Characterization of sediment transport upstream and downstream from Lake Emory on the Little Tennessee River near Franklin, North Carolina, 2014–15

Brad A. Huffman; William F. Hazell; Carolyn J. Oblinger

doi:10.3133/sir20175081

Home/Archive/USGS

USGS · sir20175081

Characterization of sediment transport upstream and downstream from Lake Emory on the Little Tennessee River near Franklin, North Carolina, 2014–15

Brad A. Huffman·William F. Hazell·Carolyn J. Oblinger

View original source ↗Read source document ↗Export metadata ↓

Abstract

Federal, State, and local agencies and organizations have expressed concerns regarding the detrimental effects of excessive sediment transport on aquatic resources and endangered species populations in the upper Little Tennessee River and some of its tributaries. In addition, the storage volume of Lake Emory, which is necessary for flood control and power generation, has been depleted by sediment deposition. To help address these concerns, a 2-year study was conducted in the upper Little Tennessee River Basin to characterize the ambient suspended-sediment concentrations and suspended-sediment loads upstream and downstream from Lake Emory in Franklin, North Carolina. The study was conducted by the U.S. Geological Survey in cooperation with Duke Energy. Suspended-sediment samples were collected periodically, and time series of stage and turbidity data were measured from December 2013 to January 2016 upstream and downstream from Lake Emory. The stage data were used to compute time-series streamflow. Suspended-sediment samples, along with time-series streamflow and turbidity data, were used to develop regression models that were used to estimate time-series suspended-sediment concentrations for the 2014 and 2015 calendar years. These concentrations, along with streamflow data, were used to compute suspended-sediment loads. Selected suspended-sediment samples were collected for analysis of particle-size distribution, with emphasis on high-flow events. Bed-load samples were also collected upstream from Lake Emory. The estimated annual suspended-sediment loads (yields) for the upstream site for the 2014 and 2015 calendar years were 27,000 short tons (92 short tons per square mile) and 63,300 short tons (215 short tons per square mile), respectively. The annual suspended-sediment loads (yields) for the downstream site for 2014 and 2015 were 24,200 short tons (75 short tons per square mile) and 94,300 short tons (292 short tons per square mile), respectively. Overall, the suspended-sediment load at the downstream site was about 28,300 short tons greater than the upstream site over the study period. As expected, high-flow events (the top 5 percent of daily mean flows) accounted for the majority of the sediment load; 80 percent at the upstream site and 90 percent at the downstream site. A similar relation between turbidity (the top 5 percent of daily mean turbidity) and high loads was also noted. In general, when instantaneous streamflows at the upstream site exceeded 5,000 cubic feet per second, increased daily loads were computed at the downstream site. During low to moderate flows, estimated suspended-sediment loads were lower at the downstream site when compared to the upstream site, which suggests that sediment deposition may be occurring in the intervening reach during those conditions. During the high-flow events, the estimated suspended-sediment loads were higher at the downstream site; however, it is impossible to say with certainty whether the increase in loading was due to scouring of lake sediment, contributions from the additional source area, model error, or a combination of one or more of these factors. The computed loads for a one-week period (December 24–31, 2015), during which the two largest high-flow events of the study period occurred, were approximately 52 percent of the 2015 annual sediment load (36 percent of 2-year load) at the upstream site and approximately 72 percent of the 2015 annual sediment load (57 percent of 2-year load) at the downstream site. Six bedload samples were collected during three events; two high-flow events and one base-flow event. The contribution of bedload to the total sediment load was determined to be insignificant for sampled flows. In general, streamflows for long-term streamgages in the study area were below normal for the majority of the study period; however, flows during the last 3 months of the study period were above normal, including the extreme events during the last week of the study period.

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 34.98725337980076° to 35.954664894270834° latitude; -84.32281494140625° to -82.81768798828125° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Brad A. Huffman, William F. Hazell, Carolyn J. Oblinger. 2017-09-06. Characterization of sediment transport upstream and downstream from Lake Emory on the Little Tennessee River near Franklin, North Carolina, 2014–15. https://doi.org/10.3133/sir20175081

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

KEEP EXPLORING

Related USGS reports

USGS2026

Karst hydrologic processes in carbonate fensters of the western Great Smoky Mountains, Tennessee, 2017–18

The geology of the Great Smoky Mountains in Tennessee and North Carolina is dominated by metamorphic strata. Along the western extent of the range, however, several carbonate fensters expose the Ordovician Jonesboro Limestone along the western edge of the Great Smoky Fault. The fensters (Cades Cove, Tuckaleechee Cove, and Wear Cove) create opportunities for allogenic recharge at the contact between the surrounding, relatively insoluble, strata and the underlying soluble formations, resulting in the development of karst systems. Karst environments have a high degree of connectivity between surface streams and groundwater systems; thus, karst aquifers are inherently vulnerable to contamination from surface activities. Karst environments also provide a specific environment for sensitive biota. Protection of the unique resources of karst environments requires an understanding of the behavior of karst aquifers, knowledge of karst recharge areas, and the mapping of groundwater flow paths. A study began in 2017 to characterize the hydrologic behavior of the karst systems in Great Smoky Mountains National Park through hydrologic monitoring, dye tracing, and seepage investigations. Hydrologic monitoring equipment was installed at several karst hydrogeologic features: two in-cave sites, two major springs, Abrams Creek in Cades Cove, and a karst depression wetland. Dye tracing was conducted to determine resurgences for the cave systems, to delineate spring recharge areas, and to estimate karst groundwater velocities. Seepage investigations along Abrams Creek helped determine if streams were sinking into the Jonesboro Limestone, either at the contact with insoluble strata or as streams flowed across the carbonate fenster. Study results indicate considerable differences in groundwater behavior between the conduit-dominated flow systems in Tuckaleechee Cove compared to the lower-gradient, colluvium-covered karst systems in Cades Cove. Study results also indicate that contaminants in the area of Bull sink and Whiteoak Sink could be rapidly transported through karst groundwater towards springs in Tuckaleechee Cove. Karst resources in Cades Cove are most likely to be affected by local events or activities taking place within the cove itself or in upper Abrams Creek upstream of the Great Smoky Fault. This study provides information on the vulnerability and uniqueness of the karst systems in the western Great Smoky Mountains, which are characterized by complex geology, high-gradient subterranean streams, deep caves, and carbonate fensters.

Tennessee↗

USGS2026

Analysis of groundwater geochemistry in the Lansing, Michigan, area

Groundwater resources in the Lansing, Michigan, area are protected by a wellhead protection plan, but previous studies have indicated that groundwater in this area may be susceptible to surface contamination. The U.S. Geological Survey, in cooperation with the Lansing Board of Water & Light, analyzed geochemical and aquifer characteristics to assess the potential for contamination of groundwater resources. Recharge age categories were determined from tritium concentrations for some wells, and by using ternary diagrams and principal component analysis for other wells. These age classifications, combined with hydrogeologic and geochemical data, were analyzed by using statistical methods to identify trends that may indicate areas of increased groundwater susceptibility. Wells with modern recharge tended to have higher sodium, chloride, and chloride:bromide ratios than did wells with mixed recharge, suggesting greater surface connectivity and potential influence from anthropogenic sources such as road salt. In contrast, wells in the mixed age category exhibited more stable geochemistry and lower constituent concentrations. Classification and regression tree models identified aquifer geometry—particularly the thickness of the uppermost bedrock unit and presence of confining layers—as key predictors of recharge age and geochemical indicators. The trends identified from these analyses could help to reveal locations in the Lansing area that are among the most susceptible to contamination.

Michigan↗

USGS2026

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.

California, Idaho, Nevada, Oregon, Utah↗