Acidic deposition contributes to a range of environmental impacts across forested landscapes, including acidification of soil and drainage water, toxic aluminum mobilization, depletion of available soil nutrient cations, and impacts to forest and aquatic species health and biodiversity. In response to decreasing levels of acidic deposition, soils and drainage waters in some regions of North America have become gradually less acidic. Thresholds of atmospheric deposition at which adverse ecological effects are manifested are called critical loads (CLs) and/or target loads (TLs). Target loads are developed based on approaches that account for spatial and temporal aspects of acidification and recovery. Exceedance represents the extent to which current or projected future levels of acidic deposition exceed the level expected to cause ecological harm. We report TLs of sulfur (S) and nitrogen (N) deposition and the potential for ecosystem recovery of watershed soils and streams in the Adirondack region of New York State, resources that have been less thoroughly investigated than lakes. Regional TLs were calculated by statistical extrapolation of hindcast and forecast simulations of 25 watersheds using the process-based model PnET-BGC coupled with empirical observations of stream hydrology and established sensitivity of sugar maple (Acer saccharum) to soil base saturation and brook trout (Salvelinus fontinalis) to stream acid neutralizing capacity (ANC). Historical impacts and the expected recovery timeline of regional soil and stream chemistry and fish community condition within the Adirondack Park were evaluated. Analysis suggests that many low-order Adirondack streams and associated watershed soils have low TLs (<40 meq/m2/yr of N+S deposition) to achieve specified benchmarks for recovery of soil base saturation or stream ANC. Acid-sensitive headwater and low-order streams and watershed soils in the region are expected to experience continued adverse effects from N and S deposition well into the future even under aggressive emissions reductions. Watershed soils and streams in the western Adirondack Park are particularly vulnerable to acidic deposition and currently in exceedance of TLs. The methods used for linking statistical and process-based models to consider chemical and biological response under varying flow conditions at the regional scale in this study can be applied to other areas of concern.
In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a throughgoing network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density.
The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service (FWS), began a study in 2019 to complete the compilation and quality assurance of water-resource threats and needs data for the 117 National Wildlife Refuges (NWRs) in the FWS Legacy Mountain-Prairie Region (LMPR) and to characterize the water-resource threats and needs of each refuge and of the LMPR itself. The LMPR encompasses the states of Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming. This report includes the compilation and quality assurance of current (April 2020) water-resource threats and needs data for the refuges in the LMPR and a statistical, graphical, and spatial characterization, including the ranking and prioritization of threat types, threat causes, and needs by the number of occurrences in the LMPR as a whole and by refuges, states, and select U.S. Environmental Protection Agency Level III Ecoregions.
A total of 540 unique threat occurrences were identified for 109 refuges in the LMPR. No threats were identified for eight refuges. About 43 percent of the threat occurrences, for 59 refuges, had a high-severity threat rating. Of the 10 most common threat types, 8 were also among the most common high-severity threat types. Water-resource threats had 72 different causes. About 83 percent of the overall common causes for threats and for high-severity threats were the same. The most common threat types overall and the most common high-severity threat types were compromised water management capability, habitat shifting/alteration, and altered flow regimes. The 20 water-resource threat types for Long Lake NWR were the most for refuges in the LMPR. Other refuges with the greatest number of threat types included Marais des Cygnes NWR (18) and Arapaho and Lee Metcalf NWRs (16 each). About 54 percent of refuges with threats had high-severity threats. Arapaho and Quivira NWRs each had 10 high-severity threat types, the maximum number of high-severity threat types for LMPR refuges.
A total of 637 unique need occurrences were identified for 114 refuges. No needs were reported for three refuges. The most common need type, a Water Resource Inventory and Assessment, was reported for 78 refuges. Two of the most common need types, repair and replace water management infrastructure and water supply/quantity monitoring, were the most common high-priority need types. Bear River Migratory Bird Refuge had the most (39) unique water-resource need types for refuges in the LMPR. Other refuges with the greatest number of need types were Baca (38), Alamosa (36), and Monte Vista (36) NWRs. The most high-priority need types for a refuge was 23, at Monte Vista NWR. Alamosa (22), Baca (22), and Lake Andes (19) NWRs were also among the top 4 refuges with the greatest number of high-priority need types.
An overall ranking scheme was developed to identify refuges that have the highest-ranking priority for conservation efforts to fulfill refuges’ statutory purposes. The count of occurrences of high-severity threats and high-priority needs were summed to determine the overall ranking value for a refuge. The 10 refuges with the highest overall ranking values, in order of ranking from higher to lower, were Alamosa, Baca, and Monte Vista NWRs (tied for highest); Lake Andes NWR, Ouray and Quivira NWRs, Bear River Migratory Bird Refuge and Flint Hills NWR, Cokeville Meadows NWR, and Arapaho NWR.
About 33 percent of overall threat occurrences were reported as under the control of the FWS to mitigate, as were 37 percent of all threat occurrences with a high-severity rating. The most common overall threat types and high-severity threat types under FWS control were compromised water management capability; habitat shifting/alteration; altered flow regimes; loss/alteration of wetland habitat; and legal challenges or fines for non-compliance with water policy, law, or regulation. A total of 68 percent of overall need occurrences and 67 percent of all high-priority need occurrences were under the control of the FWS. The most common overall need types and high-priority needs types under control were repair or replace water management infrastructure, water supply/quantity monitoring, water quality baseline monitoring, and protect habitat from invasive species. A Water Resource Inventory and Assessment was also a common overall need under FWS control, as was the high-priority need of water level monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20211007","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Bauch, N.J., Kohn, M.S., and Caruso, B.S., 2021, Characterization of water-resource threats and needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020: U.S. Geological Survey Open-File Report 2021–1007, 46 p., https://doi.org/10.3133/ofr20211007.","productDescription":"viii, 46 p.","onlineOnly":"Y","ipdsId":"IP-119415","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":384940,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1007/coverthb.jpg"},{"id":384941,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1007/ofr20211007.pdf","text":"Report","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1007"}],"country":"United States","state":"Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, Wyoming","otherGeospatial":"Legacy Mountain Prairie Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0283203125,\n 36.96744946416934\n ],\n [\n -94.63623046875,\n 37.00255267215955\n ],\n [\n -94.63623046875,\n 39.095962936305476\n ],\n [\n -95.00976562499999,\n 39.62261494094297\n ],\n [\n -94.81201171875,\n 39.85915479295669\n ],\n [\n -95.64697265625,\n 40.51379915504413\n ],\n [\n -96.1083984375,\n 41.60722821271717\n ],\n [\n -96.6357421875,\n 42.66628070564928\n ],\n [\n -96.50390625,\n 43.48481212891603\n ],\n [\n -96.416015625,\n 45.36758436884978\n ],\n [\n -96.85546875,\n 45.61403741135093\n ],\n [\n -96.52587890625,\n 45.9511496866914\n ],\n [\n -96.85546875,\n 46.81509864599243\n ],\n [\n -96.85546875,\n 47.62097541515849\n ],\n [\n -97.20703125,\n 48.1367666796927\n ],\n [\n -97.20703125,\n 48.99463598353405\n ],\n [\n -116.01562499999999,\n 49.03786794532644\n ],\n [\n -116.08154296875001,\n 48.004625021133904\n ],\n [\n -115.4443359375,\n 47.27922900257082\n ],\n [\n -114.36767578124999,\n 46.51351558059737\n ],\n [\n -114.521484375,\n 45.66012730272194\n ],\n [\n -114.14794921875,\n 45.521743896993634\n ],\n [\n -113.99414062499999,\n 45.61403741135093\n ],\n [\n -113.48876953125,\n 44.94924926661153\n ],\n [\n -112.69775390625,\n 44.38669150215206\n ],\n [\n -111.26953125,\n 44.69989765840318\n ],\n [\n -111.11572265625,\n 44.574817404670306\n ],\n [\n -111.0498046875,\n 41.983994270935625\n ],\n [\n -114.01611328125,\n 42.00032514831621\n ],\n [\n -114.06005859375,\n 36.94989178681327\n ],\n [\n -109.0283203125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.
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0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":813937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813939,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219535,"text":"70219535 - 2021 - Metabarcoding of environmental samples suggest wide distribution of eelgrass (Zostera marina) pathogens in the north Pacific","interactions":[],"lastModifiedDate":"2021-04-13T13:10:30.077483","indexId":"70219535","displayToPublicDate":"2021-04-09T08:07:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8122,"text":"Metabarcoding and Metagenomics","active":true,"publicationSubtype":{"id":10}},"title":"Metabarcoding of environmental samples suggest wide distribution of eelgrass (Zostera marina) pathogens in the north Pacific","docAbstract":"Seagrass meadows provide important ecological services to the marine environment but are declining worldwide. Although eelgrass meadows in the north Pacific are thought to be relatively healthy, few studies have assessed the presence of known disease pathogens in these meadows. In a pilot study to test the efficacy of the methods and to provide foundational disease biodiversity data in the north Pacific, we leveraged metabarcoding of environmental DNA extracted from water, sediment, and eelgrass tissue samples collected from five widely distributed eelgrass meadows in Alaska and one in Japan and uncovered wide prevalence of two classes of pathogenic organisms – Labyrinthula zosterae and other associated strains of Labyrinthula, and the Phytophthora/Halophytophthora blight species complex – known to have caused decline in eelgrass (Zostera marina) elsewhere in the species’ global distribution. Although the distribution of these disease organisms is not well understood in the north Pacific, we uncovered the presence of at least one eelgrass pathogen at every locality sampled.
Chlorine, lithium, and boron are trace elements in rhyolite but are enriched in groundwater flowing through rhyolite because they tend to partition into the fluid phase during high‐temperature fluid‐rock reactions. We present a large data set of major element and δ37Cl, δ7Li, and δ11B compositions of thermal water and rhyolite from Yellowstone Plateau Volcanic Field (YPVF). The Cl/B, Cl/Li, δ37Cl (−0.2‰ to +0.7‰), and δ11B (−6.2‰ to −5.9‰) values of alkaline‐chloride thermal waters reflect high‐temperature leaching of chlorine, lithium, and boron from rhyolite that has δ37Cl and δ11B values of +0.1‰ to +0.9‰ and −6.3‰ to −6.2‰, respectively. Chlorine and boron are not reactive, but lithium incorporation into hydrothermal alteration minerals results in a large range of Cl/Li, B/Li, and δ7Li (−1.2‰ to +3.8‰) values in thermal waters. The relatively large range in δ7Li values of thermal waters reflects a large range of values in rhyolite. Large volumes of rhyolite must be leached to account for the chloride, lithium and boron fluxes, implying deep groundwater flow through rhyolite flows and tuffs representing Yellowstone's three eruptive cycles (∼2.1 Ma). Lower Cl/B values in acid‐sulfate waters result from preferential partitioning of boron into the vapor phase and enrichment in the near‐surface water condensate. The Cl/B, Cl/Li, δ7Li (−0.3‰ to +2.1‰), and δ11B (−8.0‰ to −8.1‰) values of travertine depositing calcium‐carbonate thermal waters which discharge in the northern and southern YPVF suggest that chlorine, lithium, and boron are derived from Mesozoic siliciclastic sediments which contain detrital material from the underlying metamorphic basement.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GC009589","usgsCitation":"Cullen, J.T., Hurwitz, S., Barnes, J., Lassiter, J.C., Penniston-Dorland, S., Meixner, A., Wilckens, F., Kasemann, S., and McCleskey, R., 2021, The systematics of chlorine, lithium, and boron and δ37Cl, δ7Li, and δ11B in the hydrothermal system of the Yellowstone Plateau Volcanic Field: Geochemistry, Geophysics, Geosystems, v. 22, no. 4, e2020GC009589, 24 p., https://doi.org/10.1029/2020GC009589.","productDescription":"e2020GC009589, 24 p.","ipdsId":"IP-126599","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499914,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/cb70c4542ffa4bcc904e4e798db02100","text":"External Repository"},{"id":385278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Plateau Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0113525390625,\n 44.19795903948531\n ],\n [\n -109.9456787109375,\n 44.19795903948531\n ],\n [\n -109.9456787109375,\n 44.972570682240644\n ],\n [\n -111.0113525390625,\n 44.972570682240644\n ],\n [\n -111.0113525390625,\n 44.19795903948531\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Cullen, Jeffrey T.","contributorId":140885,"corporation":false,"usgs":false,"family":"Cullen","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":814649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":814650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnes, Jaime D.","contributorId":140886,"corporation":false,"usgs":false,"family":"Barnes","given":"Jaime D.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":814651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lassiter, John C","contributorId":257578,"corporation":false,"usgs":false,"family":"Lassiter","given":"John","email":"","middleInitial":"C","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":814652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Penniston-Dorland, Sarah","contributorId":257579,"corporation":false,"usgs":false,"family":"Penniston-Dorland","given":"Sarah","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":814653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meixner, Anette","contributorId":257580,"corporation":false,"usgs":false,"family":"Meixner","given":"Anette","email":"","affiliations":[{"id":24749,"text":"University of Bremen","active":true,"usgs":false}],"preferred":false,"id":814654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilckens, Frederike","contributorId":257583,"corporation":false,"usgs":false,"family":"Wilckens","given":"Frederike","email":"","affiliations":[{"id":24749,"text":"University of Bremen","active":true,"usgs":false}],"preferred":false,"id":814655,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kasemann, Simone A","contributorId":257585,"corporation":false,"usgs":false,"family":"Kasemann","given":"Simone A","affiliations":[{"id":24749,"text":"University of Bremen","active":true,"usgs":false}],"preferred":false,"id":814656,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814657,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70222105,"text":"70222105 - 2021 - Isotope fractionation from In Vivo methylmercury detoxification in waterbirds","interactions":[],"lastModifiedDate":"2021-07-20T12:16:15.840339","indexId":"70222105","displayToPublicDate":"2021-04-08T07:13:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5615,"text":"ACS Earth and Space Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Isotope fractionation from In Vivo methylmercury detoxification in waterbirds","title":"Isotope fractionation from In Vivo methylmercury detoxification in waterbirds","docAbstract":"The robust application of stable mercury (Hg) isotopes for mercury source apportionment and risk assessment necessitates the understanding of mass-dependent fractionation (MDF) due to internal transformations within organisms. Here, we used high energy-resolution XANES spectroscopy and isotope ratios of total mercury (δ202THg) and methylmercury (δ202MeHg) to elucidate the chemical speciation of Hg and the resultant MDF due to internal MeHg demethylation in waterbirds. In three waterbirds (Clark’s grebe, Forster’s tern, south polar skua), between 17-86% of the MeHg was demethylated to inorganic mercury (iHg) species primarily in the liver and kidneys as Hg-tetraselenolate (Hg(Sec)4) and minor Hg-dithiolate (Hg(SR)2) complexes. Tissular differences between δ202THg and δ202MeHg correlated linearly with %iHg (Hg(Sec)4 + Hg(SR)2), and were interpreted to reflect a kinetic isotope effect during in vivo MeHg demethylation. The product-reactant isotopic enrichment factor (εp/r) for the demethylation of MeHg Hg(Sec)4 was −2.2 ± 0.1‰. δ202MeHg values were unvarying within each bird regardless of Hg(Sec)4 abundance, indicating fast internal cycling or replenishment of MeHg relative to demethylation. Our findings document a universal selenium-dependent demethylation reaction in birds, provide new insights on the internal transformations and cycling of MeHg and Hg(Sec)4, and allow for mathematical correction of δ202THg values due to the MeHg Hg(Sec)4 reaction.","language":"English","publisher":"ACS Publications","doi":"10.1021/acsearthspacechem.1c00051","usgsCitation":"Poulin, B., Janssen, S.E., Rosera, T., Krabbenhoft, D.P., Eagles-Smith, C., Ackerman, J.T., Stewart, R., Kim, E., Baumann, Z., Kim, J., and Manceau, A., 2021, Isotope fractionation from In Vivo methylmercury detoxification in waterbirds: ACS Earth and Space Chemistry, v. 5, no. 5, p. 990-997, https://doi.org/10.1021/acsearthspacechem.1c00051.","productDescription":"8 p.","startPage":"990","endPage":"997","ipdsId":"IP-126702","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452767,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-03262642","text":"External Repository"},{"id":387292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":819537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah Elizabeth 0000-0002-5723-1112","orcid":"https://orcid.org/0000-0002-5723-1112","contributorId":261232,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosera, Tylor 0000-0002-3611-4654","orcid":"https://orcid.org/0000-0002-3611-4654","contributorId":221507,"corporation":false,"usgs":true,"family":"Rosera","given":"Tylor","email":"","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stewart, Robin 0000-0003-2678-9102","orcid":"https://orcid.org/0000-0003-2678-9102","contributorId":261234,"corporation":false,"usgs":true,"family":"Stewart","given":"Robin","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":819543,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kim, Eunhee","contributorId":261236,"corporation":false,"usgs":false,"family":"Kim","given":"Eunhee","email":"","affiliations":[{"id":52778,"text":"Citizens' Institute for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":819544,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baumann, Zofia","contributorId":261237,"corporation":false,"usgs":false,"family":"Baumann","given":"Zofia","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":819545,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kim, Jeong-Hoon","contributorId":261238,"corporation":false,"usgs":false,"family":"Kim","given":"Jeong-Hoon","email":"","affiliations":[{"id":39310,"text":"Korea Polar Research Institute","active":true,"usgs":false}],"preferred":false,"id":819546,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Manceau, Alain 0000-0003-0845-611X","orcid":"https://orcid.org/0000-0003-0845-611X","contributorId":194255,"corporation":false,"usgs":false,"family":"Manceau","given":"Alain","email":"","affiliations":[],"preferred":false,"id":819547,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70262781,"text":"70262781 - 2021 - Roads less travelled by— Pleistocene piracy in Washington’s northwestern Channeled Scabland","interactions":[],"lastModifiedDate":"2025-01-23T21:56:18.680423","indexId":"70262781","displayToPublicDate":"2021-04-07T15:54:55","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Roads less travelled by— Pleistocene piracy in Washington’s northwestern Channeled Scabland","docAbstract":"The Pleistocene Okanogan lobe of Cordilleran ice in north-central Washington State dammed Columbia River to pond glacial Lake Columbia and divert the river south across one or another low spot along a 230-km-long drainage divide. When enormous Missoula floods from the east briefly engulfed the lake, water poured across a few such divide saddles. The grandest such spillway into the Channeled Scabland became upper Grand Coulee.
By cutting headward to Columbia valley, upper Grand Coulee’s flood cataract opened a valve that then kept glacial Lake Columbia low and limited later floods into nearby Moses Coulee. Indeed few of the scores of last-glacial Missoula floods managed to reach it. Headward cutting of an inferred smaller cataract (Foster Coulee) had earlier lowered glacial Lake Columbia’s outlet. Such Scabland piracies explain a variety of field evidence assembled here: apparently successive outlets of glacial Lake Columbia, and certain megaflood features downcurrent to Wenatchee and Quincy basin.
Ice-rafted erratics and the Pangborn bar of foreset gravel near Wenatchee record late Wisconsin flood(s) down Columbia valley as deep as 320 m. Fancher bar, 45 m higher than Pangborn bar, also has tall foreset beds—but its gravel is partly rotted and capped by thick calcrete, thus pre-Wisconsin age, perhaps greatly so. In western Quincy basin foreset beds of basaltic gravel dip east from Columbia valley into the basin—gravel also partly rotted and capped by thick calcrete, also pre-Wisconsin. Yet evidence of late Wisconsin eastward flow to Quincy basin is sparse. This sequence suggests that upper Grand Coulee had largely opened before down-Columbia megaflood(s) early in late Wisconsin time.
A drift-obscured area of the Waterville Plateau near Badger Wells is the inconspicuous divide saddle between Columbia tributary Foster Creek drainage and Moses Coulee drainage. Before flood cataracts had opened upper Grand Coulee or Foster Coulee, and while Okanogan ice blocked the Columbia but not Foster Creek, glacial Lake Columbia (diverted Columbia River) drained over this saddle at about 654 m and down Moses Coulee. When glacial Lake Columbia stood at this high level so far west, Missoula floods swelling the lake could easily and deeply flood Moses Coulee.
Once eastern Foster Coulee cataract had been cut through, and especially once upper Grand Coulee’s great cataract receded to Columbia valley, glacial Lake Columbia stood lower, and Moses Coulee became harder to flood. During the late Wisconsin (marine isotope stage [MIS] 2), only when Okanogan-lobe ice blocked the Columbia near Brewster to form a high lake could Missoula floodwater from glacial Lake Missoula rise enough to overflow into Moses Coulee—and then only in a few very largest Missoula floods. Moses Coulee’s main excavation must lie with pre-Wisconsin outburst floods (MIS 6 or much earlier)—before upper Grand Coulee’s cataract had receded to Columbia valley.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Untangling the Quaternary period—A legacy of Stephen C. Porter","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.2548(18)","usgsCitation":"Waitt, R.B., 2021, Roads less travelled by— Pleistocene piracy in Washington’s northwestern Channeled Scabland, chap. of Untangling the Quaternary period—A legacy of Stephen C. 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The document includes an executive summary of the article, project ideas for Gwinnett County to enhance its bacterial monitoring program, and an annotated bibliography of selected references from the article. Although tailored to Gwinnett County, the project ideas are based on the state of the science of monitoring for fecal-associated pathogens and pathogen indicators in impaired surface waters and may be of interest to water resources divisions of other municipalities.
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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The intensity and frequency of storms are projected to increase in many regions of the world because of climate change. Storms can alter environmental conditions in many ecosystems. In lakes and reservoirs, storms can reduce epilimnetic temperatures from wind‐induced mixing with colder hypolimnetic waters, direct precipitation to the lake's surface, and watershed runoff. We analyzed 18 long‐term and high‐frequency lake datasets from 11 countries to assess the magnitude of wind‐ vs. rainstorm‐induced changes in epilimnetic temperature. We found small day‐to‐day epilimnetic temperature decreases in response to strong wind and heavy rain during stratified conditions. Day‐to‐day epilimnetic temperature decreased, on average, by 0.28°C during the strongest windstorms (storm mean daily wind speed among lakes: 6.7 ± 2.7 m s−1, 1 SD) and by 0.15°C after the heaviest rainstorms (storm mean daily rainfall: 21.3 ± 9.0 mm). The largest decreases in epilimnetic temperature were observed ≥2 d after sustained strong wind or heavy rain (top 5th percentile of wind and rain events for each lake) in shallow and medium‐depth lakes. The smallest decreases occurred in deep lakes. Epilimnetic temperature change from windstorms, but not rainstorms, was negatively correlated with maximum lake depth. However, even the largest storm‐induced mean epilimnetic temperature decreases were typically <2°C. Day‐to‐day temperature change, in the absence of storms, often exceeded storm‐induced temperature changes. Because storm‐induced temperature changes to lake surface waters were minimal, changes in other limnological variables (e.g., nutrient concentrations or light) from storms may have larger impacts on biological communities than temperature changes.
Stable oxygen isotope measurements on calcitic valves of benthic ostracodes (δ18Oost) from the northern Bering and Chukchi Seas were used to examine ecological and hydrographic processes governing ostracode and associated seawater δ18O values. Five cryophilic taxa were analyzed for δ18Oost values: Sarsicytheridea bradii; Paracyprideis pseudopunctillata; Heterocyprideis sorbyana; Heterocyprideis fascis; and the subarctic species Normanicythere leioderma. Controls on the stable oxygen isotope composition of ostracode calcite were investigated by first establishing species' vital effects and then comparing δ18Oost to seawater δ18O values (that ranged from −2.7 to −0.5‰), CTD temperature (−1.7 to 8.7 °C) and salinity (30–34) measured at sampling stations in the Bering and Chukchi Seas during the six summers of 2013–2018. Results from 297 δ18Oost measurements from 53 sites on the Bering and Chukchi Sea continental shelves are consistent with the temporal and spatial variation in δ18O values of continental shelf bottom water, as impacted by seasonality, regional hydrography, and physical processes (i.e., sea-ice melt and extent, vertical mixing, precipitation/evaporation). Regression statistics for δ18Oost values of two species, N. leioderma and P. pseudopunctillata, showed correlations to temperature and salinity that may facilitate prediction of water-mass characteristics when applied to sediment core records. Specifically, a significant linear regression relationship was found between δ18Oost values of N. leioderma and P. pseudopunctillata and temperature (R2 = 0.67 and 0.52, respectively). A principal component analysis confirmed temperature as the main controlling factor in the δ18Oost values of all species except S. bradii, with samples of distinct water masses grouping together. The δ18Oost values of S. bradii exhibited a narrow range of values (~3 to 4.5‰) across a temperature range of 10 °C. Due to strong vital effects and possibly other undetermined factors, the incorporation of δ18Oost in S. bradii was not driven by any obvious predominant environmental factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marmicro.2021.101979","usgsCitation":"Gemery, L., Cooper, L., Magen, C., Cronin, T.M., and Grebmeier, J., 2021, Stable oxygen isotopes in shallow marine ostracodes from the northern Bering and Chukchi Seas: Marine Micropaleontology, v. 165, 101979, 24 p., https://doi.org/10.1016/j.marmicro.2021.101979.","productDescription":"101979, 24 p.","ipdsId":"IP-119695","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":452783,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marmicro.2021.101979","text":"Publisher Index Page"},{"id":385384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gemery, Laura 0000-0003-1966-8732","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":245413,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":814910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooper, L.W.","contributorId":257751,"corporation":false,"usgs":false,"family":"Cooper","given":"L.W.","email":"","affiliations":[],"preferred":false,"id":814962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magen, C","contributorId":140084,"corporation":false,"usgs":false,"family":"Magen","given":"C","affiliations":[{"id":13382,"text":"Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida 32306","active":true,"usgs":false}],"preferred":false,"id":814963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cronin, T. M. 0000-0002-2643-0979","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":42613,"corporation":false,"usgs":true,"family":"Cronin","given":"T.","email":"","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":false,"id":814964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grebmeier, J.M.","contributorId":43932,"corporation":false,"usgs":true,"family":"Grebmeier","given":"J.M.","affiliations":[],"preferred":false,"id":814965,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227405,"text":"70227405 - 2021 - Unsaturated flow processes and the onset of seasonal deformation in slow-moving landslides","interactions":[],"lastModifiedDate":"2022-01-13T12:39:42.609244","indexId":"70227405","displayToPublicDate":"2021-04-07T06:37:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Unsaturated flow processes and the onset of seasonal deformation in slow-moving landslides","docAbstract":"Predicting rainfall-induced landslide motion is challenging because shallow groundwater flow is extremely sensitive to the preexisting moisture content in the ground. Here, we use groundwater hydrology theory and numerical modeling combined with five years of field monitoring to illustrate how unsaturated groundwater flow processes modulate the seasonal pore water pressure rise and therefore the onset of motion for slow-moving landslides. The onset of landslide motion at Oak Ridge earthflow in California’s Diablo Range occurs after an abrupt water table rise to near the landslide surface 52–129 days after seasonal rainfall commences. Model results and theory suggest that this abrupt rise occurs from the advection of a nearly saturated wetting front, which marks the leading edge of the integrated downward flux of seasonal rainfall, to the water table. Prior to this abrupt rise, we observe little measured pore water pressure response within the landslide due to rainfall. However, once the wetting front reaches the water table, we observe nearly instantaneous pore water pressure transmission within the landslide body that is accompanied by landslide acceleration. We cast the timescale to reach a critical pore water pressure threshold using a simple mass balance model that considers variable moisture storage with depth and explains the onset of seasonal landslide motion with a rainfall intensity-duration threshold. Our model shows that the seasonal response time of slow-moving landslides is controlled by the dry season vadose zone depth rather than the total landslide thickness.
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.
Neonicotinoids have been previously detected in Iowa surface waters, but less is known regarding their occurrence in groundwater. To help fill this research gap, a groundwater study was conducted in eastern Iowa and southeastern Minnesota, a corn and soybean producing area with known heavy neonicotinoid use. Neonicotinoids were studied in alluvial aquifers, a hydrogeologic setting known to be vulnerable to surface-applied contaminants. Groundwater samples were analyzed from 40 wells for six neonicotinoid compounds (acetamiprid, clothianidin, dinotefuran, imidacloprid, thiacloprid, thiamethoxam), and sulfoxaflor. Samples were analyzed using liquid chromatography tandem mass spectrometry (LC/MS/MS) with both direct aqueous injection and solid phase extraction methods. Neonicotinoids were prevalent in the alluvial aquifers with 73% of the wells having at least one neonicotinoid detection. Clothianidin (68%, max: 391.7 ng/L) was the most commonly detected, followed by imidacloprid (43%, max: 6.7 ng/L) and thiamethoxam (3%, max: 0.2 ng/L). Acetamiprid, dinotefuran, sulfoxaflor, and thiacloprid were not detected during the study. The solid phase extraction method was more sensitive than direct aqueous injection, where only clothianidin detected in 23% of samples. SPE is the preferred method for detecting low concentrations of hydrophilic pesticides in water. This study documented that the combination of heavy chemical use overlying a hydrogeologic setting vulnerable to surface applied contaminants leads to transport of neonicotinoids into an important groundwater resource.
Multiple lines of evidence suggest climate change will result in increased precipitation variability and consequently more frequent extreme events. These hydroclimatic changes will likely have significant socioecological impacts, especially across water-limited regions. Here we present an analysis of daily meteorological observations from 1976 to 2019 at 337 long-term weather stations distributed across the western United States (US). In addition to widespread warming (0.2 °C ± 0.01°C/decade, daily maximum temperature), we observed trends of reduced annual precipitation (−2.3 ± 1.5 mm/decade) across most of the region, with increasing interannual variability of precipitation. Critically, daily observations showed that extreme-duration drought became more common, with increases in both the mean and longest dry interval between precipitation events (0.6 ± 0.2, 2.4 ± 0.3 days/decade) and greater interannual variability in these dry intervals. These findings indicate that, against a backdrop of warming and drying, large regions of the western US are experiencing intensification of precipitation variability, with likely detrimental consequences for essential ecosystem services.
Information concerning the availability, use, and quality of water in St. Martin Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 46.99 million gallons per day (Mgal/d) of water were withdrawn in St. Martin Parish, including about 35.91 Mgal/d from groundwater sources and 11.08 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture (32.28 Mgal/d), rice irrigation (6.44 Mgal/d), general irrigation (2.38 Mgal/d), and livestock uses (0.06 Mgal/d), accounted for about 88 percent (41.16 Mgal/d) of the total water withdrawn. Other categories of use included public supply, which accounted for about 10 percent (4.83 Mgal/d), rural domestic, which accounted for about 2 percent (0.81 Mgal/d), and industry, which accounted for less than 1 percent (0.18 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals in St. Martin Parish peaked in 1985 at more than 68 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213007","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of St. Martin Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3007, 6 p., https://doi.org/10.3133/fs20213007.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103365","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384877,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3007/fs20213007.pdf","text":"Report","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3007"},{"id":384878,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":384876,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3007/coverthb.jpg"}],"country":"United States","state":"Louisiana","county":"St. Martin 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Martin\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Iberville Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 589.87 million gallons per day (Mgal/d) of water were withdrawn in Iberville Parish in southeastern Louisiana: 30.86 Mgal/d from groundwater sources and 559.01 Mgal/d from surface-water sources. Withdrawals for industrial use accounted for about 77 percent (452.80 Mgal/d) of the total water withdrawn in 2016. Other use categories included power generation, which accounted for about 21 percent (124.54 Mgal/d), and aquaculture, which accounted for about 1 percent (7.50 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals peaked in 1980 at 1,429.78 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213014","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Lindaman, M.A., and White, V.E., 2021, Water resources of Iberville Parish, Louisiana: U.S. Geological Survey Fact Sheet 2021–3014, 6 p., https://doi.org/10.3133/fs20213014.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103366","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384875,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":384873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3014/coverthb.jpg"},{"id":384874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3014/fs20213014.pdf","text":"Report","size":"1.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3014"}],"country":"United States","state":"Louisiana","county":"Iberville Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.4853,30.4972],[-91.4604,30.4707],[-91.4535,30.4753],[-91.4524,30.4743],[-91.4143,30.4318],[-91.4127,30.4322],[-91.3947,30.4094],[-91.3947,30.3956],[-91.3714,30.3874],[-91.3371,30.3526],[-91.3202,30.3443],[-91.3144,30.3246],[-91.1419,30.3237],[-91.1382,30.3169],[-91.1329,30.315],[-91.1255,30.3127],[-91.1213,30.3132],[-91.1171,30.3145],[-91.1128,30.315],[-91.0943,30.319],[-91.0863,30.3199],[-91.0721,30.3203],[-91.0678,30.3212],[-91.0641,30.3207],[-91.0594,30.3202],[-91.052,30.3184],[-91.0461,30.317],[-91.0398,30.3174],[-91.025,30.3201],[-91.0213,30.3146],[-91.0218,30.3128],[-91.0599,30.2132],[-91.0536,30.2122],[-91.0691,30.1817],[-91.0718,30.162],[-91.0797,30.1634],[-91.0914,30.157],[-91.0904,30.136],[-91.0905,30.125],[-91.09,30.1126],[-91.09,30.109],[-91.1069,30.1081],[-91.1061,30.0628],[-91.2244,30.0256],[-91.2222,30.0307],[-91.2222,30.0394],[-91.2238,30.0407],[-91.2275,30.043],[-91.2306,30.0421],[-91.2391,30.0307],[-91.2423,30.0298],[-91.2544,30.0426],[-91.2591,30.0504],[-91.2638,30.0546],[-91.2686,30.061],[-91.3371,30.0602],[-91.3514,30.0602],[-91.3545,30.0611],[-91.3577,30.057],[-91.3625,30.0543],[-91.3672,30.0547],[-91.3688,30.0589],[-91.3725,30.0625],[-91.3725,30.0653],[-91.3688,30.0671],[-91.3698,30.0767],[-91.3719,30.0817],[-91.3766,30.0863],[-91.3777,30.095],[-91.3814,30.0978],[-91.3898,30.0996],[-91.3893,30.1024],[-91.3925,30.1028],[-91.4642,30.1029],[-91.4706,30.1097],[-91.4727,30.1143],[-91.4684,30.1184],[-91.4679,30.1212],[-91.4648,30.1235],[-91.4637,30.1253],[-91.4616,30.1317],[-91.4727,30.1431],[-91.469,30.1486],[-91.4748,30.1564],[-91.4753,30.166],[-91.4774,30.1711],[-91.4742,30.1875],[-91.4774,30.1921],[-91.4827,30.1939],[-91.488,30.198],[-91.4906,30.2017],[-91.4901,30.2063],[-91.4885,30.2109],[-91.4816,30.2186],[-91.4821,30.2214],[-91.4795,30.2246],[-91.4763,30.2255],[-91.4753,30.2287],[-91.479,30.2319],[-91.4922,30.2369],[-91.5176,30.2415],[-91.5271,30.2411],[-91.5429,30.2397],[-91.563,30.242],[-91.5757,30.2479],[-91.5884,30.257],[-91.5911,30.2639],[-91.5911,30.2671],[-91.5879,30.2731],[-91.59,30.2772],[-91.6043,30.2895],[-91.6213,30.3105],[-91.6282,30.3302],[-91.6271,30.3357],[-91.6266,30.3426],[-91.6245,30.3545],[-91.6266,30.3586],[-91.6457,30.365],[-91.6478,30.3677],[-91.6436,30.3727],[-91.6394,30.3741],[-91.6351,30.3778],[-91.6304,30.3846],[-91.6246,30.3947],[-91.624,30.4048],[-91.6267,30.4112],[-91.6273,30.4176],[-91.6326,30.4272],[-91.6379,30.4336],[-91.6373,30.4349],[-91.6374,30.4427],[-91.6405,30.4432],[-91.6432,30.4454],[-91.6453,30.4523],[-91.657,30.4587],[-91.6581,30.4646],[-91.6682,30.4747],[-91.6825,30.4779],[-91.6926,30.481],[-91.7016,30.4925],[-91.7011,30.4975],[-91.6253,30.4972],[-91.5839,30.4967],[-91.5818,30.4825],[-91.5701,30.4826],[-91.5568,30.483],[-91.5584,30.4885],[-91.5261,30.4972],[-91.4853,30.4972]]]},\"properties\":{\"name\":\"Iberville\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (<) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 37.16031654673677\n ],\n [\n -89.296875,\n 37.33522435930639\n ],\n [\n -89.9560546875,\n 37.71859032558816\n ],\n [\n -90.8349609375,\n 36.914764288955936\n ],\n [\n -91.5380859375,\n 35.96022296929667\n ],\n [\n -92.1533203125,\n 34.66935854524543\n ],\n [\n -92.373046875,\n 33.50475906922609\n ],\n [\n -91.0546875,\n 33.100745405144245\n ],\n [\n -89.7802734375,\n 33.50475906922609\n ],\n [\n -88.9453125,\n 35.639441068973944\n ],\n [\n -88.9892578125,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Kansas River provides drinking water to about 800,000 people in northeastern Kansas. Water-treatment facilities that use the Kansas River as a water-supply source use chemical and physical processes during water treatment to remove contaminants before public distribution. Advanced notification of changing water-quality conditions near water-supply intakes allows water-treatment facilities to proactively adjust treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne, collected water-quality data at the Kansas River at Wamego (USGS site 06887500; hereafter referred to as the “Wamego site”) and De Soto (USGS site 06892350; hereafter referred to as the “De Soto site”) monitoring sites to update previously published regression models relating continuous water-quality sensor measurements, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Linear regression analysis was used to update and develop models for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and fecal indicator bacteria at the Wamego and De Soto monitoring sites using data collected during July 2012 through September 2019. The water-quality information documented in this report can be used as guidance for water-treatment processes and to characterize changes in water-quality conditions in the Kansas River over time that would not be otherwise possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211018","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Williams, T.J., 2021, Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019: U.S. Geological Survey Open-File Report 2021–1018, 18 p., https://doi.org/10.3133/ofr20211018.","productDescription":"Report: vii, 18 p.; Appendixes: 1–32; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120556","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1018/downloads","text":"Appendixes 1–32","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1018 Appendixes 1–32"},{"id":384811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1018/ofr20211018.pdf","text":"Report","size":"1.16 MB","description":"OFR 2021–1018"},{"id":384810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1018/coverthb.jpg"},{"id":384813,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.66845703124999,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 38.151837403006766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
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No abstract available.
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Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":813691,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219301,"text":"ofr20211012 - 2021 - Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces","interactions":[],"lastModifiedDate":"2021-04-06T11:29:46.334003","indexId":"ofr20211012","displayToPublicDate":"2021-04-05T07:36:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1012","displayTitle":"Implementation Plan for the Southern Pacific Border and Sierra-Cascade Mountains Provinces","title":"Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces","docAbstract":"The National Cooperative Geologic Mapping Program (NCGMP) is publishing a strategic plan titled Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge (Brock and others, in press). The plan provides a vision, mission, and goals for the program during the years 2020–2030, which are:
In order to achieve the goals outlined in the strategic plan, the NCGMP has developed an implementation plan. This plan will guide the annual review of projects carried out by USGS staff (FEDMAP) described in the plan and the development of the annual FEDMAP prospectus that will ensure the effective application of the NCGMP strategy.
This publication describes the implementation plan of the NCGMP strategy for the southern Pacific Border and Sierra-Cascade Mountains provinces, as defined by Fenneman (1917, 1928, and 1946). This implementation plan focuses on the geology of California and a sliver of Nevada surrounding Lake Tahoe. The southern Pacific Border and Sierra-Cascade Mountains provinces encompass the varied landscapes of the high Sierra Nevada, the Central Valley, and Coast Ranges in northern and central California and the Peninsular Ranges, Continental Borderland, Los Angeles Basin-San Gabriel-San Bernardino valleys, western and central Transverse Ranges, and northernmost Salton Trough in southern California. Societal demands create a need for earth-science data in each of these landscapes. The broader San Francisco Bay area, Central Valley, Los Angeles-San Gabriel-San Bernardino lowlands, and the coastal lowlands that border the Peninsular Ranges are densely populated (about 30 million people) areas at high risk of natural hazards. The mountains of the Sierra Nevada, Peninsular Ranges, and Transverse Ranges, and the coast all provide numerous recreational opportunities that attract visitors from around the world, whereas previously these ranges attracted people to mine their resources. The agricultural capacity of the Central Valley is a critical resource for the Nation that is increasingly water limited.
The southern. Pacific Border and Sierra-Cascade Mountains provinces, at the edge of the North American continent, were profoundly influenced by subduction zone tectonics during the Mesozoic and early Cenozoic (ongoing in northernmost California) and subsequently by the inception, development, and present activity of the San Andreas transform margin system. Although the geology of this region is the poster child of fundamental conceptual models of subduction zone complexes, forearc basins, ophiolite obductions, magmatic arcs, and suspect terranes, as well as hosting one of Earth’s most notorious continental transform faults—the San Andreas Fault—important questions that have important societal consequences remain to be answered. Most of California’s population reside in these provinces and live within 30 miles of an active fault (according to www.earthquakeauthority.com) yet new faults continue to be discovered, highlighting the importance of deformation off the main San Andreas Fault. Bedrock, surficial, and three-dimensional (3D) geologic maps depicting stratigraphic structure and depth to crystalline basement rocks provide critical context and information for understanding fault rupture, distributed deformation, fault connectivity, and history in addition to providing crucial data that enable forecasting of shaking amplitude and length from hypothetical earthquake scenarios.
The tectonic evolution of California produced not only stunning mountains, with associated hazards from landslides and active volcanoes, but also fertile valleys that make California the top agricultural producer in the country in terms of cash receipts (according to www.ers.usda.gov/faqs). These valleys lie atop large basins that not only store groundwater but, in many cases, host oil and gas fields, contributing to the fourth highest hydrocarbon production by State in the country in 2016 (according to https://www.aei.org/carpe-diem/animated-chart-of-us-oil-production-by-state-1981-2017). Water is a key resource increasingly stressed by growing agricultural, industrial, and residential needs. Warmer and drier conditions have led to an increased reliance on extracting groundwater resources, whose availability and quality are dictated at the first order by the 3D spatial distribution of bedrock and Quaternary surficial deposits. Thus, assessment of this critical resource is inextricably tied to knowledge of the surficial and subsurface geologic structure and material types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211012","usgsCitation":"Langenheim, V.E., Graymer, R.W., Powell, R.E., Schmidt, K.M., and Sweetkind, D.S., 2021, Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces: U.S. Geological Survey Open-File Report 2021–1012, 11 p., https://doi.org/10.3133/ofr20211012.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-121693","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":384840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1012/ofr20211012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1012/covrthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.69486597787505\n ],\n [\n -117.20214843749999,\n 34.415973384481866\n ],\n [\n -116.806640625,\n 36.491973470593685\n ],\n [\n -119.35546875000001,\n 38.34165619279595\n ],\n [\n -119.3115234375,\n 39.30029918615029\n ],\n [\n -120.10253906249999,\n 40.212440718286466\n ],\n [\n -121.86035156249999,\n 42.06560675405716\n ],\n [\n -124.3212890625,\n 42.06560675405716\n ],\n [\n -124.541015625,\n 40.51379915504413\n ],\n [\n -123.70605468750001,\n 38.71980474264237\n ],\n [\n -122.607421875,\n 37.19533058280065\n ],\n [\n -121.59667968749999,\n 35.817813158696616\n ],\n [\n -120.58593749999999,\n 34.45221847282654\n ],\n [\n -117.94921874999999,\n 33.54139466898275\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -115.26855468749999,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Colorado Plateaus aquifers constitute one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213012","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Degnan, J.R., and Musgrove, M., 2021, Groundwater quality in the Colorado Plateaus aquifers, western United States: U.S. Geological Survey Fact Sheet 2021–3012, 4 p., https://doi.org/10.3133/fs20213012.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117821","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384828,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"},{"id":384827,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3012/fs20213012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384826,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3012/covrthb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.6875,\n 33.063924198120645\n ],\n [\n -105.64453124999999,\n 34.161818161230386\n ],\n [\n -105.16113281249999,\n 36.70365959719456\n ],\n [\n -105.0732421875,\n 37.78808138412046\n ],\n [\n -105.5126953125,\n 38.51378825951165\n ],\n [\n -104.94140625,\n 39.027718840211605\n ],\n [\n -105.205078125,\n 40.38002840251183\n ],\n [\n -106.3916015625,\n 42.71473218539458\n ],\n [\n -107.841796875,\n 43.13306116240612\n ],\n [\n -110.390625,\n 42.61779143282346\n ],\n [\n -110.9619140625,\n 40.94671366508002\n ],\n [\n -109.6875,\n 40.64730356252251\n ],\n [\n -110.9619140625,\n 39.87601941962116\n ],\n [\n -112.763671875,\n 37.23032838760387\n ],\n [\n -113.115234375,\n 36.73888412439431\n ],\n [\n -111.22558593749999,\n 36.4566360115962\n ],\n [\n -111.6650390625,\n 33.211116472416855\n ],\n [\n -109.6875,\n 33.063924198120645\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
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12201 Sunrise Valley Drive, MS 413
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Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Stream Valley aquifers constitute one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213011","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Kingsbury, J.A., 2021, Groundwater quality in selected Stream Valley aquifers, western United States: U.S. Geological Survey Fact Sheet 2021–3011, 4 p., https://doi.org/10.3133/fs20213011.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117820","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384825,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"},{"id":384824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3011/fs20213011.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3011/covrthb.jpg"}],"country":"United States","state":"Colorado, Kansas, Missouri, Nebraska, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.615234375,\n 38.993572058209466\n ],\n [\n -92.98828125,\n 39.774769485295465\n ],\n [\n -94.5703125,\n 39.639537564366684\n ],\n [\n -95.49316406249999,\n 40.3130432088809\n ],\n [\n -97.0751953125,\n 42.97250158602597\n ],\n [\n -104.0625,\n 43.068887774169625\n ],\n [\n -104.1064453125,\n 40.94671366508002\n ],\n [\n -106.435546875,\n 39.470125122358176\n ],\n [\n -106.12792968749999,\n 38.09998264736481\n ],\n [\n -104.67773437499999,\n 37.020098201368114\n ],\n [\n -102.87597656249999,\n 36.94989178681327\n ],\n [\n -99.755859375,\n 36.73888412439431\n ],\n [\n -99.8876953125,\n 35.31736632923788\n ],\n [\n -96.5478515625,\n 33.94335994657882\n ],\n [\n -94.6142578125,\n 36.94989178681327\n ],\n [\n -94.52636718749999,\n 38.30718056188316\n ],\n [\n -91.2744140625,\n 38.54816542304656\n ],\n [\n -90.4833984375,\n 38.61687046392973\n ],\n [\n -90.615234375,\n 38.993572058209466\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
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12201 Sunrise Valley Drive, MS 413
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Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water. The Edwards-Trinity aquifer system constitutes one of the important aquifers being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213010","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Musgrove, M., 2021, Groundwater quality in the Edwards-Trinity aquifer system: U.S. Geological Survey Fact Sheet 2021–3010, 4 p., https://doi.org/10.3133/fs20213010.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","ipdsId":"IP-117819","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":384821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3010/fs20213010.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3010/covrthb.jpg"},{"id":384822,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XATXV1","linkHelpText":"Datasets of groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021)"}],"country":"United States","state":"Arkansas, Oklahoma, Texas","otherGeospatial":"Edwards-Trinity Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.546875,\n 28.93124697186731\n ],\n [\n -98.052978515625,\n 29.31514119318728\n ],\n [\n -97.086181640625,\n 30.467614102257855\n ],\n [\n -96.04248046875,\n 31.644028945047822\n ],\n [\n -95.328369140625,\n 32.90726224488304\n ],\n [\n -94.031982421875,\n 33.687781758439364\n ],\n [\n -93.14208984375,\n 33.779147331286474\n ],\n [\n -92.515869140625,\n 34.08906131584994\n ],\n [\n -93.878173828125,\n 34.65128519895413\n ],\n [\n -95.9326171875,\n 34.397844946449865\n ],\n [\n -97.283935546875,\n 34.252676117101515\n ],\n [\n -98.69018554687499,\n 33.137551192346145\n ],\n [\n -99.51416015625,\n 32.37068286611427\n ],\n [\n -101.348876953125,\n 32.045332838858506\n ],\n [\n -104.578857421875,\n 31.644028945047822\n ],\n [\n -103.216552734375,\n 30.401306519203583\n ],\n [\n -101.326904296875,\n 29.878755346037977\n ],\n [\n -100.546875,\n 28.93124697186731\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
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Tamarix spp. (tamarisk or saltcedar), a shrub-like tree, was intentionally introduced to the U.S. from Asia in the mid-1800s. Tamarisk thrives in today’s human-altered streamside (riparian) habitats and can be found along wetlands, rivers, lakes, and streams across the western U.S. In 2001, a biological control agent, Diorhabda spp. (tamarisk leaf beetle), was released in six states, and has since spread throughout the southwestern U.S. and northern Mexico. Beetle defoliation of tamarisk has altered tamarisk’s water use and effectiveness as erosion control, as well as dynamics of native and nonnative plant and wildlife species. The full effects of the tamarisk leaf beetle on ecosystem function remain unknown. The U.S. Geological Survey collaborates with Tribal, State, Federal agencies, and other institutions to provide current, fact-based information on the effects of tamarisk and the tamarisk leaf beetle on managed resources, and provides sound science for conservation and restoration of riparian habitats in the southwestern U.S.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203061","usgsCitation":"Nagler, P.L., Hull, J.B., van Riper, C., Shafroth, P.B., and Yackulic, C.B., 2021, The Transformation of dryland rivers: The future of introduced tamarisk in the U.S.: U.S. Geological Survey Fact Sheet 2020–3061, 6 p., https://doi.org/10.3133/fs20203061.","productDescription":"6 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-122043","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":384841,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3061/covrthb.jpg"},{"id":384842,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3061/fs20203061.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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