The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fourth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2019 water year, from October 2018 to September 2019. No changes to the previously installed data collection network were made during this period.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for all counties in the study area over the 3-month period from November to January, below-average annual rainfall for all counties, and effects from Hurricane Dorian in September 2019. Total annual rainfall for all counties ranked third among the annual totals computed for the 4 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Ortega River, Julington Creek, Pottsburg Creek, Broward River, Cedar River, Clapboard Creek, and Dunn Creek. The annual mean discharge for each of the main-stem sites was lower for the 2019 water year than for the 2018 water year. Since the beginning of the study in 2016, the St. Johns River at Astor station computed its lowest annual mean discharge, the Jacksonville station recorded its second lowest, and the Buffalo Bluff station recorded its second highest in 2019.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2018 water year, annual mean salinity at all monitoring locations was higher for the 2019 water year except at the main-stem site below Shands Bridge and at the tributary sites of Durbin Creek and Julington Creek, which remained the same. The 2019 annual mean salinity at Dunn Creek was the highest on record for that site, and Clapboard Creek and Trout River were the second highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201140","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2020, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2019: U.S. Geological Survey Open-File Report 2020–1140, 48 p., https://doi.org/10.3133/ofr20201140.","productDescription":"ix, 48 p.","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-118214","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":381275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1140/coverthb.jpg"},{"id":381276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1140/ofr20201140.pdf","text":"Report","size":"7.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1140"}],"country":"United States","state":"Florida","otherGeospatial":"St John's River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.93878173828125,\n 29.1161749329972\n ],\n [\n -81.4691162109375,\n 29.1161749329972\n ],\n [\n -81.4691162109375,\n 30.545704405480997\n ],\n [\n -81.93878173828125,\n 30.545704405480997\n ],\n [\n -81.93878173828125,\n 29.1161749329972\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Monitoring demographic response over time is valuable for understanding population dynamics of endangered species. We quantified the variation in survival patterns for three small isolated island populations of endangered waterfowl in the Hawaiian Archipelago. Laysan Teal Anas laysanensis were individually marked and the fate of 1,150 individuals were followed from different cohorts among the two reintroduced (Kure and Midway Atolls) and the single relict (Laysan Island) populations for time series of 4, 10 and 15 years respectively. We applied a non-parametric Kaplan-Meier estimator to describe variation between the populations in survival for different cohorts. For Laysan Island and Midway Atoll, we used log-rank tests to determine the effects of cohort, island and sex on survival. Birds in the Laysan Island population had significantly higher survival than those in the Midway population, and males had higher survival than females in both populations. The proportion of females surviving at Midway Atoll was 40% lower than for females on Laysan Island at year 5. The oldest bird observed from Laysan Island was at least 15.5 years old and had been ringed as an adult. The Kure Atoll founder cohort (n = 28) had 100% survival 18 months post-release, but this dropped by 39% during the first avian botulism type C outbreak. Ten of twenty-eight founders and a population of 60–70 birds persisted on Kure Atoll in 2020. We summarised mortality records to generate hypotheses to explain the cause-specific mechanisms driving the observed survival differences. Mortality data showed that the survival differences between islands in Laysan Teal survival was driven by chronic epizootics of avian botulism type C at Midway and Kure Atoll.
Previous work has proposed transfer of kinetic heat energy from low-energy broad ion beam (BIB) milling causes thermal alteration of sedimentary organic matter, resulting in increases of organic matter reflectance. Whereas, other studies have suggested the organic matter reflectance increase from BIB milling is due to decreased surface roughness. To test if reflectance increases to sedimentary organic matter (vitrinite) caused by BIB milling were related to molecular aromatization and condensation, Raman and Fourier transform infrared (FTIR) spectroscopies were used to evaluate potential compositional changes in the same vitrinite locations pre- and post-BIB milling. The same locations also were examined by atomic force microscopy (AFM) to determine topographic changes caused by BIB milling (as quantified by the areal root-mean-square roughness parameter Sq). Samples consisted of four medium volatile bituminous coals. A non-aggressive BIB milling approach was used with conditions of 5 min, 4 keV, 15°incline, 360° rotation at 25 rpm and 100% focus (1.5 kV discharge; ∼100 μA). This gentle BIB milling caused vitrinite reflectance (VRo) increases of 12 to 36% of the original values determined optically before milling (average 26% increase). When molecular proxies from FTIR (A- and C-factor, branching ratio) were plotted against each other for the same vitrinite locations pre- and post-milling, mean data points for each sample generally lie within error of a 1:1 line. Likewise, mean Raman thermal proxy [full-width half maximum of G-band (G-FWHM), Raman band separation (RBS) and D1/G band intensity ratio] values were similar for pre- and post-milled locations, also plotting within error of a 1:1 line. AFM confirms the majority (24 of 36) of pre- and post-ion milled surface pairs were smoother (lower Sq values) after BIB milling. These results are interpreted to indicate VRo increase induced by the gentle BIB milling conditions used in this study is an effect of decreased diffuse reflectance due to flatter surfaces, causing more photons to reflect directly back to the detector. Little evidence was observed for molecular aromatization and condensation of vitrinite molecules following BIB milling (with the conditions used). The presence of milling-induced artifacts, including differential milling effects dependent on location and the development of self-organized patterned structures, indicate much work remains in standardization of BIB milling before its promulgation as a routine sample preparation technique for organic petrography. These results provide better understanding of anthropogenic-induced changes to geological samples caused by the now widespread adoption of BIB milling as a disruptive innovation in sample preparation.
In 1869, John Wesley Powell completed the first well-recorded scientific river journey to explore an extensive region of the Colorado River Basin. Powell later helped to establish the U.S. Geological Survey (USGS) and served as its second director (1881–94), cementing his position in the folklore of the Survey. In 2019, the USGS marked the 150th anniversary of Powell’s first expedition with a broad-scale educational campaign as an opportunity to highlight current USGS science in the region through the lens of an exciting river expedition, with the goal of inspiring the next generation of USGS scientists. The project included a partnership with the Sesquicentennial Colorado River Exploring Expedition (SCREE), which traveled the length of the original route for ~1,000 river miles from Green River, Wyoming, to Lake Mead, Nevada, including the Grand Canyon. Small, interdisciplinary groups of USGS employees joined each segment of the journey, gathered data to be used for educational purposes, participated in community outreach events, and upon return shared their experiences with their local communities. This report documents a photographic journey of the expedition, personal vignettes from the USGS participants, Science Stories to explain the scope of the experiments, and Then and Now articles (which were published online during the expedition), to explore some of the changes that have occurred since the first expedition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1475","usgsCitation":"Scott, A., and Snow, E., 2020, The 150th anniversary of the 1869 Powell expedition—USGS participation in the Sesquicentennial Colorado River Exploring Expedition and reflections from the ~1,000-mile journey down the Green and Colorado Rivers (ver. 1.1, December 14, 2020): U.S. Geological Survey Circular 1475, 88 p., https://doi.org/10.3133/cir1475.","productDescription":"Report: vii, 88 p.; Version History","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-119749","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":381274,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1475/versionHist.txt","size":"1.17 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1475/cir1475.pdf","text":"Report","size":"24.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1475"},{"id":380830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1475/coverthb3.jpg"}],"country":"United States","state":"Arizona, Colorado, Utah, Wyoming","otherGeospatial":"Colorado River, Grand Canyon, Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.4073486328125,\n 41.6154423246811\n ],\n [\n -109.7479248046875,\n 41.32732632036622\n ],\n [\n -109.7039794921875,\n 40.88444793903562\n ],\n [\n -109.2095947265625,\n 40.84706035607122\n ],\n [\n -109.017333984375,\n 40.751418432997454\n ],\n [\n -109.09423828125,\n 40.55972134684838\n ],\n [\n -109.4677734375,\n 40.455307212131494\n ],\n [\n -109.8358154296875,\n 40.052847601823984\n ],\n [\n -110.1324462890625,\n 39.57182223734374\n ],\n [\n -110.2423095703125,\n 38.93377552819722\n ],\n [\n -110.093994140625,\n 38.49229419236133\n ],\n [\n -109.984130859375,\n 38.199338565983844\n ],\n [\n -110.1708984375,\n 38.03078569382294\n ],\n [\n -110.5828857421875,\n 37.79676317682161\n ],\n [\n -110.75866699218749,\n 37.57505900514996\n ],\n [\n -110.94543457031249,\n 37.38761749978395\n ],\n [\n -111.03881835937499,\n 37.204081555898526\n ],\n [\n -111.3848876953125,\n 37.18220222107978\n ],\n [\n -111.6375732421875,\n 37.077093191754436\n ],\n [\n -112.049560546875,\n 36.22211876039103\n ],\n [\n -112.4945068359375,\n 36.4566360115962\n ],\n [\n -113.302001953125,\n 36.25756282630298\n ],\n [\n -113.4613037109375,\n 36.01800375871416\n ],\n [\n -113.477783203125,\n 35.82672127366604\n ],\n [\n -113.97766113281249,\n 36.23984280222428\n ],\n [\n -114.0216064453125,\n 36.115690180653395\n ],\n [\n -113.917236328125,\n 35.875698032496665\n ],\n [\n -113.345947265625,\n 35.65729624809628\n ],\n [\n -113.203125,\n 36.02688935430189\n ],\n [\n -112.510986328125,\n 36.3106987841827\n ],\n [\n -112.0770263671875,\n 35.9157474194997\n ],\n [\n -111.741943359375,\n 36.06686213257888\n ],\n [\n -111.72546386718749,\n 36.48755716938576\n ],\n [\n -111.3629150390625,\n 36.932330061503144\n ],\n [\n -110.7861328125,\n 37.08585785263673\n ],\n [\n -110.40710449218749,\n 37.54893261064111\n ],\n [\n -109.8028564453125,\n 38.16047628099622\n ],\n [\n -109.0887451171875,\n 38.94232097947902\n ],\n [\n -109.22607421875,\n 39.0831721934762\n ],\n [\n -109.9346923828125,\n 38.47509432050245\n ],\n [\n -109.984130859375,\n 39.06184913429154\n ],\n [\n -109.599609375,\n 40.04864272291728\n ],\n [\n -108.8525390625,\n 40.40513069752789\n ],\n [\n -108.69873046875,\n 40.78885994449482\n ],\n [\n -109.0008544921875,\n 40.97160353279909\n ],\n [\n -109.3963623046875,\n 41.04621681452063\n ],\n [\n -109.3359375,\n 41.41801503608024\n ],\n [\n -109.2974853515625,\n 41.611335399441735\n ],\n [\n -109.4073486328125,\n 41.6154423246811\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 3, 2020; Version 1.1: December 14, 2020","contact":"https://www.usgs.gov
https://www.usgs.gov/powell150
The U.S. Geological Survey (USGS) Precipitation Chemistry Quality Assurance project (PCQA) operated five distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2017–18. The National Trends Network programs included (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate variability attributed to automated precipitation samplers. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate consistently low levels of sample contamination, generally strong analytical laboratory performance, and low overall variability in concentration data imparted by field equipment. The NADP operations moved from its 40-year home at the Illinois State Water Survey to the Wisconsin State Laboratory of Hygiene in June 2018. The PCQA programs were modified and (or) temporarily curtailed during the transition in 2018. Bias and variability of sample analysis results were evaluated for the two Central Analytical Laboratories, and ongoing monitoring will be helpful to differentiate true environmental signals from the effects of changing laboratory conditions and performance. Results of quality assurance sample analyses are provided to document that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205084","usgsCitation":"Wetherbee, G.A., and Martin, R., 2020, External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2017–18: U.S. Geological Survey Scientific Investigations Report 2020–5084, 31 p., https://doi.org/10.3133/sir20205084.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-110354","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":381227,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94RC4GD","text":"USGS data release","linkHelpText":"Data for the U.S. Geological Survey Precipitation Chemistry Quality Assurance Project for the National Atmospheric Deposition Program, 1978–2017"},{"id":381224,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5084/coverthb.jpg"},{"id":381225,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5084/sir20205084.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5084"},{"id":381226,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZKXD8N","text":"USGS data release","linkHelpText":"U.S. Geological Survey Precipitation Chemistry Quality Assurance Project Data 2017 – 2018"}],"contact":"Director, Observing Systems Division
U.S. Geological Survey
Buildings 2101, 2204 HIF
Hydrologic Instrumentation Facility
Stennis Space Center, MS 39529
Resources for addressing stream fish conservation issues are often limited and the stressors impacting fish continue to increase, so decision makers often rely on tools to prioritize locations for conservation actions. Because conservation networks already exist in many areas, incorporating these into the planning process can increase the ability of decision makers to carry out management actions. In this study we aim to identify priority areas within established networks to provide an approach which allows managers to focus efforts on the most valuable areas they control, while identifying areas outside of the network, which support species with minimal representation within the network, for acquisition or conservation partnerships. The goal of this approach is to prioritize sites to achieve high levels of species representation while also developing workable solutions. We applied a methodology incorporating established networks into a systematic conservation planning process for fish in temperate wadeable streams located in Missouri, USA. We compared how well species were represented in our approach with two commonly used alternatives: A blank slate approach which used the same systematic conservation planning technique but did not incorporate established networks, and a habitat integrity approach based solely on anthropogenic threat data. Relative to the blank slate approach, our approach required 210% more segments for representation of all species, and contained an average of 0.5 additional occurrences for the least well-represented species. Although the blank slate solution was more efficient in achieving species representation, 77% of segments in this solution were not already protected. This would likely pose a challenge for implementing conservation actions. Relative to habitat integrity-based priorities, our approach required only 38% of the number of stream segments to achieve representation of all species and contained an average of 5 additional occurrences of the least represented species, representing a substantial gain in representation. Incorporating established networks may allow managers to focus resources on areas with the greatest conservation value within established networks and to identify the most valuable areas complementary to the established networks, resulting in priorities which may be more actionable and effective than those developed by alternative approaches.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2020.515081","usgsCitation":"Sievert, N., Paukert, C.P., and Whittier, J.B., 2020, Incorporating established conservation networks into freshwater conservation planning results in more workable prioritizations: Frontiers in Environmental Science, v. 8, 515081, 13 p., https://doi.org/10.3389/fenvs.2020.515081.","productDescription":"515081, 13 p.","ipdsId":"IP-089736","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2020.515081","text":"Publisher Index Page"},{"id":395061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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B.","contributorId":272501,"corporation":false,"usgs":false,"family":"Whittier","given":"J.","email":"","middleInitial":"B.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":831995,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262421,"text":"70262421 - 2020 - The ghosts of propagation past: Haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus)","interactions":[],"lastModifiedDate":"2025-01-23T14:32:14.022314","indexId":"70262421","displayToPublicDate":"2020-12-13T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1601,"text":"Evolutionary Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The ghosts of propagation past: Haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus)","title":"The ghosts of propagation past: Haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus)","docAbstract":"Stocking of fish is an important tool for maintaining fisheries but can also significantly alter population genetic structure and erode the portfolio of within-species diversity that is important for promoting resilience and adaptability. Walleye (Sander vitreus) are a highly valued sportfish in the midwestern United States, a region characterized by postglacial recolonization from multiple lineages and an extensive history of stocking. We leveraged genomic data and recently developed analytical approaches to explore the population structure of walleye from two midwestern states, Minnesota and Wisconsin. We genotyped 954 walleye from 23 populations at ~20,000 loci using genotyping by sequencing and tested for patterns of population structure with single-SNP and microhaplotype data. Populations from Minnesota and Wisconsin were highly differentiated from each other, with additional substructure found in each state. Population structure did not consistently adhere to drainage boundaries, as cases of high intra-drainage and low inter-drainage differentiation were observed. Low genetic structure was observed between populations from the upper Wisconsin and upper Chippewa river watersheds, which are found as few as 50 km apart and were likely homogenized through historical stocking. Nevertheless, we were able to differentiate these populations using microhaplotype-based co-ancestry analysis, providing increased resolution over previous microsatellite studies and our other single SNP-based analyses. Although our results illustrate that walleye population structure has been influenced by past stocking practices, native ancestry still exists in most populations and walleye populations may be able to purge non-native alleles and haplotypes in the absence of stocking. Our study is one of the first to use genomic tools to investigate the influence of stocking on population structure in a nonsalmonid fish and outlines a workflow leveraging recently developed analytical methods to improve resolution of complex population structure that will be highly applicable in many species and systems.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13186","usgsCitation":"Bootsma, M., Miller, L., Sass, G., Euclide, P., and Larson, W., 2020, The ghosts of propagation past: Haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus): Evolutionary Applications, v. 14, no. 4, p. 1124-1144, https://doi.org/10.1111/eva.13186.","productDescription":"21 p.","startPage":"1124","endPage":"1144","ipdsId":"IP-119896","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481105,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.13186","text":"Publisher Index Page"},{"id":480943,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, 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\"}}]}","volume":"14","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Bootsma, Matthew L.","contributorId":349232,"corporation":false,"usgs":false,"family":"Bootsma","given":"Matthew L.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Loren","contributorId":349233,"corporation":false,"usgs":false,"family":"Miller","given":"Loren","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":924158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sass, Greg G.","contributorId":349235,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924160,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Euclide, Peter T.","contributorId":349234,"corporation":false,"usgs":false,"family":"Euclide","given":"Peter T.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924159,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216870,"text":"ofr20201126 - 2020 - Structure contour and isopach maps of the Wolfcamp shale and Bone Spring Formation of the Delaware Basin, Permian Basin Province, New Mexico and Texas","interactions":[],"lastModifiedDate":"2020-12-11T20:41:33.50876","indexId":"ofr20201126","displayToPublicDate":"2020-12-11T11:35:00","publicationYear":"2020","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":"2020-1126","displayTitle":"Structure Contour and Isopach Maps of the Wolfcamp Shale and Bone Spring Formation of the Delaware Basin, Permian Basin Province, New Mexico and Texas","title":"Structure contour and isopach maps of the Wolfcamp shale and Bone Spring Formation of the Delaware Basin, Permian Basin Province, New Mexico and Texas","docAbstract":"A series of structure contour and isopach maps for the Wolfcamp shale and the Bone Spring Formation of the Delaware Basin, Permian Basin Province, were generated in support of the U.S. Geological Survey 2018 assessment of undiscovered continuous oil and gas resources. The interpreted formation tops used to generate the maps are from the IHS Markit® PRODFit™ database, a commercial proprietary database. The maps in this report are reflective of the stratigraphic units on the IHS Markit type log from southeast Eddy County, New Mexico.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201126","usgsCitation":"Gaswirth, S.B., 2020, Structure contour and isopach maps of the Wolfcamp shale and Bone Spring Formation of the Delaware Basin, Permian Basin Province, New Mexico and Texas: U.S. Geological Survey Open-File Report 2020–1126, 37 p., https://doi.org/ 10.3133/ ofr20201126.","productDescription":"v, 37 p.","onlineOnly":"Y","ipdsId":"IP-119726","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":381189,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1126/ofr20201126.pdf","text":"Report","size":"9.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1126"},{"id":381188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1126/coverthb.jpg"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.171875,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 34.34343606848294\n ],\n [\n -106.171875,\n 34.34343606848294\n ],\n [\n -106.171875,\n 29.6880527498568\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
A new sequence stratigraphic framework for Turonian to Santonian (94-84 Ma) sediments is established using data from the USGS Kure Beach and Elizabethtown cores collected from the Atlantic Coastal Plain of North Carolina (NC). These sediments represent some of the oldest marine units deposited on the southeastern Atlantic Coastal Plain and record the early development of a clastic wedge atop crystalline basement. Sediments were deposited as transitional marginal-marine to marine units in a complex interplay of fluvial, estuarine, and shelf environments. Repetitive lithologies and minimal biostratigraphic control requires an integrated analysis of grain-size data, geophysical logs, biostratigraphy, and 87Sr/86Sr isotopic data to identify systems tracts and establish a sequence stratigraphic framework. From this integrated approach, three Turonian to Santonian sequences in the Elizabethtown core and six in the Kure Beach core are identified. The new sequences from oldest to youngest are Clubhouse II, Fort Fisher I, Fort Fisher II, Collins Creek I, Collins Creek II, Pleasant Creek I, and Pleasant Creek II. Sequences from North Carolina document significant shifts of global and regional sea-level during greenhouse conditions in the early Late Cretaceous. Maximum sea-level rise occurred globally during the early Turonian and is documented from the marine sediments of the Clubhouse II sequence. This sequence is unconformably overlain by terrestrial sediments deposited during a major fall in sea level and maximum progradation of the shoreline, as evidenced by the Fort Fisher I sequence. Global sea-level rise in the Coniacian resulted in the deposition of the Fort Fisher II sequence, which is present only in the Kure Beach core. Local marine circulation and erosion on the shelf is suggested by the absence of the Collins Creek I sequence at Kure Beach; this sequence is present only in the up-dip Elizabethtown core. Activation of a possible buried fault structure along the Cape Fear arch resulted in the formation of a regional depocenter during the late Coniacian to early Santonian and is reflected in the unusual thickness of the Collins Creek II and Pleasant Creek I sequences. The return to a more global sea-level influence occurred in the late Santonian with the deposition of the Pleasant Creek II sequence. A comparison of temporal distribution of sequences in the Elizabethtown and Kure Beach cores to corresponding sequences in New Jersey indicates significant differences in erosional and tectonic processes in the Cape Fear region during the Turonian and Santonian.
","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.17.4.293-314","usgsCitation":"Aleman Gonzalez, W., Self-Trail, J., Harris, W., Moore, J.P., and Farrell, K., 2020, Depositional sequence stratigraphy of Turonian to Santonian sediments, Cape Fear arch, North Carolina Coastal Plain, USA: Stratigraphy, v. 17, no. 4, p. 293-314, https://doi.org/10.29041/strat.17.4.293-314.","productDescription":"22 p.","startPage":"293","endPage":"314","ipdsId":"IP-117982","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":382554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.8111572265625,\n 36.52288052805137\n ],\n [\n -77.069091796875,\n 36.527294814546245\n ],\n [\n -78.8104248046875,\n 34.10725639663118\n ],\n [\n -78.5302734375,\n 33.8339199536547\n ],\n [\n -77.969970703125,\n 33.925129700072\n ],\n [\n -77.135009765625,\n 34.619647359797185\n ],\n [\n -76.475830078125,\n 34.710009159224946\n ],\n [\n -75.4705810546875,\n 35.26804693351555\n ],\n [\n -75.4156494140625,\n 35.7286770448517\n ],\n [\n -75.8111572265625,\n 36.52288052805137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Aleman Gonzalez, Wilma 0000-0003-3156-0126","orcid":"https://orcid.org/0000-0003-3156-0126","contributorId":223454,"corporation":false,"usgs":true,"family":"Aleman Gonzalez","given":"Wilma","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":806906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":806907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, W. Burleigh","contributorId":192889,"corporation":false,"usgs":false,"family":"Harris","given":"W. Burleigh","affiliations":[],"preferred":false,"id":806908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Jessica P.","contributorId":245725,"corporation":false,"usgs":false,"family":"Moore","given":"Jessica","email":"","middleInitial":"P.","affiliations":[{"id":49298,"text":"WVGS","active":true,"usgs":false}],"preferred":false,"id":806909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Farrell, Kathleen","contributorId":245726,"corporation":false,"usgs":false,"family":"Farrell","given":"Kathleen","affiliations":[{"id":40717,"text":"NCGS","active":true,"usgs":false}],"preferred":false,"id":806910,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216895,"text":"70216895 - 2020 - Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences","interactions":[],"lastModifiedDate":"2022-08-16T17:31:25.733964","indexId":"70216895","displayToPublicDate":"2020-12-11T08:17:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences","docAbstract":"Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
Many at-risk species lack standardized surveys across their range or quantitative data capable of detecting demographic trends. As a result, extinction risk assessments often rely on ordinal categories of risk based on explicit criteria or expert elicitation. This study demonstrates a Bayesian approach to assessing extinction risk based on this common data structure, using three freshwater mussel species being considered for listing under the US Endangered Species Act. The probability that a population is classified under each risk category was modeled as a function of projected landscape change using ordered probit regression, assuming observed categories reflect a latent, continuous probability of persistence. All three species were more likely than not (mean probability >0.5) to be classified as extirpated or low condition throughout their range based on effects of urban development and hydrologic alteration. Spatial variation in estimates revealed strongholds and high-risk areas relevant to conservation decision making. Projected change in probabilities of each risk category based on multiple land-use and climate models was generally small relative to high baseline risk resulting from past landscape changes. Assessing extinction risk based on probabilities of ordinal condition as a function of landscape patterns may provide a flexible and robust approach for many at-risk taxa by adjusting species' demographic criteria to match relative risk categories, following standardized criteria, or using expert elicitation for data-deficient species. This approach provides decision makers with a useful measure of uncertainty around ordinal classifications and provides a framework for estimating future risk based on projections of anthropogenic stressors.
White-nose syndrome (WNS) is an emerging infectious disease of hibernating bats caused by a fungus previously known as Geomyces destructans and reclassified as Pseudogymnoascus destructans. The disease was first documented in 2006 in New York, has since spread across much of eastern North America, and as of January 2012, had caused the death of at least 5.7 to 6.7 million bats. Previous studies have suggested that environmental conditions play a strong role in WNS mortality. However, to predict where and when the disease will spread to new sites is difficult because detailed site information and associated environmental data are notably sparse. This paper presents a chronology of where and when WNS was detected in North America before October 2011 and indicates who reported the infections. This paper also presents available data on WNS-infected site elevation, geology, sediment chemistry and biota, air temperature, and relative humidity.
By the end of September 2011, at least 241 known WNS-infected sites were in North America and the number of infected sites per winter season had increased each year since 2006. The progressive increase in the number of infected sites per winter season suggests that the number of WNS infections had not peaked as of the 2010–11 winter season. WNS-infected sites include caves and mines, but the sites are not restricted by elevation, lithology, or strata age. Available data on site sediment chemistry are sparse but present a wide range of values, suggesting that caves and mines may contain a great range of microenvironments that are still poorly understood. The distribution of WNS may be restricted by air temperature and relative humidity. Published air temperature values from WNS-infected sites range from −15 to 33 degrees Celsius (but most temperature values are less than 20 degrees Celsius), and relative humidity values range from 50 to 100 percent. The spread of WNS may be restricted by a cave or mine temperature threshold of 20 degrees Celsius (which is likely to be south of most of the continental United States) and by some yet to be determined threshold of low relative humidity. These results indicate that WNS may not spread south into Mexico or to Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201117","usgsCitation":"Swezey, C.S., and Garrity, C.P., 2020, Environmental data associated with sites infected with white-nose syndrome (WNS) before October 2011 in North America: U.S. Geological Survey Open-File Report 2020–1117, 67 p., https://doi.org/10.3133/ofr20201117.","productDescription":"x, 67 p.","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117667","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":381184,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1117/coverthb.jpg"},{"id":381185,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1117/ofr20201117.pdf","text":"Report","size":"19.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1117"}],"country":"Canada, United States","state":"Connecticut, Delaware, Indiana, Kentucky, Maine, Maryland, Massachsetts, Missouri, New Brunswick, New Hampshire, New Jersey, New York, North Carolina, Nova Scotia, Ohio, Oklahoma, Ontario, Pennsylvania, Quebec, Tennessee, Vermont, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.95898437499999,\n 33.797408767572485\n ],\n [\n -75.673828125,\n 35.88905007936091\n ],\n [\n -74.53125,\n 39.436192999314095\n ],\n [\n -73.65234375,\n 40.78054143186033\n ],\n [\n -70.13671875,\n 42.16340342422401\n ],\n [\n -64.775390625,\n 43.389081939117496\n ],\n [\n -59.4140625,\n 46.31658418182218\n ],\n [\n -65.390625,\n 49.724479188712984\n ],\n [\n -66.0498046875,\n 54.521081495443596\n ],\n [\n -79.4970703125,\n 54.62297813269033\n ],\n [\n -89.296875,\n 56.77680831656842\n ],\n [\n -95.44921875,\n 52.669720383688166\n ],\n [\n -95.2294921875,\n 49.03786794532644\n ],\n [\n -89.7802734375,\n 48.37084770238366\n ],\n [\n -83.935546875,\n 46.9502622421856\n ],\n [\n -81.9580078125,\n 43.70759350405294\n ],\n [\n -83.5400390625,\n 41.64007838467894\n ],\n [\n -86.8798828125,\n 41.83682786072714\n ],\n [\n -87.451171875,\n 41.57436130598913\n ],\n [\n -88.24218749999999,\n 37.47485808497102\n ],\n [\n -89.2529296875,\n 36.56260003738545\n ],\n [\n -90.04394531249999,\n 35.24561909420681\n ],\n [\n -85.5615234375,\n 34.95799531086792\n ],\n [\n -82.880859375,\n 34.994003757575776\n ],\n [\n -82.265625,\n 35.15584570226544\n ],\n [\n -81.01318359375,\n 35.08395557927643\n ],\n [\n -79.716796875,\n 34.77771580360469\n ],\n [\n -78.486328125,\n 33.815666308702774\n ],\n [\n -77.95898437499999,\n 33.797408767572485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Many approaches that have been proposed that allow users to create a Web Ontology Language (OWL) ontology from a relational database fail to include metadata that are inherent to the database tables. Without metadata, the resulting ontology lacks annotation properties. These properties are key when performing ontology alignment. This paper proposes a method to include relevant metadata through annotation properties to OWL ontologies, which furthers the ability to integrate and use data from multiple unique ontologies. The described method is applied to geospatial data collected from The National Map, a data source hosted by the U. S. Geological Survey. Following that method, an ontology was manually created that used the metadata from The National Map. Because a manual approach is prone to human error, an automated approach to storing and converting metadata into annotation properties is discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge graphs and semantic web. KGSWC 2020","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020","conferenceDate":"Nov 26–27, 2020","conferenceLocation":"Mérida, Mexico","language":"English","publisher":"Springer","doi":"10.1007/978-3-030-65384-2_4","usgsCitation":"Wagner, M.E., Fry, T.E., Bourquin, J.J., and Varanka, D.E., 2020, Creating annotations for web ontology language ontology generated from relational databases, in Knowledge graphs and semantic web. KGSWC 2020, Mérida, Mexico, Nov 26–27, 2020, p. 45-60, https://doi.org/10.1007/978-3-030-65384-2_4.","productDescription":"16 p.","startPage":"45","endPage":"60","ipdsId":"IP-120265","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":390118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Matthew Edward 0000-0002-3987-072X","orcid":"https://orcid.org/0000-0002-3987-072X","contributorId":245472,"corporation":false,"usgs":true,"family":"Wagner","given":"Matthew","email":"","middleInitial":"Edward","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fry, Tanner Edward 0000-0001-9828-0394","orcid":"https://orcid.org/0000-0001-9828-0394","contributorId":245473,"corporation":false,"usgs":true,"family":"Fry","given":"Tanner","email":"","middleInitial":"Edward","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bourquin, Jacques Jules 0000-0001-8376-138X","orcid":"https://orcid.org/0000-0001-8376-138X","contributorId":245474,"corporation":false,"usgs":true,"family":"Bourquin","given":"Jacques","email":"","middleInitial":"Jules","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806259,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216690,"text":"sir20205113 - 2020 - Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud","interactions":[],"lastModifiedDate":"2020-12-22T13:00:35.997799","indexId":"sir20205113","displayToPublicDate":"2020-12-10T07:57:47","publicationYear":"2020","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":"2020-5113","displayTitle":"Interpretation of Hydrogeologic Data to Support Groundwater Management, Bazile Groundwater Management Area, Northeast Nebraska, 2019—A Case Demonstration of the Nebraska Geocloud","title":"Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud","docAbstract":"Nitrate, age tracer, and continuous groundwater-level data were interpreted in conjunction with airborne electromagnetic (AEM) survey data to understand the movement of nitrate within the Bazile Groundwater Management Area (BGMA) in northeastern Nebraska. Previously published age tracer data and nitrate data indicated vertical stratification of groundwater quality. Younger groundwater sampled within shallow parts of the aquifer had higher concentrations of nitrate, with 70 percent exceeding the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter. In contrast, groundwater sampled from deeper parts of the aquifer indicated that nitrate concentrations were less than 2 milligrams per liter and that groundwater likely recharged prior to widespread use of commercial fertilizer.
The hydrostratigraphic interpretation of AEM profiles indicated that shallow and deep monitoring wells were often screened within the same homogenous zone of aquifer material. In contrast, test-hole logs indicated that there often are fine-grained layers within these homogenous zones that separate the shallow and deep monitoring well screens, but these fine-grained layers are not detected by the AEM technique because of decreased resolution of the AEM technique with depth.
The stratification of groundwater ages and nitrate concentrations likely was caused by groundwater-flow paths of different length, location and time of recharge, and denitrification. Within paleochannels interpreted from AEM and test-hole data, pesticides detected in groundwater generally coincide with elevated nitrate concentrations. Continuous groundwater-level data from four monitoring well nests indicated that groundwater pumping can impose or increase downward hydraulic gradients and facilitate the downward movement of nitrate into deeper parts of the High Plains aquifer. Given the density of irrigation wells within the BGMA, this effect on the hydraulic gradient is likely prevalent in other areas of the BGMA. Understanding seasonal water-level changes can allow water managers to better predict and assess the hydraulic gradient and the vulnerability of groundwater in deeper parts of the High Plains aquifer.
Nitrate, age tracer, and continuous groundwater-level data within the BGMA were interpreted in conjunction with AEM data as a case demonstration of the Nebraska Geocloud. The Nebraska Geocloud was initiated to protect taxpayer investments in AEM data collection and realize maximum benefit of these data by creating a publicly available, online digital database for long-term data storage. The Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts; the University of Nebraska-Lincoln Conservation and Survey Division, Nebraska Natural Resources Commission, Nebraska Department of Natural Resources; and the U.S. Geological Survey entered a cooperative agreement to begin a program of data management and research aimed at understanding the best use of AEM for groundwater sustainability and management. Resulting case-study interpretations are provided to guide use of the Nebraska Geocloud to assess water-quality conditions and can be used by water managers and staff to address applicable water resource problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205113","collaboration":"Prepared in cooperation with the Nebraska Natural Resources Commission; Nebraska Department of Natural Resources; and Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts","usgsCitation":"Hobza, C.M., and Steele, G.V., 2020, Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud (ver. 1.1, December 15, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5113, 46 p., https://doi.org/10.3133/sir20205113.","productDescription":"Report: viii, 45 p.; Tables: 4, 5, and 6 (.xlsx and .csv); Data Release; Version History","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112495","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":380917,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table6.xlsx","text":"Table 6","size":"16.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 6","linkHelpText":"— Pesticide concentration, nitrate concentration, and calculated apparent groundwater ages for sampled monitoring and irrigation wells with detectable concentrations of pesticides, 1995–2005"},{"id":380915,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table5.xlsx","text":"Table 5","size":"23.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 5","linkHelpText":"— Summary of selected water-quality data and groundwater age estimates from wells sampled within the Bazile Groundwater Management Area, 2000–17"},{"id":380914,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.csv","text":"Table 4","size":"6.47 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380913,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.xlsx","text":"Table 4","size":"17.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380916,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table5.csv","text":"Table 5","size":"12.1 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 5","linkHelpText":"— Summary of selected water-quality data and groundwater age estimates from wells sampled within the Bazile Groundwater Management Area, 2000–17"},{"id":380891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5113/coverthb2.jpg"},{"id":380893,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3RVXN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Interpolated groundwater-level surface, spring 2017, Bazile Groundwater Management Area, northeastern Nebraska"},{"id":380918,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table6.csv","text":"Table 6","size":"8.17 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 6","linkHelpText":"— Pesticide concentration, nitrate concentration, and calculated apparent groundwater ages for sampled monitoring and irrigation wells with detectable concentrations of pesticides, 1995–2005"},{"id":381479,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113.pdf","text":"Report","size":"4.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5113"},{"id":381480,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5113/versionHist.txt","text":"Version History","size":"585 B","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5113 Version History"}],"country":"United States","state":"Nebraska","otherGeospatial":"Bazile Groundwater Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.35009765625,\n 41.902277040963696\n ],\n [\n -96.74560546875,\n 42.52069952914966\n ],\n [\n -97.1630859375,\n 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U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The importance of monitoring shrublands to detect and understand changes through time is increasingly recognized as critical to management. This research focuses on ecological change observed over 10 yr of field observation at 126 plots and over 35 yr of the Landsat archive in a shrubland ecosystem. Field data consisting of the fractional cover of shrubs, sagebrush, herbs, litter, and bare ground components were collected to be directly comparable to Landsat time-series predictions at an ecoregion level. We used these data to test three hypotheses. First, that precipitation and temperature govern changes in the proportions of shrubland components on an interannual time scale. Second, that longer-term component change is related to climate change. Finally, that change intensity varies by shrubland communities clustered by biophysical conditions. We found that the field observations and Landsat times-series predictions generally responded similarly to interannual variation in weather, chiefly driven by precipitation. Landsat times-series data provided a reasonable means of scaling up the findings of the field observations to a larger temporal and spatial window. The results of the analysis indicate that shrubland component change intensity significantly varies by biophysical clusters, and indicate a significant increase in the cover of shrubs and sagebrush in long-term monitoring plots between 2008 and 2017 and in the Landsat time-series data across the Wyoming Basin study area from 1985 to 2017 and from 2008 to 2017. Our results indicate that the Landsat time series can be used to answer critical questions regarding the influence of climate change and the suitability of management practices in shrubland ecosystems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3311","usgsCitation":"Shi, H., Homer, C., Rigge, M.B., Postma, K., and Xian, G.Z., 2020, Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming: Ecosphere, v. 11, no. 12, e03311, 20 p., https://doi.org/10.1002/ecs2.3311.","productDescription":"e03311, 20 p.","ipdsId":"IP-113232","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":454690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3311","text":"Publisher Index Page"},{"id":389545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.972900390625,\n 40.95501133048621\n ],\n [\n -108.48999023437499,\n 40.95501133048621\n ],\n [\n -108.48999023437499,\n 42.601619944327965\n ],\n [\n -110.972900390625,\n 42.601619944327965\n ],\n [\n -110.972900390625,\n 40.95501133048621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":823501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823503,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":265826,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823504,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823505,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216166,"text":"ds1130 - 2020 - Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","interactions":[],"lastModifiedDate":"2020-12-10T13:21:13.083251","indexId":"ds1130","displayToPublicDate":"2020-12-09T07:22:02","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1130","displayTitle":"Population Estimates for Selected Breeding Seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","title":"Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","docAbstract":"Kīlauea Point National Wildlife Refuge (KPNWR) is an important seabird breeding site located at the northeastern tip of Kauaʻi in the main Hawaiian Islands. Despite the regional significance of KPNWR as one of the most important breeding sites for red-tailed tropicbirds (Phaethon rubricauda), red-footed boobies (Sula sula), and wedge-tailed shearwaters (Ardenna pacifica) in the main Hawaiian Islands, robust and accurate population surveys have not been consistently conducted and recent information is lacking. In this study, we completed comprehensive population surveys for these three species during the 2019 breeding season. Using direct censusing methods (ground-searching, visual and photographic counts), we determined that 387 red-tailed tropicbird and 5,049 red-footed booby breeding pairs nested at KPNWR in 2019. Additionally, we performed surveys of aerially displaying tropicbirds to estimate a potential population of 30 white-tailed tropicbird (Phaethon lepturus) breeding pairs at KPNWR. Using a stratified-random plot-sampling method, we estimated that 20,998 wedge-tailed shearwater pairs nested at KPNWR in 2019. The breeding population size results in this study are greater than those reported in the past for KPNWR. We suggest that the red-tailed tropicbird breeding population has increased since the mid-2000s (when population estimates were last made), whereas red-footed booby numbers likely have remained similar and 2019 results show an increase from past estimates because of the more comprehensive methods used in this study. The results of these surveys provide current and accurate population sizes for these species that can serve as (1) benchmarks for future management and monitoring at KPNWR and (2) important components of population-level assessments of seabird vulnerability to potential offshore wind energy development in the main Hawaiian Islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1130","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the U.S. Fish and Wildlife Service Kauaʻi National Wildlife Refuge Complex","usgsCitation":"Felis, J.J., Kelsey, E.C., Adams, J., Stenske, J.G., and White, L.M., 2020, Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019: U.S. Geological Survey Data Series 1130, 32 p., https://doi.org/10.3133/ds1130.","productDescription":"Report: viii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119737","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":380277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1130/ds1130.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1130"},{"id":380278,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93MPDR1","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019"},{"id":380276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1130/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Point National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.40844535827637,\n 22.218873658623284\n ],\n [\n -159.37668800354004,\n 22.218873658623284\n ],\n [\n -159.37668800354004,\n 22.238816053514743\n ],\n [\n -159.40844535827637,\n 22.238816053514743\n ],\n [\n -159.40844535827637,\n 22.218873658623284\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Information concerning the availability, use, and quality of water in Pointe Coupee Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 364 million gallons per day (Mgal/d) of water were withdrawn in Pointe Coupee Parish, including about 39.87 Mgal/d from groundwater sources and 323.72 Mgal/d from surface-water sources. Withdrawals for power generation accounted for 89 percent (323.98 Mgal/d) of the total water withdrawn. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 8 percent (29.29 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industrial, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014. The large increase in surface-water withdrawals from 1980 to 1985 is attributable to an increase of 262 Mgal/d for power-generation use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203054","usgsCitation":"White, V.E., 2020, Water resources of Pointe Coupee Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3054, 6 p., https://doi.org/10.3133/fs20203054.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102167","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":381092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3054/coverthb.jpg"},{"id":381093,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3054/fs20203054.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3054"},{"id":381094,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"Pointe Coupee Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.7504,31.0193],[-91.736,31.0153],[-91.7248,31.0084],[-91.7141,31.0066],[-91.7093,31.0098],[-91.7061,31.0167],[-91.7045,31.0176],[-91.6992,31.0199],[-91.6938,31.0203],[-91.6869,31.0176],[-91.6709,31.0108],[-91.6639,31.0035],[-91.6612,30.9967],[-91.6586,30.9944],[-91.6586,30.9916],[-91.6618,30.987],[-91.6634,30.9848],[-91.6639,30.977],[-91.6622,30.9706],[-91.6574,30.9514],[-91.6451,30.9291],[-91.6525,30.9185],[-91.661,30.8952],[-91.662,30.86],[-91.6594,30.8541],[-91.6529,30.8482],[-91.6375,30.8418],[-91.6252,30.8404],[-91.5997,30.8469],[-91.588,30.8478],[-91.5768,30.846],[-91.5709,30.8423],[-91.5667,30.8364],[-91.5624,30.8373],[-91.5592,30.84],[-91.5566,30.8487],[-91.5592,30.872],[-91.5624,30.8816],[-91.5662,30.8866],[-91.5742,30.893],[-91.5859,30.8962],[-91.6046,30.8971],[-91.6131,30.9003],[-91.6163,30.9122],[-91.6099,30.9168],[-91.5859,30.9168],[-91.5689,30.9191],[-91.5497,30.9168],[-91.5321,30.904],[-91.5214,30.8903],[-91.5156,30.8752],[-91.5156,30.8615],[-91.5219,30.8483],[-91.5273,30.8103],[-91.54,30.7912],[-91.5512,30.7806],[-91.564,30.7765],[-91.5805,30.7747],[-91.5906,30.7692],[-91.5948,30.7637],[-91.5964,30.7578],[-91.5937,30.7491],[-91.5719,30.7354],[-91.5533,30.7308],[-91.5384,30.7331],[-91.523,30.7381],[-91.5001,30.7413],[-91.4804,30.7409],[-91.4602,30.7354],[-91.4507,30.7363],[-91.4288,30.7432],[-91.398,30.7578],[-91.381,30.7591],[-91.3756,30.7559],[-91.3624,30.7394],[-91.3528,30.7225],[-91.3418,30.6727],[-91.3354,30.6603],[-91.3312,30.6585],[-91.3338,30.6539],[-91.312,30.6484],[-91.3174,30.637],[-91.3201,30.6329],[-91.3355,30.616],[-91.3498,30.6041],[-91.3631,30.59],[-91.3653,30.5877],[-91.3653,30.5845],[-91.3653,30.579],[-91.3648,30.5689],[-91.3945,30.569],[-91.3977,30.569],[-91.3993,30.569],[-91.4009,30.5621],[-91.4056,30.5557],[-91.4078,30.5406],[-91.4147,30.5406],[-91.4147,30.5255],[-91.4152,30.5191],[-91.4147,30.5118],[-91.4821,30.5114],[-91.4815,30.4972],[-91.4853,30.4972],[-91.5261,30.4972],[-91.5584,30.4885],[-91.5568,30.483],[-91.5701,30.4826],[-91.5818,30.4825],[-91.5839,30.4967],[-91.6253,30.4972],[-91.7011,30.4975],[-91.7568,30.4978],[-91.7525,30.5079],[-91.7472,30.5093],[-91.7361,30.5084],[-91.7324,30.5102],[-91.7319,30.5125],[-91.733,30.5203],[-91.7425,30.5317],[-91.7559,30.5596],[-91.7575,30.5628],[-91.757,30.5687],[-91.7549,30.5742],[-91.7544,30.5861],[-91.7512,30.5994],[-91.755,30.6126],[-91.7539,30.6176],[-91.7508,30.6231],[-91.7449,30.6254],[-91.7412,30.6327],[-91.7444,30.6401],[-91.7466,30.6588],[-91.7445,30.6625],[-91.7328,30.668],[-91.7323,30.6725],[-91.7366,30.6794],[-91.7335,30.7018],[-91.7372,30.7118],[-91.7468,30.7237],[-91.7485,30.7301],[-91.7581,30.7415],[-91.7576,30.7493],[-91.7554,30.7534],[-91.7565,30.7607],[-91.7613,30.7675],[-91.7688,30.7858],[-91.7779,30.794],[-91.787,30.7976],[-91.7987,30.8104],[-91.8067,30.8104],[-91.8089,30.8145],[-91.8078,30.8204],[-91.8041,30.8291],[-91.7977,30.8337],[-91.7978,30.8442],[-91.8154,30.8483],[-91.8202,30.8533],[-91.8202,30.8583],[-91.816,30.8634],[-91.8101,30.862],[-91.8048,30.8639],[-91.8021,30.8721],[-91.8033,30.8835],[-91.8012,30.8968],[-91.8023,30.9132],[-91.8002,30.926],[-91.8035,30.9402],[-91.8025,30.9543],[-91.8063,30.9699],[-91.8057,30.9744],[-91.8041,30.9781],[-91.7978,30.9827],[-91.7807,30.9868],[-91.777,30.9909],[-91.7754,31.0005],[-91.7728,31.0051],[-91.768,31.0092],[-91.7563,31.0143],[-91.7504,31.0193]]]},\"properties\":{\"name\":\"Pointe Coupee\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
The northern long‐eared bat (Myotis septentrionalis) is one of the bat species most affected by white‐nose syndrome. Population declines attributed to white‐nose syndrome contributed to the species’ listing as federally threatened under the 1973 Endangered Species Act. Although one of the most abundant Myotine bats in eastern North America prior to white‐nose syndrome, little is known about northern long‐eared bats in the upper Midwest, USA. We assessed the habitat associations of the northern long‐eared bats on a regional scale using occupancy models that accounted for uncertainty in nightly detection to provide needed information on the distribution as white‐nose syndrome has recently arrived in this area. We monitored bat activity using zero‐crossing frequency‐division bat detectors for 10–15 nights at 20 detector sites at each of 3 sampling areas in Michigan, USA, and 6 sampling areas in Wisconsin, USA, stratified by mesic and xeric habitat types. We constructed northern long‐eared bat nightly detection histories for our occupancy analysis using maximum likelihood estimates from 2 commercially‐available automated identification programs: Kaleidoscope and Echoclass. We sampled for a total of 2,174 detector‐nights. Both Kaleidoscope and Echoclass identified northern long‐eared bat passes on 110 detector‐nights, whereas on 1,968 detector‐nights neither program identified a northern long‐eared bat call. Only one program or the other identified northern long‐eared bat calls on 206 detector‐nights, indicating an overall agreement rate of 35% on nights when calls were detected. We analyzed these data using an occupancy analysis accounting for the potential for false positives to assess the relationship between northern long‐eared bat presence and habitat characteristics. Our analyses indicated that the probability of a false positive at a site was low (0.015; 95% CI 0.009–0.021), and detection probability, but not occupancy, declined from 2015 to 2016 for sites in Wisconsin sampled in both years. Occupancy was positively associated with distance into the forest interior (distance from nearest road).
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1138","usgsCitation":"Hyzy, B.A., Russell, R., Silvis, A., Ford, W., Riddle, J.D., and Russell, K.R., 2020, Occupancy and detectability of northern long-eared bats in the Lake States Region: Wildlife Society Bulletin, v. 44, no. 4, p. 732-740, https://doi.org/10.1002/wsb.1138.","productDescription":"9 p.","startPage":"732","endPage":"740","ipdsId":"IP-095702","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":381445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.52734374999999,\n 42.53689200787315\n ],\n [\n -87.802734375,\n 42.601619944327965\n ],\n [\n -87.6708984375,\n 44.574817404670306\n ],\n [\n -87.802734375,\n 45.042478050891546\n ],\n [\n -87.03369140625,\n 45.73685954736049\n ],\n [\n -85.4736328125,\n 46.07323062540835\n ],\n [\n -85.869140625,\n 46.649436163350245\n ],\n [\n -86.7041015625,\n 46.45299704748289\n ],\n [\n -88.00048828124999,\n 46.9502622421856\n ],\n [\n -88.9453125,\n 46.965259400349275\n ],\n [\n -90.37353515625,\n 46.63435070293566\n ],\n [\n -90.98876953125,\n 46.63435070293566\n ],\n [\n -90.76904296874999,\n 46.9052455464292\n ],\n [\n -91.97753906249999,\n 46.7248003746672\n ],\n [\n -92.28515625,\n 45.321254361171476\n ],\n [\n -91.0546875,\n 44.071800467511565\n ],\n [\n -90.52734374999999,\n 42.53689200787315\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hyzy, Brenna A.","contributorId":171457,"corporation":false,"usgs":false,"family":"Hyzy","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":806604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":806605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":806606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riddle, Jason D.","contributorId":146462,"corporation":false,"usgs":false,"family":"Riddle","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":806607,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Russell, Kevin R.","contributorId":150351,"corporation":false,"usgs":false,"family":"Russell","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":806609,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212714,"text":"ofr20201095 - 2020 - Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin","interactions":[],"lastModifiedDate":"2020-12-03T21:41:11.602515","indexId":"ofr20201095","displayToPublicDate":"2020-12-03T08:05:00","publicationYear":"2020","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":"2020-1095","displayTitle":"Compilation of Mercury Data and Associated Risk to Human and Ecosystem Health, Bad River Band of Lake Superior Chippewa, Wisconsin","title":"Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin","docAbstract":"Mercury is an environmentally ubiquitous neurotoxin, and its methylated form presents health risks to humans and other biota, primarily through dietary intake. Because methylmercury bioaccumulates and biomagnifies in living tissue, concentrations progressively increase at higher trophic positions in ecosystem food webs. Therefore, the greatest health risks are for organisms at the highest trophic positions and for humans who consume organisms such as fish from these high trophic positions. Data on environmental mercury concentrations in various media and biota provide a basis for comparison among sites and regions and for evaluating ecosystem health risks. The U.S. Geological Survey, in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa, have compiled a dataset from analyses of mercury concentrations in surface water, bed sediment, fish tissue, Rana clamitans (green frog) tissue, Haliaeetus leucocephalus (bald eagle) feathers, Lontra canadensis (North American river otter) hair, Zizania palustris (northern wild rice), and litterfall from samples collected in the Bad River watershed, Wisconsin during 2004–18. These data originated from either the Natural Resources Department or another agency based on samples collected within or near to Bad River Tribal lands before transfer to the U.S. Geological Survey for compilation and analysis at the onset of the project. This report describes the compiled mercury dataset, provides comparisons to similar measurements in the region and elsewhere, and evaluates health risks to humans and to the sampled biota. Except for litterfall, data were not collected on a consistent, regular basis over a sufficient period to evaluate temporal patterns. The reported mercury concentrations are generally similar to those reported elsewhere in the upper Great Lakes region. Reported values are consistent with atmospheric deposition as the principal source and reflect a favorable environment for mercury methylation. Fish mercury concentrations increased at higher food web positions and generally increased with length in most species measured. Sander vitreus (walleye) present the greatest risk to humans among fishes considered here because of their high trophic position and associated elevated mercury concentrations in combination with relatively high walleye consumption rates by the Native American community. Methylmercury concentrations in wild rice are generally low and likely pose little health risk. Despite reports of declining atmospheric mercury deposition across eastern North America during the past decade, a downward trend in litterfall mercury deposition was not evident in samples collected during 2012–18. Limitations in this data compilation and analysis were noted due to missing information such as collection dates and site locations for some samples. Regular monitoring of mercury in litterfall and surface waters along with periodic collection of fish would enable evaluation of temporal change in the mercury cycle that might affect future risk to humans and aquatic ecosystem inhabitants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201095","collaboration":"Prepared in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa","usgsCitation":"Burns, D.A., 2020, Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin (ver 1.1, December 2020): U.S. Geological Survey Open-File Report 2020–1095, 19 p., https://doi.org/10.3133/ofr20201095.","productDescription":"Report: vii, 19 p.; Database; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110861","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377882,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS water data for the Nation"},{"id":377880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1095/ofr20201095.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1095"},{"id":377879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1095/coverthb2.jpg"},{"id":377881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRS2C3","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Mercury data from the Bad River Watershed, Wisconsin, 2004–2018"},{"id":380931,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1095/versionHist.txt","size":"448 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.48751831054686,\n 46.54658317951774\n ],\n [\n -90.4779052734375,\n 46.57868671298067\n ],\n [\n -90.51223754882812,\n 46.599449464868584\n ],\n [\n -90.59600830078125,\n 46.63057868059483\n ],\n [\n -90.69488525390625,\n 46.69184147024343\n ],\n [\n -90.78140258789062,\n 46.71632714994794\n ],\n [\n -90.7855224609375,\n 46.66734468444288\n ],\n [\n -90.83221435546875,\n 46.62020426357956\n ],\n [\n -90.8294677734375,\n 46.57774276255591\n ],\n [\n -90.83770751953125,\n 46.39619977845332\n ],\n [\n -90.55343627929688,\n 46.409457767475764\n ],\n [\n -90.54931640625,\n 46.54280504427768\n ],\n [\n -90.48751831054686,\n 46.54658317951774\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: December 2020","contact":"Director, New York Water Science Center
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The cratered highlands located northwest of Isidis Planitia have been recognized as one of the best preserved Noachian landscapes currently exposed on Mars; the area hosts a record of diverse surface processes, diagenesis, and aqueous alteration. This region has consistently been considered a high priority for landed-mission exploration and includes the anticipated landing site of the Mars 2020 Perseverance rover within Jezero crater. Past mapping, focused on Jezero crater and the surrounding area, Nili Planum, has varied in spatial extent, map scale, and purpose, though no previous maps have provided a continuous, high-resolution geologic map at uniform scale connecting the two locations. This map represents the first, large-scale, continuous geologic map spanning both Jezero crater and Nili Planum that is based on high-resolution images.
The map area contains the majority of both Jezero crater and Nili Planum at a publication map scale of 1:75,000, which was chosen to encompass the Jezero and southern Nili Planum landing sites under consideration for the Mars 2020 mission at the time of project initiation. This map covers an area that is exactly 1° by 1° (~60 by 60 km), spanning lat 76.8° N. to long 77.8° E. and lat 17.7° to long 18.7° N. The primary base map used for this geologic map is composed of Mars Reconnaissance Orbiter’s Context Camera (CTX) images, compiled into a 6 meter per pixel (m/pixel) mosaic. A nighttime Thermal Emission Imaging System 100 m/pixel image mosaic, digital terrain models constructed from CTX images, High-Resolution Stereo Camera (HRSC) topographic data, and High Resolution Imaging Science Experiment (HiRise) images also aided in unit identification and the assessment of stratigraphic relations. We defined map units on the basis of various characteristics visible in the CTX data at map scale, such as their texture, tone, morphology, marginal characteristics, geographic location, and stratigraphic relations to other units. Some units occur solely within Jezero crater, while Nili Planum contains a sequence of units that are present across the broader northwest Isidis Planitia region. Other units occur in both Jezero crater and Nili Planum, including bedrock, aeolian, and crater units. This map publication provides a regional geologic framework that connects the geologic units across Jezero crater and Nili Planum and the history they imply, facilitates future local-scale observations by landed missions of the Jezero crater and Nili Planum region, and enables the extrapolation of units that have been defined primarily by mineralogic composition to areas where there is no existing orbital spectroscopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3464","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Sun, V.Z., and Stack, K.M., 2020, Geologic map of Jezero crater and the Nili Planum region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, pamphlet 14 p., 1 sheet, scale 1:75,000, https://doi.org/10.3133/sim3464.","productDescription":"Pamphlet: iv, 14 p.; 1 Map: 56.60 x 45.62 inches; Metadata; Database; Read Me","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436704,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3464 Geologic Map of Jezero Crater and the Nili Planum Region"},{"id":380236,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_pamphlet.pdf","text":"Pamphlet","size":"728 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464 Pamphlet"},{"id":380235,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3464/sim3464.pdf","text":"Map","size":"36.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464"},{"id":380240,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_database.zip","size":"349.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3464 Database"},{"id":380239,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Readme txt"},{"id":380238,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.xml","size":"21 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3464 Metadata xml"},{"id":380237,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.txt","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Metadata txt"},{"id":380234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3464/coverthb.jpg"},{"id":400813,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"Interactive map","linkHelpText":"- Geologic Map of Jezero Crater and the Nili Planum Region, Mars, 1:75,000. Sun and Stack (2020)"}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The purpose of this study is to characterize streamflow availability under natural (unregulated) low-flow conditions for streams in southeast Kaua‘i, Hawai‘i. The nine main study-area basins, from north to south, include Wailua River, Hanamā‘ulu, Nāwiliwili, Pūʻali, Hulēʻia, Waikomo, Lāwaʻi, and Wahiawa Streams, and Hanapēpē River. The results of this study can be used by water managers to develop technically sound instream-flow standards for the study-area streams.
Low-flow characteristics for natural streamflow conditions were represented by flow-duration discharges that are equaled or exceeded between 95 and 50 percent of the time. Short-term continuous-record stream-gaging stations that monitored low flows on Waiahi and right branch Lāwaʻi Streams were established to serve as potential index stations for partial-record sites in the study area. Continuous-record stream-gaging station on Hanapēpē River monitored natural flow during calendar year 2017 and the streamflow record during that period was used to estimate low-flow characteristics at the station. Partial-record sites were established on 3 main streams and 15 tributary streams, upstream from existing surface-water diversions. Low-flow characteristics were determined using historical and current streamflow data from continuous-record stream-gaging stations and miscellaneous sites, as well as additional data collected as part of this study. Low-flow-duration discharges for the following streams were estimated for the 59-year base period (water years 1961–2019) using two record-augmentation techniques: right branch ʻŌpaekaʻa Stream, North Fork Wailua River, north and south fork Waikoko Streams, ‘Ili‘ili‘ula Stream, north and south fork Hanamāʻulu Streams, Kamo‘oloa Stream, Pāohia Stream, Ku‘ia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River. The 95-percent flow-duration discharges (Q95) ranged from 0.018 to 42 cubic feet per second (ft3/s). The 50-percent flow-duration discharges (Q50) ranged from 1.1 to 69 ft3/s. Upper-bound estimates of low-flow duration discharges at partial-record sites on south fork Hanamāʻulu, Hanamāʻulu tributary, ʻŌmaʻo, and Pōʻeleʻele Streams were estimated based on the highest discharges measured as part of this study during Q95 to Q50 flow conditions, which were 0.44, 0.40, 0.19, and 0.22 ft3/s, respectively. Measured discharges on Nāwiliwili, Pū‘ali, and left branch Wahiawa Streams do not correlate with data at any active long-term continuous-record stream-gaging stations (10 or more complete water years of natural-flow record) and therefore low-flow duration discharges could not be estimated.
This study also estimated streamflow gains and losses using seepage-run discharge measurements in eight of the nine study basins (Pūʻali Stream basin was excluded). A majority of the streams gained flow downstream from the uppermost diversions. Measured seepage-gain rates ranged between 0.03 and 24.3 ft3/s per mile of stream reach. Seepage gains are presumed to originate mainly from groundwater discharge in the Wailua River, Hanamā‘ulu Stream, Nāwiliwili Stream, Hulēʻia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River basins. Under natural-flow conditions and flow conditions of the seepage runs, a majority of the study-area streams flow continuously from the mountains to the ocean. Where a stream discharges into a reservoir––Hanamā‘ulu and Wahiawa Streams––a dry reach may occur immediately downstream from the reservoir to the point of seepage gain in the stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205128","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Cheng, C.L., 2020, Low-flow characteristics of streams from Wailua to Hanapēpē, Kauaʻi, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5128, 57 p., https://doi.org/10.3133/sir20205128.","productDescription":"viii, 57 p.","onlineOnly":"Y","ipdsId":"IP-119175","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5128/sir20205128.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5128"},{"id":380935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5128/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.35806274414062,\n 22.02072633149476\n ],\n [\n -159.43496704101562,\n 22.051277605102463\n ],\n [\n -159.554443359375,\n 22.071641456092383\n ],\n [\n -159.6258544921875,\n 22.03345683012737\n ],\n [\n -159.6533203125,\n 21.963424936844223\n ],\n [\n -159.65744018554688,\n 21.923937190109623\n ],\n [\n -159.59838867187497,\n 21.872969071537096\n ],\n [\n -159.43222045898438,\n 21.857675083878423\n ],\n [\n -159.33334350585935,\n 21.930306923001126\n ],\n [\n -159.31823730468747,\n 21.97106645968614\n ],\n [\n -159.33059692382812,\n 22.01945321869661\n ],\n [\n -159.35806274414062,\n 22.02072633149476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pacific Islands Water Science Center
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Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
This report describes considerations for incorporating routine quality-assessment and quality-control evaluations into U.S. Geological Survey discrete water-sampling programs and projects. U.S. Geological Survey water-data science in 2020 is characterized by robustness, external reproducibility, collaborative large-volume data analysis, and efficient delivery of water-quality data. Confidence in data, or robustness, can be increased by supplementing traditional field-based quality-control data with laboratory quality control (QC) data, such as third-party blind spikes and blind blanks, laboratory blanks, and laboratory-reagent spikes. Laboratory quality-control data can provide additional information about bias and variability, method performance, and false-positive and false-negative rates that are not available from field QC data alone. Reproducibility is supported by means of standardizing metadata and documentation. Collaborative analysis brings together disparate elements of various types of quality-control review and communicates persistent data quality issues for compounds to data users internal and external to the U.S. Geological Survey. Efficient delivery of water-quality data is achieved when quality-control review is accomplished in the same expedited (near real-time) time frame as distribution of environmental results to the public and might be improved with consideration given to data versioning or to a system of alerting data users to data interpretation that might differ from originally published data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201109","usgsCitation":"Medalie, L., 2020, Considerations for incorporating quality control into water quality sampling strategies for the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1109, 5 p., https://doi.org/10.3133/ofr20201109.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120022","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":380650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1109/coverthb.jpg"},{"id":380651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1109/ofr20201109.pdf","text":"Report","size":"935 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1109"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.