The U.S. Geological Survey worked collaboratively with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Hempstead Harbor, an embayment on the northern shore of Nassau County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the harbor and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, surrounding land use, and additional water-quality data (including nutrient and stable isotopes of nitrate analyses) for the location where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Our analysis revealed an abundance of human and canine fecal contamination throughout the Hempstead Harbor landscape and that water from municipal separate storm sewer system conveyances was the most likely transport mechanism of this fecal contamination. Resuspension of bed sediment may contribute to fecal contamination in the harbor, but more targeted analyses are needed to support this finding. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the culvert at Glenwood Road and the outfall and the spillway at Skillman Street were identified as locations that contribute substantial fecal contamination to Hempstead Harbor.
","language":"English","publisher":"U.S Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215042","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2021, Using microbial source tracking to identify fecal contamination sources in an embayment in Hempstead Harbor on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2021–5042, 19 p., https://doi.org/10.3133/sir20215042.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116390","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":387786,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215042/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5042"},{"id":387710,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5042/sir20215042.XML"},{"id":387709,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5042/images"},{"id":387707,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and methodology for a study to identify fecal contamination sources using microbial source tracking in seven embayments on Long Island, New York"},{"id":387706,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"USGS water data for the nation"},{"id":387704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5042/coverthb.jpg"},{"id":387705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5042/sir20215042.pdf","text":"Report","size":"2.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5042"}],"country":"United States","state":"New York","otherGeospatial":"Hempstead Harbor, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.6197280883789,\n 40.80653332421558\n ],\n [\n -73.63929748535156,\n 40.89353200999427\n ],\n [\n -73.71345520019531,\n 40.87692019266084\n ],\n [\n -73.73680114746094,\n 40.8725069777884\n ],\n [\n -73.69972229003906,\n 40.79769722250925\n ],\n [\n -73.6197280883789,\n 40.80653332421558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Beginning with the nineteenth-century territorial surveys, the lakes and lacustrine deposits in what is now the western United States were recognized for their economic value to the expanding nation. In the latter half of the twentieth century, these systems have been acknowledged as outstanding examples of depositional systems serving as models for energy exploration and environmental analysis, many with global applications in the twenty-first century. The localities presented in this volume extend from exposures of the Eocene Green River Formation in Utah and Florissant Formation in Colorado, through the Pleistocene and Holocene lakes of the Great Basin to lakes along the California and Oregon coast. The chapters explore environmental variability, sedimentary processes, fire history, the impact of lakes on crustal flexure, and abrupt climate events in arid regions, often through the application of new tools and proxies.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/SPE536","usgsCitation":"2021, From saline to freshwater: The diversity of western lakes in space and time, v. 536, xii, 506 p., https://doi.org/10.1130/SPE536.","productDescription":"xii, 506 p.","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":391468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"536","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Starratt, Scott W. 0000-0001-9405-1746 sstarrat@usgs.gov","orcid":"https://orcid.org/0000-0001-9405-1746","contributorId":2891,"corporation":false,"usgs":true,"family":"Starratt","given":"Scott","email":"sstarrat@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":826440,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826441,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70222457,"text":"cir1477 - 2021 - Cooperative Fish and Wildlife Research Units program—2020 research abstracts","interactions":[],"lastModifiedDate":"2021-08-11T17:56:21.174783","indexId":"cir1477","displayToPublicDate":"2021-08-11T14:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1477","displayTitle":"Cooperative Fish and Wildlife Research Units Program—2020 Research Abstracts","title":"Cooperative Fish and Wildlife Research Units program—2020 research abstracts","docAbstract":"The U.S. Geological Survey (USGS) serves as the research arm of the U.S. Department of the Interior and has established a series of strategic goals that focus its efforts on serving the American people. Within the USGS, the Ecosystems Mission Area is responsible for conducting and sponsoring research that addresses the following thematic objectives under the overarching strategic goal of “Science that Supports Our Resources in Wild and Urban Spaces, and the Landscapes In-Between”:
This report provides abstracts of most of the ongoing and recently completed research investigations of the USGS Cooperative Fish and Wildlife Research Units program. The report is organized by the following major science themes that contribute to the objectives of the USGS:
Director, Cooperative Fish and Wildlife Research Units Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 303
Reston, VA 20192
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Long Island National Wildlife Refuge Complex in New York. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of five marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge-complex scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about $24,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $24,000 consistently included approaches for increasing drainage from the marsh surface within the marsh management units. The potential management benefits were derived from expected improvements in surface-water drainage and capacity for marsh elevation to keep pace with sea-level rise, and presumed increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Long Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211070","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Williams, M.R., 2021, Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2021–1070, 34 p., https://doi.org/10.3133/ofr20211070.","productDescription":"Report: vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126538","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387845,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1070/versionHist.txt","size":"640 B","linkFileType":{"id":2,"text":"txt"}},{"id":387151,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1070/ofr20211070.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1070"},{"id":387150,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1070/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.576412521044425\n ],\n [\n -73.6138916015625,\n 40.54720023441049\n ],\n [\n -73.1854248046875,\n 40.60978237983301\n ],\n [\n -72.66357421875,\n 40.77638178482896\n ],\n [\n -72.015380859375,\n 40.96330795307353\n ],\n [\n -71.795654296875,\n 41.091772220976644\n ],\n [\n -72.2625732421875,\n 41.18278832811288\n ],\n [\n -72.7294921875,\n 41.02964338716638\n ],\n [\n -73.245849609375,\n 40.94256444133327\n ],\n [\n -73.4820556640625,\n 40.967455873296714\n ],\n [\n -73.707275390625,\n 40.8595252289932\n ],\n [\n -73.8775634765625,\n 40.79301881008675\n ],\n [\n -74.0203857421875,\n 40.693134153308065\n ],\n [\n -74.0478515625,\n 40.576412521044425\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 13, 2021; Version 1.1: August 11, 2021","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
A previous report by the authors described sediment sampling and drilling by the U.S. Geological Survey (USGS) beside the American and Sacramento Rivers near Sacramento, California, in support of a U.S. Army Corps of Engineers project focused on regional flood control. The drilling was performed to define lithology, extract samples for laboratory testing, and perform borehole erosion tests (BETs). The U.S. Department of Agriculture (USDA) performed jet erodibility tests (JETs) near each drilling site, and a team from Texas A&M University performed laboratory tests with an erosion function apparatus (EFA). Collectively, the effort was intended to reveal spatial variations in sediment erodibility and provide data for use in a model to simulate morphological response to a major flood. The data collected by the USGS are available in a public data release.
This report, developed in cooperation with the U.S. Army Corps of Engineers, provides comparisons of the three types of measurements of the erodibility of riverbed sediments. The BET is performed in the field and reveals erodibility of sediments below the bed surface. The JET is likewise performed in the field but reveals only erodibility of exposed sediments. The EFA test is done in the laboratory and was performed on soils extracted from different depths beneath the bed surface, in many cases reconstituted for laboratory testing. Tests were performed at nominally similar locations but differed by meters to tens of meters in horizontal locations.
The comparison was undertaken to investigate differences among results obtained by the individual measurement approaches and to elucidate pros and cons of each method. The critical shear stress to initiate erosion and the rate of change of erosion rate per unit increase of excess shear stress, sometimes referred to as the erosion coefficient, served as the primary basis for comparison. The three test methods in some cases resulted in order of magnitude differences in estimates of these parameters. Some differences could be attributed to variances in site location or result from testing surface sediment versus a deeper layer, but systematic differences are also evident in the results. The tests performed in the laboratory using the EFA resulted in much lower values of critical shear stress and much higher values of the erosion coefficient compared to the JET tests performed by the USDA team on surface sediments. Critical shear stress was poorly resolved in the BET results because of the limited number of results per site, but the erosion coefficients derived from BET results were systematically lower than those obtained using the EFA.
A new, simplified approach is also proposed to estimate the increase in channel cross-sectional area during a large flood, given data describing the initial river cross section, riverbed erodibility parameters, and peak flood discharge and duration. The model runs until the cross section erodes to an equilibrium condition or the flood ends. Output describes the area of the cross section at the end of the simulation and the time required to reach equilibrium if it was reached within the simulated period. The model assumes unique, constant values for both the critical shear stress and the erosion coefficient and represents the fluid mechanics in a simplified way, making it of limited value for quantitative predictions. It does, however, provide an indication of which cross sections are most likely to undergo the greatest change in the design event and can be used to investigate sensitivity of erosion predictions to variability in sediment erodibility measurements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215052","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","programNote":"Cooperative Research Units","usgsCitation":"Work, P., and Livsey, D., 2021, American and Sacramento Rivers, California, erodibility measurements and model: U.S. Geological Survey Scientific Investigations Report 2021–5052, 30 p., https://doi.org/10.3133/sir20215052.","productDescription":"Report: vii, 30 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-122004","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":387634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5052/covrthb.jpg"},{"id":387637,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5052/images"},{"id":387635,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5052/sir20215052.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387636,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5052/sir20215052.xml"},{"id":387638,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 4.0, July 2021)"}],"country":"United States","state":"California","otherGeospatial":"American River, Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.58981323242188,\n 38.41378642476067\n ],\n [\n -121.34124755859375,\n 38.41378642476067\n ],\n [\n -121.34124755859375,\n 38.60292007223949\n ],\n [\n -121.58981323242188,\n 38.60292007223949\n ],\n [\n -121.58981323242188,\n 38.41378642476067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Modeling studies project that in the future surface waters in the northeast US will continue to recover from acidification over decades following reductions in atmospheric sulfur dioxide and nitrogen oxides emissions. However, these studies generally assume stationary climatic conditions over the simulation period and ignore the linkages between soil and surface water recovery from acid deposition and changing climate, despite fundamental impacts to watershed processes and comparable time scales for both phenomena. In this study, the integrated biogeochemical model PnET-BGC was applied to two montane forest watersheds in the Adirondack region of New York, USA to evaluate the recovery of surface waters from historical acidification in response to possible future changes in climate and atmospheric sulfur and nitrogen deposition. Statistically downscaled climate scenarios on average project warmer temperatures and greater precipitation for the Adirondack by the end of the century. Model simulations suggest under constant climate, acid-sensitive Buck Creek would gain 12.8 μeq L−1 of acid neutralizing capacity (ANC) by 2100 from large reductions in deposition, whereas acid insensitive Archer Creek is projected to gain 7.9 μeq L−1 of ANC. However, climate change could limit those improvements in acid-base status. Under climate change, a negative offset relative to the ANC increases with no climate change are projected for both streams by 2100. In acid-insensitive Archer Creek the negative offset (−8.5 μeq L−1) was large enough that ANC is projected to decrease by −0.6 μeq L−1, whereas in acid-sensitive Buck Creek, the negative offset (−0.4 μeq L−1) resulted in a slight decline of the projected future ANC increase to 12.4 μeq L−1. Calculated target loads for 2150 for both sites decreased when future climate change was considered in model simulations, which suggests further reductions in acid deposition may be necessary to restore ecosystem structure and function under a changing climate.
Marine phosphorites are an important part of the oceanic phosphorus cycle and are related to the effects of long-term global climate changes. We use petrography, mineralogy, rare earth elements contents, and 87Sr/86Sr-determined carbonate fluorapatite (CFA) and calcite ages to investigate the paragenesis and history of phosphatization of carbonate sediments, limestones, ferromanganese crusts, and ironstones from the summit of Rio Grande Rise (RGR), Southwest Atlantic Ocean. Phosphatization of all the rock types occurred throughout the Miocene from 20.2 to 6.8 million years ago (Ma), and occasionally during the Quaternary, mainly through the cementation of carbonate sediments by cryptocrystalline CFA, likely involving the dissolution of the smaller size fraction of foraminifera-nannofossil ooze. Porosity/permeability and abundance of fine calcite material were important factors determining the intensity of phosphatization of the various rock types. Phosphatization was initiated during a transition to a more dynamic circulation system in the South Atlantic Ocean, which remobilized phosphorus from deeper waters and increased primary productivity that culminated with the middle-Miocene Climatic Optimum between ∼17 and 14.8 Ma. The relatively shallow-water depth of RGR summit during the Miocene provided proximity to the oxygen minimum zone, a reservoir for reactive phosphorus, especially during periods of enhanced phosphorus cycling spurred by surface primary productivity. The cessation of phosphatization at RGR resulted from a rapidly cooling and dry climate that characterized the Miocene-Pliocene transition. Our results support previous observations that periods of broadly intensified ocean circulation and local hydrodynamic changes were the key paleoceanographic links to phosphorite formation.
Evolving complex mixtures of pharmaceuticals and transformation products in effluent-dominated streams pose potential impacts to aquatic species; thus, understanding the attenuation dynamics in the field and characterizing the prominent attenuation mechanisms of pharmaceuticals and their transformation products (TPs) is critical for hazard assessments. Herein, we determined the attenuation dynamics and the associated prominent mechanisms of pharmaceuticals and their corresponding TPs via a combined long-term field study and controlled laboratory experiments. For the field study, we quantified spatiotemporal exposure concentrations of five pharmaceuticals and six associated TPs in a small, temperate-region effluent-dominated stream during baseflow conditions where the wastewater plant was the main source of pharmaceuticals. We selected four sites (upstream, at, and two progressively downstream from effluent discharge) and collected water samples at 16 time points (64 samples in total, approximately twice monthly, depending on flows) for 1 year. Concurrently, we conducted photolysis, sorption, and biodegradation batch tests under controlled conditions to determine the major attenuation mechanisms. We observed 10-fold greater attenuation rates in the field compared to batch tests, demonstrating that connecting laboratory batch tests with field measurements to enhance predictive power is a critical need. Batch systems alone, often used for assessment, are useful for determining fate processes but poorly approximate in-stream attenuation kinetics. Sorption was the dominant attenuation process (t1/2<7.7 d) for 5 of 11 compounds in the batch tests, while the other compounds (n = 6) persisted in the batch tests and along the 5.1 km stream reach. In-stream parent-to-product transformation was minimal. Differential attenuation contributed to the evolving pharmaceutical mixture and created changing exposure conditions with concomitant implications for aquatic and terrestrial biota. Tandem field and laboratory characterization can better inform modeling efforts for transport and risk assessments.
Run timing and spatial locations of spawning habitats are often used to identify stocks for conservation planning or management of salmonid fishes. Although complex stock structure is most common within large watersheds with diverse habitats, even small drainages can produce multiple co-occurring spatially or temporally isolated populations or “stocks.” This project sought to address the potential existence of stock structure of Coho Salmon Oncorhynchus kisutch in a small coastal watershed on Kodiak, Alaska that supports vital subsistence and recreational fisheries and is currently managed as a single stock. We radio-tagged a total of 348 adult Coho Salmon upon freshwater entry into the Buskin River across three spawning seasons (2015–2017) and tracked in-river movements to the final locations where mortality signals were recorded. We identified two primary spawning habitats within the system: main-stem and lake tributaries, with 54% (range of 47% to 61%) of tagged fish with determined fates tracked to main-stem river spawning areas and 46% (range 39% to 53%) presumably spawning in small tributaries of the 1-km2 Buskin Lake at the headwater of the watershed. Despite distinct spatial differences in spawning locations, main-stem and tributary spawners did not differ in migration timing into freshwater (difference in run timing of main-stem versus tributary spawners = 1 d) nor body size (main-stem mean body length, mideye to tail fork = 625 mm, tributary mean = 613 mm). Unexpectedly, we determined nearly 70% of all Coho Salmon spent at least some time in Buskin Lake, including 54% of main-stem spawners, suggesting a potential role of Buskin Lake as an important staging habitat for premature migrating adult Coho Salmon who enter freshwater in advance of final maturation. We also identified areas consistently used for holding prior to spawning that could be used in spatial management planning and during times of necessary conservation to ensure integrity of the stock for the future.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10658","usgsCitation":"Stratton, M.E., Finkle, H., Falke, J.A., and Westley, P., 2021, Assessing potential stock structure of adult Coho Salmon in a small Alaska watershed: Quantifying run timing, spawning locations, and holding areas with radiotelemetry: North American Journal of Fisheries Management, v. 41, no. 5, p. 1423-1435, https://doi.org/10.1002/nafm.10658.","productDescription":"13 p.","startPage":"1423","endPage":"1435","ipdsId":"IP-128616","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Buskin River Watershed, Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.6049041748047,\n 57.73623401472855\n ],\n [\n -152.46414184570312,\n 57.73623401472855\n ],\n [\n -152.46414184570312,\n 57.79666314942287\n ],\n [\n -152.6049041748047,\n 57.79666314942287\n ],\n [\n -152.6049041748047,\n 57.73623401472855\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Stratton, M. E.","contributorId":288653,"corporation":false,"usgs":false,"family":"Stratton","given":"M.","email":"","middleInitial":"E.","affiliations":[{"id":61459,"text":"afg","active":true,"usgs":false}],"preferred":false,"id":838164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finkle, H.","contributorId":288654,"corporation":false,"usgs":false,"family":"Finkle","given":"H.","affiliations":[{"id":61459,"text":"afg","active":true,"usgs":false}],"preferred":false,"id":838165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westley, P. A. H.","contributorId":288655,"corporation":false,"usgs":false,"family":"Westley","given":"P. A. H.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":838166,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225163,"text":"70225163 - 2021 - Dynamics of green and blue water supply stress index across major global cropland basins","interactions":[],"lastModifiedDate":"2021-10-15T13:17:02.510615","indexId":"70225163","displayToPublicDate":"2021-08-09T08:13:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7749,"text":"Frontiers in Climate","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of green and blue water supply stress index across major global cropland basins","docAbstract":"Global food and water insecurity could be serious problems in the upcoming decades with growing demands from the increasing global population and more frequent effect of climatic extremes. As the available water resources are diminishing and facing continuous stress, it is crucial to monitor water demand and water availability to understand the associated water stresses. This study assessed the water stress by applying the water supply stress index (WaSSI) in relation to green (WaSSIG) and blue (WaSSIB) water resources across six major cropland basins including the Mississippi (North America), San Francisco (South America), Nile (Africa), Danube (Europe), Ganges-Brahmaputra (Asia), and Murray-Darling (Australia) for the past 17-years (2003–2019). The WaSSIG and WaSSIB results indicated that the Murray-Darling Basin experienced the most severe (maximum WaSSIG and WaSSIB anomalies) green and blue water stresses and the Mississippi Basin had the least. All basins had both green and blue water stresses for at least 35% (6 out of 17 years) of the study period. The interannual variations in green water stress were driven by both crop water demand and green water supply, whereas the blue water stress variations were primarily driven by blue water supply. The WaSSIG and WaSSIB provided a better understanding of water stress (blue or green) and their drivers (demand or supply driven) across cropland basins. This information can be useful for basin-specific resource mobilization and interventions to ensure food and water security.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2021.663444","usgsCitation":"Khand, K., Senay, G.B., Kagone, S., and Parrish, G.E., 2021, Dynamics of green and blue water supply stress index across major global cropland basins: Frontiers in Climate, v. 3, 663444, 13 p., https://doi.org/10.3389/fclim.2021.663444.","productDescription":"663444, 13 p.","ipdsId":"IP-125893","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":451244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2021.663444","text":"Publisher Index Page"},{"id":390567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2021-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":259185,"corporation":false,"usgs":false,"family":"Khand","given":"Kul Bikram","affiliations":[{"id":52326,"text":"AFDS, Contractor to USGS ERSOS Center","active":true,"usgs":false}],"preferred":false,"id":825216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":825217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":825218,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parrish, Gabriel Edwin Lee 0000-0003-4078-3516","orcid":"https://orcid.org/0000-0003-4078-3516","contributorId":267751,"corporation":false,"usgs":false,"family":"Parrish","given":"Gabriel","email":"","middleInitial":"Edwin Lee","affiliations":[{"id":55490,"text":"Innovate! Inc., Contractor to the USGS EROS Center","active":true,"usgs":false}],"preferred":false,"id":825219,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223216,"text":"70223216 - 2021 - Holocene hydroclimatic reorganizations in northwest Canada inferred from lacustrine carbonate oxygen isotopes","interactions":[],"lastModifiedDate":"2021-08-18T12:54:03.5729","indexId":"70223216","displayToPublicDate":"2021-08-09T07:50:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Holocene hydroclimatic reorganizations in northwest Canada inferred from lacustrine carbonate oxygen isotopes","docAbstract":"Sub-centennial oxygen (δ18O) isotopes of ostracod and authigenic calcite from Squanga Lake provides evidence of hydroclimatic extremes and a series of post-glacial climate system reorganizations for the interior region of northwest Canada. Authigenic calcite δ18O values range from −16‰ to −21‰ and are presently similar to modern lake water and annual precipitation values. Ostracod δ18O record near identical trends with calcite, offset by +1.7 ± 0.6‰. At 11 ka BP (kaBP = thousands of years before 1950), higher δ18O values reflect decreased precipitation−evaporation (P−E) balance from residual ice sheet influences on moisture availability. A trend to lower δ18O values until ∼8 ka BP reflects a shift to wetter conditions, and reorganization of atmospheric circulation. The last millennium and modern era are relatively dry, though not as dry as the early Holocene extreme. North Pacific climate dynamics remained an important driver of P−E balance in northwest Canada throughout the Holocene.
Life-history tradeoffs are common across taxa, but growth-survival tradeoffs—usually enhancing survival at a cost to growth—are less frequently investigated. Increased salinity (NaCl) is a prevalent anthropogenic disturbance that may cause a growth-survival tradeoff for larval amphibians. Although physiological mechanisms mediating tradeoffs are seldom investigated, hormones are prime candidates. Corticosterone (CORT) is a steroid hormone that independently influences survival and growth and may provide mechanistic insight into growth-survival tradeoffs. We conducted a 24-day experiment to test effects of salinity (<32–4000 mg/L) on growth, development, survival, CORT responses, and tradeoffs among traits of larval Northern Leopard Frogs (Rana pipiens). We also experimentally suppressed CORT signaling to determine whether CORT signaling mediates effects of salinity and a growth-survival tradeoff. Increased salinity reduced survival, growth, and development. Suppressing CORT signaling in conjunction with salinity reduced survival further but also attenuated the negative effects of salinity on growth, development, and water content. CORT of control larvae increased or was stable with growth and development but decreased with growth and development for those exposed to salinity. Therefore, salinity dysregulated CORT physiology. Across all treatments, larvae that survived had higher CORT than larvae that died. By manipulating CORT signaling, we provide strong evidence that CORT physiology mediates the outcome of a growth-survival tradeoff and enhances survival. To our knowledge, this is the first study to concomitantly measure tradeoffs between growth and survival and experimentally link these changes to CORT physiology. Identifying mechanistic links between stressors and fitness-related outcomes is critical to enhance our understanding of tradeoffs.
Stormwater discharges from municipalities are regulated by provisions in the Clean Water Act of 1972 to protect the Nation’s water resources from harmful pollutants. In 2014, the Kansas Department of Health and Environment issued new stormwater discharge permits for 17 municipalities in Johnson County, Kansas, in the northeastern part of the State. The county is largely suburban and has 20 municipalities within 22 watersheds. Municipalities in Johnson County are required to implement stormwater management programs that reduce discharges of pollutants, protect water quality, and satisfy applicable water-quality regulations.
In 2015, the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began a 4-year monitoring program designed to meet new stormwater monitoring requirements for some municipalities in Johnson County. Additional data were collected to evaluate the usefulness of continuous water-quality monitoring and different sampling methods in assessing changes in water quality. Twelve of the 22 watersheds in the county were within the sampling network for this project.
Discrete water-quality samples were collected at 25 stream sites and 2 lake sites using passive, grab, and equal-width increment sampling methods. Samples at all sites were analyzed for nutrients, Escherichia coli bacteria, total suspended solids, and suspended-sediment concentration. Ninety-nine percent of storm-event samples and 98 percent of low-flow samples were less than the Kansas Surface Water Quality Standard for nitrate plus nitrite. Eight percent of storm-event samples and 100 percent of low-flow samples were less than the total suspended solids screening value of 50 milligrams per liter. Passive samples generally had higher concentrations when compared to equal-width increment and grab samples, and grab samples and equal-width increment samples generally had similar concentrations.
Continuous water-quality data were collected at one site. Ordinary least squares regression analysis was used to relate continuous (15-minute) water-quality sensor measurements to discretely sampled constituent concentrations at one site.
Numerous factors affect water quality in urban runoff. Urban areas have many possible contaminant sources, including municipal and industrial wastewater discharges, stormwater runoff from impervious surfaces, and failing infrastructure. A better understanding of these factors can inform future monitoring efforts, leading to datasets that are representative of storm runoff and can be used to detect differences between sites and over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215041","collaboration":"Prepared in cooperation with the Johnson County Stormwater Management Program","usgsCitation":"Leiker, B.M., Rasmussen, T.J., Eslick-Huff, P.J., and Painter, C.C., 2021, Assessment of water-quality constituents monitored for total maximum daily loads in Johnson County, Kansas, January 2015 through December 2018: U.S. Geological Survey Scientific Investigations Report 2021–5041, 45 p., https://doi.org/10.3133/sir20215041.","productDescription":"Report: viii, 45 p.; Appendixes: 62 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-119343","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":387659,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91397BC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and preceding precipitation data for low-flow and storm-event samples collected in Johnson County, Kansas, from January 2015 through November 2018"},{"id":387657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5041/sir20215041.pdf","text":"Report","size":"3.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5041"},{"id":387656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5041/coverthb.jpg"},{"id":387658,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5041/sir20215041_appendixes_2to6.pdf","text":"Appendixes 2–6","size":"2.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5041 Appendixes"}],"country":"United States","state":"Kansas","county":"Johnson County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.6075,39.0437],[-94.6075,39.0399],[-94.6082,38.8463],[-94.6084,38.8341],[-94.6102,38.7376],[-95.0572,38.7395],[-95.0558,38.9816],[-95.0477,38.9778],[-95.0383,38.9771],[-95.0312,38.9773],[-95.0292,38.9813],[-95.0271,38.9881],[-95.0249,38.9962],[-95.0189,38.9987],[-95.0135,38.9991],[-95.0077,38.998],[-94.9946,38.9976],[-94.9899,38.997],[-94.9841,38.995],[-94.9789,38.9926],[-94.9755,38.9885],[-94.9704,38.9851],[-94.9645,38.9832],[-94.9575,38.982],[-94.9527,38.9828],[-94.9479,38.9845],[-94.9448,38.9871],[-94.9423,38.9898],[-94.9386,38.9933],[-94.9367,38.9964],[-94.9335,38.9995],[-94.9264,38.9998],[-94.9217,38.9996],[-94.9176,38.9977],[-94.9209,38.9919],[-94.923,38.9856],[-94.9207,38.9837],[-94.9164,38.9859],[-94.9115,38.9889],[-94.9078,38.9924],[-94.9014,39.0022],[-94.8989,39.0053],[-94.8945,39.0102],[-94.8919,39.0155],[-94.891,39.021],[-94.8875,39.0313],[-94.8824,39.0379],[-94.8768,39.0441],[-94.8681,39.052],[-94.8631,39.0564],[-94.8488,39.0578],[-94.8318,39.0546],[-94.8131,39.0486],[-94.8038,39.0456],[-94.7197,39.0435],[-94.6693,39.0433],[-94.6075,39.0437]]]},\"properties\":{\"name\":\"Johnson\",\"state\":\"KS\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.
","language":"English","publisher":"University of California Press","doi":"10.1525/elementa.2020.00076","usgsCitation":"Pershing, A., Alexander, M.A., Brady, D., Brickman, D., Curchitser, E.N., Diamond, A.W., McClenachan, L., Mills, K., Nichols, O., Pendleton, D., Record, N., Scott, J., Staudinger, M., and Wang, Y., 2021, Climate impacts on the Gulf of Maine ecosystem: A review of observed and expected changes in 2050 from rising temperatures: Elementa: Science of the Anthropocene, v. 9, no. 1, 00076, 18 p., https://doi.org/10.1525/elementa.2020.00076.","productDescription":"00076, 18 p.","ipdsId":"IP-120338","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1525/elementa.2020.00076","text":"Publisher Index 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Daniel","contributorId":260609,"corporation":false,"usgs":false,"family":"Pendleton","given":"Daniel","affiliations":[{"id":48127,"text":"Anderson Cabot Center for Marine Life","active":true,"usgs":false}],"preferred":false,"id":818168,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Record, Nicholas","contributorId":260610,"corporation":false,"usgs":false,"family":"Record","given":"Nicholas","affiliations":[{"id":52615,"text":"Bigelow Lab","active":true,"usgs":false}],"preferred":false,"id":818169,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Scott, James","contributorId":260611,"corporation":false,"usgs":false,"family":"Scott","given":"James","affiliations":[{"id":52616,"text":"CIRES","active":true,"usgs":false}],"preferred":false,"id":818170,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Staudinger, Michelle 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":206655,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":818171,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wang, Yanjun","contributorId":260612,"corporation":false,"usgs":false,"family":"Wang","given":"Yanjun","email":"","affiliations":[{"id":52613,"text":"DFO","active":true,"usgs":false}],"preferred":false,"id":818172,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70222522,"text":"sir20215055 - 2021 - Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","interactions":[],"lastModifiedDate":"2021-08-05T09:52:41.030548","indexId":"sir20215055","displayToPublicDate":"2021-08-04T08:23:21","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5055","displayTitle":"Groundwater Quality and Age of Secondary Bedrock Aquifers in the Glaciated Portion of Eastern Nebraska, 2016–18","title":"Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","docAbstract":"The Eastern Nebraska Water Resources Assessment (ENWRA) project was initiated in 2006 to assist water managers by developing a hydrogeologic framework and water budget for the glaciated portion of eastern Nebraska. Within the ENWRA area, the primary groundwater sources for municipal, domestic, and irrigation water needs are provided by withdrawals from alluvial, buried paleovalley, and the High Plains aquifer (where present). Generally, other bedrock aquifers are considered a secondary water source. However, in some areas, such as parts of Sarpy and Nemaha Counties, these secondary bedrock aquifers are the only source of water within glaciated upland areas. To improve the understanding of the quality, geochemistry, and age of groundwater from bedrock aquifers, the U.S. Geological Survey (USGS), in cooperation with the ENWRA group, which includes the Lewis and Clark, Lower Elkhorn, Lower Platte North, Lower Platte South, Nemaha, and Papio-Missouri River Natural Resources Districts, designed a study to sample 31 wells completed in the secondary bedrock aquifers and analyze samples for major ions, physical properties, nutrients, stable isotopes, and selected age tracers. Of the 31 samples collected for this report, 22 samples were collected from the Dakota aquifer contained in the Dakota Sandstone, 3 from the Niobrara aquifer contained in the Niobrara Formation of Colorado Group, and 6 from Paleozoic aquifers contained in undifferentiated Paleozoic-age units.
The results of this study indicate that major ion data collected from the Dakota aquifer can be used for assessing the quality, recharge source, and age of groundwater. Calcium bicarbonate dominant samples were characterized as modern or mixed, indicating that, in these areas, groundwater is unconfined and is recharged by precipitation and (or) surface water. If groundwater extraction rates exceed recharge rates, total dissolved solid concentrations may increase as a result of upwelling of groundwater from deeper units or formations, which can adversely affect groundwater quality. Sampling results presented in this report indicate water quality is good, but that groundwater in the Dakota aquifer with calcium bicarbonate water type may be vulnerable to surface contamination. In contrast, groundwater sampled from the Dakota aquifer, having a dominant water type other than calcium bicarbonate, generally has low dissolved oxygen and nitrate concentrations, and higher concentrations of total dissolved solids and trace elements, including iron and strontium. The geochemical characteristics of noncalcium bicarbonate samples from the Dakota aquifer indicated confining conditions and limited groundwater recharge from local precipitation. Apparent groundwater ages estimated from radiocarbon (carbon-14) sampling of noncalcium bicarbonate samples from the Dakota aquifer indicated that the time of groundwater recharge to the Dakota aquifer occurred during Pleistocene time. Depleted stable isotopes results indicate recharge during a colder climate. Groundwater under confined conditions is not easily recharged from precipitation or surface water. Future groundwater-level monitoring in locations where the Dakota aquifer appears to be confined could provide information to evaluate whether groundwater supplies remain sufficient to meet future municipal, domestic, and irrigation needs.
For the Niobrara aquifer and Paleozoic aquifers, the dominant water type was not a diagnostic indicator of recharge source, age, and groundwater quality as with the Dakota aquifer. Most likely this is because the host formation was dominated by calcium-carbonate-rich rocks; however, few samples were collected from these aquifers to be able to confirm this interpretation. Samples collected from wells completed in the Niobrara aquifer and Paleozoic aquifers and characterized as calcium sulfate water type have statistically significantly higher concentrations of total dissolved solids compared to other samples from the Niobrara aquifer and Paleozoic aquifers characterized as calcium bicarbonate. Given that six of the nine of samples collected from the Niobrara and Paleozoic aquifers indicated modern recharge, these secondary bedrock aquifers are reliant on precipitation to sustain groundwater levels and may be vulnerable to a multiyear drought. Well yields of the Niobrara and Paleozoic aquifers are dependent on the presence of secondary porosity and these units offer little storage. Samples collected from wells completed in Paleozoic aquifers were the most isotopically enriched and similar to modern precipitation and had the highest concentrations of nitrate, indicating that groundwater is affected by agricultural activities. Future groundwater sampling would be beneficial to characterize groundwater-quality changes within the Niobrara and Paleozoic aquifers over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215055","collaboration":"Prepared in cooperation with the Eastern Nebraska Water Resources Assessment","usgsCitation":"Hobza, C.M., and Flynn, A.T., 2021, Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5055, 42 p., https://doi.org/10.3133/sir20215055.","productDescription":"Report: viii, 42 p.; Dataset","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-122775","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":387641,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":387643,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.XML","linkFileType":{"id":8,"text":"xml"}},{"id":387642,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5055/images"},{"id":387640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5055"},{"id":387639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5055/coverthb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.9541015625,\n 42.779275360241904\n ],\n [\n -97.7783203125,\n 42.261049162113856\n ],\n [\n -97.58056640625,\n 41.52502957323801\n ],\n [\n -97.05322265625,\n 41.07935114946899\n ],\n [\n -96.87744140625,\n 40.64730356252251\n ],\n [\n -96.591796875,\n 39.99395569397331\n ],\n [\n -95.2734375,\n 39.977120098439634\n ],\n [\n -95.49316406249999,\n 40.3130432088809\n ],\n [\n -95.80078125,\n 40.713955826286046\n ],\n [\n -95.91064453125,\n 41.31082388091818\n ],\n [\n -96.13037109375,\n 42.00032514831621\n ],\n [\n -96.5478515625,\n 42.65012181368022\n ],\n [\n -97.0751953125,\n 42.87596410238256\n ],\n [\n -97.822265625,\n 42.89206418807337\n ],\n [\n -97.9541015625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Temporal variations of de facto wastewater reuse are relevant to public drinking water systems (PWSs) that obtain water from surface sources. Variations in wastewater discharge flows, streamflow, de facto reuse, and disinfection by-products (DBPs – trihalomethane-4 [THM4] and haloacetic acid-5 [HAA5]) over an 18-year period were examined at 11 PWSs in the Shenandoah River watershed, using more than 25,000 data records, in gaged and ungaged reaches. The relationship of de facto reuse with DBPs by year and quarter at the PWSs was examined. A linear relationship was found between THM4 and de facto reuse on an annual average basis (p = 0.050), as well as in quarters 3 (July – September) (p = 0.032) and 4 (October – December) (p = 0.031). Using a t-test (p < 0.05), the study also showed that there were significant differences in DBP levels for PWSs relative to 1% de facto reuse. This was found for THM4 based on annual average and quarter 1 (January – March) data, and for HAA5 based on quarter 3 data during the period of record.
Phytotoxins are naturally produced toxins with potencies similar/higher than many anthropogenic micropollutants. Nevertheless, little is known regarding their environmental fate and off-field transport to streams. To fill this research gap, a network of six basins in the Midwestern United States with substantial soybean production was selected for the study. Stream water (n = 110), soybean plant tissues (n = 8), and soil samples (n = 16) were analyzed for 12 phytotoxins (5 alkaloids and 7 phytoestrogens) and 2 widely used herbicides (atrazine and metolachlor). Overall, at least 1 phytotoxin was detected in 82% of the samples, with as many as 11 phytotoxins detected in a single sample (median = 5), with a concentration range from below detection to 37 and 68 ng/L for alkaloids and phytoestrogens, respectively. In contrast, the herbicides were ubiquitously detected at substantially higher concentrations (atrazine: 99% and metolachlor: 83%; the concentrations range from below detection to 150 and 410 ng/L, respectively). There was an apparent seasonal pattern for phytotoxins, where occurrence prior to and during harvest season (September to November) and during the snow melt season (March) was higher than that in December–January. Runoff events increased phytotoxin and herbicide concentrations compared to those in base-flow conditions. Phytotoxin plant concentrations were orders of magnitude higher compared to those measured in soil and streams. These results demonstrate the potential exposure of aquatic and terrestrial organisms to soybean-derived phytotoxins.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c01477","usgsCitation":"Hama, J.R., Kolpin, D., LeFevre, G., Hubbard, L.E., Powers, M.M., and Strobel, B.W., 2021, Exposure and transport of alkaloids and phytoestrogens from soybeans to agricultural soils and streams in the Midwestern United States: Environmental Science & Technology, v. 55, p. 11029-11039, https://doi.org/10.1021/acs.est.1c01477.","productDescription":"11 p.","startPage":"11029","endPage":"11039","ipdsId":"IP-129950","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451284,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.1c01477","text":"Publisher Index Page"},{"id":388886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.7685546875,\n 39.198205348894795\n ],\n [\n -89.7802734375,\n 38.8225909761771\n ],\n [\n -87.62695312499999,\n 40.48038142908172\n ],\n [\n -86.923828125,\n 41.04621681452063\n ],\n [\n -88.154296875,\n 42.779275360241904\n ],\n [\n -88.76953125,\n 43.866218006556394\n ],\n [\n -89.56054687499999,\n 43.70759350405294\n ],\n [\n -89.56054687499999,\n 44.68427737181225\n ],\n [\n -90.04394531249999,\n 46.37725420510028\n ],\n [\n -93.33984375,\n 46.40756396630067\n ],\n [\n -95.3173828125,\n 47.39834920035926\n ],\n [\n -96.767578125,\n 44.99588261816546\n ],\n [\n -97.2509765625,\n 45.42929873257377\n ],\n [\n -97.734375,\n 45.089035564831036\n ],\n [\n -94.7900390625,\n 42.87596410238256\n ],\n [\n -94.6142578125,\n 41.86956082699455\n ],\n [\n -93.8671875,\n 40.54720023441049\n ],\n [\n -92.7685546875,\n 39.198205348894795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","noUsgsAuthors":false,"publicationDate":"2021-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Hama, J. R.","contributorId":265315,"corporation":false,"usgs":false,"family":"Hama","given":"J.","email":"","middleInitial":"R.","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":822542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822543,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeFevre, G. H.","contributorId":265316,"corporation":false,"usgs":false,"family":"LeFevre","given":"G. H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":822544,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hubbard, Laura E. 0000-0003-3813-1500 lhubbard@usgs.gov","orcid":"https://orcid.org/0000-0003-3813-1500","contributorId":4221,"corporation":false,"usgs":true,"family":"Hubbard","given":"Laura","email":"lhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822545,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Powers, M. M.","contributorId":265318,"corporation":false,"usgs":false,"family":"Powers","given":"M.","email":"","middleInitial":"M.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":822546,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Strobel, B. W.","contributorId":265320,"corporation":false,"usgs":false,"family":"Strobel","given":"B.","email":"","middleInitial":"W.","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":822547,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222506,"text":"sir20215060 - 2021 - Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","interactions":[],"lastModifiedDate":"2021-08-03T11:56:55.430548","indexId":"sir20215060","displayToPublicDate":"2021-08-02T12:17:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5060","displayTitle":"Groundwater Assessment for Petroleum Hydrocarbon Compounds Associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","title":"Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18","docAbstract":"In 2013, the U.S. Geological Survey began a study in cooperation with the Defense Logistics Agency and the U.S. Air Force to estimate groundwater-flow direction, install groundwater monitoring wells, and collect soil and groundwater samples for petroleum hydrocarbon compounds to identify the presence of hydrocarbon contamination at Ellsworth Air Force Base, South Dakota, specifically around Fuels Area C. Several fuel spills of diesel fuel, jet fuel, and other petroleum products were documented on or near Fuels Area C and several studies have been done to determine the extent of petroleum hydrocarbon contamination in the subsurface.
Two-dimensional electrical resistivity tomography surveys were completed at Fuels Area C in 2014 to characterize subsurface materials and determine the depth to bedrock along survey lines. The depth to the top of the Pierre Shale from land surface along the four electrical resistivity tomography survey lines in Fuels area C ranged from about 5.4 to 8.7 meters. Resistivity lines and lithologic logs in wells in the area indicated mostly clay material with minor occurrences of sand and gravel.
Discrete groundwater levels were collected between November 2014 and June 2018 at 14 monitoring wells for use in generating a potentiometric surface in the study area around Fuels Area C. The potentiometric contours indicated that groundwater flow was from the west to east or southwest to southeast around Fuels Area C.
Soil and groundwater samples were collected at selected locations from 2014 to 2018 to better understand the presence and movement of petroleum hydrocarbons in the study area around Fuels Area C. Soil samples were collected at eight wells during installation in 2014 and three wells during installation in 2016. Groundwater samples were collected from 14 wells and a recovery sump around Fuels Area C from 2014 to 2018.
Several petroleum hydrocarbon compounds were detected, but below action levels, in soil samples collected in 2014 and 2016. Benzene and toluene were not detected in any of the soil samples from the 11 monitoring well sites. Ethylbenzene and total xylenes were detected at sites 1 and 7. Naphthalene was detected in samples from five sites (sites 1, 5, 7, 8, and 9), but concentrations were less than the Tier 1 action level of 25 milligrams per kilogram.
Gasoline-range organic compounds were detected in all soil samples collected during the installation of 11 groundwater monitoring wells within or near Fuels Area C in 2014 and 2016. Diesel-range organic compounds were detected in 9 out of the 11 soil samples collected at the 11 monitoring wells. Gasoline-range organic compound concentrations exceeded the Tier 2 assessment level for total petroleum hydrocarbons in soil samples from site 1 (5,200 milligrams per kilogram), site 5 (580 milligrams per kilogram), and site 9 (1,800 milligrams per kilogram); the remaining sites had concentrations below the Tier 2 assessment level for total petroleum hydrocarbons. The highest concentrations of diesel-range organic compounds in soil samples were from site 1 (3,600 milligrams per kilogram), site 5 (440 milligrams per kilogram), and site 14 (330 milligrams per kilogram), and only the sample from site 1 exceeded the Tier 2 assessment level for total petroleum hydrocarbons.
Petroleum hydrocarbon concentrations were measured in samples collected from 14 groundwater monitoring wells and 1 recovery sump between 2014 and 2018 in the study area around Fuels Area C. Benzene, toluene, ethylbenzene, and xylene (BTEX) compounds were detected in at least one sample collected from 10 of the 15 sites sampled in the study area from 2014 to 2018. Samples from monitoring well sites 2, 3, 6, 8, and 9 did not have any quantifiable concentrations of BTEX compounds. Multiple BTEX compounds were detected consistently in samples collected from sites 10 and 11. Few BTEX compounds were detected at sites outside of and downgradient from Fuels Area C (sites 12–14). Naphthalene was detected in 8 of the 15 sites sampled in the study area in 2014–18. Measurable concentrations of naphthalene generally were less than 5 micrograms per liter in wells sampled in the study area in 2014–18 except for samples collected at sites 5, 7, and 11.
The variability of the presence of BTEX compounds and naphthalene in wells sampled in the study area during 2014–18 likely is caused by the variability in the subsurface material, local groundwater flow, operational fueling activities, and historical spills and releases in the area. The spatial and temporal variability in the BTEX compounds and naphthalene concentrations from samples collected from 2014 to 2018 do not indicate a consistent pattern of subsurface flow or contaminate movement that would be expected if a contaminant plume migrated with the flow and movement of groundwater.
Gasoline-range organic and diesel-range organic compounds were detected in most of the groundwater samples collected in the study area around Fuels Area C in 2014–18; however, concentrations were often less than the laboratory reporting level. Median gasoline-range organic compound concentrations were greater than the laboratory reporting level at sites 1, 5, 9, 10, and 11. The highest concentrations of gasoline-range organic and diesel-range organic compounds generally were observed in samples collected from sites 10 and 11. Gasoline-range organic compound concentrations ranged from 1,500 to 9,700 micrograms per liter at site 10 and from less than 100 to 13,000 micrograms per liter at site 11. Diesel-range organic compound concentrations ranged from 9,600 to 55,000 micrograms per liter at site 10 and from 560 to 7,300 micrograms per liter at site 11.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215060","collaboration":"Prepared in cooperation with Defense Logistics Agency and Ellsworth Air Force Base","usgsCitation":"Bender, D.A., Galloway, J.M., and Medler, C.J., 2021, Groundwater assessment for petroleum hydrocarbon compounds associated with Fuels Area C, Ellsworth Air Force Base, South Dakota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2021–5060, 37 p., https://doi.org/10.3133/sir20215060.","productDescription":"Report: vi, 37 p.; Appendix Table; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-123385","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":387602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5060/coverthb.jpg"},{"id":387603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060.pdf","text":"Report","size":"6.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5060"},{"id":387606,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":387605,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060_table2.1.csv","text":"Table 2.1","size":"28.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5060 Appendix Table 2.1","linkHelpText":"— Appendix table 2.1 Water-quality results for groundwater samples collected from 14 monitoring wells in the study area around Fuels Area C, 2014–18"},{"id":387604,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5060/sir20215060_table2.1.xlsx","text":"Table 2.1","size":"42.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5060 Appendix Table 2.1","linkHelpText":"— Appendix table 2.1 Water-quality results for groundwater samples collected from 14 monitoring wells in the study area around Fuels Area C, 2014–18"},{"id":387607,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.15235137939453,\n 44.104598040381106\n ],\n [\n -103.03321838378906,\n 44.104598040381106\n ],\n [\n -103.03321838378906,\n 44.17974184575526\n ],\n [\n -103.15235137939453,\n 44.17974184575526\n ],\n [\n -103.15235137939453,\n 44.104598040381106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Post-fire hydrologic research typically focuses on the first few years after a wildfire, leading to substantial uncertainty regarding the longevity of impacts. The time needed for hydrologic function to return to pre-fire conditions is critical information for post-fire land and water management decisions. This is particularly true in Mediterranean climates, where water is scarce and in high demand, and the severity and area burned by wildfires are increasing. In part, uncertainty about hydrologic recovery is due to lack of a consistent definition or interpretation of what constitutes “recovery.” Here, we systematically reviewed empirical studies from Mediterranean climates with at least three years of post-fire hydrologic data with the objectives of (a) assessing the recovery period, (b) identifying a definition of post-fire hydrologic recovery, (c) demonstrating a simple analytical approach to aid in assessment of recovery, and (d) outlining research needs and opportunities to better quantify post-fire recovery. We assessed the hydrologic effects reported in 38 sites that were monitored for 3–20 years. Eighteen sites were considered recovered within seven years; however, the recovery time was inconsistent across sites and was not related to location, response variable, or study design. The likelihood of recovery within the study period also decreased with increasing proportion of the watershed area burned. Importantly, we have also proposed a standardized definition and an approach to quantifying hydrologic recovery that may facilitate cross-study comparisons and a deeper understanding of recovery. Specifically, we propose hydrologic recovery has occurred when a specific post-fire hydrologic function or condition of interest returns to the 95% confidence interval of the pre-fire condition. In support of this definition, we have demonstrated applying this simple approach to assess recovery and presented future research topics to improve our understanding of long-term post-fire catchment responses. In addition to the need for more studies that quantify hydrologic responses farther into the post-fire period, understanding post-fire changes in soil structural and hydraulic properties through time will improve our mechanistic understanding of post-fire hydrologic responses and recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2021.126772","usgsCitation":"Wagenbrenner, J.W., Ebel, B., Bladon, K.D., and Kinoshita, A.M., 2021, Post-wildfire hydrologic recovery in Mediterranean climates: A systematic review and case study to identify current knowledge and opportunities: Journal of Hydrology, v. 602, 126772, 16 p., https://doi.org/10.1016/j.jhydrol.2021.126772.","productDescription":"126772, 16 p.","ipdsId":"IP-105958","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":451295,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2021.126772","text":"Publisher Index Page"},{"id":388160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"602","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wagenbrenner, Joseph W. 0000-0003-3317-5141","orcid":"https://orcid.org/0000-0003-3317-5141","contributorId":264444,"corporation":false,"usgs":false,"family":"Wagenbrenner","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":821535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":821536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bladon, Kevin D. 0000-0002-4182-6883","orcid":"https://orcid.org/0000-0002-4182-6883","contributorId":264447,"corporation":false,"usgs":false,"family":"Bladon","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":821537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinoshita, Alicia M.","contributorId":245287,"corporation":false,"usgs":false,"family":"Kinoshita","given":"Alicia","email":"","middleInitial":"M.","affiliations":[{"id":49134,"text":"San Diego State University, California","active":true,"usgs":false}],"preferred":false,"id":821538,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230107,"text":"70230107 - 2021 - Chronic exposure to glyphosate in Florida manatee","interactions":[],"lastModifiedDate":"2022-03-30T15:05:55.443386","indexId":"70230107","displayToPublicDate":"2021-08-02T09:57:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Chronic exposure to glyphosate in Florida manatee","docAbstract":"Florida manatees depend on freshwater environments as a source of drinking water and as warm-water refuges. These freshwater environments are in direct contact with human activities were glyphosate-based herbicides are being used. Glyphosate is the most used herbicide worldwide and it is intensively used in Florida as a sugarcane ripener and to control invasive aquatic plants. The objective of the present study was to determine the concentration of glyphosate and its breakdown product, aminomethylphosphonic acid (AMPA), in Florida manatee plasma and assess their exposure to manatees seeking a warm-water refuge in Crystal River (west central Florida), and in South Florida. We analyzed glyphosate’s and AMPA’s concentrations in Florida manatee plasma (n = 105) collected during 2009–2019 using HPLC-MS/MS. We sampled eight Florida water bodies between 2019 and 2020, three times a year: before, during and after the sugarcane harvest using grab samples and molecular imprinted passive Polar Organic Chemical Integrative Samplers (MIP-POCIS). Glyphosate was present in 55.8% of the sampled Florida manatees’ plasma. The concentration of glyphosate has significantly increased in Florida manatee samples from 2009 until 2019. Glyphosate and AMPA were ubiquitous in water bodies. The concentration of glyphosate and AMPA was higher in South Florida than in Crystal River, particularly before and during the sugarcane harvest when Florida manatees depend on warm water refuges. Based on our results, Florida manatees were chronically exposed to glyphosate and AMPA, during and beyond the glyphosate applications to sugarcane, possibly associated with multiple uses of glyphosate-based herbicides for other crops or to control aquatic weeds. This chronic exposure in Florida water bodies may have consequences for Florida manatees’ immune and renal systems which may further be compounded by other environmental exposures such as red tide or cold stress.","language":"English","publisher":"Pergamon","doi":"10.1016/j.envint.2021.106493","usgsCitation":"De María, M., Silva-Sanchez, C., Kroll, K., Walsh, M.T., Nouri, M., Hunter, M.E., Ross, M., Clauss, T.M., and Denslow, N., 2021, Chronic exposure to glyphosate in Florida manatee: Environment International, v. 152, 106493,11 p., https://doi.org/10.1016/j.envint.2021.106493.","productDescription":"106493,11 p.","ipdsId":"IP-126668","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":451296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envint.2021.106493","text":"Publisher Index 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2021 - Projected changes of regional lake hydrologic characteristics in response to 21st century climate change","interactions":[],"lastModifiedDate":"2023-08-23T12:05:52.264045","indexId":"70247897","displayToPublicDate":"2021-08-02T07:01:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Projected changes of regional lake hydrologic characteristics in response to 21st century climate change","docAbstract":"Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatially explicit, processed-based hydrologic modeling framework implemented over the lake-rich landscape of the Northern Highlands Lake District within the United States with an ensemble of climate change scenarios for the 2050s (2041–2070) and 2080s (2071–2100). Model results quantify the effects of climate change on water budgets and lake stage elevations for 3692 lakes and highlight the importance of landscape and hydrologic setting for the response of specific lake types to climate change. All future climate projections resulted in loss of ice cover and snowpack as well as increased evaporation, but variability in climate projections (warmer conditions, wet winters combined with wet or dry summers) interacted with lake characteristics and landscape position to produce variable lake hydrologic changes. Water levels for drainage lakes (lakes with substantial surface water inflows and outflows) showed nearly no change, whereas minimum water levels for seepage lakes (minimal surface water fluxes) decreased by an average of up to 2.64 m by the end of the 21st century. Our physically based modeling approach is parsimonious and computationally efficient and can be applied to other lake-rich regions to investigate interregional variability in lake hydrologic response to future climate scenarios.
Beginning in 2015, the United States Environmental Protection Agency’s (EPA’s) National Estuary Program (NEP) started a collaboration with partners in seven estuaries along the East Coast (Barnegat Bay; Casco Bay), West Coast (Santa Monica Bay; San Francisco Bay; Tillamook Bay), and the Gulf of Mexico (GOM) Coast (Tampa Bay; Mission-Aransas Estuary) of the United States to expand the use of autonomous monitoring of partial pressure of carbon dioxide (pCO2) and pH. Analysis of high-frequency (hourly to sub-hourly) coastal acidification data including pCO2, pH, temperature, salinity, and dissolved oxygen (DO) indicate that the sensors effectively captured key parameter measurements under challenging environmental conditions, allowing for an initial characterization of daily to seasonal trends in carbonate chemistry across a range of estuarine settings. Multi-year monitoring showed that across all water bodies temperature and pCO2 covaried, suggesting that pCO2 variability was governed, in part, by seasonal temperature changes with average pCO2 being lower in cooler, winter months and higher in warmer, summer months. Furthermore, the timing of seasonal shifts towards increasing (or decreasing) pCO2 varied by location and appears to be related to regional climate conditions. Specifically, pCO2 increases began earlier in the year in warmer water, lower latitude water bodies in the GOM (Tampa Bay; Mission-Aransas Estuary) as compared with cooler water, higher latitude water bodies in the northeast (Barnegat Bay; Casco Bay), and upwelling-influenced West Coast water bodies (Tillamook Bay; Santa Monica Bay; San Francisco Bay). Results suggest that both thermal and non-thermal influences are important drivers of pCO2 in Tampa Bay and Mission-Aransas Estuary. Conversely, non-thermal processes, most notably the biogeochemical structure of coastal upwelling, appear to be largely responsible for the observed pCO2 values in West Coast water bodies. The co-occurrence of high salinity, high pCO2, low DO, and low temperature water in Santa Monica Bay and San Francisco Bay characterize the coastal upwelling paradigm that is also evident in Tillamook Bay when upwelling dominates freshwater runoff and local processes. These data demonstrate that high-quality carbonate chemistry observations can be recorded from estuarine environments using autonomous sensors originally designed for open-ocean settings.
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The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. 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The goal was to estimate long-term average spatial distributions for marine bird species using all available science-quality transect survey data and numerous bathymetric, oceanographic, and atmospheric predictor variables. We developed seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the study region. A statistical modeling framework was used to estimate numerical relationships between bird sighting data (i.e., standardized counts) and a range of temporal (e.g., Pacific Decadal Oscillation [PDO] index), spatially static (e.g., depth), and spatially dynamic (e.g., sea surface chlorophyll-a concentration) environmental variables. The estimated relationships were then used to predict spatially explicit long-term average density (individuals per km2) throughout the study area for each species/group in each of four seasons. 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