The Rainy-Lake of the Woods Basin covers 70,000 square kilometers in mid-central North America and is contained within the Provinces of Ontario and Manitoba in Canada and the State of Minnesota in the United States. This basin contains natural wilderness areas, national parks, and thousands of lakes that bring outdoor enthusiasts from around the world for hunting, fishing, backpacking, boating, and other forms of recreation. However, trade, commerce, visitors, and wildlife can inadvertently transport hitchhiking exotic invasive species that affect the functioning of natural systems by displacing native organisms, introducing diseases, and modifying predator/prey relations. In cooperation with the International Joint Commission, the U.S. Geological Survey evaluated the aquatic invasive species that pose a possible threat to North America. The outcome of this project is a set of lists of invasive species that have traits amenable or proximity to the Rainy-Lake of the Woods Basin. These lists can be referenced to further evaluate known and potential nonindigenous invasive species. The lists were derived by evaluating more than 1,500 species from several online sources including Non-Indigenous Aquatic Species, Great Lakes Aquatic Nonindigenous Species Information System, Biodiversity Information Serving Our Nation, and other State, Provincial, and Federal lists in the United States and Canada. The purpose of these lists is to be a coarse filter to determine which species pose the greatest risk to the Rainy-Lake of the Woods Basin. Using this filter, seven categories of risk assessment priorities were developed: Very High-Approaching, Very High-Present, High-Approaching, High-Present, Moderate, Low, and Native. These categories can be used by the International Rainy-Lake of the Woods Multi-Agency Arrangement Aquatic Invasive Species Subcommittee to prioritize which species will be evaluated further focusing on five risk factors: arrival risk, vulnerability assessment, ecological impact, socioeconomic impact, and beneficial impact. Based on proximity, ease of transport or introduction, and known impact to Rainy-Lake of the Woods or other impacted ecosystems, this project identified the following 10 species that could be prioritized first for risk evaluations: Bythotrephes longimanus (spiny waterflea), Faxonius rusticus (rusty crayfish), Neogobius melanostomus (round goby), Dreissena polymorpha (zebra mussel), Bithynia tentaculata (mud Bithynia or faucet snail), Potamopyrgus antipodarum (New Zealand mud snail), Butomus umbellatus (flowering rush), Nitellopsis obtusa (starry stonewort), Myriophyllum spicatum (Eurasian watermilfoil), and Phragmites australis australis (common reed).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221070","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Bell, A.H., Katona, L.R., and Vellequette, N.M., 2023, Development and application of a risk assessment tool for aquatic invasive species in the international Rainy-Lake of the Woods Basin, United States and Canada: U.S. Geological Survey Open-File Report 2022–1070, 26 p., https://doi.org/10.3133/ofr20221070.","productDescription":"Report: vi, 26 p.; 3 Datasets","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127028","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499719,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115587.htm","linkFileType":{"id":5,"text":"html"}},{"id":422425,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221070/full"},{"id":422424,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://bison.usgs.gov/","text":"USGS database","linkHelpText":"—Biodiversity Information Serving Our Nation (BISON)"},{"id":422421,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1070/images/"},{"id":422418,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1070/coverthb.jpg"},{"id":422420,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1070/ofr20221070.XML"},{"id":422419,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1070/ofr20221070.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1070"},{"id":422422,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.glerl.noaa.gov/glansis/riskAssessment.html","text":"NOAA database","linkHelpText":"—Great Lake Aquatic Nonindigenous Species Information System (GLANSIS) Risk Assessment Clearinghouse"},{"id":422423,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://nas.er.usgs.gov/","text":"USGS database","linkHelpText":"—NAS—Nonindigenous Aquatic Species"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.7568318410593,\n 50.39164460188181\n ],\n [\n -95.7568318410593,\n 47.02962883799128\n ],\n [\n -90.08788652855912,\n 47.02962883799128\n ],\n [\n -90.08788652855912,\n 50.39164460188181\n ],\n [\n -95.7568318410593,\n 50.39164460188181\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Karner blue butterfly (Lycaeides melissa samuelis, or Kbb), a federally endangered species under the U.S. Endangered Species Act in decline due to habitat loss, can be further threatened by climate change. Evaluating how climate shapes the population trend of the Kbb can help in the development of adaptive management plans. Current demographic models for the Kbb incorporate in either a density-dependent or density-independent manner. We instead created mixed density-dependent and -independent (hereafter “endo-exogenous”) models for Kbbs based on long-term count data of five isolated populations in the upper Midwest, United States during two flight periods (May to June and July to August) to understand how the growth rates were related to previous population densities and abiotic environmental conditions, including various macro- and micro-climatic variables. Our endo-exogenous extinction risk models showed that both density-dependent and -independent components were vital drivers of the historical population trends. However, climate change impacts were not always detrimental to Kbbs. Despite the decrease of population growth rate with higher overwinter temperatures and spring precipitations in the first generation, the growth rate increased with higher summer temperatures and precipitations in the second generation. We concluded that finer spatiotemporally scaled models could be more rewarding in guiding the decision-making process of Kbb restoration under climate change.
Accurate estimates of current and future habitat suitability are needed for species that may require assistance in tracking a shifting climate. Standard species distribution models (SDMs) based on occurrence data are the most common approach for evaluating climatic suitability, but these may suffer from inaccuracies stemming from disequilibrium dynamics and/or an inability to identify suitable climate regions that have no analogues within the current range. An alternative approach is to test performance with experimental introductions, and model suitability from the empirical results. We used this method with the Haleakalā silversword (Argyroxiphium sandwicense subsp. macrocephalum), using a network of out-plant plots across the top of Haleakalā volcano, Hawaiʻi. Over a ~ 5-year period, survival varied strongly across this network and was effectively explained by a simple model including mean rainfall and air temperature. We then applied this model to estimate current climatic suitability for restoration or translocation activities, to define trends in suitability over the past three decades, and to project future suitability through 2051. This empirical approach indicated that much of the current range has low suitability for long-term successful restoration, but also identified areas of high climatic suitability in a region where plants do not currently occur. These patterns contrast strongly with projections obtained with a standard SDM, which predicted continued suitability throughout the current range. Under continued climatic shifts, these results caution against the common SDM presumption of equilibrium between species’ distributions and their environment, even for long-established native species.
Informed population monitoring efforts are essential for sound management of harvested species, and adaptive strategies that provide detailed information to monitoring efforts often require data inputs from complimentary sources. Movement ecology information is seldom directly incorporated into population monitoring or adaptive harvest management strategies, yet can provide valuable information on species distributions, emigration and immigration rates, and aid in determining optimal population monitoring timing. The Rocky Mountain Population (RMP) of Sandhill Cranes is a harvested population subject to a stringent adaptive harvest management framework and an annual aerial survey to estimate population abundance, but movements of Sandhill Cranes during survey windows, and subsequent changes to harvest quotas based on their movement and distribution have not been investigated. We used seven years of GPS tracking data to estimate state-specific emigration and immigration rates, using a Bayesian multi-state capture-recapture model, among states within the RMP distribution to understand how seasonal crane movements may influence optimal aerial survey timing. We then leveraged these transition probabilities in conjunction with aerial survey count data to model how changes in aerial survey timing and movement-informed crane distribution would influence the current RMP Sandhill Crane adaptive harvest management model resulting in estimated changes to harvest allocation among states based on Sandhill Crane movement. We found that Sandhill Crane emigration from northern states began to increase the week of the aerial survey in late September, and continued to increase as autumn migration progressed into October. As expected, immigration to southern states began as emigration from northern states increased. Importantly, little movement among states occurred prior to the current aerial survey design timing. Overall, we found that current survey timing and shortly thereafter (∼1 week) did not greatly influence estimates of Sandhill Crane distribution, and did not greatly influence the harvest reallocation to each state until mid to late October (range of −42–+52 tag allocation change), much later than the current survey design would allow. Using GPS locations, we found that optimal population monitoring efforts could be improved to account for both detection and seasonal movements, while minimally influencing current adaptive harvest management strategies to stakeholders. Linking movement ecology with population monitoring efforts and subsequently adaptive harvest management strategies yields insightful information that can be beneficial for conservation planning, decision-making, and optimal species management of a migratory bird.
The Federal Highway Administration and State departments of transportation nationwide need an efficient method to assess potential adverse effects of highway stormwater runoff on receiving waters to optimize stormwater-treatment decisions. To this end, the U.S. Geological Survey, in cooperation with the Federal Highway Administration and the North Carolina Department of Transportation (NCDOT), developed a decision-support software tool based on a statewide version of the Stochastic Empirical Loading and Dilution Model (SELDM). This decision-support tool is designed to identify potential adverse effects of highway runoff by using a criterion based on a measurable change in water quality from a surrogate pollutant. The NCDOT worked with the North Carolina Department of Environmental Quality to select a 25-percent change in suspended sediment concentration as the decision-rule criterion for identifying measurable downstream water-quality change; this selection was based on available data and widely accepted stormwater monitoring uncertainties. Development of the statewide tool and its application to the Piedmont ecoregion are described in this report. Because SELDM can be applied to build a similar decision-support tool in any State, this report describes practice-ready methods that other State departments of transportation and municipal permittees can use to streamline environmental permitting and project delivery while protecting the environment.
Hydraulic design engineers can use this decision-support tool to establish stormwater-treatment goals for highway construction or improvement projects without having to learn SELDM or interpret its statistical output. The tool is a spreadsheet that determines if a selected highway segment can directly discharge highway runoff, if the highway segment can discharge runoff following treatment using a basic vegetated conveyance best management practice (BMP), or if treatment using an advanced BMP is needed to minimize effects of discharges on downstream water quality. To use the tool, hydraulic design engineers obtain upstream-basin characteristics from the U.S. Geological Survey StreamStats application and highway-site characteristics from preliminary design plans. They then enter these characteristics in the decision-support tool, which identifies the necessary stormwater-treatment goal.
The Piedmont ecoregion was used as a case study to demonstrate the type of information the decision-support tool can provide. In this ecoregion, 100 percent of direct discharges meet the water-quality criterion when the drainage-area ratio is less than about 0.007 acres of highway per square mile of upstream basin. Advanced BMPs are needed in 100 percent of basins with drainage-area ratios greater than about 50 acres per square mile. Between these drainage-area ratios, the selection of direct discharge, a basic vegetated conveyance BMP, or an advanced BMP is a function of highway-site and upstream-basin properties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235113","collaboration":"Prepared in cooperation with the Federal Highway Administration and the North Carolina Department of Transportation","usgsCitation":"Granato, G.E., Stillwell, C.C., Weaver, J.C., McDaniel, A.H., Lipscomb, B.S., Jones, S.C., and Mullins, R.M., 2023, Development of the North Carolina stormwater-treatment decision-support system by using the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5113, 25 p., https://doi.org/10.3133/sir20235113.","productDescription":"Report: vii, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141595","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Iceland's exposure to major ocean current pathways of the central North Atlantic makes it a useful location for developing long-term proxy records of past marine climate. Such records provide more detailed understanding of the full range of past variability which is necessary to improve predictions of future changes. We constructed a 225-year (1791–2015 CE) master shell growth chronology from 29 shells of Arctica islandica collected at 100 m water depth in southwest Iceland (Faxaflói). The growth chronology provides a robust age model for shell oxygen isotope (δ18Oshell) data produced at annual resolution for 251 years (1765–2015 CE). The temperature reconstruction derived from δ18Oshell shows coherence with May–October local surface temperature records and sea surface temperatures in the North Atlantic region, suggesting it is a useful proxy indicator of water temperature variability at 100 m depth within Faxaflói. Field correlations between the shell-based records and gridded sea surface temperature data reveal strong positive correlations between the 1-year lagged shell growth and temperatures within the subpolar gyre post-1972, suggesting a delayed influence of subpolar gyre dynamics on ecological indicators in southwest Iceland in recent decades. However, the shell growth chronology and δ18Oshell record generally show relatively weak and insignificant correlations with larger region climate indices including the Atlantic Multidecadal Variability, North Atlantic Oscillation, and East Atlantic pattern. Therefore the interannual variations in the newly produced shell-based records appear to reflect more local to regional dynamics around southwest Iceland than large-scale modes of climate variability.
Inland flooding, sea-level rise, and pollution pose challenges for Maine’s infrastructure and natural resources. A highly detailed, three-dimensional (3D) model of the Earth’s surface is allowing the State of Maine to address these challenges in an increasingly comprehensive and timely manner. In addition, highly accurate elevation data facilitate land development, forest management, agricultural practices, and wildlife conservation, all of which are key pillars of Maine’s economy. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed 3D model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra-producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska-Aleutian arc, specifically its pre-Holocene spatiotemporal distribution.
The Cross-Hosgri slope is a bathymetric lineament that crosses the main strand of the Hosgri fault offshore Point Estero, central California. Recently collected chirp seismic reflection profiles and sediment cores provide the basis for a reassessment of Cross-Hosgri slope origin and the lateral slip rate of the Hosgri fault based on offset of the lower slope break of the Cross-Hosgri slope. The Cross-Hosgri slope is comprised of two distinct stratigraphic units. The lower unit (unit 1) overlies the post–Last Glacial Maximum transgressive erosion surface and is interpreted as a Younger Dryas (ca. 12.85–11.65 ka) shoreface deposit based on radiocarbon and optically stimulated luminescence (OSL) ages, Bayesian age modeling, seismic facies, sediment texture, sediment infauna, and heavy mineral component. The shoreface was abandoned and partly eroded during rapid sea-level rise from ca. 11.5 to 7 ka. Unit 2 consists of fine sand and silt deposited in a midshelf environment when the rate of sea-level rise slowed between ca. 7 ka and the present. Although unit 2 provides a thin, relatively uniform cover over the lower slope break of the older shoreface, this feature still represents a valuable piercing point, providing a Hosgri fault slip rate of 2.6 ± 0.8 mm/yr. Full-waveform processing of chirp data resulted in significantly higher resolution in coarser-grained strata, which are typically difficult to interpret with more traditional envelope processing. Our novel combination of offshore radiocarbon and OSL dating is the first application to offshore paleoseismic studies, and our results indicate the utility of this approach for future marine neotectonic investigations.
Context Tidal saline wetlands (TSWs) are highly threatened from climate-change effects of sea-level rise. Studies of TSWs along the East Coast U.S. and elsewhere suggest significant likely losses over coming decades but needed are analytic tools gauged to Pacific Coast U.S. wetlands.
Objectives We predict the impacts of sea-level rise (SLR) on the elevation capital (vertical) and migration potential (lateral) resilience of TSWs along the Pacific Coast U.S. over the period 2020 to 2150 under a 1.5-m SLR scenario, and identified TSWs at risk of most rapid loss of resilience. Here, we define vertical resilience as the amount of elevation capital and lateral resilience as the amount of TSW displacement area relative to existing area.
Methods We used Bayesian network (BN) modeling to predict changes in resilience of TSWs as probabilities which can be useful in risk analysis and risk management. We developed the model using a database sample of 26 TSWs with 147 sediment core samples, among 16 estuary drainage areas along coastal California, Oregon, and Washington.
Results We found that all TSW sites would lose at least 50% of their elevation capital resilience by 2060 to just before 2100, and 100% by 2070 to 2130, depending on the site. Under a 1.5-m sea-level rise scenario, nearly all sites in California will lose most or all of their lateral migration resilience. Resilience losses generally accelerated over time. In the BN model, elevation capital resilience is most sensitive to elevation capital at time t, mean tide level at time t, and change in sea level from time 0 to time t.
Conclusions All TSW sites were projected with declines in resilience. Our model can further aid decision-making such as prioritizing sites for potential management adaptation strategies. We also identified variables most influencing resilience predictions and thus those potentially prioritized for monitoring or development of strategies to prevent loss regionally.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01762-3","usgsCitation":"Marcot, B.G., Thorne, K., Carr, J., and Guntenspergen, G.R., 2023, Foundations of modeling resilience of tidal saline wetlands to sea-level rise along the U.S. Pacific Coast: Landscape Ecology, v. 38, p. 3061-3080, https://doi.org/10.1007/s10980-023-01762-3.","productDescription":"20 p.","startPage":"3061","endPage":"3080","ipdsId":"IP-148063","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441704,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-023-01762-3","text":"Publisher Index Page"},{"id":422365,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.79508376826595,\n 32.6394986015149\n ],\n [\n -117.38046175678818,\n 33.634263181198776\n ],\n [\n -119.04924372825178,\n 34.47903231583611\n ],\n [\n -120.38379894663998,\n 34.68829360587419\n ],\n [\n -120.39855065841579,\n 35.33926414301449\n ],\n [\n -121.79626050797515,\n 36.460712237169574\n ],\n [\n -121.69218260053856,\n 36.794964571410546\n ],\n [\n -121.93473143542474,\n 37.099336500561904\n ],\n [\n -121.67841132759617,\n 37.4046675213457\n ],\n [\n -122.29904672509281,\n 38.299945698864775\n ],\n [\n -122.73038676825749,\n 38.20384318696074\n ],\n [\n -123.43481358189254,\n 39.0731465550509\n ],\n [\n -124.04491152385293,\n 40.344784634590496\n ],\n [\n -123.71975903545354,\n 41.393830664494715\n ],\n [\n -124.30182241974398,\n 42.756208502734836\n ],\n [\n -123.72352270756943,\n 43.87384063633678\n ],\n [\n -123.69957609415667,\n 46.20564287869419\n ],\n [\n -123.73190708914615,\n 47.01614568390107\n ],\n [\n -124.52407464804097,\n 48.44687072153897\n ],\n [\n -125.07770960477518,\n 48.56365821581798\n ],\n [\n -124.81324633275776,\n 47.69849413178119\n ],\n [\n -124.24511492723724,\n 46.407052517711605\n ],\n [\n -124.64743081974362,\n 43.59326210341467\n ],\n [\n -124.85817977876286,\n 42.61603559520387\n ],\n [\n -124.46177432494142,\n 41.15906667493712\n ],\n [\n -124.73844702893035,\n 40.16919745978089\n ],\n [\n -124.14464449468676,\n 39.63763990789823\n ],\n [\n -124.08963171928053,\n 38.73318027566873\n ],\n [\n -123.05064665424416,\n 37.68717586937068\n ],\n [\n -121.75049102805497,\n 35.902294376643596\n ],\n [\n -120.98156553638233,\n 35.198466337951686\n ],\n [\n -120.87397110810903,\n 34.31747679968289\n ],\n [\n -119.36572913414489,\n 34.05126341484619\n ],\n [\n -117.64709261279006,\n 33.1121239333127\n ],\n [\n -117.2603048744129,\n 32.509262003906\n ],\n [\n -116.79508376826595,\n 32.6394986015149\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2023-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Marcot, Bruce G.","contributorId":140456,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":887494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":887495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":887496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":887497,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257377,"text":"70257377 - 2023 - Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish","interactions":[],"lastModifiedDate":"2024-08-21T16:13:36.214575","indexId":"70257377","displayToPublicDate":"2023-11-02T09:00:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish","docAbstract":"Estimates of juvenile survival are critical for informing population dynamics and the ecology of fish, yet these demographic parameters are difficult to measure. Here, we demonstrate that advances in animal tracking technology provide opportunities to evaluate survival of juvenile tagged fish. We implemented a whole-lake telemetry array in conjunction with small acoustic tags (including tags < 1.0 g) to track the fate of stocked juvenile cisco (Coregonus artedi) as part of a native species restoration effort in the Finger Lakes region of New York, USA. We used time-to-event modeling to characterize the survival function of stocked fish, where we infer mortality as the cessation of tag detections. Survival estimates revealed distinct stages of juvenile cisco mortality including high immediate post-release mortality, followed by a period of elevated mortality during an acclimation period. By characterizing mortality over time, the whole-lake biotelemetry effort provided information useful for adapting stocking practices that may improve survival of stocked fish, and ultimately the success of the species reintroduction effort. The combination of acoustic technology and time-to-event modeling to inform fish survival may have wide applicability across waterbodies where receiver arrays can be deployed at scale and where basic assumptions about population closure can be satisfied.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-023-46330-6","usgsCitation":"Koeberle, A., Pearsall, W., Hammers, B., Mulhall, D., McKenna Jr., J., Chalupnicki, M., and Sethi, S.A., 2023, Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish: Scientific Reports, v. 13, e18956, 12 p., https://doi.org/10.1038/s41598-023-46330-6.","productDescription":"e18956, 12 p.","ipdsId":"IP-152742","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":441707,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-46330-6","text":"Publisher Index Page"},{"id":433009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Finger Lakes region, Keuka Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.25902648249861,\n 42.667712412436\n ],\n [\n -77.25902648249861,\n 42.40324220590065\n ],\n [\n -77.06119645246562,\n 42.40324220590065\n ],\n [\n -77.06119645246562,\n 42.667712412436\n ],\n [\n -77.25902648249861,\n 42.667712412436\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2023-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Koeberle, Alexander","contributorId":342552,"corporation":false,"usgs":false,"family":"Koeberle","given":"Alexander","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":910186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearsall, Webster","contributorId":342553,"corporation":false,"usgs":false,"family":"Pearsall","given":"Webster","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammers, Brad","contributorId":342555,"corporation":false,"usgs":false,"family":"Hammers","given":"Brad","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mulhall, Daniel","contributorId":342558,"corporation":false,"usgs":false,"family":"Mulhall","given":"Daniel","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKenna Jr., James E. 0000-0002-1428-7597","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":342562,"corporation":false,"usgs":false,"family":"McKenna Jr.","given":"James E.","affiliations":[{"id":62863,"text":"Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":910190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chalupnicki, Marc 0000-0002-3792-9345","orcid":"https://orcid.org/0000-0002-3792-9345","contributorId":242991,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":910191,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sethi, Suresh Andrew 0000-0002-9369-487X ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-487X","contributorId":252537,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":911332,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249994,"text":"70249994 - 2023 - Field observations and long short-term memory modeling of spectral wave evolution at living shorelines in Chesapeake Bay, USA","interactions":[],"lastModifiedDate":"2023-11-12T13:51:58.025735","indexId":"70249994","displayToPublicDate":"2023-11-02T07:46:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5444,"text":"Applied Ocean Research","active":true,"publicationSubtype":{"id":10}},"title":"Field observations and long short-term memory modeling of spectral wave evolution at living shorelines in Chesapeake Bay, USA","docAbstract":"Living shorelines as a nature-based solution for climate change adaptation were constructed in many places around the world. The success of this type of projects requires long-term monitoring for adaptive management. The paper presents a novel framework leveraging scientific machine learning methods for accurate and rapid prediction of long-term hydrodynamic forcing impacting living shorelines using short-term measurements of water levels and wind waves in the largest estuary in the U.S. Different from existing data-driven wave prediction models focusing on significant wave heights, this study is focused on the prediction of wave energy spectra in shallow water using winds and tides as the input feature and short-term measurements of wave spectra and water depths as the label. Long Short-Term Memory (LSTM) models were developed using four-month wave measurements in the stormy seasons to predict integral wave parameters and energy spectra for multiple years. The developed models accurately predicted wave heights, peak periods, and energy spectra around the living shorelines, capturing complex wave dynamics, such as wave generation by wind, nonlinear wave-wave interactions, and depth-limited wave breaking in the shallow water of a large estuary. The validated models were then used to determine the long-term wave forcing impacting the living shorelines based on the modeled wave characteristics and spectra. Model results show that the surrogate models utilizing LSTM to predict wave spectra in the frequency domain enable long-term predictions of spectral wave evolution with a minimal computational cost. Our findings provide valuable insights into the efficacy of living shorelines in attenuating wave energy and demonstrate the utility of this approach in assessing the effectiveness of such living shoreline structures.
The 6 February 2023 Kahramanmaraş, Turkey (Türkiye), earthquake sequence produced > 500 km of surface rupture primarily on the left‐lateral East Anatolian (~345 km) and Çardak (~175 km) faults. Constraining the length and magnitude of surface displacement on the causative faults is critical for loss estimates, recovery efforts, rapid identification of impacted infrastructure, and fault displacement hazard analysis. To support these efforts, we rapidly mapped the surface rupture from satellite data with support from remote sensing and field teams, and released the results to the public in near‐real time. Detailed surface rupture mapping commenced on 7 February and continued as high‐resolution (< 1.0 m/pixel) optical images from WorldView satellites (2023 Maxar) became available. We interpreted the initial simplified rupture trace from subpixel offset fields derived from Advanced Land Observation Satellite2 and Sentinel‐1A synthetic aperture radar image pairs available on 8 and 10 February, respectively. The mapping was released publicly on 10 February, with frequent updates, and published in final form four months postearthquake (Reitman, Briggs, et al., 2023). This publicly available, rapid mapping helped guide fieldwork and constrained U.S. Geological Survey finite‐fault and loss estimate models, as well as stress change estimates and dynamic rupture models.
The James Tributary Summary outlines change over time for a suite of monitored tidal water quality parameters and associated potential drivers of those trends for the period 1985 – 2021 and provides a brief description of the current state of knowledge explaining these observed changes. Water quality parameters described include surface (above pycnocline) total nitrogen (TN), surface total phosphorus (TP), surface water temperature (WTEMP), spring and summer (June, July, August) surface chlorophyll a, summer bottom (below pycnocline) dissolved oxygen (DO) concentrations, and Secchi disk depth (a measure of water clarity). Results for annual bottom TP, bottom TN, surface ortho-phosphate (PO4), surface dissolved inorganic nitrogen (DIN), surface total suspended solids (TSS), and summer surface DO concentrations are provided in an Appendix. Drivers discussed include physiographic watershed characteristics, changes in TN, TP, and sediment loads from the watershed to tidal waters, expected effects of changing land use, and implementation of nutrient management and natural resource conservation practices. Factors internal to estuarine waters that also play a role as drivers are described including biogeochemical processes, physical forces such as wind-driven mixing of the water column and increase in rainfall intensity and volume, and biological factors such as phytoplankton biomass and the presence of submerged aquatic vegetation. Continuing to track water quality response and investigating these influencing factors are important steps to understanding water quality patterns and changes in the James River. The intended audiences for this report include, but are not limited to, 1) technical managers within jurisdictions who are looking at tidal water quality data and trying to understand why patterns are occurring, 2) local watershed organizations that are trying to understand these analyses and working to connect them to their local area(s), and 3) federal, state, and academic researchers. Figure 1 presents a conceptual model highlighting these intended audiences. Our goal is for the Tributary Summary documents to be sources of readily available background for change over time in tidal water quality observed with monitoring data. The intended purpose of the Tributary Summary documents is to help answer questions related to water quality, show how landscape factors drive water quality change over time, provide support for management decisions that may alter water quality trends and living resources conditions, and highlight where there may be information or knowledge gaps.
","language":"English","publisher":"Chesapeake Bay Program","usgsCitation":"Sullivan, B.M., Gootman, K., Gunnerson, A., Johnson, C., Mason, C.A., Perry, E., Bhatt, G., Keisman, J.L., Webber, J.S., Harcum, J., Lane, M., Devereux, O., Zhang, Q., Murphy, R., Karrh, R., Butler, T., Van Note, V., and Wei, Z., 2023, James Tributary summary: A summary of trends in tidal water quality and associated factors, 1985-2021, 77 p.","productDescription":"77 p.","ipdsId":"IP-155491","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":422387,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeakebay.net/who/group/integrated-trends-analysis-team","linkFileType":{"id":5,"text":"html"}},{"id":422427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Preliminary documentation of coseismic ground failure triggered by the February 6, 2023 Türkiye earthquake sequence","interactions":[],"lastModifiedDate":"2024-08-01T14:58:15.621963","indexId":"70256619","displayToPublicDate":"2023-11-01T09:49:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1517,"text":"Engineering Geology","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary documentation of coseismic ground failure triggered by the February 6, 2023 Türkiye earthquake sequence","docAbstract":"The devastating Kahramanmaraş earthquake sequence occurred on February 6, 2023. Two main events, Mw 7.8 and Mw 7.5 occurred 9 hours apart, affected 11 cities in Turkey, and subjected an area of ∼90,000 km2 to shaking levels known to trigger landslides (peak ground acceleration > 0.08 g). Extensive landsliding was expected given the hilly terrain affected by this significant ground shaking—about 15% of the topography is steeper than 20°—but was not initially apparent in early satellite imagery, mostly because of obscuring snow that fell just after the earthquakes. However, after a more detailed investigation using high-resolution satellite images, aerial photos, and a field survey, we confirmed that this earthquake sequence did, indeed, trigger numerous landslides. In this study, we present those findings and provide a preliminary characterization of the spatial distribution, general characteristics, and dominant types of landslides and hillslope deformation triggered by the earthquake sequence. We mapped 3673 coseismic landslides, mostly concentrated in the northern half of the impacted area. Rock falls are the most abundant landslide type, but bedrock rotational landslides, translational slides and lateral spreads are also numerous. Surface rupture through mountainous terrain caused several large, and in some cases fatal, landslides. Incipient landslides and ground cracks are also widespread, especially in the north. Lithology, spatial variability of ground shaking, and topographic relief appear to be the main variables controlling the spatial distribution of coseismic landslides. There are few detailed studies of earthquake-triggered landslides in arid and semi-arid regions such as this one, nor for such complex earthquake sequences. Therefore, this contribution provides valuable information for future hazard and modeling efforts in arid and semi-arid regions.
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To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.
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\"}}]}","volume":"345","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andreadisa, Konstantinos","contributorId":342567,"corporation":false,"usgs":false,"family":"Andreadisa","given":"Konstantinos","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Abhiskek","contributorId":342569,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhiskek","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Caitlyn","contributorId":316779,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910197,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249866,"text":"70249866 - 2023 - Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk","interactions":[],"lastModifiedDate":"2023-11-02T14:02:20.152878","indexId":"70249866","displayToPublicDate":"2023-11-01T08:51:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk","docAbstract":"Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-46195-9","usgsCitation":"Sanchez, G.M., Petrasova, A., Skrip, M.M., Collins, E., Lawrimore, M.A., Vogler, J.B., Terando, A., Vukomanovic, J., Mitasova, H., and Meentemeyer, R.K., 2023, Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk: Scientific Reports, v. 13, 18869, 15 p., https://doi.org/10.1038/s41598-023-46195-9.","productDescription":"18869, 15 p.","ipdsId":"IP-144281","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":441720,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-46195-9","text":"Publisher Index Page"},{"id":422331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","county":"Berkeley County, Charleston County, Dorchester County","city":"Charleston, Moncks Corner, 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0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":887448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vukomanovic, Jelena","contributorId":316275,"corporation":false,"usgs":false,"family":"Vukomanovic","given":"Jelena","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":887459,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mitasova, Helena 0000-0002-6906-3398","orcid":"https://orcid.org/0000-0002-6906-3398","contributorId":296416,"corporation":false,"usgs":false,"family":"Mitasova","given":"Helena","email":"","affiliations":[{"id":7091,"text":"North Carolina State 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designed to identify favorable areas for geothermal resources have traditionally been focused on near-surface information, namely data that can be compiled into a 2D map. However, these methods fail to account for the third dimension: depth. As a result, they do not incorporate deep crustal and mantle features like heat sources. Geophysical methods with multi-scale capabilities, such as magnetotellurics (MT), provide tools to image deeper structures and bottom-up control on location of near surface hydrothermal systems in 3D. This study briefly demonstrates the advantage of understanding bottom-up control on hydrothermal systems to aid assessment and characterization. A regional 3D electrical resistivity model of the Great Basin is developed from MT data that images the near surface down to the mantle. From the 3D model, electrical conductance (depth integrated electrical conductivity) maps are created at logical depth intervals to identify anomalies. The conductance maps image discrete zones of high-conductance between 15-20 km depth indicative of fluid collection at the brittle-ductile transition; high-conductance zones around Moho depths (30-50 km) suggestive of partial melt; high-conductance zones in the upper mantle indicative of higher temperature and larger melt fraction, and low-conductance zones indicative of lithospheric material descending in the mantle. One anomalous zone of low-conductance in the mantle is under the north central Nevada, suggesting vertical mantle flow transport of heat to the crust related to sinking lithospheric material.","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Using the Earth to save the Earth (GRC transactions)","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Peacock, J., and Bedrosian, P.A., 2023, Importance of understanding bottom-up control when characterizing geothermal systems, in Using the Earth to save the Earth (GRC transactions), v. 47, p. 1797-1807.","productDescription":"11 p.","startPage":"1797","endPage":"1807","ipdsId":"IP-154701","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":424332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424331,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034877","linkFileType":{"id":5,"text":"html"}}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":892010,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":892011,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250023,"text":"70250023 - 2023 - Exploring centennial barrier-inlet evolution: Insights from undeveloped and developed phases at Barnegat Inlet, New Jersey","interactions":[],"lastModifiedDate":"2023-11-14T13:02:59.992292","indexId":"70250023","displayToPublicDate":"2023-11-01T07:02:07","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring centennial barrier-inlet evolution: Insights from undeveloped and developed phases at Barnegat Inlet, New Jersey","docAbstract":"This study aims to identify the natural processes and the subsequent responses to coastal engineering and development on the alongshore evolution of the IB-BI-LBI inlet-barrier system. The primary focus will be the quantification of barrier island and inlet sediment partitioning at decadal to centennial timescales, from 1839-1941. We analyze historical alongshore evolution and track coastal engineering efforts at the Island Beach–Barnegat Inlet–Long Beach Island, NJ barrier-inlet system, which has transitioned from natural to highly developed over the past 180 years. We build a quantitative mass-balance framework that tracks sediment reservoir volumes and transport fluxes within the barrier-inlet system to describe both the natural and developed alongshore evolution of this system. We find that minor coastal engineering efforts, including the construction of small-scale wood and stone jetties, not only shift sediment transport locally, but also shift system-wide sediment transport based on inlet-barrier island interactions and sediment partitioning. Better understanding these different modes of past evolution can help to guide coastal management strategies as beach nourishment increases in cost, sea level-rise accelerates, and extreme storm patterns change.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The Proceedings of the Coastal Sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0130","usgsCitation":"Nichols-O’Neill, S., Lorenzo-Trueba, J., Ciarletta, D.J., and Miselis, J.L., 2023, Exploring centennial barrier-inlet evolution: Insights from undeveloped and developed phases at Barnegat Inlet, New Jersey, in The Proceedings of the Coastal Sediments 2023, p. 1403-1414, https://doi.org/10.1142/9789811275135_0130.","productDescription":"12 p.","startPage":"1403","endPage":"1414","ipdsId":"IP-148130","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":422572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.15617443689175,\n 39.81674675488196\n ],\n [\n -74.15617443689175,\n 39.71752217758285\n ],\n [\n -74.06965710290756,\n 39.71752217758285\n ],\n [\n -74.06965710290756,\n 39.81674675488196\n ],\n [\n -74.15617443689175,\n 39.81674675488196\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Nichols-O’Neill, Shane","contributorId":297142,"corporation":false,"usgs":false,"family":"Nichols-O’Neill","given":"Shane","email":"","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":888021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorenzo-Trueba, Jorge","contributorId":297143,"corporation":false,"usgs":false,"family":"Lorenzo-Trueba","given":"Jorge","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":888022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888024,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250481,"text":"70250481 - 2023 - The Arctic Rivers Project: Using an equitable co-production framework for integrating meaningful community engagement and science to understand climate impacts","interactions":[],"lastModifiedDate":"2023-12-13T12:43:35.882553","indexId":"70250481","displayToPublicDate":"2023-11-01T06:38:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17107,"text":"Community Science","active":true,"publicationSubtype":{"id":10}},"title":"The Arctic Rivers Project: Using an equitable co-production framework for integrating meaningful community engagement and science to understand climate impacts","docAbstract":"As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community-engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project-specific knowledge co-production protocols. This paper provides a novel model of design and implementation to co-produce knowledge with communities across a large study domain.
Seismic images of Earth’s interior have revealed two continent-sized anomalies with low seismic velocities, known as the large low-velocity provinces (LLVPs), in the lowermost mantle. The LLVPs are often interpreted as intrinsically dense heterogeneities that are compositionally distinct from the surrounding mantle. Here we show that LLVPs may represent buried relics of Theia mantle material (TMM) that was preserved in proto-Earth’s mantle after the Moon-forming giant impact. Our canonical giant-impact simulations show that a fraction of Theia’s mantle could have been delivered to proto-Earth’s solid lower mantle. We find that TMM is intrinsically 2.0–3.5% denser than proto-Earth’s mantle based on models of Theia’s mantle and the observed higher FeO content of the Moon. Our mantle convection models show that dense TMM blobs with a size of tens of kilometres after the impact can later sink and accumulate into LLVP-like thermochemical piles atop Earth’s core and survive to the present day. The LLVPs may, thus, be a natural consequence of the Moon-forming giant impact. Because giant impacts are common at the end stages of planet accretion, similar mantle heterogeneities caused by impacts may also exist in the interiors of other planetary bodies.
Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector) to consider the impact of a ML model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January–September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance). We used a generalized linear mixed model to examine the influence of ML model performance and tagging method on tagging efficiency. We found that ML bounding boxes offer significant improvement in tagging efficiency when labelling data compared to unassisted tagging. Additionally, the time taken to label with bounding boxes was not statistically different from an unassisted tagging approach. However, we found that gains in efficiency are contingent on the ML algorithm’s performance and that incorrect ML predictions, particularly the 4.2% false positive and 3.6% false negative predictions, can slow the tagging process compared to a non-hybrid approach. These findings indicate that although practitioners usually forgo the production of bounding boxes when selecting a data labelling process due to the increased effort, ML bounding box-assisted tagging can offer an efficient method for labeling. More broadly, ML-assisted data labelling offers an opportunity to accelerate the analysis of trail camera imagery, but an assessment of the ML model’s performance can illuminate whether the hybrid-tagging approach is ultimately a help or hinderance.
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In addition to point and non-point source nutrient inputs, factors such as hydrology, geomorphology, temperature, light, and biogeochemical transformations influence nutrient dynamics in surface waters, allowing for the formation of steep spatial gradients and patchiness. Documenting nutrient variability is also necessary to identify sources, quantify transformation rates, and understand drivers. Due to logistical and cost constraints, it is often unfeasible to measure concentrations of nutrients in surface waters using discrete sampling followed by laboratory analysis at a resolution high enough to identify steep spatial gradients and patchiness. Because of these constraints, data generated from discrete sampling are limited in space and time, often missing key variabilities. Recent advancements of in situ nitrate plus nitrite (NO3- and NO2-) sensor technology has enabled highly temporally and spatially resolved NO3- concentration measurements in aquatic ecosystems. However, comparable information about ammonium (NH4+) concentrations remains unavailable. To address this need, U.S. Geological Survey collaborated with Timberline Instruments to modify their commercially available benchtop TL-2800 ammonia analyzer to collect high-frequency continuous (1 unique sample measurement per second) NH4+ concentration measurements at a micromolar (0.5 µM) resolution in flow-through mode while receiving water pumped from a moving boat. 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Advances in sensor design and sUAS capabilities have led to rapid advances in the amount and type of geophysical data that can be acquired using sUAS-based survey designs (Gavazzi et al., 2019). Here we showcase the utility of a single sUAS (the Matrice 600 Pro and accompanying sensor package) that collects magnetic, thermal infra-red (TIR) and gas (CO2, SO2, H2S, water vapor) data for use in geothermal resource exploration and monitoring, with case studies in eastern California and Iceland. The work highlights the flexibility of modern sUAS systems for single-team acquisition of multiple independent but coupled geophysical data which allow for a multidisciplinary approach to geothermal systems research. We summarize the workflows involved in collecting each dataset as well as several common issues encountered both during data collection and data processing.
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