The oxidative weathering of sulfidic rock can profoundly impact watersheds through the resulting export of acidity and metals. Weathering leaves a record of mineral transformation, particularly involving minor redox-sensitive phases, that can inform the development of conceptual and quantitative models. In sulfidic sedimentary rocks, however, variations in depositional history, diagenesis and mineralization can change or overprint the distributions of these trace minerals, complicating the interpretation of weathering signatures. Here we show that a combination of bulk mineralogical and geochemical techniques, micrometer-resolution X-ray fluorescence microprobe analysis and rock magnetic measurements, applied to drill core samples and single weathered fractures, can provide data that enable the development of a geochemically consistent weathering model.
This work focused on one watershed in the Upper Colorado River Basin sitting within the Mesaverde Formation, a sedimentary sandstone bedrock with disseminated sulfide minerals, including pyrite and sphalerite, that were introduced during diagenesis and subsequent magmatic-hydrothermal mineralization. Combined analytical methods revealed the pathways of iron (Fe), carbonate and silicate mineral weathering and showed how pH controls element retention or release from the actively weathering fractured sandstone. Drill core logging, whole rock X-ray diffraction, and geochemical measurements document the progression from unweathered rock at depth to weathered rock at the surface. X-ray microprobe analyses of a 1-cm size weathering profile along a fracture surface are consistent with the mobilization of Fe(II) and Fe(III) into acidic pore water from the dissolution of primary pyrite, Fe-sphalerite, chlorite, and minor siderite and pyrrhotite. These reactions are followed by the precipitation of secondary minerals such as of goethite and jarosite, a Fe-(oxyhydr)oxide and hydrous Fe(III) sulfate, respectively. Microscale analyses also helped explain the weathering reactions responsible for the mineralogical transformations observed in the top and most weathered section of the drill core. For example, dissolution of feldspar and chlorite neutralizes the acidity generated by Fe and sulfide mineral oxidation, oversaturating the solution in both Fe-oxides. The combination of X-ray spectromicroscopy and magnetic measurements show that the Fe(III) product is goethite, mainly present either as a coatings on fracture surfaces in the actively weathering region of the core or more homogeneously contained within the unconsolidated regolith at the top of the core. Low-temperature magnetic data reveal the presence of ferromagnetic Fe-sulfide pyrrhotite that, although it occurs at trace concentrations, could provide a qualitative proxy for unweathered sulfide minerals because the loss of pyrrhotite is associated with the onset of oxidative weathering. Pyrrhotite loss and goethite formation are detectable through room-temperature magnetic coercivity changes, suggesting that rock magnetic measurements can determine weathering intensity in rock samples at many scales. This work contributes evidence that the weathering of sulfidic sedimentary rocks follows a geochemical pattern in which the abundance of sulfide minerals controls the generation of acidity and dissolved elements, and the pH-dependent mobility of these elements controls their export to the ground- and surface-water.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2022.11.005","usgsCitation":"Carrero, S., Slotznick, S.P., Fakra, S.C., Sitar, M.C., Bone, S.E., Mauk, J.L., Manning, A.H., Swanson-Hysell, N., Williams, K.H., Banfield, J.F., and Gilbert, B., 2023, Mineralogical, magnetic and geochemical data constrain the pathways and extent of weathering of mineralized sedimentary rocks: Geochimica et Cosmochimica Acta, v. 343, p. 180-195, https://doi.org/10.1016/j.gca.2022.11.005.","productDescription":"16 p.","startPage":"180","endPage":"195","ipdsId":"IP-144517","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":444613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/9rx7w6vm","text":"Publisher Index Page"},{"id":416368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.02298126226637,\n 54.47123140197621\n ],\n [\n -107.02298126226637,\n 53.04438952769195\n ],\n [\n -106.457428641961,\n 53.04438952769195\n ],\n [\n -106.457428641961,\n 54.47123140197621\n ],\n [\n -107.02298126226637,\n 54.47123140197621\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.17636327510458,\n 38.98415958443957\n ],\n [\n -107.17636327510458,\n 38.76066749873635\n ],\n [\n -106.78664053376538,\n 38.76066749873635\n ],\n [\n -106.78664053376538,\n 38.98415958443957\n ],\n [\n -107.17636327510458,\n 38.98415958443957\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"343","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carrero, Sergio","contributorId":304474,"corporation":false,"usgs":false,"family":"Carrero","given":"Sergio","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":870596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slotznick, Sarah P.","contributorId":298122,"corporation":false,"usgs":false,"family":"Slotznick","given":"Sarah","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":870597,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fakra, Sirine C.","contributorId":304475,"corporation":false,"usgs":false,"family":"Fakra","given":"Sirine","email":"","middleInitial":"C.","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":870598,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sitar, M. Cole","contributorId":304476,"corporation":false,"usgs":false,"family":"Sitar","given":"M.","email":"","middleInitial":"Cole","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":870599,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bone, Sharon E.","contributorId":304477,"corporation":false,"usgs":false,"family":"Bone","given":"Sharon","email":"","middleInitial":"E.","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":870600,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mauk, Jeffrey L. 0000-0002-6244-2774 jmauk@usgs.gov","orcid":"https://orcid.org/0000-0002-6244-2774","contributorId":4101,"corporation":false,"usgs":true,"family":"Mauk","given":"Jeffrey","email":"jmauk@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":870601,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":870602,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Swanson-Hysell, Nicholas L.","contributorId":304479,"corporation":false,"usgs":false,"family":"Swanson-Hysell","given":"Nicholas L.","affiliations":[],"preferred":false,"id":870606,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Kenneth H.","contributorId":268895,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":870607,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Banfield, Jillian F.","contributorId":152634,"corporation":false,"usgs":false,"family":"Banfield","given":"Jillian","email":"","middleInitial":"F.","affiliations":[{"id":18952,"text":"Department of Earth and Planetary Science, University of California Berkeley, CA 94720, USA","active":true,"usgs":false}],"preferred":false,"id":870608,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gilbert, Benjamin","contributorId":304478,"corporation":false,"usgs":false,"family":"Gilbert","given":"Benjamin","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":870603,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240465,"text":"70240465 - 2023 - Four decades of regional wet deposition, local bulk deposition, and stream-water chemistry show the influence of nearby land use on forested streams in Central Appalachia☆","interactions":[],"lastModifiedDate":"2023-02-08T12:39:04.453935","indexId":"70240465","displayToPublicDate":"2023-02-03T06:35:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Four decades of regional wet deposition, local bulk deposition, and stream-water chemistry show the influence of nearby land use on forested streams in Central Appalachia☆","docAbstract":"Hydrologic monitoring began on two headwater streams (<1 km2) on the University of Kentucky's Robinson Forest in 1971. We evaluated stream-water (1974–2013) and bulk-deposition (wet + dust) (1984–2013) chemistry in the context of regional wet-deposition patterns that showed decreases in both sulfate and nitrate concentrations as well as proximal surface-mine expansion. Decadal time steps (1974–83, 1984–93, 1994–2003, 2004–2013) were used to quantify change. Comparison of the first two decades showed similarly decreased sulfate (minimum flow-adjusted annual-mean concentration of ≈13.5 mg/L in 1982 to 8.8 mg/L in 1992) and increased pH (6.6–6.8) in both streams, reflecting contemporaneous changes in both bulk and wet deposition. In contrast, concentrations of nitrate (0.14 to >0.25 mg/L) and base cations increased between these two decades, coinciding with expansion of surface mining between 1985 and 1995. In 2004, stream-water pH (6.7 in 2004), sulfate (9.2 mg/L), and nitrate (>0.11 mg/L) were similar to 1982, despite wet-deposition concentrations being lower. Base-cation concentrations were higher in the stream adjacent to ongoing surface mining relative to the stream situated near the middle of the experimental forest. However, pH decreased to approximately 5.7 by 2013 for both streams, which, combined with a shift in dominant cations from calcium to magnesium and potassium, indicates that the soil-buffering capacity of this landscape has been exceeded. Ratios of bulk deposition and stream-water concentrations indicate enrichment of sulfate (1.7–25.2) and cations (0.5–64.8), but not nitrogen (0.1–5.6), indicating that the Forest is not nitrogen saturated and that ongoing changes in water-quality are sulfate driven. When concentrations were adjusted to account for changes in streamflow (climate) over the 4 decades, external influences (land management/regulation) explained most change. The amount and direction of change differed among constituents, both between consecutive decades and between the first and last decades, reflecting the influence of localized surface mining even as regional wet deposition continued to improve due to the Clean Air Act. The implication is that localized stressors have the potential to out-pace the benefits of national environmental policies for communities that depend on local water-resources in similar environments.
Declining body size is believed to be a universal response to climate warming and has been documented in numerous studies of marine and anadromous fishes. The Salmonidae are a family of coldwater fishes considered to be among the most sensitive species to climate warming; however, whether the shrinking body size response holds true for freshwater salmonids has yet to be examined at a broad spatial scale. We compiled observations of individual fish lengths from long-term surveys across the Northern Hemisphere for 12 species of freshwater salmonids and used linear mixed models to test for spatial and temporal trends in body size (fish length) spanning recent decades. Contrary to expectations, we found a significant increase in length overall but with high variability in trends among populations and species. More than two-thirds of the populations we examined increased in length over time. Secondary regressions revealed larger-bodied populations are experiencing greater increases in length than smaller-bodied populations. Mean water temperature was weakly predictive of changes in body length but overall minimal influences of environmental variables suggest that it is difficult to predict an organism's response to changing temperatures by solely looking at climatic factors. Our results suggest that declining body size is not universal, and the response of fishes to climate change may be largely influenced by local factors. It is important to know that we cannot assume the effects of climate change are predictable and negative at a large spatial scale.
A submerged, low-relief nearshore berm was constructed in the Pacific Ocean near the mouth of the Columbia River, USA, using 216,000 m3 of sediment dredged from the adjacent navigation channel. The material dredged from the navigation channel was placed on the northern flank of the ebb-tidal delta in water depths between 12 and 15 m and created a distinct feature that could be tracked over time. Field measurements and numerical modeling were used to evaluate the transport pathways, time scales, and physical processes responsible for dispersal of the berm and evaluate the suitability of the location for operational placement of dredged material to enhance the sediment supply to eroding beaches onshore of the placement site. Repeated multibeam bathymetric surveys characterized the initial berm morphology and dispersion of the berm between September 22, 2020, and March 10, 2021. During this time, the volume of sediment within the berm decreased by about 40%to 127,000 m3, the maximum height decreased by almost 60%, and the center of the deposit shifted onshore over 200 m. Observations of berm morphology were compared with predictions from a three-dimensional hydrodynamic and sediment transport model application to refine poorly constrained model input parameters including sediment transport coefficients, bed schematization, and grain size. The calibrated sediment transport model was used to predict the amount, timing, and direction of transport outside of the observed survey area. Model simulations predicted that tidal currents were weak in the vicinity of the berm and wave processes including enhanced bottom stresses and asymmetric bottom orbital velocities resulted in dominant onshore movement of sediment from the berm toward the coastline. Roughly 50% of the berm volume was predicted to disperse away from the initial placement site during the 169 day hindcast. Between 9 and 17% of the initial volume of the berm was predicted to accumulate along the shoreface of a shoreline reach experiencing chronic erosion directly onshore of the placement site. Scenarios exploring alternate placement locations suggested that the berm was relatively effective in enhancing the sediment supply along the eroding coastline north of the inlet. The transferable monitoring and modeling framework developed in this study can be used to inform implementation of strategic nearshore placements and regional sediment management in complex, high-energy coastal environments elsewhere.
Water samples were collected from July through December 2016 from 9 production wells and 13 domestic wells in the Mohawk River Basin, and from 17 production wells and 17 domestic wells in the western New York River Basins. The samples were collected and processed by using standard U.S. Geological Survey methods and were analyzed for 320 physicochemical properties and constituents, including dissolved gases, major ions, nutrients, trace elements, pesticides, volatile organic compounds, radionuclides, and indicator bacteria, to characterize groundwater quality in the basins. Analytical results are provided in the companion U.S. Geological Survey data release titled “Groundwater Quality Data From the Mohawk and Western New York River Basins, New York, 2016.”
The Mohawk River Basin study area covers 3,500 square miles in New York. Of the 22 wells sampled in the Mohawk River Basin, 8 are completed in sand and gravel, and 14 are completed in bedrock aquifers. Most constituents in the samples from the Mohawk River Basin were present in concentrations below the maximum contaminant levels used in public supply drinking-water regulations by the New York State Department of Health and the U.S. Environmental Protection Agency. Values for some of the properties and concentrations of some constituents—pH, color, iron, manganese, aluminum, sodium, chloride, dissolved solids, radon-222, and heterotrophic plate count—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards.
The western New York River Basins study area covers 5,340 square miles in western New York and includes parts of the Lake Erie and Niagara River Basins, the western Lake Ontario Basin (between the Niagara River and Genesee River Basins), and the Allegheny River Basin. Of the 34 wells sampled in the western New York River Basins, 16 are completed in sand and gravel, and 18 are completed in bedrock aquifers. Most constituents in the samples from the western New York River Basins were present in concentrations below the maximum contaminant levels used in public supply drinking-water regulations by the New York State Department of Health and the U.S. Environmental Protection Agency. Values for some of the properties and concentrations of some constituents—color, chloride, sodium, dissolved solids, iron, manganese, aluminum, arsenic, barium, radon-222, methane, total coliform bacteria, fecal coliform bacteria, and Escherichia coli bacteria—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221021","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Gaige, D.L., Scott, T.-M., Reddy, J.E., and Keefe, M.R., 2023, Groundwater quality in the Mohawk and western New York River Basins, New York, 2016: U.S. Geological Survey Open-File Report 2022–1021, 38 p., https://doi.org/10.3133/ofr20221021.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115618","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":412503,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNH96T","text":"USGS data release","linkHelpText":"Groundwater quality data from the Mohawk and western New York River Basins, New York, 2016"},{"id":412502,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1021/images/"},{"id":412500,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1021"},{"id":412499,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1021/ofr20221021.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1021"},{"id":412498,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1021/coverthb.jpg"},{"id":412501,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1021/ofr20221021.XML"},{"id":499717,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114305.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk and New York River basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.84977657608984,\n 43.556764188166994\n ],\n [\n -75.84977657608984,\n 41.81434325258104\n ],\n [\n -73.94567088326258,\n 41.81434325258104\n ],\n [\n -73.94567088326258,\n 43.556764188166994\n ],\n [\n -75.84977657608984,\n 43.556764188166994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm3/cm3). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.
Pipestone National Monument is a 301-acre site sacred to many Native American Tribes, providing cultural exhibits and walking trails to Pipestone Creek, Winnewissa Falls, and historical pipestone quarries for numerous visitors each year. However, the Minnesota Pollution Control Agency has determined turbidity and fecal coliform bacteria occur in Pipestone Creek in high enough numbers to be a potential health hazard. Concerns also were raised about exposure risk from waterfall mist to visitors and staff. The U.S. Geological Survey and the National Park Service collaborated on a study to collect 21 water-quality samples from 8 creek sites and 3 quarries in 2018 and analyzed them for over 250 water-quality parameters and contaminants. Additional samples were collected in August 2019 to assess the waterfall mists from Winnewissa Falls. Nutrient concentrations in the creek and quarries were elevated in 2018, indicating they are affected by agricultural inputs. All sample concentrations for nitrate and total nitrogen in Pipestone Creek exceeded Minnesota standards and U.S. Environmental Protection Agency nutrient criteria. Minnesota standards and U.S. Environmental Protection Agency nutrient criteria for total phosphorus also were exceeded in some of the quarry samples. Twenty of 210 micropollutants had measurable concentrations: 13 pesticides, 5 pharmaceuticals, and 2 other types of micropollutants. Atrazine, deethylatrazine, and metolachlor ethanesulfonic acid were detected in all 21 samples collected during the study. The five pharmaceuticals detected were acetaminophen, gabapentin, gemfibrozil, metformin, and oxycodone. Gabapentin (10 of 21 samples) and metformin (8 of 21 samples) were the most commonly detected pharmaceuticals. None of the detected micropollutant concentrations exceeded any Minnesota standards or U.S. Environmental Protection Agency aquatic life benchmarks, except the acute toxicity benchmark for nonvascular plants for atrazine. Two cyanotoxins, anatoxin-a and microcystin, were detected, but concentrations were below U.S. Environmental Protection Agency guidelines for swimming or recreation. Notably, total coliform, fecal coliform, and Escherichia coli were detected in all creek samples, and concentrations generally decreased downstream, suggesting contamination potentially occurred upstream from the monument. Mycobacterium avium ssp. paratuberculosis was not detected in any creek sediment samples but was detected in three water samples from the creek. Three organisms were detected in the 2019 water and mist sampling from Winnewissa Falls. Two of these organisms can cause illness in humans (Cryptosporidium and Legionella), and a third (ruminant Bacteroides) is an indicator of manure contamination. Despite few samples, pathogen-positive water samples and air sampling demonstrated the feasibility and utility of the mist sampling approach outlined in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225122","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Krall, A.L., King, K.A., Christensen, V.G., Stokdyk, J.P., Scudder Eikenberry, B.C., and Stevenson, S.A., 2023, Creek and quarry water quality at Pipestone National Monument and pilot study of pathogen detection methods in waterfall mist at Winnewissa Falls, Pipestone, Minnesota, 2018–19: U.S. Geological Survey Scientific Investigations Report 2022–5122, 80 p., https://doi.org/10.3133/sir20225122.","productDescription":"Report: ix, 80 p.; Data Release; Dataset","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-117666","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500465,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114302.htm","linkFileType":{"id":5,"text":"html"}},{"id":412366,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5122/sir20225122.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412365,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5122/sir20225122.pdf","text":"Report","size":"6.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5122"},{"id":412364,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5122/coverthb.jpg"},{"id":412544,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225122/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412369,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412368,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BB1EUV","text":"USGS data release","linkHelpText":"Algal toxins and Mycobacterium avium ssp. paratuberculosis measured in surface-water, quarry-water, and sediment samples collected at Pipestone National Monument, Pipestone, Minnesota, 2018–19"},{"id":412367,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5122/images"}],"country":"United States","state":"Minnesota","city":"Pipestone","otherGeospatial":"Winnewissa Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.34104470371224,\n 44.02847804709168\n ],\n [\n -96.34104470371224,\n 43.992813051913146\n ],\n [\n -96.29197039079662,\n 43.992813051913146\n ],\n [\n -96.29197039079662,\n 44.02847804709168\n ],\n [\n -96.34104470371224,\n 44.02847804709168\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
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Salt marshes with lush grass meadows teeming with shorebirds are iconic features of the Buzzards Bay coast and provide opportunities for recreation, aesthetic enjoyment, as well as important environmental benefits. These productive coastal wetlands are important because they protect properties from storm surges, remove nutrients from the water and carbon from the atmosphere, and provide critical habitats for fish, shellfish, and birds.
Found where the land meets the sea, salt marshes are naturally dynamic features that change with rising seas, waves, ice, and storms. In the past, humans purposely altered salt marshes by filling them to create buildable land or digging drainage ditches. These major alterations harmed marsh structure and health. In recent decades, however, marshes are degrading because of more diffuse and complex pressures such as nutrient pollution, sea level rise, major storms, and crab overgrazing. As a result, at many places along the East Coast, marshes have crumbling banks and large areas where the plants have died, leaving behind mudflats. The Buzzards Bay Coalition and the Buzzards Bay National Estuary Program began field monitoring of salt marshes around Buzzards Bay in 2019 to document changes (map below shows sites). We partnered with the U.S. Geological Survey and the Woodwell Climate Research Center to use aerial tools to investigate how different characteristics of the long-term marsh sites and their watersheds affect the marsh’s current health and likely future. This report brings together the results of on the ground monitoring with data from aerial imagery to look at marsh status at 12 long-term monitoring sites based on existing stressors, current marsh conditions, and potential for adaptation.
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Lee","contributorId":303379,"corporation":false,"usgs":false,"family":"Blaney","given":"Lee","email":"","affiliations":[{"id":38069,"text":"University of Maryland, Baltimore County","active":true,"usgs":false}],"preferred":false,"id":867425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cantwell, Mark","contributorId":303380,"corporation":false,"usgs":false,"family":"Cantwell","given":"Mark","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":867426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fowler, Lara","contributorId":303381,"corporation":false,"usgs":false,"family":"Fowler","given":"Lara","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":867427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ihde, Thomas F.","contributorId":261321,"corporation":false,"usgs":false,"family":"Ihde","given":"Thomas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":867428,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mank, Mark","contributorId":303383,"corporation":false,"usgs":false,"family":"Mank","given":"Mark","email":"","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":867430,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Majcher, Emily H. 0000-0001-7144-6809","orcid":"https://orcid.org/0000-0001-7144-6809","contributorId":203335,"corporation":false,"usgs":true,"family":"Majcher","given":"Emily","middleInitial":"H.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867431,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Onyullo, George 0000-0002-5234-4871","orcid":"https://orcid.org/0000-0002-5234-4871","contributorId":261308,"corporation":false,"usgs":false,"family":"Onyullo","given":"George","email":"","affiliations":[{"id":52806,"text":"District of Columbia - Dept of Energy and Env","active":true,"usgs":false}],"preferred":false,"id":867432,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Phillips, Scott W. 0000-0002-1637-9428 swphilli@usgs.gov","orcid":"https://orcid.org/0000-0002-1637-9428","contributorId":191221,"corporation":false,"usgs":true,"family":"Phillips","given":"Scott","email":"swphilli@usgs.gov","middleInitial":"W.","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":867433,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240636,"text":"70240636 - 2023 - Ecology and ecosystem impacts of submerged and floating aquatic vegetation in the Sacramento-San Joaquin Delta","interactions":[],"lastModifiedDate":"2023-02-10T13:13:30.325947","indexId":"70240636","displayToPublicDate":"2023-02-01T07:11:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Ecology and ecosystem impacts of submerged and floating aquatic vegetation in the Sacramento-San Joaquin Delta","docAbstract":"Substantial increases in non-native aquatic vegetation have occurred in the upper San Francisco Estuary over the last 2 decades, largely from the explosive growth of a few submerged and floating aquatic plant species. Some of these species act as ecosystem engineers by creating conditions that favor their further growth and expansion as well as by modifying habitat for other organisms. Over the last decade, numerous studies have investigated patterns of expansion and turn-over of aquatic vegetation species; effects of vegetation on ecosystem health, water quality, and habitat; and effects of particular species or communities on physical processes such as carbon and sediment dynamics. Taking a synthetic approach to evaluate what has been learned over the last few years has shed light on just how significant aquatic plant species and communities are to ecosystems in the Sacramento-San Joaquin Delta. Aquatic vegetation affects every aspect of the physical and biotic environment, acting as ecosystem engineers on the landscape. Furthermore, their effects are constantly changing across space and time, leaving many unanswered questions about the full effects of aquatic vegetation on Delta ecosystems and what future effects may result, as species shift in distribution and new species are introduced. Remaining knowledge gaps underlie our understanding of aquatic macrophyte effects on Delta ecosystems, including their roles and relationships with respect to nutrients and nutrient cycling, evapotranspiration and water budgets, carbon and sediment, and emerging effects on fish species and their habitats. This paper explores our current understanding of submerged and floating aquatic vegetation (SAV and FAV) ecology with respect to major aquatic plant communities, observed patterns of change, interactions between aquatic vegetation and the physical environment, and how these factors affect ecosystem services and disservices within the upper San Francisco Estuary.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2023v20iss4art3","usgsCitation":"Christman, M.A., Khanna, S., Drexler, J.Z., and Young, M.J., 2023, Ecology and ecosystem impacts of submerged and floating aquatic vegetation in the Sacramento-San Joaquin Delta: San Francisco Estuary and Watershed Science, v. 20, no. 4, 3, 32 p., https://doi.org/10.15447/sfews.2023v20iss4art3.","productDescription":"3, 32 p.","ipdsId":"IP-144768","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":444640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2023v20iss4art3","text":"Publisher Index Page"},{"id":412940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.65414668802397,\n 38.53804340076624\n ],\n [\n -122.65414668802397,\n 37.37783082362128\n ],\n [\n -121.1606403257308,\n 37.37783082362128\n ],\n [\n -121.1606403257308,\n 38.53804340076624\n ],\n [\n -122.65414668802397,\n 38.53804340076624\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Christman, Mairgareth A.","contributorId":206436,"corporation":false,"usgs":false,"family":"Christman","given":"Mairgareth","email":"","middleInitial":"A.","affiliations":[{"id":37330,"text":"Delta Stewardship Council, Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":864044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khanna, Shruti","contributorId":205167,"corporation":false,"usgs":false,"family":"Khanna","given":"Shruti","email":"","affiliations":[{"id":37041,"text":"Department of Land, Air, and Water Resources, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":864045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":864046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":864047,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243560,"text":"70243560 - 2023 - Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","interactions":[],"lastModifiedDate":"2023-05-12T12:20:15.008523","indexId":"70243560","displayToPublicDate":"2023-02-01T07:09:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","title":"Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","docAbstract":"Aim
Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.
Location
North America.
Methods
We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.
Results
GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.
Main Conclusions
Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13659","usgsCitation":"Hightower, J.N., Crawford, D.L., Thogmartin, W.E., Aldinger, K.R., Barker Swarthout, S., Buehler, D.A., Confer, J., Friis, C., Larkin, J., Lowe, J.D., Piorkowski, M., Rohrbaugh, R., Rosenberg, K.V., Smalling, C.G., Wood, P.B., Vallender, R., and Roth, A.M., 2023, Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers: Diversity and Distributions, v. 29, no. 2, p. 254-271, https://doi.org/10.1111/ddi.13659.","productDescription":"18 p.","startPage":"254","endPage":"271","ipdsId":"IP-138007","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":444642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13659","text":"Publisher Index Page"},{"id":435475,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AS9YAC","text":"USGS data release","linkHelpText":"Blue-winged and Golden-winged Warbler Breeding Season Occurrences in North America, 1932-2021"},{"id":416983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hightower, Jessica N.","contributorId":204645,"corporation":false,"usgs":false,"family":"Hightower","given":"Jessica","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":872370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crawford, Dolly L.","contributorId":299588,"corporation":false,"usgs":false,"family":"Crawford","given":"Dolly","email":"","middleInitial":"L.","affiliations":[{"id":64892,"text":"Pennsylvania Western University","active":true,"usgs":false}],"preferred":false,"id":872371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":872372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldinger, Kyle R.","contributorId":171892,"corporation":false,"usgs":false,"family":"Aldinger","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barker Swarthout, Sara","contributorId":176239,"corporation":false,"usgs":false,"family":"Barker Swarthout","given":"Sara","email":"","affiliations":[{"id":34544,"text":"Cornell Lab of Ornithology, Cornell University","active":true,"usgs":false}],"preferred":false,"id":872374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":872375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Confer, John","contributorId":305334,"corporation":false,"usgs":false,"family":"Confer","given":"John","email":"","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":872376,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Friis, Christian","contributorId":194605,"corporation":false,"usgs":false,"family":"Friis","given":"Christian","email":"","affiliations":[],"preferred":false,"id":872377,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Larkin, Jeff","contributorId":199993,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":872378,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lowe, James D.","contributorId":305336,"corporation":false,"usgs":false,"family":"Lowe","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":872379,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Piorkowski, Martin","contributorId":305338,"corporation":false,"usgs":false,"family":"Piorkowski","given":"Martin","email":"","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":872380,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rohrbaugh, Ronald W.","contributorId":305340,"corporation":false,"usgs":false,"family":"Rohrbaugh","given":"Ronald W.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":872381,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":872382,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Smalling, Curtis G.","contributorId":191724,"corporation":false,"usgs":false,"family":"Smalling","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":33352,"text":"Audubon North Carolina","active":true,"usgs":false}],"preferred":false,"id":872383,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wood, Petra B.","contributorId":305342,"corporation":false,"usgs":false,"family":"Wood","given":"Petra","email":"","middleInitial":"B.","affiliations":[{"id":66214,"text":"West Virginia Cooperative Fish and Wildlife Research Unit,","active":true,"usgs":false}],"preferred":false,"id":872384,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vallender, Rachel","contributorId":194966,"corporation":false,"usgs":false,"family":"Vallender","given":"Rachel","email":"","affiliations":[{"id":34540,"text":"Canadian Museum of Nature","active":true,"usgs":false},{"id":27312,"text":"Canadian Wildlife Service, Environment and Climate Change Canada, 6 Bruce Street, Mount","active":true,"usgs":false}],"preferred":false,"id":872385,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Roth, Amber M.","contributorId":191723,"corporation":false,"usgs":false,"family":"Roth","given":"Amber","email":"","middleInitial":"M.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false},{"id":27866,"text":"University of Maine, Department of Wildlife, Fisheries, and Conservation Biology, Orono, ME","active":true,"usgs":false},{"id":25614,"text":"School of Forest Resources, University of Maine","active":true,"usgs":false}],"preferred":false,"id":872386,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70240935,"text":"70240935 - 2023 - Agricultural conservation practices could help offset climate change impacts on cyanobacterial harmful algal blooms in Lake Erie","interactions":[],"lastModifiedDate":"2024-05-20T16:23:27.294825","indexId":"70240935","displayToPublicDate":"2023-02-01T06:36:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural conservation practices could help offset climate change impacts on cyanobacterial harmful algal blooms in Lake Erie","docAbstract":"Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.
Finchite (IMA2017-052), Sr(UO2)2(V2O8)·5H2O, is the first uranium mineral known to contain essential Sr. The new mineral occurs as yellow-green blades up to ~10 µm in length in surface outcrops of the calcrete-type uranium deposit at Sulfur Springs Draw, Martin County, Texas, U.S.A. Crystals of finchite were subsequently discovered underground in the Pandora mine, La Sal, San Juan County, Utah, U.S.A., as diamond-shaped golden-yellow crystals reaching up to 1 mm. The crystal structure of finchite from both localities was determined using single-crystal X-ray diffraction and is orthorhombic, Pcan, with a = 10.363(6) Å, b = 8.498(5) Å, c = 16.250(9) Å, V = 1431.0(13) Å3, Z = 4 (R1 = 0.0555) from Sulfur Springs Draw; and a = 10.3898(16), b = 8.5326(14), c = 16.3765(3) Å, V = 1451.8(4) Å3, Z = 4 (R1 = 0.0600) from the Pandora mine. Electron-probe microanalysis provided the empirical formula (Sr0.88K0.17Ca0.10Mg0.07Al0.03Fe0.02)Σ1.20(UO2)2(V2.08O8)·5H2O for crystals from Sulfur Springs Draw, and (Sr0.50Ca0.28Ba0.22K0.05)Σ0.94(U0.99O2)2(V2.01O8)·5H2O for crystals from the Pandora mine, based on 17 O atoms per formula unit. The structure of finchite contains uranyl vanadate sheets based upon the francevillite topology. Finchite is a possible immobilization species for both uranium and the dangerous radionuclide 90Sr because of the relative insolubility of uranyl vanadate minerals in water.
During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality.
The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R2), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit.
In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.
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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","conferenceDate":"January 24-25, 2023","conferenceLocation":"Flagstaff, AZ","language":"English","collaboration":"Bureau of Reclamation","usgsCitation":"Deemer, B., Voichick, N., Sabol, T.A., Andrews, C.M., and Mihalevich, B.A., 2023, Appendix 1: Lake Powell water quality monitoring, in Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program, Flagstaff, AZ, January 24-25, 2023, p. 134-142.","productDescription":"9 p.","startPage":"134","endPage":"142","ipdsId":"IP-147768","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":416490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416481,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/uc/progact/amp/twg/2023-01-26-twg-meeting/20230126-AnnualReportingMeeting-ProceedingsFY2022AnnualReportingMeeting-508-UCRO.pdf"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.98991581400394,\n 36.92878972693423\n ],\n [\n -109.82511758909659,\n 36.92878972693423\n ],\n [\n -109.82511758909659,\n 38.054642642406435\n ],\n [\n -111.98991581400394,\n 38.054642642406435\n ],\n [\n -111.98991581400394,\n 36.92878972693423\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voichick, Nicholas 0000-0002-9716-5906 nvoichick@usgs.gov","orcid":"https://orcid.org/0000-0002-9716-5906","contributorId":203632,"corporation":false,"usgs":true,"family":"Voichick","given":"Nicholas","email":"nvoichick@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mihalevich, Bryce Anthony 0000-0001-5492-221X","orcid":"https://orcid.org/0000-0001-5492-221X","contributorId":304586,"corporation":false,"usgs":true,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"Anthony","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871019,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241025,"text":"70241025 - 2023 - Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","interactions":[],"lastModifiedDate":"2023-03-07T12:47:56.075162","indexId":"70241025","displayToPublicDate":"2023-01-31T06:40:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","docAbstract":"Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.
The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and Escherichia coli at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225130","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., 2023, Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5130, 14 p., https://doi.org/10.3133/sir20225130.","productDescription":"Report: vii, 14 p.; 14 Appendixes; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141414","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":412468,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225130/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412446,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412445,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5130/downloads","text":"Appendixes 1–14","linkFileType":{"id":1,"text":"pdf"}},{"id":412443,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500484,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114304.htm","linkFileType":{"id":5,"text":"html"}},{"id":412442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.pdf","text":"Report","size":"5.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5130"},{"id":412441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5130/coverthb.jpg"},{"id":412444,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5130/images"}],"country":"United States","state":"Kansas","city":"Topeka","otherGeospatial":"Kansas River, Topeka Weir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.66443090940162,\n 39.06286764035286\n ],\n [\n -95.67191642810837,\n 39.06117240426602\n ],\n [\n -95.64218228435746,\n 39.06262546626132\n ],\n [\n -95.64239021543237,\n 39.064239944513304\n ],\n [\n -95.63864745607941,\n 39.07925282377511\n ],\n [\n -95.63812762839112,\n 39.08401430643988\n ],\n [\n -95.63251348936144,\n 39.0836915042114\n ],\n [\n -95.63199366167355,\n 39.08054410506932\n ],\n [\n -95.61452745135817,\n 39.07780010407046\n ],\n [\n -95.61463141689583,\n 39.07465244209993\n ],\n [\n -95.6078736569523,\n 39.057620349038984\n ],\n [\n -95.60225951792262,\n 39.05794327053502\n ],\n [\n -95.59716520658046,\n 39.05826619055418\n ],\n [\n -95.58572899744581,\n 39.0606880436211\n ],\n [\n -95.58884796357322,\n 39.063432710002644\n ],\n [\n -95.59300658507682,\n 39.070778203846714\n ],\n [\n -95.59092727432522,\n 39.08958241237616\n ],\n [\n -95.60589831173833,\n 39.09127696606694\n ],\n [\n -95.60849745017785,\n 39.08732294411979\n ],\n [\n -95.61702262426043,\n 39.08691946002881\n ],\n [\n -95.62222090113984,\n 39.08611248492275\n ],\n [\n -95.63532055887627,\n 39.090712119361\n ],\n [\n -95.64166245666956,\n 39.08917894121052\n ],\n [\n -95.65101935505261,\n 39.08361080342297\n ],\n [\n -95.65663349408229,\n 39.076266645265974\n ],\n [\n -95.66058418451098,\n 39.067145912321024\n ],\n [\n -95.66443090940162,\n 39.06286764035286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223016","usgsCitation":"Dahm, K.G., Jones, D.K., Anderson, P.J., Dick, M.C., Hawbaker, T.J., and Horton, R.J., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST): U.S. Geological Survey Fact Sheet 2022–3016, 4 p., https://doi.org/10.3133/fs20223016.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-133121","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":416352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3016"},{"id":416351,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.xml"},{"id":411878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3016/coverthb.jpg"},{"id":411879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.pdf","text":"Report","size":"5.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3016"},{"id":416350,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3016/images"}],"country":"Mexico, United Statetes","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
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Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2023.119679","usgsCitation":"Zhang, C., McIntosh, K.D., Sienkiewicz, N., Stelzer, E., Graham, J.L., and Lu, J., 2023, Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States: Water Research, v. 235, 119679, 16 p., https://doi.org/10.1016/j.watres.2023.119679.","productDescription":"119679, 16 p.","ipdsId":"IP-147638","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":444674,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10123349","text":"External Repository"},{"id":419308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lu, Jingrang","contributorId":288917,"corporation":false,"usgs":false,"family":"Lu","given":"Jingrang","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection 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northeastern Minnesota","docAbstract":"This study compares the results of two regional steady-state U.S. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Flow (MODFLOW) models constructed to quantify the hydrologic changes in the St. Louis River Basin from iron mining on the Mesabi Iron Range in northeastern Minnesota. The U.S. Geological Survey collaborated in this study with bands of the Minnesota Chippewa Tribe, and the Minnesota Pollution Control Agency to inform management decisions about aquatic resources in the St. Louis River Basin. A model constructed and calibrated to represent average 1995–2015 mining conditions produced regional groundwater heads and flows. A pre-mining scenario model was constructed from this mining model but had the land and bedrock surfaces restored to pre-mining topographies and had modeled mining features (mine pits, tailings basins, waste-rock piles, and mining-disturbed areas) eliminated to represent general pre-mining stratigraphy and hydrogeology. Many of the features important to the hydrology of this mining area (like individual mine pits) are difficult to represent in groundwater models and required the use of modeling tools to indirectly account for their effects. The difference between the results of these two models represents mining’s effects on the hydrology in the Mesabi Iron Range area of the St Louis River Basin. The mining and pre-mining regional models also can provide boundary conditions and initial properties for future local or site-specific groundwater-flow models in the area.
Total groundwater flow through the mining model is 171 million cubic feet per day. Areal recharge is the largest source of groundwater (78 and 81 percent of total groundwater flow in the mining and pre-mining scenario models, respectively). Seepage from streams and lakes provides another 17 percent of the total groundwater flow through both models. Water leaves aquifers through seepage to streams (discharge as base flow, 43 percent in both models) and areal seepage to the land surface (surface seepage), for example to wetlands (45 and 49 percent, mining and pre-mining scenario models respectively).
Comparison of the results from the mining and pre-mining scenario models shows that iron mining has produced measurable hydrologic changes in the St. Louis River Basin, but that most of those changes and the highest magnitude changes occur near the mining features. Flow changes to and from surface-water bodies like streams and wetlands were analyzed in detail because of their importance in sustaining surface waters and aquatic life. Overall, groundwater flow in the mining model was 3.62 million cubic feet per day (2.2 percent) greater than total pre-mining model groundwater flow. This was caused by an increase in recharge from tailings basins and a decrease in discharge from surface seepage. Groundwater discharge to mine pits was the largest change in groundwater flows between the models (a change representing 2.8 percent of total pre-mining model groundwater flow). Net recharge to groundwater from tailings basins (2.4 percent), net decrease in surface seepage from groundwater (2.7 percent), and net increase in seepage to streams (1.0 percent) were all in this same range of total pre-mining model groundwater flow. Groundwater lost through mine-pit withdrawals was nearly offset by groundwater gained through recharge from tailings basins. However, because losses and gains occurred in different areas, the effect of mining can have more substantial effects on local areas than the model-wide averages represent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225124","collaboration":"Prepared in cooperation with bands of the Minnesota Chippewa Tribe, the Great Lakes Indian Fish & Wildlife Commission, and the Minnesota Pollution Control Agency","usgsCitation":"Cowdery, T.K., Baker, A.C., Haserodt, M.J., Feinstein, D.T., and Hunt, R.J., 2023, Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota: U.S. Geological Survey Scientific Investigations Report 2022–5124, 59 p., https://doi.org/10.3133/sir20225124.","productDescription":"Report: viii, 59 p.; 2 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-122102","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":412380,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412376,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5124/images"},{"id":412373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5124/coverthb.jpg"},{"id":412374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5124"},{"id":500466,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114303.htm","linkFileType":{"id":5,"text":"html"}},{"id":412504,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412378,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","linkHelpText":"Soil-water-balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995–2010"},{"id":412377,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6KSBJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT simulations of regional groundwater flow under mining and pre-mining scenarios near the Mesabi Iron Range within the St. Louis River Basin, northeastern Minnesota"}],"country":"United States","state":"Minnesota","otherGeospatial":"Mesabi Iron Range, St Louis River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.333,\n 48\n ],\n [\n -93.3333,\n 47\n ],\n [\n -91.666,\n 47\n ],\n [\n -91.666,\n 48\n ],\n [\n -93.333,\n 48\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Improvements in timber harvest practices and reductions in harvest volumes over the past half century are commonly presumed to have reduced sediment loads in many western US rivers. However, direct assessments in larger watersheds are relatively sparse. Here, we compare 2019–21 sediment concentrations against those of the late 1970s in the Bogachiel and Calawah River watersheds, adjacent and similarly sized (~300 km2) basins in the western Olympic Mountains of Washington State. The Calawah River watershed has experienced significant land-cover disturbance, including a large 1951 fire, extensive post-fire salvage logging, and relatively high rates of timber harvest through the 1990s. In contrast, the Bogachiel River watershed did not burn, and experienced only modest timber harvest that largely post-dated 1970s sediment monitoring. Channel-width trends suggest the Calawah River was still recovering from 1950s disturbances in the late 1970s. We found that 2019–21 suspended-sediment loads in the Calawah River were 2.3–2.6 times lower than would have been expected based on 1970s sediment rating curves, while recent loads in the Bogachiel River were a factor of 1.4 ± 1.0 lower. We consider the plausibility and possible explanations of declining concentrations in the less-disturbed Bogachiel River. Suspended-sediment yields in the Bogachiel River were two times higher than yields in the Calawah River, which is attributed to a combination of modestly higher precipitation, more efficient runoff generation, and more extensive and erodible Quaternary valley fills in the Bogachiel River. Regional shifts in flood hydrology have also influenced suspended-sediment loads in both watersheds. Our results then document a significant decline in suspended-sediment concentrations in the Calawah River over the past half century. Reduced land-cover disturbance provides the simplest and most likely explanation for this decline, though the wide range of possible concentration changes in the Bogachiel River leaves open possibilities that other processes (human, natural, or methodologic) could be a factor.