The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services, is undertaking a study on per- and polyfluoroalkyl substances (PFAS) in soils and biosolids. The study will characterize PFAS concentrations in shallow soil and selected biosolids throughout the State of New Hampshire, conduct laboratory experiments to improve understanding of how mobile PFAS are in the environment, and implement a site-specific field study of PFAS transport from soil to water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip208","collaboration":"Prepared in cooperation with the New Hampshire Department of Environmental Services","usgsCitation":"Tokranov, A.K., Schlosser, K.E.A., Marts, J.M., Drouin, A.F., Santangelo, L.M., and Welch, S.M., 2021, Per- and polyfluoroalkyl substances (PFAS) in New Hampshire soils and biosolids: U.S. Geological Survey General Information Product 208, 2 p., https://doi.org/10.3133/gip208.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123740","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384461,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/208/coverthb2.jpg"},{"id":384462,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/208/gip208.pdf","text":"Report","size":"895 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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
No abstract available.
","language":"English","publisher":"New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Blake, J., 2021, Geology and calcite deposition of Fort Stanton-Snowy River Cave: Lite Geology, v. 48, p. 7-8.","productDescription":"2 p.","startPage":"7","endPage":"8","ipdsId":"IP-125320","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":384809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384785,"type":{"id":15,"text":"Index Page"},"url":"https://geoinfo.nmt.edu/publications/periodicals/litegeology/current/home.cfml"}],"country":"United States","state":"New Meico","otherGeospatial":"Fort Stanton-Snowy River Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.43280029296875,\n 32.637061996573436\n ],\n [\n -105.43304443359374,\n 32.637061996573436\n ],\n [\n -105.43304443359374,\n 33.277731642555224\n ],\n [\n -106.43280029296875,\n 33.277731642555224\n ],\n [\n -106.43280029296875,\n 32.637061996573436\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813304,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219109,"text":"70219109 - 2021 - Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry","interactions":[],"lastModifiedDate":"2021-03-24T12:13:12.940737","indexId":"70219109","displayToPublicDate":"2021-03-18T07:10:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry","docAbstract":"Riverbeds are hotspots for microbially-mediated reactions that exhibit pronounced variability in space and time. It is challenging to resolve biogeochemical mechanisms in natural riverbeds, as uncontrolled settings complicate data collection and interpretation. To overcome these challenges, laboratory flumes are often used as proxies for natural riverbed systems. Flumes capture spatiotemporal variability and thus allow for controlled investigations of riverbed biogeochemistry. These investigations implicitly rely on the assumption that the flume microbiome is similar to the microbiome of natural riverbeds. However, this assumption has not been tested and it is unknown how the microbiome of a flume compares to natural aquatic settings, including riverbeds. To evaluate the fundamental assumption that a flume hosts a microbiome similar to natural riverbed systems, we used 16s rRNA gene sequencing and publicly available data to compare the sediment microbiome of a single large laboratory flume to a wide variety of natural ecosystems including lake and marine sediments, river, lake, hyporheic, soil, and marine water, and bank and wetland soils. Richness and Shannon diversity metrics, analyses of variance, Bray-Curtis dissimilarity, and analysis of the common microbiomes between flume and river sediment all indicated that the flume microbiome more closely resembled natural riverbed sediments than other ecosystems, supporting the use of flume experiments for investigating natural microbially-mediated biogeochemical processes in riverbeds.
","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2021.596260","usgsCitation":"Kaufman, M., Warden, J.G., Cardenas, M.B., Stegen, J.C., Graham, E.B., and Brown, J., 2021, Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry: Frontiers in Water, v. 21, no. 3, 12 p., https://doi.org/10.3389/frwa.2021.596260.","productDescription":"12 p.","ipdsId":"IP-096581","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":453034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.596260","text":"Publisher Index Page"},{"id":384625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaufman, Matthew H.","contributorId":255711,"corporation":false,"usgs":false,"family":"Kaufman","given":"Matthew H.","affiliations":[{"id":29861,"text":"The University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":812810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warden, John G. 0000-0003-1384-458X","orcid":"https://orcid.org/0000-0003-1384-458X","contributorId":215846,"corporation":false,"usgs":true,"family":"Warden","given":"John","email":"","middleInitial":"G.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cardenas, M. Bayani","contributorId":181932,"corporation":false,"usgs":false,"family":"Cardenas","given":"M.","email":"","middleInitial":"Bayani","affiliations":[],"preferred":false,"id":812812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stegen, James C.","contributorId":255712,"corporation":false,"usgs":false,"family":"Stegen","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":812813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Emily B.","contributorId":202683,"corporation":false,"usgs":false,"family":"Graham","given":"Emily","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":812814,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Joseph","contributorId":255713,"corporation":false,"usgs":false,"family":"Brown","given":"Joseph","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":812815,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220293,"text":"70220293 - 2021 - Enumerating white-tailed deer using unmanned aerial vehicles","interactions":[],"lastModifiedDate":"2021-04-30T12:15:02.541705","indexId":"70220293","displayToPublicDate":"2021-03-18T07:06:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Enumerating white-tailed deer using unmanned aerial vehicles","docAbstract":"The white‐tailed deer (Odocoileus virginianus) is an ecologically important species in forests of North America. Effective management of forests requires accurate, precise estimates of deer population abundance to plan and justify management actions. Spotlight surveys in combination with distance sampling are a common method of estimating deer population abundance; however, spotlight surveys are known to have serious drawbacks such as high costs and sampling biases. Therefore, we tested the effectiveness of enumerating deer from unmanned aerial vehicle (UAV) flights, conducted 1 and 6 March 2018, to develop population and density estimates in 2 United States National Parks: Harpers Ferry National Historic Park (HAFE) and Monocacy National Battlefield (MONO). Concurrent spotlight surveys at MONO enabled us to compare estimates obtained by the 2 methods. Deer density estimates by 4 observers of UAV‐obtained thermal imagery from HAFE were 94.5 ± 3.9 deer/km2. Concurrent UAV and spotlight surveys at MONO found 19.7 ± 0.5 deer/km2 and 6.4 ± 4.9 deer/km2, respectively; suggesting that spotlight surveys may significantly underestimate deer densities. Despite the logistical challenges to UAV operation, our findings demonstrate that UAVs will become an invaluable tool for wildlife management as technology improves. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
Data collection, along with hydraulic and fluvial egg transport modeling, was completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam in Kentucky and Indiana. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for Hypophthalmichthys nobilis (bighead carp).
A copy of an existing step-backwater model of Ohio River flows was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows were selected to represent typical conditions ranging from a high-streamflow event to a seasonal dry-weather event, with two streamflows between these extremes for this reach of the Ohio River. Outputs from the hydraulic model, a range of five water temperatures observed in water-quality data, and four potential spawning locations were used as input to the Fluvial Egg Drift Simulator to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run.
Results from the Fluvial Egg Drift Simulator scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical or biological factors such as excess turbulence, fertilization failure, predation, or starvation) indicate that most eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the model reach. The overall mean percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24 to 30 degrees Celsius. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature and high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. Although the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information about conditions favorable to spawning and recruitment habitat for bighead carp in the Ohio River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215005","collaboration":"Biological Threats and Invasive Species Research Program","usgsCitation":"Ostheimer, C.J., Boldt, J.A., and Buszka, P.M., 2021, Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator: U.S. Geological Survey Scientific Investigations Report 2021–5005, 30 p., https://doi.org/10.3133/sir20215005.","productDescription":"Report: v, 30 p.; 2 Data Releases","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-116266","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":384390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5005/coverthb.jpg"},{"id":384391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5005/sir20215005.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5005"},{"id":384392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQHEPU","text":"USGS data release","linkHelpText":"Velocity and water-quality surveys in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, October 27–November 4, 2016, and June 26–29, 2017"},{"id":384393,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JHLGZL","text":"USGS data release","linkHelpText":"Geospatial data and models for the simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator"}],"country":"United States","state":"Indiana, Kentucky","otherGeospatial":"Ohio River, Markland Locks and Dam, McAlpine Locks and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.660400390625,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.40194908237822\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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6460 Busch Blvd., Suite 100
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Floods are one of the most costly and frequent natural disasters in Nevada. For example, the 1997 New Year’s flood has been estimated to have caused more than $1 billion in damage across northern Nevada (Truckee River Flood Management Authority, 2017). In 2014, more than 2 miles of Interstate 15 in southern Nevada was heavily damaged by the remnants of Hurricane Norbert combined with monsoonal rains (Sutko, 2015). Flooding in Nevada is highly variable in cause and the season of the year. Flooding can be caused by snowmelt, rain on snow, and flash flooding during thunderstorms. Peak streamflow estimates are critical for planning by government agencies; designation of flood zones; and design of infrastructure including culverts, bridges, and roadways. In order to provide accurate estimates of flood frequencies, long-term data collection of peak streamflows would be needed because the accuracy of estimates improves with longer datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213015","collaboration":"Prepared in cooperation with Nevada Department of Transportation","usgsCitation":"Schmidt, K., 2021, Peak streamflow determinations in Nevada: A cooperative program with the USGS and Nevada Department of Transportation: U.S. Geological Survey, Fact Sheet 2021-3015, 4 p., https://doi.org/10.3133/fs20213015.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-112970","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":384413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3015/covrthb.jpg"},{"id":384414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3015/fs20213015.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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\"}}]}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
Freshly erupted volcanic ash contains a range of soluble elements, some of which can generate harmful effects in living cells and are considered potentially toxic elements (PTEs). This work investigates the leaching dynamics of ash-associated PTEs in order to optimize a method for volcanic ash respiratory hazard assessment. Using three pristine (unaffected by precipitation) ash samples, we quantify the release of PTEs (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) and major cations typical of ash leachates (Mg, Na, Ca, K) in multiple simulated lung fluid (SLF) preparations and under varying experimental parameters (contact time and solid to liquid ratio). Data are compared to a standard water leach (WL) to ascertain whether the WL can be used as a simple proxy for SLF leaching. The main findings are: PTE concentrations reach steady-state dissolution by 24 h, and a relatively short contact time (10 min) approximates maximum dissolution; PTE dissolution is comparatively stable at low solid to liquid ratios (1:100 to 1:1000); inclusion of commonly used macromolecules has element-specific effects, and addition of a lung surfactant has little impact on extraction efficiency. These observations indicate that a WL can be used to approximate lung bioaccessible PTEs in an eruption response situation. This is a useful step towards standardizing in vitro methods to determine the soluble-element hazard from inhaled ash.
Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses.
Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.
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","language":"English","publisher":"Springer","doi":"10.1007/s10584-021-02979-4","usgsCitation":"Cherkauer, K.A., Bowling, L., Byun, K., Chaubey, I., Chin, N., Ficklin, D., Hamlet, A., Kines, S., Lee, C., Neupane, R., Pignotti, G., Rahman, S., Singh, S., Valappil-Femeena, P., and Williamson, T.N., 2021, Climate change impacts and strategies for adaptation for water resource management in Indiana: Climatic Change, v. 163, 21, 20 p., https://doi.org/10.1007/s10584-021-02979-4.","productDescription":"21, 20 p.","ipdsId":"IP-108809","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":490070,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Climate_change_impacts_and_strategies_for_adaptation_for_water_resource_management_in_Indiana/24780879","text":"External 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Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
","tableOfContents":"
Hydroacoustics have revolutionized how the U.S. Geological Survey (USGS) measures streamflow by increasing the efficiency and quality of the measurement. However, the ability to determine the full range of streamflow at a streamflow-gaging station remains limited because in-person flow measurements still must be made by qualified personnel. As a result, streamflow during flood events typically is measured infrequently in comparison to the duration of the event, usually after the peak flow has occurred. To overcome these difficulties, the USGS has developed the Trolley Operated Automatic Discharge System (TOADS), an automated system for measuring streamflow without the need for onsite personnel. Investment by USGS in TOADS and other innovative technologies and methods provides substantial improvements to flood assessment and watershed management, making the USGS the continued world leader in surface-water hydrology.
Streamflow measurements made with TOADS are analogous to a moving-boat measurement, which measures the flow at a point in a river by moving from bank to bank and measuring water velocities at various depths below the boat. The TOADS uses hydroacoustic technology to profile water velocity across a river while moving vertically through the water column to measure flow at multiple depths. Use of TOADS to measure streamflow can save substantial time and money, provide improved flow ratings by taking numerous targeted automated measurements over a range of conditions, and provide a safe alternative to standard boat measurements when river conditions are hazardous. The TOADS can be programmed to measure flow based on a variety of triggers (including river stage, amount of flow, time of day) and can take repeated measurements at user-specified intervals during floods, droughts, and other events of interest.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Analysis of the water balance of the southwestern United States (SWUS) during 1900 through 2018 was used to evaluate the magnitude of the turn-of-the-century (TOC) drought in the SWUS. Results indicate that the warm season (April through September) soil moisture and runoff during the TOC drought were among the lowest values of the 1900 through 2018 period. Additionally, increases in temperature were identified as a significant driver of low soil moisture and runoff conditions during the warm season. In contrast, during the cool seasons (October through March) and the water year (October 1 through September 30) during the TOC drought, soil moisture and runoff did not indicate extremely dry conditions even though temperatures were the highest of the 1900 through 2018 period.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abbfc1","usgsCitation":"McCabe, G.J., and Wolock, D.M., 2021, Water balance of the turn-of-the-century drought in the Southwestern United States: Environmental Research Letters, v. 16, 044015, 9 p., https://doi.org/10.1088/1748-9326/abbfc1.","productDescription":"044015, 9 p.","ipdsId":"IP-122582","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abbfc1","text":"Publisher Index Page"},{"id":405904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Soputhwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 40\n ],\n [\n -108.08349609375,\n 40\n ],\n [\n -124,\n 40\n ],\n [\n -120.05859375,\n 34.161818161230386\n ],\n [\n -117.24609374999999,\n 32.39851580247402\n ],\n [\n -114.9609375,\n 32.76880048488168\n ],\n [\n -111.1376953125,\n 31.42866311735861\n ],\n [\n -108.2373046875,\n 31.316101383495624\n ],\n [\n -108.06152343749999,\n 31.765537409484374\n ],\n [\n -106.61132812499999,\n 31.765537409484374\n ],\n [\n -103.9306640625,\n 29.305561325527698\n ],\n [\n -103.0517578125,\n 29.075375179558346\n ],\n [\n -102.26074218749999,\n 29.954934549656144\n ],\n [\n -100.986328125,\n 29.6880527498568\n ],\n [\n -99.7998046875,\n 27.800209937418252\n ],\n [\n -99.5361328125,\n 27.68352808378776\n ],\n [\n -100,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":850205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850206,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219123,"text":"70219123 - 2021 - Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health","interactions":[],"lastModifiedDate":"2021-03-24T11:41:22.219976","indexId":"70219123","displayToPublicDate":"2021-03-16T06:33:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health","docAbstract":"Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2021.620715","usgsCitation":"Sepulveda, A., Hoegh, A.B., Gage, J.A., Caldwell Eldridge, S.L., Birch, J.M., Stratton, C., Hutchins, P.R., and Barnhart, E.P., 2021, Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health: Frontiers in Ecology and Evolution, v. 9, 13 p., https://doi.org/10.3389/fevo.2021.620715.","productDescription":"13 p.","ipdsId":"IP-123750","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.620715","text":"Publisher Index Page"},{"id":384620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana","otherGeospatial":"Yellowstone River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.400390625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 43.89789239125797\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":812862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gage, Joshua A.","contributorId":255726,"corporation":false,"usgs":false,"family":"Gage","given":"Joshua","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":812864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birch, James M.","contributorId":255728,"corporation":false,"usgs":false,"family":"Birch","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":812865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stratton, Christian","contributorId":217711,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":812866,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812867,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812868,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219207,"text":"70219207 - 2021 - Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","interactions":[],"lastModifiedDate":"2021-05-18T14:07:03.389177","indexId":"70219207","displayToPublicDate":"2021-03-15T13:32:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3042,"text":"Petroleum Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","docAbstract":"Geological carbon sequestration (GCS) is necessary to help meet emissions reduction goals, but groundwater contamination may occur if CO2 and/or brine were to leak out of deep storage formations into the shallow subsurface. For this study, a natural analogue was investigated: in the Virgin River Basin of southwest Utah, water with moderate salinity and high CO2 concentrations is leaking upward into shallow aquifers that contain heavy metal-bearing concretions. The aquifer system is comprised of the Navajo and Kayenta formations, which are pervasive across southern Utah and have been considered as a potential GCS injection unit where they are sufficiently deep. Numerical models of the site were constructed based on measured water chemistry and head distributions from previous studies. Simulations were used to improve understanding of the rate and distribution of the upwelling flow into the aquifers, and to assess the reactive transport processes that may occur if the upwelling fluids were to interact with a zone of iron oxide and other heavy metals, representing the concretions that are common in the area. Various mineralogies were tested, including one in which Pb+2 was adsorbed onto ferrihydrite, and another in which it was bound within a solid mixture of litharge (PbO) and hematite (Fe2O3). Results indicate that metal mobilization depends strongly on the source zone composition and that Pb+2 transport can be naturally attenuated by gas phase formation and carbonate mineral precipitation. These findings could be used to improve risk assessment and mitigation strategies at geological carbon sequestration sites.
","language":"English","publisher":"The Geological Society of London","doi":"10.1144/petgeo2020-109","usgsCitation":"Plampin, M.R., Blondes, M., Sonnenthal, E., and Craddock, W.H., 2021, Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah: Petroleum Geoscience, v. 27, no. 3, petgeo2020-109, 15 p., https://doi.org/10.1144/petgeo2020-109.","productDescription":"petgeo2020-109, 15 p.","ipdsId":"IP-120620","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":384769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"47\",\"properties\":{\"name\":\"Utah\",\"nation\":\"USA 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Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":813215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":813217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218647,"text":"sir20215004 - 2021 - Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","interactions":[],"lastModifiedDate":"2021-03-16T11:43:44.760872","indexId":"sir20215004","displayToPublicDate":"2021-03-15T08:46:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5004","displayTitle":"Numerical Simulation of the Effects of Groundwater Withdrawal and Injection of High-Salinity Water on Salinity and Groundwater Discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","title":"Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","docAbstract":"Kaloko-Honokōhau National Historical Park (KAHO) is located on the west coast of the island of Hawaiʻi and contains water resources exposed in fishponds, anchialine pools, and marine waters that are cultural resources and that provide habitat for threatened, endangered, and other culturally important native species. KAHO’s water resources are sustained by and dependent on groundwater discharge. In 1978, the year of KAHO authorization, the lands immediately surrounding KAHO were undeveloped and zoned for conservation purposes; at present, most surrounding lands are either developed or zoned for industrial, commercial, or residential use. Urbanization of the North Kona District has increased the need for additional drinking and nonpotable (irrigation) water. Because KAHO’s water resources may be affected by existing and proposed groundwater withdrawals and injections of high-salinity water in the surrounding area, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to refine the understanding of how groundwater withdrawals and injection of high-salinity water may affect KAHO’s water resources.
Changes in KAHO water resources, in terms of changes in salinity and groundwater discharge, were modeled for selected scenarios of groundwater withdrawal and high-salinity water injection in the aquifer. The numerical model was developed using the model code SUTRA, which accounts for density-dependent flow and salinity transport, and included the coastal-confined groundwater system beneath the coastal freshwater-lens system. Model results indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the salinity of KAHO’s anchialine pools, which provide habitat for the endangered orangeblack Hawaiian damselfly (Megalagrion xanthomelas). The magnitude of the effect is dependent on the amount and location of the withdrawal. Greater withdrawal rates cause greater increases in salinity in KAHO, other factors being equal. For a given withdrawal rate, the greatest increase in salinity in KAHO is associated with wells withdrawing groundwater in an area inland of KAHO and the least increase in salinity is associated with wells near the coast. Model results also indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the groundwater discharge, in terms of the freshwater component (water with zero salinity) of the discharge, through KAHO. Greater withdrawal rates cause greater reductions in freshwater discharge through KAHO. For a given withdrawal rate, the greatest reduction in freshwater discharge through KAHO is associated with wells near the north boundary of KAHO and the least reduction is associated with wells near the coast to the north and south of KAHO.
Injection of high-salinity water that is denser than ocean water can affect the salinity of damselfly habitat in KAHO, with the magnitude of the effect dependent on the location of the injection. Model results indicate that salinity may either increase or decrease in the anchialine pools that provide damselfly habitat in KAHO, depending on the site of injection. Injection inland of KAHO and at sites immediately north and south of KAHO causes a simulated decrease in salinity at the damselfly habitat, whereas injection farther north and south of KAHO causes an increase in salinity. Injection of high-salinity water also causes a reduction in freshwater discharge through KAHO, with the greatest reduction associated with distant injection wells to the north and south of KAHO and the least reduction associated with wells located near and immediately inland from KAHO.
The numerical groundwater models developed for this study have a number of limitations. Lack of understanding of the subsurface geology constrains the ability to accurately model the groundwater-flow system. The models developed for this study are nonunique, cannot account for local-scale heterogeneities in the aquifer, and contain uncertainties related to recharge, boundary conditions, assigned parameter values in the model, and representations of the different hydrogeological features. Confidence in model results can be improved by addressing these and other limitations. In spite of these limitations, the three-dimensional numerical model developed for this study provides a useful conceptual understanding of the potential effects of additional withdrawals and injections on groundwater resources in KAHO. Further evaluation of the ecologic effects of the simulated changes in groundwater quality and quantity in KAHO is needed but is beyond the scope of this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215004","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Oki, D.S., 2021, Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2021–5004, 59 p., https://doi.org/10.3133/sir20215004.","productDescription":"Report: viii, 59 p.; Data Release","numberOfPages":"59","ipdsId":"IP-119308","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZ3EVJ","linkHelpText":"SUTRA Model Used to Evaluate the Effects of Groundwater Withdrawal and Injection, Kaloko-Honokōhau National Historical Park, Hawaiʻi"},{"id":383762,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5004/sir20215004.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5004/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaloko-Honokōhau National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.03761672973633,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.66829132832601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, conducts research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2019, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2019 included deploying a portable seismic array near Steamboat Geyser in Norris Geyser Basin that recorded signals from seven major water eruptions; deploying a semipermanent Global Positioning System array; surveying soil carbon dioxide flux and temperature and operating an eddy covariance system to make continuous measurements; collecting and analyzing water samples from Shoshone Geyser Basin, the outlets of Shoshone and Lewis Lakes, Cinder Pool in Norris Geyser Basin, and several locations along Obsidian Creek; exploring and documenting a new thermal area near Tern Lake that was discovered in 2018; measuring specific conductance along major rivers to determine the chloride flux and total heat output of the Yellowstone hydrothermal system; conducting an inventory of hydrothermal features in Norris Geyser Basin and Upper Geyser Basin as part of a park-wide project that began in 2018; and sampling of tree rings and silica sinter deposits in the Upper Geyser Basin to better understand hydrothermal activity over time.
Continuing the pattern that started in 2018, Steamboat Geyser, in Norris Geyser Basin, erupted 48 times in 2019—a new record for a calendar year! Overall, however, noteworthy geyser activity in Yellowstone National Park was much reduced relative to the previous year. Thermal features on Geyser Hill in the Upper Geyser Basin had returned to their normal activity styles after Ear Spring’s September 2018 eruption and did not show any significant changes in 2019. Giant Geyser, also in the Upper Geyser Basin, did not experience any eruptions after March 2019. Seismicity was reduced relative to previous years, and deformation of Norris Geyser Basin, which started as uplift in 2015 and paused in late 2018, shifted to subsidence in late 2019. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1473","usgsCitation":"Yellowstone Volcano Observatory, 2021, Yellowstone Volcano Observatory 2019 annual report: U.S. Geological Survey Circular 1473, 35 p., https://doi.org/10.3133/cir1473.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"N","ipdsId":"IP-119028","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384319,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1473/cir1473.pdf","text":"Report","size":"62 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1473/covrthb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.731414013769\n ],\n [\n -109.281005859375,\n 43.731414013769\n ],\n [\n -109.281005859375,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 43.731414013769\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"Crex Meadows Wildlife Area (Crex) is a 30,000-acre property in Burnett County, Wisconsin. Crex is managed by the Wisconsin Department of Natural Resources (WDNR) with the goal of providing public recreation opportunities while also protecting the quality of native ecological communities and species on the property. The WDNR’s management strategy includes controlling water levels at flowages in Crex using a system of dikes, water control structures, ditches, and a diversion pump. For the past several decades there has been concern among nearby landowners that the water manage-ment strategy at Crex may be contributing to groundwater flooding in adjacent, privately held properties. This issue has been particularly contentious during periods when regional groundwater elevations are already high. This study was conducted in response to those concerns. For the study, a network of 12 monitoring wells was installed in and to the west of Crex. Groundwater elevations were recorded in the wells before, during, and after water-level changes in the western Crex flowages to assess if groundwater elevations to the west of Crex are detectably affected by the flowage water levels.
This study successfully collected groundwater elevations in 11 study wells during a 3-month period in 2019 when water elevations in the Dike 6 flowage and Erickson flowage were lowered and then raised. The data logger at a 12th location failed and no data were recorded. The groundwater elevation trends in these study wells were compared with groundwater elevation trends at a regional U.S. Geological Survey well to provide information for determining if changing the flowage elevations had a noticeable response in the study wells west of Crex Meadows. This analysis was done by (1) evaluating study well groundwater elevation trends compared to the regional well, (2) using a scatter plot of study well and regional well data during raising and lowering periods,
(3) assessing horizontal hydraulic gradient data during the study period, and (4) assessing the cumulative departure from the mean groundwater elevation for each well.
Overall, regional groundwater elevations had a down-ward trend before and during the flowage lowering period and then had an upward trend during the flowage raising period. This pattern was observed in the regional well and in all the study wells adjacent to and several miles from the flowages. The similarity in patterns indicates that precipitation and regional groundwater flow conditions were the dominant drivers of the system during the study period. The scatter plot and cumulative departure from the mean analysis showed that in addition to regional trends, wells 1, 6, and 7 were likely affected by the changes in the flowage water levels. Overall, at least on the timescale of this study, water management at Crex likely did not have detectable effects on wells outside the Crex property. Wells installed on the Crex property including the wells in the lakebeds of the flowages (wells 1 and 7) and possibly well 6 east of the flowages showed what seems to be minor affects due to water management at Crex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205149","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Haserodt, M.J., and Fienen, M.N., 2020, Assessment of groundwater trends near Crex Meadows, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5149, 32 p., https://doi.org/10.3133/sir20205149.","productDescription":"vi, 36 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-117629","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385958,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5149/versionHist.txt","text":"Version History","size":"1.69 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5149 Version History"},{"id":385957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5149/sir20205149.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5149"},{"id":384275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5149/coverthb3.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Crex Meadows","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.51930236816406,\n 45.81540082150529\n ],\n [\n -92.51861572265625,\n 45.829756159282766\n ],\n [\n -92.51861572265625,\n 45.84506443975059\n ],\n [\n -92.52616882324219,\n 45.84506443975059\n ],\n [\n -92.52754211425781,\n 45.87853662114514\n ],\n [\n -92.55226135253906,\n 45.882360730184025\n ],\n [\n -92.55088806152344,\n 45.90768880475299\n ],\n [\n -92.60856628417967,\n 45.90386643939614\n ],\n [\n -92.67105102539061,\n 45.897654534346906\n ],\n [\n -92.68272399902344,\n 45.88618457602257\n ],\n [\n -92.68135070800781,\n 45.867062714815475\n ],\n [\n -92.69096374511719,\n 45.817315080406246\n ],\n [\n -92.691650390625,\n 45.80008438131991\n ],\n [\n -92.68753051757812,\n 45.79338211440398\n ],\n [\n -92.64770507812499,\n 45.79338211440398\n ],\n [\n -92.61543273925781,\n 45.813965084145295\n ],\n [\n -92.57972717285156,\n 45.817315080406246\n ],\n [\n -92.57492065429688,\n 45.82688538784564\n ],\n [\n -92.54539489746094,\n 45.82640691154487\n ],\n [\n -92.54539489746094,\n 45.81109349837976\n ],\n [\n -92.51930236816406,\n 45.81540082150529\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 15, 2021; Version 1.1: May 26, 2021","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
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Austin, Texas 78754-4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"Geophysical investigations documenting enhanced magnetic susceptibility (MS) within the water table fluctuation zone at hydrocarbon contaminated sites suggest that MS can be used as a proxy for investigating microbial mediated iron reduction during intrinsic bioremediation. Here, we investigated the microbial community composition over a 5-year period at a hydrocarbon-contaminated site that exhibited transient elevated MS responses. Our objective was to determine the key microbial populations in zones of elevated MS. We retrieved sediment cores from the petroleum-contaminated site near Bemidji, MN, United States, and performed MS measurements on these cores. We also characterized the microbial community composition by high-throughput 16S rRNA gene amplicon sequencing from samples collected along the complete core length. Our spatial and temporal analysis revealed that the microbial community composition was generally stable throughout the period of investigation. In addition, we observed distinct vertical redox zonations extending from the upper vadose zone into the saturated zone. These distinct redox zonations were concomitant with the dominant microbial metabolic processes as follows: (1) the upper vadose zone was dominated by aerobic microbial populations; (2) the lower vadose zone was dominated by methanotrophic populations, iron reducers and iron oxidizers; (3) the smear zone was dominated by iron reducers; and (4) the free product zone was dominated by syntrophic and methanogenic populations. Although the common notion is that high MS values are caused by high magnetite concentrations that can be biotically formed through the activities of iron-reducing bacteria, here we show that the highest magnetic susceptibilities were measured in the free-phase petroleum zone, where a methanogenic community was predominant. This field study may contribute to the emerging knowledge that methanogens can switch their metabolism from methanogenesis to iron reduction with associated magnetite precipitation in hydrocarbon contaminated sediments. Thus, geophysical methods such as MS may help to identify zones where iron cycling/reduction by methanogens is occurring.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2021.598172","usgsCitation":"Beaver, C.L., Atekwana, E.A., Bekins, B.A., Ntarlagiannis, D., Slater, L., and Rossbach, S., 2021, Methanogens and their syntrophic partners dominate zones of enhanced magnetic susceptibility at a petroleum contaminated site: Frontiers in Earth Science, v. 9, 598172, 18 p., https://doi.org/10.3389/feart.2021.598172.","productDescription":"598172, 18 p.","ipdsId":"IP-125198","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.598172","text":"Publisher Index Page"},{"id":384449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, Carol L.","contributorId":255451,"corporation":false,"usgs":false,"family":"Beaver","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atekwana, Estella A.","contributorId":255452,"corporation":false,"usgs":false,"family":"Atekwana","given":"Estella","email":"","middleInitial":"A.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":812377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":812378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ntarlagiannis, Dimitrios","contributorId":255453,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rossbach, Silvia","contributorId":255455,"corporation":false,"usgs":false,"family":"Rossbach","given":"Silvia","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812381,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219910,"text":"70219910 - 2021 - The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","interactions":[],"lastModifiedDate":"2021-04-19T11:51:47.992809","indexId":"70219910","displayToPublicDate":"2021-03-15T06:56:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","docAbstract":"The Panola Mountain Research Watershed (PMRW) is a 41‐hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long‐term study has enabled insights that were initially unforeseen but are also unachievable in short‐term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location‐independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14127","usgsCitation":"Aulenbach, B.T., Hooper, R.P., van Meerveld, H.J., Burns, D., Freer, J.E., Shanley, J.B., Huntington, T., McDonnell, J.J., and Norman E. Peters, 2021, The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed: Hydrological Processes, v. 35, no. 4, e14127, 14 p., https://doi.org/10.1002/hyp.14127.","productDescription":"e14127, 14 p.","ipdsId":"IP-125152","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":385149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Richard P 0000-0002-3329-9622","orcid":"https://orcid.org/0000-0002-3329-9622","contributorId":257488,"corporation":false,"usgs":false,"family":"Hooper","given":"Richard","email":"","middleInitial":"P","affiliations":[{"id":52045,"text":"Tufts University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":814372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Meerveld, H. J. 0000-0002-7547-3270","orcid":"https://orcid.org/0000-0002-7547-3270","contributorId":257489,"corporation":false,"usgs":false,"family":"van Meerveld","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":52048,"text":"University of Zurich, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":814373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freer, James E. 0000-0001-6388-7890","orcid":"https://orcid.org/0000-0001-6388-7890","contributorId":188139,"corporation":false,"usgs":false,"family":"Freer","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":814375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDonnell, Jeffery J. 0000-0002-3880-3162","orcid":"https://orcid.org/0000-0002-3880-3162","contributorId":62723,"corporation":false,"usgs":false,"family":"McDonnell","given":"Jeffery","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Norman E. Peters 0000-0002-0637-9424","orcid":"https://orcid.org/0000-0002-0637-9424","contributorId":207130,"corporation":false,"usgs":false,"family":"Norman E. Peters","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":814379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221418,"text":"70221418 - 2021 - Using bottom trawls to monitor subsurface water clarity in marine ecosystems","interactions":[],"lastModifiedDate":"2021-06-15T11:46:28.05606","indexId":"70221418","displayToPublicDate":"2021-03-15T06:44:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3194,"text":"Progress in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Using bottom trawls to monitor subsurface water clarity in marine ecosystems","docAbstract":"Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.