The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, by 7 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the sixth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. Prior reports in this series documented data collected from October 2015 to September 2020. This report contains information pertinent to data collection during the 2021 water year, from October 2020 to September 2021. There were no modifications this year to the previously installed monitoring network. Data at each station were compared for the length of the project and on a yearly basis to show the annual variability of discharge and salinity in the project area.
Discharge and salinity varied widely during the 2021 water year data collection period, which included above-average rainfall for four of the five counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 6 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Clapboard Creek, Ortega River, Pottsburg Creek at U.S. 90, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, Cedar River, and Broward River, whose annual mean discharge was lowest. Annual mean discharge at 7 of the 10 tributary monitoring sites was higher for the 2021 water year than for the 2020 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 6 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2021 water year than for the 2020 water year and ranked second among the annual totals computed for the 6 years considered for this study.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2020 water year, annual mean salinity at all monitoring locations was lower for the 2021 water year except at the tributary site of Durbin Creek, which remained the same. The 2021 annual mean salinity at all sites ranked second lowest since the beginning of the study in 2016 except at Julington Creek and Racy Point, which tied for lowest, and Durbin Creek, which had the same value for each year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221111","issn":"ISSN 2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2023, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021: U.S. Geological Survey Open-File Report 2022–1111, 48 p., https://doi.org/10.3133/ofr20221111.","productDescription":"Report: x, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-139675","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":413532,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221111/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412288,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.XML","linkFileType":{"id":8,"text":"xml"}},{"id":412285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1111/coverthb.jpg"},{"id":412286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":412287,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1111/images"},{"id":412289,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.31115628870195,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 30.583300030597925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
A three-dimensional numerical model of groundwater flow was developed and calibrated for the unconsolidated New Jersey Coastal Plain aquifers underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, to evaluate groundwater flow pathways of per- and polyfluoroalkyl substances (PFAS) contamination associated with use of aqueous film forming foam (AFFF) at the base. The regional subsurface flow model spans an area of approximately 518 square miles around JBMDL and is based on a previously developed hydrogeologic framework of the area. Steady-state flow in the unconsolidated aquifers was simulated using the MODFLOW 6 groundwater flow model, which is able to account for hydrostratigraphic pinchouts and discontinuities in the Coastal Plain aquifers underlying JBMDL. To account for local patterns of fluid flow driving advective subsurface migration of PFAS, the grid was refined using quadtree meshes spanning 21 areas where historical AFFF use was identified, five off-site reconnaissance areas identified by AFCEC as areas in which the occurrence of PFAS is most likely to pose a potential danger to local drinking water supplies, and along streams that behave as drains in the base-flow-dominated Coastal Plain.
Following grid refinement, four physical processes known to govern subsurface flow were introduced to the model. These included effective precipitation recharge, discharge to streams and stream-connected wetlands, regional inflows and outflows along the model bottom, and withdrawals from wells, each of which were incorporated into the model as either external or internal boundary conditions. To account for effective precipitation recharge, a specified-flow boundary was assigned along the top of the model. Similarly, regional flows predicted using the modified U.S Geological Survey’s New Jersey Coastal Plain Regional Aquifer System Analysis model were treated as specified-flow boundary conditions along the bottom of the model. Base-flow losses were treated as drains along streams delineated using a 10-foot LiDAR dataset. Drains were also assigned to cells falling within stream-connected National Hydrologic Database wetlands. Finally, well-pumpage data mined from the New Jersey Water Transfer database were added to the model to account for extraction of groundwater through pumping from industrial-supply and drinking-water-supply wells. Along model edges established at groundwater divides, where the net flux of water across the boundary is equal to zero, natural no-flow boundary conditions were imposed.
The refined flow model was calibrated using the parameter-estimation (PEST) program, which adjusts model parameters by performing a gradient search over the sum-of-squared-error objective function until the parameter set that produces simulated water levels and base flows most closely matches 544 water levels and 20 estimated base flows and closely adheres to initial parameter estimates. Based on the analysis of calibration residuals, the model did not appear to be affected by significant model structural error.
The MODPATH particle-tracking algorithm was used to estimate advective transport paths of PFAS in the vicinity of JBMDL. Forward tracking was used to determine paths of PFAS away from AFFF source areas to streams, wetlands, pumping wells, and geographic areas that PFAS may contaminate. Additionally, reverse tracking was used to determine particle pathlines away from off-site PFAS reconnaissance areas, or areas within which all sources of PFAS might be advectively transported into subsurface drinking-water supplies, to locations at land surface that may indicate a source of PFAS.
The coupled and calibrated groundwater flow and particle-tracking transport model provide valuable tools for predicting the relative extent of PFAS contamination from onsite legacy source areas. The calibrated model also provides measures of water-level and base-flow observation influence that can help guide future data-collection efforts related to groundwater and surface water sampling for PFAS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221112","collaboration":"Prepared in cooperation with the U.S. Air Force","usgsCitation":"Fiore, A.R., and Colarullo, S.J., 2023, Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018: U.S. Geological Survey Open-File Report 2022–1112, 41 p., 2 pls., https://doi.org/10.3133/ofr20221112.","productDescription":"Report: ix, 41 p.; 2 Plates: 35.00 x 45.00 inches and 45.00 x 30.00 inches; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129806","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":412124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK4CZS","text":"USGS data release","linkHelpText":"MODFLOW6 and MODPATH7 used to simulate regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412125,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.XML"},{"id":412123,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1112"},{"id":412121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1112/coverthb.jpg"},{"id":412126,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1112/images/"},{"id":412129,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate1.pdf","text":"Plate 1","size":"212 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 1 to 15 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 4 and 14, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.pdf","text":"Report","size":"7.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1112"},{"id":412130,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate2.pdf","text":"Plate 2","size":"200 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 16 to 21 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 16 to 19, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":499723,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114286.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.77016941849112,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 40.156458843115274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
In 2021, the U.S. Geological Survey will collect water-quality samples and environmental data from 3 sites in Koocanusa Reservoir and from 1 site in the Kootenai River. The transboundary Koocanusa Reservoir is in southeastern British Columbia, Canada, and northwestern Montana, United States, and was formed with the construction of Libby Dam on the Kootenai River 26 kilometers upstream from Libby, Montana. Two of the reservoir sites and the Kootenai River site, in the Libby Dam tailwater (the outflow of the reservoir flow into the Kootenai River), are equipped with automated, high-frequency ServoSipper water samplers. At the two reservoir sites, these samplers are mounted to pontoon platforms and automatically collect samples from multiple depths; a ServoSipper sampler was deployed at one site in 2019, and another ServoSipper sampler will be deployed at a second site in 2021. Discrete water-quality samples will be collected monthly at two depths at the river site and at two of the reservoir sites. The goal of this project is to collect multidepth, high-frequency vertical and temporal water-quality samples and data to understand the limnological and biological processes that control variations and trends in selenium concentrations and loads throughout Koocanusa Reservoir and in the Libby Dam tailwater at the southern end of the reservoir. This sampling and analysis plan documents the organization, sampling and data-collection scheme and design, pre- and post-collection processes, and quality-assurance and quality-control procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221113","usgsCitation":"Caldwell Eldridge, S.L., Schaar, M.A., Reese, C.B., Bussell, A.M., and Chapin, T., 2023, Sampling and analysis plan for the Koocanusa Reservoir and upper Kootenai River, Montana, water-quality monitoring program, 2021: U.S. Geological Survey Open-File Report 2022–1113, 32 p., https://doi.org/10.3133/ofr20221113.","productDescription":"ix, 32 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-137190","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412312,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.XML"},{"id":412310,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1113/coverthb.jpg"},{"id":412311,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1113"},{"id":412313,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1113/images"},{"id":412323,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221113/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Koocanusa Reservoir, Upper Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10472590374278,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 49.02558777092872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, collected borehole geophysical data and completed simple aquifer tests to estimate the thickness and hydraulic properties of surficial deposits. The purpose of data collection was to create generalized contour maps of Pierre Shale elevation and surficial deposit thickness within and near Ellsworth Air Force Base (study area). Natural gamma and electromagnetic induction data were collected to refine or determine surficial deposit thickness at selected wells. Additionally, data from previous geophysical studies and driller logs were compiled and combined with results from natural gamma and electromagnetic induction data to provide a more spatially complete image of the subsurface. Borehole nuclear magnetic resonance (bNMR) data were collected to estimate hydraulic conductivity and water content of surficial deposits overlying Pierre Shale. Simple aquifer tests using water slugs (slug tests) were completed to estimate hydraulic conductivity of surficial deposits, and results were compared to hydraulic conductivity estimates from bNMR data. All data used to construct maps and estimate hydraulic properties are provided in an accompanying U.S. Geological Survey data release (available at https://doi.org/10.5066/P9FLR79F).
Generalized contour maps were constructed using results from 26 borehole geophysical logs, 35 geophysical transects from previous studies, and 304 wells with driller logs. Pierre Shale elevation generally followed land-surface topography, sloping from high elevations in the north to lower elevations in the south. Topographic highs of Pierre Shale, where present, could act as groundwater divides that potentially affect groundwater flow direction. Surficial deposit thickness varied spatially and ranged from 0 to 86 feet. Surficial deposits generally were thickest in higher elevation areas near ephemeral streams in the northern part of the study area. Hydraulic conductivity estimated from bNMR results using two analytical methods ranged from 0.1 to 2,314 feet per day, whereas hydraulic conductivity estimated from slug tests ranged from 0.001 to 193 feet per day. Hydraulic conductivity estimates from slug tests were plotted with surficial deposit thickness contours instead of bNMR estimates because bNMR estimates were determined to overestimate hydraulic conductivity. Hydraulic conductivity values generally were greater in the southwestern part of study area than the northeastern part.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3502","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., Eldridge, W.G., Anderson, T.M., and Phillips, S.N., 2023, Maps of elevation of top of Pierre Shale and surficial deposit thickness with hydraulic properties from borehole geophysics and aquifers tests within and near Ellsworth Air Force Base, South Dakota, 2020–21: U.S. Geological Survey Scientific Investigations Map 3502, 25-p. pamphlet, 2 sheets, https://doi.org/10.3133/sim3502.","productDescription":"Report: vii, 25 p.; 2 Sheets: 36.00 x 36.00 inches; 2 Data Releases; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138395","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":500208,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114281.htm","linkFileType":{"id":5,"text":"html"}},{"id":412159,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3502/images"},{"id":412158,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3502/sim3502.XML"},{"id":412155,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3502/sim3502.pdf","text":"Pamphlet","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502"},{"id":412250,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3502/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":412161,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":412157,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3502/sim3502_sheet02.pdf","text":"Sheet 2","size":"7.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502, sheet 2","linkHelpText":"—Map showing contours of depth to Pierre Shale, hydraulic conductivity, and groundwater velocity of surficial deposits"},{"id":412156,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3502/sim3502_sheet01.pdf","text":"Sheet 1","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502, sheet 1","linkHelpText":"—Map showing elevation contours of the top of Pierre Shale from well logs and electrical resistivity tomography data"},{"id":412162,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412154,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3502/coverthb.jpg"},{"id":412160,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FLR79F","text":"USGS data release","linkHelpText":"Datasets used to create maps of Pierre Shale elevation and surficial deposit thickness within and near Ellsworth Air Force Base, South Dakota, 2021"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.17044722999105,\n 44.19638858668296\n ],\n [\n -103.17044722999105,\n 44.0880305228894\n ],\n [\n -103.00984038772282,\n 44.0880305228894\n ],\n [\n -103.00984038772282,\n 44.19638858668296\n ],\n [\n -103.17044722999105,\n 44.19638858668296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
As a part of the Texas Water Development Board groundwater availability modeling program, the U.S. Geological Survey developed the Gulf Coast Land Subsidence and Groundwater-Flow model (hereinafter, the “GULF model”) and ensemble to simulate groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system (the study area) in Texas from predevelopment (1897) through 2018. Since the publication of a previous groundwater model for the greater Houston area in 2012, there have been changes to the distribution of groundwater withdrawals and advances in modeling tools. To reflect these changes and to simulate more recent conditions, the GULF model was developed in cooperation with the Harris-Galveston and Fort Bend Subsidence Districts to provide an updated Groundwater Availability Model.
Since the early 1900s, most of the groundwater withdrawals in the study area have been from three of the hydrogeologic units that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers and, more recently, from the Catahoula confining unit. Withdrawals from these hydrogeologic units are used for municipal supply, commercial and industrial use, and irrigation purposes. Withdrawals of large quantities of groundwater in the greater Houston area have caused widespread groundwater-level declines in the Chicot, Evangeline, and Jasper aquifers of more than 300 feet (ft). Early development of the aquifer system, which began before 1900, resulted in nearly 50 percent of the eventual historical groundwater-level minimums having been reached as early as 1946 in some areas. These groundwater-level declines led to more than 9 ft of land-surface subsidence—historically in central and southeastern Harris County and Galveston County, but more recently in northern, northwestern, and western Harris County, Montgomery County, and northern Fort Bend County—from depressurization and compaction of clay and silt layers interbedded in the aquifer sediments.
In a generalized conceptual model of the Gulf Coast aquifer system, water enters the groundwater system in topographically high outcrops of the hydrogeologic units in the northwestern part of the aquifer system. Groundwater that does not discharge to streams flows to intermediate and deep zones of the aquifer system southeastward of the outcrop areas where it is discharged by wells and by upward leakage in topographically low areas near the coast. The uppermost parts of the aquifer system, which include outcrop areas, are under water-table (unconfined) conditions where the groundwater is not confined under pressure. As depth increases in the aquifer system and interbedded clay and silt layers accumulate, water-table conditions evolve into confined conditions where the groundwater is under pressure.
Groundwater flow and land-surface subsidence in the GULF model and ensemble were simulated by using MODFLOW 6 with the Skeletal Storage, Compaction, and Subsidence package. The model consists of six layers, one for each of the five hydrogeologic units in the northern part of the Gulf Coast aquifer system and a surficial top layer that includes part of each hydrogeologic unit. Transient groundwater flow was simulated during 1897–2018 by using a combination of multiyear, annual, and monthly stress periods. An initial steady-state stress period was configured to represent predevelopment mean annual inflows and outflows. The subsidence package used in the GULF model and ensemble uses a head-based subsidence formulation that simulates the delayed drainage response from clay and silt sediment to changes in groundwater levels.
The GULF model and ensemble were history matched to groundwater-level observations at selected wells, land-surface subsidence at benchmarks, aquifer compaction at borehole extensometers, and vertical displacement from Global Positioning System stations. A Bayesian framework was used to represent uncertainty in modeled parameters and simulated outputs of interest. History matching and uncertainty quantification were performed by using a Monte Carlo approach enabled through iterative ensemble smoother software to produce an ensemble of models fit to historical data. The iterative ensemble smoother substantially reduced the computational demand of parameter estimation by approximating the first-order relation between model inputs and outputs, thereby allowing 183,207 adjustable parameters to be used for history matching at a relatively low computational and time cost.
The history-matched parameter values are within the ranges of previously published values and agree with the current understanding of the spatial and temporal patterns of parameter uncertainty for the Gulf Coast aquifer system. A good agreement between the observed (or estimated) and simulated groundwater levels, land-surface subsidence, compaction, and vertical displacement was obtained across the modeled area based on qualitative and quantitative comparisons. Ensemble mean annual groundwater-flow rates to the Chicot, Evangeline, Jasper aquifers and Catahoula confining unit were 0.0–0.49 inch (in.), 0.09–0.33 in., 0.01–0.07 in., and 0.01–0.05 in., respectively. GULF model mean annual groundwater-flow rates to the Chicot, Evangeline, and Jasper aquifers and Catahoula confining unit were 0.31 in., 0.19 in., 0.03 in., and 0.03 in., respectively.
The GULF-model-simulated recharge to the outcrop area was the largest inflow (75 percent), and recharge to other areas was 25 percent of the model inflow. The simulated outflows included (1) net surface-water/groundwater exchange with study area streams (50 percent), (2) groundwater use (49 percent), and (3) net surface-water/groundwater exchange with the Gulf of Mexico (1 percent). The sum of the simulated values of the outflows (1,041,973 acre-feet per year [acre-ft/yr]) and the elastic expansion of the fine-grained sediment and numerical solver error (339 acre-ft/yr) minus the inflows (654,172 acre-ft/yr) represents the reduction of storage from the Gulf Coast aquifer system (388,140 acre-ft/yr). Most of the storage depletion is caused by the long-term groundwater-level declines that have resulted primarily in inelastic compaction.
The GULF model was used to estimate Jasper aquifer compaction at selected benchmarks in Montgomery County and northern Harris County, which are the primary locations of Jasper aquifer groundwater use. Simulated Jasper aquifer compaction in northern Harris County was between 0.2 and 0.5 ft, or between about 5 and 16 percent of simulated subsidence at the benchmark locations. Simulated Jasper aquifer compaction in Montgomery County was between 0.8 and 1.2 ft, or between about 33 and 57 percent of simulated subsidence at the benchmark locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1877","issn":"ISSN 2330-7102","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District and the Fort Bend Subsidence District","usgsCitation":"Ellis, J.H., Knight, J.E., White, J.T., Sneed, M., Hughes, J.D., Ramage, J.K., Braun, C.L., Teeple, A., Foster, L., Rendon, S.H., and Brandt, J., 2023, Hydrogeology, land-surface subsidence, and documentation of the Gulf Coast Land Subsidence and Groundwater-Flow (GULF) model, southeast Texas, 1897–2018 (ver. 1.1, November 2023): U.S. Geological Survey Professional Paper 1877, 425 p., https://doi.org/10.3133/pp1877.","productDescription":"Report: xx, 425 p., 8 Appendixes; Data Release","numberOfPages":"450","onlineOnly":"Y","ipdsId":"IP-127938","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":500160,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114230.htm","linkFileType":{"id":5,"text":"html"}},{"id":422702,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/pp1877/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":411889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM8A1P","text":"USGS Data Release","linkHelpText":"MODFLOW 6 model and ensemble used in the simulation of groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system, 1897–2018"},{"id":422705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/pp1877/coverthb2.jpg"},{"id":411888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/pp1877/pp1877.pdf","text":"Report","size":"184 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.54245107965883,\n 31.199747848944256\n ],\n [\n -96.33297842340923,\n 30.997489619299927\n ],\n [\n -96.79440420465926,\n 30.136679255787612\n ],\n [\n -96.02536123590899,\n 28.551820525825022\n ],\n [\n -95.36068838434645,\n 28.86498475853952\n ],\n [\n -94.72348135309639,\n 29.28746086219381\n ],\n [\n -94.65207022028405,\n 29.402380282489133\n ],\n [\n -94.23458975153387,\n 29.574516044800063\n ],\n [\n -93.82809561090883,\n 29.670020494605353\n ],\n [\n -93.89401357965892,\n 29.803574466610613\n ],\n [\n -93.6907665093464,\n 30.05113045792723\n ],\n [\n -93.67428701715903,\n 30.307554456695556\n ],\n [\n -93.6687938530968,\n 30.563309394138372\n ],\n [\n -93.49301260309677,\n 30.841976559030968\n ],\n [\n -93.4765331109094,\n 31.077503645282718\n ],\n [\n -93.54245107965883,\n 31.199747848944256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 13, 2023; Version 1.1: November 28, 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Snow Lake, a glacially dammed lake on the Snow Glacier near Seward, Alaska, drains rapidly every 14 months–3 years, causing flooding along the Snow River. Highway, railroad, and utility infrastructure on the lower Snow River floodplain is vulnerable to flood damage. Historical hydrology, geomorphology, and two-dimensional hydraulic and sediment transport modeling were used to assess the flood risks from Snow Lake outburst floods. Floods have become more frequent, peaked more rapidly, and have had generally higher peaks over the last 20 years as the Snow Glacier has thinned, translating to a greater potential for flood damage. Rapidly shifting channel locations and the occasional introduction of large volumes of debris to the river also threaten infrastructure on the floodplain and in the channel. An assessment of the historical channel planform between 1951 and 2019 showed that there have been more and less stable segments along the lower Snow River and that channel migration has generally been toward the east. An analysis of floodplain elevations using 2008 light detection and ranging (lidar) showed that the main channel is relatively high compared to floodplain channels that carry floodwaters along the railroad grade, so that once the main channel banks are overtopped water rapidly disperses throughout the floodplain. A two-dimensional flow and sediment transport model was developed, and its simulation results were compared to three past outburst floods from 2007, 2017, and 2019. Despite the complex floodplain and channel geometry, coarse resolution of the mesh, and sediment input data, the model successfully simulated areas of observed scour along the railroad grade and at the guidebank to the highway bridge. The modeled water-surface elevations generally replicated peak elevations recorded at a streamgage in the middle of the model domain and at pressure transducers installed on the floodplain and main channel, although there were discrepancies on the rising limb and some locations had a poorer fit than others. A model of a hypothetical check flood, approximately 150 percent of the largest recorded outburst flood, was developed to provide hydraulic variables to use when planning for infrastructure upgrades.
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
This guide is intended to assist with characterizing injury to freshwater benthic macroinvertebrates (BMIs) in Natural Resource Damage Assessment and Restoration (NRDAR) cases. The contents are narrowly focused on insects, crustaceans, snails, and other invertebrate fauna that are typically considered part of BMI communities and are not intended to address studies of injury to larger benthic taxa such as freshwater mussels, crayfish, or benthic fishes or amphibians. Although some percentage of the community functions as predators, BMIs are predominantly primary consumers (for example, scrapers, shredders, and filterer/gatherer feeding groups) that play an essential role in converting carbon and nitrogen from plant tissues into animal biomass for higher-order consumers, especially in flowing waters. Aquatic contaminants can disrupt the quantity and quality of energy transferred (ecosystem function) by reducing invertebrate biomass and diversity. Additionally, the accumulation of toxic residues in invertebrate tissues may be a source of exposure leading to adverse effects in higher trophic levels. The goal of NRDAR BMI assessments is to establish direct linkages of contaminant exposure to injuries reflected by changes in community structure (for example, reduced density and taxa richness) or by effects at the individual population level (for example, survival, growth, and reproduction). BMIs are infrequently the U.S. Department of Interior (DOI)-managed resource in a NRDAR case, with managed resources more frequently including migratory birds, fish, or other insectivorous vertebrates. Therefore, it is critical to have clearly defined objectives for evaluating BMIs and an understanding of how invertebrate data relate to the quantification of injuries to the DOI-managed resource. This guide is intended to assist decisions on whether or not to proceed with BMI studies, use of existing information and data for screening purposes, and what types of studies can support a BMI-injury determination. This document is intended to provide general considerations and best practices for assessing BMIs. Relevant guidance and references are listed throughout the report as sources for specific methods and analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221110","usgsCitation":"Soucek, D.J., Farag, A.M., Besser, J.M., and Steevens, J.A., 2023, Guide for benthic invertebrate studies in support of Natural Resource Damage Assessment and Restoration: U.S. Geological Survey Open-File Report 2022–1110, 11 p., https://doi.org/10.3133/ofr20221110.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-139162","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":411372,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221110/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411347,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1110/coverthb.jpg"},{"id":411348,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1110/ofr20221110.pdf","text":"Report","size":"1.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1110"},{"id":411349,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1110/ofr20221110.XML"},{"id":411350,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1110/images"}],"contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Natural Trap Cave, located in the Big Horn Mountains of north-central Wyoming, has a history of trapping and preserving a range of North American fauna that plummeted into the deep vertical entrance. These animal remains were buried and preserved within sediments of the main chamber and, in turn, have helped elucidate the procession of faunal dynamics during the latest glacial cycle. The cave location, south of the Laurentide and Cordilleran Ice Sheets, and proximal to Yellowstone, is at an ideal geographical juncture to provide insights to ecological changes in North America. The sediments that the animals are buried in inform us about transport and deposition both inside and outside of the cave that relate to catchment dynamics. We report on a series of optically stimulated luminescence (OSL) ages derived from samples obtained within the cave during excavation work in 2014 and in 2018. We also examine chronology produced by argon, tephrochronology, fission track, and luminescence techniques that have been used for understanding the infilling of the cave. The cave sediment ages and in situ measured gamma spectroscopy as measured in this study helped resolve an improved chronological age model when combined with previous data.
The suite of OSL ages is interpreted through the stratigraphic relationships (and vertebrates contained within) which requires the use of an adequate age model; we use either the central age model or minimum age model where appropriate and with justification. Lowest sediments are dated to ∼150 ka with a hiatus at ∼130 to 52 ka. Above this, sediment deposition and entrainment of paleontological materials are representative of Pleistocene and early Holocene times, between 37 ± 6 ka and 7.6 ± 0.5 ka. The stratigraphic architecture suggests that deposition of materials into the cave is episodic and rapid, followed by quiescent periods where hydrologic scour, heavy overland flow, or possibly a cryo-hydrologic process may have altered unit relationships. Thus, the complementary geochronometers and the characteristics of quartz versus feldspar luminescence signals improve temporal interpretations of these complex deposits. This adapted understanding of mixing also sets the stage for future work with the aim to improve our understanding of ages and sources for ash units within these cave deposits. The three ash units recognized in the cave may represent an in-situ reworking of the same ash or may be representative of previously undocumented eruptions from the Yellowstone Caldera.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quaint.2022.01.005","usgsCitation":"Mahan, S.A., Wood, J.R., Lovelace, D.M., Laden, J., McGuire, J., and Meachen, J., 2023, Luminescence ages and new interpretations of the timing and deposition of Quaternary sediments at Natural Trap Cave, Wyoming: Quaternary International, v. 647-648, p. 22-35, https://doi.org/10.1016/j.quaint.2022.01.005.","productDescription":"14 p.","startPage":"22","endPage":"35","ipdsId":"IP-130446","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":445542,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quaint.2022.01.005","text":"Publisher Index Page"},{"id":435584,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K8OYLG","text":"USGS data release","linkHelpText":"Data Release for Luminescence: Luminescence data for Natural Trap Cave, Wyoming"},{"id":407047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Natural Trap Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.27438354492188,\n 44.79158175909386\n ],\n [\n -107.92282104492188,\n 44.79158175909386\n ],\n [\n -107.92282104492188,\n 45.00219463609633\n ],\n [\n -108.27438354492188,\n 45.00219463609633\n ],\n [\n -108.27438354492188,\n 44.79158175909386\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"647-648","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":852335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, John R.","contributorId":265642,"corporation":false,"usgs":false,"family":"Wood","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":852336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovelace, Dave M 0000-0002-0154-4777","orcid":"https://orcid.org/0000-0002-0154-4777","contributorId":296740,"corporation":false,"usgs":false,"family":"Lovelace","given":"Dave","email":"","middleInitial":"M","affiliations":[{"id":64159,"text":"University of Wisconsin-Madison, Dept. of Geoscience","active":true,"usgs":false}],"preferred":false,"id":852337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laden, Juan","contributorId":296741,"corporation":false,"usgs":false,"family":"Laden","given":"Juan","email":"","affiliations":[],"preferred":false,"id":852338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, Jenny","contributorId":269803,"corporation":false,"usgs":false,"family":"McGuire","given":"Jenny","email":"","affiliations":[{"id":56035,"text":"GA Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":852339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meachen, Julie 0000-0002-2526-2045","orcid":"https://orcid.org/0000-0002-2526-2045","contributorId":296742,"corporation":false,"usgs":false,"family":"Meachen","given":"Julie","email":"","affiliations":[{"id":64161,"text":"Des Moines University","active":true,"usgs":false}],"preferred":false,"id":852340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239066,"text":"sir20225123 - 2022 - Estimated effects of pumping on groundwater storage and Walker River stream efficiencies in Smith and Mason Valleys, west-central Nevada","interactions":[],"lastModifiedDate":"2022-12-28T13:01:23.048714","indexId":"sir20225123","displayToPublicDate":"2022-12-27T07:56:08","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5123","displayTitle":"Estimated Effects of Pumping on Groundwater Storage and Walker River Stream Efficiencies in Smith and Mason Valleys, West-Central Nevada","title":"Estimated effects of pumping on groundwater storage and Walker River stream efficiencies in Smith and Mason Valleys, west-central Nevada","docAbstract":"The Walker River originates in the Sierra Nevada Mountains and flows nearly 160 miles to its terminus at Walker Lake in west-central Nevada. The river provides a source of irrigation water for tens of thousands of acres of agricultural lands in California and Nevada and is the principal source of inflow to Walker Lake. Extraction of groundwater for agricultural use became prevalent in the late 1950s and early 1960s to supplement irrigation demands not met by surface-water diversions during times of drought. There is growing concern that continued groundwater withdrawals within the Walker River Basin are likely contributing to depleted streamflow of the Walker River and the long-term depletion of groundwater storage in the basin. This report documents changes in groundwater storage-volume and trends in Walker River stream efficiency, a measure of change in flow due to gaining or losing conditions, in the two largest agricultural valleys in the Walker River Basin, Smith and Mason Valleys, for a multi-decade period. Groundwater-level maps from previous studies were used for the beginning (1970) and middle (2006) points of this study. Groundwater levels measured from 1991–95 and 2016–20 were used to construct median groundwater-level maps that represented conditions in 1995 and 2020. Valley wide groundwater-level change was calculated by comparing groundwater-level maps for the periods 1970–95, 1996–2006, and 2007–20 and by observing the overall change from 1970 to 2020. Groundwater storage-volume change was calculated using groundwater-level change and previously defined specific yield values. Between 1970 and 2020, groundwater storage-volume declined 287,600 acre-feet in Smith Valley and 269,000 acre-feet in Mason Valley. Using groundwater storage-volume decline and annual groundwater pumpage rates, a maximum groundwater pumpage rate can be computed to support management of water resources. In Smith Valley, groundwater pumping in excess of 22,300 acre-feet per year would likely result in groundwater storage decline. In Mason Valley, groundwater pumping in excess of 75,200 acre-feet per year would likely result in groundwater storage decline. Stream efficiency was calculated using continuous streamflow data and monthly diversion volumes on two reaches: (1) the West Walker River in Smith Valley, from 1948 to 2020 and (2) the Walker River in Mason Valley, from 1958 to 2020. Stream efficiency during non-irrigation season in Smith and Mason Valleys declined at a statistically significant rate of 1.1 and 0.6 percent per year, respectively. Trends in stream efficiency corresponded to occurrence of prolonged drought, deviation from average annual streamflows, and total groundwater pumpage. Long-term declines in groundwater storage-volume and stream efficiency demonstrate that the alluvial aquifer system is becoming increasingly depleted, such that the river can no longer replenish groundwater storage while simultaneously balancing groundwater and surface-water withdrawals. The introduction of supplemental groundwater pumpage was intended to offset surface-water deficits during dry years; however, pumpage occurs even in years when average or above average streamflows meet surface-water demands. Reliance on supplemental groundwater pumpage has resulted in widespread groundwater storage-volume decline and decreased stream efficiency. With each successive drought cycle, the ability of Walker River to sustain streamflows and convey water downstream has diminished. Above average wet periods have a marginal and short-lived effect on rebounding the groundwater levels outside of the river corridor. Moreover, if the trend continues, each future drought cycle may further reduce groundwater supplies and that may further decrease streamflow reliability.
Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
Multi-dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two-dimensional (2D) and three-dimensional (3D) hydrodynamic models for a 1.6-km reach of a large gravel-bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth-averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near-bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high-resolution bathymetry used as model input. Model predictions of winter-run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear-flowing, relatively shallow large rivers with sufficient accuracy to support multi-dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.
Urbanization can substantially alter sediment and nutrient loadings to streams. Although a growing body of literature has documented these processes, conditions may vary widely by region and physiographic province (PP). Substantial investments are made by localities to meet federal, state, and local water-quality goals and locally relevant monitoring data are needed to appropriately set standards and track progress. In 2016, a long-term stormwater monitoring program was initiated to characterize water-quality and streamflow conditions and compute average annual nutrient- and sediment-loading rates across the three dominant land-use types—commercial (COM), high-density residential, and single-family residential (SFR)—in the Hampton Roads metropolitan region within the Coastal Plain PP in southeastern Virginia. This report summarizes the first five years of data collection to (1) assess patterns in streamflow and water chemistry across the three major land-use types in the region; (2) compute annual sediment and nutrient loads; and (3) compare annual loading rates to those in other urbanized regions.
Patterns in watershed hydrology characteristics and conditions were similar to those observed in other urban monitoring studies. Base-flow indices were lower and stream flashiness indices were higher in the study watersheds compared to those in less developed reference watersheds. These patterns reflect a decrease in infiltration and consequent increase in storm runoff as a result of urbanization. Stream flashiness was strongly positively related to degree of impervious land cover and negatively to watershed area. Hydrologic metrics varied across the land-use gradient, reflecting greater and more rapid runoff in the COM watersheds than in SFR watersheds. Event-based analyses conducted exclusively on periods of runoff highlight longer duration events, longer time-to-peak streamflow, and a longer lag between peak precipitation and peak streamflow in SFR watersheds, and higher stormflow yields, runoff ratios, and peak flows in COM watersheds. Event-based metrics varied seasonally because of regional meteorological patterns.
Concentrations of total suspended solids (TSS) and total phosphorus (TP) were positively correlated to streamflow, whereas concentrations of total nitrogen (TN) varied little across the hydrologic regime. Phosphorus composition varied spatially and seasonally—the proportion of orthophosphate (PO43-) was highest in samples collected from stations draining residential land-use types and was elevated in summer and fall. Nitrogen composition varied with hydrologic condition: nitrate plus nitrite (NO3-) dominance during base flow shifted to total organic nitrogen (TON) dominance during periods of runoff. For all three major constituents (TSS, TP, and TN), concentrations were highest in SFR watersheds, whereas yields were greatest in COM watersheds. This seeming contradiction in concentration and yield across land-use types occurred because of spatial differences in streamflow yield.
The network average TSS yield in Hampton Roads was lower than that in comparable networks in Fairfax County, Virginia, and Gwinnett County, Georgia, a difference that may reflect dissimilarities in the topographic and soil characteristics of the Coastal Plain versus those in Piedmont PPs, as well as differences in engineered concrete stormwater conveyances versus earthen streams. The average annual TP yield in Hampton Roads was higher than averages reported in comparison studies and was primarily driven by elevated PO43-. Elevated PO43- yields may be related to unique soil and geological features of the Coastal Plain PP that limit phosphorus retention. Total nitrogen yields in the Hampton Roads and Fairfax County networks were similar; however, composition did vary, with greater total organic nitrogen yields in Hampton Roads and greater NO3- yields in Fairfax County.
Cross-correlation analyses and mass-volume curves were used to assess the timing of sediment and nutrient loadings. The majority of TSS and TP was typically transported during the initial phase of a storm-runoff event, a phenomenon commonly termed the “first flush.” Although TN concentrations typically peaked within an hour of peak streamflow, reflecting the particulate dominance of TN during stormflows, and loadings were greater during the early phase of most storm events, the stricter first-flush criterion was rarely met. This suggests that the most abundant sources of TN in these watersheds are not as directly connected to the stormwater-conveyance system as are TSS and TP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225111","isbn":"978-1-4113-4488-4","collaboration":"Prepared in cooperation with the Hampton Roads Planning District Commission","usgsCitation":"Porter, A.J., 2022, Stormwater quantity and quality in selected urban watersheds in Hampton Roads, Virginia, 2016–2020: U.S. Geological Survey Scientific Investigations Report 2022–5111, 77 p., https://doi.org/10.3133/sir20225111.","productDescription":"Report: xi, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140434","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":409552,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225111/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5111"},{"id":409543,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XMPEND","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess stormwater quality and quantity in twelve urban watersheds in Hampton Roads, Virginia, 2016–2020"},{"id":409542,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5111/images/"},{"id":409539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5111"},{"id":409541,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.XML"},{"id":409538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5111/coverthb.jpg"}],"country":"United States","state":"Virginia","city":"Hampton Roads","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.14335620670732,\n 36.90313369880009\n ],\n [\n -76.31812685340054,\n 37.07905097755574\n ],\n [\n -76.51786473533596,\n 37.10821243098252\n ],\n [\n -76.55620727517176,\n 37.13025388344366\n ],\n [\n -76.59365716752067,\n 37.16650105362895\n ],\n [\n -76.62219115065432,\n 37.1295423598058\n ],\n [\n -76.47773786104024,\n 37.03208397181615\n ],\n [\n -76.4179948338545,\n 36.95587973488442\n ],\n [\n -76.23519900440459,\n 36.804670263024434\n ],\n [\n -76.11036282819533,\n 36.821803342395526\n ],\n [\n -76.14335620670732,\n 36.90313369880009\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Imaging sonar was used to assess the behavior, abundance, and timing of fish at the entrances to the Selective Water Withdrawal (SWW) intake structure located in the forebay of Round Butte Dam, Oregon during the spring of 2021. The purposes of the SWW are (1) to direct surface currents in the forebay to attract and collect downriver migrating juvenile salmonid smolts (Chinook salmon [Oncorhynchus tshawytscha], sockeye salmon [O. nerka], and steelhead [O. mykiss]) from Lake Billy Chinook and (2) to enable operators of the SWW to withdraw water from surface and benthic elevations in the reservoir to manage downriver water temperatures. Part of the evaluation, to determine how well the structure performs at collecting juvenile salmonids, needs (A) to regularly assess how fish are approaching the entrance, and (B) to determine if operational flows could be optimized to increase the attraction of smolts present in the forebay of Lake Billy Chinook. The primary goals of this study were (1) to assess the abundance and behaviors of smolt-size fish observed near the SWW and (2) to provide data of the effect of two-night generation operation timing conditions on movements and behaviors of fish near the entrance to the SWW structure. The purpose of this assessment is to improve downstream passage solutions.
Two imaging sonar units were deployed during the spring 2021 smolt out-migration period. One unit monitored fish movements near the south entrance and one unit monitored movements near the north entrance of the SWW. Both smolt and bull trout (Salvelinus confluentus)-size fish were regularly observed near the entrances with greater abundances observed during night, corresponding with greater discharge through the SWW than during the day when discharge was reduced. Differences in fish abundance were observed between the night generation operation timing conditions, with increased fish counts observed when elevated discharge was extended to 6:00 a.m., rather than when discharges have been traditionally reduced in the early morning at 4:00 a.m. Fish of all size groups were primarily observed near the center of the SWW, and greater abundances of fish were observed at the south entrance. Increased counts of bull trout-size fish coincided with the increased abundances of smolt-size fish. Overall, the results indicate that (A) smolt-size fish were more abundant near the entrance of the SWW during periods of increased discharge, (B) bull trout-size fish were present at the SWW, and (C) fish were more numerous at the SWW when night generation operation timing was extended later into the morning hours rather than the traditional operation timing flow reduction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221098","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., and Hatton, T.W., 2022, Evaluation of fish behavior at the entrances to a Selective Water Withdrawal structure in Lake Billy Chinook, Oregon, 2021: U.S. Geological Survey Open-File Report 2022–1098, 28 p., https://doi.org/10.3133/ofr20221098.","productDescription":"viii, 28 p.","onlineOnly":"Y","ipdsId":"IP-139510","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":409425,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1098/images"},{"id":409424,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221098/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1098"},{"id":409423,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1098/ofr20221098.pdf","text":"Report","size":"36.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1098"},{"id":409422,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1098/coverthb.jpg"},{"id":409426,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1098/ofr20221098.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Billy Chinook","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5557866634069,\n 44.75388913871075\n ],\n [\n -121.5557866634069,\n 44.38885839267408\n ],\n [\n -121.07886358532556,\n 44.38885839267408\n ],\n [\n -121.07886358532556,\n 44.75388913871075\n ],\n [\n -121.5557866634069,\n 44.75388913871075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements.
In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge.
A normalized variability index (VN) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the VN values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding VN values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error.
The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225102","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Soong, D.T., and Over, T.M., 2022, Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2022–5102, 54 p., https://doi.org/10.3133/sir20225102.","productDescription":"Report: ix, 54 p.; 2 Data releases; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120412","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":409202,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S2IID","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2011 Land Cover Conterminous United States"},{"id":409201,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC21B0","text":"USGS data release","linkHelpText":"Models, inputs, and outputs for estimating the uncertainty of discharge simulations for the Lake Michigan Diversion using the Hydrological Simulation Program – FORTRAN model"},{"id":409196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5102/coverthb.jpg"},{"id":409197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.pdf","text":"Report","size":"8.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5102"},{"id":409198,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.XML"},{"id":409199,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5102/images"},{"id":409200,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Illinois, Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.34881814497747,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 42.492126793048925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Construction and operation of dams provide sources of clean drinking water, support large-scale irrigation, generate hydroelectricity, control floods, and improve river navigation. Yet these benefits are not without cost. Dams affect the natural flow regime, downstream sediment fluxes, and riverine and riparian ecosystems. The Jemez Canyon Dam in New Mexico was constructed in 1953 by the U.S. Army Corps of Engineers with authorizations for flood control and sediment retention. Water managers of the dam use various operational techniques to restore peak streamflow, improve sediment management, and restore altered ecosystem processes, while maintaining the authorized purposes of the dam. This study focuses on four distinct reservoir management operation periods implemented at the Jemez Canyon Dam: (1) predam (pre-1953), (2) a seasonal 24-hour hold pool (1953–79), (3) a permanent pool (1979–2001), and (4) dry reservoir (2001–18).
Results of this study indicate successful flood control and reduction in peak instantaneous streamflow events following construction of the dam, specifically documented in 1958 and 2013. During the second water management operation period, moderate sediment retention (also defined as trap efficiency, which is the percentage of incoming sediments trapped within a reservoir during a given time) occurred (between 41.0 and 67.0 percent of sediments were retained). During the third period (1979–2001), between 61.2 and 99.8 percent of sediments were retained. During the fourth period (2001–18), at least 1,909 acre-feet of accumulated sediment were remobilized. The estimated dam trap efficiency during the fourth water management operation period was −37.2 percent, indicating that more sediments were being removed from the Jemez Canyon Reservoir than were being deposited. These remobilized sediments supplemented the natural sediment delivery in the Jemez River to the middle Rio Grande. The current (2022) dry reservoir operation allows sediment delivery during periods when flooding is not a concern while still providing flood control when needed.
Suspended-sediment particle size data indicate potential coarsening of suspended sediments during the fourth water management operation period, likely resulting from erosion of coarse bed sediments deposited in the reservoir. Suspended-sediment particle size data during the first and fourth water management operation periods indicate that finer sediment mobilized during monsoon season than during snowmelt. Also, suspended-sediment concentrations during the predam and post-hold pool periods indicate concentrations were higher during monsoon season than during snowmelt. Seasonal variations in suspended-sediment concentration and particle size may help dam managers make operational decisions by increasing the understanding of particle size, concentration, and variation of suspended sediment during a given year. The seasonality of suspended-sediment transport can also vary, depending not only on concentration and particle size, but on precipitation. The maximum annual suspended-sediment loads occurred during all three seasonal categories analyzed in this study: snowmelt, monsoon, and the remainder of the year. This indicates that, in addition to sediment particle size and concentration, understanding the variability of transport mechanisms of suspended-sediment load can also guide optimal water management operations at a dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225081","isbn":"978-1-4113-4481-5","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Brown, J.E., Matherne, A.M., Reale, J.K., and Miltenberger, K.E., 2022, Suspended-sediment transport and water management, Jemez Canyon Dam, New Mexico, 1948–2018: U.S. Geological Survey Scientific Investigations Report 2022–5081, 30 p., https://doi.org/10.3133/sir20225081.","productDescription":"Report: vii, 30 p.; 2 Datasets","numberOfPages":"42","onlineOnly":"N","ipdsId":"IP-107586","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":408900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5081/coverthb.jpg"},{"id":408901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.pdf","text":"Report","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5081"},{"id":408902,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.XML"},{"id":409231,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225081/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408905,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408904,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS Earth Resources Observation and Science Center database","linkHelpText":"—EarthExplorer"},{"id":408903,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5081/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.61655311950582,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.438783063650746\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
During the last 12 decades (1900–2020), the amounts of raw materials used in the United States have increased significantly due to economic development, technological innovations, and population growth. Data on materials are presented here to provide an overview of the annual quantities (measured in physical terms) required for the standard of living in the United States and to provide insights into the consumption trajectory that developing countries may follow. The consumption patterns driving the use of raw materials were analyzed through the lens of economic disruptions and expansions during this period, illustrating the linkages between the selected materials and economic development. These annual material inputs to the U.S. economy (excluding food or fuel) were also analyzed in a global context. The data used were gathered by various agencies and compiled by the U.S. Geological Survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1164","collaboration":"National Minerals Information Center","usgsCitation":"Matos, G.R., 2022, Materials flow in the United States—A global context, 1900–2020: U.S. Geological Survey Data Report 1164, 23 p., https://doi.org/10.3133/dr1164. 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U.S. Geological Survey
991 National Center
Reston, VA 20192
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 60th annual report of the River Master of the Delaware River. It covers the 2013 River Master report year, the period from December 1, 2012 to November 30, 2013.
During the report year, precipitation in the upper Delaware River Basin was 44.50 inches or 100 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high until October 2013 when it decreased below 80 percent combined capacity. The lowest combined storage of the report year was 70.2 percent of combined capacity on November 26, 2013. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the Montague flow objective for the Delaware River at the Montague, New Jersey streamgage on 71 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on July 16, 2013 to temporarily increase releases to provide thermal protection below Cannonsville Reservoir.
The quality of water in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221068","isbn":"978-1-4113-4486-0","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2012–November 30, 2013: U.S. Geological Survey Open-File Report 2022–1068, 99 p., https://doi.org/10.3133/ofr20221068.","productDescription":"xii, 99 p.","numberOfPages":"99","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123834","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":501821,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113786.htm","linkFileType":{"id":5,"text":"html"}},{"id":408577,"rank":4,"type":{"id":31,"text":"Publication 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Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
Groundwater-stream connectivity across mountain watersheds is critical for supporting streamflow during dry times and keeping streams cool during warm times, yet U.S. Geological Survey (USGS) stream measurements are often sparse in headwaters. Starting in 2019, the USGS Next Generation Water Observing System Program developed a multiscale methods and technology testbed approach to monitoring groundwater discharge to streams in the Neversink Reservoir watershed in the Catskill Mountains of New York. Groundwater discharge dynamics are complex across space and time because of geographic variability, topography, and preferential groundwater flow patterns, and the monitoring of discharge processes necessitates an innovative approach that includes emerging water tracing methods and enhanced local geologic mapping. This fact sheet describes the multiscale monitoring approach applied in the Neversink Reservoir watershed and specifically how the varied data types are complimentary in understanding groundwater-stream connectivity, with elements transferable to other mountain watersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223077","usgsCitation":"Briggs, M.A., Gazoorian, C.L., Doctor, D.H., and Burns, D.A., 2022, A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York: U.S. Geological Survey Fact Sheet 2022–3077, 6 p., https://doi.org/10.3133/fs20223077.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140810","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501511,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113787.htm","linkFileType":{"id":5,"text":"html"}},{"id":435646,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3TYOZ","text":"USGS data release","linkHelpText":"Stream Temperature, Dissolved Radon, and Stable Water Isotope Data Collected along Headwater Streams in the Upper Neversink River Watershed, NY, USA (ver. 2.0, April 2023)"},{"id":408663,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3077/images/"},{"id":408662,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.XML"},{"id":408661,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3077"},{"id":408660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3077"},{"id":408659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3077/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.20847061475345,\n 42.188348269216135\n ],\n [\n -74.59712682347332,\n 42.248700527889696\n ],\n [\n -75.01085762630467,\n 41.923046154032335\n ],\n [\n -74.86354438590236,\n 41.71281459504877\n ],\n [\n -74.42317182682854,\n 41.88105505768368\n ],\n [\n -74.19906764196153,\n 42.012767732647546\n ],\n [\n -74.20847061475345,\n 42.188348269216135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Manager, Next Generation Water Observing System
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"},{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from calendar year 2019, and additionally uses streamflow statistics from November and December 2018. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2019, total groundwater withdrawals were estimated to be 3,070 acre-feet (acre-ft), industrial withdrawals were 670 acre-ft, and municipal withdrawals were estimated to be 2,400 acre-ft. Total withdrawals during 2019 were about 58 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline after 2005 and cessation of mining operations in 2019.
Water levels measured in 2019 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 10 of 16 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 16 wells ranged from +8.2 feet (ft) to −40.0 ft, and the median change was −1.7 ft. Water levels also showed decline in 16 of 18 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −38.8 ft, with changes across all 18 wells ranging from +12.9 ft to −185.0 ft.
Spring flow was measured at four springs in 2019. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2019), Dinnebito Wash near Sand Springs 09401110 (1993 to 2019), Polacca Wash near Second Mesa 09400568 (1994 to 2019), and Pasture Canyon Springs 09401265 (2004 to 2019). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was indicated at Moenkopi Wash and Dinnebito Wash.
In 2019, water samples collected from four springs and three wells in the Black Mesa area were analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Concentrations of dissolved solids, chloride, and sulfate increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids and chloride at Burro Spring have varied for the period of record, but there is no statistical trend in the data; however, concentrations of sulfate from Burro Spring now show a trend towards lower concentrations. No statistical trend tests were performed for the three wells sampled in 2019 since less historical water-quality data were available for comparison.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221086","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2022, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2018–2019: U.S. Geological Survey Open-File Report 2022–1086, 47 p., https://doi.org/10.3133/ofr20221086.","productDescription":"vii, 47 p.","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-119897","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":501833,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113767.htm","linkFileType":{"id":5,"text":"html"}},{"id":408436,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211124","text":"Open-File Report 2021-1124","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2016–2018"},{"id":408427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1086/covrthb.jpg"},{"id":408428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1086/ofr20221086.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1086"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 34.94899072578227\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
This study characterized the concentration and quality of dissolved organic carbon (DOC) in the McKenzie River, a relatively undeveloped watershed in western Oregon, and its link to forming disinfection by-products (DBPs) in treated drinking water. The study aimed to identify the primary source(s) of DOC in source water for the Eugene Water & Electric Board’s (EWEB) conventional treatment plant on the McKenzie River near river mile 11, upstream of Hayden Bridge. The two classes of regulated compounds examined—trihalomethanes (THMs) and haloacetic acids (HAAs)—form when organic carbon in raw source water reacts with chlorine and (or) bromine during water treatment.
The objectives of the study were to:
Eleven storms were sampled synoptically in upstream-to-downstream fashion to provide a “snapshot” of water quality conditions at four sites on the McKenzie River from Frissell Bridge (6 miles downstream from Trail Bridge Reservoir) to the EWEB water treatment plant at Hayden Bridge and nine contributing tributaries. Storms included late summer and early autumn “first flush” events and late autumn, winter, and spring storms spanning a range in streamflows from 3,000 to 26,000 cubic feet per second as measured in the main stem McKenzie River at the EWEB water intake.
Water samples were analyzed for DOC concentrations and optical properties (fluorescence and ultraviolet absorbance [UVA]) across a range of wavelengths to characterize the quantity and quality of dissolved organic matter (DOM) in the McKenzie River at the drinking water intake and upstream locations. Paired sets of source and finished water samples were collected at the EWEB treatment plant to identify DOC quality parameters in raw source water that might predict DBP concentrations in finished drinking water.
DOC concentrations were relatively low in the McKenzie River (0.4–3 milligrams per liter [mg/L]; average 1.5 mg/L) but much higher in the tributaries. The highest DOC concentrations occurred during “first flush” storms in October 2012 and September 2013; the highest value (16 mg/L) was measured at the 52nd Street stormwater outfall. The average DOC concentration in the lower basin-tributaries was 3.8 mg/L; three middle basin tributaries—Quartz, Gate, and Haagen Creeks, which drain private forestland with less coniferous forest compared with other higher elevation tributaries— had slightly lower average DOC concentrations (2.8 mg/L). These middle-basin watersheds may be important sources of DOC and DBP precursors to the McKenzie River, even more so than the lower basin tributaries, depending on their flows (and loads). This is particularly true after the September 2020 Holiday Farm fire, which burned much of this area.
DOC concentrations increased 68 percent in the McKenzie River between the uppermost reference site at Frissell Bridge and Vida; this includes drainage from Quartz Creek, Blue River Lake and Cougar Reservoir, which all contributed DOC to the main stem. In contrast, the lowermost tributaries draining most of the agricultural and urban land did not have a large effect on DOC in the McKenzie River despite their higher DOC concentrations because of their presumed relatively low streamflows and, consequently, DOC loads. Apart from the continuous flow monitors in the McKenzie River and some tributaries (Blue River and South Fork McKenzie River, and streamflow at Hayden Bridge and Vida, Camp Creek and some other locations), streamflow was not assessed during sample collection for this study. This lack of streamflow data precludes a detailed analysis of loads, which is discussed in the future studies section.
All DBP concentrations in finished drinking water were less than EPA maximum contaminant levels (MCLs) of 0.080 mg/L for the four trihalomethanes (THM4) and 0.060 mg/L for five haloacetic acids (HAA5). During the 11 storm sampling events the maximum summed concentrations were about 0.040 mg/L for both THM4 and HAA5. Compliance monitoring samples, collected separately by EWEB, yielded some higher concentrations—0.046 mg/L THM4 and 0.047 HAA5—during the December 2012 storm. The corresponding benchmark quotient (BQ) values, which indicate how close a measured DBP concentration is to the MCL, were 0.58 and 0.78, respectively, for THM4 and HAA5. Compared with a similar 2007–08 McKenzie River study that did not target storm events, concentrations of THM4 and HAA5 in finished water were 68 percent and 33 percent higher, respectively, during the current study.
Due to the high dilution rates in the McKenzie River main stem, many of the individual fluorescence excitation-emission measurements were low (<0.1 Raman units) and approached analytical detection limits. Parallel factor analysis (PARAFAC) resulted in a five-component model (C1–C5) that represents five unique organic fluorophores. Components C1, C2, and C3 represent DOM associated with soil-derived, humic-like, more degraded organic matter. In contrast, components C4 and C5 represent “fresher” DOM, derived from terrestrial and aquatic plants, including algae and cyanobacteria that are common in the McKenzie River and its tributaries and reservoirs. The fluorescence data and PARAFAC modeling suggest that most of the DOC in the McKenzie River originated from terrestrial sources (primarily components C1 and C2). The largest increases in DOC in the main stem occurred in the reach upstream of Vida, from inflows by Quartz Creek, Blue River, South Fork McKenzie River, and other tributaries.
Concentrations of DBPs in EWEB’s finished drinking water were positively correlated with DOC concentrations in raw source water (THM4, p<0.05; HAA5, p<0.01) for paired samples collected 12−24 hours apart. DOC concentrations were significantly positively correlated (p<0.001) with laboratory-based fluorescent dissolved organic matter (fDOM) measurements, suggesting fDOM as a useful parameter for monitoring and predicting DOC concentration in surface water and DBP concentrations in finished water.
Of all the PARAFAC components in surface water, C5 had the highest correlations with DBPs in finished water (rho = 0.77–0.84, p<0.01), followed by components C1 and C2 (rho = 0.75 and 0.71, respectively, p<0.01). This C5 carbon is associated with recently produced DOM, possibly from decomposed terrestrial and aquatic vegetation. Model loadings of these three components were considerably higher in the sampled tributaries relative to the main stem McKenzie River, with most of the observed increases in the main stem apparent at Vida. This points to Quartz Creek or other tributaries in the reach between Frissell Bridge and the sampling site near Vida (South Fork McKenzie and Blue Rivers) as potentially key contributors of DOM source material that leads to the production of DBPs in treated drinking water. A limited load analysis showed that the reservoirs contributed 8–37 percent of the instantaneous DOC loads observed at Vida at the time of sampling, which suggests other sources such as Quartz Creek and other streams in the reach between Frissell Bridge and Vida are more important.
Random forest analyses identified PARAFAC components C1 and C5 and fluorescence peaks A, C, M, T and N as the best predictors for HAA5 concentrations in finished drinking water, explaining 62.5 percent of the variation. The best predictors for THM4 were C1, C4 + C5, and peaks T, A, and N, which explained 33 percent of the variation.
Several land cover and vegetation classes were correlated with DOC concentration and other optical measurements. The percentage of evergreen forest in each of the subwatersheds sampled was negatively correlated (p<0.001) with DOC concentration and many optical indicators of DOM quantity: UVA254, fDOM, and all of the fluorescence peaks. In contrast, mixed (deciduous) forest was positively correlated (p<0.001) with DOC, fDOM, UVA254, and several fluorescence peaks, demonstrating the importance of deciduous leaf fall in generating DOC and DBP precursors.
The high level of human activities in the middle and lower portion of the basin—including timber harvesting and road construction on private forestland, agricultural, rural, industrial, and urban development—have resulted in the greatest loss in native coniferous and mixed deciduous forests in the basin. DOC loading from these tributaries and reservoir releases, which contain DOC from terrestrial and aquatic productivity, both enrich the McKenzie River. Concentrations of DOC increased an average of 71 percent (range 30–120 percent) in the McKenzie River between Frissell Bridge, the upstream reference site, and Vida. PARAFAC components C1, C2, and C5—which were correlated with DBPs in finished water—increased, on average, 109–136 percent (range 20–250 percent) in this same Frissell-to-Vida reach. These increases occur from input of tributaries in the middle basin such as Quartz Creek and others, as noted above.
Future monitoring, field, and lab studies can improve our understanding of seasonal and spatial sources of organic carbon contributing DBP precursors to the McKenzie River and allow detection of long-term trends resulting from the recent Holiday Farm Fire, which burned 173,393 acres of forestland, including riparian areas along the main stem, and numerous structures, homes, and outbuildings in September 2020. Future studies could examine DOC fluxes and flushing of carbon from the watershed, investigate the role of precipitation amount and intensity in mobilizing carbon and sediment, and evaluate impacts to aquatic communities and human health as part of a post-fire assessment. Other areas ripe for study include evaluating the impacts of potential temperature increases on carbon sequestration and decomposition in the burned and unburned forests and identifying practices that foster sequestration of carbon in forest soils.
The use of fluorescence sensors such as fDOM to monitor the concentration and composition of raw water supplies may be improved for detection of specific DBP precursors, to provide continuous and real-time information to treatment plant operators. Future studies that monitor DOM amount and quality, and DBP Formation Potential (FP), particularly during storm events, paired with streamflow measurements, as suggested above, could help identify areas that contribute high DOC loads and thus help managers identify the key areas to focus restoration activities. Other studies could examine treatment options for currently regulated DBPs and potentially unregulated compounds, including advanced biological treatments for their removal.
This study was a collaboration between the U.S. Geological Survey (USGS) and EWEB in Eugene, Oregon, with additional funding provided from USGS Cooperative Matching Funds Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225010","collaboration":"Prepared in Cooperation with Eugene Water & Electric Board","usgsCitation":"Carpenter, K.D., Kraus, T.E., Hansen, A.M., Downing, B.D., Goldman, J.H., Haynes, J., Donahue, D., and Morgenstern, K., 2022, Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water: U.S. Geological Survey Scientific Investigations Report 2022–5010, 50 p., https://doi.org/10.3133/sir20225010.","productDescription":"Report: viii, 50 p.; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117763","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":408395,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QPSIG3","text":"USGS data release","description":"USGS data release.","linkHelpText":"Absorbance and fluorescence measurements and concentrations of disinfection by-products in source water and finished water in the McKenzie River Basin, Oregon: 2012-2014"},{"id":408300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5010"},{"id":408302,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.XML"},{"id":408299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5010/coverthb2.jpg"},{"id":408301,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5010/images"},{"id":408366,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010_table1.1.xlsx","text":"Table 1.1","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5010 table 1.1"}],"country":"United States","state":"Oregon","otherGeospatial":"McKenzie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.125,\n 43.8\n ],\n [\n -121.875,\n 43.8\n ],\n [\n -121.875,\n 44.3\n ],\n [\n -123.125,\n 44.3\n ],\n [\n -123.125,\n 43.8\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
The Bushy Park Reservoir is a relatively shallow impoundment in southeastern South Carolina. The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.
The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.
The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.
Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.
For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.
For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll a. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll a, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.
With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll a model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.
Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.
Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.39794921875,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 32.676372772089806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
Relations between stage (water level) and discharge of streamflow through a natural channel are the result of time-varying processes, which are commonly described by time-varying stage-discharge ratings. Hydrographers with the U.S. Geological Survey successfully maintain the accuracy of streamflow data by manually applying time-tested approaches to adapt ratings to temporal changes in hydraulic conditions. The difficulty with the manual approach is that it is a subjective, time-consuming process that requires considerable skill and experience to implement. In addition, manual adjustments of ratings make quantification of resulting streamflow data uncertainties problematic. A computer-aided adaptive stage-discharge estimation approach is proposed to track sequential changes in the relation between stage and discharge at continuous-record streamgages. In this report, adaptations are based strictly on discrete measurement data that are then used to compute the magnitudes and uncertainties of streamflow. The approach entails the parameterization of a cubic regression spline (CRS) for the stage-discharge relation based on an existing rating or on a set of discrete measurements. A state-space model is then parameterized to track temporal changes in stage-discharge relations beginning with the initial CRS parameterization using discrete measurements. Finally, Kalman estimation is used with the state-space model to estimate the magnitude and uncertainty of flows. In a case study using data from streamgage U.S. Geological Survey 04122500 Marquette River at Scottville, Michigan, a five-parameter CRS model was estimated from data in an existing stage-discharge rating to provide an initial CRS parameter set for a state-space model. The initial CRS parameters were updated sequentially in a state-space model based on periodic discrete measurements of stage and discharge that spanned a 30-year period for this analysis. Additional analysis is needed to determine the timing of rapidly varying shifts more precisely in stage-discharge relations than the relatively infrequent discrete measurements currently enabled. Unit streamflow estimates based on flow in a local streamgaging network may provide a basis for adapting a stage-discharge rating at unit time intervals by augmenting discrete measurement data within the state-space model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225083","usgsCitation":"Holtschlag, D.J., 2022, A computer-aided approach for adapting stage-discharge ratings and characterizing uncertainties of streamflow data with discrete measurements: U.S. Geological Survey Scientific Investigations Report 2022–5083, 36 p., https://doi.org/10.3133/sir20225083.","productDescription":"viii, 36 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-130821","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407483,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5083/images"},{"id":407482,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.XML"},{"id":407480,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5083/coverthb.jpg"},{"id":407481,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.pdf","text":"Report","size":"47.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5083"}],"country":"United States","state":"Michigan","otherGeospatial":"Pere Marquette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.20147705078125,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 43.56845179881218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The semi-arid Walla Walla River Basin (WWRB) spans 1777 square miles in the states of Washington and Oregon and supports a diverse agricultural region as well as cities and rural communities that are partially reliant on groundwater. Historically, surface water and groundwater data have been collected in the WWRB by several entities including federal, state, local, and tribal governments; irrigation districts; universities; and non-profits. This report describes the surface and groundwater data collection by the U.S. Geological Survey from February 2018 to April 2022 to provide the Washington State Department of Ecology and other stakeholders basic knowledge of existing water resources in the WWRB, Washington. Additionally, the data were collected to build a long-term groundwater dataset, with the intent to provide data for better understanding to assist in informed decisions about groundwater use, management, and conservation throughout the basin, and for future inclusion in a conceptual model of the groundwater-flow system (conceptual model). Data were collected and compiled for 237 sites—191 wells and 46 surface-water discharge sites. A small annual network of deep basalt wells was established in February 2018 to commence the data collection. In March 2020 and April 2021, additional field inventories were performed by locating and measuring groundwater wells in the WWRB. A subset of the inventoried wells were selected for an annual or a quarterly water level network to be measured until July 2024. In August 2020, field reconnaissance identified 46 surface-water sites to be measured for discharge, estimating gaining and losing reaches in streams for groundwater influences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1163","collaboration":"Prepared in cooperation with Washington Department of Ecology","usgsCitation":"Fasser, E.T., and Dunn, S.B., 2022, Groundwater and surface-water data collection for the Walla Walla River Basin, Washington, 2018–22: Data Report 1163, 8 p., https://doi.org/10.3133/dr1163.","productDescription":"Report: vi, 8 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-140692","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":407252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1163/coverthb.jpg"},{"id":407253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1163"},{"id":407254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1163/images"},{"id":407255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.XML"},{"id":407256,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D2C2DK","text":"USGS data release","description":"USGS data release","linkHelpText":"Dataset of groundwater and surface water data collection for the Walla Walla Basin in Washington, 2018–2022"},{"id":407257,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1163/full","text":"Report"},{"id":501271,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113584.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Walla Walla River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.21264648437499,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey, using calibrated ratings of the lift gates, to support the U.S. Fish and Wildlife Service in managing lake level. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure.
Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221073","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency","usgsCitation":"Heal, E.N., Diehl, T.H., and Garrett, J.W., 2022, Preliminary models relating lake level gate operation and discharge at Reelfoot Lake in Tennessee and Kentucky: U.S. Geological Survey Open-File Report 2022–1073, 27 p., https://doi.org/10.3133/ofr20221073.","productDescription":"Report: vii, 27 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-103756","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":406684,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GY1UF4","text":"USGS data release","linkHelpText":"Preliminary model data for lake level gate operation and discharge at Reelfoot Lake—Tennessee and Kentucky"},{"id":501823,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113520.htm","linkFileType":{"id":5,"text":"html"}},{"id":406683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.pdf","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1073"},{"id":406682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1073/coverthb.jpg"},{"id":406685,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":406844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1073/images"},{"id":406845,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.xml"},{"id":409059,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221073/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1073"}],"country":"United States","state":"Kentucky, Tennessee","otherGeospatial":"Reelfoot Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46441650390625,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.30350540784278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Williston Basin has been a leading oil and gas producing area for more than 50 years. While oil production initially peaked within the Williston Basin in the mid-1980s, production rapidly increased in the mid-2000s, largely because of improved horizontal (directional) drilling and hydraulic fracturing methods. In 2012, energy development associated with the Bakken Formation was identified as a priority requiring collaboration toward improved timeliness of issuing permits for new wells combined with reasonable measures to maintain environmental quality. Shortly thereafter, the Bakken Federal Executive Group was created to address common challenges associated with energy development. The Bakken Federal Executive Group partner agencies identified a gap in current understanding of the cumulative environmental challenges attributed to energy development throughout the area, resulting in an effort to aggregate scientific data and identify additional research and information needs related to natural resources within areas of energy development in the Williston Basin. As part of this effort, water resources in the area (including groundwater; streams and rivers; and lakes, reservoirs, and wetlands) were characterized and described in terms of physical occurrence, flow characteristics, recharge, water quality, and water use. Similarly, waters produced during energy-development activities also were characterized even though these waters are not considered usable resources within the area. Groundwater resources were characterized by the major hydrogeologic units, or aquifers, identifying the units that supply most groundwater used for domestic, stock, agricultural, and industrial purposes. The groundwater characterization included other deeper hydrogeologic units in the Williston Basin that may be a useable source of water with treatment, have utility as a reservoir for reinjection of produced waters, or be a source of minerals and energy resources. A generalized groundwater budget and flow system identifying the sources of recharge (stream infiltration, precipitation, and movement [leakage] from other aquifers) and the general groundwater flow direction is included for each of the major hydrogeologic units. Rivers and streams within the Williston Basin with 10 or more years of continuous streamflow data were identified. For a subset of these sites, streamflow characteristics, including the monthly and annual mean flow, were generated to identify seasonal and interannual changes in streamflow and thus provide information on the drivers and reliability of streamflow at the seasonal or multiyear scale. Daily streamflow and annual extreme flows (peak and low flow) also were estimated for the subset of sites. The daily streamflow and annual extreme flow values provide information on short-term or extreme events that are relevant to infrastructure design and evaluating spills, leaks, or accidental discharges of water or petroleum products. Surface-water features (lakes, ponds, and wetlands) were classified using the Cowardin system and identified on the National Wetlands Inventory maps generated by the U.S. Fish and Wildlife Service. The spatial distribution of the surface-water features was analyzed by State, county, and specifically in comparison to the Prairie Pothole Region. The proximity of the surface-water features to energy development infrastructure (specifically oil or gas well pads) was evaluated. It was determined that, although oil or gas wells are often near a surface-water feature, most surface-water features do not have wells nearby, with the exception of wells in the Prairie Pothole Region. Water-quality data were aggregated from two data sources: (1) the Water-Quality Portal, sponsored by the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), and National Water Quality Monitoring Council; and (2) a data compilation completed as part of the USGS National Water-Quality Assessment project. The Water-Quality Portal integrates publicly available water-quality data from databases maintained by the USGS, EPA, and U.S. Department of Agriculture, including water-quality data from Tribal, State, and local databases. Water-quality data for 15 commonly measured water-quality constituents were aggregated for groundwater, rivers and streams, and lakes and reservoirs. For each aggregated dataset (groundwater, rivers and streams, and lakes and reservoirs), analyses of the water-quality data included summary statistics, maps of spatial distribution of constituent values, boxplots of constituent values by timeframe or hydrogeologic unit, spatial comparisons of site locations and constituent values to petroleum well density, and comparisons of the constituent values measured to EPA drinking-water standards/guidelines. Produced water includes all fluids brought to the surface along with the targeted hydrocarbons as part of the oil and gas exploration and extraction processes. These fluids may include formation water (waters that co-exist with rock/oil/gas), hydraulic fracturing fluids, and other combinations of water and chemicals used during oil and gas well drilling, development, treatments, recompletions, and workovers. Produced water datasets were aggregated from two sources: the USGS National Produced Waters Geochemical database (ver. 2.1) and a series of projects focused specifically on sampling produced water in the Williston Basin from 2010 to 2014. The National Produced Waters Geochemical database was useful for a general understanding of produced-water chemistry. Produced waters are characterized by extreme salinity and contain elevated concentrations of other constituents (including arsenic, barium, cadmium, lead, zinc, radium-226/radium-228, and ammonium) that could negatively affect water and aquatic resources if released. Produced waters also have a generally unique chemical (isotopic) signature that may be useful in tracking water from different geologic units; for example, the oxygen/deuterium and strontium ratio values measured in brine waters from the Bakken Formation are distinct from brines collected from other geologic units in the Williston Basin.
Water-use information related to energy production in the area also was aggregated and summarized. The summary of water use is not limited to oil and gas production but includes water used to produce all types of energy resources in the Williston Basin, including coal/lignite, thermoelectric power, oil and gas, hydropower, biomass and biofuels, wind, geothermal, and solar. Each State has its own methods for regulating and reporting water usage within its jurisdiction. These methods can introduce problems when examining water use from sources, such as the Missouri River or Fox Hills aquifer, that are shared across political boundaries. Without the one-to-one match for usage types and amounts used from a water source, it is difficult to develop a comprehensive water budget for the water source being evaluated. A large amount of freshwater is required to prepare a well for oil and gas well production; in some cases, 3 to 7 million gallons of water are needed per well. The EPA estimates that hydraulic fracturing in the Williston Basin uses between 70 to 140 billion gallons per year. Water also is used for myriad other purposes related to ancillary oil and gas extraction. In addition to water used for immediate energy development, the expanded human workforce migrating into the area and other support staff who have moved into the area during the development also use water.
Research and information needs were identified that could be relevant in the evaluation of the effects of energy development on water resources. Information needs related to the evaluation of groundwater resources include the following: improved potentiometric-surface maps for glacial units; availability of a uniform stream network digital geographic coverage that spans the international boundary with Canada; enhanced surface-water use information with regards to the gain and loss of streamflow to shallow groundwater, which would increase understanding groundwater and surface-water interactions; and expanded geophysical assessments. Gaps in the availability of streamflow data include the lack of information on ice-jam flooding despite potential for effects to infrastructure (pipelines, roads, and facilities) and an understanding of the cumulative effects of largely undocumented stock and diversion dams. Although this study resulted in the aggregation of a large quantity of water-quality data, the availability of consistently collected, systematically processed and reported data over large parts of the Williston Basin is sparse. Few samples have been analyzed for constituents that may indicate the effect of energy development on water resources. Constituents that could be considered include boron, chloride, bromide, iodine, fluoride, manganese, lithium, radium, strontium isotopes, volatile organic compounds, and isotopes of inorganic ions (such as hydrogen and carbon). Collaboration between Tribal, Federal, State, and local entities to identify a common study design, common monitoring constituents, and consistent sampling locations would generate datasets with broad utility and would likely result in overall cost savings for monitoring over time. Similarly, there is a need for standardized sample collection, processing, laboratory analytical methods, and the collection of ancillary data for produced waters sampling. Additional characterization of the range of chemical, microbial, and isotopic compositions and quantities of “end-member” produced waters, and the collection of time-series datasets to document the changes in produced waters during and after well development also were needs identified during this study. Water-use estimates would be improved through the implementation of comprehensive studies of water use from groundwater and surface-water sources using consistent methodologies across the Williston Basin. The submission of chemical and water data related to hydraulic fracturing collected by the oil and gas industry would add to the quantity of available data. Consistent implementation of regulations and monitoring controls across political boundaries (State, county, and international) would further improve the consistency of data available for the estimates of water use.
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U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601