A comprehensive study to evaluate water-quality trends, while considering natural hydroclimatic variability, in the Red River of the North Basin and assess water-quality conditions for the Red River of the North crossing the international boundary near Emerson, Manitoba, Canada (the binational site), was completed by the U.S. Geological Survey in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada. The international Red River of the North Basin encompasses 3 U.S. States (South Dakota, North Dakota, and Minnesota) and 1 Canadian Province (Manitoba). Water quality in the Red River of the North Basin is of concern for both Federal governments and State and Provincial governments. Water-quality objectives have been previously established for selected dissolved ions and recently (2019) proposed for selected nutrients for the binational site.
In the current (2020) study, water-quality data from State, Provincial, and Federal agencies in the United States and Canada for sites in the Red River of the North Basin from 1970 to 2017 were compiled and used for trend analysis. Trend analysis using a water-quality dataset from multiple agencies that collect water-quality data for various objectives presented multiple challenges. The trend-analysis approach was able to accommodate differences in water-quality data caused by field-collection and laboratory-analytical method differences, disparities in sampling frequencies, and spatial and temporal gaps in data. Most of these challenges were overcome by the statistical tool, R–QWTREND, which identifies trends in concentration unrelated to variability in streamflow.
The integrated basin approach used in the current study, combined with comparing current data trends with historical trends, provided valuable insights into understanding how water quality is changing spatially (34 sites analyzed for a recent period, 2000–15) and temporally (5 sites analyzed for a 45-year historical period, 1970–2015) within the Red River of the North Basin. One of the most consistent spatial and temporal changes observed in the current study was increasing concentrations of sulfate among tributary and main-stem sites since 2000. For some sites, increases were detected starting as early as 1985. Total dissolved solids and chloride concentrations had spatial and temporal patterns like sulfate. Although R–QWTREND removes the variability in constituent concentration caused by natural streamflow variability, all variability in sulfate caused by hydroclimatic variability may not be captured because of changes in hydrologic pathways and changes in the contributions of sulfate from various natural sources.
Nutrient concentrations demonstrated less consistent spatial and temporal changes than sulfate, and changes in nutrient concentrations were assumed to be more closely tied to human-induced rather than natural changes. Nitrate-plus-nitrite concentrations were mostly increasing in the upper Red River of the North subbasin, and for nitrate plus nitrite and total nitrogen, the Sheyenne River subbasin had consistent decreasing concentrations. Since 2000, total phosphorus has decreased in the upper Red River of the North subbasin, but total phosphorus concentration has increased for sites in the lower Red River of the North subbasin, and for some main-stem sites, concentrations have been increasing since 1985. Unlike sulfate, the pattern in historical trends for total phosphorus for the main-stem sites differed from tributary sites, indicating that human-induced changes affected tributaries and main-stem sites differently.
The more detailed evaluation of flow-averaged water-quality conditions for the binational site provided an understanding of how loads have changed over time and what proportion of the year and season concentrations are expected to exceed water-quality objectives. In a basin with highly variable streamflow like the Red River of the North, the trend in flow-averaged load (assuming streamflow conditions are the same year after year) provided a robust measure of change over time. Increasing concentrations of sulfate, chloride, total dissolved solids, and total phosphorus since 1985 for the binational site resulted in longer periods of exceedance of water-quality objectives per year occurring over time. For total nitrogen, decreasing concentrations resulted in shorter periods of exceedance per year during 1980 to 2015, but concentrations were still expected to exceed the water-quality objective about half the year. Periods of when exceedances were likely to occur during the year were affected by the source and transport mechanisms of the constituent.
Trend results from this effort identified how water quality has changed across the basin, and further investigation would help to identify causes for the trends observed here. Information from the current study provides a basis for future trend attribution studies, evaluation of water-quality objectives, and development of comprehensive strategies for reducing nutrients to desired targets and establishes a baseline for tracking future progress in the Red River of the North Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205079","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada","usgsCitation":"Nustad, R.A., and Vecchia, A.V., 2020, Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017: U.S. Geological Survey Scientific Investigations Report 2020–5079, 75 p., https://doi.org/10.3133/sir20205079.","productDescription":"Report: ix, 75 p.; 2 Tables; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-113881","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5079/coverthb.jpg"},{"id":377260,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C9JAMY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis—Data supporting water-quality trend analysis in the Red River of the North basin, 1970–2017"},{"id":377258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5079"},{"id":377259,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079_tables_2_and_3.xlsx","text":"Tables 2 and 3","size":"60.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5079 Tables 2 and 3"},{"id":377261,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","description":"USGS Data Release","linkHelpText":"— U.S. Geological Survey National Water Information System database"}],"country":"United States, Canada","state":"Minnesota, North Dakota, South Dakota, Manitoba","otherGeospatial":"Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27294921875,\n 50.14874640066278\n ],\n [\n -98.85498046875,\n 49.710272582105695\n ],\n [\n -100.81054687499999,\n 49.38237278700955\n ],\n [\n -100.7666015625,\n 48.58932584966975\n ],\n [\n -99.86572265625,\n 47.040182144806664\n ],\n [\n -98.525390625,\n 46.7248003746672\n ],\n [\n -98.76708984374999,\n 46.37725420510028\n ],\n [\n -98.63525390624999,\n 45.96642454131025\n ],\n [\n -97.91015624999999,\n 45.55252525134013\n ],\n [\n -97.14111328125,\n 45.321254361171476\n ],\n [\n -95.77880859375,\n 45.89000815866184\n ],\n [\n -95.2294921875,\n 46.28622391806706\n ],\n [\n -95.1416015625,\n 46.73986059969267\n ],\n [\n -95.0537109375,\n 47.68018294648414\n ],\n [\n -94.59228515625,\n 47.79839667295524\n ],\n [\n -94.306640625,\n 48.07807894349862\n ],\n [\n -94.54833984375,\n 48.29781249243716\n ],\n [\n -95.1416015625,\n 48.23930899024907\n ],\n [\n -95.2734375,\n 48.850258199721495\n ],\n [\n -95.42724609375,\n 49.1242192485914\n ],\n [\n -96.7236328125,\n 50.02185841773444\n ],\n [\n -97.27294921875,\n 50.14874640066278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503 1608
Mountain View Road,
Rapid City, SD 57702
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
In 2019, the U.S. Geological Survey, in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group, studied the magnitude and recurrence interval of streamflow (discharge) needed to initiate bed movement of gravel-sized and finer sediment in a segment of the Colorado River in Colorado to better understand sediment movement and its relation to flow regimes of the river. The study area extended from the confluence of the Blue and Colorado Rivers near Kremmling, Colorado, downstream to the confluence of the Eagle and Colorado Rivers near Dotsero, Colo. Bed movement occurred more frequently and at lower streamflows from State Bridge to Catamount Bridge compared to the study area upstream from State Bridge. As a result, the flushing flow was characterized in the study area using two definitions: the “upstream flushing flow” for locations above State Bridge and the “downstream flushing flow” for locations below State Bridge.
Acoustic data from stationary hydrophones continuously deployed in the spring and summer of 2019 and longitudinal hydrophone acoustic profiles manually collected in summer 2019 were used to identify the streamflow needed for incipient gravel-bed movement and establish flushing flows defined for this study. The upstream flushing flow was defined as 3,000 cubic feet per second (ft3/s) at streamgage 09058000 Colorado River near Kremmling, Colo. (the Kremmling streamgage) based on the underwater acoustic data from the downstream location at the Radium stationary site (2,950 ft3/s at the Kremmling streamgage which was rounded to 3,000 ft3/s). The downstream flushing flow was defined as 2,400 ft3/s at the Kremmling streamgage or 3,100 ft3/s at streamgage 09060799 Colorado River at Catamount Bridge, Colo. (the Catamount Bridge streamgage) based on the more conservative streamflow associated with the flushing flow defined using underwater acoustic data from the downstream location at the above Catamount Bridge stationary site (2,310 ft3/s at the Kremmling streamgage which was rounded to 2,400 ft3/s and 3,040 ft3/s at the Catamount Bridge streamgage which was rounded to 3,100 ft3/s).
The annual series of peak-streamflow data at the Kremmling streamgage were used to estimate annual exceedance probability (AEP) streamflows to compare to the flushing flow. Results from the Denver Water Platte and Colorado Simulation Model were used to generate daily peak-streamflows for a future conditions scenario provided for this report. The upstream flushing flow of approximately 3,000 ft3/s at the Kremmling streamgage has an AEP near 0.50 (2-year return period) depending on the period of historical record and an AEP near 0.43 (2.33-year return period) for the future period. The downstream flushing flow of approximately 2,400 ft3/s at the Kremmling streamgage has an AEP near 0.67 (1.5-year return period) depending on the period of historical record and an AEP near 0.67 (1.5-year return period) for the future period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205069","collaboration":"Prepared in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group and the Colorado River Water Conservation District","usgsCitation":"Kohn, M.S., Marineau, M.D., Hempel, L.A., and McDonald, R.R., 2020, Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2020–5069, 39 p., https://doi.org/10.3133/sir20205069.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114386","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":376916,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J5L78O","text":"USGS data release","linkHelpText":"Acoustic, Spatial, and Sediment Size Data 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Daily mean streamflow was estimated for all the nontidal parts of the Chesapeake Bay riverine system with the Unit Flows in Networks of Channels computer application using measured streamflow at the most downstream gage of selected rivers. The streamflows estimated by the Unit Flows in Networks of Channels computer application were aggregated at the 12-digit Hydrologic Unit Code level, after which base flow was estimated by two hydrograph-separation methods. Based on six sites selected for comparison, modeled streamflows are typically within an order of magnitude of measured streamflows, and monthly mean streamflows are in better agreement than daily streamflows. For the six selected sites, the base-flow values calculated by the two hydrograph-separation methods were compared. The monthly base-flow values also were in better agreement than the daily base-flow values. The modeled data were animated to better visualize spatial and temporal variability of streamflow and base-flow index.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205055","usgsCitation":"Buffington, P.C., and Capel, P.D., 2020, Estimating streamflow and base flow within the nontidal Chesapeake Bay riverine system: U.S. Geological Survey Scientific Investigations Report 2020–5055, 26 p., https://doi.org/10.3133/sir20205055.","productDescription":"Report: v, 26 p.; Figure Animations: Figures 15–18; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098068","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":376516,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","linkHelpText":"— National Water Information System database"},{"id":376515,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P906K5GZ","text":"USGS data 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watershed."},{"id":376512,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5055/sir20205055_fig16_animation.mp4","text":"Figure 16 Animation","size":"56.6 MB","description":"SIR 2020–5055 Figure 16","linkHelpText":"— Monthly base-flow index (BFI) animation for the watershed of the Susquehanna River, upstream from Harrisburg, Pennsylvania."},{"id":376511,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5055/sir20205055_fig15_animation.mp4","text":"Figure 15 Animation","size":"51.0 MB","description":"SIR 2020–5055 Figure 15","linkHelpText":"— Monthly mean streamflow animation for the watershed of the Susquehanna River, upstream from Harrisburg, Pennsylvania."},{"id":376510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5055/sir20205055.pdf","text":"Report","size":"3.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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National Water-Quality Assessment (NAWQA) Science Team
12201 Sunrise Valley Drive
Reston, VA 20192
Surface-water and suspended-sediment samples were collected and analyzed by the U.S. Geological Survey for multiple current-use pesticides and pesticide degradates approximately every 2 weeks at up to five sites in the Yolo Bypass and Cache Slough Complex before, during, and after augmented flow pulses in summer and fall 2016 and 2018 as well as during ambient flow conditions in summer and fall 2017 (no flow pulse). In 2016, augmented flows occurred during the summer (July) and required the pumping of Sacramento River water by local Reclamation Districts into the Colusa Basin Drain and Yolo Bypass Toe Drain. In contrast, augmented flows in 2018 occurred in the fall (August–September) and used agricultural tailwater (primarily rice field discharge water) to create the flow pulse. Water samples were analyzed by the U.S. Geological Survey for a suite of 175 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry laboratory methods. Suspended sediments filtered from the water samples were analyzed for 143 pesticides and degradates by gas chromatography with mass spectrometry.
During the study, 53 pesticides were detected, and all the samples contained mixtures of multiple pesticides at concentrations ranging from below method detection limits to 8,780 nanograms per liter. Pesticides used in growing rice were the dominant pesticides present at four of the five sites sampled and urban-use pesticides dominated at the remaining site. Overall, total pesticide concentrations tended to be higher at sites in the northern part of the Yolo Bypass and lower at southern sites, except for the farthest downstream site which received additional pesticide inputs from the Sacramento River. Flow-pulse water source influenced total pesticide concentrations in the Yolo Bypass and Cache Slough Complex, and the highest total pesticide concentrations at each site were detected either immediately before or during the flow pulse generated with agricultural tailwater in 2018. Data gathered during this study will aid the California Department of Water Resources and other agencies working in the region in adaptively managing pulse flows in the Yolo Bypass and Cache Slough Complex, as one of several California Natural Resources Agency’s Delta Smelt Resiliency strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201076","collaboration":"Prepared in cooperation with the California Department of Water Resources and the State and Federal Contractors Water Agency","usgsCitation":"Orlando, J.L., De Parsia, M., Sanders, C., Hladik, M., and Frantzich, J., 2020, Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California: U.S. Geological Survey Open-File Report 2020–1076, 101 p., https://doi.org/10.3133/ofr20201076.","productDescription":"Report: vi, 101 p.; Data release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-109449","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"U.S. Geological Survey, 2019, National Water Information System: U.S. Geological Survey Web interface"},{"id":376628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1076/ofr20201076.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1076/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85760498046875,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The headwaters of the Snake River are in the mountains of northwestern Wyoming. Maintaining the recognized high quality of water in Grand Teton National Park is a National Park Service (NPS) priority. To characterize and understand the water resources of Grand Teton National Park, the NPS established a monitoring program to monitor the quality of area surface waters. Beginning in 2006, water was sampled by the NPS and analyzed for a range of chemical species at the Snake River above Jackson Lake at Flagg Ranch streamgage 13010065 (hereafter referred to as “Snake River at Flagg Ranch”), a site where the U.S. Geological Survey (USGS) previously sampled and analyzed water from 1986 through 2004. The USGS, in cooperation with the NPS, evaluated water-quality data collected by both entities to determine if discharge and total dissolved solids (referred to as dissolved solids) have changed in the Snake River at the Flagg Ranch.
To understand potential changes with time in dissolved solids, discharge was analyzed between January 1986 and December 2018, which corresponds with the time period when water-quality data were collected. Mean annual discharge varied during this time, with high, low, mean, and median flows generally increasing from 1986 through 1998, decreasing through 2005, and then generally increasing through 2018.
Combining water-quality data collected by the USGS and NPS provides a longer, more complete dataset for analyses. During the period of time when NPS was the sampling agency, specific conductance data were collected, but dissolved-solids data were not. The specific conductance data from both agencies were evaluated to determine if combining the data was justified. The interquartile ranges of data collected by both agencies are similar, and rapid, large changes in values during the period of transition between USGS and NPS sampling do not occur. The USGS and NPS datasets are not statistically different in the spring, summer, or fall, but are statistically different in the winter. The winter differences could be a function of the lack of wintertime NPS sampling, which excludes higher-concentration, lower-discharge data or a function of changes in the actual concentration in the stream. Although there is some difference in the winter datasets, the similarity in sampling methods and general overall data characteristics justifies combining the data for trend analyses.
Because the dissolved-solids parameter is useful for managers, it is often calculated from specific conductance using a linear regression model when dissolved-solids data are absent. For this study, creating a modeled dataset of dissolved solids for the NPS data collection period of time provided a longer, more complete dataset of dissolved-solids concentrations.
The concentrations of dissolved solids over time are identified by season and indicate that samples collected in the fall and winter have higher concentrations than samples collected in spring and summer. Specifically, the mean dissolved-solids concentrations in fall and winter are around 188 milligrams per liter (mg/L), whereas the mean concentrations are around 130 mg/L in spring and summer. This difference is generally attributed to the dilution of spring and summer samples by snowmelt generated runoff during the high-flow period of the year.
Trend analyses of dissolved-solids concentrations and loads indicate that an upward trend in concentration from 1986 to 2018 is likely, and a downward trend in load is highly likely. Comparing 1986 to 2018, dissolved-solids concentration is estimated to have increased by 2.25 mg/L (1.4 percent). During that same period, the dissolved-solids load is estimated to have decreased 11.8 million kilograms per year (12-percent decrease). This decrease is consistent with the estimated decrease in annual mean of daily mean discharge. Because 10 percent of the total change in dissolved-solids load is related to a change in the concentration-discharge relationship and 2 percent is related to changes in discharge, the decreased load is related less to changes in discharge and more to landscape scale processes that are affecting the concentration-discharge relationship.
As noted above, the data collected by the USGS and NPS are generally comparable with regards to sampling and analytical methods, and data collected by both agencies were used as one dataset for trend analyses. The current NPS sampling schedule, however, is creating a dataset biased towards lower concentration dissolved-solids data, which occurs during higher summer flows, by only sampling during April through November. From 1986 to 2018, the percentage of NPS samples is small enough that the effect on trends is expected to be minimal. Because of the importance of low flow (winter season) data, it is likely that an April through November sampling regime may affect the ability to detect trends or determine seasonality in the future. Collection of winter data in particular is important based on the findings that the changes in the modeled concentration-discharge relationship over time have been most pronounced during the winter (represented by February) months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205062","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Miller, O.L., and Eddy-Miller, C.A., 2020, Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018: U.S. Geological Survey Scientific Investigations Report 2020–5062, 19 p., https://doi.org/10.3133/sir20205062.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-116863","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":376357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5062/coverthb.jpg"},{"id":376358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5062/sir20205062.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5062"}],"country":"United States","state":"Wyoming","otherGeospatial":"Flagg Ranch watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05804443359375,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.081666311450526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
Helena, MT 59601
Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
West Valley City, UT 84119–2047
Groundwater susceptibility to contamination was investigated by using environmental tracer-based groundwater age metrics in the south Atlantic and Gulf Coast principal aquifer systems of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands. Samples of dissolved gas, tritium, sulfur hexafluoride, tritiogenic helium, and carbon-14 were collected from 231 public supply wells in the 3 principal aquifer systems. Dissolved gas models were used to characterize recharge conditions and they identified recharge mechanisms that ranged from rapid, but short-lived, water table rises (possibly associated with large scale flooding), to slower diffuse recharge not associated with large water table fluctuations. Dissolved gas and geochemical correction models were used to calculate and (or) correct tracer concentrations before input to lumped parameter models of groundwater age. Lumped parameter models that were fit to tracer concentrations indicated groundwater was relatively old across the aquifer systems, with an estimated mean age of about 30,000 years. Estimates of groundwater age were related to hydrogeology, with increasing groundwater ages associated with greater depth, confinement, and distance from the recharge zone. Young groundwater with mean ages less than 2,000 years generally was in unconfined parts of the aquifer system, except for local areas of heavy groundwater extraction from unconfined aquifer units where estimated mean ages were up to 15,000 years. Lumped parameter model optimized age distributions describe the relative contribution of differing flow paths to the mean age, and a composite distribution of all samples from the three aquifer systems indicated that about 15 percent of the total sampled water had an age of less than 100 years. Various metrics of susceptibility, to land surface and geogenic contamination sources, derived from the age distributions, indicated geogenic sources as the primary threat to groundwater quality in the aquifer systems. Values of the susceptibility index (unitless) and fraction of recharge since 2,000 and 15,000 years before present are provided for assessment of individual well susceptibility. The data and interpretation methods presented here provide an additional means of investigating the susceptibility and sustainability of groundwater resources of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands aquifer systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205050","collaboration":"National Water-Quality ProgramNAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
A field investigation intended to measure the potential for erosion of sediments beside the American and Sacramento Rivers near Sacramento, California, is described. The study featured two primary components: (1) drilling and soil sampling to reveal lithology, down to depths matching the local river thalweg, where possible, and (2) borehole erosion tests (BETs) as described by Briaud and others (2017) at many of the same locations. The latter test involved drilling a vertical hole, measuring its diameter profile, inserting a hollow drilling rod to almost the bottom of the hole, and pumping fluid through the drilling rod at a known discharge for a chosen time interval. The hole was then resurveyed to establish an erosion rate (change in borehole radius divided by duration of flow event) as a function of depth, and the test was repeated. This test was performed with water as the erosive fluid at 12 locations, with 1 test repeated with drilling mud. Lithology holes were drilled at these same locations and an additional five locations. Drilling operations took place on river left and river right on the American River and river left (left bank, when looking downstream) on the Sacramento River.
The drilling to acquire sediment samples and reveal lithology involved the use of a mobile drilling rig equipped with a 6-inch (in.) auger, a 140-pound pneumatic hammer to drive split spoon and Calmod samplers, and a piston to push Shelby tube samplers to obtain samples of clayey material. Blow count (hammer blows per 6-in. sampler advance) was recorded while sampling, and the process was logged using standard U.S. Army Corps of Engineers (USACE), Sacramento District procedures. Sediment samples were identified and described in the field per ASTM D2488 and then delivered to a USACE laboratory and to Texas A&M University for additional laboratory analysis.
The BETs were performed with the same drilling rig that performed the drilling for definition of lithology. In most instances, tests were limited to regions above the water table, to avoid slumping of the borehole and heaving sands pushing into the hole. Most of the tests featured sediments that were primarily silty sand or sandy silt.
The testing procedure involved comparing borehole profiles before and after passing an assumed constant discharge through a drilling rod to the bottom of the drilled hole. Discharge and water losses were logged during the testing procedure, and water losses into the walls of the drilled hole were typically less than 5 percent of the introduced volume. For the tests performed with water, the coefficient of variation of the discharge ranged from 4.5 to 28 percent, with a mean of 13 percent, but the mean discharge appeared to be reasonably steady over the typical test duration of 10–30 minutes. It was thus assumed that discharge was constant and water losses during the tests were neglected. Coefficients of variation of the discharge for the three tests performed with drilling mud were much higher (20–50 percent), but erosion rates were much smaller.
Resolution of the borehole caliper-reported diameter was 0.1 in. and several of the tests lasted for 10 minutes. With boreholes measured twice, before and after each test, and averaged, these numbers correspond to an apparent erosion rate (radius change divided by test duration) of 0.3 inches per hour (in/hr), which is a theoretical lower bound on what could be measured with this approach and equipment. In practice, 0.5 in/hr appears to be a more realistic lower bound on the detectable erosion rate, based on inspection of computed changes and erosion rates.
Three flow speeds (5, 8, and 12 feet per second; ft/s) were targeted for the tests. Because of equipment limitations, it was not possible in the field to reach an average of 12 ft/s throughout any given borehole, although much higher flow speeds were reached locally in some cases. Most tests featured at least two different flow rates, and the borehole was typically surveyed at least twice for each condition, to allow averaging to reduce the influence of random diameter measurement errors. Errors arising from out-of-round boreholes appeared to be uncommon.
Briaud and others (2017) recommend stepped increases in the flow rate during a borehole test. This approach was taken during initial testing but proved to be problematic. The drilled hole would be enlarged by the first (smaller) discharge, and then it would be difficult to reach the desired higher flow speed because of the larger annulus between the drilled hole and the drilling rod that supplied the water for testing. This was largely solved by starting with a high discharge and, in many cases, maintaining it for subsequent tests with the average flow speed decreasing as the hole enlarged.
Several different measures of erosion rate were computed and investigated by comparison to lithological profiles. The vertically averaged erosion rate for each hole was computed, but this result does not reveal vertical variability of erodibility; and the mean flow speed within the hole is not a good representation of the speed when attempting to determine a relationship between erosion rate and flow speed. Instead, for each 6-inch layer within the hole, vertically averaged erosion rates and local flow speeds were computed and plotted. Where possible, the soil type for each layer was identified. For later laboratory analysis, project protocol dictated collection of Shelby tube samples whenever clay was encountered.
Plots of erosion rate versus flow speed displayed scatter that indicate that several other factors influence the erosion potential of the soil. Blow count was not a good predictor variable; it is better correlated with soil type than erodibility.
Soils were classified as sand, silt, or clay, depending on which soil type dominated within a sample. In general, those classified as sand and silt did not reveal clear patterns allowing erosion rate to be computed directly from flow speed, but the test results define the range and bounds on the erosion rate. Results for clay were slightly clearer with the erosion rate increasing with flow speed, once a threshold had been reached. In this case, the erosion rate appeared to change near a speed of 7 ft/s; above this threshold, erosion rates jumped from less than 2 in/hr to greater than 3 in/hr.
Even for soils with similar classifications, large differences in erodibility were observed between sites and in different layers within an individual hole. One potential means of dealing with this problem would be to perform more tests at each site to allow establishment of relationships between flow speed and erodibility for individual layers within a borehole. The maximum number of tests performed at a site in this study was four, but in some cases, results are available for only one or two flow events. Comparison of data to a set of Erosion Function Apparatus tests that provide better resolution of the vertical variation in the erosion rate versus flow speed relationship would allow further investigation of this idea.
It was hypothesized that drilling mud could expand the utility of the test in soft sands by reducing the likelihood of slumping that would be interpreted as erosion. The one test that was performed with drilling mud indicated that it greatly reduced the erosion rate of the soils encountered. It yielded very different results from the test performed at the same site with water.
Erosion rate is often expressed as a function of shear stress applied to a soil. In order to compute shear stress on the walls of the drilled hole, one must assume a form for the relationship between flow speed and shear stress and select a friction factor that is often estimated empirically from head loss, observed water-surface profiles, surface roughness, or other data not available in this report. One methodology for computing shear stress from flow speed is discussed in this report, but the test results have been presented in terms of erosion rate versus flow speed to avoid assuming values that are not verifiable via the field data collected in this study. Erosion rate was computed from directly measured values (sequential borehole profiles) and flow speed was computed directly from measured quantities (discharge and borehole geometry).
The BET has seen limited application, primarily in clayey soils, whereas most of the soils encountered in this study were primarily sand or silt. The objective of the BET is to determine the erodibility of in situ soil below the ground or riverbed surface. The BET is simple in principle and has the advantage of revealing erodibility of in situ sediments below the ground or riverbed surface; it appears to be very useful in clayey soils, based on previously published work, but is more difficult to apply in sandy soils where slumping and water losses within the hole during testing are more likely to occur. The BET did reveal a large variation in the results both laterally and vertically, even for the same soil-type classification. It is thus recommended that the results be applied considering these spatial variations rather than attempting to universally assign an erosion-rate relationship to a particular soil type. Results have been provided showing the results by site and by sediment classification (sand, silt, and clay), to allow either approach. Where possible, it is important to rely on site-specific results because the erosion-rate relationship for a given soil type varied by site.
Data collected during this project have been made publicly available online via the U.S. Geological Survey (USGS) Sciencebase database. The measured borehole profiles, discharge, lithology log sheets, and photos are available in the data release that accompanies this report (see Work and Livsey (2019) in the “Selected References” section for the appropriate link).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205063","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Work, P., and Livsey, D., 2020, Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California: U.S. Geological Survey Scientific Investigations Report 2020–5063, 92 p., https://doi.org/10.3133/sir20205063.","productDescription":"Report: vii, 92 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-110364","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376205,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5063/coverthb.jpg"},{"id":376206,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5063/sir20205063.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376207,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 3.0, July 2020)"}],"country":"United States","state":"California","city":"Sacramento","otherGeospatial":"American River, Sacramento Rivers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.7010498046875,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.38903340675905\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, investigated the effects of stormwater runoff from bridge decks on stream water quality conditions in South Carolina. The investigation assessed 5 bridges in 3 physiographic provinces in South Carolina (Piedmont, Upper Coastal Plain, and Lower Coast Plain) that had a range of bridge, traffic, and hydrologic characteristics. The five selected South Carolina bridge sites (coincident with U.S. Geological Survey stations) and corresponding highways were Lynches River at Effingham (station 02132000; U.S. Highway 52), North Fork Edisto River at Orangeburg (station 02173500; U.S. Highway 301), Turkey Creek above Huger (station 02172035; South Carolina Highway 41), South Fork Edisto River near Denmark (station 02173000; U.S. Highway 321), and Fishing Creek at Highway 5 below York (station 021473415; South Carolina Highway 5). Bridge decks at the selected sites used open chutes, scuppers, and downspouts to drain stormwater directly into the receiving water at evenly spaced intervals.
Stream water, sediment, and biological samples were collected and analyzed for a variety of constituents to evaluate the stream conditions for this study. Five to six stream samples were collected at transects upstream and downstream from each selected bridge site using the equal-width-increment technique during observable stormwater runoff. Routine samples of the receiving waters were collected 12 to 14 times at the upstream transect during nonstorm conditions. Samples were analyzed for physical properties, suspended sediment, nutrients, major ions, trace metals, polycyclic aromatic hydrocarbons, and Escherichia coli. Bridge-deck sediment and streambed sediment at upstream and downstream transects were collected once at each bridge site and analyzed for metals and semivolatile organic compounds that include polycyclic aromatic hydrocarbons. Benthic macroinvertebrate community surveys were conducted once using Hester-Dendy multiplate artificial substrate samplers deployed at multiple upstream and downstream transects concurrently.
Statistical analysis of the water-quality data determined that stormwater runoff from bridges did not significantly degrade physical properties, nor nutrient, trace-metal, Escherichia coli, and suspended-sediment concentrations at the selected sites beyond the variability at the upstream transect (no bridge influence) during the study period. During storm sampling at the bridge sites, water-quality conditions were statistically similar upstream and downstream from each bridge, except for greater turbidity, total nitrogen, and total organic nitrogen plus ammonia concentrations found downstream from the bridge site on Fishing Creek; higher total chromium concentrations detected downstream from the bridge site on Turkey Creek; and increased Escherichia coli concentrations found downstream from the bridge site on the North Fork Edisto River. Total recoverable lead, cadmium, and copper concentrations were the only trace metals that periodically exceeded the South Carolina Department of Health and Environmental Control freshwater aquatic-life criteria at some bridge sites (lead, copper, and cadmium in Turkey Creek; cadmium and lead in Fishing Creek; lead in the South Fork Edisto River and Lynches River), but the exceedances occurred more frequently during routine sampling upstream from the bridge sites than during storm sampling at upstream and downstream transects. In general, stormwater runoff from the bridge decks did not seem to be the major source of metal enrichment in receiving waters during the study period. North Fork and South Fork Edisto Rivers and Turkey Creek had only one storm sample that exceeded South Carolina Department of Health and Environmental Control recreational criterion for Escherichia coli at both the upstream and downstream locations, while Fishing Creek had more frequent exceedances. Polycyclic aromatic hydrocarbons were detected infrequently in the stream samples.
In general, sediment trace-metal concentrations were below the threshold and probable effect concentration at all bridge sites, except for the chromium concentration (45.1 milligrams per kilogram) detected upstream from the bridge site on Fishing Creek that exceeded the threshold effect concentration of 43.4 milligrams per kilogram. Based on enrichment ratios less than 1.5, bridge-deck runoff did not seem to be affecting trace-metal accumulation in the streambed sediment downstream from the bridge sites, except for lead at the bridge site on the Lynches River and manganese at the bridge site on Fishing Creek.
Individual polycyclic aromatic compound concentrations and the sum of 18 compounds did not exceed any threshold and probable effect concentrations, indicating polycyclic aromatic hydrocarbon concentrations in the streambed sediment at downstream and upstream transects were not likely to affect the health of benthic macroinvertebrate communities. Although the cumulative polycyclic aromatic hydrocarbon concentrations in downstream sediment at the sites on Turkey and Fishing Creeks were well below the threshold effect concentration of 1,610 micrograms per kilogram, the 3- to 100-fold increase in downstream concentrations indicated a strong probability of a bridge-deck runoff source.
Overall, benthic macroinvertebrate community health downstream from the bridge sites did not seem to be affected by bridge-deck runoff based on several multivariate analyses that indicated statistically similar benthic macroinvertebrate communities at upstream and downstream transects. Of the five bridge sites in this study, the site on Turkey Creek seemed to have the least healthy benthic macroinvertebrate communities because of the lowest Ephemeroptera, Plecoptera, and Trichoptera spp. (mayflies, stoneflies, and caddisflies, respectively) taxa, species richness, and diversity; and the highest biotic indices, indicative of poorer ecological health, at upstream and downstream transects. This ecological finding was not unexpected because of seasonal periods of negligible flow when dissolved-oxygen concentrations fell below 4 milligrams per liter during the study period. Of the five bridge sites in this study, the site on the South Fork Edisto River seemed to have healthier benthic macroinvertebrate communities because of the greater mean Ephemeroptera, Plecoptera, and Trichoptera spp. taxa; and lower mean biotic indices at upstream and downstream transects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205046","collaboration":"Prepared in cooperation with South Carolina Department of Transportation","usgsCitation":"Journey, C.A., Petkewich, M.D., Conlon, K.J., Caldwell, A.W., Clark, J.M., Riley, J.W., and Bradley, P.M., 2020, Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018: U.S. Geological Survey Scientific Investigations Report 2020–5046, 101 p., https://doi.org/10.3133/sir20205046.","productDescription":"xii, 101 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099513","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376048,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes.xlsx","text":"Appendixes 1-3","size":"312 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":376047,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5046"},{"id":376046,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSV2Y","text":"USGS data release","linkHelpText":"Water-, Sediment-, and Biological-Quality Data for Waters Receiving Runoff from Five Bridges in South Carolina, 2013 to 2018"},{"id":376045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5046/coverthb.jpg"},{"id":376051,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes_csv.zip","text":"Appendixes 1-3 (CSV)","size":"34.5 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.
The purpose of this report is to document the development of Escherichia coli (E. coli) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.
Multiple linear regression models were developed to estimate E. coli concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.
Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of E. coli concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as E. coli, the uncertainty of E. coli laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for E. coli concentration from 2012 to 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p., https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.23611450195312,\n 41.166249339092\n ],\n [\n -95.78155517578124,\n 41.15901221836655\n ],\n [\n -95.7843017578125,\n 41.37783904584602\n ],\n [\n -95.95321655273436,\n 41.37886950966323\n ],\n [\n -96.23611450195312,\n 41.37165592008984\n ],\n [\n -96.23611450195312,\n 41.166249339092\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.
The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.
The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.
The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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1400 Independence Road
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Karst is a landscape formed from the dissolution of soluble rock or rock containing minerals that are easily dissolved from within the rock. The landscape is characterized by sinkholes, caves, losing streams, springs, and underground drainage systems, which rapidly move water through the karst. The two forms of karst in New York State include carbonate karst, which forms in carbonate rock (limestone, marble, and dolostone), and evaporite karst, which forms in rock that contains the evaporite minerals gypsum and halite.
Past and recent studies of karst across the State have shown that areas of focused recharge in karstic carbonate rock allow contaminants to enter aquifer systems with little attenuation. Focused areas of recharge need to be identified to help prevent such contamination from sources on or adjacent to the karst. The New York State Departments of Environmental Conservation and Health are collaborating with the agricultural community to make farmers and farm-planning advisors more aware of karst and how to manage daily farming activities to reduce their impact on surface water and groundwater resources, especially in karst areas. There is also a need to make regulators, planners, and the general public aware of New York’s karst resources and to properly protect and manage these resources to protect the quality of groundwater and surface water that can flow into, through, and from karst bedrock.
Using publicly available geospatial data, karst bedrock and closed depressions over or near karst rock were identified across New York. Carbonate, evaporite, and marble geologic units were selected from a statewide 1:250,000-scale bedrock geology dataset. The selected geologic units were intersected with 7.5-minute quadrangle maps to define the study area.
The U.S. Geological Survey has compiled an inventory of closed depressions from statewide digital contour data, scanned 7.5-minute topographic maps known as a digital raster graphics, and light detection and ranging (lidar) digital elevation models. Analysis of the data resulted in the identification of 5,023 closed depressions statewide. The inventory was conducted to eliminate duplication of results from analysis of the three data sources. A series of overlay analyses was conducted using the closed depressions and thematic data known to be key factors in determining the probability of a closed depression contributing to focused groundwater recharge; the thematic data include bedrock geology, soil type, soil infiltration rate, and land cover.
Though the extent of karst development is important in understanding the interaction between surface water and groundwater in karst terrains, some of the worst cases of groundwater contamination in karst can occur where only minor karst features might be present. The presence of karst—be it a short section of a solutioned fracture or an extensive cave system—requires careful consideration, forward-looking environmental planning, and consistent water-quality protection to preserve New York State’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205030","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Kappel, W.M., Reddy, J.E., and Root, J.C., 2020, Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features: U.S. Geological Survey Scientific Investigations Report 2020–5030, 74 p., https://doi.org/10.3133/sir20205030.","productDescription":"Report: viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090019","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":375401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5030/coverthb.jpg"},{"id":375404,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGN5IJ","text":"USGS data release","linkHelpText":"Data for statewide assessment of New York’s karst aquifers with an inventory of closed-depression and focused-recharge features"},{"id":375534,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5030"},{"id":375482,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030_table1.pdf","text":"Table 1","size":"140 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Stratigraphic column of New York State bedrock indicating those units in which karst features might be present"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Groundwater-level conditions, generalized groundwater potentiometric surfaces, and generalized flow directions at the decommissioned Naval Air Warfare Center in West Trenton, New Jersey, were evaluated for calendar year 2018. Groundwater levels measured continuously in five on-site wells and one nearby off-site well were plotted as hydrographs for January 1, 2018, through December 31, 2018. Groundwater levels measured in 110 wells on June 18, 2018, were contoured as generalized potentiometric surfaces on maps and sections. Generalized groundwater-flow directions inferred from the June 2018 data are shown in the maps and sections.
Groundwater levels in six monitoring wells fluctuated in response to seasonal changes, precipitation, and pumping from “pump-and-treat” (P&T) wells. Record high precipitation totals in November, combined with a shutdown of three P&T wells in November, resulted in annual high water levels in late November for five of the six wells monitored. Annual high groundwater levels that occur during the fall are uncharacteristic of the typical timing of annual high water levels, which usually occur in the spring following low evapotranspiration during the winter months, compared to annual low water levels, which usually occur in fall because of high evapotranspiration during the summer months. The annual high water levels occurred following a 3-day precipitation event totaling 3.50 inches from November 24-26, which also caused the largest 1-day water-level increase for five of the six wells in 2018.
The groundwater-level contour maps and sections include generalized flow directions. Given the heterogeneity of the site’s fractured rock aquifers, contours and associated groundwater-flow directions shown on the maps and sections should be considered as broad conceptualizations. A nearly vertical fault striking southwest to northeast separates the northwestern part of the site underlain by the Lockatong Formation from the southeastern part, which is underlain by the Stockton Formation. In the Lockatong Formation, general groundwater-flow directions were toward P&T wells. The P&T wells limited the flow of groundwater in the Lockatong Formation from the site into the adjacent areas and contained most groundwater contamination within the site. A groundwater divide bisected the site; groundwater in the western part generally flowed to P&T wells 8BR, 15BR, 20BR, 29BR, 56BR, 91BR, and BRP-2, and groundwater in the eastern part generally flowed to P&T well 48BR. A groundwater divide also was present in the Stockton Formation. Groundwater west of the divide in the Stockton Formation generally flowed toward P&T well 22BR, and groundwater east of the divide generally flowed south and southeast, away from the site. Saprolite and fill from land surface to depths of 25 feet below land surface exhibit similar properties to those of porous media, and water levels in surficial wells were contoured using a porous media aquifer approach. Water levels in these surficial wells indicate that groundwater in the saprolite and fill flowed predominantly toward Gold Run and, to a lesser extent, the West Ditch spring that drains to Gold Run. In addition, some shallow groundwater was captured by the cone of depression in the fractured bedrock and was attributed to P&T well 48BR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201016","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Fiore, A.R., and Lacombe, P.J., 2020, Groundwater levels and generalized potentiometric surfaces, former Naval Air Warfare Center, West Trenton, New Jersey, 2018: U.S. Geological Survey Open-File Report 2020–1016, 28 p., https://doi.org/10.3133/ofr20201016.","productDescription":"Report: v, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104199","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":375290,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1016/ofr20201016.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1016"},{"id":375285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1016/coverthb.jpg"},{"id":375288,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98N1GWV","text":"USGS data release","linkHelpText":"Reported groundwater levels and groundwater pump-and-treat withdrawals, former Naval Air Warfare Center, West Trenton, New Jersey, 2018"}],"country":"United States","state":"New Jersey","city":"West Trenton","otherGeospatial":"Former Naval Air Warfare Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.81613278388977,\n 40.26694411855267\n ],\n [\n -74.80834364891052,\n 40.26694411855267\n ],\n [\n -74.80834364891052,\n 40.27319835024231\n ],\n [\n -74.81613278388977,\n 40.27319835024231\n ],\n [\n -74.81613278388977,\n 40.26694411855267\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
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The U.S. Geological Survey’s Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic response and streamflow. This study developed, calibrated, and assessed a PRMS model that simulates near-native or naturalized streamflow conditions in the Upper Rio Grande Basin. A PRMS model framework of 1,021 hydrologic response units was constructed for the basin. Subbasins within the larger Upper Rio Grande Basin range from snow-dominated northern basins to monsoon driven southern basins. The 1,021 hydrologic response units were grouped into 133 subareas within the basin, and solar radiation and potential evapotranspiration data were used to calibrate corresponding PRMS parameters in each subarea independently. Nine subbasins with streamgages distributed across the basin were identified as “near-native” subbasins, or those basins with low anthropogenic disturbance. Model parameters that affect streamflow were calibrated for the near-native subbasins, and the calibrated parameters were distributed to the remaining hydrologic response units on the basis of terrain, soil, and vegetation conditions linked to a distribution and weighting algorithm developed for this study. The parameter distribution method was validated in three of the nine near-native subbasins. Calibration results demonstrated that the PRMS model developed in this study with distributed model parameters for the entire Upper Rio Grande Basin was successful in applying local information to improve model performance over the National Hydrologic Model, and that the new model is appropriate to use to simulate near-native conditions throughout the basin. The result is a model that can simulate naturalized flow and other variables that affect the water budget (including soil moisture, evapotranspiration, recharge) at the daily time step for current and future climate conditions, and that can also be used in conjunction with other models developed for the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205026","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"Chavarria, S.B., Moeser, C.D., and Douglas-Mankin, K.R., 2020, Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin: U.S. Geological Survey Scientific Investigations Report 2020–5026, 38 p., https://doi.org/10.3133/sir20205026.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-111974","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":436948,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ML93QB","text":"USGS data release","linkHelpText":"Hydrologic simulations using projected climate data as input to the Precipitation-Runoff Modeling System (PRMS) in the Upper Rio Grande Basin"},{"id":375137,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YOPYW7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data for the application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin"},{"id":375136,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5026/sir20205026.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5026"},{"id":375135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5026/coverthb.jpg"}],"country":"United States","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.74316406249999,\n 31.466153715024294\n ],\n [\n -106.0400390625,\n 31.052933985705163\n ],\n [\n -105.380859375,\n 30.90222470517144\n ],\n [\n -105.0732421875,\n 31.12819929911196\n ],\n [\n -105.5126953125,\n 32.175612478499325\n ],\n [\n -105.2490234375,\n 32.80574473290688\n ],\n [\n -105.732421875,\n 33.211116472416855\n ],\n [\n -105.16113281249999,\n 33.797408767572485\n ],\n [\n -104.8974609375,\n 34.66935854524543\n ],\n [\n -105.380859375,\n 35.460669951495305\n ],\n [\n -104.5458984375,\n 36.80928470205937\n ],\n [\n -104.94140625,\n 38.03078569382294\n ],\n [\n -106.34765625,\n 38.54816542304656\n ],\n [\n -107.314453125,\n 37.92686760148135\n ],\n [\n -106.8310546875,\n 37.33522435930639\n ],\n [\n -108.06152343749999,\n 35.99578538642032\n ],\n [\n -107.75390625,\n 34.488447837809304\n ],\n [\n -108.19335937499999,\n 33.61461929233378\n ],\n [\n -108.984375,\n 32.65787573695528\n ],\n [\n -108.80859375,\n 31.541089879585808\n ],\n [\n -108.45703125,\n 31.27855085894653\n ],\n [\n -107.7978515625,\n 32.287132632616384\n ],\n [\n -107.05078125,\n 32.39851580247402\n ],\n [\n -106.74316406249999,\n 31.466153715024294\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
A potentiometric-surface map for spring 2018 was created for the Mississippi River Valley alluvial (MRVA) aquifer using available groundwater-altitude data from 1,126 wells completed in the MRVA aquifer and from the altitude of the top of the water surface in area rivers from 66 streamgages. Personnel from Arkansas Natural Resources Commission, Arkansas Department of Health, Arkansas Geological Survey, Illinois Department of Agriculture, Illinois State Water Survey, Louisiana Department of Natural Resources, Louisiana Department of Transportation and Development, Mississippi Department of Environmental Quality, Missouri Department of Natural Resources, Yazoo Mississippi Delta Joint Water Management District, U.S. Department of Agriculture-Natural Resources Conservation Service, and U.S. Geological Survey (USGS) routinely collect groundwater-level data from wells screened in the MRVA aquifer. The USGS and the U.S. Army Corps of Engineers routinely collect data on river stage and streamflow for the rivers overlying the MRVA aquifer area. The potentiometric-surface map for 2018 was created utilizing existing groundwater and surface-water altitudes to support investigations to characterize the MRVA aquifer as part of the USGS Water Availability and Use Science Program.
Sufficient data were available to map the potentiometric surface of the MRVA aquifer for spring 2018 for about 87 percent of the aquifer area. The potentiometric contours ranged from 10 to 340 feet above North American Vertical Datum of 1988. The regional direction of groundwater flow was generally to the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flowed into the depression, and near rivers, where flow can be from aquifer to the river or from the river into the aquifer. There are large depressions in the potentiometric-surface map in the lower one-half of the Cache region and in most of the Grand Prairie and Delta regions.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The U.S. Geological Survey, in partnership with The Nature Conservancy, analyzed existing water-quality and streamflow data from three locations in the Upper White River Basin, Indiana, to estimate annual mean concentrations and fluxes and to identify and quantify changes in water quality and streamflow over time. Water-quality data used in the analyses were collected between water years 1992 and 2017. Annual mean-daily concentrations and fluxes of total suspended solids, total phosphorus as phosphorus, nitrate plus nitrite as nitrogen, and total Kjeldahl nitrogen as nitrogen were estimated for U.S. Geological Survey streamgage locations in Indiana on the Upper White River at Muncie, near Nora, and near Centerton. In addition, flow-normalized annual mean-daily concentrations and fluxes of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated and used to assess changes in these constituents between water years 1997 and 2017. Flow normalization is a process that attempts to remove the effects of year-to-year variation in streamflow on concentrations and fluxes without removing the effects associated with seasonal and long-term (multiyear) trends in streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203030","collaboration":"Prepared in cooperation with The Nature Conservancy with generous support from the Nina Mason Pulliam Charitable Trust","usgsCitation":"Koltun, G.F., and Hauswald, C., 2020, Trends in streamflow, nutrients, and total suspended solids in the Upper White River Basin, Indiana: U.S. Geological Survey Fact Sheet 2020–3030, 6 p., https://doi.org/10.3133/fs20203030.","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-114324","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":374596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3030/coverthb.jpg"},{"id":374597,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3030/fs20203030.pdf","text":"Report","size":"9.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3030"}],"country":"United States","state":"Indiana","otherGeospatial":"Upper White River Basin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-86.6546,39.6001],[-86.6522,39.6087],[-86.6463,39.6128],[-86.6403,39.6201],[-86.6404,39.6305],[-86.6654,39.6305],[-86.6858,39.63],[-86.6853,39.6884],[-86.6849,39.7773],[-86.6845,39.8648],[-86.6929,39.8643],[-86.6937,39.9228],[-86.6938,39.9528],[-86.6946,40.0402],[-86.6961,40.1282],[-86.6962,40.1785],[-86.2424,40.1807],[-86.2435,40.2152],[-86.242,40.3013],[-86.2423,40.3734],[-86.2429,40.3884],[-86.2422,40.4029],[-85.8624,40.407],[-85.8621,40.3784],[-85.5784,40.3794],[-85.4451,40.3792],[-85.2205,40.379],[-85.2182,40.3073],[-85.1302,40.3082],[-85.0186,40.3092],[-84.901,40.3096],[-84.8064,40.3102],[-84.8079,40.1741],[-84.8106,40.1351],[-84.8112,40.1265],[-84.8131,40.006],[-84.8603,40.0066],[-84.8952,40.0061],[-85.2014,40.0042],[-85.2013,39.875],[-85.2133,39.8751],[-85.2205,39.8748],[-85.2214,39.7895],[-85.243,39.7902],[-85.3017,39.789],[-85.3519,39.7894],[-85.4651,39.7886],[-85.5765,39.7858],[-85.5968,39.786],[-85.6333,39.7862],[-85.6338,39.6987],[-85.6876,39.6987],[-85.7993,39.6993],[-85.913,39.6976],[-85.9518,39.6969],[-85.9523,39.638],[-85.9521,39.347],[-85.9812,39.3466],[-85.9902,39.3467],[-86.0247,39.3464],[-86.0854,39.3452],[-86.0919,39.3452],[-86.1008,39.3453],[-86.1377,39.3445],[-86.249,39.342],[-86.3566,39.3404],[-86.3816,39.3399],[-86.4631,39.3391],[-86.5732,39.3395],[-86.6309,39.3413],[-86.6309,39.3481],[-86.6323,39.4696],[-86.6859,39.47],[-86.686,39.5144],[-86.6861,39.5262],[-86.6706,39.5339],[-86.6533,39.5475],[-86.6491,39.5552],[-86.6528,39.5666],[-86.6546,39.5865],[-86.6552,39.5965],[-86.6546,39.6001]]]},\"properties\":{\"name\":\"Boone\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229
Environmental groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016, and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public-water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013–14 and quality-control samples collected in 2012–15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1124","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. 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U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Five volcanic-hydrothermal systems in the central Aleutians Islands were sampled for water and gas geochemistry in 2015 to provide baseline data to help predict future volcanic unrest. Some areas had not been sampled in 20–30 years (Makushin volcano, Geyser Bight), and other areas had minimal to no prior sampling (Tana volcano and Fisher Caldera). The chemical and isotopic data of the waters show a wide variety of characteristics typical of hydrothermal settings. Stable isotopic analyses of the waters show no evidence for primary magmatic water, rather that waters have a meteoric origin that is variably influenced by boiling and evaporation processes. The carbon and helium isotopic analyses of gases suggest they contain a primary magmatic component typical of the upper mantle at most locations, and the CO2/S ratios show that these gases have been modified by interactions with groundwater along the flow paths. Some areas demonstrate stable compositions since the last sampling (for example, Akutan hydrothermal areas), with some being remarkably steady over very long periods (for example, Geyser Bight). Other areas show modifications because of either lower amounts of upwelling from hydrothermal sources or lower amounts of magmatic influence on the surface chemistry (for example, Upper Glacial valley of Makushin, an informally named valley leading south of the volcano toward Makushin Bay to the south). Finally, this report highlights that previously unsampled regions in the Aleutian Islands, such as Tana volcano and Fisher Caldera (the latter found to have one of the highest helium isotopic signatures ever measured in the Aleutian Islands), show evidence of ongoing subsurface magmatism that warrants continued investigation in terms of volcanic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205043","collaboration":"","usgsCitation":"Werner, C., Kern, C., and Kelly, P. K., 2020, Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015: U.S. Geological Survey Scientific Investigations Report 2020–5043, 35 p., https://doi.org/10.3133/sir20205043.","productDescription":"Report: viii, 35 p.; 2 Tables","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118716","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":374537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5043/coverthb.jpg"},{"id":374538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374539,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table1.pdf","text":"Table 1","size":"200 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":374540,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table2.pdf","text":"Table 2","size":"130 KB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","county":"","city":"","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.2333984375,\n 52.81604319154934\n ],\n [\n -168.96972656249997,\n 52.669720383688166\n ],\n [\n -168.4423828125,\n 52.8691297276852\n ],\n [\n -168.28857421875,\n 53.08082737207479\n ],\n [\n -167.62939453124997,\n 53.1335898292448\n ],\n [\n -166.75048828125,\n 53.30462107510271\n ],\n [\n -166.13525390625,\n 53.657661020298\n ],\n [\n -164.99267578125,\n 54.00776876193478\n ],\n [\n -164.11376953125,\n 54.23955053156177\n ],\n [\n -164.33349609375,\n 54.67383096593114\n ],\n [\n -164.68505859375,\n 54.88924640307589\n ],\n [\n -165.41015625,\n 54.648412502316695\n ],\n [\n -166.57470703125,\n 54.316523240258256\n ],\n [\n -168.77197265625,\n 53.605544099238\n ],\n [\n -169.2333984375,\n 52.81604319154934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Volcano Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling.
The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data.
The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent
agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17.
Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary.
From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value.
The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205028","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Smith, E.A., Kiesling, R.L., and Hayter, E.J., 2020, Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5028, 31 p., https://doi.org/10.3133/sir20205028.","productDescription":"Report: viii, 31 p.; Data Release; Dataset","onlineOnly":"Y","ipdsId":"IP-113167","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437002,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U1XXG0","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC model scenarios for velocity profiles around Munger Landing, selected years (2012-2019)"},{"id":374450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5028/coverthb.jpg"},{"id":374451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5028/sir20205028.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5028"},{"id":374452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990OUS6","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC hydrodynamic model for discharge and temperature simulations: 2016–17"},{"id":374455,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS Water Data for the Nation"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.548828125,\n 46.62869257083747\n ],\n [\n -92.0050048828125,\n 46.62869257083747\n ],\n [\n -92.0050048828125,\n 47.07199249565323\n ],\n [\n -92.548828125,\n 47.07199249565323\n ],\n [\n -92.548828125,\n 46.62869257083747\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, has been collecting topographic data at 10 study areas along Fountain Creek, Colorado, annually since 2012. The 10 study areas are located between Colorado Springs and the terminus of Fountain Creek at the Arkansas River in Pueblo. The purpose of this report is to present elevation maps based on topographic surveys collected in 2015 and 2019 and to present maps of elevation change that occurred between 2015 and 2019 at all 10 study areas. Elevation and elevation-change maps were developed in ArcGIS from topographic surveys collected at each study area using real-time kinematic Global Navigation Satellite Systems during the winter months (January through April) of 2015 and 2019. Elevation-change maps were created using statistically defined minimum levels of change detection asso-ciated with the 68-percent confidence limit and the 95-percent confidence limit. Study areas along Fountain Creek underwent a range of geomorphic responses between 2015 and 2019 that often depended on the dominant channel pattern of the study area. The results of this ongoing monitoring effort can be used to assess long-term changes in land-surface elevation and to advance understanding of the geomorphic response to possible alterations in flow conditions on Fountain Creek.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3456","collaboration":"Prepared in cooperation with Colorado Springs Utilities","usgsCitation":"Hempel, L., 2020, Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–19: \nU.S. Geological Survey Scientific Investigations Map 3456, 10 sheets, 9 p., https://doi.org/10.3133/sim3456.","productDescription":"Report: vi, 9 p.; 11 Sheets: 12.20 x 13.45 inches or smaller; Read Me; Data Release","onlineOnly":"Y","ipdsId":"IP-112462","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":391173,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3481","text":"Elevation and Elevation-Change Maps of Fountain Creek, Southeastern Colorado, 2015–20"},{"id":374401,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R00MWF","text":"USGS data 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layers."},{"id":374293,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet4.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 04","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 04","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":374294,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet5.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 05","size":"33.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Eevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 05","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":374299,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet10.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 10","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 10","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":374300,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheets1to10.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Areas 01-10","size":"345 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Areas 01-10","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.00457763671874,\n 38.35027253825765\n ],\n [\n -104.5404052734375,\n 38.35027253825765\n ],\n [\n -104.5404052734375,\n 39.15988184949157\n ],\n [\n -105.00457763671874,\n 39.15988184949157\n ],\n [\n -105.00457763671874,\n 38.35027253825765\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
To improve flood-frequency estimates at rural streams in Alabama, annual exceedance probability flows at gaged locations and regional regression equations used to estimate annual exceedance probability flows at ungaged locations were developed by using current geospatial data, new analytical methods, and annual peak-flow data through September 2015 at 242 streamgages in Alabama and surrounding States. The regional regression equations were derived from statistical analyses of annual peak-flow data and basin characteristics for a subset of 217 streamgages. Four flood regions were identified based on residuals from the regional regression analyses and contain sites with similar basin characteristics. A separate set of equations was derived for estimating flood frequency and magnitude for small rural streams using a subset of 40 small basin streamgages. A large river analysis was also completed for 14 selected large-river streamgages in Alabama. Annual exceedance probability flows presented in this report reflect additional streamflow data collected since the previous study of flood magnitude and frequency in Alabama, which included streamflow through September 2003.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205032","collaboration":"Alabama Department of Transportation","usgsCitation":"Anderson, B.T., 2020, Magnitude and frequency of floods in Alabama, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5032, 148 p., https://doi.org/10.3133/sir20205032.","productDescription":"Report: vii, 148 p.; 1 Plate: 20.00 x 30.00 inches; Data Release","numberOfPages":"160","onlineOnly":"N","ipdsId":"IP-104043","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374279,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5032/sir20205032_plate01.pdf","text":"Plate 1","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5032 plate 1","linkHelpText":"—Locations of Flood Regions and Streamgages in Alabama"},{"id":374278,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5032/sir20205032.pdf","text":"Report","size":"6.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5032"},{"id":374277,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5032/coverthb.jpg"},{"id":374280,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYSZLL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood regions and annual exceedance probability flows for Alabama streams, data through 2015"}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.39599609375,\n 30.41078179084589\n ],\n [\n -87.36328125,\n 30.391830328088137\n ],\n [\n -87.5390625,\n 30.789036751261136\n ],\n [\n -87.47314453125,\n 31.034108344903512\n ],\n [\n -85.078125,\n 31.071755902820133\n ],\n [\n -84.96826171874999,\n 32.26855544621476\n ],\n [\n -85.62744140625,\n 34.95799531086792\n ],\n [\n -88.04443359375,\n 34.994003757575776\n ],\n [\n -88.48388671874999,\n 32.02670629333614\n ],\n [\n -88.39599609375,\n 30.41078179084589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 58th Annual Report of the River Master of the Delaware River. It covers the 2011 River Master report year, the period from December 1, 2010, to November 30, 2011.
During the report year, precipitation in the upper Delaware River Basin was 71.43 inches or 162 percent of the long-term average. On December 1, 2010, combined usable storage in the New York City reservoirs in the upper Delaware River Basin was 230.430 billion gallons or 85.1 percent of combined storage capacity. The reservoirs were at about 100 percent of usable capacity on May 31, 2011. Combined storage remained high (above 80 percent combined capacity) through November 2011. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 5 days during the report year (July 24–28, 2011). Conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year.
The quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201020","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2020, Report of the River Master of the Delaware River for the period December 1, 2010–November 30, 2011: U.S. Geological Survey Open-File Report 2020–1020, 127 p., https://doi.org/10.3133/ofr20201020.","productDescription":"x, 127 p.","numberOfPages":"141","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-112762","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":374239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1020/coverthb.jpg"},{"id":374254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1020/ofr20201020.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1020"}],"country":"United States","state":"New York, New Jersey, Pennsylvania ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.673583984375,\n 39.7240885773337\n ],\n [\n -73.828125,\n 39.7240885773337\n ],\n [\n -73.828125,\n 42.67435857693381\n ],\n [\n -76.673583984375,\n 42.67435857693381\n ],\n [\n -76.673583984375,\n 39.7240885773337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
Passive groundwater sampling is defined as the collection of a water sample from a well without the use of purging by a pump or retrieval by a bailer (Interstate Technology and Regulatory Council [ITRC], 2006; American Society for Testing and Materials [ASTM], 2014). No purging means that advection of water is not involved in collecting the water sample from the well. Passive samplers rely on diffusion as the primary process that drives their collection of chemical constituents. Diffusion is the transport of chemicals caused by the presence of a chemical gradient. Chemicals tend to move or diffuse from areas of higher concentration to areas of lower concentration to reach an average or equilibrium concentration. Passive sampling of groundwater relies on the ambient exchange of groundwater in the formation with water in the screened or open interval of a well. In this report, the term formation is used to describe all saturated hydrogeologic units that yield water to a well. If the well opening is unclogged and free of a film of deposits from physical turbidity or chemical precipitation, then the exchange of groundwater is likely adequate, and the water in the open interval will be representative of water in the formation. In some cases, the passive sample from the well opening can be more representative of groundwater from the formation than a sample collected by pumping if pumping induces mixing of water in the open interval with stagnant casing water that has undergone chemical alteration (Harte and others, 2018). In most cases, passive sampling will better represent the ambient groundwater chemistry flowing through the open interval of a well because pumping may capture water of different chemistry from downgradient or lateral areas that would not normally pass through the well. Three basic types of passive samplers are discussed in this report. The first type of passive sampler is the equilibrium-membrane type, which includes a semi-permeable membrane through which chemicals diffuse or permeate. Permeation is simply the process of water or chemicals moving through openings in the membrane. The authors contend that permeation is dominated by diffusion for many of the passive samplers discussed in this report. Some passive equilibrium-membrane-type samplers allow most types of chemical constituents through, whereas others allow the diffusion of only selected groups of chemicals. Once the chemical constituents are inside the membrane, they are retained by the equilibration of concentrations inside the sampler with those outside the sampler. The second type of passive sampler is an equilibrium-thief type, which has no semi-permeable membrane. Chemical constituents simply move through the openings in the body of the sampler either initially through advection and dispersion or over time primarily by diffusion. Chemical constituents reach equilibrium between the water in the sampler and the water in the well and are captured in the sampler when the sampler is closed. The third type of passive sampler is an accumulation-type sampler that contains sorptive media. Selected chemical constituents are sorbed onto the media that the sampler contains for later extraction and analysis. Although passive samplers have been available for more than 15 years (from present [2020]), their use by U.S. Geological Survey (USGS) hydrologists and hydrologic technicians to monitor groundwater quality largely has been limited to selected research studies. The authors believe that this may be the result of (1) a lack of exposure of most USGS personnel to passive samplers and the uses of these samplers and (2) the lack of a USGS-approved protocol for the proper use of these samplers by USGS personnel. This report is an effort to fill those two needs. The focus of this report is on hydraulic, hydrologic, and chemical considerations in the application of passive samplers and interpretation of groundwater chemistry results obtained using passive samplers in wells. This report describes the differences between purging and passive sampling methods in groundwater and explains how and why passive samplers work. The report points out the advantages and limitations of passive samplers in general and for each particular type of passive sampler. Important considerations to be taken into account prior to the use of passive samplers are discussed, such as defining the data-quality objectives, the water-quality constituents to be sampled, sample volumes required for analysis, well construction of the sampling network, and the geologic formations that will be sampled. Potential applications of passive samplers also are discussed, such as chemical-vertical profiling of wells. A general field protocol for the deployment, recovery, and sample collection using these devices is described, and some overall guidance for the practitioner with application examples is given. Comparison methods used to evaluate results from passive sampling versus purge sampling also are discussed.
","largerWorkTitle":"","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D8","collaboration":"","usgsCitation":"Imbrigiotta, T.E., and Harte, P.T., 2020, Passive sampling of groundwater wells for determination of water chemistry: U.S. Geological Survey Techniques and Methods, chap. 8, section D, book 1, 80 p., https://doi.org/10.3133/tm1d8.\n","productDescription":"ix, 80 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082895","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":373983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d8/coverthb.jpg"},{"id":373984,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d8/tm1d8.pdf","text":"Report","size":"4.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1-D8"}],"publicComments":"This report is Chapter 8 of Section D: Water quality in Book 1: Collection of water data by direct measurement","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648