In 2003 and 2004, the U.S. Geological Survey investigated the availability of groundwater resources in a 160-square mile area of coastal New Hampshire (Figure 1) using a regional groundwater flow model (Mack, 2009). At that time, population growth and increasing water demand prompted concern for the sustainability of the region’s groundwater resources in a fractured-crystalline bedrock-aquifer with little storage. The groundwater flow model developed for the previous study incorporated detailed water-use information for 2003-4 and simulated the effects of projected increases in water use. However, poor stream representation may reduce the effectiveness of the original model head simulations. Improvements to the model, made by incorporating the USGS’s MODLFOW-2005 Newton formulation (MODFLOW-NWT, Niswonger and others, 2011) and by more accurately representing stream characteristics, are presented in an example simulating approximate changes in water use. Groundwater heads in an area of relatively larger population change, near the center of the Seacoast’s fractured bedrock aquifer, were simulated with the upgraded model using published 2004, and approximated 2015, water use rates. This area is situated at a local topographic high point and near the junction of three towns, where drainages flow westward, toward Great Bay, and eastward, toward the Atlantic Ocean (Figure 1).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the MODFLOW and more 2017 conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"MODFLOW and More 2017","conferenceDate":"May 21-24, 2017","language":"English","usgsCitation":"Mack, T., 2017, Preliminary-assessment and upgrade of a groundwater flow model of the Seacoast Bedrock Aquifer, New Hampshire, in Proceedings of the MODFLOW and more 2017 conference, May 21-24, 2017, p. 40-44.","productDescription":"5 p.","startPage":"40","endPage":"44","ipdsId":"IP-087643","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":386128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire","otherGeospatial":"Seacoast Bedrock Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.71418762207031,\n 43.04079076668198\n ],\n [\n -70.71075439453125,\n 43.071395809535375\n ],\n [\n -70.78628540039062,\n 43.08493742707592\n ],\n [\n -70.8570098876953,\n 43.1405770781429\n ],\n [\n -70.9881591796875,\n 43.033764503405315\n ],\n [\n -70.98953247070311,\n 42.82663145362289\n ],\n [\n -70.81443786621094,\n 42.82209892875648\n ],\n [\n -70.74783325195311,\n 42.976520698105524\n ],\n [\n -70.71418762207031,\n 43.03777960950732\n ],\n [\n -70.71418762207031,\n 43.04079076668198\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mack, Thomas J. 0000-0002-0496-3918","orcid":"https://orcid.org/0000-0002-0496-3918","contributorId":218727,"corporation":false,"usgs":true,"family":"Mack","given":"Thomas J.","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":815071,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215552,"text":"70215552 - 2017 - Yellowstone River compact commission sixty-sixth annual report 2017","interactions":[],"lastModifiedDate":"2022-04-18T14:09:05.043145","indexId":"70215552","displayToPublicDate":"2017-12-31T09:46:57","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Yellowstone River compact commission sixty-sixth annual report 2017","docAbstract":"No abstract available.
","language":"English","publisher":"Yellowstone River Compact Commission","usgsCitation":"Kilpatrick, J.M., Tyrrell, P., and Langel, J., 2017, Yellowstone River compact commission sixty-sixth annual report 2017: Cooperator Report, xvi, 40 p.","productDescription":"xvi, 40 p.","ipdsId":"IP-103413","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":382759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379639,"type":{"id":11,"text":"Document"},"url":"https://water.usgs.gov/water-resources/YRCC-docs/YRCCAnnualReport2017_508Compliant.pdf"}],"country":"United States","state":"Montana, North Dakota, Wyoming","otherGeospatial":"Yellowstone River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.6505126953125,\n 47.85740289465826\n ],\n [\n -104.0185546875,\n 47.97889140226657\n ],\n [\n -104.732666015625,\n 47.57652571374621\n ],\n [\n -107.5836181640625,\n 46.524855311033406\n ],\n [\n -109.19860839843749,\n 45.75602615586017\n ],\n [\n -110.621337890625,\n 45.84793427349226\n ],\n [\n -111.0772705078125,\n 45.336701909968134\n ],\n [\n -110.9234619140625,\n 44.680371641890375\n ],\n [\n -110.863037109375,\n 43.56447158721811\n ],\n [\n -110.46203613281249,\n 42.98053954751642\n ],\n [\n -109.599609375,\n 42.69454866207692\n ],\n [\n -108.91845703124999,\n 42.30575300304638\n ],\n [\n -106.5399169921875,\n 43.01268088642034\n ],\n [\n -105.0732421875,\n 43.66389797397276\n ],\n [\n -104.6392822265625,\n 44.449467536006935\n ],\n [\n -104.150390625,\n 46.78877728793222\n ],\n [\n -103.9251708984375,\n 47.37603463349758\n ],\n [\n -103.6505126953125,\n 47.85740289465826\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kilpatrick, John M. 0000-0002-1180-3752 jmkilpat@usgs.gov","orcid":"https://orcid.org/0000-0002-1180-3752","contributorId":1010,"corporation":false,"usgs":true,"family":"Kilpatrick","given":"John","email":"jmkilpat@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":809298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tyrrell, Patrick","contributorId":248516,"corporation":false,"usgs":false,"family":"Tyrrell","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":809299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langel, Jan","contributorId":248517,"corporation":false,"usgs":false,"family":"Langel","given":"Jan","email":"","affiliations":[],"preferred":false,"id":809300,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217856,"text":"70217856 - 2017 - Soil acidification and Beech Bark Disease influence the composition and structure of Sugar Maple-Beech Forests","interactions":[],"lastModifiedDate":"2021-02-08T14:08:45.196726","indexId":"70217856","displayToPublicDate":"2017-12-31T08:07:52","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Soil acidification and Beech Bark Disease influence the composition and structure of Sugar Maple-Beech Forests","docAbstract":"No abstract available.
","language":"English","publisher":"New York State Energy Research","collaboration":"New York State Energy Research and Development Authority; USGS","usgsCitation":"Sullivan, T.J., McDonnell, T.C., Lawrence, G.B., Antidormi, M.R., Dovciak, M., Zarfos, M.R., and Bailey, S., 2017, Soil acidification and Beech Bark Disease influence the composition and structure of Sugar Maple-Beech Forests, iv, 18 p.","productDescription":"iv, 18 p.","ipdsId":"IP-086720","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":383097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383096,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/About/Publications/Research-and-Development-Technical-Reports/Environmental-Research-and-Development-Technical-Reports"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.45410156250001,\n 42.71473218539461\n ],\n [\n -73.03710937500001,\n 42.71473218539461\n ],\n [\n -73.03710937500001,\n 45.089035564831015\n ],\n [\n -75.45410156250001,\n 45.089035564831015\n ],\n [\n -75.45410156250001,\n 42.71473218539461\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Timothy J.","contributorId":196720,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":809919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonnell, Todd C. 0000-0002-5231-105X","orcid":"https://orcid.org/0000-0002-5231-105X","contributorId":196721,"corporation":false,"usgs":false,"family":"McDonnell","given":"Todd","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":809920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dovciak, Martin","contributorId":196723,"corporation":false,"usgs":false,"family":"Dovciak","given":"Martin","email":"","affiliations":[],"preferred":false,"id":809921,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zarfos, Michael R. 0000-0002-2902-4773","orcid":"https://orcid.org/0000-0002-2902-4773","contributorId":196724,"corporation":false,"usgs":false,"family":"Zarfos","given":"Michael","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":809925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bailey, Scott W.","contributorId":248803,"corporation":false,"usgs":false,"family":"Bailey","given":"Scott W.","affiliations":[{"id":50019,"text":"USDA Forest Service, Hubbard Brook Experiment Station","active":true,"usgs":false}],"preferred":false,"id":809922,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195218,"text":"70195218 - 2017 - The thermal regime and species composition of fish and invertebrates in Kelly Warm Spring, Grand Teton National Park, Wyoming","interactions":[],"lastModifiedDate":"2018-03-19T10:42:30","indexId":"70195218","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"The thermal regime and species composition of fish and invertebrates in Kelly Warm Spring, Grand Teton National Park, Wyoming","docAbstract":"We evaluated the thermal regime and relative abundance of native and nonnative fish and invertebrates within Kelly Warm Spring and Savage Ditch, Grand Teton National Park, Wyoming. Water temperatures within the system remained relatively warm year-round with mean temperatures >20 °C near the spring source and >5 °C approximately 2 km downstream of the source. A total of 7 nonnative species were collected: Convict/Zebra Cichlid (Cichlasoma nigrofasciatum), Green Swordtail (Xiphophorus hellerii), Tadpole Madtom (Noturus gyrinus), Guppy (Poecilia reticulata), Goldfish (Carassius auratus), red-rimmed melania snail (Melanoides tuberculata), and American bullfrog tadpoles (Lithobates catesbeianus). Nonnative fish (Zebra Cichlids and Green Swordtails), red-rimmed melania snails, and bullfrog tadpoles dominated the upper 2 km of the system. Abundance estimates of the Zebra Cichlid exceeded 12,000 fish/km immediately downstream of the spring source. Relative abundance of native species increased movingdownstream as water temperatures attenuated with distance from the thermally warmed spring source; however, nonnative species were captured 4 km downstream from the spring. Fish diseases were prevalent in both native and nonnative fish from the Kelly Warm Spring pond. Clinostomum marginatum, a trematode parasite, was found in native species samples, and the tapeworm Diphyllobothrium dendriticum was present in samples from nonnative species. Diphyllobothrium dendriticum is rare in Wyoming. Salmonella spp. were also found in some samples of nonnative species. These bacteria are associated with aquarium fish and aquaculture and are generally not found in the wild.
","language":"English","publisher":"Monte L. Bean Life Science Museum, Brigham Young University","doi":"10.3398/064.077.0405","usgsCitation":"Harper, D., and Farag, A., 2017, The thermal regime and species composition of fish and invertebrates in Kelly Warm Spring, Grand Teton National Park, Wyoming: Western North American Naturalist, v. 77, no. 4, p. 440-449, https://doi.org/10.3398/064.077.0405.","productDescription":"10 p.","startPage":"440","endPage":"449","ipdsId":"IP-083872","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488736,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsarchive.byu.edu/wnan/vol77/iss4/4","text":"External Repository"},{"id":351236,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park, Kelly Warm Spring","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.61748623847961,\n 43.63864915229675\n ],\n [\n -110.61528682708739,\n 43.63864915229675\n ],\n [\n -110.61528682708739,\n 43.63986428872045\n ],\n [\n -110.61748623847961,\n 43.63986428872045\n ],\n [\n -110.61748623847961,\n 43.63864915229675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7c1e77e4b00f54eb229308","contributors":{"authors":[{"text":"Harper, David 0000-0001-7061-8461 david_harper@usgs.gov","orcid":"https://orcid.org/0000-0001-7061-8461","contributorId":169848,"corporation":false,"usgs":true,"family":"Harper","given":"David","email":"david_harper@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":727507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farag, Aida 0000-0003-4247-6763 aida_farag@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6763","contributorId":200690,"corporation":false,"usgs":true,"family":"Farag","given":"Aida","email":"aida_farag@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":727508,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196914,"text":"70196914 - 2017 - Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia","interactions":[],"lastModifiedDate":"2018-05-10T14:36:58","indexId":"70196914","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia","docAbstract":"The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.016.0406","usgsCitation":"Jennings, C.A., and Sundmark, A.P., 2017, Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia: Southeastern Naturalist, v. 16, no. 4, p. 546-566, https://doi.org/10.1656/058.016.0406.","productDescription":"21 p.","startPage":"546","endPage":"566","ipdsId":"IP-077977","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Charlie Elliot Wildlife Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.30908203125,\n 32.7503226078097\n ],\n [\n -83.03466796874999,\n 32.7503226078097\n ],\n [\n -83.03466796874999,\n 33.779147331286474\n ],\n [\n -84.30908203125,\n 33.779147331286474\n ],\n [\n -84.30908203125,\n 32.7503226078097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2dc","contributors":{"authors":[{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sundmark, Aaron P.","contributorId":204804,"corporation":false,"usgs":false,"family":"Sundmark","given":"Aaron","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":735042,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196942,"text":"70196942 - 2017 - Effects of isolation on ant assemblages depend on microhabitat","interactions":[],"lastModifiedDate":"2018-05-14T13:19:19","indexId":"70196942","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of isolation on ant assemblages depend on microhabitat","docAbstract":"How isolation affects biological communities is a fundamental question in ecology and conservation biology. Local diversity (α) and regional diversity (γ) are consistently lower in insular areas. The pattern of species turnover (β diversity) and the influence of isolation on competitive interactions are less predictable. Differences in communities across microhabitats within an isolated patch could contribute to the variability in patterns related to isolation. Trees form characteristically dense and sparse patches (low vs. high isolation) in floating marshes in coastal Louisiana, and canopy and root areas around these trees could support distinct ant communities. Consequently, trees in floating marshes provide an ideal environment to study the effects of isolation on community assemblages in different microhabitats. We sampled ant communities in 120 trees during the summer of 2016. We found ant α diversity was not different between the canopy and roots, and the magnitude and directional effects of isolation on ants were inconsistent between the canopy and root areas. In the roots of sparse sites, ant diversity (α, β, and γ) was lower, species composition was changed, and the signature of interspecific competition was more prominent compared to dense sites. In the canopy, however, significant differences between dense and sparse sites were only detected in α and γ diversity, and ant species co‐occurrence was not significantly different from a random distribution. The inconsistent responses of ants in canopy and root areas to isolation may be due to the differences of species pool size, environmental harshness, and species interactions between strata. In addition, these findings indicate that communities in distinct microenvironments can respond differentially to habitat isolation. We suggest incorporating organisms from different microhabitats into future research to better understand the influence of isolation on the assembly of biological communities.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2049","usgsCitation":"Chen, X., Adams, B., Layne, M., Swarzenski, C.M., Norris, D., and Hooper-Bui, L., 2017, Effects of isolation on ant assemblages depend on microhabitat: Ecosphere, v. 8, no. 12, p. 1-12, https://doi.org/10.1002/ecs2.2049.","productDescription":"e02049; 12 p.","startPage":"1","endPage":"12","onlineOnly":"Y","ipdsId":"IP-091131","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":469226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2049","text":"Publisher Index Page"},{"id":354098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-20","publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2d8","contributors":{"authors":[{"text":"Chen, Xuan","contributorId":204821,"corporation":false,"usgs":false,"family":"Chen","given":"Xuan","email":"","affiliations":[{"id":36987,"text":"Louisiana State University, College of Coast and Environment","active":true,"usgs":false}],"preferred":false,"id":735093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Benjamin","contributorId":204822,"corporation":false,"usgs":false,"family":"Adams","given":"Benjamin","email":"","affiliations":[{"id":36988,"text":"University of Louisville, Dept. Biology","active":true,"usgs":false}],"preferred":false,"id":735094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Layne, Michael","contributorId":204823,"corporation":false,"usgs":false,"family":"Layne","given":"Michael","email":"","affiliations":[{"id":36987,"text":"Louisiana State University, College of Coast and Environment","active":true,"usgs":false}],"preferred":false,"id":735095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swarzenski, Christopher M. 0000-0001-9843-1471 cswarzen@usgs.gov","orcid":"https://orcid.org/0000-0001-9843-1471","contributorId":656,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Christopher","email":"cswarzen@usgs.gov","middleInitial":"M.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":735092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norris, David O.","contributorId":156306,"corporation":false,"usgs":false,"family":"Norris","given":"David O.","affiliations":[],"preferred":false,"id":735096,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hooper-Bui, Linda","contributorId":204824,"corporation":false,"usgs":false,"family":"Hooper-Bui","given":"Linda","email":"","affiliations":[{"id":36987,"text":"Louisiana State University, College of Coast and Environment","active":true,"usgs":false}],"preferred":false,"id":735097,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198174,"text":"70198174 - 2017 - Population expansion of Humpback chub in western Grand Canyon and hypothesized mechanisms","interactions":[],"lastModifiedDate":"2018-07-19T09:43:34","indexId":"70198174","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3451,"text":"Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Population expansion of Humpback chub in western Grand Canyon and hypothesized mechanisms","docAbstract":"Humpback chub, Gila cypha, is an endangered warm water fish endemic to the Colorado River basin of southwestern North America. In Grand Canyon National Park, cold hypolimnetic water-release temperatures from Glen Canyon Dam have largely precluded successful spawning and recruitment of humpback chub in the mainstem Colorado River. Therefore, the species has utilized the warmer, more saline, and free-flowing Little Colorado River for its primary spawning habitat and continued existence. Based on long-term fish sampling efforts, we document local recruitment and population expansion of humpback chub in the western Grand Canyon and hypothesize that this is a result of recent warmer mainstem water temperatures. Continued recruitment and population expansion of humpback chub in the western Grand Canyon could potentially reduce extinction risk by providing population redundancy and less reliance upon the Little Colorado River for the species survival in the Grand Canyon.
","language":"English","publisher":"Southwestern Association of Naturalists","doi":"10.1894/0038-4909-62.4.285","usgsCitation":"VanHaverbeke, D.R., Stone, D.M., Dodrill, M., Young, K.L., and Pillow, M.J., 2017, Population expansion of Humpback chub in western Grand Canyon and hypothesized mechanisms: Southwestern Naturalist, v. 62, no. 4, p. 285-292, https://doi.org/10.1894/0038-4909-62.4.285.","productDescription":"8 p.","startPage":"285","endPage":"292","ipdsId":"IP-087852","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438117,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76972GB","text":"USGS data release","linkHelpText":"Humpback chub (Gila cypha) and mean daily water temperature data, western Grand Canyon - 2000 to 2016"},{"id":355813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.071044921875,\n 35.50987173838399\n ],\n [\n -111.0333251953125,\n 35.50987173838399\n ],\n [\n -111.0333251953125,\n 37.12966595484084\n ],\n [\n -114.071044921875,\n 37.12966595484084\n ],\n [\n -114.071044921875,\n 35.50987173838399\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc50be4b0f5d57878eaee","contributors":{"authors":[{"text":"VanHaverbeke, David R.","contributorId":206440,"corporation":false,"usgs":false,"family":"VanHaverbeke","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":37331,"text":"US Fish and Wildlife Service, 2500 S. Pine Knoll Drive, Flagstaff, AZ 86001","active":true,"usgs":false}],"preferred":false,"id":740426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Dennis M.","contributorId":58237,"corporation":false,"usgs":false,"family":"Stone","given":"Dennis","email":"","middleInitial":"M.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":740427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":740425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Kirk L.","contributorId":204247,"corporation":false,"usgs":false,"family":"Young","given":"Kirk","email":"","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":740428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pillow, Michael J.","contributorId":206441,"corporation":false,"usgs":false,"family":"Pillow","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37331,"text":"US Fish and Wildlife Service, 2500 S. Pine Knoll Drive, Flagstaff, AZ 86001","active":true,"usgs":false}],"preferred":false,"id":740429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193663,"text":"sir20175133 - 2017 - Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States","interactions":[],"lastModifiedDate":"2018-01-02T13:23:03","indexId":"sir20175133","displayToPublicDate":"2017-12-29T15:45:00","publicationYear":"2017","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":"2017-5133","title":"Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States","docAbstract":"A suite of hydrologic models has been developed for the Apalachicola-Chattahoochee-Flint River Basin (ACFB) as part of the National Water Census, a U.S. Geological Survey research program that focuses on developing new water accounting tools and assessing water availability and use at the regional and national scales. Seven hydrologic models were developed using the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, process-based system that simulates the effects of precipitation, temperature, land cover, and water use on basin hydrology. A coarse-resolution PRMS model was developed for the entire ACFB, and six fine-resolution PRMS models were developed for six subbasins of the ACFB. The coarse-resolution model was loosely coupled with a groundwater model to better assess the effects of water use on streamflow in the lower ACFB, a complex geologic setting with karst features. The PRMS coarse-resolution model was used to provide inputs of recharge to the groundwater model, which in turn provide simulations of groundwater flow that were aggregated with PRMS-based simulations of surface runoff and shallow-subsurface flow. Simulations without the effects of water use were developed for each model for at least the calendar years 1982–2012 with longer periods for the Potato Creek subbasin (1942–2012) and the Spring Creek subbasin (1952–2012). Water-use-affected flows were simulated for 2008–12. Water budget simulations showed heterogeneous distributions of precipitation, actual evapotranspiration, recharge, runoff, and storage change across the ACFB. Streamflow volume differences between no-water-use and water-use simulations were largest along the main stem of the Apalachicola and Chattahoochee River Basins, with streamflow percentage differences largest in the upper Chattahoochee and Flint River Basins and Spring Creek in the lower Flint River Basin. Water-use information at a shorter time step and a fully coupled simulation in the lower ACFB may further improve water availability estimates and hydrologic simulations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175133","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"LaFontaine, J.H., Jones, L.E., and Painter, J.A., 2017, Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2017–5133, 112 p., https://doi.org/10.3133/sir20175133.","productDescription":"Report: x, 112 p.; Data Release","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075742","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5133/sir20175133.pdf","text":"Report","size":"32.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5133"},{"id":350251,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175141","text":"Scientific Investigations Report 2017-5141","linkHelpText":"- Groundwater-Flow Budget for the Lower Apalachicola-Chattahoochee-Flint River Basin in Southwestern Georgia and Parts of Florida and Alabama, 2008–12"},{"id":350202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5133/coverthb.jpg"},{"id":350244,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2F1R","text":"USGS data release","description":"USGS data release","linkHelpText":"Model Input and Output for Hydrologic Simulations of the Apalachicola-Chattahoochee-Flint River Basin using the Precipitation Runoff Modeling System"}],"country":"United States","otherGeospatial":"Apalachicola-Chattahoochee-Flint River Basin","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 29.726222319395504\n ],\n [\n -83.397216796875,\n 29.726222319395504\n ],\n [\n -83.397216796875,\n 34.88593094075317\n ],\n [\n -85.660400390625,\n 34.88593094075317\n ],\n [\n -85.660400390625,\n 29.726222319395504\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
As part of the National Water Census program in the Apalachicola-Chattahoochee-Flint (ACF) River Basin, the U.S. Geological Survey evaluated the groundwater budget of the lower ACF, with particular emphasis on recharge, characterizing the spatial and temporal relation between surface water and groundwater, and groundwater pumping. To evaluate the hydrologic budget of the lower ACF River Basin, a groundwater-flow model, constructed using MODFLOW-2005, was developed for the Upper Floridan aquifer and overlying semiconfining unit for 2008–12. Model input included temporally and spatially variable specified recharge, estimated using a Precipitation-Runoff Modeling System (PRMS) model for the ACF River Basin, and pumping, partly estimated on the basis of measured agricultural pumping rates in Georgia. The model was calibrated to measured groundwater levels and base flows, which were estimated using hydrograph separation.
The simulated groundwater-flow budget resulted in a small net cumulative loss of groundwater in storage during the study period. The model simulated a net loss in groundwater storage for all the subbasins as conditions became substantially drier from the beginning to the end of the study period. The model is limited by its conceptualization, the data used to represent and calibrate the model, and the mathematical representation of the system; therefore, any interpretations should be considered in light of these limitations. In spite of these limitations, the model provides insight regarding water availability in the lower ACF River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175141","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"Jones, L.E., Painter, Jaime, LaFontaine, Jacob, Sepulveda, Nicasio, and Sifuentes, D.F., 2017, Groundwater-flow budget for the lower Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia and parts of \nFlorida and Alabama, 2008–12: U.S. Geological Survey Scientific Investigations Report 2017–5141, 76 p., \nhttps://doi.org/10.3133/sir20175141.","productDescription":"Report: viii, 76 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5141/coverthb.jpg"},{"id":350249,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175133","text":"Scientific Investigations Report 2017-5133","linkHelpText":"- Simulations of Hydrologic Response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States"},{"id":350247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5141/sir20175141.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5141"},{"id":350248,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DV1HCG","text":"USGS data release","description":"USGS data release"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.5,\n 30.5\n ],\n [\n -83.75,\n 30.5\n ],\n [\n -83.75,\n 32.25\n ],\n [\n -85.5,\n 32.25\n ],\n [\n -85.5,\n 30.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
The Devonian-age Marcellus Shale and the Ordovician-age Utica Shale, which have the potential for natural gas development, underlie Pike County and neighboring counties in northeastern Pennsylvania. In 2015, the U.S. Geological Survey, in cooperation with the Pike County Conservation District, conducted a study that expanded on a previous more limited 2012 study to assess baseline shallow groundwater quality in bedrock aquifers in Pike County prior to possible extensive shale-gas development. Seventy-nine water wells ranging in depths from 80 to 610 feet were sampled during June through September 2015 to provide data on the presence of methane and other aspects of existing groundwater quality in the various bedrock geologic units throughout the county, including concentrations of inorganic constituents commonly present at low values in shallow, fresh groundwater but elevated in brines associated with fluids extracted from geologic formations during shale-gas development. All groundwater samples collected in 2015 were analyzed for bacteria, dissolved and total major ions, nutrients, selected dissolved and total inorganic trace constituents (including metals and other elements), radon-222, gross alpha- and gross beta-particle activity, dissolved gases (methane, ethane, and propane), and, if sufficient methane was present, the isotopic composition of methane. Additionally, samples from 20 wells distributed throughout the county were analyzed for selected man-made volatile organic compounds, and samples from 13 wells where waters had detectable gross alpha activity were analyzed for radium-226 on the basis of relatively elevated gross alpha-particle activity.
Results of the 2015 study show that groundwater quality generally met most drinking-water standards for constituents and properties included in analyses, but groundwater samples from some wells had one or more constituents or properties, including arsenic, iron, manganese, pH, bacteria, sodium, chloride, sulfate, total dissolved solids, and radon-222, that did not meet (commonly termed failed or exceeded) primary or secondary maximum contaminant levels (MCLs) or Health Advisories (HA) for drinking water. Except for iron, dissolved and total concentrations of major ions and most trace constituents generally were similar. Only 1 of 79 well-water samples had any constituent that exceeded a MCL, with an arsenic concentration of about 30 micrograms per liter (µg/L) that was higher than the MCL of 10 µg/L. However, total arsenic concentrations were higher than the HA of 2 µg/L in samples from another 12 of 79 wells (about 15 percent). Secondary maximum contaminant levels (SMCLs) were exceeded most frequently by pH and concentrations of iron and manganese. The pH was outside of the SMCL range of 6.5–8.5 in samples from 24 of 79 wells (30 percent), ranging from 5.5 to 9.2; more samples had pH values less than 6.5 than had pH values greater than 8.5. Total iron concentrations typically were much greater than dissolved iron concentrations, indicating substantial presence of iron in particulate phase, and exceeded the SMCL of 300 µg/L more often [35 of 79 samples (44 percent)] than dissolved iron concentrations [samples from 8 of 79 wells (10 percent)]. Total manganese concentrations exceeded the SMCL of 50 µg/L in samples from 31 of 79 wells (39 percent) and the HA of 300 µg/L in samples from 13 of 79 wells (about 16 percent). A few (1–2) samples had concentrations of sodium, chloride, sulfate, or TDS higher than the SMCLs of 60, 250, 250, and 500 mg/L, respectively. However, dissolved sodium concentrations were higher than the HA of 20 mg/L in samples from 15 of 79 wells (nearly 20 percent). Total coliform bacteria were detected in samples from 25 of 79 wells (32 percent) but Escherichia coli were not detected in any sample. Radon-222 activities ranged from 11 to 5,100 picocuries per liter (pCi/L), with a median of 1,440 pCi/L, and exceeded the proposed and the alternate proposed drinking-water standards of 300 and 4,000 pCi/L, respectively, in samples from 60 of 79 wells (75 percent) and in samples from 2 of 79 wells (3 percent), respectively.
Groundwater samples from all wells were analyzed for dissolved methane by one contract laboratory that determined water from 19 of the 79 wells (24 percent) had concentrations of methane greater than the reporting level of 0.010 milligrams per liter (mg/L) with a maximum methane concentration of 2.5 mg/L. Methane concentrations in 18 replicate samples submitted to a second laboratory for dissolved gas and isotopic analysis generally were higher by as much as a factor of 2.7 from those determined by the first laboratory, indicating potential bias related to combined sampling and analytical methods, and therefore, caution needs to be used when comparing methane results determined by different methods. The isotopic composition of methane in 9 of 10 samples with sufficient dissolved methane (about 0.3 mg/L) for isotopic analysis is consistent with values reported for methane of microbial origin produced through carbon dioxide reduction; an isotopic shift in 1 or 2 samples may indicate subsequent methane oxidation. The low concentrations of ethane relative to methane in these samples further indicate that the methane may be of microbial origin. Groundwater samples with relatively elevated methane concentrations (near or greater than 0.3 mg/L) also had chemical compositions that differed in some respects from groundwater with relatively low methane concentrations (less than 0.3 mg/L) by having higher pH (greater than 8) and higher concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide and chloride/bromide ratios indicative of mixing with a small amount of brine of probable natural occurrence.
The spatial distribution of groundwater compositions differs by topographic setting and lithology and generally shows that (1) relatively dilute, slightly acidic, oxygenated, calcium-carbonate type waters tend to occur in the uplands underlain by the undivided Poplar Gap and Packerton members of the Catskill Formation in southwestern Pike County; (2) waters of near neutral pH with the highest amounts of hardness (calcium and magnesium) generally occur in areas of intermediate altitudes underlain by other members of the Catskill Formation; and (3) waters with pH values greater than 8, low oxygen concentrations, and the highest arsenic, sodium, lithium, bromide, and methane concentrations can be present in deep wells in uplands but most frequently occur in stream valleys, especially at low altitudes (less than about 1,200 feet above North American Vertical Datum of 1988) where groundwater may be discharging regionally, such as to the Delaware River in northern and eastern Pike County. Thus, the baseline assessment of groundwater quality in Pike County prior to gas-well development shows that shallow (less than about 1,000 feet deep) groundwater generally meets primary drinking-water standards for inorganic constituents but varies spatially, with methane and some constituents present in high concentrations in brine (and connate waters from gas and oil reservoirs) present at low to moderate concentrations in some parts of Pike County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175110","collaboration":"Prepared in cooperation with the Pike County Conservation District","usgsCitation":"Senior, L.A., and Cravotta, C.A., III, 2017: Baseline assessment of groundwater quality in Pike County, Pennsylvania, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5110, 181 p., https://doi.org/10.3133/sir20175110.","productDescription":"Report: xii, 181 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088156","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":350196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5980c3c0e4b0a38ca278a8c9","text":"USGS Data Release","linkHelpText":"Field properties and results of laboratory analysis of groundwater samples collected from 79 wells in Pike County, Pennsylvania, 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Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070-2424
http://pa.water.usgs.gov
Geochemical Modeling for Mine Site Characterization and Remediation is the fourth of six volumes in the Management Technologies for Metal Mining Infl uenced Water series about technologies for management of metal mine and metallurgical process drainage.
This handbook describes the important components of hydrogeochemical modeling for mine environments, primarily those mines where sulfi de minerals are present—metal mines and coal mines.
It provides general guidelines on the strengths and limitations of geochemical modeling and an overview of its application to the hydrogeochemistry of both unmined mineralized sites and those contaminated from mineral extraction and mineral processing.
The handbook includes an overview of the models behind the codes, explains vital geochemical computations, describes several modeling processes, provides a compilation of codes, and gives examples of their application, including both successes and failures.
Hydrologic modeling is also included because mining contaminants most often migrate by surface water and groundwater transport, and contaminant concentrations are a function of water residence time as well as pathways.
This is an indispensable resource for mine planners and engineers, environmental managers, land managers, consultants, researchers, government regulators, nongovernmental organizations, students, stakeholders, and anyone with an interest in mining influenced water.
The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service collected data on water and bottom-sediment chemistry to be used to evaluate a new water rights acquisition program designed to enhance wetland habitat in Stillwater National Wildlife Refuge and in Lahontan Valley, Churchill County, Nevada. The area supports habitat critical to the feeding and resting of migratory birds travelling the Pacific Flyway. Information about how water rights acquisitions may affect the quality of water delivered to the wetlands is needed by stakeholders and Stillwater National Wildlife Refuge managers in order to evaluate the effectiveness of this approach to wetlands management. A network of six sites on waterways that deliver the majority of water to Refuge wetlands was established to monitor the quality of streamflow and bottom sediment. Each site was visited every 4 to 6 weeks and selected water-quality field parameters were measured when flowing water was present. Water samples were collected at varying frequencies and analyzed for major ions, silica, and organic carbon, and for selected species of nitrogen and phosphorus, trace elements, pharmaceuticals, and other trace organic compounds. Bottom-sediment samples were collected for analysis of selected trace elements.
Dissolved-solids concentrations exceeded the recommended criterion for protection of aquatic life (500 milligrams per liter) in 33 of 62 filtered water samples. The maximum arsenic criterion (340 micrograms per liter) was exceeded twice and the continuous criterion was exceeded seven times. Criteria protecting aquatic life from continuous exposure to aluminum, cadmium, lead, and mercury (87, 0.72, 2.5, and 0.77 micrograms per liter, respectively) were exceeded only once in filtered samples (27, 40, 32, and 36 samples, respectively). Mercury was the only trace element analyzed in bottom-sediment samples to exceed the published probable effect concentration (1,060 micrograms per kilogram).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1072","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Thodal, C.E., 2017, Chemical quality of water and bottom sediment, Stillwater National Wildlife Refuge, Lahontan Valley, Nevada: U.S. Geological Survey Data Series Report 1072, 38 p., plus supplemental data, https://doi.org/10.3133/ds1072.","productDescription":"Report: vi, 38 p.; Supplemental Data","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-083557","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":350181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1072/ds1072.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1072"},{"id":350180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1072/coverthb.jpg"},{"id":350182,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1072/ds1072_suppData.xlsx","text":"Supplemental Data","size":"575 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1072"}],"country":"United States","state":"Nevada","otherGeospatial":"Stillwater National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.55484008789061,\n 39.46164364205549\n ],\n [\n -118.49853515625,\n 39.46111351521458\n ],\n [\n -118.50265502929688,\n 39.47489550075251\n ],\n [\n -118.48205566406251,\n 39.47807557129829\n ],\n [\n -118.48068237304686,\n 39.50615988027491\n ],\n [\n -118.46214294433592,\n 39.505100301007545\n ],\n [\n -118.46420288085936,\n 39.54958871848275\n ],\n [\n -118.44291687011719,\n 39.549059262117225\n ],\n [\n -118.4497833251953,\n 39.564941195531496\n ],\n [\n -118.4051513671875,\n 39.564411856338054\n ],\n [\n -118.40789794921875,\n 39.57552713084889\n ],\n [\n -118.38867187500001,\n 39.576585635482296\n ],\n [\n -118.39073181152344,\n 39.607804249995105\n ],\n [\n -118.37013244628905,\n 39.60727523813919\n ],\n [\n -118.37150573730467,\n 39.63530729658601\n ],\n [\n -118.36395263671875,\n 39.63636488778663\n ],\n [\n -118.36051940917969,\n 39.678126804900295\n ],\n [\n -118.3447265625,\n 39.680240661158805\n ],\n [\n -118.34541320800781,\n 39.72303232864369\n ],\n [\n -118.553466796875,\n 39.72303232864369\n ],\n [\n -118.55484008789061,\n 39.46164364205549\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.
Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented streamflow distribution maps are foundational work intended to support the development of additional streamflow distribution maps that include statistical constraints on the selected flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3390","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., Sanocki, C.A., Lorenz, D.L., and Jacobsen, K.E., 2017, Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota: U.S. Geological Survey Scientific Investigations Map 3390, pamphlet 16 p., 2 sheets, https://doi.org/10.3133/sim3390.","productDescription":"Pamphlet: vii, 16 p.; 2 Sheets: 22.0 inches x 11.0 inches; Data Releases","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060395","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":350215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3390/sim3390.pdf","text":"Pamphlet","size":"976 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Pamphlet"},{"id":350216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet1.pdf","text":"Sheet 1","size":"480 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Sheet 1"},{"id":350217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet2.pdf","text":"Sheet 2","size":"590 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":350214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3390/coverthb.jpg"},{"id":350218,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72V2DMN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the Cannon River drainage basin, southeast Minnesota, 1995-2010"},{"id":350219,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995-2010"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cannon River Drainage Basin, St. Louis River Drainage Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.25,\n 46.63435070293566\n ],\n [\n -91.5,\n 46.63435070293566\n ],\n [\n -91.5,\n 47.89056441663247\n ],\n [\n -93.25,\n 47.89056441663247\n ],\n [\n -93.25,\n 46.63435070293566\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8,\n 43.75\n ],\n [\n -92.75,\n 43.75\n ],\n [\n -92.75,\n 44.6\n ],\n [\n -93.8,\n 44.6\n ],\n [\n -93.8,\n 43.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Streambed scour potential was evaluated at 52 river- and stream-spanning bridges in Alaska that lack a quantitative scour analysis or have unknown foundation details. All sites were evaluated for stream stability and long-term scour potential. Contraction scour and abutment scour were calculated for 52 bridges, and pier scour was calculated for 11 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for sites where the modeled water surface was higher than the superstructure of the bridge. In most cases, hydraulic models of the 1- and 0.2-percent annual exceedance probability floods (also known as the 100- and 500-year floods, respectively) were used to derive hydraulic variables for the scour calculations. Alternate flood values were used in scour calculations for sites where smaller floods overtopped a bridge or where standard flood-frequency estimation techniques did not apply. Scour also was calculated for large recorded floods at 13 sites.
Channel instability at 11 sites was related to human activities (in-channel mining, dredging, and channel relocation). Eight of the dredged sites are located on active unstable alluvial fans and were graded to protect infrastructure. The trend toward aggradation during major floods at these sites reduces confidence in scour estimates.
Vertical contraction and pressure flow occurred during the 0.2-percent or smaller annual exceedance probability floods at eight sites. Contraction scour exceeded 5 feet (ft) at four sites, and total scour at piers (pier scour plus contraction scour) exceeded 5 ft at four sites. Debris accumulation increased calculated pier scour at six sites by an average of 2.4 ft. Total scour at abutments exceeded 5 ft at 10 sites. Scour estimates seemed excessive at two piers where equations did not account for channel armoring, and at four abutments where failure of the embankment and attendant channel widening would reduce scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175149","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities","usgsCitation":"Beebee, R.A., Dworsky, K.L., and Knopp, S.J., 2017, Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2013–15: U.S. Geological Survey Scientific Investigations Report 2017-5149, 67 p., https://doi.org/10.3133/sir20175149.","productDescription":"Report: vi, 67 p.; Appendix","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-083845","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":350212,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5149/sir20175149.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
https://alaska.usgs.gov/
Climate change refugia, areas buffered from climate change relative to their surroundings, are of increasing interest as natural resource managers seek to prioritize climate adaptation actions. However, evidence that refugia buffer the effects of anthropogenic climate change is largely missing.
Focusing on the climate-sensitive Belding’s ground squirrel (Urocitellus beldingi), we predicted that highly connected Sierra Nevada meadows that had warmed less or shown less precipitation change over the last century would have greater population persistence, as measured by short-term occupancy, fewer extirpations over the twentieth century, and long-term persistence measured through genetic diversity.
Across California, U. beldingi were more likely to persist over the last century in meadows with high connectivity that were defined as refugial based on a suite of temperature and precipitation factors. In Yosemite National Park, highly connected refugial meadows were more likely to be occupied by U. beldingi. More broadly, populations inhabiting Sierra Nevada meadows with colder mean winter temperatures had higher values of allelic richness at microsatellite loci, consistent with higher population persistence in temperature-buffered sites. Furthermore, both allelic richness and gene flow were higher in meadows that had higher landscape connectivity, indicating the importance of metapopulation processes. Conversely, anthropogenic refugia, sites where populations appeared to persist due to food or water supplementation, had lower connectivity, genetic diversity, and gene flow, and thus might act as ecological traps. This study provides evidence that validates the climate change refugia concept in a contemporary context and illustrates how to integrate field observations and genetic analyses to test the effectiveness of climate change refugia and connectivity.
Climate change refugia will be important for conserving populations as well as genetic diversity and evolutionary potential. Our study shows that in-depth modeling paired with rigorous fieldwork can identify functioning climate change refugia for conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40665-017-0036-5","usgsCitation":"Morelli, T.L., Maher, S.P., Lim, M.C., Kastely, C., Eastman, L.M., Flint, L.E., Flint, A.L., Beissinger, S.R., and Moritz, C., 2017, Climate change refugia and habitat connectivity promote species persistence: Climate Change Responses, v. 4, 8, 12 p., https://doi.org/10.1186/s40665-017-0036-5.","productDescription":"8, 12 p.","ipdsId":"IP-091107","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":469229,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40665-017-0036-5","text":"Publisher Index Page"},{"id":376819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.94873046875,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 37.51844023887861\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2017-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maher, Sean P.","contributorId":7998,"corporation":false,"usgs":true,"family":"Maher","given":"Sean","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lim, Marisa C. W.","contributorId":236825,"corporation":false,"usgs":false,"family":"Lim","given":"Marisa","email":"","middleInitial":"C. W.","affiliations":[],"preferred":false,"id":794324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kastely, Christina","contributorId":236826,"corporation":false,"usgs":false,"family":"Kastely","given":"Christina","email":"","affiliations":[],"preferred":false,"id":794325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eastman, Lindsey M.","contributorId":236827,"corporation":false,"usgs":false,"family":"Eastman","given":"Lindsey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beissinger, Steven R.","contributorId":100534,"corporation":false,"usgs":true,"family":"Beissinger","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moritz, Craig","contributorId":149462,"corporation":false,"usgs":false,"family":"Moritz","given":"Craig","email":"","affiliations":[{"id":17742,"text":"Research School of Biology, The Australian Nat'l U, Acton, Australia","active":true,"usgs":false}],"preferred":false,"id":794366,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70193484,"text":"sir20175131 - 2017 - An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016","interactions":[],"lastModifiedDate":"2018-01-02T12:58:43","indexId":"sir20175131","displayToPublicDate":"2017-12-22T12:45:00","publicationYear":"2017","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":"2017-5131","title":"An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016","docAbstract":"The Great Lakes Areas of Concern (AOCs) are considered to be the most severely degraded areas within the Great Lakes basin, as defined in the Great Lakes Water Quality Agreement and amendments. Among the 43 designated AOCs are four Lake Michigan AOCs in the State of Wisconsin. The smallest of these AOCs is the Sheboygan River AOC, which was designated as an AOC because of sediment contamination from polychlorinated biphenyl compounds (PCBs), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals. The Sheboygan River AOC has 9 of 14 possible Beneficial Use Impairments (BUIs), which must be addressed to improve overall water-quality, and to ultimately delist the AOC. One of the BUIs associated with this AOC is the “degradation of phytoplankton and zooplankton populations,” which can be removed from the list of impairments when it has been determined that zooplankton community composition and structure at the AOC do not differ significantly from communities at non-AOC comparison sites. In 2012 and 2014, the U.S. Geological Survey collected plankton (phytoplankton and zooplankton) community samples at the Sheboygan River AOC and selected non-AOC sites as part of a larger Great Lakes Restoration Initiative study evaluating both the benthos and plankton communities in all four of Wisconsin’s Lake Michigan AOCs. Although neither richness nor diversity of phytoplankton or zooplankton in the Sheboygan River AOC were found to differ significantly from the non-AOC sites in 2012, results from the 2014 data indicated that zooplankton diversity was significantly lower, and so rated as degraded, when compared to the Manitowoc and Kewaunee Rivers, two non-AOC sites of similar size, land use, and close geographic proximity.
As a follow-up to the 2014 results, zooplankton samples were collected at the same locations in the AOC and non-AOC sites during three sampling trips in spring, summer, and fall 2016. An analysis of similarity indicated no significant difference between the zooplankton community composition and structure in the AOC and non-AOC sites. Zooplankton taxa richness in the AOC was rated as “not degraded” in 2016 because of significantly higher taxa richness values in samples collected from the Sheboygan River AOC, compared with the non-AOC sites as a group (that is, data pooled from both non-AOC sites). Zooplankton diversity in 2016, however, was characterized as “degraded” in the AOC on the basis of significantly lower (p<0.05) values in samples collected from the AOC compared with those collected from the non-AOC sites as a group. Annual variation in zooplankton community composition and structure at the Sheboygan River AOC was significantly different among all 3 years sampled, as indicated by an analysis of similarity test. Zooplankton richness was significantly higher in 2014 than in both 2012 and 2016, and diversity was significantly higher in 2012 than in both 2014 and 2016. Postremediation recovery can often be complicated by non-AOC-related stressors such as nutrients, invasive species, and extremes in flow, which could affect the recovery of zooplankton communities in the Sheboygan River AOC. The effect of the stressors on postremediation recovery underscores the importance of sampling multiple years when assessing the effectiveness of remediation activities. The results from this study will be used by the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency to determine if restoration efforts have been effective in removing the plankton BUI and to monitor future conditions in the AOC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175131","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency","usgsCitation":"Olds, H.T., Scudder Eikenberry, B.C., Burns, D.J., and Bell, A.H., 2017, An evaluation of the zooplankton community at the Sheboygan River Area of Concern and non-Area of Concern comparison sites in western Lake Michigan rivers and harbors in 2016: U.S. Geological Survey Scientific Investigations Report 2017–5131, 15 p., https://doi.org/10.3133/sir20175131.\n","productDescription":"Report: vii, 15 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090820","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":349967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5131/sir20175131.pdf","text":"Report","size":"2.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5131"},{"id":349966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5131/coverthb.jpg"},{"id":349968,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QV3KD9","text":"USGS data release","linkHelpText":"Zooplankton Community Data at the Sheboygan River Area of Concern and Non-Areas of Concern Comparison Sites in Western Lake Michigan Rivers and Harbors in 2016"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.7313232421875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 43.74530493763506\n ],\n [\n -87.4676513671875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 44.484749436619964\n ],\n [\n -87.7313232421875,\n 43.74530493763506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The U.S. Geological Survey estimated mean undiscovered, technically recoverable resources of 8.7 billion barrels of oil and 25 trillion cubic feet of natural gas (associated and nonassociated) in conventional accumulations in the Cretaceous Nanushuk and Torok Formations in the National Petroleum Reserve in Alaska, adjacent State and Native lands, and State waters. The estimated undiscovered oil resources in the Nanushuk and Torok Formations are significantly higher than previous estimates, owing primarily to recent, larger than anticipated oil discoveries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173088","usgsCitation":"Houseknecht, D.W., Lease, R.O., Schenk, C.J., Mercier, T.J., Rouse, W.A., Jarboe, P.J., Whidden, K.J., Garrity, C.P., Lewis, K.A., Heller, S.J., Craddock, W.H., Klett, T.R., Le, P.A., Smith, R.A., Tennyson, M.E., Gaswirth, S.B., Woodall, C.A., Brownfield, M.E., Leathers-Miller, H.M., and Finn, T.M., 2017, Assessment of undiscovered oil and gas resources in the Cretaceous Nanushuk and Torok Formations, Alaska North Slope, and summary of resource potential of the National Petroleum Reserve in Alaska, 2017: U.S. Geological Survey Fact Sheet 2017–3088, 4 p., https://doi.org/10.3133/fs20173088.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-092895","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438122,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSJKNF","text":"USGS data release","linkHelpText":"USGS Alaska Petroleum Systems Project: Northern Alaska Province, Nanushuk and Torok Formations Assessment Units and Assessment Input Forms"},{"id":350008,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20103102","text":"Fact Sheet 2010–3102: ","linkHelpText":"2010 Updated Assessment of Undiscovered Oil and Gas Resources of the National Petroleum Reserve in Alaska (NPRA)"},{"id":350003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3088/coverthb.jpg"},{"id":350004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3088/fs20173088.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3088"},{"id":350009,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20053043","text":"Fact Sheet 2005–3043: ","linkHelpText":"Oil and Gas Assessment of Central North Slope, Alaska, 2005"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170,\n 67\n ],\n [\n -145,\n 67\n ],\n [\n -145,\n 71.5\n ],\n [\n -170,\n 71.5\n ],\n [\n -170,\n 67\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
National Center, MS 913
Reston, VA 20192
Pavlof Volcano is one of the most frequently active volcanoes in the Aleutian Island arc, having erupted more than 40 times since observations were first recorded in the early 1800s . The volcano is located on the Alaska Peninsula (lat 55.4173° N, long 161.8937° W), near Izembek National Wildlife Refuge. The towns and villages closest to the volcano are Cold Bay, Nelson Lagoon, Sand Point, and King Cove, which are all within 90 kilometers (km) of the volcano (fig. 1). Pavlof is a symmetrically shaped stratocone that is 2,518 meters (m) high, and has about 2,300 m of relief. The volcano supports a cover of glacial ice and perennial snow roughly 2 to 4 cubic kilometers (km3) in volume, which is mantled by variable amounts of tephra fall, rockfall debris, and pyroclastic-flow deposits produced during historical eruptions. Typical Pavlof eruptions are characterized by moderate amounts of ash emission, lava fountaining, spatter-fed lava flows, explosions, and the accumulation of unstable mounds of spatter on the upper flanks of the volcano. The accumulation and subsequent collapse of spatter piles on the upper flanks of the volcano creates hot granular avalanches, which erode and melt snow and ice, and thereby generate watery debris-flow and hyperconcentrated-flow lahars.
Seismic instruments were first installed on Pavlof Volcano in the early 1970s, and since then eruptive episodes have been better characterized and specific processes have been documented with greater certainty. The application of remote sensing techniques, including the use of infrasound data, has also aided the study of more recent eruptions. Although Pavlof Volcano is located in a remote part of Alaska, it is visible from Cold Bay, Sand Point, and Nelson Lagoon, making distal observations of eruptive activity possible, weather permitting. A busy air-travel corridor that is utilized by a numerous transcontinental and regional air carriers passes near Pavlof Volcano. The frequency of air travel across the region results in a relatively large number of airborne observations of eruptive activity. During the 2014 Pavlof eruptions, the Alaska Volcano Observatory received observations and photographs from pilots and local observers, which aided evaluation of the eruptive activity and the areas affected by eruptive products.
This report outlines the chronology of events associated with the 2014 eruptive activity at Pavlof Volcano, provides documentation of the style and character of the eruptive episodes, and reports briefly on the eruptive products and impacts. The principal observations are described and portrayed on maps and photographs, and the 2014 eruptive activity is compared to historical eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175129","usgsCitation":"Waythomas, C.F., Haney, M.M., Wallace, K.L., Cameron, C.E., and Schneider, D.J., 2017, The 2014 eruptions of Pavlof Volcano, Alaska: U.S. Geological Survey Scientific Investigations Report 2017-5129, 27 p., https://doi.org/10.3133/sir20175129. ","productDescription":"vi, 27 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-075355","costCenters":[{"id":121,"text":"Alaska Volcano Observatory","active":false,"usgs":true}],"links":[{"id":350485,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix1.xlsx","text":"Appendix 1","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350486,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2a.xlsx","text":"Appendix 2A","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350487,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2b.xlsx","text":"Appendix 2B","size":"150 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5129"},{"id":350185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5129/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Pavlof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.2,\n 55.25\n ],\n [\n -161.7,\n 55.25\n ],\n [\n -161.7,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
Located in south-central Texas, the Geronimo Creek and Plum Creek watersheds have long been characterized by elevated nitrate concentrations. From April 2015 through March 2016, an assessment was done by the U.S. Geological Survey, in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board, to characterize nitrate concentrations and to document possible sources of elevated nitrate in these two watersheds. Water-quality samples were collected from stream, spring, and groundwater sites distributed across the two watersheds, along with precipitation samples and wastewater treatment plant (WWTP) effluent samples from the Plum Creek watershed, to characterize endmember concentrations and isotopic compositions from April 2015 through March 2016. Stream, spring, and groundwater samples from both watersheds were collected during four synoptic sampling events to characterize spatial and temporal variations in water quality and chemical loadings. Water-quality and -quantity data from the WWTPs and stream discharge data also were considered. Samples were analyzed for major ions, selected trace elements, nutrients, and stable isotopes of water and nitrate.
The dominant land use in both watersheds is agriculture (cultivated crops, rangeland, and grassland and pasture). The upper part of the Plum Creek watershed is more highly urbanized and has five major WWTPs; numerous smaller permitted wastewater outfalls are concentrated in the upper and central parts of the Plum Creek watershed. The Geronimo Creek watershed, in contrast, has no WWTPs upstream from or near the sampling sites.
Results indicate that water quality in the Geronimo Creek watershed, which was evaluated only during base-flow conditions, is dominated by groundwater, which discharges to the stream by numerous springs at various locations. Nitrate isotope values for most Geronimo Creek samples were similar, which indicates that they likely have a common source (or sources) of nitrate. Nitrate sources in the Geronimo Creek watershed include a predominance of nitrate from fertilizer applications, as well as a contribution from septic systems. Additional nitrate loading from these sources is ongoing. Chemical loadings of dissolved solids, chloride, and sulfate varied little among sampling events and were low at most sites because of low streamflow.
In contrast to the Geronimo Creek watershed, nitrate sources in the Plum Creek watershed are dominated by effluent discharge from the major WWTPs in the upper and central parts of the watershed. Results indicate that discharge from these WWTPs accounts for the majority of base flow in the watershed. Nitrate concentrations in Plum Creek were dependent on flow conditions, with the highest concentrations measured at lower flows, when flow is dominated by WWTP effluent discharge. In addition to WWTP effluent discharge, the Plum Creek watershed, similar to the Geronimo Creek watershed, also is affected by historical and current loading of nitrate from fertilizer applications and from septic systems in the watershed. Chemical loadings of dissolved solids, chloride, sulfate, and nitrate in Plum Creek at lower flow conditions are highest at the upstream sites and decrease downstream as distance from the WWTPs increases, which is consistent with WWTP effluent as an important control on water quality. Under higher flow conditions, however, nitrate loads to Plum Creek increased by about a factor of three. These higher nitrate loads cannot be accounted for by WWTP effluent discharge from the five major WWTPs in the watershed. This additional loading indicates that nitrate is exported from the northeastern part of the watershed. In the lower part of the Plum Creek watershed, higher concentrations of dissolved solids, chloride, and sulfate occur, which might be affected by produced water associated with oil and gas exploration, or mixing with saline groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175121","collaboration":"Prepared in cooperation with the Guadalupe-Blanco River Authority and the Texas State Soil and Water Conservation Board","usgsCitation":"Lambert, R.B., Opsahl, S.P., and Musgrove, MaryLynn, 2017, Water quality, sources of nitrate, and chemical loadings in the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016: U.S. Geological Survey Scientific Investigations Report 2017–5121, 49 p., https://doi.org/10.3133/sir20175121.","productDescription":"Report: x, 49 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087883","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":350189,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23Z5S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release for water-quality and chemical loading data from the Geronimo Creek and Plum Creek watersheds, south-central Texas, April 2015–March 2016"},{"id":350187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5121/coverthb.jpg"},{"id":350188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5121/sir20175121.pdf","text":"Report","size":"3.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5121"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 29.5\n ],\n [\n -97.4167,\n 29.5\n ],\n [\n -97.4167,\n 30.1667\n ],\n [\n -98.1667,\n 30.1667\n ],\n [\n -98.1667,\n 29.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The U.S. Geological Survey (USGS), in support of the Massachusetts Estuaries Project (MEP), delineated groundwater-contributing areas to various hydrologic receptors including ponds, streams, and coastal water bodies throughout southeastern Massachusetts, including portions of the Plymouth-Carver aquifer system and all of Cape Cod. These contributing areas were delineated over a 6-year period from 2003 through 2008 by using previously published regional USGS groundwater-flow models for the Plymouth-Carver region (Masterson and others, 2009), the Sagamore (western) and Monomoy (eastern) flow lenses of Cape Cod (Walter and Whealan, 2005), and lower Cape Cod (Masterson, 2004). The original USGS groundwater-contributing areas were subsequently revised in some locations by the MEP to remove modeling artifacts or to make the contributing areas more consistent with site-specific hydrologic conditions without further USGS review. This report describes the process used to create the USGS groundwater-contributing areas and provides these model results in their original format in a single, publicly accessible publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1074","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Carlson, C.S., Masterson, J.P., Walter, D.A., and Barbaro, J.R., 2017, Development of simulated groundwater-contributing areas to selected streams, ponds, coastal water bodies, and production wells in the Plymouth-Carver region and Cape Cod, Massachusetts: U.S. Geological Survey Data Series 1074, 17 p., https://doi.org/10.3133/ds1074.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087594","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69H2Z","text":"USGS data release","description":"USGS data release","linkHelpText":"Simulated Groundwater-Contributing Areas to Selected Streams, Ponds, Coastal Water Bodies, and Production Wells, Plymouth-Carver Region and Cape Cod, Massachusetts"},{"id":350102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1074/coverthb.jpg"},{"id":350103,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1074/ds1074.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1074"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod, Plymouth-Carver Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.015625,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 42.167475010395336\n ],\n [\n -71.015625,\n 42.167475010395336\n ],\n [\n -71.015625,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Agricultural producers in North Dakota are aware of concerns about degrading water quality, and many of the producers are interested in implementing conservation practices to reduce the export of nutrients from their farms. Producers often implement conservation practices without knowledge of the water quality of the runoff from their farm or if conservation practices they may implement have any effect on water quality. In response to this lack of information, the U.S. Geological Survey, in cooperation with North Dakota State University Extension Service and in coordination with an advisory group consisting of State agencies, agricultural producers, and commodity groups, implemented a monitoring study as part of a Discovery Farms program in North Dakota in 2007. Three data-collection sites were established at each of three farms near Underwood, Embden, and Dazey, North Dakota. The purpose of this report is to describe runoff and water-quality characteristics using data collected at the three Discovery Farms during 2008–16. Runoff and water-quality data were used to help describe the implications of agricultural conservation practices on runoff and water-quality patterns.
Runoff characteristics of monitoring sites at the three farms were determined by measuring flow volume and precipitation. Runoff at the Underwood farm monitoring sites generally was controlled by precipitation in the area, antecedent soil moisture conditions, and, after 2012, possibly by the diversion ditch constructed by the producer. Most of the annual runoff was in March and April each year during spring snowmelt. Runoff characteristics at the Embden farm are complex because of the mix of surface runoff and flow through two separate drainage tile systems. Annual flow volumes for the drainage tiles sites (sites E2 and E3) were several orders of magnitude greater than measured at the surface water site E1. Site E1 generally only had runoff briefly in March and April during spring snowmelt and during only a few large rain events throughout 2009–16. Flow was somewhat continuous at sites E2 and E3 throughout the year during years of increased precipitation, such as in 2010 and 2011. At Dazey farm, annual flow volumes at the most downstream site D3 for 2010–15 ranged from 88 acre-feet (2012) to 12,060 acre-feet (2010). The largest monthly runoff volumes at D1 (most upstream site; combination of data from site D1a [original site] and site D1b [relocated site]) and D3 were in March and April during spring snowmelt runoff and rain events.
At Underwood farm, total ammonia and total phosphorus had the highest concentrations at the most upstream site (U1) and decreased sequentially at sites U2 and U3 downstream. Total ammonia and total phosphorus concentrations at the sites for Underwood farm also generally were higher than measured at sites for the Dazey and Embden farms. At Embden farm, nitrate plus nitrite concentrations were lowest at site E1 (surface-water site) and highest at sites E2 and E3 (drainage tile sites). Nitrate plus nitrite concentrations at sites E2 and E3 also were the highest among all the sites at all three farms. Median total nitrate plus nitrite concentrations for sites E1, E2, and E3 were 0.22, 13, and 10 milligrams per liter as nitrogen, respectively. Nutrient concentrations generally were greater at site D1 (most upstream site) compared to site D3 (most downstream site) at Dazey farm. Higher concentrations at site D1, which is farther upstream and closer to potential sources of nutrients, compared to lower concentrations at site D3, which is farther downstream and receives more runoff, indicates that dilution may be the reason concentrations decrease downstream.
Annual loads for chloride at all three Underwood sites were the greatest in 2011 and the least in 2012, which coincided with years of the greatest and least annual flow volume, respectively. Total ammonia had a similar pattern at the three sites. Nitrate plus nitrite loads displayed a different pattern than chloride and total ammonia, indicating possible different sources. Chloride, total ammonia, total phosphorus, and suspended sediment were transported past site U1 mostly in March and the least from July through October. Monthly nitrate plus nitrite loads had a different pattern than the other constituents, indicating other possible sources such as fertilizer application in the surrounding cropland.
Annual loads for Embden farm were considerably greater at sites E2 and E3 compared to site E1. Annual yields for all constituents also were substantially greater at sites E2 and E3 compared to site E1, mainly because of a combination of higher flow volumes and small contributing drainage areas at sites E2 and E3 compared to site E1.
The greatest annual loads at Dazey farm site D3 for chloride, nitrate plus nitrite, and suspended sediment were in 2010 and 2011, and zero loads were estimated for 2012 because no flow was measured at the site. Mean monthly loads generally were greatest for most constituents in March and April at sites D1 and D3 except for suspended sediment that had the greatest monthly loads in May.
To mitigate runoff and water-quality effects of their operations, the producers implemented various agricultural conservation practices before and during the Discovery Farms monitoring. Even though it was difficult to quantify the effects of the agricultural conservation practices implemented at the farms, the data collected from the Discovery Farms program provided a better understanding of some of the variables that affect runoff and water quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175124","collaboration":"Prepared in cooperation with North Dakota State University Extension Service","usgsCitation":"Galloway, J.M., and Nustad, R.A., 2017, Runoff and water-quality characteristics of three Discovery Farms in North Dakota, 2008–16: U.S. Geological Survey Scientific Investigations Report 2017–5124, 68 p.,\nhttps://doi.org/10.3133/sir20175124.","productDescription":"ix, 68 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-088875","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5124/sir20175124.pdf","text":"Report","size":"3.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Dakota Water Science Center, North Dakota Office
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
Wastewater discharged to wells and ponds and wastes buried in shallow pits and trenches at facilities at the Idaho National Laboratory (INL) have contributed contaminants to the eastern Snake River Plain (ESRP) aquifer in the southwestern part of the INL. This report describes the correlation between subsurface stratigraphy in the southwestern part of the INL with information on the presence or absence of wastewater constituents to better understand how flow pathways in the aquifer control the movement of wastewater discharged at INL facilities. Paleomagnetic inclination was used to identify subsurface basalt flows based on similar inclination measurements, polarity, and stratigraphic position. Tritium concentrations, along with other chemical information for wells where tritium concentrations were lacking, were used as an indicator of which wells were influenced by wastewater disposal.
The basalt lava flows in the upper 150 feet of the ESRP aquifer where wastewater was discharged at the Idaho Nuclear Technology and Engineering Center (INTEC) consisted of the Central Facilities Area (CFA) Buried Vent flow and the AEC Butte flow. At the Advanced Test Reactor (ATR) Complex, where wastewater would presumably pond on the surface of the water table, the CFA Buried Vent flow probably occurs as the primary stratigraphic unit present; however, AEC Butte flow also could be present at some of the locations. At the Radioactive Waste Management Complex (RWMC), where contamination from buried wastes would presumably move down through the unsaturated zone and pond on the surface of the water table, the CFA Buried Vent; Late Basal Brunhes; or Early Basal Brunhes basalt flows are the flow unit at or near the water table in different cores.
In the wells closer to where wastewater disposal occurred at INTEC and the ATR-Complex, almost all the wells show wastewater influence in the upper part of the ESRP aquifer and wastewater is present in both the CFA Buried Vent flow and AEC Butte flow. The CFA Buried Vent flow and AEC Butte flow are also present in wells at and north of CFA and are all influenced by wastewater contamination. All wells with the AEC Butte flow present have wastewater influence and 83 percent of the wells with the more prevalent CFA Buried Vent flow have wastewater influence. South and southeast of CFA, most wells are not influenced by wastewater disposal and are completed in the Big Lost Flow and the CFA Buried Vent flow. Wells southwest of CFA are influenced by wastewater disposal and are completed in the Big Lost flow and CFA Buried Vent flow at the top of the aquifer. Basalt stratigraphy indicates that the CFA Buried Vent flow is the predominant flow in the upper part of the ESRP aquifer at and near the RWMC as it is present in all the wells in this area. The Late Basal Brunhes flow, Middle Basal Brunhes flow, Early Basal Brunhes flow, South Late Matuyama flow, and Matuyama flow are also present in various wells influenced by waste disposal.
Some wells south of RWMC do not show wastewater influence, and the lack of wastewater influence could be due to low hydraulic conductivities. Several wells south and southeast of CFA also do not show wastewater influence. Low hydraulic conductivities or ESRP subsidence are possible causes for lack of wastewater south of CFA.
Multilevel monitoring wells completed much deeper in the aquifer show influence of wastewater in numerous basalt flows. Well Middle 2051 (northwest of RWMC) does not show wastewater influence in its upper three basalt flows (CFA Buried Vent, Late Basal Brunhes, and Middle Basal Brunhes); however, wastewater is present in two deeper flows (the Matuyama and Jaramillo flows). Well USGS 131A (southwest of CFA) and USGS132 (south of RWMC) both show wastewater influence in all the basalt flows sampled in the upper 600 feet of the aquifer. Wells USGS 137A, 105, 108, and 103 completed along the southern boundary of the INL all show wastewater influence in several basalt flows including the G flow, Middle and Early Basal Brunhes flows, the South Late Matuyama flow and the Matuyama flow; however, the strongest wastewater influence appears to be in the South Late Matuyama flow. The concentrations of wastewater constituents in deeper parts of these wells support the concept of groundwater flow deepening in the southwestern part of the INL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175148","collaboration":"DOE/ID-22245Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
The rare-earth elements (REEs) are 15 elements that range in atomic number from 57 (lanthanum) to 71 (lutetium); they are commonly referred to as the “lanthanides.” Yttrium (atomic number 39) is also commonly regarded as an REE because it shares chemical and physical similarities and has affinities with the lanthanides. Although REEs are not rare in terms of average crustal abundance, the concentrated deposits of REEs are limited in number.
Because of their unusual physical and chemical properties, the REEs have diverse defense, energy, industrial, and military technology applications. The glass industry is the leading consumer of REE raw materials, which are used for glass polishing and as additives that provide color and special optical properties to the glass. Lanthanum-based catalysts are used in petroleum refining, and cerium-based catalysts are used in automotive catalytic converters. The use of REEs in magnets is a rapidly increasing application. Neodymium-iron-boron magnets, which are the strongest known type of magnets, are used when space and weight are restrictions. Nickel-metal hydride batteries use anodes made of a lanthanum-based alloys.
China, which has led the world production of REEs for decades, accounted for more than 90 percent of global production and supply, on average, during the past decade. Citing a need to retain its limited REE resources to meet domestic requirements as well as concerns about the environmental effects of mining, China began placing restrictions on the supply of REEs in 2010 through the imposition of quotas, licenses, and taxes. As a result, the global rare-earth industry has increased its stockpiling of REEs; explored for deposits outside of China; and promoted new efforts to conserve, recycle, and substitute for REEs. New mine production began at Mount Weld in Western Australia, and numerous other exploration and development projects noted in this chapter are ongoing throughout the world.
The REE-bearing minerals are diverse and often complex in composition. At least 245 individual REE-bearing minerals are recognized; they are mainly carbonates, fluorocarbonates, and hydroxylcarbonates (n = 42); oxides (n = 59); silicates (n = 85); and phosphates (n = 26).
Many of the world’s significant REE deposits occur in carbonatites, which are carbonate igneous rocks. The REEs also have a strong genetic association with alkaline magmatism. The systematic geologic and chemical processes that explain these observations are not well understood. Economic or potentially economic REE deposits have been found in (a) carbonatites, (b) peralkaline igneous systems, (c) magmatic magnetite-hematite bodies, (d) iron oxide-copper-gold (IOCG) deposits, (e) xenotime-monazite accumulations in mafic gneiss, (f) ion-absorption clay deposits, and (g) monazite-xenotime-bearing placer deposits. Carbonatites have been the world’s main source for the light REEs since the 1960s. Ion-adsorption clay deposits in southern China are the world’s primary source of the heavy REEs. Monazite-bearing placer deposits were important sources of REEs before the mid-1960s and may be again in the future. In recent years, REEs have been produced from large carbonatite bodies mined at the Mountain Pass deposit in California and, in China, at the Bayan Obo deposit in Nei Mongol Autonomous Region, the Maoniuping deposit in Sichuan Province, the Daluxiang deposit in Sichuan Province, and the Weishan deposit in Anhui Province. Alkaline igneous complexes have recently been targeted for exploration because of their enrichments in the heavy REEs.
Information relevant to the environmental aspects of REE mining is limited. Little is known about the aquatic toxicity of REEs. The United States lacks drinking water standards for REEs. The concentrations of REEs in environmental media are influenced by their low abundances in crustal rocks and their limited solubility in most groundwaters and surface waters. The scarcity of sulfide minerals, including pyrite, minimizes or eliminates concerns about acid-mine drainage for carbonatite-hosted deposits and alkaline-intrusion-related REE deposits. For now, insights into environmental responses of REE mine wastes must rely on predictive models.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802O","isbn":"978-1-4113-3991-0","usgsCitation":"Van Gosen, B.S., Verplanck, P.L., Seal, R.R., II, Long, K.R., and Gambogi, Joseph, 2017, Rare-earth elements, chap. O of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. O1–O31, https://doi.org/10.3133/pp1802O.","productDescription":"viii, 31 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050900","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":334627,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/o/pp1802o.pdf","text":"Report","size":"4.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 O"},{"id":334626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/o/coverthb1.jpg"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov
Antimony is an important mineral commodity used widely in modern industrialized societies. The element imparts strength, hardness, and corrosion resistance to alloys that are used in many areas of industry, including in lead-acid storage batteries. Antimony’s leading use is as a fire retardant in safety equipment and in household goods, such as mattresses. The U.S. Government has considered antimony to be a critical mineral mainly because of its use in military applications. The great majority of the world’s antimony comes from China, and much of the remainder is shipped to China for smelting. Antimony resources are unevenly distributed around the world. China has the bulk of the world’s identified resources; other countries that have identified antimony resources include Bolivia, Canada, Mexico, Russia, South Africa, Tajikistan, and Turkey. Resources in the United States are located mainly in Alaska, Idaho, Montana, and Nevada. The most significant antimony mineral deposits occur in geologic environments with a thick sequence of siliciclastic sedimentary rocks in areas with significant fault and fracture systems. The most common antimony ore mineral is stibnite (Sb2 S3 ), but more than 100 other minerals also contain antimony. The presence of antimony in surface waters and groundwaters results primarily from rock weathering, soil runoff, and anthropogenic sources. Global emissions of antimony to the atmosphere average 6,100 metric tons per year. Empirical data suggest that the acid-generating potential of antimony mine waste is low.
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U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov