Sediment transport, turbidity, and dissolved oxygen were evaluated during six consecutive water years (2013–2018) of drawdowns of a flood control reservoir in the upper Willamette Valley, Oregon, USA. The drawdowns were conducted to allow volitional passage of endangered juvenile chinook salmon through the dam's regulating outlets by lowering the reservoir elevation to a point where the historical streambed was exposed and transported water and sediment through the reservoir dam. Sediment loads during the drawdown were highest in the first year of monitoring, with a computed value of 40,200 metric tons over a 5-day drawdown, followed by 5 years of lower sediment loads and lower sediment transport rates, suggesting that much of the stored sediment within the reservoir thalweg was transported out of the reservoir in the early years of the consecutive drawdowns. Suspended sediment concentrations (SSC) computed using turbidity and streamflow data resulted in maximum SSC at the onset of the drawdowns, with the highest computed values occurring during the water year 2017 drawdown at 17,500 mg/L (turbidity = 2,990 FNU), and average drawdown SSC values ranging from 654 to 3,950 mg/L for the six years of monitoring. Computed SSC were on the lower range of concentrations that could be harmful to out-migrating juvenile salmon published in other studies. High amounts of particulate organic matter and sand-sized material in drawdown SSC samples affected relations between turbidity and SSC, requiring the use of multiple surrogate regression models over short time frames. Dissolved oxygen minimum values were recorded in two of the monitoring years, with a minimum value of 0.71 and 3.4 mg/L recorded at the onset of the drawdowns in water years 2016 and 2018, respectively. Dissolved oxygen values below 4 mg/L lasted for 1 h, suggesting a rapidly expressed chemical oxygen demand. The response of suspended sediment loads and SSC highlight the site-specific nature of reservoir drawdowns, and the need for evaluation of expected sediment responses for drawdowns being considered at other locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.113068","usgsCitation":"Schenk, L.N., and Bragg, H.M., 2021, Sediment transport, turbidity, and dissolved oxygen responses to annual streambed drawdowns for downstream fish passage in a flood control reservoir: Journal of Environmental Management, v. 295, 113068, 11 p., https://doi.org/10.1016/j.jenvman.2021.113068.","productDescription":"113068, 11 p.","ipdsId":"IP-119744","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":387132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Fall Creek Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.69325256347656,\n 43.923862711777446\n ],\n [\n -122.73548126220703,\n 43.9429004110983\n ],\n [\n -122.69565582275389,\n 43.95130472827632\n ],\n [\n -122.65342712402344,\n 43.97305156068593\n ],\n [\n -122.66578674316406,\n 43.97972228837853\n ],\n [\n -122.71076202392577,\n 43.96069638244953\n ],\n [\n -122.75333404541016,\n 43.959460723283826\n ],\n [\n -122.76226043701173,\n 43.958472177448414\n ],\n [\n -122.76191711425781,\n 43.93820336335502\n ],\n [\n -122.7509307861328,\n 43.93721446391471\n ],\n [\n -122.73616790771484,\n 43.93251696697599\n ],\n [\n -122.70767211914064,\n 43.92336814487696\n ],\n [\n -122.69256591796876,\n 43.92287357386489\n ],\n [\n -122.69325256347656,\n 43.923862711777446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bragg, Heather M. 0000-0002-0013-4573 hmbragg@usgs.gov","orcid":"https://orcid.org/0000-0002-0013-4573","contributorId":239645,"corporation":false,"usgs":true,"family":"Bragg","given":"Heather","email":"hmbragg@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819010,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221701,"text":"70221701 - 2021 - New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","interactions":[],"lastModifiedDate":"2021-11-01T15:36:01.701983","indexId":"70221701","displayToPublicDate":"2021-06-18T10:07:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1751,"text":"Geobiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","title":"New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","docAbstract":"In order to reconstruct the ecosystem structure of chemosynthetic environments in the fossil record, geochemical proxies must be developed. Here, we present a suite of novel compound-specific isotope parameters for tracing chemosynthetic production with a focus on understanding nitrogen dynamics in deep-sea cold seep environments. We examined the chemosymbiotic bivalve Bathymodiolus childressi from three geographically distinct seep sites on the NE Atlantic Margin and compared isotope data to non-chemosynthetic littoral mussels to test whether water depth, seep activity, and/or mussel bed size are linked to differences in chemosynthetic production. The bulk isotope analysis of carbon (δ13C) and nitrogen (δ15N), and δ15N values of individual amino acids (δ15NAA) in both gill and muscle tissues, as well as δ15NAA-derived parameters including trophic level (TL), baseline δ15N value (δ15NPhe), and a microbial resynthesis index (ΣV), were used to investigate specific geochemical signatures of chemosynthesis. Our results show that δ15NAA values provide a number of new proxies for relative reliance on chemosynthesis, including TL, ∑V, and both δ15N values and molar percentages (Gly/Glu mol% index) of specific AA. Together, these parameters suggested that relative chemoautotrophy is linked to both degree of venting from seeps and mussel bed size. Finally, we tested a Bayesian mixing model using diagnostic AA δ15N values, showing that percent contribution of chemoautotrophic versus heterotrophic production to seep mussel nutrition can be directly estimated from δ15NAA values. Our results demonstrate that δ15NAA analysis can provide a new set of geochemical tools to better understand mixotrophic ecosystem function and energetics, and suggest extension to the study of ancient chemosynthetic environments in the fossil record.
","language":"English","publisher":"Wiley","doi":"10.1111/gbi.12458","usgsCitation":"Vokhshoori, N., McCarthy, M., Close, H., Demopoulos, A., and Prouty, N.G., 2021, New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi): Geobiology, v. 19, no. 6, p. 601-617, https://doi.org/10.1111/gbi.12458.","productDescription":"17 p.","startPage":"601","endPage":"617","ipdsId":"IP-121092","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Vokhshoori, Natasha","contributorId":260681,"corporation":false,"usgs":false,"family":"Vokhshoori","given":"Natasha","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":818469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Matt","contributorId":260682,"corporation":false,"usgs":false,"family":"McCarthy","given":"Matt","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":818470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Close, Hilary G.","contributorId":199931,"corporation":false,"usgs":false,"family":"Close","given":"Hilary","middleInitial":"G.","affiliations":[],"preferred":false,"id":818471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":222192,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":818472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818473,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229497,"text":"70229497 - 2021 - The marine terraces of Santa Cruz Island, California: Implications for glacial isostatic adjustment models of last-interglacial sea-level history","interactions":[],"lastModifiedDate":"2022-03-09T12:47:41.369958","indexId":"70229497","displayToPublicDate":"2021-06-18T06:43:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"The marine terraces of Santa Cruz Island, California: Implications for glacial isostatic adjustment models of last-interglacial sea-level history","docAbstract":"Glacial isostatic adjustment (GIA) models hypothesize that along coastal California, last interglacial (LIG, broadly from ~130 to ~115 ka) sea level could have been as high as +11 m to +13 m, relative to present, substantially higher than the commonly estimated elevation of +6 m. Areas with low uplift rates can test whether such models are valid. Marine terraces on Santa Cruz Island have previously been reported to occur at low (<10 m) elevations, but ages of many such localities are not known. Using lidar imagery as a base, marine terraces on Santa Cruz Island were newly mapped, elevations were measured, fossils were collected for U-series dating (corals), strontium isotope compositions and amino acid geochronology (mollusks), and paleozoogeography (all taxa). Sr isotope compositions of mollusks from the highest of three marine terraces give ages of ~2.5 Ma to 1.9 Ma, along with Pliocene ages, from shells interpreted to be reworked. U-series ages of corals from the western part of the island indicate that low-elevation terraces north of the Santa Cruz Island fault correlate to the LIG. Where corals are lacking, amino acid ratios and faunal aspects support terrace correlation to the LIG high stand of sea. Elevations of most terrace localities north of the east-west trending Santa Cruz Island fault, in both the western and eastern parts of the island, range from 5.75 m to 8 m above sea level, well below the modeled paleo-sea-level range. Subsidence is ruled out as a mechanism for explaining the lower-than-modeled elevations, because higher-elevation terraces are present along much of the Santa Cruz Island coast north of the fault, indicating long-term tectonic uplift. The low elevations of the LIG terrace fragments are, however, consistent with a low rate of uplift derived from the higher, ~2.5–1.9 Ma terrace. A number of other localities on the Pacific Coast, also dated to the LIG, have marine terrace elevations below the modeled level. GIA models may have overestimated last interglacial sea level by a substantial amount and need to be revised if used for forecasts for future sea-level rise.
In a 1998 paper entitled “Guts don’t fly: small digestive organs in obese bar-tailed godwits,” Piersma and Gill (1998) showed that the digestive organs were tiny and the fat loads huge in individuals suspected of embarking on a non-stop flight from Alaska to New Zealand. It was suggested that prior to migratory departure, these godwits would shrink the digestive organs used during fuel deposition and boost the size and capacity of exercise organs to optimize flight performance. Here we document the verity of the proposed physiomorphic changes by comparing organ sizes and body composition of bar-tailed godwits Limosa lapponica baueri collected in modesty midway during their fueling period (mid-September; fueling, n = 7) with the previously published data for godwits that had just departed on their trans-Pacific flight (October 19; flying, n = 9). Mean total body masses for the two groups were nearly identical, but nearly half of the body mass of fueling godwits consisted of water, while fat constituted over half of total body mass of flying godwits. The two groups also differed in their fat-free mass components. The heart and flight muscles were heavier in fueling godwits, but these body components constituted a relatively greater fraction of the fat-free mass in flying godwits. In contrast, organs related to digestion and homeostasis were heavier in fueling godwits, and most of these organ groups were also relatively larger in fueling godwits compared to flying godwits. These results reflect the functional importance of organ and muscle groups related to energy acquisition in fueling godwits and the consequences of flight-related exertion in flying godwits. The extreme physiomorphic changes apparently occurred over a short time window (≤1 month). We conclude that the inferences made on the basis of the 1998 paper were correct. The cues and stimuli which moderate these changes remain to be studied.
Fate and transport modeling of water-borne contaminants is a data demanding and costly endeavor, requiring considerable expes such, it becomes important to know when a complex modeling approach is required, and when a simpler approach is adequate. This is the main objective herein, where a conservative mixing model is used to characterize the transport of As, Cu, Fe, and SO4. The study area is divided into three sectors, corresponding to the upstream, middle, and downstream portions of the Elqui River Basin, Chile. In Sector 1, acidic conditions result in the conservative transport of constituents that are sourced from acid rock drainage. In Sector 2, pH increases and transport is influenced by pH-dependent reactions and the subsequent settling of the particulate phase. In Sector 3, there are no additional constituent inputs, and the constituents are conservatively transported downstream. Conservative transport within Sector 3 is confirmed through the development of a regression model that provides monthly estimates of SO4 load. Whereas SO4 and Cu concentrations are adequately approximated by the conservative mixing model, estimates of As and Fe concentrations exhibit larger errors, due to the more reactive behavior of these constituents. The fact that the simple, conservative mixing model describes SO4 transport is a valuable result, as this constituent is known to be one of the primary indicators of mining-related contamination in rivers. The approach could also be a useful starting point for further evaluations of the effects of climate change and hydrological variability on the water quality of rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3823","usgsCitation":"Rossi, C., Oyarzun, J., Pasten, P., Runkel, R.L., Núñez, J., Duhalde, D., Maturana, H., Rojas, E., Arumí, J., Castillo, D., and Oyarzun, R., 2021, Assessment of a conservative mixing model for the evaluation of constituent behavior below river confluences, Elqui River Basin, Chile: River Research and Applications, v. 37, no. 7, p. 967-978, https://doi.org/10.1002/rra.3823.","productDescription":"12 p.","startPage":"967","endPage":"978","ipdsId":"IP-117538","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":390393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Elqui River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.4605712890625,\n -30.741835717889778\n ],\n [\n -68.8348388671875,\n -30.741835717889778\n ],\n [\n -68.8348388671875,\n -29.176145182559758\n ],\n [\n -71.4605712890625,\n -29.176145182559758\n ],\n [\n -71.4605712890625,\n -30.741835717889778\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Catalina","contributorId":267243,"corporation":false,"usgs":false,"family":"Rossi","given":"Catalina","email":"","affiliations":[{"id":55453,"text":"U. La Serena","active":true,"usgs":false}],"preferred":false,"id":824827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oyarzun, Jorge","contributorId":267244,"corporation":false,"usgs":false,"family":"Oyarzun","given":"Jorge","email":"","affiliations":[{"id":55453,"text":"U. La Serena","active":true,"usgs":false}],"preferred":false,"id":824828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pasten, Pablo","contributorId":267245,"corporation":false,"usgs":false,"family":"Pasten","given":"Pablo","affiliations":[{"id":55454,"text":"Pontificia Universidad Católica","active":true,"usgs":false}],"preferred":false,"id":824829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824830,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Núñez, Jorge","contributorId":267246,"corporation":false,"usgs":false,"family":"Núñez","given":"Jorge","affiliations":[{"id":55453,"text":"U. La Serena","active":true,"usgs":false}],"preferred":false,"id":824831,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duhalde, Denisse","contributorId":267247,"corporation":false,"usgs":false,"family":"Duhalde","given":"Denisse","email":"","affiliations":[{"id":55453,"text":"U. La Serena","active":true,"usgs":false}],"preferred":false,"id":824832,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maturana, Hugo","contributorId":267248,"corporation":false,"usgs":false,"family":"Maturana","given":"Hugo","email":"","affiliations":[{"id":27795,"text":"Universidad Católica del Norte","active":true,"usgs":false}],"preferred":false,"id":824833,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rojas, Eduardo","contributorId":267249,"corporation":false,"usgs":false,"family":"Rojas","given":"Eduardo","email":"","affiliations":[{"id":55453,"text":"U. La Serena","active":true,"usgs":false}],"preferred":false,"id":824834,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Arumí, José L.","contributorId":267250,"corporation":false,"usgs":false,"family":"Arumí","given":"José L.","affiliations":[{"id":49667,"text":"Universidad de Concepción","active":true,"usgs":false}],"preferred":false,"id":824835,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Castillo, Daniela","contributorId":267251,"corporation":false,"usgs":false,"family":"Castillo","given":"Daniela","email":"","affiliations":[{"id":55455,"text":"Universidad de La Serena","active":true,"usgs":false}],"preferred":false,"id":824837,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Oyarzun, Ricardo","contributorId":267252,"corporation":false,"usgs":false,"family":"Oyarzun","given":"Ricardo","email":"","affiliations":[{"id":55455,"text":"Universidad de La Serena","active":true,"usgs":false}],"preferred":false,"id":824838,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70250112,"text":"70250112 - 2021 - Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling","interactions":[],"lastModifiedDate":"2023-11-21T11:53:06.136867","indexId":"70250112","displayToPublicDate":"2021-06-17T09:16:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling","docAbstract":"Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13122368","usgsCitation":"Stanislawski, L.V., Shavers, E.J., Wang, S., Jiang, Z., Usery, E., Moak, E., Duffy, A., and Schott, J., 2021, Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling: Remote Sensing, v. 13, no. 12, 2368, 27 p., https://doi.org/10.3390/rs13122368.","productDescription":"2368, 27 p.","ipdsId":"IP-128026","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":451842,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13122368","text":"Publisher Index Page"},{"id":422726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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These islands are expected to change drastically over the coming century due to accelerated sea-level rise and changes in frequency and intensity of storm events. The dynamic nature of barrier islands coupled with the importance of these environments make it critical for natural resource managers to understand how habitats on barrier islands are changing or may change over time to determine when and where management actions may be needed. In this study, we applied a habitat change assessment framework, which included exploring areal coverage and distribution changes and change component analysis. Change component analysis, which breaks differences into net gain/loss and allocation difference (i.e., habitat oscillation), has not previously been used to study barrier island habitat evolution. Here, we demonstrate the approach using habitat predictions from a geomorphic modeling effort on Dauphin Island, Alabama (USA). We explored differences of habitat predictions for potential island configurations with and without a beach and dune restoration action under future conditions related to sea level and storminess. We found a potential linkage between landward migration of barrier islands and exchange, an output of change component analysis. The hypothesis may be tested to explore whether this linkage applies over space and time and whether the approach is applicable to monitoring landward migration of coastal wetlands. Collectively, our results highlight the utility of change component analysis for monitoring and quantifying barrier island habitat change and migration.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-021-00971-w","usgsCitation":"Enwright, N., Wang, L., Dalyander, P., Wang, H., Osland, M., Mickey, R.C., Jenkins, R., and Godsey, E., 2021, Assessing habitat change and migration of barrier islands: Estuaries and Coasts, v. 44, p. 2073-2086, https://doi.org/10.1007/s12237-021-00971-w.","productDescription":"14 p.; Data Release","startPage":"2073","endPage":"2086","ipdsId":"IP-121508","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":499827,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/geoanth_pubs/276","text":"External Repository"},{"id":395610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417852,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V4Z7OK"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33969116210938,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2021-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217771,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Lei","contributorId":193279,"corporation":false,"usgs":false,"family":"Wang","given":"Lei","email":"","affiliations":[],"preferred":false,"id":833595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. 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Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":833596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":222383,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833597,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":219805,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833598,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833599,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833600,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Godsey, Elizabeth 0000-0003-4621-7857","orcid":"https://orcid.org/0000-0003-4621-7857","contributorId":222094,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","affiliations":[{"id":34200,"text":"Army Corp of Engineers","active":true,"usgs":false}],"preferred":false,"id":833601,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221549,"text":"70221549 - 2021 - Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","interactions":[],"lastModifiedDate":"2021-06-23T12:28:09.472652","indexId":"70221549","displayToPublicDate":"2021-06-17T06:59:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","docAbstract":"In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112560","usgsCitation":"Ye, S., Rogan, J., Zhu, Z., Hawbaker, T., Hart, S., Andrus, R.A., Meddens, A.J., Hicke, J.A., Eastman, J.R., and Kulakowski, D., 2021, Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance: Remote Sensing of Environment, v. 263, 112560, 16 p., https://doi.org/10.1016/j.rse.2021.112560.","productDescription":"112560, 16 p.","ipdsId":"IP-124774","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112560","text":"Publisher Index Page"},{"id":386642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0283203125,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"263","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ye, Su","contributorId":260471,"corporation":false,"usgs":false,"family":"Ye","given":"Su","email":"","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogan, John","contributorId":260472,"corporation":false,"usgs":false,"family":"Rogan","given":"John","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":818019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":818020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Sarah J.","contributorId":260474,"corporation":false,"usgs":false,"family":"Hart","given":"Sarah J.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":818021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrus, Robert A.","contributorId":260475,"corporation":false,"usgs":false,"family":"Andrus","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meddens, Arjan J.H.","contributorId":260476,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","middleInitial":"J.H.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818023,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hicke, Jeffery A.","contributorId":260477,"corporation":false,"usgs":false,"family":"Hicke","given":"Jeffery","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":818024,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eastman, J. Ronald","contributorId":260480,"corporation":false,"usgs":false,"family":"Eastman","given":"J.","email":"","middleInitial":"Ronald","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818025,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kulakowski, Dominik","contributorId":260482,"corporation":false,"usgs":false,"family":"Kulakowski","given":"Dominik","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818026,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221472,"text":"sir20215032 - 2021 - Permeable groundwater pathways and tritium migration patterns from the HANDLEY underground nuclear test, Pahute Mesa, Nevada","interactions":[],"lastModifiedDate":"2021-06-17T10:26:00.248996","indexId":"sir20215032","displayToPublicDate":"2021-06-16T13:00:45","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5032","displayTitle":"Permeable Groundwater Pathways and Tritium Migration Patterns from the HANDLEY Underground Nuclear Test, Pahute Mesa, Nevada","title":"Permeable groundwater pathways and tritium migration patterns from the HANDLEY underground nuclear test, Pahute Mesa, Nevada","docAbstract":"The HANDLEY nuclear test was detonated at about 2,700 feet below the water table on March 26, 1970, in Pahute Mesa, south-central Nevada. Measured tritium concentrations in boreholes ER-20-12 and PM-3 indicate that a shallow tritium plume has migrated more than 1 mile (mi) downgradient from the HANDLEY test within a semi-perched aquifer and deeper tritium plumes have migrated 4.5 miles (mi) within underlying regional aquifers. Boreholes ER-20-12 and PM-3 are in an area of moderate-to-low transmissivity, but observation of tritium moving 4.5 mi within 40 years of the detonation indicates that high-transmissivity intervals exist. However, the location of these permeable pathways is unknown.
This report integrates geologic, hydrologic, and tritium data to infer the location of permeable pathways near and downgradient from the HANDLEY test. Numerical groundwater-flow and tritium-transport models were developed to estimate hydraulic and transport properties between the HANDLEY test and boreholes ER-20-12 and PM-3. Recharge, hydraulic-conductivity, specific-yield, specific-storage, and effective-porosity distributions were estimated with the numerical models by fitting simulated water-level altitudes, vertical-head differences, aquifer-test transmissivities, tritium concentrations, and drawdowns in wells PM-3-1 and PM-3-2 to measured equivalents. Drawdowns were estimated in wells PM-3-1 and PM-3-2 in response to groundwater withdrawals during the drilling of borehole ER-20-12. A modified hydrostratigraphic framework model (mHFM) was developed that incorporates hydrostratigraphic units (HSUs) from the Pahute Mesa–Oasis Valley hydrostratigraphic framework model (PMOV HFM). HSUs in the mHFM were modified from the PMOV HFM by grouping HSUs that, conceptually, are hydraulically similar and splitting HSUs based on water-level, aquifer-test, and tritium data.
Shallow and deeper tritium plumes have migrated to borehole ER-20-12 from the HANDLEY test. The shallow plume migrated from the HANDLEY test through the Timber Mountain welded tuff aquifer, whereas the deeper plumes moved through the Belted Range aquifer (BRA) and modified pre-Belted Range lava flow aquifer (mPBRLFA). Simulated tritium concentrations indicate that the leading edges of tritium plumes reached borehole ER-20-12 by 1990. From 1970 to 2020, the simulated tritium load mostly occurs between borehole ER-20-12 and the HANDLEY test.
An unmapped permeable feature was simulated between borehole ER-20-12 and the downgradient Ribbon Cliff structural zone. This permeable feature hydraulically connects the BRA and mPBRLFA with the Tiva Canyon aquifer (TCA). The TCA is the most transmissive unit in the study area. Simulated tritium from the deeper plumes moves through the permeable feature downgradient from borehole ER-20-12 and then migrates toward well PM-3-1 through the TCA. The leading edge of the deeper simulated tritium plumes reaches well PM-3-1 by 2010.
The mHFM and PMOV HFM do not include a permeable HSU at the water table near borehole PM-3, which is necessary for numerical flow and transport models to match measured water levels, transmissivities, and tritium concentrations in well PM-3-2. Consistently higher measured tritium concentrations in shallow well PM-3-2, compared to deeper well PM-3-1, and a downward vertical gradient between these wells indicate that a permeable feature exists near the water table that causes faster tritium migration toward the shallow well. Reevaluation of the PMOV HFM and geologic investigations, such as drilling another well, are needed to more precisely understand the shallow permeable pathway from the Handley test to well PM-3-2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215032","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management, under Interagency Agreement DE-EM0004969","usgsCitation":"Jackson, T.R., 2021, Permeable groundwater pathways and tritium migration patterns from the HANDLEY underground nuclear test, Pahute Mesa, Nevada: U.S. Geological Survey Scientific Investigations Report 2021–5032, 49 p., https://doi.org/10.3133/sir20215032.","productDescription":"Report: vii, 49 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-120498","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":386552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5032/coverthb.jpg"},{"id":386553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5032/sir20215032.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5032"},{"id":386554,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRDQSN","text":"USGS data release","description":"USGS data release.","linkHelpText":"MODFLOW-2005 and MT3DMS models and supplemental data used to simulate groundwater flow and tritium transport from the HANDLEY underground nuclear test, Pahute Mesa, southern Nevada"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.333984375,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
A description of historical and ambient water quality conditions is often required as part of navigational studies. This paper describes a series of tools developed by the USGS that can aid navigation managers in developing water quality assessments. The tools use R, a statistical software program, and provide methods to retrieve historical streamflow and water quality data, summarize observations, model concentrations and fluxes, and estimate seasonal, annual, and decadal trends. The utility of these tools is demonstrated by providing an analysis of the seasonal variability and long-term trends of nitrate plus nitrite, orthophosphate, and suspended sediment concentrations and fluxes at nine sites in the Mississippi River Basin. Trends in annual mean concentration and flux showed fairly stable nitrate plus nitrite at most of the nine sites, with increases in the Upper Mississippi and Missouri Rivers and decreases on the Illinois River over a 40-year period beginning in 1980. Orthophosphate concentration or flux increased at almost all sites over a similar time period. Conversely, a concurrent steady decline in suspended sediment concentrations and fluxes was noted at sites throughout the basin.
Infiltration-induced landslides threaten transportation infrastructure around the world, and impose both direct costs through repair and remediation work and indirect costs through lost economic activity. Therefore, finding the most cost-effective techniques to mitigate slope failures that can impact critical infrastructure links is desirable. The Straight Creek landslide, which affects a segment of Interstate 70 in Summit County, Colorado (USA), has experienced seasonal failure driven by rapid springtime snowmelt infiltration since the early 1970s, allowing changes in its stability to be studied. Past studies have established that seasonal failure is driven by pore-water pressure increase caused by the rapid infiltration of snowmelt and the hydraulic conductivity contrast between upper slope materials and the highway embankment. Two remediation designs have been applied to the site, including lightweight caissons beneath the highway surface in 2011 and 2012, and horizontal drains near the slide toe in 2012. The effects of the lightweight caissons and horizontal drains, as well as an alternative drain design that would extend into the hillslope above the highway embankment, are evaluated within a rigorous hydro-mechanical simulation framework along with a method to generate a field of local factor of safety. Model results show that the effect of the lightweight caissons on the factor of safety is no more than 1% during times of critical instability, as they do not affect the seasonal changes in hydrology that cause destabilizing decreases in effective stress along the failure surface. Horizontal drains are intended to reduce pore-water pressures, but the location of existing drains limit their efficacy due to the low hydraulic conductivity of subsurface materials underneath the highway. Model results indicate that these drains are only partially responsible for a reduction in movement rate since their installation, which is also due to lower annual cumulative snowmelt infiltration levels since 2012. Results also show that an alternative drain design could result in increased stability during critical periods by intercepting downslope subsurface flow before it arrives at the hydraulic conductivity contrast at the embankment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2021.106240","usgsCitation":"Hinds, E., Lu, N., Mirus, B.B., Godt, J.W., and Wayllace, A., 2021, Evaluation of techniques for mitigating snowmelt infiltration-induced landsliding in a highway embankment: Engineering Geology, v. 291, 106240, 11 p., https://doi.org/10.1016/j.enggeo.2021.106240.","productDescription":"106240, 11 p.","ipdsId":"IP-117388","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451867,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enggeo.2021.106240","text":"Publisher Index Page"},{"id":386641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Straight Creek slide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.9521484375,\n 39.75471275080197\n ],\n [\n -105.79147338867188,\n 39.75471275080197\n ],\n [\n -105.79147338867188,\n 39.930800820752765\n ],\n [\n -105.9521484375,\n 39.930800820752765\n ],\n [\n -105.9521484375,\n 39.75471275080197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"291","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hinds, Eric","contributorId":218084,"corporation":false,"usgs":false,"family":"Hinds","given":"Eric","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":818027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Ning","contributorId":191360,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","email":"","affiliations":[{"id":12620,"text":"U.S. Army Corp. of Engineers","active":true,"usgs":false}],"preferred":false,"id":818028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":818029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wayllace, Alexandra","contributorId":203213,"corporation":false,"usgs":false,"family":"Wayllace","given":"Alexandra","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":818031,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274339,"text":"70274339 - 2021 - When Wyoming became Superior: Oblique convergence along the southern Trans-Hudson orogen","interactions":[],"lastModifiedDate":"2026-03-27T16:19:34.178613","indexId":"70274339","displayToPublicDate":"2021-06-16T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"When Wyoming became Superior: Oblique convergence along the southern Trans-Hudson orogen","docAbstract":"The Trans-Hudson orogen (THO) is one of the best-preserved Proterozoic orogens on Earth, largely unaffected by subsequent tectonism, yet its southern extent lies concealed beneath the North American Central Plains. A new 3D resistivity model over the southern orogen is developed and interpreted alongside borehole, potential field, and seismic reflection data. We present the first synoptic crustal view of the southern THO and a new tectonic model of the orogen. Our model reveals high-conductivity belts marking paleo-subduction zones while the orogen center consists of deeply exhumed relatively dense and mostly magnetic juvenile crust preserved between the deformed margins of the Wyoming and Superior cratons. Complex structure along the western margin suggests convergence began with oblique subduction and the northward transport of severed fragments of the Wyoming Province. High-conductivity belts are in places offset from upper-crustal geophysical boundaries, consistent with the thrusting of accreted arcs over the Archean margins during terminal closure.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL092970","usgsCitation":"Bedrosian, P.A., and Finn, C., 2021, When Wyoming became Superior: Oblique convergence along the southern Trans-Hudson orogen: Geophysical Research Letters, v. 48, no. 13, e2021GL092970, 10 p., https://doi.org/10.1029/2021GL092970.","productDescription":"e2021GL092970, 10 p.","ipdsId":"IP-128575","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501577,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501605,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl092970","text":"Publisher Index Page"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.52047674158729,\n 55.2786050391222\n ],\n [\n -111.52047674158729,\n 41.422595132286716\n ],\n [\n -91.30332888849887,\n 41.422595132286716\n ],\n [\n -91.30332888849887,\n 55.2786050391222\n ],\n [\n -111.52047674158729,\n 55.2786050391222\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"13","noUsgsAuthors":false,"publicationDate":"2021-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":229711,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957954,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221407,"text":"ofr20211059 - 2021 - Surface-mining modeling for USGS coal assessments","interactions":[],"lastModifiedDate":"2021-06-16T11:43:56.91242","indexId":"ofr20211059","displayToPublicDate":"2021-06-15T15:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1059","displayTitle":"Surface-Mining Modeling for USGS Coal Assessments","title":"Surface-mining modeling for USGS coal assessments","docAbstract":"The value of national coal deposits is determined for the Solid Fuel Energy Resources Assessment and Research Project through economic evaluations using hypothetical mining models. Deposits near the surface are evaluated with the U.S. Geological Survey (USGS) surface-mining model, which is patterned after the standard mining techniques and infrastructures of commercial mining projects. The USGS surface-mining model uses these commercial mining project techniques as guides to develop a quantitative measurement to distinguish between potential recoverable resources and reserves by comparing total project cost and market value.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211059","usgsCitation":"Pierce, P.E., 2021, Surface-mining modeling for USGS coal assessments: U.S. Geological Survey Open-File Report 2021–1059, 13 p., https://doi.org/10.3133/ofr20211059.","productDescription":"iv, 13 p.","onlineOnly":"Y","ipdsId":"IP-117620","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":386474,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1059/coverthb.jpg"},{"id":386475,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1059/ofr20211059.pdf","text":"Report","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1059"}],"contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Controlling range expansion of invasive carp (specifically Hypophthalmichthys spp.) on the Tennessee River is important to conserve the ecological and economic benefits provided by the river. We collaborated with State and Federal agencies (the stakeholder group) to develop a decision framework and decision support model to evaluate strategies to control carp expansion in the Tennessee River. Using this decision framework, we assessed the efficacy of various barrier strategies (technologies and locations) on reducing bigheaded carp (Hypophthalmichthys nobilis [bighead carp] and Hypophthalmichthys molitrix [silver carp]) relative abundance under different patterns and magnitudes of population growth and movement. We also assessed whether or not these strategies induced tradeoffs between reducing bigheaded carp relative abundance and other considerations for public satisfaction, effects on lock operation, and native species. For the purpose of comparing options to control carp in a quantitative framework, we codeveloped a carp population dynamics model with the stakeholder group. We then used the model to compare invasive carp management options within the Tennessee River system. The actions we considered included barrier placement at lock and dam systems and targeted removal through harvest, which were believed to impede upstream carp spread and establishment. To account for the uncertainty in carp population growth and movement rates, the group developed four population models that varied in the underlying population dynamics and population growth rates. The models affected population growth through either the stock-recruitment relation or intrinsic density-dependent growth rate. We then tasked the stakeholder group to test various strategies using the model. We then developed a more formal optimization framework and solved for strategies that performed well under scenarios of barrier effectiveness, movement rate, recruitment frequency, fishing mortality, and variation in population growth rate. The results of our qualitative and quantitative analyses indicated that strategies designed to first protect reservoirs just above the leading edge of carp invasion by installing barriers and removing fish below that point would perform best; however, this depended on barrier effectiveness. When barrier effectiveness was high, simply cutting off the presumed source of carp and blocking the leading the edge was enough to stop carp invasion; however, lower effectiveness meant that more barriers would be needed to slow, but not completely stop, carp invasion. We discuss what these findings mean in terms of future monitoring and management efforts to reduce the potential for expanding carp invasion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211068","programNote":"Biological Threats Research Program","usgsCitation":"Post van der Burg, M., Smith, D.R., Cupp, A.R., Rogers, M.W., and Chapman, D.C., 2021, Decision analysis of barrier placement and targeted removal to control invasive carp in the Tennessee River Basin: U.S. Geological Survey Open-File Report 2021–1068, 18 p., https://doi.org/10.3133/ofr20211068.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-129842","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386492,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1068/coverthb2.jpg"},{"id":386493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1068/ofr20211068.pdf","text":"Report","size":"979 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1068"}],"country":"United States","state":"Kentucky","otherGeospatial":"Tennessee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.516845703125,\n 37.01132594307015\n ],\n [\n -87.12158203125,\n 37.42252593456307\n ],\n [\n -85.111083984375,\n 38.212288054388175\n ],\n [\n -84.13330078125,\n 38.74551518488265\n ],\n [\n -84.48486328124999,\n 39.036252959636606\n ],\n [\n -85.078125,\n 39.13006024213511\n ],\n [\n -85.97900390625,\n 38.98503278695909\n ],\n [\n -87.022705078125,\n 38.54816542304656\n ],\n [\n -87.945556640625,\n 38.24680876017446\n ],\n [\n -88.72558593749999,\n 37.70120736474139\n ],\n [\n -88.78051757812499,\n 37.32648861334206\n ],\n [\n -88.472900390625,\n 36.99377838872517\n ],\n [\n -88.516845703125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Post-wildfire changes to hydrologic and geomorphic systems can lead to widespread sediment redistribution. Understanding how sediment moves through a watershed is crucial for assessing hazards, developing debris flow inundation models, engineering sediment retention solutions, and quantifying the role that disturbances play in landscape evolution. In this study, we used terrestrial and airborne lidar to measure sediment redistribution in the 2016 Fish Fire, in the San Gabriel Mountains in southern California, USA. The lidar areas are in two adjacent watersheds, at spatial scales of 900 m2 to 4 km2, respectively. Terrestrial lidar data were acquired prior to rainfall, and two subsequent surveys show erosional change after rainstorms. Two airborne lidar flights occurred (1) 7 months before, and (2) 14 months after the fire ignition, capturing the erosional effects after rainfall. We found hillslope erosion dominated the overall sediment budget in the first rainy season after wildfire. Only 7% of the total erosion came from the active channel bed and channel banks, and the remaining 93% of eroded sediment was derived from hillslopes. Within the channelized portion of the watershed erosion/deposition could be generally described with topographic metrics used in a stream power equation. Observed sediment volumes were compared with four empirical models and one process-based model. We found that the best predictions of sediment volume were obtained from an empirical model developed in the same physiographic region. Moreover, this study showed that post-wildfire erosion rates in the San Gabriel Mountains attain the same magnitude as millennial time scale bedrock erosion rates.
Pesticides occur in urban streams globally, but the relation of occurrence to urbanization can be obscured by regional differences. In studies of five regions of the United States, we investigated the effect of region and urbanization on the occurrence and potential toxicity of dissolved pesticide mixtures. We analyzed 225 pesticide compounds in weekly discrete water samples collected during 6–12 weeks from 271 wadable streams; development in these basins ranged from undeveloped to highly urbanized. Sixteen pesticides were consistently detected in 16 urban centers across the five regions—we propose that these pesticides comprise a suite of urban signature pesticides (USP) that are all common in small U.S. urban streams. These USPs accounted for the majority of summed maximum pesticide concentrations at urban sites within each urban center. USP concentrations, mixture complexity, and potential toxicity increased with the degree of urbanization in the basin. Basin urbanization explained the most variability in multivariate distance-based models of pesticide profiles, with region always secondary in importance. The USPs accounted for 83% of pesticides in the 20 most frequently occurring 2-compound unique mixtures at urban sites, with carbendazim+prometon the most common. Although USPs were consistently detected in all regions, detection frequencies and concentrations varied by region, conferring differences in potential aquatic toxicity. Potential toxicity was highest for invertebrates (benchmarks exceeded in 51% of urban streams), due most often to the neonicotinoid insecticide imidacloprid and secondarily to organophosphate insecticides and fipronil. Benchmarks were rarely exceeded in urban streams for plants (at 3% of sites) or fish (<1%). We propose that the USPs identified here would make logical core (nonexclusive) constituents for monitoring dissolved pesticides in U.S. urban streams, and that unique mixtures containing imidacloprid, fipronil, and carbendazim are priority candidates for mixtures toxicity testing.
Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.
Tree hyraxes (Dendrohyrax) are one of only three genera currently recognized in Procaviidae, the only extant family in the mammalian order Hyracoidea. Their taxonomy and natural history have received little attention in recent decades. All tree hyrax populations of Guineo-Congolian forests of Africa are currently treated as a single species, Dendrohyrax dorsalis, the western tree hyrax, but many other groups of mammals distributed across this large biome have been shown to consist of several different species, each restricted to a distinct biogeographical region. We analysed variation in loud-call structure, pelage colour, skull morphometrics and mitochondrial genomes in populations across much of the range of D. dorsalis. This integrative approach uncovered considerable cryptic variation. The population found between the Niger and Volta Rivers in West Africa is particularly distinctive, and we describe it herein as a new species. Our study highlights the need to revise the taxonomy of the genus Dendrohyrax in light of modern systematics and current understanding of its distribution. It also adds to a growing body of evidence that the Niger–Volta interfluvium has a distinct meso-mammal fauna. Unfortunately, the fauna of this region is under major threat and warrants much greater conservation attention.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/zoolinnean/zlab029","usgsCitation":"Oates, J.F., Woodman, N., Gaubert, P., Sargis, E.J., Wiafe, E.D., Lecompte, E., Dowsett-Lemaire, F., Dowsett, R.J., Bi, S.G., Ikemeh, R.A., Djagoun, C., Tomsett, L., and Bearder, S.K., 2021, A new species of tree hyrax (Procaviidae: Dendrohyrax) from West Africa and the significance of the Niger–Volta interfluvium in mammalian biogeography: Zoological Journal of the Linnean Society, zlab029, https://doi.org/10.1093/zoolinnean/zlab029.","productDescription":"zlab029","ipdsId":"IP-127431","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/zoolinnean/zlab029","text":"Publisher Index Page"},{"id":386520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"West Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -19.33593749999999,\n 2.811371193331128\n ],\n [\n 22.14843750000001,\n 2.811371193331128\n ],\n [\n 22.14843750000001,\n 36.59788913307022\n ],\n [\n -19.33593749999999,\n 36.59788913307022\n ],\n [\n -19.33593749999999,\n 2.811371193331128\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Oates, John F. 0000-0001-5943-4557","orcid":"https://orcid.org/0000-0001-5943-4557","contributorId":260296,"corporation":false,"usgs":false,"family":"Oates","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":52558,"text":"Department of Anthropology, Hunter College CUNY, New York, NY","active":true,"usgs":false}],"preferred":false,"id":817704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":817705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaubert, Philippe 0000-0002-1375-9935","orcid":"https://orcid.org/0000-0002-1375-9935","contributorId":149820,"corporation":false,"usgs":false,"family":"Gaubert","given":"Philippe","email":"","affiliations":[{"id":17834,"text":"Institut des Sciences de l'Evolution de Montpellier (ISEM) – UM2-CNRS-IRD, Université de Montpellier, Montpellier Cedex 05, France","active":true,"usgs":false}],"preferred":false,"id":817706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sargis, Eric J. 0000-0003-0424-3803","orcid":"https://orcid.org/0000-0003-0424-3803","contributorId":203885,"corporation":false,"usgs":false,"family":"Sargis","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":36741,"text":"Department of Anthropology, Yale University, P.O. Box 208277, New Haven, CT 06520, USA","active":true,"usgs":false}],"preferred":false,"id":817707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiafe, Edward D. 0000-0001-5938-9901","orcid":"https://orcid.org/0000-0001-5938-9901","contributorId":260297,"corporation":false,"usgs":false,"family":"Wiafe","given":"Edward","email":"","middleInitial":"D.","affiliations":[{"id":52559,"text":"Le Pouget, 30440 Sumène, France","active":true,"usgs":false}],"preferred":false,"id":817708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lecompte, Emilie 0000-0002-5711-7395","orcid":"https://orcid.org/0000-0002-5711-7395","contributorId":260298,"corporation":false,"usgs":false,"family":"Lecompte","given":"Emilie","email":"","affiliations":[{"id":52560,"text":"Laboratoire Evolution et Diversité Biologique, IRD / CNRS / UPS, Université Paul Sabatier, 31062 Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":817709,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dowsett-Lemaire, Francoise","contributorId":260299,"corporation":false,"usgs":false,"family":"Dowsett-Lemaire","given":"Francoise","email":"","affiliations":[{"id":52559,"text":"Le Pouget, 30440 Sumène, France","active":true,"usgs":false}],"preferred":false,"id":817710,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dowsett, Robert J.","contributorId":260300,"corporation":false,"usgs":false,"family":"Dowsett","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":52559,"text":"Le Pouget, 30440 Sumène, France","active":true,"usgs":false}],"preferred":false,"id":817711,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bi, Sery Gonedele 0000-0001-8823-4319","orcid":"https://orcid.org/0000-0001-8823-4319","contributorId":260301,"corporation":false,"usgs":false,"family":"Bi","given":"Sery","email":"","middleInitial":"Gonedele","affiliations":[{"id":52562,"text":"Département de Génétique, Université Félix Houphouët-Boigny, 01 BP V34 Abidjan, Côte d’Ivoire","active":true,"usgs":false}],"preferred":false,"id":817712,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ikemeh, Rachel A. 0000-0002-0342-9625","orcid":"https://orcid.org/0000-0002-0342-9625","contributorId":260302,"corporation":false,"usgs":false,"family":"Ikemeh","given":"Rachel","email":"","middleInitial":"A.","affiliations":[{"id":52563,"text":"SW/Niger Delta Forest Project, New Garki, Abuja, Nigeria","active":true,"usgs":false}],"preferred":false,"id":817713,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Djagoun, Chabi 0000-0002-6352-2450","orcid":"https://orcid.org/0000-0002-6352-2450","contributorId":260303,"corporation":false,"usgs":false,"family":"Djagoun","given":"Chabi","email":"","affiliations":[{"id":52564,"text":"Laboratoire d’Ecologie Appliquée, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, 01 B.P. 526 Cotonou, Benin","active":true,"usgs":false}],"preferred":false,"id":817714,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tomsett, Louise","contributorId":260304,"corporation":false,"usgs":false,"family":"Tomsett","given":"Louise","email":"","affiliations":[{"id":52565,"text":"Department of Life Sciences, Natural History Museum, London, SW7 5BD, UK","active":true,"usgs":false}],"preferred":false,"id":817715,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bearder, Simon K.","contributorId":260305,"corporation":false,"usgs":false,"family":"Bearder","given":"Simon","email":"","middleInitial":"K.","affiliations":[{"id":52566,"text":"School of Social Sciences, Oxford Brookes University, Oxford OX3 0BP, UK","active":true,"usgs":false}],"preferred":false,"id":817716,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70221485,"text":"70221485 - 2021 - Effects of tidally varying river flow on entrainment of juvenile salmon into Sutter and Steamboat Sloughs","interactions":[],"lastModifiedDate":"2021-06-17T11:39:33.433744","indexId":"70221485","displayToPublicDate":"2021-06-15T06:37:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Effects of tidally varying river flow on entrainment of juvenile salmon into Sutter and Steamboat Sloughs","docAbstract":"Globally, anthrax outbreaks pose a serious threat to people, livestock, and wildlife. Furthermore, environmental change can exacerbate these outbreak dynamics by altering the host–pathogen relationship. However, little is known about how the quantitative spatial dynamics of host movement and environmental change may affect the spread of Bacillus anthracis, the causative agent of anthrax. Here, we use real-time observations and high-resolution tracking data from a population of common hippopotamus (Hippopotamus amphibius) in Tanzania to explore the relationship between river hydrology, H. amphibius movement, and the spatiotemporal dynamics of an active anthrax outbreak. We found that extreme river drying, a consequence of anthropogenic disturbances to our study river, indirectly facilitated the spread of B. anthracis by modulating H. amphibius movements. Our findings reveal that anthrax spread upstream in the Great Ruaha River (~3.5 km over a 9-day period), which followed the movement patterns of infected H. amphibius, who moved upstream as the river dried in search of remaining aquatic refugia. These upstream movements can result in large aggregations of H. amphibius. However, despite these aggregations, the density of H. amphibius in river pools did not influence the number of B. anthracis-induced mortalities. Moreover, infection by B. anthracis did not appear to influence H. amphibius movement behaviors, which suggests that infected individuals can vector B. anthracis over large distances right up until their death. Finally, we show that contact rates between H. amphibius- and B. anthracis-infected river pools are highly variable and the frequency and duration of contacts could potentially increase the probability of mortality. While difficult to obtain, the quantitative insights that we gathered during a real-time anthrax outbreak are critical to better understand, predict, and manage future outbreaks.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3540","usgsCitation":"Stears, K., Schmitt, M.H., Turner, W.C., McCauley, D., Muse, E.A., Kiwango, H., Matheyo, D., and Mutayoba, B.M., 2021, Hippopotamus movements structure the spatiotemporal dynamics of an active anthrax outbreak: Ecosphere, v. 12, no. 6, e03540, 14 p., https://doi.org/10.1002/ecs2.3540.","productDescription":"e03540, 14 p.","ipdsId":"IP-121950","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3540","text":"Publisher Index Page"},{"id":396541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","otherGeospatial":"Ruaha National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 33.95874023437499,\n -8.697784143504906\n ],\n [\n 34.87884521484374,\n -8.697784143504906\n ],\n [\n 34.87884521484374,\n -7.917793352627911\n ],\n [\n 33.95874023437499,\n -7.917793352627911\n ],\n [\n 33.95874023437499,\n -8.697784143504906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Stears, Keenan","contributorId":287054,"corporation":false,"usgs":false,"family":"Stears","given":"Keenan","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmitt, Melissa H.","contributorId":287055,"corporation":false,"usgs":false,"family":"Schmitt","given":"Melissa","email":"","middleInitial":"H.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muse, Epaphras A.","contributorId":287060,"corporation":false,"usgs":false,"family":"Muse","given":"Epaphras","email":"","middleInitial":"A.","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kiwango, Halima","contributorId":287062,"corporation":false,"usgs":false,"family":"Kiwango","given":"Halima","email":"","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matheyo, Daniel","contributorId":287063,"corporation":false,"usgs":false,"family":"Matheyo","given":"Daniel","email":"","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836461,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mutayoba, Benezeth M.","contributorId":287064,"corporation":false,"usgs":false,"family":"Mutayoba","given":"Benezeth","email":"","middleInitial":"M.","affiliations":[{"id":61457,"text":"Sokoine University of Agriculture","active":true,"usgs":false}],"preferred":false,"id":836462,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224310,"text":"70224310 - 2021 - Spatial Gaussian processes improve multi-species occupancy models when range boundaries are uncertain and nonoverlapping","interactions":[],"lastModifiedDate":"2021-09-21T12:39:44.167853","indexId":"70224310","displayToPublicDate":"2021-06-14T07:37:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Spatial Gaussian processes improve multi-species occupancy models when range boundaries are uncertain and nonoverlapping","docAbstract":"The latest release of MODFLOW 6, the current core version of the MODFLOW groundwater modeling software, debuted a new package dubbed the “mover” (MVR). Using a generalized approach, MVR facilitates the transfer of water among any arbitrary combination of simulated features (i.e., pumping wells, stream, drains, lakes, etc.) within a MODFLOW 6 simulation. Four “rules” controlling the amount of water transferred from a providing feature to a receiving feature are currently available. In this way, MVR can represent natural connections between features, for example streams entering or exiting lakes, and perhaps more interestingly, it also can transfer water among simulated features to more accurately simulate water management. An example model representative of an agricultural setting demonstrates some of the available MVR connections. For example, an irrigation event that transfers surface water from an irrigation delivery ditch to multiple cropped areas demonstrates a “one-to-many” connection that is possible within MVR. Conversely, irrigation or precipitation runoff from multiple fields may be routed to a particular stream segment using “many-to-one” MVR connections. MVR supports many additional connection types, several of which are demonstrated by the included example problem.
Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.
A three-dimensional hydrogeologic framework of the Hot Springs anticlinorium beneath Hot Springs National Park, Arkansas, was constructed to represent the complex hydrogeology of the park and surrounding areas to depths exceeding 9,000 feet below ground surface. The framework, composed of 6 rock formations and 1 vertical fault emplaced beneath the thermal springs, was discretized into 19 layers, 429 rows, and 576 columns and incorporated into a 3-dimensional steady-state groundwater-flow model constructed in MODFLOW-2005. Historical daily mean thermal spring flows were simulated for one stress period of approximately 34 years (1980–2014), chosen to represent the period of record for historical climate data used in the quantification of the boundary conditions. The groundwater-flow model was manually calibrated to historical daily mean thermal spring flows of 88,000 cubic feet per day observed over a 12-year period of record (1990–1995 and 1998–2005) at the thermal springs collection system. Calibration was achieved by calculating starting heads and general head boundary conditions from the Bernoulli equation and then adjusting the horizontal and vertical hydraulic conductivities of the rock formations and vertical fault and the hydraulic conductance of head-dependent flux boundaries. The groundwater-flow model was coupled to a surface-water model developed in the Precipitation-Runoff Modeling System (PRMS) by using PRMS-simulated gravity drainage as a specified flux recharge boundary condition in the groundwater-flow model. Together, the coupled models were used to (1) locate the areas of groundwater recharge to the thermal springs in the discretized hydrogeologic framework by using forward and reverse particle-tracking capabilities of MODPATH, (2) simulate the effects of variable recharge rates on the spring flows at the thermal springs, and (3) assess possible effects of climate and land-use change on the long-term variability of spring flows at the thermal springs.
Forward and backward particle-tracking maps indicated that the most prevalent areas of recharge in the discretized hydrogeologic framework used in this study were within about 0.6–0.9 mile of the thermal springs. Forward particle tracking indicated a recharge area southwest of the thermal springs that corresponded to a location where the predominant lithologies are the Arkansas Novaculite, Hot Springs Sandstone, and Bigfork Chert. Backward particle tracking indicated a second localized area of recharge to the northeast of the thermal springs that corresponded to a location where the dominant lithology is the Bigfork Chert. The groundwater-flow model indicated that the most probable recharge formations are the Arkansas Novaculite, Bigfork Chert, and Hot Springs Sandstone.
The simulated effects of climate and land-use changes on the variability of the spring-flow rates at the thermal springs generally resulted in reductions of thermal spring flow attributed to urban development and more extreme climates characterized by elevated mean surface air temperatures. The groundwater-flow model predicted a linear relation between the thermal spring discharge and the cumulative recharge volume applied to the hydrogeologic framework, and the positive slope of the predicted relation between recharge and simulated thermal spring flow indicates that more extreme precipitation events that supply more recharge may in fact increase the thermal spring-flow rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215045","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hart, R.M., Ikard, S.J., Hays, P.D., and Clark, B.R., 2021, Effects of climate and land-use change on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas: U.S. Geological Survey Scientific Investigations Report 2021–5045, 38 p., https://doi.org/10.3133/sir20215045.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-091576","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":386401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5045/coverthb.jpg"},{"id":386402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5045/sir20215045.pdf","text":"Report","size":"43.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5045"},{"id":386403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBJVVL","text":"USGS data release","linkHelpText":"Model inputs and outputs for simulating and predicting the effects of climate and land-use changes on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas"},{"id":386404,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5045/images"}],"country":"United States","state":"Arkansas","otherGeospatial":"Hot Springs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1475830078125,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.487881874939866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211