In crude oil contaminant plumes, the dissolved organic carbon (DOC) is mainly hydrocarbon degradation intermediates only partly quantified by the diesel range total petroleum hydrocarbon (TPHd) method. To understand potential biological effects of degradation intermediates, we tested three fractions of DOC: (1) solid-phase extract (HLB); (2) dichloromethane (DCM-total) extract used in TPHd; and (3) DCM extract with hydrocarbons isolated by silica gel cleanup (DCM-SGC). Bioactivity of extracts from five wells spanning a range of DOC was tested using an in vitro multiplex reporter system that evaluates modulation of the activity of 46 transcription factors; extracts were evaluated at concentrations equivalent to the well water samples. The aryl hydrocarbon receptor (AhR) and pregnane X receptor (PXR) transcription factors showed the greatest upregulation, with HLB exceeding DCM-total, and no upregulation in the hydrocarbon fraction (DCM-SGC). The HLB extracts were further studied with HepG2 chemically activated luciferase expression (CALUX) in vitro assays at nine concentrations ranging from 40 to 0.01 times the well water concentrations. Responses decreased with distance from the source but were still present at two wells without detectable hydrocarbons. Thus, our in vitro assay results indicate that risks associated with degradation intermediates of hydrocarbons in groundwater will be underestimated when protocols that remove these chemicals are employed.
Digital flood-inundation maps for a 9.9-mile reach of Dardenne Creek, St. Charles County, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Transportation, St. Charles County, and the Cities of O’Fallon and St. Peters, Mo. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages 05514860 Dardenne Creek at Old Town St. Peters, Mo., and 05587450 Mississippi River at Grafton, Illinois. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= drcm7 and https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= grfi2, which also forecasts flood hydrographs at these sites (sites DRCM7 and GRFI2).
Flood profiles were computed for the Dardenne Creek stream reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relation at the USGS streamgages 05514840 Dardenne Creek at O’Fallon, Mo., and 05514860 Dardenne Creek at Old Town St. Peters, Mo., and the documented high-water marks from the flood of December 2015.
The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 16 ft, or near bankfull, to 32 ft at the reference streamgage 05514860. Stages in the lower Dardenne Creek can be affected by backwater from the Mississippi River; therefore, several sets of water-surface profiles were developed representing the extent of varying levels of backwater as referenced to the USGS streamgage 05587450 on the Mississippi River at Grafton, Ill. The upper stage for each map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) at the streamgage location. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.26-ft vertical accuracy and 0.71-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with real-time information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and postflood recovery efforts.
Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
As part of CRMS, Digital Orthophoto Quarter Quadrangles (DOQQs) for the coastal region of Louisiana are created for years when coastwide land-water classifications are required. A DOQQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been corrected. These images combine the image characteristics of a photo with the geometric qualities of a map. The DOQQs generated for this project consist of four components or spectral bands of information: blue, green, red and very-near infrared (VNIR). These images are referred to as Color Infrared (CIR) digital imagery.
CRMS site-level assessments of land-water coverage will be based off of color-infrared photography acquired for coastal Louisiana and clipped to each 1-km2 CRMS-Wetlands site. Unless otherwise noted as a specific preliminary condition, all vegetation such as scrub-shrub, emergent vegetation, and forested areas will fall under the land classification, while open water, non-vegetated, regularly flooded mud flats, and aquatic vegetation beds will be characterized as water.
CRMS imagery contracts are managed and issued by the United States Geological Survey (USGS) Wetland and Aquatic Research Center.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"A standard operating procedures manual for the Coastwide Reference Monitoring System-Wetlands and the System-Wide Assessment and Monitoring Program: Methods for site establishment, data collection, and quality assurance/quality control","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","collaboration":"Coastal Protection and Restoration Authority of Louisiana","usgsCitation":"Folse, T.M., McGinnis, T., Sharp, L.A., West, J.L., Hymel, M.K., Troutman, J.P., Weifenbach, D., Boshart, W.M., Rodrigue, L.B., Richardi, D.C., Wood, W.B., Miller, C.M., Robinson, E.M., Freeman, A.M., Stagg, C., Couvillion, B., and Beck, H., 2020, Imagery, 5 p.","productDescription":"5 p.","startPage":"10-1","endPage":"10-5","ipdsId":"IP-120085","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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Therefore, improved methods for identifying small amounts of oil on birds are needed. Because ultraviolet (UV) light can be used to identify thin crude oil films in water and on substrate that are not visually apparent under normal lighting conditions, we hypothesized that UV light could be useful for detecting small amounts of oil present on the plumage of birds. We evaluated black skimmers (Rynchops niger), brown pelicans (Pelecanus occidentalis), clapper rails (Rallus crepitans), great egrets (Ardea alba), and seaside sparrows (Ammodramus maritimus) exposed to areas affected by the Deepwater Horizon oil spill in the Gulf of Mexico as well as from reference areas from 20 June, 2010 to 23 February, 2011. When visually assessed without UV light, 19.6% of birds evaluated from areas affected by the spill were determined to be oiled (previously published data), whereas when examined under UV light, 56.3% of the same birds were determined to have oil exposure. Of 705 individuals examined in areas potentially impacted by the spill, we found that fluorescence under UV light assessment identified 259 oiled birds that appeared to be oil-free on visual exam, supporting its utility as a simple tool for improving detection of modestly oiled birds in the field. Further, UV assessment revealed an increase in qualitative severity of oiling (approximate % of body surface oiled) in 40% of birds compared to what was determined on visual exam. Additionally, black skimmers, brown pelicans, and great egrets exposed to oil as determined using UV light experienced oxidative injury to erythrocytes, had decreased numbers of circulating erythrocytes, and showed evidence of a regenerative hematological response in the form of increased reticulocytes. This evidence of adverse effects was similar to changes identified in birds with oil exposure as determined by visual examination without UV light, and is consistent with hemolytic anemia likely caused by oil exposure. Thus, UV assessment proved useful for enhancing detection of birds exposed to oil, but did not increase detection of birds experiencing clinical signs of anemia compared to standard visual oiling assessment. We conclude that UV light evaluation can help identify oil exposure in many birds that would otherwise be identified visually as unexposed during oil spill events.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02255-8","usgsCitation":"Fallon, J.A., Smith, E.P., Shoch, N., Paruk, J., Adams, E., Evers, D., Jodice, P.G., Perkins, M., Meatty, D.E., and Hopkins, W., 2020, Ultraviolet-assisted oiling assessment improves detection of oiled birds experiencing clinical signs of hemolytic anemia after exposure to the Deepwater Horizon oil spill: Ecotoxicology, v. 29, p. 1399-1408, https://doi.org/10.1007/s10646-020-02255-8.","productDescription":"10 p.","startPage":"1399","endPage":"1408","ipdsId":"IP-107889","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467280,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s10646-020-02255-8","text":"External Repository"},{"id":393957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 26.15543796871355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Fallon, J. A.","contributorId":270956,"corporation":false,"usgs":false,"family":"Fallon","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, E. P.","contributorId":270957,"corporation":false,"usgs":false,"family":"Smith","given":"E.","email":"","middleInitial":"P.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoch, N.","contributorId":270958,"corporation":false,"usgs":false,"family":"Shoch","given":"N.","email":"","affiliations":[{"id":56232,"text":"Adirondack Center for Loon Conservation","active":true,"usgs":false}],"preferred":false,"id":830211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paruk, J. D.","contributorId":270959,"corporation":false,"usgs":false,"family":"Paruk","given":"J. D.","affiliations":[{"id":56233,"text":"Saint Joseph's College of Maine","active":true,"usgs":false}],"preferred":false,"id":830212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, E. A.","contributorId":270960,"corporation":false,"usgs":false,"family":"Adams","given":"E. A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830213,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evers, D. C.","contributorId":270961,"corporation":false,"usgs":false,"family":"Evers","given":"D. C.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830214,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":830215,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perkins, M.","contributorId":270962,"corporation":false,"usgs":false,"family":"Perkins","given":"M.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":830216,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meatty, D. E.","contributorId":270963,"corporation":false,"usgs":false,"family":"Meatty","given":"D.","email":"","middleInitial":"E.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830217,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hopkins, W. A.","contributorId":270964,"corporation":false,"usgs":false,"family":"Hopkins","given":"W. A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830218,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215193,"text":"70215193 - 2020 - Assessing the potential for spectrally based remote sensing of salmon spawning locations","interactions":[],"lastModifiedDate":"2020-10-10T13:13:47.748404","indexId":"70215193","displayToPublicDate":"2020-08-12T08:10:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential for spectrally based remote sensing of salmon spawning locations","docAbstract":"Remote sensing tools are increasingly used for quantitative mapping of fluvial habitats, yet few techniques exist for continuous sampling of aquatic organisms, such as spawning salmonids. This study assessed the potential for spectrally based remote sensing of salmon spawning locations (i.e., redds) using data acquired from unmanned aircraft systems (UAS) along a large, gravel‐bed river. We developed a novel, semi‐automated approach for detecting salmon redds by applying machine learning classification and object detection techniques to UAS‐based imagery. We found that both true colour (RGB) and hyperspectral imagery could be used to identify salmon redds, though with varying degrees of accuracy. Redds were mapped with accuracies of ~0.75 from RGB imagery using logistic regression and support vector machines (SVM) classification algorithms, but this type of data could not be used to identify redds using Object‐based Image Analysis (OBIA). The hyperspectral imagery was more useful for mapping salmon redds, with accuracies greater than 0.9 for both logistic regression and SVM classifiers; OBIA of the hyperspectral data resulted in redd detection accuracies up to 0.86. The hyperspectral imagery also yielded complementary physical habitat information including water depth and substrate composition, which we quantified on the basis of a spectrally based chlorophyll absorption ratio. Overall, the hyperspectral imagery more effectively identified salmon spawning locations than RGB images and was more conducive to the classification approaches we evaluated. Each type of remotely sensed data had advantages and limitations, which are important for potential users to understand when incorporating UAS‐based data collection into river ecosystem studies.
We describe the baseline coupled model configuration and simulation characteristics of GFDL's Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon-chemistry-climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system interactions. ESM4.1 features doubled horizontal resolution of both atmosphere (2° to 1°) and ocean (1° to 0.5°) relative to GFDL's previous-generation coupled ESM2-carbon and CM3-chemistry models. ESM4.1 brings together key representational advances in CM4.0 dynamics and physics along with those in aerosols and their precursor emissions, land ecosystem vegetation and canopy competition, and multiday fire; ocean ecological and biogeochemical interactions, comprehensive land-atmosphere-ocean cycling of CO2, dust and iron, and interactive ocean-atmosphere nitrogen cycling are described in detail across this volume of JAMES and presented here in terms of the overall coupling and resulting fidelity. ESM4.1 provides much improved fidelity in CO2 and chemistry over ESM2 and CM3, captures most of CM4.0's baseline simulations characteristics, and notably improves on CM4.0 in (1) Southern Ocean mode and intermediate water ventilation, (2) Southern Ocean aerosols, and (3) reduced spurious ocean heat uptake. ESM4.1 has reduced transient and equilibrium climate sensitivity compared to CM4.0. Fidelity concerns include (1) moderate degradation in sea surface temperature biases, (2) degradation in aerosols in some regions, and (3) strong centennial scale climate modulation by Southern Ocean convection.
The Taiya River flows through the Chilkoot Trail Unit of Klondike Gold Rush National Historical Park in southeast Alaska, which was founded to preserve cultural and historical resources and further understanding of natural processes active in the surrounding coastal-to-subarctic basin. Riverine processes exert an important influence on ecologically important boreal toad (Anaxryus boreas boreas), salmon [chum salmon (Oncorhynchus keta), pink salmon (O. gorbushca), and coho salmon (O. kisutch)], and eulachon (Thaleichthys pacificus) habitats, erosion of the historic ghost town of Dyea and other cultural and historical artifacts, and recreational opportunities in the lower 7.5 kilometers (km) of the Taiya River valley bottom. Recurrent consideration of hydroelectric development in West Creek upstream of the park since the 1980s has included proposals for damming and diverting West Creek, which could alter the delivery of water and sediment to this section of the Taiya River. To improve understanding of the hydrologic dependence of park resources for the purposes of guiding effective monitoring and conservation, this study, conducted by the U.S. Geological Survey in cooperation with the National Park Service, used a review of hydrologic data, collection of discrete suspended sediment data, geomorphic mapping, and analysis of historical aerial and ground photographs in a reconnaissance of formative geomorphic processes and hydrologic conditions in the lower 7.5 km of the Taiya River valley bottom.
Streamflow and suspended sediment data collected at the U.S. Geological Survey streamgages on the Taiya River and West Creek, combined with historical data, document conditions consistent with streams draining strongly glacierized basins in Alaska. Suspended sediment concentrations from samples collected concurrently over six varying flow levels during 2017–18 ranged from 6 to 284 milligrams per liter (mg/L) for the Taiya River and 13 to 162 mg/L for West Creek, which are similar to or slightly higher than historical values. For the common period of record (1970–77), correlation of daily mean discharge between the two streams was strongest (Pearson’s r = 0.97) during the prolonged May–October high-flow season and weakest (r = 0.90) during the November–April low-flow season, when West Creek daily mean discharge was proportionally higher. For the Taiya River, streamflow data compared between the available periods of record (1970–77 and 2004–17) showed no decadal-scale patterns in mean annual discharge but did show a shift toward an earlier spring snowmelt pulse. Notable flooding in the Taiya River Basin includes glacial lake outburst floods from the Nourse River valley prior to and during the 1897–98 Gold Rush, a 2002 glacial lake outburst flood from the West Creek valley, and a 1967 rainfall-generated flood.
Geomorphic mapping identified four categories of surfaces in the valley bottom—active main stem, abandoned main stem, alluvial fans, and emergent tidal surfaces. Using the maps, main-stem surfaces were subdivided into age categories to identify channel migration patterns from prior to 1940s to 2018. The valley bottom is dominated by active or abandoned channels of the Taiya River except at the extensive low-angle West Creek fan. The active main stem presently supports a mostly single-thread channel with bars and a few sloughs, but the channel actively moved and sometimes was braided within multiple, wider unvegetated corridors in 1894 and earlier. An inventory of 29 off-main-stem channels identified for the study indicates that abandoned main stem channels provide local topographic lows that can intercept groundwater or sustain tributary flow, facilitating the formation of most nonestuarine wetlands in the valley and sustaining important boreal toad breeding habitat.
Within the active main stem corridor, the channel has episodically built and reworked meanders and bars, eroding more than one-half of the historic Dyea townsite, in response to glacially controlled delivery of water and sediment, flooding, inputs from West Creek, local features including large woody debris and beaver dams, and rapid uplift from isostatic rebound. West Creek has constructed a large, persistent fan, provoked kilometer-scale Taiya River channel change near the confluence, constructively added to high-season streamflow that affects Taiya River channel migration capacity, disproportionately contributed early-season streamflow, and possibly contributed to groundwater levels in the valley bottom. The progressive narrowing and stability of the main stem corridor, possibly a result of reduction in the magnitude or frequency of glacial lake outburst floods or glacial sediment delivery to streams, indicates less active future reworking of abandoned main-stem surfaces or regeneration of wetland features. The fluvial history of the Taiya River valley bottom collectively indicates continued channel change within a limited corridor, relative stability in wetland locations but uncertainty in stability of groundwater supply to them, and channel incision and extension in response to uplift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Curran, J.H., 2020, Hydrology and geomorphology of the Taiya River near the West Creek Tributary, southeast Alaska: U.S. Geological Survey Scientific Investigations Report 2020–5059, 57 p., https://doi.org/10.3133/sir20205059.","productDescription":"Report: viii, 57 p.; Data Release","numberOfPages":"57","onlineOnly":"Y","ipdsId":"IP-102183","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":376975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5059/covrthb.jpg"},{"id":376976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5059/sir20205059.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376977,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XP1SE7","linkHelpText":"Geomorphic surface and channel boundaries for the lower 7.5 kilometers of the Taiya River Valley, southeast Alaska, 2018"}],"country":"United States","state":"Alaska","otherGeospatial":"Taiya River Near the West Creek Tributary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.5927734375,\n 57.71588512774503\n ],\n [\n -135,\n 57.657157596582984\n ],\n [\n -132.64892578125,\n 57.621875380195455\n ],\n [\n -132.64892578125,\n 59.877911874831156\n ],\n [\n -137.61474609375,\n 59.877911874831156\n ],\n [\n -137.5927734375,\n 57.71588512774503\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025
Kelp forests and rocky reefs are among the most recognized marine ecosystems and provide the primary habitat for several species of fishes, invertebrates, and algal assemblages (Stephens and others, 2006). In addition, kelp forests have been shown to be important carbon dioxide sinks (Wilmers and others, 2012) and are an important source of nearshore marine primary production (Duggins and others, 1989). These highly dynamic ecosystems are extremely variable, and both top-down and bottom-up ecological controls drive this rich trophic environment. Giant kelp (Macrocystis pyrifera) forests and the species that inhabit these ecosystems are influenced by several environmental conditions, such as wave exposure, water temperature, water clarity, bottom depth and composition, species composition, and the density of kelp and other algal assemblages (Schiel and Foster, 2015). However, in addition to “normal” variability, kelp forests can undergo extreme regime shifts from kelp canopy forested areas to barrens characterized by high densities of urchins and encrusting coralline algae (Harrold and Reed, 1985).
San Nicolas Island (SNI), outermost of the California Channel Islands, is home to a diverse group of terrestrial and marine organisms and includes kelp bed and rocky reef habitats (fig. 1). The SNI kelp forests not only provide food and shelter for fishes and invertebrates within the habitat, but also they support higher trophic level consumers such as marine birds and several marine mammal species including the southern sea otter (Enhydra lutris nereis), a major predator on sea urchins and other marine invertebrates.
Owing to concern about the vulnerability of the California population, the U.S. Fish and Wildlife Service (USFWS) translocated 140 southern sea otters from the central California coast to SNI between 1987 and 1990. Although only approximately 14 translocated otters are thought to have remained at SNI (U.S. Fish and Wildlife Service, 2012), their population at the island has increased and is currently greater than 120 individuals (Hatfield and others, 2019). Sea otters are a natural part of the kelp forest ecosystem, but their presence has implications for community dynamics as they repopulate a region from which they were extirpated in the 19th century. At SNI, sea otters have been concentrated mostly around the west end of the island, with some use of the south side and very little, but expanding, use of the northeast side. An ecosystem shift from urchin dominated to kelp dominated, that occurred at a site at the west end of the island in the early 2000s, though initiated by sea urchin disease, was likely facilitated to some degree by sea otter foraging (Kenner and Tinker, 2018).
These ecosystems also are the target of many fisheries, including urchin and lobster. Urchin fisheries, which target the larger red sea urchin, may release the smaller but more mobile purple sea urchin from competitive control (Dayton and others, 1998). Lobster fisheries may release purple sea urchins from predatory control (Lafferty, 2004). Owing to the distance from the mainland, however, SNI kelp forests and reefs have been somewhat protected from the degree of harvest and other anthropogenic impacts experienced by the southern California mainland. Invasive species are another issue, and there are a few invasive subtidal macroalgae of concern in southern California waters. Although the brown alga Sargassum muticum has been established at the island for decades, S. horneri has only recently been seen at SNI and, so far, the invasive kelp Undaria pinnatifida and the green alga Caulerpa taxifolia have not been observed there. Sargassum horneri, in particular, has demonstrated a capability to outcompete native kelps at some of the other Channel Islands but it is unclear what indirect effects it may have on community structure (Marks and others, 2015).
Because the surrounding kelp forests fall within the management boundary of the SNI Integrated Natural Resources Management Plan (INRMP; U.S. Navy, 2015), USGS works with the Navy to provide surveys of this ecologically important ecosystem that inform natural resource managers of trends in the population abundance of particular species. In addition, long-term surveys allow for an understanding of potential changes in species diversity and community composition as a result of trophic or other interactions.
The U.S. Geological Survey (USGS) implemented a kelp forest monitoring program for the U.S. Navy at San Nicolas Island in 2014, building on sites and methods established by USFWS scientists in 1980 (appendix 1). This report focuses on data collected during sampling expeditions to these sites in fall 2018 (October 2–5) and spring 2019 (April 3–6). Together they will be herein referred to as year 5 because, although the trips were made in different calendar years, they were approximately 6 months apart and were conducted under the fifth year of this contract. The previous sampling year (fall 2017 and spring 2018) is referred to as year 4. The year 5 data are compared with data collected during eight trips from fall 2014 through spring 2018. Differences in counts between these expeditions can result from seasonal factors, stochastic variation, or sampling error, but temporal comparison can reveal population trends. Where appropriate, long-term data collected during the 33 years prior to the implementation of these slightly revised protocols will be presented in order to lend some context to the observations reported here.
Genus and species names used in this report are those currently recognized as valid in the Integrated Taxonomic Information System (ITIS.gov). Upon first use, the name recognized as valid by the World Register of Marine Species (WoRMS; marinespecies.org) is shown in brackets if different. The exception is Sargassum horneri which does not show up in any discernable form in ITIS.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201091","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J.A., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report: U.S. Geological Survey Open-File Report 2020–1091, 93 p., https://doi.org/10.3133/ofr20201091.","productDescription":"ix, 93 p.","onlineOnly":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377300,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1091/coverthb.jpg"},{"id":377301,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1091/ofr20201091.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1091"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Naval Facility San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.60197448730467,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.19675310661128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The lateral extent and vertical stability of salt marshes experiencing rising sea levels depend on interacting drivers and feedbacks with potential for non‐linear behaviors. A two‐dimensional transect model was developed to examine changes in marsh and upland forest lateral extent and to explore controls on marsh inland transgression. Model behavior demonstrates limited and abrupt forest retreat with long‐term upland boundary migration rates controlled by slope, sea level rise (SLR), high water events and biotic‐abiotic interactions. For low to moderate upland slopes the landward marsh edge is controlled by the interaction of these inundation events and forest recovery resulting in punctuated transgressive events. As SLR rates increase, the importance of the timing and frequency of water level deviations diminishes, and migration rates revert back to a slope‐SLR dominated process.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088998","usgsCitation":"Carr, J., Guntenspergen, G.R., and Kirwan, M.L., 2020, Modelling marsh-forest boundary transgression in response to storms and sea-level rise: Geophysical Research Letters, v. 47, no. 17, e2020GL088998, 10 p., https://doi.org/10.1029/2020GL088998.","productDescription":"e2020GL088998, 10 p.","ipdsId":"IP-112010","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455681,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088998","text":"Publisher Index Page"},{"id":436830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQ27F5","text":"USGS data release","linkHelpText":"Water levels (November 11 2016 through November 11 2017) for four wells and Light intensity data (October 1 2015 through September 2019): from marsh to upland forest, for Moneystump Marsh, Blackwater National Wildlife Refuge, Maryland"},{"id":377420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":795926,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196827,"text":"sim3406 - 2020 - Geomorphic map of western Whatcom County, Washington","interactions":[],"lastModifiedDate":"2021-11-29T11:25:56.313865","indexId":"sim3406","displayToPublicDate":"2020-08-10T14:26:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3406","displayTitle":"Geomorphic Map of Western Whatcom County, Washington","title":"Geomorphic map of western Whatcom County, Washington","docAbstract":"Western Whatcom County has a rich history of glaciation, sea-level change, fluvial erosion and deposition, landsliding, nearby volcanic activity, and human landscape modification. This lidar-derived geomorphic map interprets this history from the form and position of the Earth’s surface.
The geomorphic record is broken into nine phases, beginning with the peak of the Vashon stade of the Fraser glaciation of Armstrong and others (1965) (phase 1), shortly after 16,000 years ago. The Cordilleran ice sheet was ≥1.6 km thick in the Bellingham area. Glacial lineations on high ground demonstrate that ice flow was from north to south. Storage of water in ice sheets at this time resulted in global sea level ~120 m lower than at present. The weight of the ice sheet depressed the land so that local relative sea level was at least 150 m higher than at present. As the ice sheet melted and thinned, it floated, broke up, and was replaced by salt water. The margin of the ice sheet—or at least its grounding line—retreated to the northeast of the map area during or before phase 2. Marine deposition, currents, and waves smoothed earlier-formed surfaces in the western part of the map area. Global sea level rose (because of melting of continental ice sheets), but the Fraser Lowland rose even faster (due to glacio-isostatic rebound following the loss of ice-sheet load), and thus local relative sea level fell.
The Cordilleran ice sheet readvanced during the Sumas stade of Armstrong and others (1965). Oldest Sumas moraines formed when relative sea level at Bellingham was ~55 m (phase 3). Younger moraines formed when relative sea level at Bellingham was ~25 m (phase 4). The amount of Sumas ice retreat and readvance between these times is unknown. Younger Sumas events are marked by local moraines, progressive isostatic rebound and lowering of relative sea level, and changes in the flow of ice-marginal water. During phase 5, the southeast margin of the ice sheet advanced, perhaps because capture of ice-marginal drainage by the Samish River (east and south of the map area) meant the ice sheet was no longer trimmed by high-discharge flow along Squalicum channel. Farther west and north, the ice margin retreated between phases 4 and 5. Phases 6 through 9 may mark stillstands during further ice retreat. There were glacial outburst floods (jökulhlaups) during phases 7 and 8, and perhaps during phase 5.
When Sumas ice left the area, perhaps about 11,500 years ago, the Nooksack River appears to have discharged northeast through Sumas Valley to the Fraser River. Details of the switch to its modern course are speculative, but archaeological and sediment-supply arguments suggest that the modern Nooksack River delta south of Ferndale formed within the past 5,000 years.
The foothills of the North Cascades are decorated with abundant post-glacial deep-seated landslides. Anomalously high late Holocene beaches are found at Birch Bay, Neptune Beach, perhaps at Maple Beach on the east side of Point Roberts, and perhaps at the northwest corner of the Lummi Peninsula. These beaches may have been uplifted by earthquakes that did not rupture the surface.
The low-relief landscape shaped by the Cordilleran ice sheet, along with fluvial infilling of low areas, resulted in abundant wetland, at least 70 percent of which has been diked and (or) drained to control flooding and facilitate farming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3406","collaboration":"Prepared in cooperation with Whatcom County and the Washington State Department of Natural Resources","usgsCitation":"Kovanen, D.J., Haugerud, R.A., and Easterbrook, D.J., 2020, Geomorphic map of western Whatcom County, Washington (ver. 1.1, November 2021): U.S. Geological Survey Scientific Investigations Map 3406, pamphlet 42 p., scale 1:50,000, https://doi.org/10.3133/sim3406.","productDescription":"Pamphlet: vi, 42 p.; Plate: 65.10 x 39.00 inches; Metadata; Read Me; 4 Databases; Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086454","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":392128,"rank":11,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3406/versionHist.txt"},{"id":377137,"rank":10,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/XML_metadata.zip","text":"XML_metadata","size":"109 KB","linkFileType":{"id":6,"text":"zip"},"description":"XML_metadata.zip"},{"id":377136,"rank":9,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-simple.zip","text":"SIM3406-simple","size":"13.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-simple.zip"},{"id":377135,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-open.zip","text":"SIM3406-open","size":"13.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-open.zip"},{"id":377134,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-gdb.zip","text":"SIM3406-gdb","size":"62.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-gdb.zip"},{"id":377133,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3406/00Readme.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Read Me"},{"id":377132,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.xml","size":"18 KB xml","description":"SIM 3406 Metadata xml"},{"id":377131,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.txt","size":"17 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Metadata text"},{"id":377130,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406.pdf","text":"Map","size":"16.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406"},{"id":377129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_pamphlet_v1.1.pdf","text":"Pamphlet","size":"11.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406 Pamphlet"},{"id":377128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3406/coverthb.jpg"}],"country":"United States","state":"Washington","county":"Whatcom County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1185,\n 48.6169\n ],\n [\n -122.2390,\n 48.6169\n ],\n [\n -122.2390,\n 49.0156\n ],\n [\n -123.1185,\n 49.0156\n ],\n [\n -123.1185,\n 48.6169\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: November 2021","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025
Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559
A comprehensive study to evaluate water-quality trends, while considering natural hydroclimatic variability, in the Red River of the North Basin and assess water-quality conditions for the Red River of the North crossing the international boundary near Emerson, Manitoba, Canada (the binational site), was completed by the U.S. Geological Survey in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada. The international Red River of the North Basin encompasses 3 U.S. States (South Dakota, North Dakota, and Minnesota) and 1 Canadian Province (Manitoba). Water quality in the Red River of the North Basin is of concern for both Federal governments and State and Provincial governments. Water-quality objectives have been previously established for selected dissolved ions and recently (2019) proposed for selected nutrients for the binational site.
In the current (2020) study, water-quality data from State, Provincial, and Federal agencies in the United States and Canada for sites in the Red River of the North Basin from 1970 to 2017 were compiled and used for trend analysis. Trend analysis using a water-quality dataset from multiple agencies that collect water-quality data for various objectives presented multiple challenges. The trend-analysis approach was able to accommodate differences in water-quality data caused by field-collection and laboratory-analytical method differences, disparities in sampling frequencies, and spatial and temporal gaps in data. Most of these challenges were overcome by the statistical tool, R–QWTREND, which identifies trends in concentration unrelated to variability in streamflow.
The integrated basin approach used in the current study, combined with comparing current data trends with historical trends, provided valuable insights into understanding how water quality is changing spatially (34 sites analyzed for a recent period, 2000–15) and temporally (5 sites analyzed for a 45-year historical period, 1970–2015) within the Red River of the North Basin. One of the most consistent spatial and temporal changes observed in the current study was increasing concentrations of sulfate among tributary and main-stem sites since 2000. For some sites, increases were detected starting as early as 1985. Total dissolved solids and chloride concentrations had spatial and temporal patterns like sulfate. Although R–QWTREND removes the variability in constituent concentration caused by natural streamflow variability, all variability in sulfate caused by hydroclimatic variability may not be captured because of changes in hydrologic pathways and changes in the contributions of sulfate from various natural sources.
Nutrient concentrations demonstrated less consistent spatial and temporal changes than sulfate, and changes in nutrient concentrations were assumed to be more closely tied to human-induced rather than natural changes. Nitrate-plus-nitrite concentrations were mostly increasing in the upper Red River of the North subbasin, and for nitrate plus nitrite and total nitrogen, the Sheyenne River subbasin had consistent decreasing concentrations. Since 2000, total phosphorus has decreased in the upper Red River of the North subbasin, but total phosphorus concentration has increased for sites in the lower Red River of the North subbasin, and for some main-stem sites, concentrations have been increasing since 1985. Unlike sulfate, the pattern in historical trends for total phosphorus for the main-stem sites differed from tributary sites, indicating that human-induced changes affected tributaries and main-stem sites differently.
The more detailed evaluation of flow-averaged water-quality conditions for the binational site provided an understanding of how loads have changed over time and what proportion of the year and season concentrations are expected to exceed water-quality objectives. In a basin with highly variable streamflow like the Red River of the North, the trend in flow-averaged load (assuming streamflow conditions are the same year after year) provided a robust measure of change over time. Increasing concentrations of sulfate, chloride, total dissolved solids, and total phosphorus since 1985 for the binational site resulted in longer periods of exceedance of water-quality objectives per year occurring over time. For total nitrogen, decreasing concentrations resulted in shorter periods of exceedance per year during 1980 to 2015, but concentrations were still expected to exceed the water-quality objective about half the year. Periods of when exceedances were likely to occur during the year were affected by the source and transport mechanisms of the constituent.
Trend results from this effort identified how water quality has changed across the basin, and further investigation would help to identify causes for the trends observed here. Information from the current study provides a basis for future trend attribution studies, evaluation of water-quality objectives, and development of comprehensive strategies for reducing nutrients to desired targets and establishes a baseline for tracking future progress in the Red River of the North Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205079","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada","usgsCitation":"Nustad, R.A., and Vecchia, A.V., 2020, Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017: U.S. Geological Survey Scientific Investigations Report 2020–5079, 75 p., https://doi.org/10.3133/sir20205079.","productDescription":"Report: ix, 75 p.; 2 Tables; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-113881","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5079/coverthb.jpg"},{"id":377260,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C9JAMY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis—Data supporting water-quality trend analysis in the Red River of the North basin, 1970–2017"},{"id":377258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5079"},{"id":377259,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079_tables_2_and_3.xlsx","text":"Tables 2 and 3","size":"60.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5079 Tables 2 and 3"},{"id":377261,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","description":"USGS Data Release","linkHelpText":"— U.S. Geological Survey National Water Information System database"}],"country":"United States, Canada","state":"Minnesota, North Dakota, South Dakota, Manitoba","otherGeospatial":"Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27294921875,\n 50.14874640066278\n ],\n [\n -98.85498046875,\n 49.710272582105695\n ],\n [\n -100.81054687499999,\n 49.38237278700955\n ],\n [\n -100.7666015625,\n 48.58932584966975\n ],\n [\n -99.86572265625,\n 47.040182144806664\n ],\n [\n -98.525390625,\n 46.7248003746672\n ],\n [\n -98.76708984374999,\n 46.37725420510028\n ],\n [\n -98.63525390624999,\n 45.96642454131025\n ],\n [\n -97.91015624999999,\n 45.55252525134013\n ],\n [\n -97.14111328125,\n 45.321254361171476\n ],\n [\n -95.77880859375,\n 45.89000815866184\n ],\n [\n -95.2294921875,\n 46.28622391806706\n ],\n [\n -95.1416015625,\n 46.73986059969267\n ],\n [\n -95.0537109375,\n 47.68018294648414\n ],\n [\n -94.59228515625,\n 47.79839667295524\n ],\n [\n -94.306640625,\n 48.07807894349862\n ],\n [\n -94.54833984375,\n 48.29781249243716\n ],\n [\n -95.1416015625,\n 48.23930899024907\n ],\n [\n -95.2734375,\n 48.850258199721495\n ],\n [\n -95.42724609375,\n 49.1242192485914\n ],\n [\n -96.7236328125,\n 50.02185841773444\n ],\n [\n -97.27294921875,\n 50.14874640066278\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
Since the 1970s, temporal variations of hydrothermal discharge and thermal output from the numerous hydrothermal features in the Yellowstone Plateau Volcanic Field (YPVF) have been studied by measuring the chloride flux in the major rivers. In this study, the sources, fate, and flux of solutes in the Fall River and its major tributaries, in southwest Yellowstone National Park, were determined. The considerable precipitation in southwest YPVF and high groundwater flow through Quaternary rhyolites results in river solute fluxes that originate from shallow non-thermal groundwater and deep-thermal water. Specific conductance serves as a surrogate measure for thirteen riverine solute concentrations. Combining continuous 15-minute specific conductance and discharge data, the annual chloride, arsenic, fluoride, and silica fluxes from the Fall River were determined to be 11%, 5%, 25%, and 19% of the total flux exiting YPVF. Approximately 11% of the Fall River chloride flux is from non-thermal waters, which is larger than the previous estimate of 4 to 6%. Furthermore, a large proportion of fluoride and silica in the Fall River are derived from water-rock interaction in the shallow non-thermal groundwater system and the non-thermal weathering rate (30 ± 2 t/yr·km2) is higher than other rivers draining the Yellowstone caldera. Consequently, 73 ± 3% of the annual total dissolved solid flux in the Fall River is from thermal sources. Synoptic sampling of river water and discharge measurements was performed during low-flow conditions that allowed for the determination of solute sources and their downstream fate. It was determined that chloride, sodium, arsenic, rubidium, lithium, and boron are primarily (>89%) associated with thermal waters and the Bechler River is the primary source of most hydrothermal solutes in the Fall River, but the major source of arsenic is Boundary Creek. Using the chloride inventory method, the thermal water discharge from several thermal areas was also determined.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.107021","usgsCitation":"McCleskey, R., Hurwitz, S., White, E.B., Roth, D.A., Susong, D., Hungerford, J., and Olson, L.A., 2020, Sources, fate, and flux of riverine solutes in the Southwest Yellowstone Plateau Volcanic Field, USA: Journal of Volcanology and Geothermal Research, v. 403, 107021, 15 p., https://doi.org/10.1016/j.jvolgeores.2020.107021.","productDescription":"107021, 15 p.","ipdsId":"IP-118755","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":382915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.03881835937499,\n 43.476840397778936\n ],\n [\n -108.929443359375,\n 43.476840397778936\n ],\n [\n -108.929443359375,\n 45.01141864227728\n ],\n [\n -111.03881835937499,\n 45.01141864227728\n ],\n [\n -111.03881835937499,\n 43.476840397778936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"403","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":809801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":809802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Erin B 0000-0003-2066-670X","orcid":"https://orcid.org/0000-0003-2066-670X","contributorId":224483,"corporation":false,"usgs":false,"family":"White","given":"Erin","email":"","middleInitial":"B","affiliations":[{"id":40891,"text":"National Park Service: Yellowstone, WY, US","active":true,"usgs":false}],"preferred":false,"id":809805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":2340,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":809803,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Susong, David 0000-0003-0415-5221","orcid":"https://orcid.org/0000-0003-0415-5221","contributorId":229551,"corporation":false,"usgs":false,"family":"Susong","given":"David","affiliations":[{"id":41666,"text":"USGS Utah Water Science Center (emeritus)","active":true,"usgs":false}],"preferred":false,"id":809804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hungerford, Jefferson","contributorId":243584,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":809806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olson, Lonnie A.","contributorId":248775,"corporation":false,"usgs":false,"family":"Olson","given":"Lonnie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":809814,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212497,"text":"70212497 - 2020 - Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion","interactions":[],"lastModifiedDate":"2020-08-18T14:46:14.015324","indexId":"70212497","displayToPublicDate":"2020-08-07T09:40:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":686,"text":"Air, Soil and Water Research","active":true,"publicationSubtype":{"id":10}},"title":"Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion","docAbstract":"In northwestern Mexico and the southwestern United States, limited water supplies and fragile landscapes jeopardize world-renowned biological diversity. Simple rock detention structures have been used to manage agricultural water for over a thousand years and are now being installed to restore ecohydrological functionality but with little scientific evidence of their success. The impacts, design, and construction of such structures has been debated among local restoration practitioners, management, and permitting agencies. This article presents archeological documentation, local contentions, and examples of available research assessments of rock detention structures in the Madrean Archipelago Ecoregion. A US Geological Survey study to quantify impacts of rock detention structures using remote-sensing analyses, hydrologic monitoring, vegetation surveys, and watershed modeling is discussed, and results rendered in terms of the critical restoration ecosystem services provided. This framework provides a means for comparing management actions that might directly or indirectly impact human populations and assessing tradeoffs between them.","language":"English","publisher":"Sage Journals","doi":"10.1177/1178622120946337","usgsCitation":"Norman, L., 2020, Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion: Air, Soil and Water Research, v. 13, 13 p., https://doi.org/10.1177/1178622120946337.","productDescription":"13 p.","ipdsId":"IP-114137","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1178622120946337","text":"Publisher Index Page"},{"id":377603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Chihuahua, New Mexico, Sonora","otherGeospatial":"Madrean Archipelago Ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.610107421875,\n 29.372601506681402\n ],\n [\n -108.00659179687499,\n 29.372601506681402\n ],\n [\n -108.00659179687499,\n 33.486435450999885\n ],\n [\n -111.610107421875,\n 33.486435450999885\n ],\n [\n -111.610107421875,\n 29.372601506681402\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796583,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70212590,"text":"70212590 - 2020 - Focused fluid flow along the Nootka Fault Zone and continental slope, Explorer-Juan de Fuca plate boundary","interactions":[],"lastModifiedDate":"2020-08-25T13:31:23.062953","indexId":"70212590","displayToPublicDate":"2020-08-07T08:55:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Focused fluid flow along the Nootka Fault Zone and continental slope, Explorer-Juan de Fuca plate boundary","docAbstract":"Geophysical and geochemical data indicate there is abundant fluid expulsion in the Nootka fault zone (NFZ) between the Juan de Fuca and Explorer plates and the Nootka continental slope. Here we combine observations from > 20 years of investigations to\ndemonstrate the nature of fluid-flow along the NFZ, which is the seismically most active region off Vancouver Island. Seismicity reaching down to the upper mantle is linked to near-seafloor manifestation of fluid flow through a network of faults. Along the two main fault traces, seismic reflection data imaged bright spots 100 300 m below seafloor that lie above changes inbasement topography. The bright spots are conformable to sediment layering, show opposite-toseafloor reflection polarity, and are associated with frequency-reduction and velocity push-down indicating the presence of gas in the sediments. Two seafloor mounds ~15 km seaward of the Nootka slope are underlain by deep, non-conformable high amplitude reflective zones. Measurements in the water column above one mound revealed a plume of warm water, and bottom-video observations imaged hydrothermal vent system biota. Pore fluids from a core at this mound contain predominately microbial methane (C1) with a high proportion of ethane (C2) yielding C1/C2 ratios < 500 indicating a possible slight contribution from a deep source. We infer the reflective zones beneath the two mounds are basaltic intrusions that create hydrothermal circulation within the overlying sediments. Across the Nootka continental slope, gas hydrate related bottom-simulating reflectors are widespread and occur at depths indicating heat-flow values of 80 90 mW/m2.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GC009095","usgsCitation":"Riedel, M., Rohr, K..., Spence, G.D., Kelley, D., Delaney, J., Lapham, L., Pohlman, J., Hyndman, R., and Willoughby, E., 2020, Focused fluid flow along the Nootka Fault Zone and continental slope, Explorer-Juan de Fuca plate boundary: Geochemistry, Geophysics, Geosystems, v. 21, no. 8, e2020GC009095, 26 p., https://doi.org/10.1029/2020GC009095.","productDescription":"e2020GC009095, 26 p.","ipdsId":"IP-120436","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gc009095","text":"Publisher Index Page"},{"id":377719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.2216796875,\n 41.83682786072714\n ],\n [\n -120.36621093749999,\n 41.83682786072714\n ],\n [\n -120.36621093749999,\n 49.06666839558117\n ],\n [\n -127.2216796875,\n 49.06666839558117\n ],\n [\n -127.2216796875,\n 41.83682786072714\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Riedel, M.","contributorId":238948,"corporation":false,"usgs":false,"family":"Riedel","given":"M.","affiliations":[{"id":47829,"text":"GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstr. 1 – 3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":796931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rohr, K .M. M.","contributorId":238949,"corporation":false,"usgs":false,"family":"Rohr","given":"K","email":"","middleInitial":".M. M.","affiliations":[{"id":47832,"text":"Geological Survey of Canada – Pacific, 9860 West Saanich Road, Sidney BC, V8L 4B2, Canada","active":true,"usgs":false}],"preferred":false,"id":796932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spence, G. D.","contributorId":238950,"corporation":false,"usgs":false,"family":"Spence","given":"G.","email":"","middleInitial":"D.","affiliations":[{"id":47833,"text":"School of Earth and Ocean Sciences, University of Victoria, Bob Wright Centre A405, Victoria, BC, V8W 2Y2, Canada","active":true,"usgs":false}],"preferred":false,"id":796933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelley, D.","contributorId":238951,"corporation":false,"usgs":false,"family":"Kelley","given":"D.","affiliations":[{"id":47834,"text":". School of Oceanography, University of Washington, 1503 NE Boat Street, Seattle, WA, 98195, USA","active":true,"usgs":false}],"preferred":false,"id":796934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Delaney, J.","contributorId":238952,"corporation":false,"usgs":false,"family":"Delaney","given":"J.","email":"","affiliations":[{"id":47835,"text":"School of Oceanography, University of Washington, 1503 NE Boat Street, Seattle, WA, 98195, USA","active":true,"usgs":false}],"preferred":false,"id":796935,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lapham, L.","contributorId":189178,"corporation":false,"usgs":false,"family":"Lapham","given":"L.","affiliations":[],"preferred":false,"id":796936,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pohlman, John 0000-0002-3563-4586","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":220804,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":796937,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hyndman, R.D.","contributorId":238953,"corporation":false,"usgs":false,"family":"Hyndman","given":"R.D.","affiliations":[{"id":47836,"text":"Geological Survey of Canada – Pacific, 9860 West Saanich Road, Sidney BC, V8L 4B2, Canada 3. School of Earth and Ocean Sciences, University of Victoria, Bob Wright Centre A405, Victoria, BC, V8W 2Y2, Canada","active":true,"usgs":false}],"preferred":false,"id":796938,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Willoughby, E.C.","contributorId":238954,"corporation":false,"usgs":false,"family":"Willoughby","given":"E.C.","email":"","affiliations":[{"id":47836,"text":"Geological Survey of Canada – Pacific, 9860 West Saanich Road, Sidney BC, V8L 4B2, Canada 3. School of Earth and Ocean Sciences, University of Victoria, Bob Wright Centre A405, Victoria, BC, V8W 2Y2, Canada","active":true,"usgs":false}],"preferred":false,"id":796939,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211816,"text":"70211816 - 2020 - Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2020-08-07T20:03:25.527836","indexId":"70211816","displayToPublicDate":"2020-08-06T14:44:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico","docAbstract":"Turbidity currents deliver sediment rapidly from the continental shelf to the slope and beyond; and can be triggered by processes such as shelf resuspension during oceanic storms; mass failure of slope deposits due to sediment- and wave-pressure loadings; and localized events that grow into sustained currents via self-amplifying ignition. Because these operate over multiple spatial and temporal scales, ranging from the eddy-scale to continental-scale; coupled numerical models that represent the full transport pathway have proved elusive though individual models have been developed to describe each of these processes. Toward a more holistic tool, a numerical workflow was developed to address pathways for sediment routing from terrestrial and coastal sources, across the continental shelf and ultimately down continental slope canyons of the northern Gulf of Mexico, where offshore infrastructure is susceptible to damage by turbidity currents. Workflow components included: 1) a calibrated simulator for fluvial discharge (Water Balance Model - Sediment; WBMsed); 2) domain grids for seabed sediment textures (dbSEABED); bathymetry, and channelization; 3) a simulator for ocean dynamics and resuspension (the Regional Ocean Modeling System; ROMS); 4) A simulator (HurriSlip) of seafloor failure and flow ignition; and 5) A Reynolds-averaged Navier–Stokes (RANS) turbidity current model (TURBINS). Model simulations explored physical oceanic conditions that might generate turbidity currents, and allowed the workflow to be tested for a year that included two hurricanes. Results showed that extreme storms were especially effective at delivering sediment from coastal source areas to the deep sea, at timescales that ranged from individual wave events (~hours), to the settling lag of fine sediment (~days).
","language":"English","publisher":"MDPI","doi":"10.3390/jmse8080586","usgsCitation":"Harris, C.K., Syvitski, J., Arango, H., Meiburg, E.H., Cohen, S., Jenkins, C., Birchler, J.J., Hutton, E.W., Kniskern, T.A., Radhakrishnan, S., and Auad, G., 2020, Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico: Journal of Marine Science and Engineering, v. 8, no. 8, 586, 28 p., https://doi.org/10.3390/jmse8080586.","productDescription":"586, 28 p.","ipdsId":"IP-109071","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse8080586","text":"Publisher Index Page"},{"id":377178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.53662109375,\n 27.410785702577023\n ],\n [\n -83.56201171875,\n 27.410785702577023\n ],\n [\n -83.56201171875,\n 30.581179257386985\n ],\n [\n -97.53662109375,\n 30.581179257386985\n ],\n [\n -97.53662109375,\n 27.410785702577023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Harris, Courtney K.","contributorId":19620,"corporation":false,"usgs":false,"family":"Harris","given":"Courtney","email":"","middleInitial":"K.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":795214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Syvitski, Jaia","contributorId":237738,"corporation":false,"usgs":false,"family":"Syvitski","given":"Jaia","email":"","affiliations":[],"preferred":false,"id":795215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arango, H.G.","contributorId":103772,"corporation":false,"usgs":true,"family":"Arango","given":"H.G.","email":"","affiliations":[],"preferred":false,"id":795216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meiburg, E. H.","contributorId":237739,"corporation":false,"usgs":false,"family":"Meiburg","given":"E.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":795217,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cohen, Sagy","contributorId":202461,"corporation":false,"usgs":false,"family":"Cohen","given":"Sagy","email":"","affiliations":[{"id":36450,"text":"Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":795218,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jenkins, C.J.","contributorId":61244,"corporation":false,"usgs":true,"family":"Jenkins","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":795219,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":795220,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hutton, E. W. H.","contributorId":20940,"corporation":false,"usgs":true,"family":"Hutton","given":"E.","email":"","middleInitial":"W. H.","affiliations":[],"preferred":false,"id":795221,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kniskern, T. A.","contributorId":42807,"corporation":false,"usgs":false,"family":"Kniskern","given":"T.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":795222,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Radhakrishnan, S.","contributorId":237740,"corporation":false,"usgs":false,"family":"Radhakrishnan","given":"S.","email":"","affiliations":[],"preferred":false,"id":795223,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Auad, Guillermo","contributorId":78120,"corporation":false,"usgs":true,"family":"Auad","given":"Guillermo","email":"","affiliations":[],"preferred":false,"id":795224,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70215007,"text":"70215007 - 2020 - Evaluation of acute and chronic toxicity of nickel and zinc to 2 sensitive freshwater benthic invertebrates using refined testing methods","interactions":[],"lastModifiedDate":"2020-10-29T15:13:00.246324","indexId":"70215007","displayToPublicDate":"2020-08-06T11:44:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of acute and chronic toxicity of nickel and zinc to 2 sensitive freshwater benthic invertebrates using refined testing methods","docAbstract":"The US Environmental Protection Agency (USEPA) is reviewing the protectiveness of the national ambient water quality criteria (WQC) for nickel (Ni) and zinc (Zn) and compiling toxicity databases to update the WQC. An amphipod (Hyalella azteca) and a unionid mussel (Lampsilis siliquoidea) have shown high sensitivity to Ni and Zn in previous studies. However, there remained uncertainties regarding the influence of test duration (48 vs 96 h) and the presence and absence of food in acute exposures with the amphipod, and there were also concerns about poor control of amphipod growth and reproduction and mussel growth in chronic exposures. We conducted acute 48‐ and 96‐h water‐only toxicity tests to evaluate the influence of feeding and test durations on the toxicity of dissolved Ni and Zn to the amphipod; we also used recently refined test methods to conduct chronic Ni and Zn toxicity tests to evaluate the sensitivity of the amphipod (6‐wk exposure) and the mussel (4‐ and 12‐wk exposures). The 96‐h 50% effect concentrations (EC50s) of 916 µg Ni/L and 99 µg Zn/L from acute amphipod tests without feeding decreased from the 48‐h EC50s by 62 and 33%, respectively, whereas the 96‐h EC50s of 2732 µg Ni/L and 194 µg Zn/L from the tests with feeding decreased from the 48‐h EC50s by 10 and 26%, indicating that the presence or absence of food had apparent implications for the 96‐h EC50. Our chronic 6‐wk EC20s for the amphipod (4.5 µg Ni/L and 35 µg Zn/L) were 50 to 67% lower than the 6‐wk EC20s from previous amphipod tests, and our chronic 4‐wk EC20s for the mussel (41 µg Ni/L and 66 µg Zn/L) were similar to or up to 42% lower than the 4‐wk EC20s from previous mussel tests. The lower EC20s from the present study likely reflect more accurate estimates of inherent sensitivity to Ni and Zn due to the refined test conditions. Finally, increasing the chronic test duration from 4 to 12 wk substantially increased the toxicity of Zn to the mussel, whereas the 4‐ and 12‐wk Ni effect needs to be re‐evaluated to understand the large degree of variation in organism responses observed in the present study.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4841","usgsCitation":"Wang, N., Kunz, J.L., Cleveland, D.M., Steevens, J.A., Hammer, E.J., Van Genderen, E., Ryan, A.C., and Schlekat, C., 2020, Evaluation of acute and chronic toxicity of nickel and zinc to 2 sensitive freshwater benthic invertebrates using refined testing methods: Environmental Toxicology and Chemistry, v. 39, no. 11, p. 2256-2268, https://doi.org/10.1002/etc.4841.","productDescription":"13 p.","startPage":"2256","endPage":"2268","ipdsId":"IP-119074","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":436833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DH1ORZ","text":"USGS data release","linkHelpText":"Chemical and biological data from acute and chronic nickel and zinc exposure bioassays to two sensitive freshwater benthic invertebrates"},{"id":379093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. 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Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":800527,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Genderen, Eric","contributorId":242622,"corporation":false,"usgs":false,"family":"Van Genderen","given":"Eric","affiliations":[{"id":48485,"text":"International Zinc Association, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":800528,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ryan, Adam C.","contributorId":175564,"corporation":false,"usgs":false,"family":"Ryan","given":"Adam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":800529,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schlekat, Christian E.","contributorId":242623,"corporation":false,"usgs":false,"family":"Schlekat","given":"Christian E.","affiliations":[{"id":48488,"text":"NiPERA Inc., Durham, NC","active":true,"usgs":false}],"preferred":false,"id":800530,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211637,"text":"70211637 - 2020 - A hybrid approach for predictive soil property mapping using conventional soil survey data","interactions":[],"lastModifiedDate":"2020-09-10T20:19:02.656543","indexId":"70211637","displayToPublicDate":"2020-08-06T10:36:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3420,"text":"Soil Science Society of America Journal","active":true,"publicationSubtype":{"id":10}},"title":"A hybrid approach for predictive soil property mapping using conventional soil survey data","docAbstract":"Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.","language":"English","publisher":"Wiley","doi":"10.1002/saj2.20080","usgsCitation":"Nauman, T.W., and Duniway, M.C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 170-1194, https://doi.org/10.1002/saj2.20080.","productDescription":"25 p.","startPage":"170","endPage":"1194","onlineOnly":"Y","ipdsId":"IP-108106","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436834,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SK0DO2","text":"USGS data release","linkHelpText":"Predictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead"},{"id":377090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado, Wyoming, Utah, Nevada, Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.61132812499999,\n 35.67514743608467\n ],\n [\n -107.490234375,\n 39.50404070558415\n ],\n [\n -108.720703125,\n 42.68243539838623\n ],\n [\n -110.302734375,\n 42.5530802889558\n ],\n [\n -112.1484375,\n 41.21172151054787\n ],\n [\n -113.818359375,\n 38.06539235133249\n ],\n [\n -115.6201171875,\n 37.3002752813443\n ],\n [\n -116.3671875,\n 36.527294814546245\n ],\n [\n -112.587890625,\n 34.56085936708384\n ],\n [\n -106.61132812499999,\n 35.67514743608467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794893,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}