitoring and maintenance of existing chutes, and characterizing the habitat that the chutes provide. This report describes the methods used to monitor suspended sediment at two Missouri River chutes and presents the results of the data analysis to help understand the suspended-sediment characteristics of each chute and the effect the chutes have on the Missouri River. Upper Hamburg chute, near Nebraska City, Nebraska, and Kansas chute, near Peru, Nebraska, were selected for monitoring. At each study site, monthly discrete samples were collected from April through October in the Missouri River main-channel transects upstream from the chute inlet, downstream from the chute outlet, at the outlet (downstream transect) of both chutes, and at the inlet (upstream transect) of Kansas chute. In addition, grab samples from all chute sampling locations were collected using autosamplers. Suspended-sediment concentration (SSC) and grain-size metrics were determined for all samples (discrete and grab). Continuous water-quality monitors recorded turbidity and water temperature at 15-minute intervals at the three chute sampling locations. Two acoustic Doppler velocimeters, one within each chute, measured water depth and current velocities continuously. The depth and velocity data were used to estimate streamflow within each chute. The sampling design was developed to understand the suspended-sediment differences within each chute and between the chute and the Missouri River main channel during discrete sampling. The sampling design also allowed for site-specific surrogate relations between SSC and turbidity to be developed, which could be used to compute real-time estimates of SSC and sediment loads within the chutes. Real-time estimates of SSC and sediment loads enable a better understanding of sediment transport within the chutes during times when physical samples are not collected, including periods of high flow. High flows during the summer of 2011 resulted in substantial alterations to both studied chutes; therefore, the U.S. Army Corps of Engineers repaired and modified both chutes during 2012. These unforeseen repairs and modifications within the chutes added uncertainty to the analysis because concentrations were altered by construction equipment and flow alteration. Daily suspended-sediment and suspended-silt loads were estimated based on surrogate relations with turbidity. A linear regression was used to estimate equal-width increment (EWI)-equivalent SSC from autosampler SSC before using the model-calibration dataset to determine the best-fit model for prediction of SSC from the turbidity and, in some cases, discharge. Correlation between suspended-sand concentration ( SSandC ) in EWI samples and concurrent samples collected by an autosampler was low; therefore, SSandC was excluded from development of surrogate relations because a large part of the calibration dataset was from autosamples. Instead, SSandC was estimated as SSC minus suspended-silt-clay concentration ( SSiltC ). At all sites, the best-fit models included the base-10 logarithm of concentration and turbidity, and at Kansas chute upstream, the base-10 logarithm of streamflow was also included in the best-fit models. These surrogate models were used to estimate continuous time series of SSC and SSiltC . Estimated concentrations of suspended sediment were used to estimate instantaneous and daily loads for total suspended sediment, suspended silt-clay, and suspended sand. Estimated daily suspended-sediment loads were not significantly different between upstream and downstream transects within the Kansas chute, and most individual daily loads within the chute were not significantly different between upstream and downstream transects when evaluated using overlap in daily 95-percent confidence intervals. The comparison of daily load values for upstream and downstream chute transects, as estimated from turbidity-based surrogate models for Kansas chute, documents the daily dynamic nature of sediment transport within the chute with a temporal resolution that is not practical with discrete suspended-sediment sampling alone. Comparisons of concentrations and loads from EWI samples collected from different transects within a study site resulted in few significant differences, but comparisons are limited by small sample sizes and large within-transect variability. When comparing the Missouri River upstream transect to the chute inlet transect, similar results were determined in 2012 as were determined in 2008—the chute inlet affected the amount of sediment entering the chute from the main channel. In addition, the Kansas chute is potentially affecting the sediment concentration within the Missouri River main channel, but small sample size and construction activities within the chute limit the ability to fully understand either the effect of the chute in 2012 or the effect of the chute on the main channel during a year without construction. Finally, some differences in SSC were detected between the Missouri River upstream transects and the chute downstream transects; however, the effect of the chutes on the Missouri River main-channel sediment transport was difficult to isolate because of construction activities and sampling variability.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 40.513277131087484° to 40.61681920737131° latitude; -95.78601837158203° to -95.70259094238281° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brenda K. Densmore, David L. Rus, Matthew T. Moser, Brent M. Hall, Michael J. Andersen. 2016-02-04. Sediment loads and transport at constructed chutes along the Missouri River - Upper Hamburg Chute near Nebraska City, Nebraska, and Kansas Chute near Peru, Nebraska. https://doi.org/10.3133/sir20165002

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

Scientific Investigations Report↗

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗