The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2022, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2022 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate interactions between hydrothermal features and influences from external influences, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, new mapping of ash-flow deposits on the Sour Creek dome, installation of a new continuous gas monitoring station near Mud Volcano, sampling of gas emissions and thermal waters around Yellowstone National Park to monitor water chemistry over space and time, research into the age and history of Steamboat Geyser in Norris Geyser Basin, and assessment of thermal output based on satellite imagery and chloride flux in rivers.
The most noteworthy event of the year was not geophysical, but meteorological. Combined runoff from rain and snowmelt caused substantial flooding in Yellowstone National Park, which caused damage to park roads and infrastructure. Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 11 water eruptions in 2022, the lowest number of annual eruptions in the current eruptive sequence. Total seismicity—2,429 located earthquakes—was slightly less than the 2,773 earthquakes located in 2021 and at the upper end of the historical average range of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year. Satellite deformation measurements indicated the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim in 2021, but satellite data spanning 2022 show no uplift in that area. Throughout 2022, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1508","usgsCitation":"Yellowstone Volcano Observatory, 2023, Yellowstone Volcano Observatory 2022 annual report: U.S. Geological Survey Circular 1508, 49 p., https://doi.org/10.3133/cir1508.","productDescription":"v, 49 p.","numberOfPages":"49","onlineOnly":"N","ipdsId":"IP-149199","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1508/covrthb.jpg"},{"id":416683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1508/cir1508.pdf","text":"Report","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499548,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114711.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.22714973143812,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 45.10797687707381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"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":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
Reliable estimates of the magnitude and frequency of floods are an important part of the framework for hydraulic-structure design and flood-plain management in Georgia, South Carolina, and North Carolina. Annual peak flows measured at U.S. Geological Survey streamgages are used to compute flood‑frequency estimates at those streamgages. However, flood‑frequency estimates also are needed at ungaged stream locations. A process known as regionalization was used to develop regression equations to estimate the magnitude and frequency of floods at ungaged locations.
A multistate approach was used to update estimates of the magnitude and frequency of floods in rural, ungaged basins in Georgia, South Carolina, and North Carolina. Annual peak-flow data through September 2017 were analyzed for 965 streamgages with 10 or more years of data on rural streams in Georgia, South Carolina, North Carolina, and adjacent parts of Alabama, Florida, Tennessee, and Virginia. Flood‑frequency estimates of the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows, which correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively, were computed for the 965 streamgages following national guidelines. As part of the computation of flood‑frequency estimates for the streamgages, an updated value for the regional skew coefficient (0.048) was developed using a Bayesian generalized least squares regression model. The new regional skew has a mean square error or average variance of prediction of 0.092. Additionally, basin characteristics for these stations were computed using a geographical information system.
Exploratory analyses on the 965 streamgages confirmed the five hydrologic regions for Georgia, South Carolina, and North Carolina defined in a previous rural flood‑frequency study. From the 965 streamgages, streamgages with 30 or more years of record were used to complete a peak-flow trend analysis. Of the 965 streamgages, 164 streamgages were found to be redundant and were excluded from the regional regression analyses. Data from the remaining 801 streamgages (292 in Georgia, 75 in South Carolina, 303 in North Carolina, 15 in Alabama, 12 in Florida, 39 in Tennessee, and 65 in Virginia) were used in a regional regression analysis relating basin characteristics to flood‑frequency estimates. This analysis, based on generalized least squares regression, was used to develop a set of predictive equations to estimate the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows for rural, ungaged basins in Georgia, South Carolina, and North Carolina. The final set of predictive equations are all functions of drainage area and percentage of the drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 35.8 to 44.4 percent.
Flood‑frequency estimates also were computed for 72 regulated (for example, a streamgage where flow is altered by a dam or weir) streamgages in Georgia, South Carolina, and North Carolina with 20 or more years of post-regulation record using data through water year 2019. The water year is the annual period from October 1 through September 30 and is designated by the year in which the period ends. Of the 72 regulated streamgages, 18 had pre-regulated periods of record that also were analyzed as part of this study. Flow adjustments were applied to historic peaks and large floods from the pre-regulated period, if available, for use in the post-regulation frequency analysis. Estimates of large floods provide valuable information in frequency analysis and, thus, were included in the post-regulation frequency analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235006","collaboration":"Prepared in cooperation with the Georgia Department of Transportation (Engineering Division, Office of Bridge Design and Maintenance), South Carolina Department of Transportation (Hydraulic Design Support Office), North Carolina Department of Transportation (Division of Highways, Hydraulics Unit), and the North Carolina Department of Crime Control and Public Safety (Division of Emergency Management, Floodplain Mapping Program)","usgsCitation":"Feaster, T.D., Gotvald, A.J., Musser, J.W., Weaver, J.C., Kolb, K.R., Veilleux, A.G., and Wagner, D.M., 2023, Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results: U.S. Geological Survey Scientific Investigations Report 2023–5006, 75 p., https://doi.org/10.3133/sir20235006.","productDescription":"Report: ix, 75 p.; 2 Data 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Reliable flood-frequency estimates are important for hydraulic structure design and floodplain management in Georgia, South Carolina, and North Carolina. Annual peak streamflows (hereafter, referred to as peak flows) measured at 965 U.S. Geological Survey streamgages were used to compute flood-frequency estimates with annual exceedance probabilities (AEPs) of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent. These AEPs correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A subset of these streamgages (801) were used to develop equations to predict the AEP flood flows at ungaged stream locations. This study was completed by the USGS in cooperation with the Georgia, South Carolina, and North Carolina Departments of Transportation and the North Carolina Department of Crime Control and Public Safety, and the results are summarized in this fact sheet. The complete results and the supporting data are presented in the companion scientific investigations report and data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233011","collaboration":"Prepared in cooperation with the Georgia Department of Transportation (Engineering Division, Office of Bridge Design and Maintenance), South Carolina Department of Transportation (Hydraulic Design Support Office), North Carolina Department of Transportation (Division of Highways, Hydraulics Unit), and the North Carolina Department of Crime Control and Public Safety (Division of Emergency Management, Floodplain Mapping Program)","usgsCitation":"Feaster, T.D., Gotvald, A.J., Musser, J.W., Weaver, J.C., and Kolb, K.R., 2023, Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Summary: U.S. Geological Survey Fact Sheet 2023–3011, 6 p., https://doi.org/10.3133/fs20233011.","productDescription":"Report: 6 p.; 2 Data 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Hydrologic and geophysical data were collected to support updates to an existing groundwater-flow model of Hinkley Valley, California, in the Mojave Desert about 80 miles northeast of Los Angeles, California. These data provide information on predevelopment (pre-1930) water levels, groundwater recharge, and selected hydrologic properties of aquifer materials.
A predevelopment groundwater-level map, drawn using water-level measurements from 48 wells collected as early as 1918, showed groundwater movement from recharge areas along the Mojave River to evaporative discharge areas near the margin of Harper (dry) Lake in Water Valley. During predevelopment conditions, depth to water ranged from near land surface along the Mojave River to above land surface near Harper (dry) Lake, consistent with flowing wells in Water Valley at that time. Depths to water in much of Hinkley Valley downgradient from the Lockhart fault were less than 20 feet below land surface. By 2017, water-level declines as a result of agricultural pumping, were as much as 60 feet near the Hinkley compressor station.
Areal recharge from infiltration of precipitation on the valley floor is negligible. Average annual recharge as infiltration of runoff from upland drainages to Hinkley and Water Valleys averages 64.7 acre-feet per year. In most years recharge does not occur; in years when it occurs, recharge to Hinkley Valley is typically about 296 acre-feet. In contrast, average recharge as infiltration of streamflow from the Mojave River from 1931 to 2015 was between 13,400 and 17,100 acre-feet per year; in some years recharge from the Mojave River exceeded 100,000 acre-ft. Estimates of predevelopment groundwater movement through Hinkley Gap and groundwater discharge to Harper (dry) Lake ranged from 570 to 1,900 and 820 to 2,460 acre-feet per year, respectively; at the time of this study in 2017, groundwater movement through Hinkley Gap was estimated to be about 83 acre-feet per year.
Hydraulic-conductivity values estimated from slug-test data for 95 monitoring wells ranged from less than 0.1 to 680 feet per day (ft/d); values generally decreased with depth. Median hydraulic-conductivity values calculated from nuclear magnetic resonance (NMR) data for Mojave River alluvium and near-shore lake deposits were 73 and 11 ft/d, respectively; median hydraulic-conductivity values for locally derived alluvium and weathered bedrock were 6 and 2 ft/d, respectively. Hydraulic-conductivity values, estimated from NMR data for formerly saturated deposits overlying the 2017 water table, were as high as 300 ft/d near the Hinkley compressor station. Downgradient from the Hinkley compressor station, formerly saturated deposits had hydraulic-conductivity values of about 150 ft/d, which were higher than values in saturated material. Coarse-textured, permeable material in formerly saturated deposits above the 2017 water table may have allowed groundwater, released from the Hinkley compressor station that may have contained Cr(VI), to move rapidly downgradient.
The Lockhart fault is an impediment to groundwater flow within Hinkley Valley. Groundwater-flow directions from horizontal point-velocity probe data were deflected to the west on the upgradient side of the fault compared to the nominal direction of groundwater flow estimated from water-level data. Younger groundwater was present on the upgradient and downgradient sides of the fault, and older groundwater with unadjusted carbon-14 ages as old as 5,650 years before present was in water from wells within splays of the Lockhart fault, consistent with limited groundwater movement across the fault. As a result, groundwater and Cr(VI) released from the Hinkley compressor station moved to the northwest along the downgradient side of the fault.
Coupled well-bore flow and depth-dependent water-quality data show water from wells C-01 and IW-03 within the Q4 2015 (October–December 2015) regulatory Cr(VI) plume was yielded from thin layers within the aquifer that are composed of well-sorted lake-margin (beach) deposits that likely have high lateral and longitudinal connectivity. Collectively, data show highly permeable deposits above the regional water table and thin permeable deposits within saturated portions of the upper aquifer that may have conducted groundwater and Cr(VI) downgradient when releases from the Hinkley compressor station first occurred.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885H","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Groover, K.D., Izbicki, J.A., Seymour, W.A., Brown, A.N., Bayless, R.E., Johnson, C.D., Pappas, K.L., Smith, G.A., Clark, D.A., Larsen, J., Dick, M.C., Flint, L.E., Stamos, C.L., and Warden, J.G., 2023, Predevelopment water levels, groundwater recharge, and selected hydrologic properties of aquifer materials, Hinkley and Water Valleys, California, Chapter H of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-H, 64 p., https://doi.org/10.3133/pp1885H.","productDescription":"Report: x, 64 p.; Data Release; 5 Appendixes","numberOfPages":"64","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":417466,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416347,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.5.xlsx","text":"Appendix table H 1.5","linkFileType":{"id":3,"text":"xlsx"}},{"id":416346,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.4.xlsx","text":"Appendix table H 1.4","linkFileType":{"id":3,"text":"xlsx"}},{"id":416345,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.3.xlsx","text":"Appendix table H 1.3","linkFileType":{"id":3,"text":"xlsx"}},{"id":416344,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.2.xlsx","text":"Appendix table H 1.2","linkFileType":{"id":3,"text":"xlsx"}},{"id":416343,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.1.xlsx","text":"Appendix table H 1.1","linkFileType":{"id":3,"text":"xlsx"}},{"id":416293,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/h/images"},{"id":416291,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/h/pp1885h.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416290,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/h/covrthb.jpg"},{"id":416292,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/h/pp1885h.xml"},{"id":416289,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BUXAX1","text":"Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018","description":"Groover, K.D., Izbicki, J.A., Larsen, J.D., Dick, M.C., Nawikas, J., and Kohel, C.A., 2021, Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9BUXAX1."}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Hexavalent chromium, Cr(VI), was discharged in cooling wastewater to unlined surface ponds from 1952 to 1964 and reached the underlying unconsolidated aquifer at the Pacific Gas and Electric Company (PG&E) Hinkley compressor station in the Mojave Desert, 80 miles northeast of Los Angeles, California. A suite of environmental tracers was analyzed in water samples collected from more than 100 wells to characterize the source, age, and geochemical evolution of groundwater within and near the Cr(VI) plume in Hinkley and Water Valleys. This information was used to help determine the extent of Cr(VI) associated with releases from the Hinkley compressor station and to identify Cr(VI) associated with natural sources.
The source of water in most wells, indicated by stable oxygen and hydrogen isotope values for water, delta oxygen-18 and delta deuterium, was recharge by infiltration of intermittent surface flows in the Mojave River. With the exception of small flows in 1958, the Mojave River was largely dry between 1952 and 1969. This dry period spans the period of Cr(VI) releases from the Hinkley compressor station; 1952–69 also spans the period of high tritium levels in precipitation resulting from the atmospheric testing of nuclear weapons and, as a consequence, tritium concentrations in groundwater in Hinkley Valley were comparatively low. Groundwater ages (time since recharge) increased downgradient from the Mojave River and with depth. Tritium, measured by helium ingrowth with a study reporting level of 0.05 tritium unit, was detected in water from 51 percent of wells, with detectable tritium as far as 7 miles downgradient from the Mojave River. Tritium concentrations were higher, and tritium/helium-3 groundwater ages younger, in water from wells near the Mojave River and in water from shallower wells downgradient. Agricultural pumping has decreased groundwater levels as much as 60 feet since 1952. As a result of this pumping, some groundwater containing tritium, and presumably anthropogenic Cr(VI), has been removed from the aquifer. The distribution of wells having carbon-14 activities near or greater than 100-percent modern carbon, consistent with post-1952 recharge water, was similar to the distribution of wells containing detectable tritium. Carbon-14 activities as low as 8.9-percent modern carbon, with carbon-14 ages (unadjusted for reactions with aquifer materials) of almost 20,000 years before present (ybp), were sampled in water from some deep wells. Hexavalent chromium concentrations in older groundwater were as high as 11 micrograms per liter but did not exceed 3.6 micrograms per liter in older water from wells completed in “Mojave-type” deposits (composed of felsic Mojave River stream and near-shore lake deposits sourced from the Mojave River); this value may represent an upper limit on Cr(VI) concentrations in groundwater within Mojave-type deposits that likely approximates background Cr(VI) concentrations in the study area. Chlorofluorocarbons were released to the atmosphere and hydrologic cycle as a result of industrial activity beginning in the 1930s. Chlorofluorocarbon data were not generally suitable for groundwater-age dating in Hinkley and Water Valley because of nonatmospheric contributions from local sources.
Strontium-87/86 isotope ratios and stable chromium isotopes, delta chromium-53, provide information on the geochemical evolution of groundwater in the aquifer. Highly radiogenic strontium-87/86 ratios greater than 0.71000 were present in water from wells completed in coarse-textured Mojave-type deposits having low chromium concentrations but were not diagnostic of these materials. Nonradiogenic strontium-87/86 ratios less than 0.70950 were diagnostic of weathered materials in the northern subarea of Hinkley and in Water Valley that were eroded from Miocene (23–5 million ybp) deposits east of the study area. Values for delta chromium-53 ranged from near 0 to 2.8 parts per thousand (‰) difference. The extent of reductive fractionation, mixing with native groundwater, and longitudinal dispersion within the October–December 2015 (Q4 2015) regulatory Cr(VI) plume can be estimated on the basis of the delta chromium-53 isotope composition of groundwater within the plume. Reduction of Cr(VI) to trivalent chromium, Cr(III), can occur in the presence of natural reductants in oxic groundwater. Although not diagnostic of anthropogenic chromium at the concentrations of interest near the Q4 2015 regulatory Cr(VI) plume margin, delta chromium-53 data indicate anthropogenic Cr(VI) within the plume is not conservative and has reacted with aquifer materials; these reactions have removed some anthropogenic Cr(VI) from groundwater.
Environmental tracers, and the distribution of modern (post-1952) and premodern (pre-1952) groundwater, inform understanding of the extent of anthropogenic and naturally occurring Cr(VI) near the Q4 2015 regulatory Cr(VI) plume and the understanding of geochemical processes occurring in and near the margins of the Cr(VI) plume. The oxygen and hydrogen isotope compositions of water, tritium/helium-3 groundwater-age data, and carbon-14 data were used with mineralogy and chemistry data as part of a summative-scale analysis to determine the Cr(VI) plume extent later in this professional paper (chapter G).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885F","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Warden, J.G., Izbicki, J.A., Sültenfuß, J., Scheiderich, K., and Fitzpatrick, J., 2023, Environmental tracers of groundwater source, age, and geochemical evolution, Chapter F of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-F, 74 p., https://doi.org/10.3133/pp1885F.","productDescription":"Report: xii, 74 p.; 2 Data Releases; 2 Appendixes","numberOfPages":"74","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416331,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/f/tables/pp1885f_appendtable_f.2.1.xlsx","text":"Appendix table 2.1","linkFileType":{"id":3,"text":"xlsx"}},{"id":416277,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/f/covrthb.jpg"},{"id":417464,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416275,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416276,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HUPMG0","text":"Grain size, mineralogic, and trace-element data from field samples near Hinkley, California","description":"Morrison, J.M., Benzel, W.M., Holm-Denoma, C.S., and Bala, S., 2018, Grain size, mineralogic, and trace-element data from field samples near Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUPMG0."},{"id":416278,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/f/pp1885f.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416279,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/f/pp1885f.xml"},{"id":416280,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/f/images"},{"id":416330,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/f/tables/pp1885f_appendtable_f.1.1.csv","text":"Appendix table 1.1","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with Gwinnett County Department of Water Resources, established the Long-Term Trend Monitoring program in 1996 to monitor and analyze the hydrologic and water-quality conditions in Gwinnett County, Georgia. Gwinnett County is a suburban to urban area northeast of the city of Atlanta in north-central Georgia. The monitoring program currently consists of 15 watersheds ranging in size from 1.3 to about 161 square miles. This report synthesizes watershed characteristics and hydrologic and water-quality monitoring data collected for water years (WYs) 2002–20.
The 15 study watersheds were characterized for land-surface elevations, average land-surface slopes, septic densities, sanitary sewer densities, and detention pond areas. Temporal patterns in watershed characteristics were determined for land cover (2001–19), percent imperviousness (2000–20), population density (2000–20), and building density (1950–2022). In 2001, most of the watersheds had at least 45 percent of their land cover composed of developed land cover groups, and by 2019, at least 59 percent of each watershed was developed. Land cover changes occurred most rapidly between 2004 and 2008 at most watersheds. Percent imperviousness in the study watersheds varied substantially and ranged from 14.75 to 55.13 percent in 2019.
Precipitation and runoff were quantified at all study watersheds for WYs 2002–20, and the hydrologic cycle was evaluated both annually and seasonally. Several 1-year or longer droughts occurred during this period. Study area precipitation averaged 51.5 inches per year and runoff averaged 22.5 inches per year. Variations in annual runoff were largely determined by annual precipitation but were also dependent upon watershed storage. Runoff varied seasonally because of high evapotranspiration rates in the summer and changes in base flow associated with seasonal changes in watershed storage. Fifty-one percent of runoff in the study area occurred as base flow. Watersheds with higher imperviousness had higher stormflows because of increased surface runoff and lower base flows because of reduced infiltration that recharges watershed storage.
Turbidity, water temperature, and specific conductance were continuously measured at each study site. These constituents varied seasonally, diurnally, and with streamflow. A minimum of two base-flow and six stormflow samples were collected per year at each watershed and were analyzed for 21 water-quality constituents (water temperature, laboratory specific conductance, pH, and turbidity, biochemical and chemical oxygen demand, suspended sediments, nutrients, base cations, trace metals, and total dissolved solids). Concentrations of most particulate constituents were approximately one-half or more orders of magnitude higher in stormflow samples than in base-flow samples. Total copper and zinc stormflow concentrations exceeded the national recommended aquatic life criteria for acute conditions to varying degrees.
Annual loads and yields were estimated for 12 constituents (which include suspended sediments, nutrients, base cations, trace metals, and total dissolved solids) using a surrogate regression model approach and the Beale load estimator. Loads were typically higher for years with higher runoff. The proportional range of annual loads for total suspended solids, suspended-sediment concentrations, total phosphorus, and total lead, however, were 3.2 to 4.8 times larger than for annual runoff. Higher-than-expected annual sediment loads occurred in the years that also had some of the highest peak flows during the period, indicating that large storms are responsible for much of the sediment transport. Large development projects in proximity to streams also were related to years with high sediment loads. Yields from the Crooked Creek and North Fork Peachtree Creek watersheds were typically among the highest for 8 of the 12 constituents. These watersheds had the two highest amounts of developed medium plus high intensity land cover and the two highest percentages of imperviousness. Moderate to strong correlations were identified between seven of the constituent yields and the percentage of developed medium and high intensity land cover groups. Temporal trends in concentrations and loads were identified for 140 of the 300 possible watershed-time period-constituent combinations. There were substantially more negative than positive temporal trends identified during WYs 2003–10, whereas the number of negative and positive temporal trends were similar during WYs 2010–20. Measures of sediment transport had the most negative temporal trends. A few watersheds had consistent trends across several constituents; however, these trends did not appear to be associated with temporal changes in development or imperviousness.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions and trends for the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality and determine changes in water-quality conditions. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
https://www.usgs.gov/centers/lsawsc
The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, undertook a study to update and extend results from a previous study (Koltun, 2019, https://doi.org/10.3133/sir20195119), using data from 3 additional years and newer estimation methods. Koltun (2019) assessed trends in streamflow, precipitation, and estimated annual mean concentrations and flux of nitrate plus nitrite, total Kjeldahl nitrogen, total phosphorus, and total suspended solids (TSS) for USGS streamflow gages on the upper White River at Muncie, near Nora, and near Centerton, Indiana. Annual mean and maximum daily streamflows had statistically significant upward trends at all study gages between water years 1978 and 2020. An abrupt increase in streamflow occurred around water year 2001. Annual total precipitation at the Indianapolis International Airport increased between calendar years 1932 and 2020 at an average rate of 0.089 inches per year.
The current study assessed the magnitude, direction, and likelihood of change in flow-normalized concentrations and flux of TSS, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen between water years 1997 and 2019. With two exceptions, concentration and flux changes that were statistically significant in Koltun (2019, https://doi.org/10.3133/sir20195119), which reported changes between water years 1997 and 2017, still have the same statistically significant change directions. The reliability of the current trend result for TSS is uncertain because of a large gap in the TSS record for the Centerton gage.
For each constituent, spatial patterns were examined in the sampled distribution of nutrient and TSS concentration data from 20 mainstem, tributary, and distributary locations in the upper White River Basin. The largest median concentrations of TSS, total phosphorus, and total Kjeldahl nitrogen were associated with mainstem upper White River sites downstream from Indianapolis. The median total phosphorus and total Kjeldahl nitrogen concentrations were elevated relative to bracketing upstream/downstream mainstem sites at the upper White River site immediately downstream from Muncie.
Data on several anthropogenic factors that could influence the concentrations and fluxes of nutrients and TSS were gathered and analyzed to better understand the factors’ spatial and temporal variations. Those anthropogenic factors included population, land cover, cropping and operational tillage practices, fertilizer application, and upgrades to wastewater treatment systems and delivery processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235025","collaboration":"Prepared in cooperation with The Nature Conservancy with generous support from the Nina Mason Pulliam Charitable Trust","usgsCitation":"Koltun, G.F., 2023, Trends in environmental, anthropogenic, and water-quality characteristics in the upper White River Basin, Indiana: U.S. Geological Survey Scientific Investigations Report 2023–5025, 46 p., https://doi.org/10.3133/sir20235025.","productDescription":"Report: x, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-139275","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":415439,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O6C9L3","text":"USGS data release","linkHelpText":"Model data archive—Trends in selected environmental, anthropogenic, and water-quality characteristics in the upper White River Basin, Indiana, 1991–2020"},{"id":415441,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5025/images/"},{"id":415438,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235025/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5025"},{"id":415437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5025/sir20235025.pdf","text":"Report","size":"5.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5025"},{"id":415436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5025/coverthb.jpg"},{"id":415440,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5025/sir20235025.XML"},{"id":415718,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20233009","text":"Fact Sheet 2023–3009","linkHelpText":"- Potential Drivers of Change in Fluxes of Nutrients and Total Suspended Solids in the Upper White River Basin, Indiana, Water Years 1997–2019"},{"id":500879,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114664.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Indiana","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.808361236281,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 40.56108404734479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Changes in riparian vegetation cover and composition occur in relation to flow regime, geomorphic template, and climate, and can have cascading effects on aquatic and terrestrial ecosystems. Tracking such changes over time is therefore an important part of monitoring the condition and trajectory of riparian ecosystems. Maintaining diverse, self-sustaining riparian vegetation comprised of mostly native species is identified in the Glen Canyon Dam Long-Term Experimental and Management Plan as a key resource objective for the section of the Colorado River between Glen Canyon Dam and Lake Mead. The U.S. Geological Survey Grand Canyon Monitoring and Research Center implemented an annual monitoring program in 2014 to assess the status and trends of riparian vegetation along this section of river, particularly as they relate to flow regime. In this report, we summarize plant species composition and cover data collected under the annual monitoring program from 2014 to 2019, with special consideration given to the hydrologic position, associated geomorphic feature class, local climate patterns, native and nonnative species, and floristic region for key vegetation metrics and species. We divided the study area into four river segments (referred to as Glen Canyon, Marble Canyon, eastern Grand Canyon, and western Grand Canyon) on the basis of geography and floristic composition and calculated each recorded plant species’ relative frequency and foliar cover by river segment. These data were then used to evaluate species composition relationships among river segments, hydrologic zones, geomorphic features, and sampling years through ordination analysis. Temporal trends in our focal resource objectives—species richness, total foliar cover, proportion of native to nonnative species richness, proportion of native to nonnative species cover, Tamarix cover, Pluchea sericea cover, and Baccharis species cover—were assessed using mixed-effects models. Four patterns related to species composition emerged: (1) species composition of fixed-site sandbars differed from that of randomly selected sites (including randomly selected sandbars), (2) species composition of Glen Canyon sites differed from that of other previously identified floristic regions, (3) species composition differed across hydrologic zones related to dam operations, and (4) species composition within river segments did not change across years. For temporal patterns, four main findings emerged: (1) trends differed between fixed-sites and randomly selected sites; (2) although few directional changes were observed from 2014 to 2019, Baccharis species cover increased at randomly selected sites in areas influenced by daily water fluctuations; (3) native species cover and richness were greater than nonnative species cover and richness across all hydrologic zones; and (4) the temporal trend metrics used here can be used across floristic groups, enabling assessment of the Colorado River ecosystem as a whole. In addition to these findings, lists of recorded plant species are included as appendixes. The variations and patterns in vegetation status and trends presented in this report can be used as a baseline against which future monitoring can be compared.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231026","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2023, Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019: U.S. Geological Survey Open-File Report 2023–1026, 55 p., https://doi.org/10.3133/ofr20231026.","productDescription":"Report: vii, 55 p.; Data Release","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-132835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499774,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114661.htm","linkFileType":{"id":5,"text":"html"}},{"id":415675,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1026/images"},{"id":415674,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1026/ofr20231026.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415673,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1026/covrthb.jpg"},{"id":415672,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEHY2S","text":"Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019","description":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2022, Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9KEHY2S."}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.06028247701303,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 36.94784441270309\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"In 2021, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, drilled and constructed borehole TAN-2336 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory in southeastern Idaho. Borehole TAN-2336 initially was cored from the depths of 34.0–255.8 ft below land surface (BLS) to collect continuous geologic data and then redrilled to complete construction as a monitoring well completed to about 255 ft BLS. Three sediment layers are described in geophysical data, but only one was recovered in core and described as fine sand with evidence of ash (pumice) near 203 ft BLS. Basalt texture for borehole TAN-2336 generally was described as aphanitic, phaneritic, diktytaxitic, and porphyritic. Basalt flows varied from highly fractured to dense with high to low vesiculation.
Geophysical data were examined with photographed core material to make lithologic descriptions as well as suggest zones where groundwater flow was anticipated. Primary pathways for groundwater, fractured basalt, occur in two areas with the first occurrence near 232.0 ft BLS and the second occurrence near 248.6 ft BLS in borehole TAN-2336. The first occurrence was identified near the top of the water column (232.0 ft BLS) and is more pronounced than the bottom interval (248.6 ft BLS). The location of these fractures in borehole TAN-2336 appear to impact the aquifer tests that were conducted following final well construction. Single-well aquifer tests were completed July 14, 2021, to provide estimates of transmissivity and hydraulic conductivity. Estimates for transmissivity and hydraulic conductivity during aquifer test 1 were 1.24×103 feet squared per day (ft2/d) and 1.76 feet per day (ft/d), respectively. Estimates for transmissivity and hydraulic conductivity during aquifer test 2 were 1.22×103 ft2/d and 1.75 ft/d, respectively. The transmissivity and hydraulic conductivity estimates for well TAN-2336 were within range of those considered from previous aquifer tests in other wells near Test Area North.
Water-quality samples were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for select inorganic constituents showed concentrations consistent with signatures from regional groundwater. Water-quality samples analyzed for stable isotopes of oxygen and hydrogen are consistent with signatures from irrigation and agricultural recharge inputs to the aquifer. Results for trichloroethene, vinyl chloride, and strontium-90 were all measured above their respective maximum contaminant levels (MCLs) for public drinking water supplies. The nutrient concentration results are likely being impacted by the remediation amendment introduced to the aquifer to address trichloroethylene concentrations from past waste-disposal activities. These waste-disposal activities have resulted in volatile organic compound and radiochemical detections in groundwater samples collected at well TAN-2336.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235020","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22260","usgsCitation":"Twining, B.V., Treinen, K.C., and Trcka, A.R., 2023, Completion summary for Borehole TAN-2336 at Test Area North, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2023–5020, 33 p. plus appendixes, https://doi.org/10.3133/sir20235020.","productDescription":"Report: vii, 33 p.; Appendix: 2","additionalOnlineFiles":"Y","ipdsId":"IP-137450","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":414342,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.XML"},{"id":414336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5020/coverthb.jpg"},{"id":414337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020"},{"id":414340,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235020/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5020"},{"id":500714,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114615.htm","linkFileType":{"id":5,"text":"html"}},{"id":414341,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5020/images"},{"id":414339,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix2.pdf","text":"Appendix 2","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 2"},{"id":414338,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix1.pdf","text":"Appendix 1","size":"218 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 1"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.07738340728746,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 43.34536223650912\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The Rio Grande, in southern Albuquerque, New Mexico, is a Clean Water Act Section 303(d) Category 5 impaired reach for Escherichia coli (E. coli). The reach is 5 miles in length, extending from Tijeras Arroyo south to the Isleta Pueblo boundary. An evaluation of E. coli and microbial source tracking markers (human-, canine-, and waterfowl-specific sources) was conducted by the U.S. Geological Survey to determine the extent and source of fecal bacteria within the impaired reach of the Rio Grande, primarily during the dry season (November through June) in 2020 and 2021. Samples were collected in the river cross section at three locations within each site and collected during both the dry season and the wet season, thereby providing data over a range of flow conditions to better understand the extent and source of fecal bacteria. Because fecal microorganisms may readily attach to sediments, riverbed material samples were also collected. During the dry season, E. coli concentrations in water were primarily detected below the New Mexico Surface Water Quality Standard of 410 colony forming units per 100 milliliters and mostly human and canine sources were detected. However, approximately 40 percent of the water samples exceeded the Isleta Pueblo water quality standard of 88 colony forming units per 100 milliliters. E. coli concentrations in bed material were detected at low concentrations, and the bed material was a sandy substrate, with little fine-grained material, a suitable habitat that would allow for bacterial growth during the dry season. Significant spatial and temporal differences, where p-values were less than 0.05, were found in water-quality samples for E. coli (seasonal) and the human tracker concentrations (between sites and within a cross section of a site). Given the lack of correlation between discharge and E. coli concentration and the human marker being most prevalent in the study area, the sources of E. coli in the dry season are likely nonpoint sources. The results from this study will help decision makers determine the efficacy of their best management practices and guide new practices to improve water quality in the reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235019","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Travis, R.E., Wilkins, K.L., and Kephart, C.M., 2023, Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21: U.S. Geological Survey Scientific Investigations Report 2023–5019, 48 p., https://doi.org/10.3133/sir20235019.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-139896","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":414732,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.XML","size":"328 KB","linkFileType":{"id":8,"text":"xml"}},{"id":414632,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q2ECYV","text":"U.S. Geological Survey data release—Fecal bacteria and microbial source tracking marker data in the Rio Grande, Albuquerque, New Mexico 2017–2020"},{"id":414629,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5019/images"},{"id":414627,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5019/coverthb.jpg"},{"id":414626,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5019"},{"id":500713,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114616.htm","linkFileType":{"id":5,"text":"html"}},{"id":414733,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235019/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande, South Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 35.5\n ],\n [\n -107,\n 34.75\n ],\n [\n -106.25,\n 34.75\n ],\n [\n -106.25,\n 35.5\n ],\n [\n -107,\n 35.5\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
Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil properties, groundwater flow, land use, and other features that describe the hydrogeology and geochemistry of the aquifer system. Four models were developed to predict the probability of manganese concentrations greater than a detection level of 10 micrograms per liter (μg/L) and greater than three threshold concentrations (50, 150, and 300 μg/L) relevant to drinking-water quality. One model was developed to predict the probability of 1,4-dioxane concentrations greater than a detection level of 0.07 μg/L. The 1,4-dioxane model was limited geographically to Suffolk County because of data availability. Predictions were made for two layers in the upper glacial aquifer and three layers in the Magothy aquifer, which are the upper two of the three major aquifers of the Long Island aquifer system.
The objective of the study described in this report was to demonstrate the application of the methods rather than to develop precise estimates of manganese or 1,4-dioxane concentrations at any given location. The predictive models developed in the study are considered preliminary in the sense that they are an initial effort at developing these kinds of models specifically for Long Island. The models could be improved by the inclusion of additional data, by the use of methods to improve the modeling of infrequent high concentrations of manganese and 1,4-dioxane (above threshold concentrations), and by including more explanatory variables that specifically describe conditions and contaminant sources on Long Island. Nonetheless, the distribution of model predictions and the influence of explanatory variables in the models were consistent with the expected relations between contaminant concentrations and groundwater-flow-system characteristics and the distribution of manmade sources.
Mapped predictions indicated that manganese detections were more probable in the upper glacial aquifer and along the southern shore of Long Island, consistent with the distribution of anoxic conditions in groundwater in the Long Island aquifer system. Manganese was infrequently predicted at concentrations greater than thresholds of concern for drinking-water quality in any of the aquifer layers. Detections of 1,4-dioxane were predicted in the western, more highly developed parts of Suffolk County, in the upper glacial aquifer and the top and middle layers of the Magothy aquifer, and in northwestern Suffolk County in the bottom layer of the Magothy aquifer. Although preliminary in nature and based on limited data, these mapped predictions can be used to generally identify areas where manganese and 1,4-dioxane may be present at concentrations of concern to prioritize areas for future monitoring and to guide future modeling and mapping efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225120","programNote":"National Water Quality Program","usgsCitation":"DeSimone, L.A., 2023, Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5120, 34 p., https://doi.org/10.3133/sir20225120.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133571","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":414438,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5120/images/"},{"id":414437,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.XML"},{"id":414436,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225120/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5120"},{"id":500463,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114612.htm","linkFileType":{"id":5,"text":"html"}},{"id":414439,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AT9YG","text":"USGS data release","linkHelpText":"Data and model archive for preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York"},{"id":414434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5120/coverthb.jpg"},{"id":414435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5120"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.05146015978082,\n 40.628474760922984\n ],\n [\n -73.96502494019428,\n 40.542103435896706\n ],\n [\n -73.54649650851006,\n 40.545560430280744\n ],\n [\n -73.20985407432973,\n 40.61811609149555\n ],\n [\n -72.74128419972719,\n 40.738867255336714\n ],\n [\n -72.19082832762075,\n 40.90411303840304\n ],\n [\n -71.79504600635465,\n 41.08266452105815\n ],\n [\n -72.259066658874,\n 41.20257103045407\n ],\n [\n -72.71853808930965,\n 41.00374947032347\n ],\n [\n -73.1643618534946,\n 41.010615404965876\n ],\n [\n -73.52375039809249,\n 40.948796241204036\n ],\n [\n -73.76485916851937,\n 40.873160815851264\n ],\n [\n -73.87858972060721,\n 40.79055078444986\n ],\n [\n -74.01961560519632,\n 40.72163048139012\n ],\n [\n -74.05146015978082,\n 40.68714353955147\n ],\n [\n -74.05146015978082,\n 40.628474760922984\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Des Moines Water Works (DMWW) is one of the largest water providers in Iowa and as population growth continues, demand for drinking water is increasing. DMWW uses groundwater and surface water as raw water sources to supply the City of Des Moines and surrounding communities. In response to current and future demands, DMWW is in need of a thorough understanding of local groundwater resources, specifically the Des Moines River alluvial aquifer. The Des Moines River alluvial aquifer is hydraulically connected to the Des Moines River and consists of alluvial deposits and glacial outwash sands and gravels. To ensure a sustainable groundwater supply, additional information to better understand and manage groundwater availability within the Des Moines River alluvial aquifer would be beneficial. Beginning in 2018, DMWW partnered with the U.S. Geological Survey to construct a groundwater-flow model to increase understanding of the hydrologic system in the Des Moines area. The model hydrogeologic framework will be enhanced by using multiple geophysical methods of data collection in the Des Moines River, Beaver Creek, and the Des Moines River alluvial aquifer that could provide a better understanding of the geology in the model area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233006","usgsCitation":"Thomas, J.C., Spring, M.A., Gruhn, L.R., and Bristow, E.L., 2023, Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022: U.S. Geological Survey Fact Sheet 2023–3006, 4 p., https://doi.org/10.3133/fs20233006.","productDescription":"Report: 4 p.; 2 Data Releases; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136349","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":414104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3006"},{"id":414105,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.XML","description":"FS 2023–3006"},{"id":414103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3006/coverthb2.jpg"},{"id":414112,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS dataset","linkHelpText":"—National Hydrography Dataset— USGS National Hydrography Dataset Best Resolution for Hydrologic Unit 4 – 2001"},{"id":414109,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":414110,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499566,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114475.htm","linkFileType":{"id":5,"text":"html"}},{"id":414138,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":414113,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3006/images"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Des Moines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.69895287733283,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.66093681949087\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
Winslow Township and the Camden County Municipal Utility Authority (CCMUA) developed a plan to shut down the Winslow sewage-treatment facility and associated effluent infiltration facility and transfer the effluent to the CCMUA sewage-treatment facility on the Delaware River in Camden, New Jersey. Winslow Township reduced groundwater withdrawals from the Kirkwood-Cohansey aquifer system to offset groundwater recharge lost with the cessation of effluent infiltration. The U.S. Geological Survey, in cooperation with Winslow Township and the CCMUA, collected data to evaluate conditions prior to cessation of effluent infiltration and installed two continuous-record streamflow-gaging stations. Streamflow measurements also were made at two low-flow partial-record sites, and groundwater levels were measured in 17 wells at high and low water-level periods (May and September 2010). A groundwater-flow model provides estimated changes in base flow of the Great Egg Harbor River under several groundwater-withdrawal and effluent infiltration scenarios.
Water levels were measured in an observation well 480 feet (ft) from the infiltration lagoons during 1971–2010. A downward trend in water levels in the well prior to 1985 is attributed in part to increased impervious surfaces and groundwater withdrawals associated with development in the area that began in the early 1970s. From late 1985 to 2010, there was an upward trend in water levels in the well that is attributed to the construction of nearby effluent infiltration lagoons in 1985 and the increasing rate of effluent infiltration during the period. Recent and historical measurements made at the four surface-water sites were correlated with same-day discharges measured at three nearby index stations to estimate continuous low-flow record at the sites. Effects on base flow caused by reductions in groundwater withdrawals or the cessation of effluent infiltration in Winslow Township could not be ascertained from the available data with the statistical and analysis methods used.
Groundwater discharge to streams (base flow) was simulated with a groundwater-flow model of the Great Egg Harbor and Mullica River Basins. Simulated monthly base flows using 2008–10 withdrawal rates and effluent recharge (Scenario 1) are generally about 1.5 million gallons per day (Mgal/d) greater than simulated base flows using 2003–07 withdrawal rates (Baseline Scenario) because of the 1.57 Mgal/d reduction in average withdrawals by Winslow Township from the Kirkwood-Cohansey aquifer system from 2003–07 to 2008–10. Simulated monthly base flows using 2008–10 withdrawals but without effluent infiltration (Scenario 2) are very similar to, but typically slightly lower than, Baseline Scenario base flows.
Three hypothetical future distributions of groundwater withdrawals from existing Winslow Township wells are simulated, each without effluent infiltration and using the same groundwater withdrawal rate as Scenario 2, but with different hypothetical distributions of withdrawals among existing Winslow Township wells. The Scenario 3 and 4 base flows are greater than the Baseline Scenario base flows in all months, and the Scenario 5 base flows are less than the Baseline Scenario base flows in all months. The simulation results indicate that a reduction in average withdrawals from the Kirkwood-Cohansey aquifer system by 1.57 Mgal/d offsets the reduction of effluent infiltration by about the same rate, resulting in nearly unchanged base flows in the Great Egg Harbor River near Blue Anchor (01410820).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235002","collaboration":"Prepared in cooperation with the Township of Winslow and the Camden County Municipal Utilities Authority","usgsCitation":"Carleton, G.B., and Pope, D.A., 2023, Hydrologic effects of possible changes in water-supply withdrawals from, and effluent recharge to, the Kirkwood-Cohansey aquifer system, Winslow Township, Camden County, New Jersey: U.S. Geological Survey Scientific Investigations Report 2023–5002, 16 p., https://doi.org/10.3133/sir20235002.","productDescription":"Report: vii, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057410","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":413542,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7154G0Z","text":"USGS data release","linkHelpText":"MODFLOW-2000 model used to evaluate the effects of possible changes in water-supply withdrawals from, and effluent recharge to, the Kirkwood-Cohansey aquifer system, Winslow Township, Camden County, New Jersey"},{"id":500487,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114441.htm","linkFileType":{"id":5,"text":"html"}},{"id":413541,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5002/images/"},{"id":413538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5002/sir20235002.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5002"},{"id":413537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5002/coverthb.jpg"},{"id":413539,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235002/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5002"},{"id":413540,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5002/sir20235002.XML"}],"country":"United States","state":"New Jersey","county":"Camden County","otherGeospatial":"Winslow Township","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75,\n 39.833\n ],\n [\n -75,\n 39.5833\n ],\n [\n -74.833,\n 39.5833\n ],\n [\n -74.833,\n 39.833\n ],\n [\n -75,\n 39.833\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
The WaterML2: Part 3 - Surface Hydrology Features (HY_Features) Conceptual Model was published by OGC in 2018. This report documents the use of HY_Features concepts in support of two key tasks: (1) local to continental hydrologic modeling; and (2) referencing river corridor data to hydrographic networks. The presented use cases are applicable in hydroscience research and assessments, water resources engineering practices, and drought and flood responses.
Before the HY_Features conceptual model there was no internationally recognized standard for the design of software and data for the hydroscience and engineering community. This report presents progress towards a logical data model that interprets the abstract HY_Features concepts for use in geospatial workflows, modeling applications, and web data systems that integrate hydrologic data.
The use cases addressed include: (1) hydrologic model control volume definition; (2) hydrologic network connectivity; (3) characterization of catchments with landscape and atmospheric data; (4) river corridor characterization; (5) hydrologic location; and (6) flow network location. Each use case is described briefly along with an analysis of the information requirements. This report presents a summary of the logical model designed to satisfy the needs of these use cases and a summary of updates and changes proposed for HY_Features.
Changes for consideration by the HY_Features Standards Working Group include the following.
Provide more clarity on the inherited properties and associations of features that \"realize\" the catchment and nexus concepts from HY_Features.
Add nexus realization feature types to represent the outlet of catchments that are \"frontal\" (terminate to the ocean or a large waterbody) or \"inland sinks.\"
Add a \"HY_Flowline\" feature as a superclass of HY_Flowpath providing linear referencing on waterbodies that are not catchment realizations.
Add an association or interface to support connection between surface catchments and hydrogeologic units.
Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used to make monthly mean streamflow and base-flow predictions at 162 ungaged sites in the study area. Daily streamflow observations and computed base flow from 247 streamgages were used as the basis for the development of these RF models. RF models were constructed from basin and climatic characteristics and related to observed monthly mean streamflow values; models were used to compute monthly base-flow estimates from selected streamgages in and adjacent to the Mississippi River Alluvial Plain extent, which includes streamflows from parts of Alabama, Arkansas, Colorado, Florida, Illinois, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, New Mexico, Tennessee, and Texas. The explanatory variables for the models were selected to represent physical characteristics and climatic time series for the contributing drainage basins to the streamgages and ungaged locations of interest. The Nash-Sutcliffe efficiency between observed and simulated monthly mean streamflow was greater than 0.80 for 155 of the 247 streamgages, with a median Nash-Sutcliffe efficiency value of 0.83. The streamflow and base-flow simulations can be used to improve inflow values and to verify the Mississippi River Alluvial Plain groundwater flow model. The statistical model, input data, and response data (simulated monthly mean streamflows) are available as a U.S. Geological Survey software release and a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225079","programNote":"Water Availability and Use Science Program","usgsCitation":"Dietsch, B.J., Asquith, W.H., Breaker, B.K., Westenbroek, S.M., and Kress, W.H., 2023, Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018: U.S. Geological Survey Scientific Investigations Report 2022–5079, 17 p., https://doi.org/10.3133/sir20225079.","productDescription":"Report: v, 17 p.; Tables: 4; Data Release; Dataset; Software Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105480","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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monthly mean streamflows with estimated flows for the random forest models using leave-one-out cross validation in the Mississippi embayment regional aquifer system, 1901–2016."},{"id":413439,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5079/sir20225079_table3.1.xlsx","text":"Table 3.1","size":"35.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022–5079 Table 3.1","linkHelpText":"—Performance metrics of comparing the observed monthly mean streamflows with estimated flows for the random forest models using leave-one-out cross validation in the Mississippi embayment regional aquifer system, 1901–2016."},{"id":413436,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5079/sir20225079_table1.1.xlsx","text":"Table 1.1","size":"41.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022–5079 Table 1.1","linkHelpText":"—U.S. Geological Survey streamgages used to train and evaluate performance in the 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flows with estimated base flows for the model trained with all gaged sites in the Mississippi embayment regional aquifer system, 1901–2018."},{"id":413434,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5079/sir20225079.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":413435,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5079/images"}],"country":"United States","state":"Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.03025800131559,\n 37.28445113180966\n ],\n [\n -89.46951716923496,\n 37.3543169113709\n ],\n [\n -90.7872946729918,\n 37.28445113180966\n ],\n [\n -91.35833159128646,\n 36.75839141479749\n ],\n [\n 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U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The objective of the work presented in this report is to develop equations that can be used to extend the base flow record at multiple partial-record streamgaging stations at Acadia National Park in eastern coastal Maine based on nearby continuous-record streamgaging stations. Daily mean streamflow values at U.S. Geological Survey continuous-record streamgaging station Otter Creek near Bar Harbor, Maine (station 01022840) had stronger correlations with instantaneous measurements during base flow conditions from 2006 to 2020 at 14 partial-record streamgaging stations at Acadia National Park than the other four continuous-record streamgaging stations tested for use as index stations. Index stations are continuous-record stations on hydrologically similar streams that have the potential to be used to extend the record at the partial-record station. Base flow is that part of streamflow that is sustained primarily by groundwater discharge. It is not attributable to direct precipitation or melting snow. Five of the partial-record stations had strong correlations with Otter Creek (correlation coefficient greater than 0.90) and relatively low root mean square errors (from 0.04 to 0.19). An additional four partial-record stations had fair correlations with Otter Creek (correlation coefficient from 0.79 to 0.9) and relatively low root mean square errors (from 0.05 to 0.19). For these 10 stations, maintenance of variance extension type 1 (MOVE.1) record extension equations computed in this report provide a reasonable method for extending the partial record, estimating summer monthly means and medians, and estimating daily mean streamflow values at these sites on days with no streamflow (discharge) measurements. Four of the partial-record stations have weak correlations (less than 0.78) or high root mean square error values (greater than 9) or both, indicating that record extension techniques are not appropriate for these partial-record stations using currently [2022] available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225126","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lombard, P.J., 2023, Estimating streamflow for base flow conditions at partial-record streamgaging stations at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2022–5126, 13 p., https://doi.org/10.3133/sir20225126.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-143769","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":413317,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZP8XHG","text":"USGS data release","linkHelpText":"Data and code to support MOVE.1 regression equations for streamflow at partial-record streamgaging stations at Acadia National Park, Maine:"},{"id":413315,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.XML"},{"id":413312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5126/coverthb.jpg"},{"id":413313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5126"},{"id":413316,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5126/images/"},{"id":413864,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225126/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5126"},{"id":500481,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114380.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.16726259768689,\n 44.32624433734378\n ],\n [\n -68.17739845831488,\n 44.36973371484888\n ],\n [\n -68.21954229987337,\n 44.38307924264697\n ],\n [\n -68.23981402112935,\n 44.41090441296549\n ],\n [\n -68.28035746364178,\n 44.41928748605292\n ],\n [\n -68.29422758871185,\n 44.406712425764226\n ],\n [\n -68.33050330043281,\n 44.35371506667144\n ],\n [\n -68.38971806515461,\n 44.35562227826236\n ],\n [\n -68.40305472387581,\n 44.31937464393428\n ],\n [\n -68.39025153150348,\n 44.27890346604781\n ],\n [\n -68.3390387620142,\n 44.21661505589944\n ],\n [\n -68.3112985118746,\n 44.217762064180675\n ],\n [\n -68.2883594588743,\n 44.24566571236582\n ],\n [\n -68.30329651664208,\n 44.285396004838105\n ],\n [\n -68.24514868461804,\n 44.281958867796675\n ],\n [\n -68.1982036459197,\n 44.29532438221068\n ],\n [\n -68.16726259768689,\n 44.32662596339111\n ],\n [\n -68.16726259768689,\n 44.32624433734378\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Condit Dam was removed from river kilometer (rkm) 5.3 of the White Salmon River, Washington, in 2011 and 2012 after blocking upstream passage of anadromous fish for nearly 100 years. The dam removal opened habitat upstream and improved habitat downstream with addition of cobble and gravel to a reach depauperate of spawning and rearing habitat. We assessed juvenile anadromous salmonid abundance and distribution in the subbasin from 2016 through 2021 to evaluate the efficacy of natural recolonization. We sampled for outmigrant smolts and other life-history stages at a rotary screw trap at rkm 2.3 and for juvenile abundance at sites in Buck and Rattlesnake Creeks, two primary tributaries upstream from the former dam location.
We estimated smolt abundance of steelhead (Oncorhynchus mykiss) and coho salmon (O. kisutch) at the screw-trap site during most years of the study. High flow and missed trapping days in 2017 precluded estimates, and the trap was not fished during 2020 because of the onset of the COVID-19 pandemic. Steelhead smolt-abundance estimates ranged from 3,581 to 5,851 fish; coho salmon smolt-abundance estimates ranged from 1,093 to 1,773 fish, although in 2021, only 2 coho salmon smolt were captured and no estimate was made.
Other species and life stages also were captured in the screw trap. Steelhead and coho salmon fry and parr, and Chinook salmon (O. tshawytscha) fry were captured, indicating the presence and likely use of improved habitat downstream from the former dam site by multiple life stages and spawning success upstream from the screw-trap site. Chinook salmon fry were captured, indicating spawning success upstream from the screw-trap site. Fry numbers varied greatly by day and year. Yearly variation in Chinook and coho salmon fry numbers may have been influenced by high flows following spawning causing redd scour and egg-to-fry mortality. Three bull trout (Salvelinus confluentus) were caught in the screw trap, one in June 2018, one in June 2019, and one in June 2021. All three bull trout showed smolt characteristics and were tagged with passive integrated transponders (PITs). The bull trout captured in June 2018 was detected at Bonneville Dam Corner Collector several days later, indicating likely anadromy. We also captured lamprey in the screw trap: 44 during 2018, 31 during 2019, and 11 during 2021; we believe most were adult brook lamprey (Lampetra richardsoni), although some could have been Pacific lamprey (Entosphenus tridentatus) macropthalmia.
We confirmed the presence of juvenile steelhead (through smolt origin data) and coho salmon in Mill, Buck, and Rattlesnake Creeks, which are all upstream from the former site of Condit Dam. Juvenile salmonid abundance sampling at a site in Buck Creek during 2016–20 indicated the presence of juvenile coho salmon in all years except 2020. Total salmonid abundance (steelhead and coho salmon combined) at the Buck Creek site each year exceeded abundance in sampling prior to dam removal in 2009 and 2010. Juvenile salmonid abundance sampling in Rattlesnake Creek during 2016–20 indicated the presence of juvenile coho salmon in 2017, 2018, and 2019. Total juvenile salmonid abundance at the Rattlesnake Creek site was highly variable, sometimes exceeding and sometimes less than abundance prior to dam removal during 2001–05. During the period covered by this report, adult salmonid returns to the Columbia River were decreasing, largely because of marine survival. The extent to which this basin-wide decrease affected adult returns and juvenile populations in the White Salmon River subbasin is not known.
Despite a period of poor marine survival, PIT-tagged smolt and juvenile steelhead and coho salmon from the screw trap and tributaries returned to Bonneville Dam. Smolt-to-adult return rates from the screw trap to Bonneville Dam were similar to those in other nearby rivers during this period. However, data are still incomplete for some years and sample sizes were low. Future tagging and monitoring would be beneficial to track this valuable metric.
Genetic samples from steelhead smolt and parr collected at the screw trap and some main-stem electrofishing during 2016 were analyzed for Genetic Stock Identification (GSI) by CRITFC. Preliminary data showed that White Salmon River fish were the most common at about 42 percent, with 19 percent typing to Hood River, Oregon stock, and about 26 percent typing to Skamania stock, a common hatchery stock in the area. Winter and summer runs were represented in the samples.
Juvenile salmonid sampling in the White Salmon River, Washington, following removal of Condit Dam, demonstrated that anadromous salmonids are using newly opened habitat upstream from the former dam site and improved lower river habitat. Steelhead and coho salmon smolts are being produced upstream from the former dam site, and some have returned to Bonneville Dam as adults. Chinook salmon spawning upstream from our smolt trap site are producing fry. These results are encouraging for success of the strictly natural recolonization strategy. However, declines in anadromous runs to the larger Columbia River Basin also likely have affected the White Salmon runs and our data may not reflect full capacity of the White Salmon River subbasin juvenile production. Continued abundance, distribution, and GSI monitoring will help to track the evolution of anadromous fish in the White Salmon River under a natural recolonization strategy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221117","collaboration":"Prepared in cooperation with Yakama Nation Fisheries and Mid-Columbia Fisheries Enhancement Group","usgsCitation":"Jezorek, I.G., and Hardiman, J.M., 2023, Juvenile salmonid monitoring to assess natural recolonization following removal of Condit Dam on the White Salmon River, Washington, 2016–21: U.S. Geological Survey Open-File Report 2022–1117, 23 p., https://doi.org/10.3133/ofr20221117.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-137364","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":413140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1117/coverthb.jpg"},{"id":413143,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1117/images"},{"id":413142,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221117/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1117"},{"id":413141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1117/ofr20221117.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1117"},{"id":499726,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114378.htm","linkFileType":{"id":5,"text":"html"}},{"id":413144,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1117/ofr20221117.XML"}],"country":"United States","state":"Washington","otherGeospatial":"White Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78283038944755,\n 46.100301136884156\n ],\n [\n -121.78283038944755,\n 45.67990372212273\n ],\n [\n -121.22276550358751,\n 45.67990372212273\n ],\n [\n -121.22276550358751,\n 46.100301136884156\n ],\n [\n -121.78283038944755,\n 46.100301136884156\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
Censored and uncensored generalized additive models (GAMs) were developed using streamflow data from 941 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of daily streamflow for streams draining to the Gulf of Mexico. The modeled decadal statistics comprise no-flow fractions and L-moments of logarithms of nonzero streamflow for six decades (1950–2009). These statistics represent metrics of decadal flow-duration curves (dFDCs) derived from about 10 million daily mean streamflows. The L-moments comprise the mean, coefficient of L-variation, and the third through fifth L-moment ratios. The GAMs were fit to the statistics from 941 streamgages and 2,750 streamgage-decades by using watershed properties such as basin area and slope, decadal precipitation and temperature, and decadal values of flood storage and urban development percentages. The GAMs then estimated decadal statistics for 9,220 prediction locations (stream reaches) coincident with outlets of level-12 hydrologic unit codes. Both entire dataset (whole model) and leave-one-watershed-out model results are reported. No-flow fractions are censored data, and Tobit extensions to GAMs were used to model ephemeral streamflow conditions. Conversely, uncensored GAMs were used for estimation of the L-moments. The GAMs are shown, by coverage probabilities, to construct reliable 95-percent prediction limits. An example shows how no-flow fractions and L-moments may be used to approximate dFDCs by using selected probability distributions (mathematical formulas) including the asymmetric exponential power, generalized normal, and kappa distributions.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211