Conservation efforts on private lands are important for biodiversity conservation. On private lands in South Carolina, in the southeastern United States, forestry management practices (prescribed burning, thinning, herbicide application) are used to improve upland pine habitat for wildlife and timber harvest and are incentivized through U.S. Department of Agriculture Farm Bill cost-share programs. Because many forest-dependent avian species have habitat requirements created primarily through forest management, data are needed on the effectiveness of these management activities. We studied privately owned loblolly pine (Pinus taeda) stands in the South Carolina Piedmont region. Our objective was to understand how management practices influence avian species richness and abundance at local (forest stand) and landscape levels in relatively small stands (average ~28 ha). We surveyed 49 forest stands during 2 bird breeding seasons with traditional point counts and vegetation surveys. We evaluated the effects of management on pine stand characteristics, avian species richness, and abundance of state-designated bird species of concern. Repeated burning and thinning shifted stand conditions to open pine woodlands with reduced basal area and herbaceous understories. Stands with lower basal area supported greater avian species richness. Some species increased in abundance in response to active management (e.g., Brown-headed Nuthatch [Sitta pusilla] and Indigo Bunting [Passerina cyanea]), but relationships varied. Some species responded positively to increases in forest quantity at a landscape scale (1–5 km; e.g., Northern Bobwhite [Colinus virginianus]). We found species-rich avian communities and species of conservation concern on working timber lands, indicating that incentivized forest management on private lands can provide valuable habitat for wildlife.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa048","usgsCitation":"Wood, J., Tegeler, A., and Ross, B., 2020, Vegetation management on private forestland can increase avian species richness and abundance: Ornithological Applications, v. 122, no. 4, duaa048, 16 p., https://doi.org/10.1093/condor/duaa048.","productDescription":"duaa048, 16 p.","ipdsId":"IP-106496","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454896,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa048","text":"Publisher Index Page"},{"id":393413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Carolina\",\"nation\":\"USA \"}}]}","volume":"122","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, J.M.","contributorId":270361,"corporation":false,"usgs":false,"family":"Wood","given":"J.M.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tegeler, A.K.","contributorId":270363,"corporation":false,"usgs":false,"family":"Tegeler","given":"A.K.","email":"","affiliations":[{"id":56153,"text":"2South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215885,"text":"ofr20201115 - 2020 - Southern (California) sea otter population status and trends at San Nicolas Island, 2017–2020","interactions":[],"lastModifiedDate":"2020-11-03T12:47:24.250221","indexId":"ofr20201115","displayToPublicDate":"2020-11-02T08:26:49","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":"2020-1115","displayTitle":"Southern (California) Sea Otter Population Status and Trends at San Nicolas Island, 2017–2020","title":"Southern (California) sea otter population status and trends at San Nicolas Island, 2017–2020","docAbstract":"The southern sea otter (Enhydra lutris nereis) population at San Nicolas Island, California, has been monitored annually since the translocation of 140 sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and type across years. In 2017, the U.S. Navy and the U.S. Fish and Wildlife Service initiated a sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The monitoring program, at its basic level, includes quarterly seasonal surveys of population abundance, distribution, and foraging activity. From 2017 to 2020, we measured a 22-percent per annum increase in population abundance (95-percent confidence interval =11–34 percent) with 114 total individuals as of February 2020. Coinciding with recent population growth, the sea otter distribution, which previously tended to concentrate on the west side, appears to have shifted toward an expansion of use in the north and especially greater seasonal use in the north and south during winter and spring. Foraging data were collected on a total of 2,675 foraging dives in 167 foraging bouts, and the majority of identified prey on successful dives (n=1,335) were sea urchins (940) followed by snails (240) and crabs (78). Small numbers of lobsters (26), octopus (16), and abalone (5) also were identified. Estimates of energy intake rates averaged 17.3 kilocalories per minute (95-percent confidence interval =15.6–19.0 kilocalories per minute) and suggest possible variations across years and seasons, but confidence intervals based on specific years of data were relatively wide. In addition to abundance, trends, distribution, and forage energy intake across seasons and years, these replicated surveys provide information on the precision of data achieved by quarterly survey effort. We used precision estimates and conducted simulation analyses to assess the power of detecting 10-percent or greater decreases in population growth rates and how this power is likely to change with years of observation, survey effort, and the size of decrease. These results can be useful to the planning of future monitoring and research of sea otters at San Nicolas Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201115","collaboration":"Wildlife ProgramDirector, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Increasing specific conductance (SC) and chloride concentrations [Cl] negatively affect many stream ecosystems. We characterized spatial variability in SC, [Cl], and exceedances of Environmental Protection Agency [Cl] criteria using nearly 30 million high-frequency observations (2–15 min intervals) for SC and modeled [Cl] from 93 sites across three regions in the eastern United States: Southeast, Mid-Atlantic, and New England. SC and [Cl] increase substantially from south to north and within regions with impervious surface cover (ISC). In the Southeast, [Cl] weakly correlates with ISC, no [Cl] exceedances occur, and [Cl] concentrations are constant with time. In the Mid-Atlantic and New England, [Cl] and [Cl] exceedances strongly correlate with ISC. [Cl] criteria are frequently exceeded at sites with greater than 9–10% ISC and median [Cl] higher than 30–80 mg/L. Tens to hundreds of [Cl] exceedances observed annually at most of these sites help explain previous research where stream ecosystems showed changes at (primarily nonwinter) [Cl] as low as 30–40 mg/L. Mid-Atlantic chronic [Cl] exceedances occur primarily in December–March. In New England, exceedances are common in nonwinter months. [Cl] is increasing at nearly all Mid-Atlantic and New England sites with the largest increases at sites with higher [Cl].
Mercury (Hg) is a global pollutant that affects aquatic biota in otherwise pristine settings such as the Adirondack region of New York State. Bioaccumulation of Hg is especially problematic in sensitive landscapes, where inorganic mercury from atmospheric deposition is readily converted, via natural processes, to methylmercury (MeHg), the toxic form that is taken up and biomagnified in aquatic food webs. There is great interest in monitoring MeHg in aquatic biota across these sensitive regions to evaluate responses to changes in Hg emissions. Aquatic insects, such as dragonfly larvae, have great potential as MeHg “biosentinels,” but currently are not widely used for this purpose. An important practical consideration in the use of aquatic insects for MeHg biomonitoring is whether total mercury (THg) is a suitable surrogate for MeHg, which is much more technically challenging and expensive to analyze than is THg. The objective of this project was to assess the suitability of THg as a surrogate for MeHg in stream-dwelling insects. Specifically, existing data on immature aquatic insects from nine Adirondack streams were used to characterize MeHg to THg ratios (i.e., MeHg%), and variation in these ratios (e.g., among sites, seasons, taxa) in predator and primary consumer insects, examine how well THg in different groups tracks measured stream water MeHg (i.e., filtered MeHg; FMeHg), and explore the influence of trophic position (indicated by nitrogen stable isotopes; δ15N) on the observed MeHg% patterns.
Three broad insect feeding groups were included in this analysis: predators, shredders, and scrapers. Predators had the highest MeHg% (median 94%), and MeHg% did not differ significantly among any of the taxa considered: stoneflies, damselflies, and three families of dragonflies (darners, common skimmers, and clubtails). Darners and common skimmers, the most numerous and abundant predators, were combined for further analyses. Site medians for these “selected dragonflies” were all at least 90% (summer-fall collections) and MeHg% did not differ significantly among sites. The correlation between FMeHg and THg in selected dragonflies was nearly as strong as that of FMeHg and dragonfly MeHg. In contrast, median MeHg% in shredders (northern caddisflies) and scrapers (flathead mayflies), which are both primary consumers, was lower overall (medians 52% and 35%, respectively), more variable, and less-well representative of FMeHg than predators. Stable isotope results indicate that variation in feeding position is an important influence on some of the MeHg% patterns observed in this study. This study’s findings suggest that THg is likely to be a suitable surrogate for MeHg in predatory aquatic insects from Adirondack streams, but do not support the use of THg in primary consumers for regional MeHg monitoring.
","language":"English","publisher":"New York State Energy Research and Development Authority","usgsCitation":"Riva-Murray, K., 2020, Ratios of methylmercury to total mercury in predator and primary consumer insects from Adirondack streams in New York State: Summary Report 20-32, vi, 15 p.","productDescription":"vi, 15 p.","ipdsId":"IP-103615","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":382274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382272,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/About/Publications/Research-and-Development-Technical-Reports/Environmental-Research-and-Development-Technical-Reports#eco"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.805908203125,\n 44.02442151965934\n ],\n [\n -73.85009765625,\n 44.02442151965934\n ],\n [\n -73.85009765625,\n 44.5435052132082\n ],\n [\n -74.805908203125,\n 44.5435052132082\n ],\n [\n -74.805908203125,\n 44.02442151965934\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":2984,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808421,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220613,"text":"70220613 - 2020 - Council monitoring and assessment program (CMAP): A framework for using the monitoring program inventory to conduct gap assessments for the Gulf of Mexico Region","interactions":[],"lastModifiedDate":"2021-05-21T15:36:24.768626","indexId":"70220613","displayToPublicDate":"2020-10-31T10:23:22","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"284","title":"Council monitoring and assessment program (CMAP): A framework for using the monitoring program inventory to conduct gap assessments for the Gulf of Mexico Region","docAbstract":"Executive Summary Under the Resources and Ecosystem Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act), the Gulf Coast Ecosystem Restoration Council (RESTORE Council or Council) is required to report on the progress of funded projects and programs. Systematic monitoring of restoration at the project-specific and programmatic-levels (watershed and Gulf of Mexico) enables consistent reporting and gives the public confidence that the restoration investments selected by the RESTORE Council will be evaluated and adaptively managed accordingly. Monitoring information that has been collected at different spatial and temporal scales can be used as the foundation to illustrate progress towards comprehensive ecosystem restoration goals and objectives that promote holistic Gulf of Mexico recovery (see ‘RESTORE Council Background’ at the beginning of this report for additional Council information).
Currently, federal, state and local agencies, universities, private industry, and non-governmental organizations (NGOs) are conducting monitoring activities at various scales around the Gulf of Mexico. In addition, each RESTORE Council-funded project will, at a minimum, perform project-specific monitoring. This collection of monitoring activities was inventoried and coordinated into a network of existing programs by the Council-funded RESTORE Council Monitoring and Assessment Program (CMAP), which will suggest opportunities for efficiencies and collaborative cross-program review of performance with other Gulf ecosystem recovery efforts. CMAP was designed and funded to inventory and integrate existing monitoring efforts, improve discovery and accessibility of existing monitoring data, and ensure the collected information supports management decisions.
The fundamental approach to building the CMAP Gulf of Mexico water quality monitoring, habitat monitoring, and mapping network was to: 1. Adopt, or construct as needed, a comprehensive inventory of existing habitat and water quality observation, monitoring, and mapping programs in the Gulf of Mexico (hereafter referred to as the “Inventory”; NOAA and USGS, 2019a); 2. Evaluate the suitability/applicability of each program and its existing and prospective data for use in restoration activities; 3. Develop a process to use the Inventory to conduct gap assessments; 4. Develop a catalog of baseline assessments conducted in the Gulf of Mexico (NOAA and USGS, 2019b); and 5. Develop a searchable monitoring information portal/database to enable access to collected information and products.
","language":"English","publisher":"National Oceanic and Atmospheric Administration (NOAA)","doi":"10.25923/mrdd-h727","usgsCitation":"Bosch, J., Burkart, H.B., Chivoiu, B., Clark, R., Clement, C., Enwright, N., Giordano, S., Jeffrey, C., Johnson, E., Hart, R., Hile, S.D., Howell, J.S., Laurenzano, C., Lee, M., McCloskey, T., McTigue, T., Meyers, M.B., Miller, K.E., Mize, S., Monaco, M.E., Owen, K., Rebich, R., Rendon, S.H., Robertson, A., Sample, T., Sanks, K.M., Steyer, G., Suir, K., Swarzenski, C.M., and Thurman, H.R., 2020, Council monitoring and assessment program (CMAP): A framework for using the monitoring program inventory to conduct gap assessments for the Gulf of Mexico Region: NOAA Technical Memorandum 284, ii, 55 p., https://doi.org/10.25923/mrdd-h727.","productDescription":"ii, 55 p.","startPage":"55 p.","ipdsId":"IP-119233","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research 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0000-0001-6751-5568","orcid":"https://orcid.org/0000-0001-6751-5568","contributorId":218508,"corporation":false,"usgs":true,"family":"Mize","given":"Scott","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816225,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Monaco, Mark E.","contributorId":200279,"corporation":false,"usgs":false,"family":"Monaco","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":816226,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Owen, Kevin","contributorId":218509,"corporation":false,"usgs":false,"family":"Owen","given":"Kevin","email":"","affiliations":[{"id":39855,"text":"NOAA 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Center","active":true,"usgs":true}],"preferred":true,"id":816228,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Robertson, Ali","contributorId":218623,"corporation":false,"usgs":false,"family":"Robertson","given":"Ali","email":"","affiliations":[],"preferred":false,"id":816229,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sample, Thomas 0000-0002-3960-8334","orcid":"https://orcid.org/0000-0002-3960-8334","contributorId":218510,"corporation":false,"usgs":true,"family":"Sample","given":"Thomas","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816230,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sanks, Kelly Marie 0000-0002-5966-2370","orcid":"https://orcid.org/0000-0002-5966-2370","contributorId":228881,"corporation":false,"usgs":true,"family":"Sanks","given":"Kelly","email":"","middleInitial":"Marie","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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cswarzen@usgs.gov","orcid":"https://orcid.org/0000-0001-9843-1471","contributorId":656,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Christopher","email":"cswarzen@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":816233,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Thurman, Hana Rose 0000-0001-7097-5362","orcid":"https://orcid.org/0000-0001-7097-5362","contributorId":258258,"corporation":false,"usgs":true,"family":"Thurman","given":"Hana","email":"","middleInitial":"Rose","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":816234,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70215395,"text":"70215395 - 2020 - Upper Mississippi River system weighted wind fetch analysis (1989, 2000, 2010/2011)","interactions":[],"lastModifiedDate":"2021-01-28T15:36:34.090546","indexId":"70215395","displayToPublicDate":"2020-10-31T09:27:42","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7574,"text":"Contract Report","active":true,"publicationSubtype":{"id":4}},"title":"Upper Mississippi River system weighted wind fetch analysis (1989, 2000, 2010/2011)","docAbstract":"Wind fetch is defined as the unobstructed distance that wind can travel over water in a constant direction. Fetches are limited by landforms surrounding the body of water. Fetch is an important characteristic of open water because longer fetches can result in larger wind-generated waves. The larger waves, in turn, can increase shoreline erosion and sediment resuspension (Rohweder and others 2012). Increases in sediment resuspension lead to increases in water turbidity, which in turn decreases light penetration and, therefore, create conditions less conducive to aquatic plant growth (Giblin and others 2010).
A wind fetch model was developed by David Finlayson, U. S. Geological Survey, Pacific Science Center, while he was a Ph.D. student at the University of Washington (Finlayson 2005). This method calculates effective fetch using the recommended procedure of the Shore Protection Manual (USACE 1984). Scientists at the United States Geological Survey, Upper Midwest Environmental Sciences Center (UMESC) and the United States Army Corps of Engineers (USACE) further refined this model (Rohweder and others 2012) and structured it to operate using the most recent version of the ArcMap Geographic Information System platform (Esri, 2019). At the time the analysis was performed, the version of ArcMap used was 10.7.1. The model refined in 2012 was used for the analyses described in this report.
Using this model, UMESC performed an analysis to model weighted wind fetch for the Upper Mississippi River System (UMRS) corresponding to three separate time periods of land cover spatial data acquisition (1989, 2000, and 2010/2011). The purpose of the analysis was to examine how fetch varies over time and space within the UMRS for potential management applications. For more detailed information on the wind fetch model, examine the USGS Open-File Report by Rohweder and others (2012).
","language":"English","publisher":"U.S. Army Corps of Engineers, Mississippi River Restoration Program","usgsCitation":"Rohweder, J.J., and Rogala, J.T., 2020, Upper Mississippi River system weighted wind fetch analysis (1989, 2000, 2010/2011): Contract Report, ii, 26 p.","productDescription":"ii, 26 p.","ipdsId":"IP-119011","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":382758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382757,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://umesc.usgs.gov/documents/reports/2020/umrr_ltrm_weighted_wind_fetch_101620.pdf"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 39.8928799002948\n ],\n [\n -88.9453125,\n 40.64730356252251\n ],\n [\n -87.64892578125,\n 41.44272637767212\n ],\n [\n -87.71484375,\n 41.918628865183045\n ],\n [\n -88.0224609375,\n 42.27730877423709\n ],\n [\n -88.83544921874999,\n 41.83682786072714\n ],\n [\n -89.45068359374999,\n 41.393294288784865\n ],\n [\n -90.50537109375,\n 40.39676430557203\n ],\n [\n -90.3955078125,\n 39.8928799002948\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.5166015625,\n 37.77071473849609\n ],\n [\n -90,\n 38.53097889440024\n ],\n [\n -90.15380859375,\n 39.2832938689385\n ],\n [\n -90.68115234375,\n 40.53050177574321\n ],\n [\n -89.8681640625,\n 41.96765920367816\n ],\n [\n -89.89013671875,\n 42.47209690919285\n ],\n [\n -91.07666015625,\n 44.11914151643737\n ],\n [\n -94.2626953125,\n 45.98169518512228\n ],\n [\n -94.85595703125,\n 46.10370875598026\n ],\n [\n -95.16357421875,\n 45.5679096098613\n ],\n [\n -92.92236328125,\n 44.29240108529005\n ],\n [\n -91.73583984374999,\n 43.068887774169625\n ],\n [\n -90.98876953125,\n 41.85319643776675\n ],\n [\n -91.91162109375,\n 40.56389453066509\n ],\n [\n -91.73583984374999,\n 39.45316112807394\n ],\n [\n -90.24169921875,\n 38.048091067457236\n ],\n [\n -90,\n 37.405073750176925\n ],\n [\n -89.5166015625,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":802002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":802003,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268708,"text":"70268708 - 2020 - On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","interactions":[],"lastModifiedDate":"2025-07-07T16:11:01.608215","indexId":"70268708","displayToPublicDate":"2020-10-30T11:09:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21978,"text":"Frontiers in Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","docAbstract":"Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.578785","usgsCitation":"Nguyen, P., Bador, M., Alexander, L., Lane, T., and Funk, C., 2020, On the robustness of annual daily precipitation maxima estimates over Monsoon Asia: Frontiers in Climate Services, v. 2, 578785, 19 p., https://doi.org/10.3389/fclim.2020.578785.","productDescription":"578785, 19 p.","ipdsId":"IP-121958","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492046,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.578785","text":"Publisher Index Page"},{"id":491743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Monsoon Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 67.82681208821384,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 28.08149631086141\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 148.23729356007158,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 45.2939171288528\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 90.4186427916805,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n 10.095537024904786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nguyen, Phuong-Loan","contributorId":357544,"corporation":false,"usgs":false,"family":"Nguyen","given":"Phuong-Loan","affiliations":[{"id":85452,"text":"ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Sydney, New South Wales, Australia","active":true,"usgs":false}],"preferred":false,"id":941695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bador, Margot","contributorId":223056,"corporation":false,"usgs":false,"family":"Bador","given":"Margot","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Lisa","contributorId":223054,"corporation":false,"usgs":false,"family":"Alexander","given":"Lisa","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, Todd P.","contributorId":357545,"corporation":false,"usgs":false,"family":"Lane","given":"Todd P.","affiliations":[{"id":85454,"text":"2School of Earth Science and ARC Centre of Excellence for Climate Extremes, The University of Melbourne, Melbourne, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":941698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@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":941699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227642,"text":"70227642 - 2020 - Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data","interactions":[],"lastModifiedDate":"2022-01-24T14:57:06.932262","indexId":"70227642","displayToPublicDate":"2020-10-30T08:48:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data","docAbstract":"Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.
A geochemical characterization of groundwater in the Big Chino subbasin of Arizona was conducted by the U.S. Geological Survey, in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project, to understand groundwater evolution through the study area and the source of water to springs along the gaining reach of the Verde River just downstream from its confluence with Granite Creek. Samples were collected between 2011 and 2018 in groundwater wells completed in basin-fill and carbonate aquifers and at selected springs, including two discrete springs discharging along the aforementioned stretch of the Verde River. Five newly installed monitoring wells completed in the carbonate aquifer were sampled in 2018. Water-quality results obtained from these samples include the first known geochemical data for carbonate groundwater beneath the basin-fill in the Big Chino subbasin downgradient from Walnut Creek near Paulden, Arizona, as well as other parts of the study area without previous data. Groundwater samples were collected and analyzed for major ions, arsenic, nutrients, stable isotopes of oxygen and hydrogen (δ18O and δ2H), strontium isotopes (87Sr/86Sr), carbon-14, isotopes of carbon (δ13C), and noble gases.
Significant differences in groundwater geochemistry between the basin-fill and carbonate aquifers were driven primarily by higher pH, tritium, and δ18O and δ2H in the basin-fill aquifer samples and higher specific conductance and higher concentrations of calcium, sodium, bicarbonate, fluoride, and arsenic in the carbonate aquifer samples. All but one sample from the carbonate aquifer and two samples from the basin-fill aquifer exceeded the U.S. Environmental Protection Agency (EPA) drinking water standard for arsenic of 10 micrograms per liter. One basin-fill aquifer sample exceeded the EPA drinking water standard for fluoride of 4 milligrams per liter, and one carbonate aquifer sample exceeded the EPA secondary drinking water standard for fluoride of 2 milligrams per liter. A component of modern groundwater recharged following aboveground nuclear testing beginning in the mid-1950s is present in some basin-fill and spring groundwater from this study. Groundwater that can be dated using radiocarbon decay is also present in the study area, with four groundwater samples indicating possible recharge during the Pleistocene with groundwater ages ranging from approximately 34,600 to 13,300 years before present. Other groundwater sampled during this study that can dated using radiocarbon decay ranged in age from about 7,500 to 1,100 years before present, indicating possible recharge during the Holocene.
The gaining reach of the Verde River downstream from the confluence with Granite Creek shows areal changes in temperature, pH, and specific conductance, indicating multiple zones of groundwater input. Surface-water samples for analyses of δ18O and δ2H have been collected at the Verde River near Paulden, Ariz. streamgage (09503700) during discharge measurements since 2009, and a trend analysis of the δ18O and δ2H data indicated no significant trend exists for the 10-year period of record. Additional groundwater samples from the carbonate aquifer beneath the basin-fill upgradient and downgradient from Walnut Creek would provide valuable information to understand groundwater evolution along the Big Chino subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205094","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project","usgsCitation":"Beisner, K.R., and Jones, C.J.R., 2020, Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18: U.S. Geological Survey Scientific Investigations Report 2020–5094, 49 p., https://doi.org/10.3133/sir20205094.","productDescription":"Report: viii, 49 p.; 2 Appendixes; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113409","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":379927,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HMZNIK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Carbon and strontium isotopic data for rock, soil, and soil gas from the Big Chino Sub-Basin, Arizona, 2017 and 2018"},{"id":379924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.csv","text":"Appendix 1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"},{"id":379923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094.pdf","text":"Report","size":"37.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5094"},{"id":379926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P909LD47","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water quality parameters in the Verde River below Granite Creek, Arizona, June 2018"},{"id":379922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5094/coverthb.jpg"},{"id":379925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.xlsx","text":"Appendix 1","size":"33.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"}],"country":"United States","state":"Arizona","otherGeospatial":"Big Chino subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.18389892578125,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 34.3366324743773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
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Puget Sound is a critical part of the Pacific Northwest, both culturally and economically. Eelgrass beds are an important feature of Puget Sound and are known to influence fish assemblages. As part of a larger site-characterization effort, and to gain a better understanding of the fish assemblages in Bellingham Bay, Washington, four eelgrass beds (Zostera marina) along the shoreline were surveyed. Fish were captured from 24 through 26 September 2019 by using three beach-seine hauls per eelgrass bed. In total, 12 hauls yielded 2,135 fish that comprised 20 species from 14 families. Shiner perch (Cymatogaster aggregata) accounted for 52 percent of the total catch. The other common species included three-spine stickleback (Gasterosteus aculeatus), bay pipefish (Syngnathus leptorhynchus), saddleback gunnel (Pholis ornata), Pacific staghorn sculpin (Leptocottus armatus), and Pacific sand lance (Ammodytes personatus). Total catch and species richness were highest at the two locations closest to the urban center of Bellingham; however, species diversity and evenness were highest at the two eelgrass beds farthest from the city center. Descriptions of fish assemblages in eelgrass beds are expected to be useful in the development of future process-based investigations by study partners and will focus on the movements of sediments and contaminants and their influence on biota.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1131","usgsCitation":"Andrews, M.I., and Liedtke, T.L., 2020, Fish assemblages in eelgrass beds of Bellingham Bay, Washington, Northern Puget Sound, 2019: U.S. Geological Survey Data Series 1131, 11 p., https://doi.org/10.3133/ds1131.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-117153","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":379932,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1131/ds1131.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1131"},{"id":379931,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1131/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Bellingham Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.68020629882812,\n 48.45561965661709\n ],\n [\n -122.42752075195314,\n 48.45561965661709\n ],\n [\n -122.42752075195314,\n 48.79510425169179\n ],\n [\n -122.68020629882812,\n 48.79510425169179\n ],\n [\n -122.68020629882812,\n 48.45561965661709\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.
Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108734","usgsCitation":"Harrity, E.J., Stevens, B., and Conway, C.J., 2020, Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment: Biological Conservation, v. 250, 108734,10 p., https://doi.org/10.1016/j.biocon.2020.108734.","productDescription":"108734,10 p.","ipdsId":"IP-116214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River Basin, Salton Sink","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.840087890625,\n 32.52828936482526\n ],\n [\n -114.796142578125,\n 32.44024912337551\n ],\n [\n -114.08752441406249,\n 32.697177359290635\n ],\n [\n -114.246826171875,\n 33.55055114384406\n ],\n [\n -114.29077148437499,\n 33.8247936182649\n ],\n [\n -113.873291015625,\n 34.31621838080741\n ],\n [\n -114.356689453125,\n 34.88593094075317\n ],\n [\n -114.63134765625001,\n 34.858890491257796\n ],\n [\n -115.916748046875,\n 33.128351191631566\n ],\n [\n -114.840087890625,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrity, Eamon J.","contributorId":275852,"corporation":false,"usgs":false,"family":"Harrity","given":"Eamon","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216654,"text":"70216654 - 2020 - Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","interactions":[],"lastModifiedDate":"2020-11-27T17:09:15.373856","indexId":"70216654","displayToPublicDate":"2020-10-28T11:04:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7439,"text":"Frontiers in Ecology and Evolution Section Biogeography and Macroecology","active":true,"publicationSubtype":{"id":10}},"title":"Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","docAbstract":"Biological soil crusts (biocrusts) on the Colorado Plateau may fuel carbon (C) and nitrogen (N) cycling of soil heterotrophic organisms throughout the region. Late successional moss and lichen biocrusts, in particular, can increase soil C and N availability, but some data suggest these biocrust types will be replaced by early successional cyanobacterial biocrusts as the region undergoes warming and aridification. In this study, we evaluated the short-term interactive effects of biocrust successional state and elevated temperature on soil heterotrophic C and N cycling (specifically, soil respiration, N2O emissions, microbial biomass C and N, and soluble C and N). We collected soils following an 87-day greenhouse mesocosm experiment where the soils had been topped with different biocrust successional states (moss-dominated, cyanobacteria-dominated, or no biocrust) and had experienced different temperatures (ambient and warmed), under an artificial precipitation regime. Following this pre-incubation mesocosm phase, the soils were assessed using a short-term (2-day) laboratory incubation to determine the cumulative effect of the elevated temperature and altered biocrust successional state on the temperature sensitivity of soil heterotrophic C and N cycling. We found that there were interactive effects of biocrust successional state and exposure to warmer temperatures during the mesocosm phase under greenhouse conditions on the rate and temperature sensitivity of soil heterotrophic C and N cycling in laboratory incubations. Soils collected from beneath late successional biocrusts exhibited higher C and N cycling rates than those from beneath early successional crusts, while warming reduced both the magnitude and the temperature sensitivity of C and N cycling. The inhibiting effect of warming, was most evident in soils from beneath late successional biocrusts, which, during the mesocosm phase, also exhibited the greatest reductions in gross primary production and respiration in response to the warming treatment. Taken together, these data suggest that an overall effect of climate warming may be increasing resource limitation of the soil heterotrophic C and N cycles in the region, which may magnify alterations associated with the changes in biocrust community structure documented in previous studies. Overall, results from this study suggest that soil heterotrophic biogeochemical cycling is affected by interactions between temperature and the biocrust community that lives atop the mineral soil, with important implications for C and N cycling into the future.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.467157","usgsCitation":"Tucker, C., Ferrenberg, S., and Reed, S., 2020, Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling: Frontiers in Ecology and Evolution Section Biogeography and Macroecology, v. 8, 467157, 17 p., https://doi.org/10.3389/fevo.2020.467157.","productDescription":"467157, 17 p.","ipdsId":"IP-110869","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.467157","text":"Publisher Index Page"},{"id":380845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","city":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.66140747070312,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.50519140240356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrenberg, Scott","contributorId":217143,"corporation":false,"usgs":false,"family":"Ferrenberg","given":"Scott","affiliations":[{"id":39569,"text":"Department of Biology, New Mexico State University, Las Cruces, NM 88001, USA","active":true,"usgs":false}],"preferred":false,"id":805740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216386,"text":"70216386 - 2020 - Mussel community assessment tool for the Upper Mississippi River system","interactions":[],"lastModifiedDate":"2020-11-13T14:53:45.631508","indexId":"70216386","displayToPublicDate":"2020-10-28T08:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Mussel community assessment tool for the Upper Mississippi River system","docAbstract":"Upper Mississippi River (UMR) resource managers need a quantitative means of evaluating the health of mussel assemblages to measure effects of management and regulatory actions, assess restoration techniques, and inform regulatory tasks. Our objective was to create a mussel community assessment tool (MCAT), consisting of a suite of metrics and scoring criteria, to consistently compare the relative health of UMR mussel assemblages. We developed an initial MCAT using quantitative data from 25 sites and 10 metrics. Metrics fell in five broad groups: conservation status and environmental sensitivity, taxonomic composition, population processes, abundance, and diversity. Metric scoring categories were based on quartile analysis: 25% scoring as good, 50% scoring as fair, and 25% scoring as poor. Scores were meant to facilitate establishing management priorities and mitigation options for the conservation of mussels. Scoring categories assumed that a healthy mussel assemblage consists of species with a variety of reproductive and life-history strategies, a low percentage of tolerant species, and a high percentage of sensitive species; shows evidence of adequate recruitment, a variety of age classes, and low mortality; and has high abundance, species richness, and species and tribe evenness. Metrics were validated using a modified Delphi technique. MCAT metrics generally reflected the professional opinions of UMR resource managers and provided a consistent evaluation technique with uniform definitions that managers could use to evaluate mussel assemblages. Additional data sets scored a priori by UMR resource managers were used to further validate metrics, resulting in data from 33 sites spanning over 980 km of the UMR. Initial and revised MCAT scores were similar, indicating that data represent the range of mussel assemblages in the UMR. Mussel assemblages could be evaluated using individual metrics or a composite score to suit management purposes. With additional data, metrics could be calibrated on a local scale or applied to other river systems.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v23i2.2020.109-123","usgsCitation":"Dunn, H.L., Zigler, S.J., and Newton, T., 2020, Mussel community assessment tool for the Upper Mississippi River system: Freshwater Mollusk Biology and Conservation, v. 23, no. 2, p. 109-123, https://doi.org/10.31931/fmbc.v23i2.2020.109-123.","productDescription":"15 p.","startPage":"109","endPage":"123","ipdsId":"IP-100031","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":454949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v23i2.2020.109-123","text":"Publisher Index Page"},{"id":380503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.9560546875,\n 38.85682013474361\n ],\n [\n -90.3076171875,\n 39.13006024213511\n ],\n [\n -91.043701171875,\n 39.690280594818034\n ],\n [\n -91.307373046875,\n 40.26276066437183\n ],\n [\n -90.791015625,\n 41.1290213474951\n ],\n [\n -90.87890625,\n 41.31907562295139\n ],\n [\n -90.2197265625,\n 41.566141964768384\n ],\n [\n -89.9560546875,\n 42.032974332441405\n ],\n [\n -90.50537109375,\n 42.54498667313236\n ],\n [\n -90.59326171875,\n 42.70665956351041\n ],\n [\n -90.90087890624999,\n 42.867912483915305\n ],\n [\n -90.966796875,\n 43.16512263158296\n ],\n [\n -90.999755859375,\n 43.492782808225\n ],\n [\n -91.1865234375,\n 43.96909818325171\n ],\n [\n -91.790771484375,\n 44.35527821160296\n ],\n [\n -92.1533203125,\n 44.53567453241317\n ],\n [\n -92.779541015625,\n 44.87144275016589\n ],\n [\n -93.087158203125,\n 44.68427737181225\n ],\n [\n -92.2412109375,\n 44.315987905196906\n ],\n [\n -91.834716796875,\n 44.02442151965934\n ],\n [\n -91.5380859375,\n 43.8503744993026\n ],\n [\n -91.241455078125,\n 43.22118973298753\n ],\n [\n -91.25244140624999,\n 43.092960677116295\n ],\n [\n -91.1865234375,\n 42.71473218539458\n ],\n [\n -90.758056640625,\n 42.50450285299051\n ],\n [\n -90.3955078125,\n 42.09007006868398\n ],\n [\n -90.50537109375,\n 41.672911819602085\n ],\n [\n -91.19750976562499,\n 41.50034959128928\n ],\n [\n -91.29638671875,\n 41.19518982948959\n ],\n [\n -91.20849609375,\n 41.03793062246529\n ],\n [\n -91.395263671875,\n 40.73893324113601\n ],\n [\n -91.62597656249999,\n 40.413496049701955\n ],\n [\n -91.746826171875,\n 40.027614437486655\n ],\n [\n -91.483154296875,\n 39.42770738465604\n ],\n [\n -90.966796875,\n 39.172658670429946\n ],\n [\n -90.703125,\n 38.79690830348427\n ],\n [\n -89.9560546875,\n 38.85682013474361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Heidi L.","contributorId":244888,"corporation":false,"usgs":false,"family":"Dunn","given":"Heidi","email":"","middleInitial":"L.","affiliations":[{"id":49009,"text":"EcoAnalysts, Inc.","active":true,"usgs":false}],"preferred":false,"id":804848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zigler, Steven J. 0000-0002-4153-0652 szigler@usgs.gov","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":2410,"corporation":false,"usgs":true,"family":"Zigler","given":"Steven","email":"szigler@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804850,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216696,"text":"70216696 - 2020 - Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery","interactions":[],"lastModifiedDate":"2020-12-02T12:43:37.139714","indexId":"70216696","displayToPublicDate":"2020-10-28T07:17:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery","docAbstract":"Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12213524","usgsCitation":"Gao, F., Anderson, M., and Hively, W.D., 2020, Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery: Remote Sensing, v. 12, no. 21, 22 p., https://doi.org/10.3390/rs12213524.","productDescription":"22 p.","ipdsId":"IP-123386","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12213524","text":"Publisher Index Page"},{"id":380903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"21","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":805911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":805912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":805913,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","interactions":[{"subject":{"id":47512,"text":"twri04A3 - 2002 - Statistical methods in water resources","indexId":"twri04A3","publicationYear":"2002","noYear":false,"displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources"},"predicate":"SUPERSEDED_BY","object":{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","indexId":"tm4A3","publicationYear":"2020","noYear":false,"title":"Statistical methods in water resources"},"id":1}],"lastModifiedDate":"2024-08-13T14:02:36.434133","indexId":"tm4A3","displayToPublicDate":"2020-10-27T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A3","displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources","docAbstract":"This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.
The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A3","usgsCitation":"Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.]","productDescription":"Report: xxii, 458 p.; Data Release","numberOfPages":"484","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-089727","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":418371,"rank":5,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/tm/04/a03/Errata_Sheet.pdf","text":"Errata Sheet","size":"136 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Errata Sheet"},{"id":379731,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://store.usgs.gov/product/533012","text":"Print Version Available"},{"id":374999,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JWL6XR","text":"USGS data release","linkHelpText":"Statistical Methods in Water Resources - Supporting Materials"},{"id":375013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a03/tm4a3.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A3"},{"id":375000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a03/coverthb.jpg"}],"publicComments":"Techniques and Methods 4-A3 supersedes Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.","contact":"Chief, Analysis and Prediction Branch
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12201 Sunrise Valley Dr., Mail Stop 415
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This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; Version 1.1: January 11, 2021","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The utilization, preservation, and conservation of the Nation’s resources requires well-informed management decisions. The North Atlantic and Appalachian Region (NAAR) of the U.S. Geological Survey (USGS) supports science-based decision making for Federal, State, and local policymakers to meet the challenges of today and into the future. The science centers in the NAAR have well-deserved reputations as world leaders in delivering unbiased science. We help protect the lives and property of our families, friends, neighbors, and the Nation by providing the data and scientific interpretation that decision makers need to make informed choices on a myriad of topics. Many of our jobs include inherent risk. When others are moving themselves and their families to higher ground during storms, NAAR employees can be found heading toward high water to ensure that accurate streamflow and storm-tide data continue to be collected and delivered to the public and first responders.
In March 2020, the world changed, and the NAAR staff adapted to it. Despite the challenges, the NAAR has had an incredibly productive year. I am not just citing publications (with our labs and field offices closed in the spring, centers increased annual publications by 10 to 40 percent compared with 2019) or partnerships (new science initiatives and partnerships are up significantly as well). Leaders at the center level created the right environments for their teams to be safe but still meet and exceed their program goals. Our vast data collection networks were maintained and enhanced. Our laboratories met holding times and quality-control objectives. When folks asked for help, our staff provided. Some solutions were not perfect at first, but they just kept trying. What started as a short-term inconvenience may now have become the new normal, but in quickly adapting, the NAAR staff showed dedication and wisdom, made the region a little safer, and just might change the world. This general information product highlights just a few of the many accomplishments of the NAAR staff during these challenging times and offers a taste of all the great work being done by the USGS community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip207","isbn":"978-1-4113-4381-8","usgsCitation":"U.S. Geological Survey, 2020, Meeting the challenge—U.S. Geological Survey North Atlantic and Appalachian Region fiscal year 2020 in review: U.S. Geological Survey General Information Product 207, 20 p., https://doi.org/10.3133/gip207.","productDescription":"20 p.","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123417","costCenters":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":379535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/207/gip207.pdf","text":"Report","size":"5.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 207"},{"id":379534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/207/coverthb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Kentucky, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"Appalachian region, North Atlantic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.76171875,\n 36.56260003738545\n ],\n [\n -73.7841796875,\n 40.34654412118006\n ],\n [\n -70.6640625,\n 41.178653972331674\n ],\n [\n -69.873046875,\n 41.73852846935917\n ],\n [\n -70.7080078125,\n 42.68243539838623\n ],\n [\n -69.3896484375,\n 43.929549935614595\n ],\n [\n -67.5,\n 44.465151013519616\n ],\n [\n -66.70898437499999,\n 45.1510532655634\n ],\n [\n -67.67578124999999,\n 45.85941212790755\n ],\n [\n -67.939453125,\n 47.21956811231547\n ],\n [\n -69.2578125,\n 47.54687159892238\n ],\n [\n -70.57617187499999,\n 45.583289756006316\n ],\n [\n -71.71875,\n 45.058001435398275\n ],\n [\n -74.970703125,\n 45.058001435398275\n ],\n [\n -76.201171875,\n 44.213709909702054\n ],\n [\n -76.9482421875,\n 43.35713822211053\n ],\n [\n -78.31054687499999,\n 43.45291889355465\n ],\n [\n -78.837890625,\n 43.197167282501276\n ],\n [\n -78.8818359375,\n 42.71473218539458\n ],\n [\n -80.5078125,\n 41.86956082699455\n ],\n [\n -80.419921875,\n 40.17887331434696\n ],\n [\n -82.44140625,\n 38.44498466889473\n ],\n [\n -82.9248046875,\n 38.61687046392973\n ],\n [\n -84.6826171875,\n 39.13006024213511\n ],\n [\n -86.17675781249999,\n 38.03078569382294\n ],\n [\n -87.8466796875,\n 37.92686760148135\n ],\n [\n -88.3740234375,\n 37.54457732085582\n ],\n [\n -89.1650390625,\n 37.33522435930639\n ],\n [\n -89.4287109375,\n 36.59788913307022\n ],\n [\n -75.76171875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Regional Director
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12201 Sunrise Valley Drive
Reston, VA 20192
The Midcontinent Rift System (MRS) is a 1.1 Ga sequence of voluminous basaltic eruptions and multiple intrusions followed by widespread sedimentation that extends across the Midcontinent and northern Great Lakes region of North America. Previous workers have commonly used seismic-reflection data (Great Lakes International Multidisciplinary Program on Crustal Evolution [GLIMPCE] line A) to demonstrate that the northern rift margin in central Lake Superior developed as a normal growth fault that was structurally inverted to a reverse fault during a compressional event after rifting had ended. A prominent, curvilinear aeromagnetic anomaly that extends from Isle Royale, Michigan, to Superior Shoal in central Lake Superior, Ontario (the IR-SS anomaly), is commonly presented as a manifestation of this reverse fault. We have integrated multidisciplinary geophysical analyses (seismic-reflection, seismic-refraction, aeromagnetic, and gravity), physical-property information (density, magnetic susceptibility and remanence, and compressional-wave velocity), and geologic concepts to develop an alternate interpretation of the rift margin along GLIMPCE line A, where it intersects the IR-SS anomaly. Our new model indicates that a normal fault is the dominant structure at the northern rift margin along line A, contrary to the original rift-margin paradigm, which asserts that compressional structures are the dominant features preserved today. Integral to this alternate model is a newly interpreted, prerift sedimentary basin intruded by sills in northern Lake Superior. Our alternate model of the northern rift margin has implications for interpreting the style, scale, and timing of extension, rift-related intrusion, and compression during development of the MRS.
Lava that erupted during the 2014–2015 Holuhraun eruption in Iceland flowed into a proglacial river system, resulting in aqueous cooling of the lava and an ephemeral hydrothermal system. We carried out a monitoring study of this system from 2015 to 2018 to document the cooling of the lava over this time, using thermocouple measurements and data-logging sensors. The heat loss rate from advection through this hydrothermal system in August 2015 was ~5.5 × 108 W; since eruption, aqueous cooling likely accounted for ~1% of the total heat loss from the lava. This estimate excludes steam losses from fumaroles as well as any groundwater that was not released to the surface, and thus is a lower bound. Near the terminus of the flow, advection of heat by flowing water may have locally accounted for tens of percent of the total cooling of that part of the flow. Our data quantify the importance of water cooling for this lava flow and can be compared with models to better understand lava–water interactions more generally. We also provide detailed methods for simple, low-cost monitoring of similar instances in the future.
Autonomous underwater vehicles (AUVs) and animal telemetry have become important tools for understanding the relationships between aquatic organisms and their environment, but more information is needed to guide the development and use of AUVs as effective animal tracking platforms. A forward-facing acoustic telemetry receiver (VR2Tx 69 kHz; VEMCO, Bedford, Nova Scotia) attached to a novel AUV (gliding robotic fish) was tested in a freshwater lake to (1) compare its detection efficiency (i.e., the probability of detecting an acoustic signal emitted by a tag) of acoustic tags (VEMCO model V8-4H 69 kHz) to stationary receivers and (2) determine if detection efficiency was related to distance between tag and receiver, direction of movement (toward or away from transmitter), depth, or pitch.
Detection efficiency for mobile (robot-mounted) and stationary receivers were similar at ranges less than 300 m, on average across all tests, but detection efficiency for the mobile receiver decreased faster than for stationary receivers at distances greater than 300 m. Detection efficiency was higher when the robot was moving toward the transmitter than when moving away from the transmitter. Detection efficiency decreased with depth (surface to 4 m) when the robot was moving away from the transmitter, but depth had no significant effect on detection efficiency when the robot was moving toward the transmitter. Detection efficiency was higher when the robot was descending (pitched downward) than ascending (pitched upward) when moving toward the transmitter, but pitch had no significant effect when moving away from the transmitter.
Results suggested that much of the observed variation in detection efficiency is related to shielding of the acoustic signal by the robot body depending on the positions and orientation of the hydrophone relative to the transmitter. Results are expected to inform hardware, software, and operational changes to gliding robotic fish that will improve detection efficiency. Regardless, data on the size and shape of detection efficiency curves for gliding robotic fish will be useful for planning future missions and should be relevant to other AUVs for telemetry. With refinements, gliding robotic fish could be a useful platform for active tracking of acoustic tags in certain environments.
","language":"English","publisher":"Springer","doi":"10.1186/s40317-020-00219-7","usgsCitation":"Ennasr, O., Holbrook, C., Hondorp, D.W., Krueger, C., Coleman, D., Solanki, P., Thon, J., and Tan, X., 2020, Characterization of acoustic detection efficiency using a gliding robotic fish as a mobile receiver platform: Animal Biotelemetry, v. 8, no. 32, 13 p., https://doi.org/10.1186/s40317-020-00219-7.","productDescription":"13 p.","ipdsId":"IP-122951","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00219-7","text":"Publisher Index Page"},{"id":436745,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S75TSB","text":"USGS data release","linkHelpText":"Acoustic detection performance of gliding robotic fish in Higgins Lake, Michigan, USA, 2016-2018"},{"id":380901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"32","noUsgsAuthors":false,"publicationDate":"2020-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ennasr, Osama 0000-0002-8353-6446","orcid":"https://orcid.org/0000-0002-8353-6446","contributorId":245318,"corporation":false,"usgs":false,"family":"Ennasr","given":"Osama","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":805906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coleman, Demetris","contributorId":245319,"corporation":false,"usgs":false,"family":"Coleman","given":"Demetris","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805907,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Solanki, Pratap","contributorId":245320,"corporation":false,"usgs":false,"family":"Solanki","given":"Pratap","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805908,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thon, John","contributorId":245321,"corporation":false,"usgs":false,"family":"Thon","given":"John","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805909,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":805910,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70215466,"text":"ofr20201104 - 2020 - Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017","interactions":[],"lastModifiedDate":"2020-10-25T17:23:47.879673","indexId":"ofr20201104","displayToPublicDate":"2020-10-23T12:20:00","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":"2020-1104","displayTitle":"Evaluation of the U.S. Geological Survey Streamgage Network in South Carolina, 2017","title":"Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017","docAbstract":"The U.S. Geological Survey (USGS) has been monitoring streamflow in South Carolina since the late 1800s. From the beginning, the USGS streamgage network in South Carolina has been dynamic, with streamgages being added or removed depending on their purpose and the availability of funding from Federal, State, and local partners. Streamflow monitoring is important for acquiring real-time data during flood events, but also for collecting long-term data that can be used to compute the magnitude and frequency of floods and to frame flood events in a historical perspective. These data are also critical for being able to develop regional regression equations that can be used to estimate flood characteristics at ungaged locations, which is important for infrastructure planning and design. The historical flooding that occurred in South Carolina in 2015, 2016, and 2018 highlighted the importance of collecting these data. Therefore, the USGS, in cooperation with the South Carolina Department of Transportation, evaluated the USGS streamgage network in South Carolina for the purpose of helping guide decisions concerning future streamgage location selection, both spatially and in terms of the range of drainage basin characteristics that are typically important in flood-frequency analyses. The results of this evaluation are presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201104","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Kolb, K.R., 2020, Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017: U.S. Geological Survey Open-File Report 2020–1104, 15 p., https://doi.org/10.3133/ofr20201104.","productDescription":"Report: vii, 15 p.; 1 Plate: 40.00 x 40.00 inches; Appendixes 1-3; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116207","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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1770 Corporate Drive
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The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface water and groundwater resources. This chapter, NFM A6.2, provides guidance and protocols for the measurement of dissolved oxygen, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.2, version 3.0, by Stewart A, Rounds, Franceska D. Wilde, and George F. Ritz. Dissolved oxygen is routinely measured when water samples are collected, is often continually measured at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring dissolved oxygen described in this chapter is applicable to most natural waters.
Before 2017, the USGS NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0—U.S. Geological Survey, 2018) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
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12201 Sunrise Valley Drive, MS 432
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