This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that AWiFS has an interior geometric performance in the range of −16.080 (−0.268 pixel) to 35.520 meters (m; 0.592 pixel) in easting and −25.680 (−0.428 pixel) to 23.400 m (0.390 pixel) in northing in band-to-band registration, an exterior geometric error of −64.262 (−1.071 pixels) to −19.059 m (−0.318 pixel) in easting and −29.028 (−0.484 pixel) to 41.249 m (0.687 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 2.29–2.36 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.030–0.035.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030G","usgsCitation":"Ramaseri Chandra, S.N., Kim, M., Christopherson, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on Resourcesat-2 Advanced Wide Field Sensor, chap. G of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors (ver. 1.2, August 2024): U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030G.","productDescription":"Report: iv, 17 p.; Version History","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126658","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":392291,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/g/versionHist.txt","text":"Version History","size":"1.8 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2021–1030G Version History"},{"id":391064,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/g/images"},{"id":391063,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/g/ofr20211030g.xml","text":"Report","size":"79.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1030G xml"},{"id":433255,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/g/ofr20211030g.pdf","text":"Report","size":"2.2 MB","description":"OFR 2021–1030G"},{"id":391061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/g/coverthb3.jpg"}],"edition":"Version 1.0: September 28, 2021; Version 1.1: November 30, 2021; Version 1.2: August 29, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Permian Basin, in west Texas and southeastern New Mexico is one of the largest conventional oil and gas reservoirs in the United States and is becoming one of the world’s largest continuous oil and gas (COG) reservoirs. Advances in technology have enabled oil and gas to be extracted from reservoirs that historically were developed using conventional, or vertical, well drilling techniques. Conventional oil and gas reservoirs have discrete deposits that are well defined and are typically trapped by an overlying geologic formation or caprock, whereas COG reservoirs contain deposits that are distributed evenly throughout the geologic formation, typically have much lower permeability (the capacity of a porous rock to transmit a fluid) than the conventional deposits, and require specialized horizontal extraction techniques. The methods to extract the oil from the two different reservoirs require differing amounts of water, and the horizontal extraction methods typically require substantially more water. In 2015, the U.S. Geological Survey started a topical study to quantify water used during COG development. The Permian Basin, which contains both types of reservoirs (continuous and conventional), was the second basin in the United States in the U.S. Geological Survey’s topical study to quantify water used during COG development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213053","usgsCitation":"Houston, N.A., Ball, G.P., Galanter, A.E., Valder, J.F., McShane, R.R., Thamke, J.N., and McDowell, J.S., Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–2019, with Comparisons to the Williston Basin, North Dakota and Montana: U.S. Geological Survey Fact Sheet 2021–3053, 4 p., https://doi.org/10.3133/fs20213053.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-124923","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":390943,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3053/images"},{"id":390942,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3053/fs20213053.xml","text":"Report","size":"37.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"FS 2021–3053 xml"},{"id":390941,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3053/fs20213053.pdf","text":"Report","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053"},{"id":390940,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3053/coverthb.jpg"}],"country":"United States","state":"Montana, New Mexico, North Dakota, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.9296875,\n 48.980216985374994\n ],\n [\n -108.720703125,\n 48.922499263758255\n ],\n [\n -108.017578125,\n 48.28319289548349\n ],\n [\n -107.57812499999999,\n 47.81315451752768\n ],\n [\n -108.369140625,\n 47.45780853075031\n ],\n [\n -108.369140625,\n 47.040182144806664\n ],\n [\n -107.05078125,\n 46.86019101567027\n ],\n [\n -105.556640625,\n 46.07323062540835\n ],\n [\n -103.0078125,\n 45.02695045318546\n ],\n [\n -100.81054687499999,\n 45.089035564831036\n ],\n [\n -99.755859375,\n 47.100044694025215\n ],\n [\n -100.107421875,\n 49.03786794532644\n ],\n [\n -107.9296875,\n 48.980216985374994\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.095703125,\n 34.252676117101515\n ],\n [\n -103.64501953125,\n 33.90689555128866\n ],\n [\n -104.56787109374999,\n 33.578014746143985\n ],\n [\n -105.0732421875,\n 32.879587173066305\n ],\n [\n -105.1171875,\n 32.02670629333614\n ],\n [\n -104.56787109374999,\n 31.42866311735861\n ],\n [\n -104.150390625,\n 30.713503990354965\n ],\n [\n -103.16162109375,\n 30.44867367928756\n ],\n [\n -102.23876953125,\n 30.391830328088137\n ],\n [\n -101.40380859375,\n 30.183121842195515\n ],\n [\n -100.5908203125,\n 29.878755346037977\n ],\n [\n -100.2392578125,\n 29.592565403314087\n ],\n [\n -99.77783203125,\n 29.82158272057499\n ],\n [\n -99.66796875,\n 30.80791068136646\n ],\n [\n -99.73388671874999,\n 31.353636941500987\n ],\n [\n -100.283203125,\n 31.615965936476076\n ],\n [\n -100.34912109375,\n 31.914867503276223\n ],\n [\n -100.61279296875,\n 32.491230287947594\n ],\n [\n -100.32714843749999,\n 32.91648534731439\n ],\n [\n -100.107421875,\n 33.706062655101206\n ],\n [\n -100.45898437499999,\n 34.19817309627726\n ],\n [\n -100.8544921875,\n 34.32529192442733\n ],\n [\n -102.12890625,\n 34.488447837809304\n ],\n [\n -102.72216796875,\n 34.397844946449865\n ],\n [\n -103.095703125,\n 34.252676117101515\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The U.S. Geological Survey installed 10 rain gages and 12 calibrated H-flumes to measure rainfall and runoff volumes at 10 locations in Ohio Department of Transportation highway median-strip catchments. Data were collected to facilitate comparisons of rainfall and runoff volumes at study sites before and after stormwater best management practices (BMPs) were installed and between sites with different BMPs. The BMP treatments comprised removing the top layer of the existing soil, rototilling the remaining soil to a 6-inch depth, mixing the soils with one of two soil amendments (compost with sand or shale) at one of two thicknesses (4 inches or 6 inches), topping with a compost blanket, seeding, and installing erosion control matting. The overall treatment used at a given study site is referred to as “BMP.” At two locations where soil amendments were installed, a second “control” site was installed to measure runoff from an adjacent catchment in the same median strip where no soil amendment was installed. This no-treatment option (no soil amendment) was considered its own class of BMP.
Rainfall and runoff data were collected during periods when air temperatures were above freezing (including all months except January, February, and parts of December and March) from 2018 to 2020. The data collection period for each study site was divided into “pre-BMP” and “post-BMP” periods. Equipment to measure rainfall and runoff was installed and data were collected from April to December 2018 before installation of soil amendments (the pre-BMP period). The post-BMP period started between April and May of 2019 at the first measured rainfall after soil amendments were installed. Rainfall and runoff monitoring continued through September 2020. For control sites, the post-BMP periods were assigned to start with the first measured rainfall in the 2019 data collection season.
A rainfall-runoff “event” was defined as beginning at the time of the first measured rainfall and ending when rainfall and runoff (if any) ceased and remained ceased for at least 3 hours. A value referred to as “event runoff percentage,” defined as the total volume of runoff during an event expressed as a percentage of the total volume of rainfall falling over the catchment, was computed for each event. The distribution of rainfall totals associated with events was similar between the pre-BMP and post-BMP periods; however, there were appreciable between-site differences in the distribution of event runoff percentages during the pre-BMP and post-BMP periods.
Empirical distribution function (EDF) tests were performed with and without data from events that resulted in no runoff to determine whether the distribution of event runoff percentages changed from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal in the pre-BMP and post-BMP periods was rejected (α=0.05) in at least one of the two tests for four sites (one site with a shale amendment and three sites with sand amendments). Mean event runoff percentages at each of those four sites decreased from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal was not rejected for the other six sites’ draining catchments with soil amendments or the two control sites. EDF tests performed on event rainfall totals indicated no statistically significant changes between the pre-BMP and post-BMP period distributions for any of the sites.
Double-mass analyses of cumulative runoff were performed for two pairs of closely spaced sites (each pair located in a common median strip): one site in each pair drained a catchment where soil amendments were installed, and the other (a control) drained a catchment without soil amendments. Those double-mass analyses indicated a small reduction in runoff from the pre-BMP to post-BMP period at the site whose catchment received the sand and compost amendment, but no perceptible reduction in runoff at the site whose catchment received the shale and compost amendment.
Regression analyses indicated that (a) three rainfall factors (event rainfall totals, total rainfall for the previous 7 days, and a cross product of the factors) and the intercept term were the four most important factors explaining event runoff percentages, (b) the effect of amendment type on event runoff percentage was small in comparison to the rainfall and intercept terms, (c) event runoff percentages tended to be lower for sites with shale amendments than sites with sand amendments; however, event runoff percentages tended to be lower for control sites than for sites with shale or sand amendments, and (d) event runoff percentages increased with increasing amendment thickness. The counterintuitive results that event runoff percentages increased with increasing amendment thickness and that control sites tended to have lower event runoff percentages than sites draining soil-amended catchments likely reflects unmeasured factors that existed at the sites before BMPs were installed rather than the effect of the BMP treatments.
Although not definitive, some support for the conclusion that the sand amendment was generally more effective at reducing runoff than the shale amendment was provided by results from the EDF tests, double-mass analyses, and runoff statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215114","collaboration":"Prepared in cooperation with ms consultants","usgsCitation":"Whitehead, M.T., and Koltun, G.F., 2021, Assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio, 2018–20 (ver. 1.1, December 2021): U.S. Geological Survey Scientific Investigations Report 2021–5114, 27 p., https://doi.org/10.3133/sir20215114.","productDescription":"Report: vii, 27 p.; Data Release; Version History","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-118944","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":390957,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P945PKJ7","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Dataset for analyses in assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio"},{"id":390955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5114/coverthb2.jpg"},{"id":392682,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.pdf","text":"Report","size":"5.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5114"},{"id":390958,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5114 xml"},{"id":392683,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5114/versionHist.txt","text":"Version History","size":"3.07 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":390959,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5114/images"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.6279296875,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 39.639537564366684\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2021; Version 1.1: December 2021","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd.
Ste 100
Columbus, OH 43229–1737
Identifying the physical habitat characteristics associated with riverine freshwater mussel assemblages is challenging but crucial for understanding the causes of mussel declines. The occurrence of mussels in multispecies beds suggests that common physical factors influence or limit their occurrence. Fine-scale geomorphic and hydraulic factors (e.g., scour, bed stability) are predictive of mussel-bed occurrence, but they are computationally challenging to represent at intermediate or riverscape scales. We used maximum entropy (MaxEnt) modeling to evaluate associations between riverscape-scale hydrogeomorphic variables and mussel-bed presence along 530 river km of the Meramec River basin, USA, to identify river reaches that are fundamentally suitable for mussels as well as those that are not. We obtained the locations of mussel beds from an existing, multiyear dataset, and we derived river variables from high-resolution, open-source datasets of aerial imagery and topography. Mussel beds occurred almost exclusively in reaches identified by our model as suitable; these were characterized by laterally stable channels, absence of adjacent bluffs, proximity to gravel bars, higher stream power, and larger areas of contiguous water (a proxy for drought vulnerability). We validated our model findings based on model sensitivity using a set of mussel-bed locations not used in model development. These findings can inform how resource managers allocate survey, monitoring, and conservation efforts.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc-d-20-00002","usgsCitation":"Keymanesh, K., Rosenberger, A.E., Lindner, G., Bouska, K.L., and McMurray, S.E., 2021, Riverscape-scale modeling of fundamentally suitable habitat for mussel assemblages in an Ozark River system, Missouri: Freshwater Mollusk Biology and Conservation, v. 24, no. 2, p. 43-58, https://doi.org/10.31931/fmbc-d-20-00002.","productDescription":"16 p.","startPage":"43","endPage":"58","ipdsId":"IP-113472","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc-d-20-00002","text":"Publisher Index Page"},{"id":433559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.23145192205784,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 38.57316922340365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keymanesh, K.","contributorId":317234,"corporation":false,"usgs":false,"family":"Keymanesh","given":"K.","email":"","affiliations":[],"preferred":false,"id":908903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindner, G.","contributorId":341798,"corporation":false,"usgs":false,"family":"Lindner","given":"G.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMurray, Stephen E.","contributorId":206918,"corporation":false,"usgs":false,"family":"McMurray","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225582,"text":"sir20215020 - 2021 - Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming","interactions":[],"lastModifiedDate":"2022-06-16T19:45:30.631881","indexId":"sir20215020","displayToPublicDate":"2021-10-27T10:00:17","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5020","displayTitle":"Geologic and Hydrogeologic Characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, Northern Greater Denver Basin, Southeastern Laramie County, Wyoming","title":"Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming","docAbstract":"In cooperation with the Wyoming State Engineer’s Office, the U.S. Geological Survey studied the geologic and hydrogeologic characteristics of Cenozoic and Upper Cretaceous strata at a location in southeastern Laramie County within the Wyoming part of the Cheyenne Basin, the northern subbasin of the greater Denver Basin. The study aimed to improve understanding of the aquifers/aquifer systems in these strata, motivated in part by declining groundwater levels and interest in exploring future groundwater supplies. Based on detailed geologic characterization using information obtained by drilling and coring a 960-foot-(ft) deep exploratory borehole, and comparisons with previously published descriptions, identified Cenozoic lithostratigraphic units included 40 ft of Quaternary older alluvial fan deposits consisting of an unconsolidated mixture of sand and gravel with lesser quantities of silt and clay in varying proportions and the underlying 407.3-ft-thick White River Formation of late Eocene-Oligocene age consisting largely of mudrocks with sparse thin beds of sandstone, muddy gravel, and conglomeratic mudrocks. Identified Upper Cretaceous lithostratigraphic units included the 351.6-ft-thick Lance Formation, consisting of terrestrial sedimentary rocks including mudrocks (muddy shale and silty and sandy shale, siltstone, claystone, and mudstone) interbedded with much smaller quantities of very fine- to medium-grained muddy and silty sandstone and coal; the 79.6-ft-thick Fox Hills Sandstone, consisting of a transitional marine sequence of muddy or silty sandstone present in five individual beds; and 86.7 ft of the upper transition member of the Pierre Shale, consisting largely of marine sedimentary rocks such as muddy shale. Beds of the upper and lower Fox Hills Sandstone were separated by tongues of the Lance Formation and upper transition member of the Pierre Shale, respectively.
The White River hydrogeologic unit, consisting of the entire White River Formation or Group at the study site, did not contain any substantial secondary permeability features in the mudrocks that composed almost all the unit. A monitoring well (BR–1) was completed in the White River aquifer with the well screen open to the only coarse-grained unit (muddy sandstone) that had sufficient thickness and permeability to be considered as an aquifer. Sampling of the well for a broad suite of constituents indicated groundwater generally was of excellent quality except dissolved arsenic was detected at a concentration greater than the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level, and dissolved sodium was measured at a concentration greater than several EPA Drinking Water Advisory Levels (DWAs) for the constituent. Well development, well purging for groundwater sampling, and calculated aquifer properties indicated the sandstone aquifer screened by monitoring well BR–1 was not very productive. Analysis of the well water-level responses in BR–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response with wellbore-storage effects. Hydraulic properties estimated based on these responses yielded values of hydraulic conductivity (K, 0.057 foot per day [ft/d]), specific storage (Ss, 1.6×10−6 per foot [ft−1]) and porosity (n, 0.43). Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated an upward trend (+1.13 foot per year [ft/yr]) during the period analyzed, September 5, 2014, to September 30, 2017.
Lithologic characteristics of the Lance hydrogeologic unit, consisting of the entire Lance Formation at the study site, indicated a potential aquifer in a “sandy” interval in the upper part of the unit. Most of the Lance hydrogeologic unit below the “sandy” interval consisted of various low-permeability lithologies unlikely to yield substantial quantities of water. This lower part of the hydrogeologic unit likely functions as a confining unit separating the underlying Lance-Fox Hills aquifer. A geologic cross section constructed for this study indicated fine-grained sediments composed most of the Lance Formation/hydrogeologic unit not only at the study location, but also throughout southern Laramie County along the line of section and throughout the Wyoming and Colorado parts of the Cheyenne Basin. A monitoring well (LN–1) completed in a sandstone bed in the “sandy” interval of the Lance hydrogeologic unit produced a mean of about 23 gallons per minute (gal/min) during well development, indicating sandstone beds can form moderately productive confined subaquifers in this part of the hydrogeologic unit. Analysis of the well water-level responses in well LN–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response. Hydraulic properties estimated based on these responses yielded values for a lower bounding K of 0.60 ft/d, Ss of 1.6×10−6 ft−1, and n of 0.38. Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated a downward trend (−0.86 ft/yr) during the period analyzed (November 8, 2014, to September 30, 2017). Analyses for a broad suite of constituents in samples from well LN–1 indicated groundwater quality generally was excellent, although dissolved sodium was measured at a concentration greater than two EPA DWA levels for the constituent.
Because of the absence of any overlying or intertonguing sandstone beds belonging to the lower/basal part of the Lance Formation, the Lance-Fox Hills aquifer at the study site consisted only of the five sandstone beds of the Fox Hills Sandstone. The cross section constructed for this study illustrated how the Fox Hills Sandstone, and thus, most of the Lance-Fox Hills aquifer, consists of a series of sandstone bodies that overlap (shingle) upward to the east across southern Laramie County. These bodies collectively form a fairly continuous body of sandstone, thus potentially forming an areally extensive aquifer across southern Laramie County, and by extension, throughout most of the formation’s extent in the Wyoming part of the Cheyenne Basin, as is the case in the Colorado part of the basin. A monitoring well (FH–1) completed in part of the thickest sandstone bed of the Lance-Fox Hills aquifer was moderately to highly productive and easily produced 25 to 30 gal/min after development. Substantially larger water production rates likely could be obtained by penetrating the full thickness of this bed and by completing a well open to the other overlying and underlying sandstone beds of the aquifer. Analysis of the water-level responses in well FH–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response. Hydraulic properties computed based on these responses yielded values for a lower bounding estimate for K of 0.26 ft/d, for Ss of 1.0×10−6 ft−1, and for n of 0.41. Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated a downward trend (−1.74 ft/yr) during the period analyzed, December 19, 2014, to September 30, 2017. Sampling of monitoring well FH–1 and two production wells completed in the Fox Hills Sandstone in other parts of Laramie County indicated groundwater quality generally is excellent, although pH exceeded a recommended EPA aesthetic drinking-water standard (Secondary Maximum Contaminant Level) in two of three sampled wells, total dissolved solids concentrations exceeded the Secondary Maximum Contaminant Level in one of the two sampled production wells, and dissolved sodium was measured in all three sampled wells at a concentration greater than two EPA DWA levels for the constituent. The Wyoming Class II agricultural (irrigation) sodium adsorption ratio standard of 8 was exceeded in all three sampled wells, indicating these waters are not suitable for irrigation use.
Computed vertical hydraulic gradients indicated a strong potential for downward flow throughout the groundwater system at the study site, including from the low-yielding aquifer in the upper White River Formation/hydrogeologic unit (monitoring well BR–1) to the sandstone subaquifer in the Lance Formation/hydrogeologic unit (monitoring well LN–1), and from the Lance subaquifer (monitoring well LN–1) to the sandstone bed/aquifer that composes much of the Lance-Fox Hills aquifer thickness at the study site (monitoring well FH–1). However, large hydraulic-head differences between wells indicated high resistance to vertical flow attributable to the low vertical hydraulic conductivity of intervening strata, which consisted almost entirely of low-permeability mudrocks. The confined nature of the sandstone aquifers monitored by the various wells coupled with dissimilarities between groundwater-level fluctuations and trends in groundwater levels indicated downward flow through the intervening strata (primarily mudrocks in the various lithostratigraphic/hydrogeologic units) between the examined sets of wells likely was small.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215020","collaboration":"Prepared in cooperation with the Wyoming State Engineer’s Office","usgsCitation":"Bartos, T.T., Galloway, D.L., Hallberg, L.L., Dechesne, M., Diehl, S.F., and Davidson, S.L., 2021, Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming: U.S. Geological Survey Scientific Investigations Report 2021–5020, 219 p., 1 pl., https://doi.org/10.3133/sir20215020.","productDescription":"Report: xvii, 219 p.; Appendix Table; Plate: 42.00 x 63.00 inches; Data Release; Dataset","numberOfPages":"242","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110049","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":390939,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS groundwater data for Wyoming, in USGS water data for the Nation"},{"id":390938,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPLA74","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Atmospheric-loading frequency response functions and groundwater levels filtered for the effects of atmospheric loading and solid Earth tides for three USGS monitoring wells, southeastern Laramie County, Wyoming, 2014–2017"},{"id":390936,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020_plate.pdf","text":"Plate","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020 Plate","linkHelpText":"— Construction of monitoring wells BR–1, LN–1, and FH–1, and geophysical logs, generalized lithology, and interpreted lithostratigraphy for exploratory borehole LC–F1, southeastern Laramie County, Wyoming"},{"id":390937,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020_table1.1.pdf","text":"Table 1.1","size":"500 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020 Appendix Table","linkHelpText":"— Description of core collected from exploratory borehole LC–F1, southeastern Laramie County, Wyoming"},{"id":390934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5020/coverthb.jpg"},{"id":390935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020"}],"country":"United States","state":"Wyoming","county":"Laramie County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.6506,41.651],[-104.6491,41.5656],[-104.0521,41.5654],[-104.052,41.3949],[-104.0526,41.0236],[-104.0528,41.0017],[-104.1399,41.0019],[-104.4725,41.0027],[-104.4875,41.0027],[-104.5606,41.0028],[-104.5679,41.0028],[-104.6087,41.0046],[-104.6134,41.0048],[-104.6337,41.0056],[-104.6648,41.0047],[-104.6837,41.0041],[-104.7013,41.0035],[-104.83,40.9996],[-104.8341,40.9996],[-104.9385,40.9995],[-104.9425,40.9995],[-105.1109,40.9993],[-105.2763,40.9998],[-105.2774,41.6567],[-105.1706,41.6535],[-105.0575,41.6537],[-104.9419,41.6537],[-104.6506,41.651]]]},\"properties\":{\"name\":\"Laramie\",\"state\":\"WY\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Mineralogical and geochemical studies of interbedded black and gray mudstones in the Triassic part of the Triassic-Jurassic Otuk Formation (northern Alaska) document locally abundant barite and pyrite plus diverse redox signatures. These strata, deposited in an outer shelf setting at paleolatitudes of ~45 to 60°N, show widespread sedimentological evidence for bioturbation. Barite occurs preferentially in black mudstones (TOC = 0.93–6.46 wt%), forming displacive euhedral crystals with pyrite inclusions and rims, and late albite inclusions or intergrowths. Pyrite also occurs as small (3–20 μm) framboids, discontinuous laminae, euhedral and anhedral crystals, and replacements of barite and fossils (mainly radiolarians). Paragenetically early wurtzite is present as clusters of very small (1–3 μm) aggregates of radiating crystals 0.5 to 1.0 μm long with cores of organic matter that overgrow framboidal pyrite; later wurtzite forms 10- to 30-μm bladed crystals. Equant grains (3–30 μm) and small (20 μm) angular clusters of zinc sulfide that include <1-μm-long, comb-like structures are sphalerite or wurtzite, or both. Minor siderite forms euhedral crystals intergrown with albite that enclose wurtzite and barite. Illite shows intergrowths with sphalerite; rare K-feldspar is intergrown with barite. Formation of these minerals and assemblages is attributed to early diagenetic processes.
Whole-rock geochemical data for 15 samples show large ranges in redox proxies including Post Archean Average Shale (PAAS)-normalized enrichment factors (EFs) for V, U, Mo, and Re, and Al-normalized ratios for V, U, and Mo. Results for most black mudstones, with or without abundant barite and/or pyrite, suggest deposition within an oxygen minimum zone. Cerium anomalies, PAAS-normalized and calculated on a detrital-free basis, range widely from 0.49 to 0.96 and may reflect diagenetic overprinting by Ce-depleted fluids. Considering data for both black and gray mudstones, the overall geochemical pattern together with evidence from pyrite framboid sizes suggest that redox conditions fluctuated greatly from euxinic to oxic, like the redox profiles reported for modern shelf sediments offshore Peru and Namibia. The euxinic redox signatures in some Otuk black mudstones may correlate with widespread Early to Middle Triassic ocean anoxic events proposed for other regions.
Calculations of median EFs for trace elements in Otuk black mudstones reveal both enrichments and depletions. Normalizations to the median composition of the three least-mineralized black mudstones show that barite- and/or pyrite-rich samples display large (>50%) positive changes for Li (+80.4%), V (+75.6%), Sr (+75.9%), Ba (+790%), Cu (+92.1%), Ni (+169%), Ag (+156%), Au (+3091%), As (+109%), Sb (+476%), and Se (+205%); Zn shows a moderate positive change of +42.1%. Moderate negative changes are evident only for Ge (−47.2%) and W (−30.6%). The local enrichments may reflect one or more factors including redox variations in bottom waters and pore fluids, element mobility during diagenesis, and selective fractionation into minerals such as barite, pyrite, and wurtzite. Anomalously low U/Al and UEF values, compared to those for other modern and ancient organic-rich sediments and sedimentary rocks, are attributed to increased solubility and loss of U during bioturbation-related oxygenation in the subsurface.
Physicochemical constraints on barite, pyrite, and wurtzite formation are informed by use of a pH-fO2 plot constructed at 10 °C. Based on paragenetic evidence for multistage deposition of these three minerals, together with the presence of illite intergrown with ZnS and K-feldspar with barite, proposed diagenetic trends involve an increase in pH and fO2 related to the ingress of sulfate-rich pore fluids during bioturbation, followed by a return to lower then higher pH and fO2 conditions linked to carbon, sulfur, barium, and iron cycling during diagenesis. Labile Ba of marine pelagic origin was mobilized from organic-rich sediment upward to the sulfate-methane transition zone where barite precipitated during the interaction of reduced Ba- and CH4-rich fluids with sulfate-bearing pore fluids. The formation of paragenetically early wurtzite (ZnS) crystals, as well as locally high EF values for Cu, Ni, Ag, and Au, is attributed to metal enrichment of pore fluids, with sources being derived in part from water-column deposition from hydrothermal plumes related to coeval Triassic seafloor vent systems including a volcanogenic massive sulfide deposit in British Columbia and the Wrangellia Large Igneous Province in Alaska.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120568","usgsCitation":"Slack, J.F., McAleer, R.J., Shanks, W., and Dumoulin, J.A., 2021, Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska: Chemical Geology, v. 585, 120568, 22 p., https://doi.org/10.1016/j.chemgeo.2021.120568.","productDescription":"120568, 22 p.","ipdsId":"IP-130237","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450344,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120568","text":"Publisher Index Page"},{"id":418739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.604267717169,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 71.70733094087223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"585","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":877040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":877041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanks, Wayne (Pat)","contributorId":240838,"corporation":false,"usgs":true,"family":"Shanks","given":"Wayne (Pat)","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":877042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":877043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225619,"text":"70225619 - 2021 - Effects of sea ice decline and summer land use on polar bear home range size in the Beaufort Sea","interactions":[],"lastModifiedDate":"2021-10-28T13:48:10.388947","indexId":"70225619","displayToPublicDate":"2021-10-26T08:47:32","publicationYear":"2021","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 sea ice decline and summer land use on polar bear home range size in the Beaufort Sea","docAbstract":"Animals responding to habitat loss and fragmentation may increase their home ranges to offset declines in localized resources or they may decrease their home ranges and switch to alternative resources. In many regions of the Arctic, polar bears (Ursus maritimus) exhibit some of the largest home ranges of any quadrupedal mammal. Polar bears are presently experiencing a rapid decline in Arctic sea ice extent and a change in sea ice composition. For the Southern Beaufort Sea subpopulation of polar bears, this has resulted in a divergent movement pattern where most of the subpopulation remains on the sea ice in the summer melt season while the remainder move to land. We evaluated the effects of summer land use and maternal denning on the annual and seasonal utilization distribution size (i.e., home range) of adult female polar bears in the Southern Beaufort Sea subpopulation over 30 yr (1986–2016) during a period of rapid sea ice decline. For bears that remained on the summer sea ice, model-derived mean annual utilization distributions were 64% larger in 1999–2016 (
In active volcanic regions, high-temperature chemical reactions in the hydrothermal system consume CO2 sourced from magma or from the deep crust, whereas reactions with silicates at shallow depths mainly consume atmospheric CO2. Numerous studies have quantified the load of dissolved solids in rivers that drain volcanic regions to determine chemical weathering rates and atmospheric CO2 consumption rates. However, the balance between thermal and non-thermal components to riverine fluxes in these areas remains poorly constrained, hindering accurate estimates of atmospheric CO2 consumption rates. Here we use the Ge/Si ratio and the stable silicon isotopes (δ30Si) as tracers for quantifying non-thermal silicon contributions in rivers draining the Yellowstone Plateau Volcanic Field, USA. The Ge/Si ratio (µmol.mol−1) was determined for seven thermal water samples (183 ± 22), eight rivers (35 ± 23) and six creeks flowing into Yellowstone Lake (5 ± 3) during base flow and during peak water discharge following snowmelt. The δ30Si value (‰) was determined for thermal waters (−0.09 ± 0.04), Yellowstone River at Yellowstone Lake outlet (1.91 ± 0.23) and creek samples (0.82 ± 0.29). The calculated atmospheric CO2 consumption associated with non-thermal waters flowing through Yellowstone's rivers during peak discharge is ∼3.03 ton.km−2.yr−1, which is ∼2% of the annual mean atmospheric CO2 consumption in other volcanic regions. This study highlights the significance of quantifying seasonal variations in chemical weathering rates for improving estimates of atmospheric CO2 consumption rates in active volcanic regions.
The U.S. Geological Survey, in cooperation with the Alabama Department of Transportation, evaluated the role of culvert construction in altering streams and habitats of benthic macroinvertebrate communities at selected study sites in the northern East Gulf Coastal Plain of Alabama during 2011–19. Analysis included examinations of changes in stream channel geometry, suspended sediment, turbidity, and benthic macroinvertebrate communities.
Topographic surveys of stream channel cross sections, upstream and downstream of the culvert, were conducted before and after construction. Changes in channel geometry (cross-sectional area, top width, mean depth, and thalweg slope) were assessed by using paired sample t-tests to compare before- and after-construction channel geometry measurements. Statistically significant changes in stream channel geometry between the before- and after-construction measurements were observed at four of the six study sites. Analysis of the channel geometry data indicates that 1 site had no measured changes, and thalweg reach slopes were inverted at 4 of the 12 study reaches—2 measured in before-construction reaches and 2 measured in after-construction reaches.
Surface-water samples were collected during selected storm events for suspended sediment and turbidity analyses. Samples were simultaneously collected upstream and downstream of the culvert construction reaches during all three phases of construction (before, during, and after). Analysis focused on the parity of upstream to downstream simultaneous samples. The mean upstream to downstream paired ratios of sediment concentrations and turbidity from the after-construction phase indicate that colloidal and noncolloidal sediments were passing through the construction reaches at two of the six sites, noncolloidal sediments were being trapped in the construction reaches at two sites, and colloidal and noncolloidal sediments were being removed from the construction reach at two sites.
Benthic macroinvertebrates were collected and identified at five of the six sites from instream habitats that were available in sampled areas both upstream and downstream of the culvert construction reaches. Differences between upstream and downstream reaches and the Wilcoxon rank sum statistic were used to examine changes in metrics of benthic macroinvertebrate communities between before- and after-construction phases. Benthic macroinvertebrate sampling results did not indicate that culvert construction caused impairment to communities at study sites. No tolerance metrics suggested a major change in the pollution tolerance of the communities. The same upstream to downstream patterns in abundance-weighted tolerance values were observed in the before- and after-construction periods at each site. At one site, the difference between upstream and downstream richness-based tolerance values increased, but the after-construction upstream and downstream richness-based tolerance values were lower (indicating a less pollution-tolerant macroinvertebrate community) than in the before-construction period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215096","collaboration":"Prepared in cooperation with the Alabama Department of Transportation","usgsCitation":"Pugh, A.L., and Gill, A.C., 2021, Effects of culvert construction on streams and macroinvertebrate communities at selected sites in the East Gulf Coastal Plain of Alabama, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5096, 52 p., https://doi.org/10.3133/sir20215096.","productDescription":"Report: vii, 52 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-097029","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":390797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P906BOVO","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Aerial imagery, benthic macroinvertebrate, topographic survey, and soil survey datasets collected for a study of effects of culverts on the natural conditions of streams in the East Gulf Coastal Plain of Alabama, 2010–2019"},{"id":390796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5096/sir20215096.pdf","text":"Report","size":"15.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5096"},{"id":390795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5096/coverthb.jpg"},{"id":390798,"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":"Alabama","otherGeospatial":"East Gulf Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.08837890625,\n 34.69646117272349\n ],\n [\n -88.165283203125,\n 34.69646117272349\n ],\n [\n -88.505859375,\n 31.98012335736804\n ],\n [\n -88.363037109375,\n 30.315987718557867\n ],\n [\n -88.121337890625,\n 30.268556249047727\n ],\n [\n -87.747802734375,\n 30.173624550358536\n ],\n [\n -87.418212890625,\n 30.35391637229704\n ],\n [\n -87.264404296875,\n 30.477082932837682\n ],\n [\n -87.4072265625,\n 30.581179257386985\n ],\n [\n -87.451171875,\n 30.741835717889792\n ],\n [\n -87.593994140625,\n 30.86451022625836\n ],\n [\n -87.5390625,\n 31.005862904624205\n ],\n [\n -84.979248046875,\n 30.996445897426373\n ],\n [\n -85.067138671875,\n 31.175209828310845\n ],\n [\n -85.045166015625,\n 31.306715155075167\n ],\n [\n -85.067138671875,\n 31.456782472114313\n ],\n [\n -85.078125,\n 31.71882222408327\n ],\n [\n -85.0341796875,\n 31.970803930433096\n ],\n [\n -84.88037109375,\n 32.25926542645933\n ],\n [\n -84.9462890625,\n 32.35212281198644\n ],\n [\n -85.045166015625,\n 32.46342595776104\n ],\n [\n -85.23193359375,\n 32.48196313217176\n ],\n [\n -85.6494140625,\n 32.54681317351514\n ],\n [\n -86.044921875,\n 32.57459172113418\n ],\n [\n -86.693115234375,\n 32.648625783736726\n ],\n [\n -87.132568359375,\n 32.685619853722\n ],\n [\n -87.4951171875,\n 32.85190345738802\n ],\n [\n -87.73681640625,\n 33.109948297894285\n ],\n [\n -87.82470703125,\n 33.53223722395908\n ],\n [\n -87.91259765625,\n 34.10725639663118\n ],\n [\n -87.95654296875,\n 34.379712580462204\n ],\n [\n -88.08837890625,\n 34.69646117272349\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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640 Grassmere Park, Suite 100
Nashville, TN 37211
Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.737416","usgsCitation":"Satterthwaite, E.V., Bax, N.J., Miloslavich, P., Ratnarajah, L., Canonico, G., Dunn, D., Simmons, S.E., Carini, R., Evans, K., Allain, V., Appeltans, W., Batten, S., Benedetti-Cecchi, L., Bernard, A.T., Bristol, R., Benson, A., Buttigieg, P.L., Gerhardinger, L.C., Chiba, S., Davies, T.E., Duffy, J., Giron-Nava, A., Hsu, A.J., Kraberg, A.C., Kudela, R.M., Lear, D., Montes, E., Muller-Karger, F., O’Brien, T.D., Obura, D., Provoost, P., Pruckner, S., Rebelo, L., Selig, E.R., Kjesbu, O.S., Starger, C., Stuart-Smith, R.D., Vierros, M., Waller, J.S., Weatherdon, L.V., Wellman, T., and Zivian, A., 2021, Establishing the foundation for the global observing system for marine life: Frontiers in Marine Science, v. 8, 737416, 19 p., https://doi.org/10.3389/fmars.2021.737416.","productDescription":"737416, 19 p.","ipdsId":"IP-127529","costCenters":[{"id":191,"text":"Colorado Water Science 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Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":829553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunn, Daniel","contributorId":206672,"corporation":false,"usgs":false,"family":"Dunn","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":829554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Simmons, Samantha E.","contributorId":156320,"corporation":false,"usgs":false,"family":"Simmons","given":"Samantha","email":"","middleInitial":"E.","affiliations":[{"id":20313,"text":"Marine Mammal Commission","active":true,"usgs":false}],"preferred":false,"id":829555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carini, Roxanne J.","contributorId":270549,"corporation":false,"usgs":false,"family":"Carini","given":"Roxanne 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carbon dioxide (CO2) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH4) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH4 emissions (FCH4) is complicated by confounding influences of water table level and temperature on CH4 production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH4 at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US-Los) and Allequash Creek (US-ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US-Los being larger in scale and having a more open canopy. Co-located sites with multi-year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH4 without interference due to differing climate. Daily average FCH4 from US-Los reached a maximum of 47.7 ηmol CH4 m−2 s−1 during the study period, while US-ALQ was more than double at 117.9 ηmol CH4 m−2 s−1. The lagged influence of GPP on temperature-normalized FCH4 (Tair-FCH4) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH4 at US-ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH4 due to this study has implications for climate modeling and more accurate carbon budgeting.
Baseflow is critical to sustaining streamflow in the Upper Colorado River Basin. Therefore, effective water resources management requires estimates of baseflow response to climatic changes. This study provides the first estimates of projected baseflow changes from historical (1984 – 2012) to thirty-year periods centered around 2030, 2050, and 2080 under warm/wet, median, and hot/dry climatic conditions using a hybrid statistical-deterministic baseflow model. Total baseflow supplied to the Lower Colorado River Basin may decline by up to 33%, although this value may increase in the near future by 6% under warm/wet conditions. The percentage of baseflow lost during in-stream transport is projected to increase by 1 - 5% relative to historical conditions. Results highlight that climate driven changes in high elevation hydrology have impacts on basin-wide water availability. Study results have implications for human and ecological water availability in one of the most heavily managed watersheds in the world.
Land-surface deformation (subsidence) caused by groundwater withdrawal is identified as an undesirable result in the Pajaro Valley Water Management Agency’s Basin Management Plan and California’s Sustainable Groundwater Management Act. In Pajaro Valley, groundwater provides nearly 90 percent of the total water supply. To aid the development of sustainable groundwater management criteria, the U.S. Geological Survey, in cooperation with the Pajaro Valley Water Management Agency, performed an analysis of land-surface deformation (subsidence and uplift) in Pajaro Valley for 2015–18, using Interferometric Synthetic Aperture Radar and continuous Global Positioning System methods. Land-surface deformation results were then compared with subsurface geology and groundwater altitudes to better understand the hydromechanical response of the coastal aquifer system. The results indicate the land surface is generally stable with only small magnitudes (less than 1 inch) of seasonal land-surface deformation (subsidence in the summer and uplift in the winter) during 2015–18. During this time, the largest magnitude of land-surface deformation was less than 2 inches of subsidence and was localized in one area just north of the city limits of Watsonville, California. Groundwater altitudes during 2015–18 demonstrated seasonal variability and annual to multi-annual increases after reaching historical lows by the mid-1990s. The small magnitudes of land-surface deformation coupled with groundwater-altitude increases in most areas indicate that the subsidence likely is largely elastic and recoverable. The Corralitos-Pajaro Valley groundwater basin contains fine-grained (clay) sediments that have the potential for permanent aquifer-system compaction and resultant land subsidence. However, groundwater altitudes throughout the Pajaro Valley have increased above historical lows, and observed increases in groundwater altitudes coincided with changes in groundwater management activities. Observed relations between groundwater management activities and groundwater altitudes indicate that management of groundwater supplies could minimize the potential for permanent land-surface deformation in Pajaro Valley.
","language":"English","publisher":"U.S. Geological","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211101","collaboration":"Prepared in cooperation with the Pajaro Valley Water Management Agency","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., Earll, M.M., Sneed, M., and Henson, W., 2021, Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18: U.S. Geological Survey Open-File Report 2021–1101, 16 p., https://doi.org/10.3133/ofr20211101.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-118756","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":390883,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FNARQO","linkHelpText":"Interferometric Synthetic Aperture Radar and Water Level Data, Pajaro Valley, Santa Cruz and Monterey Counties, California, 1970–2018"},{"id":390882,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1101/images"},{"id":390881,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.xml"},{"id":390880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1101/covrthb.jpg"}],"country":"United States","state":"California","county":"Monterey County, Santa Cruz County","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.89468383789061,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.71356812817935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Alluvial aquifers in Iowa have more wells with nitrate exceeding drinking-water standards than other aquifers; are susceptible to contamination by organic contaminants; and have high concentrations of naturally occurring iron and manganese in depositional areas that contain abundant organic matter. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, Iowa, studied the Cedar River alluvial aquifer in Linn County, Iowa, from 1990 to 2019 to understand the effect of municipal pumping on spatial and temporal hydrologic and water-quality variability. The Cedar River alluvial aquifer is the source of water for the city of Cedar Rapids, Iowa. Withdrawal of large quantities of water for municipal and industrial supply has altered the normal flow of water in the alluvial aquifer. Pumping induces flow from the Cedar River and the underlying bedrock aquifer into the alluvial aquifer.
Water quality in the alluvial aquifer varies along the Cedar River. Changes in nitrate, ammonia, manganese, and iron in the alluvial aquifer are seen as the upstream free-flowing reach of the Cedar River transitions to a partially regulated downstream reach, likely because of differences in reduction-oxidation conditions in the aquifer, which are controlled by infiltration from the Cedar River under normal conditions and when wells are being pumped. Nitrate, normally found in oxygenated environments, had the highest concentrations in the most upstream wells in the Seminole well field and the lowest concentrations in the most downstream wells in the East well field. In contrast, ammonia, manganese, and iron, normally found in greatest abundance in anoxic (reducing) conditions, had the greatest concentrations in the most downstream wells. Additionally, dissolved nitrate plus nitrite nitrogen concentrations in wells were substantially less and manganese concentrations were greater in production wells near backwater wetlands in contrast to wells near the Cedar River.
Temporal variability in water quality in the alluvial aquifer was driven by pumping that increased flow from the Cedar River into the alluvial aquifer and ultimately led to changes in reduction-oxidation conditions of the aquifer. Increasing dissolved nitrate plus nitrite nitrogen concentrations in the Cedar River from 1990 to 2019 were mirrored in the alluvial aquifer. Anoxic conditions are prevalent in the alluvial aquifer next to the Cedar River when the aquifer is not under pumping stress. However, production well pumping caused induced infiltration of oxygenated river water into the aquifer resulting in increased dissolved nitrate plus nitrite nitrogen concentrations and pesticides and decreased naturally occurring dissolved iron and manganese.
Hydrologic and water-quality conditions in the Cedar River alluvial aquifer from 1990 to 2019 provide baseline conditions needed to evaluate the effects of current and future nutrient reduction efforts and land-use changes in the Cedar River Basin on water quality of the Cedar River alluvial aquifer and its source water, the Cedar River. This summary and analysis provide information that can assist the City of Cedar Rapids Utilities Water Department in managing groundwater resources, and provides information that could be used develop a groundwater-quality model to characterize variability over larger areas of the alluvial aquifer, allowing water providers to plan for future water needs of their users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215110","usgsCitation":"Kalkhoff, S.J., 2021, Hydrologic and water-quality conditions in the Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019: U.S. Geological Survey Scientific Investigations Report 2021–5110, 61 p., https://doi.org/10.3133/sir20215110.","productDescription":"Report: ix, 61 p.; Data Release; Dataset","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-121189","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5110/coverthb.jpg"},{"id":390748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5110/sir20215110.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5110"},{"id":390749,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7VKOU","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic and water quality data from the Cedar River and Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019"},{"id":390750,"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":"Iowa","county":"Linn County","otherGeospatial":"Cedar River Alluvial Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3649,42.2964],[-91.3651,42.2082],[-91.3653,42.1215],[-91.3661,42.0343],[-91.3669,41.948],[-91.3677,41.8603],[-91.4836,41.8608],[-91.5989,41.8612],[-91.716,41.862],[-91.8318,41.8617],[-91.8329,41.9485],[-91.8338,42.0366],[-91.8342,42.1242],[-91.8328,42.2087],[-91.8319,42.2987],[-91.7153,42.2971],[-91.5969,42.2959],[-91.4809,42.296],[-91.3649,42.2964]]]},\"properties\":{\"name\":\"Linn\",\"state\":\"IA\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Great Dismal Swamp is a peat wetland in the Coastal Plain of southeastern Virginia and northeastern North Carolina. Timber harvesting and the construction of ditches to drain the swamp and facilitate the harvesting are collectively implicated in changes that altered the wetland forests, caused subsidence and decomposition of the peat, and increased the risk of fire. In response to these changes, managers have implemented strategies to control water levels and rewet the swamp using a network of 64 adjustable-height, water-control structures on the ditches. Rewetting the swamp is intended to re-establish the original wetland-forest types, reduce the risk of fire, reduce subsidence and decomposition of the peat, enhance peat accretion, and reduce the risk of fire. Knowledge of responses of the swamp to hydrologic controls, however, is critical to developing and implementing effective management goals and strategies. Because the 2008 South One fire reemphasized the need for this knowledge, the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service began studies in 2009 to identify critical hydrologic controls and responses to these controls.
These studies identified water sources, topography, the two-layered hydraulic characteristics of the peat, the absence of peat in some areas, the ditch and road network, water-control structures on the ditches, the Dismal Swamp Canal and associated infrastructure, and wetland forests as the primary hydrologic controls. Precipitation is the only water source across much of the swamp. The eastward flow of streams and groundwater from the Isle of Wight Plain, across the Suffolk scarp, and into the swamp are additional water sources to the western part of the swamp. Vertical differences in the hydraulic characteristics of the peat reflect an upper peat having a high hydraulic conductivity and specific yield overlying a lower peat and sand having lower hydraulic conductivity and specific yield. The upper peat forms the main aquifer for the storage, flow, and release of water from the swamp. Maintaining water in the upper peat is critical to water availability to the wetland forests because of these properties.
Groundwater flows from the swamp into the ditches and the Dismal Swamp Canal where it discharges into nearby streams. Discharge typically is to the closest ditch except where a spoil-pile road that impedes flow intervenes between the swamp and the ditch. When groundwater levels in a ditch are about 2 feet lower than levels in the other three ditches surrounding a part of the swamp, however, most groundwater typically discharges to the ditch having the lower level. This occurs even if a spoil-pile road intervenes between the swamp and the ditch having the lower level. Flow to a single ditch shifts watershed boundaries and groundwater divides toward the ditches having higher water levels and demonstrates how flow and discharge are controlled by ditch water levels. Consequently, managing water levels based on these and other hydrologic controls and responses is critical to achieving management objectives.
The chemistry of water across the swamp shows the effects of the peat. Dissolved organic carbon concentrations in the groundwater are among the highest reported globally, ranging from 55 to 195 milligrams per liter. The pH of groundwater and ditch water is commonly less than 4.0 standard units because of organic acids. A relation between the pH and specific conductance of groundwater and ditch water reflects water sources, flow paths, and the chemical evolution, as waters from the different sources mix and flow along the paths.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205100","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Speiran, G.K., and Wurster, F.C., 2021, Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies: U.S. Geological Survey Scientific Investigations Report 2020-5100, 104 p., https://doi.org/10.3133/sir20205100.","productDescription":"xii, 104 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108950","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":436139,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZVW9C8","text":"USGS data release","linkHelpText":"Hydrologic, water-quality, fire, forest-cover, and other data, the Great Dismal Swamp, Virginia and North Carolina"},{"id":390256,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205100/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2020-5100"},{"id":390255,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5100/images"},{"id":390252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5100/coverthb.jpg"},{"id":390253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5100"},{"id":390254,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.XML"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Great Dismal Swamp","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.651611328125,\n 36.575835338491736\n ],\n [\n -76.65710449218749,\n 36.41244153535644\n ],\n [\n -76.5142822265625,\n 36.32397712011261\n ],\n [\n -76.3714599609375,\n 36.36822190085109\n ],\n [\n -76.25061035156251,\n 36.4345419190089\n ],\n [\n -76.2835693359375,\n 36.85325222344016\n ],\n [\n -76.4483642578125,\n 36.87522650673951\n ],\n [\n -76.61865234374999,\n 36.84006462037767\n ],\n [\n -76.651611328125,\n 36.575835338491736\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030H","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J., Anderson, C., Stensaas, G.L., and Kim, M., 2021, System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor (ver. 1.2, December 2024), chap. H of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 20 p., https://doi.org/10.3133/ofr20211030H.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126659","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":433262,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/h/versionHist.txt","text":"Version History","size":"2.07 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390427,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/h/images"},{"id":390426,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.xml","size":"75.7 kB","linkFileType":{"id":8,"text":"xml"}},{"id":390425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–H"},{"id":390424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/h/coverthb4.jpg"},{"id":464526,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030H/full"}],"edition":"Version 1.0: October 21, 2021; Version 1.1: August 29, 2024; Version 1.2: December 2, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Increases in nitrogen applications to the land surface since the 1950s have led to a cascade of negative environmental impacts, including degradation of drinking water supplies, nutrient enrichment of aquatic ecosystems and contributions to global climate change. In this study, groundwater, streambed porewater, and stream sampling were used to establish trends in nitrate concentrations and how redox gradients influence nitrate transport across diverse glacial terranes. Decadal sampling has found that elevated nitrate concentrations in shallow groundwater beneath cropland have been sustained for decades. Redox gradients established in the saturated zone using dissolved O2, iron, nitrate and excess N2 from denitrification suggest that nitrate-bearing zones are thin in glacial terranes dominated by fine materials. These thin nitrate-bearing zones lead to suboxic, low nitrate streambed porewater and limit the contributions of nitrate to streams from slow-flow groundwater. In contrast, thick oxic zones in more coarse-grained glacial terranes allow nitrate to reach deeper groundwater, resulting in streambed porewater with elevated nitrate concentrations and causing a large portion of stream nitrate to be derived from slow-flow groundwater. Groundwater age tracer data indicate that denitrification occurs more quickly in the terrane dominated by fine material than in the more coarse-grained terrane. The quicker depletion of nitrate in the more fine-grained terrane suggests that the thinner oxic zone in this terrane is due, in part, to the greater availability and reactivity of electron donors in this terrane than in the more coarse-grained terrane. Groundwater age tracer data and hydrograph separation analysis suggest that saturated zone lag times between when changes in land use practices occur and when changes in stream water are fully observed may vary widely across hydrogeologic settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.150200","usgsCitation":"Tesoriero, A.J., Stratton, L., and Miller, M., 2021, Influence of redox gradients on nitrate transport from the landscape to groundwater and streams: Science of the Total Environment, v. 800, p. 1-12, https://doi.org/10.1016/j.scitotenv.2021.150200.","productDescription":"150200, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-123707","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436140,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVKF1K","text":"USGS data release","linkHelpText":"Dissolved Gas Modeling Results for Groundwater Samples Collected in the Western Lake Michigan Drainages and Eastern Iowa Basins Study Areas of the United States: 2007, 2017"},{"id":390680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, 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 -94.658203125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"800","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stratton, Laurel E. 0000-0001-8567-8619","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":215056,"corporation":false,"usgs":true,"family":"Stratton","given":"Laurel E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225518,"text":"70225518 - 2021 - Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2021-10-20T15:36:49.819571","indexId":"70225518","displayToPublicDate":"2021-10-20T10:26:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","docAbstract":"Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224031,"text":"ofr20211089 - 2021 - Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","interactions":[],"lastModifiedDate":"2021-10-20T14:18:57.158711","indexId":"ofr20211089","displayToPublicDate":"2021-10-20T10:20:00","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":"2021-1089","displayTitle":"Managed Aquifer Recharge Suitability—Regional Screening and Case Studies in Jordan and Lebanon","title":"Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","docAbstract":"The U.S. Geological Survey, at the request of the U.S. Agency for International Development, led a 5-year regional project to develop and apply methods for water availability and suitability mapping for managed aquifer recharge (MAR) in the Middle East and North Africa region. A regional model of surface runoff for the period from 1984 to 2015 was developed to characterize water availability using remote sensing data on climate, vegetation, and topography in Jordan, Lebanon, and surrounding areas. Surface runoff was accumulated to characterize potential streamflow available for MAR and these data were combined with land surface slope to prepare a regional screening map of MAR suitability, illustrating suitability mapping concepts and methods. The application of the methods is demonstrated by the evaluation of water availability and suitability for potential MAR in study areas in Jordan and Lebanon. Locations suitable for MAR are present in both Jordan and Lebanon, but limitations exist in both countries, related primarily to water availability in Jordan and land areas of suitable terrain in Lebanon. An additional feasibility study including field investigations would likely provide decision makers with essential information for further development of the use of MAR in Jordan, Lebanon, and the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211089","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Goode, D.J., ed., 2021, Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon: U.S. Geological Survey Open-File Report 2021–1089, 87 p., https://doi.org/10.3133/ofr20211089.","productDescription":"Report: xi, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124064","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436143,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":390660,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"- Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":389216,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971ZVHF","text":"USGS data release","linkHelpText":"Assembly of satellite-based rainfall datasets in situ data and rainfall climatology contours for the MENA region"},{"id":389217,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TXLT1X","text":"USGS data release","linkHelpText":"Modeling accumulated surface runoff and water availability for aquifer storage and recovery in the MENA region from 1984–2015"},{"id":389215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1089/ofr20211089.pdf","text":"Report","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1089"},{"id":389214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1089/coverthb.jpg"}],"country":"Jordan, Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[35.54567,32.39399],[35.71992,32.70919],[36.83406,32.31294],[38.79234,33.37869],[39.19547,32.16101],[39.00489,32.01022],[37.00217,31.50841],[37.99885,30.5085],[37.66812,30.33867],[37.50358,30.00378],[36.74053,29.86528],[36.50121,29.50525],[36.06894,29.19749],[34.95604,29.35655],[34.9226,29.50133],[35.42092,31.10007],[35.39756,31.48909],[35.54525,31.7825],[35.54567,32.39399]]],[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]]},\"properties\":{\"name\":\"Jordan\"}}]}","contact":"U.S. Geological Survey
Office of International Programs
917 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
directoroip@usgs.gov
A concentrated animal feeding operation (CAFO) established in Newton County, Arkansas, near Big Creek, a tributary of the Buffalo National River, raised concern about potential degradation of water quality in the karst watershed. In this study, isotopic tools were combined with standard geochemical approaches to characterize nutrient sources and dynamics in the Big Creek watershed. An isotopic and geochemical reference database of potential nutrient sources in the Big Creek watershed was constructed based on samples collected from representative potential sources. Nutrient sources and stream samples were analyzed for delta (δ)15N-NO3, δ18O NO3, and a suite of selected dissolved ions. Data provide evidence of modification of potential local nutrient source signatures by nitrification, atmospheric deposition, evaporation, and denitrification. Samples taken from the CAFO waste pond, a septic system, field and parking lot runoff, fertilizer, and hog manure exhibited different δ15N-NO3 and δ18O-NO3 values as compared to stream samples. Stream δ15N-NO3 and δ18O-NO3 values cannot be explained by direct input of any one of these potential sources without modification of the isotopic composition by mixing or fractionation. Big Creek nitrate isotope values (-3.4 per mil [‰] to 6.7‰ δ15N-NO3 and -7.6 to 9.1‰ δ18O-NO3) were similar to values expected from nitrification of nitrogen stored in soils sampled in the watershed (2.8 to 7.6‰ δ15N-NO3 and 3.4 to 4.8‰ δ18O-NO3).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"conferenceTitle":"2020 KIG workshop","conferenceDate":"October 19-20, 2021","conferenceLocation":"Online","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Sokolosky, K., and Hays, P.D., 2021, Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas, in U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021, v. 8, Online, October 19-20, 2021, p. 54-63.","productDescription":"10 p.","startPage":"54","endPage":"63","ipdsId":"IP-117025","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":396600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/sir20205019"},{"id":396602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Big Creek, Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6307373046875,\n 36.50522086338427\n ],\n [\n -94.4549560546875,\n 35.40696093270201\n ],\n [\n -91.7578125,\n 35.420391545750746\n ],\n [\n -91.768798828125,\n 36.50522086338427\n ],\n [\n -94.6307373046875,\n 36.50522086338427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":836807,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Spangler, Lawrence E. 0000-0003-3928-8809 spangler@usgs.gov","orcid":"https://orcid.org/0000-0003-3928-8809","contributorId":973,"corporation":false,"usgs":true,"family":"Spangler","given":"Lawrence","email":"spangler@usgs.gov","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836808,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Sokolosky, Kelly","contributorId":287479,"corporation":false,"usgs":false,"family":"Sokolosky","given":"Kelly","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":836794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hays, Phillip D. 0000-0001-5491-9272 pdhays@usgs.gov","orcid":"https://orcid.org/0000-0001-5491-9272","contributorId":4145,"corporation":false,"usgs":true,"family":"Hays","given":"Phillip","email":"pdhays@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836795,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100925","usgsCitation":"DeSimone, L.A., and Ransom, K.M., 2021, Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning: Journal of Hydrology: Regional Studies, v. 37, 100925, 20 p., https://doi.org/10.1016/j.ejrh.2021.100925.","productDescription":"100925, 20 p.","ipdsId":"IP-126500","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450397,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100925","text":"Publisher Index Page"},{"id":436146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M64CD1","text":"USGS data release","linkHelpText":"Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA"},{"id":390662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York, North Carolina, Pennsylvania, Virginia","city":"Baltimore, New York, Philadelphia, Richmond, Washington D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.1142578125,\n 41.22824901518529\n ],\n [\n -73.63037109375,\n 40.94671366508002\n ],\n [\n -75.816650390625,\n 39.78321267821705\n ],\n [\n -78.321533203125,\n 38.30718056188316\n ],\n [\n -78.079833984375,\n 35.200744801724014\n ],\n [\n -77.069091796875,\n 35.06597313798418\n ],\n [\n -76.80541992187499,\n 34.94899072578227\n ],\n [\n -76.475830078125,\n 35.092945313732635\n ],\n [\n -76.387939453125,\n 35.34425514918409\n ],\n [\n -76.036376953125,\n 35.29943548054545\n ],\n [\n -75.509033203125,\n 35.84453450421662\n ],\n [\n -75.882568359375,\n 36.43012234551576\n ],\n [\n -75.904541015625,\n 37.055177106660814\n ],\n [\n -75.860595703125,\n 37.28279464911045\n ],\n [\n -75.201416015625,\n 38.07404145941957\n ],\n [\n -74.893798828125,\n 38.496593518947584\n ],\n [\n -75.289306640625,\n 39.16414104768742\n ],\n [\n -74.94873046875,\n 39.138581990583525\n ],\n [\n -75.069580078125,\n 38.976492485539396\n ],\n [\n -74.87182617187499,\n 38.865374851611634\n ],\n [\n -74.124755859375,\n 39.715638134796336\n ],\n [\n -73.883056640625,\n 40.38839687388361\n ],\n [\n -74.058837890625,\n 40.50544628405211\n ],\n [\n -71.74072265625,\n 40.98819156349393\n ],\n [\n -72.1142578125,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225698,"text":"70225698 - 2021 - Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","interactions":[],"lastModifiedDate":"2021-11-03T12:50:00.117476","indexId":"70225698","displayToPublicDate":"2021-10-20T07:46:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","docAbstract":"Historically, Cisco Coregonus artedi and Lake Whitefish Coregonus clupeaformis were abundant throughout the Laurentian Great Lakes, but overharvest, habitat degradation, and interactions with exotic species caused most populations to collapse by the mid-1900s. Strict commercial fishery regulations and improved environmental and ecological conditions allowed Cisco to partially recover only in Lake Superior, whereas Lake Whitefish recovered in all the upper Great Lakes (Superior, Michigan, and Huron). The differential responses of Cisco and Lake Whitefish to improved environmental and ecological conditions in lakes Michigan and Huron have led to questions about potential negative interactions between these species. To provide context for fishery managers, we tested for positive and negative correlations between historical (1929–1970) Cisco and Lake Whitefish commercial gill net catch per effort (CPE; kg/km of net) at a variety of spatial scales in Michigan waters of the upper Great Lakes. The three best-fit spatial models—LAKEWIDE, REGIONAL 10, and SIMPLE—all had similar levels of support (scaled second-order Akaike Information Criterion < 3.0), and we used these models to determine whether there was a significant correlation between Cisco and Lake Whitefish CPE (positive and negative). There was either no correlation between Cisco and Lake Whitefish CPE or a positive correlation for most (12 of 13) pairwise (Cisco–Lake Whitefish) comparisons. We identified no strong positive or negative correlations in the lakewide (LAKEWIDE) or reduced (SIMPLE) models. In the regional model (REGIONAL 10), we identified strong and positive correlations between Cisco and Lake Whitefish CPE in two regions (ρ = 0.59–0.71) and a weak negative correlation in one region (ρ = −0.45). Collectively, our findings suggest that Cisco and Lake Whitefish CPE were largely independent of each other; thus, these species likely did not interact to the detriment of one another in Michigan waters of the upper Great Lakes during 1929–1970.
Karst hydrogeologic systems represent challenging and unique conditions to scientists attempting to study groundwater flow and contaminant transport. Karst terrains are characterized by distinct and beautiful landscapes, caverns, and springs, and many of the exceptional karst areas are designated as national or state parks. The range and complexity of landforms and groundwater flow systems associated with karst terrains are enormous, perhaps more than any other type of aquifer.
The U.S. Geological Survey (USGS) Karst Interest Group (KIG), formed in 2000, is a loosely knit, grassroots organization of USGS and non-USGS scientists and researchers devoted to fostering better communication among scientists working on, or interested in, karst aquifers. The primary mission of the KIG is to encourage and support interdisciplinary collaboration and technology transfer among scientists working in karst areas. To accomplish its mission, the KIG has organized a series of workshops. To date (2021), eight KIG workshops, including the workshop documented in this report, have been held. This workshop is the first virtual workshop. The abstracts and extended abstracts provide a snapshot in time of past and current karst-related studies. The field trip guide is included in the proceedings volume even though the field trip will not occur in person.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205019","usgsCitation":"Kuniansky, E.L., and Spangler, L.E., eds., 2021, U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021: U.S. Geological Survey Scientific Investigations Report 2020–5019, 147 p., https://doi.org/10.3133/sir20205019.","productDescription":"iv, 147 p.","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114317","costCenters":[{"id":448,"text":"National Water Availability and Use Program","active":false,"usgs":true}],"links":[{"id":390521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5019/coverthb.jpg"},{"id":390522,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5019/sir20205019.pdf","text":"Report","size":"9.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5019"}],"contact":"Water Mission Area
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
https://www.usgs.gov/mission-areas/water-resources/science/karst-aquifers