Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USA LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe.
","language":"English","publisher":"Pensoft","doi":"10.3897/VCS.67537","usgsCitation":"Comer, P.J., Hak, J.C., Dockter, D., and Smith, J., 2022, Integration of vegetation classification with land cover mapping: Lessons from regional mapping efforts in the Americas: Vegetation Classification and Survey, p. 29-43, https://doi.org/10.3897/VCS.67537.","productDescription":"15 p.","startPage":"29","endPage":"43","ipdsId":"IP-128344","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/vcs.67537","text":"Publisher Index Page"},{"id":398727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Comer, Patrick J. 0000-0002-5869-2105","orcid":"https://orcid.org/0000-0002-5869-2105","contributorId":258190,"corporation":false,"usgs":false,"family":"Comer","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":840550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hak, Jon C","contributorId":290233,"corporation":false,"usgs":false,"family":"Hak","given":"Jon","email":"","middleInitial":"C","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":840551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216392,"corporation":false,"usgs":false,"family":"Dockter","given":"Daryn","affiliations":[],"preferred":false,"id":840552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Jim","contributorId":191054,"corporation":false,"usgs":false,"family":"Smith","given":"Jim","email":"","affiliations":[],"preferred":false,"id":840553,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238946,"text":"70238946 - 2022 - Fishway Entrance Palisade","interactions":[],"lastModifiedDate":"2023-01-10T16:06:21.874488","indexId":"70238946","displayToPublicDate":"2022-02-14T10:01:03","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9958,"text":"Final Technical Report","active":true,"publicationSubtype":{"id":1}},"title":"Fishway Entrance Palisade","docAbstract":"This technical report summarizes the work that was conducted by the University of Massachusetts Amherst and the United States Geological Survey (USGS), along with other project partners, on the Fishway Entrance Palisade (EP), a projected funded through the Department of Energy’s (DOE) funding opportunity titled ‘Innovative Solutions for Fish Passage at Hydropower Dams’ (DE‐FOA‐0001662). The period of performance ranged from September 1, 2018 through September 30, 2021. \n\nThe EP is a novel fish passage engineering technology designed to provide more favorable entry conditions for fish and to reduce costs relative to conventional fishway auxiliary water systems (AWS). The EP project has four primary components.\n\nFirst, the Northeast United States Auxiliary Water Systems Database was created (Northeast Fishway Auxiliary Water Systems Database Section). The database, developed with material provided by the U.S. Fish and Wildlife Service, contains information on fishway type (e.g., lift, Denil, pool and weir) and Auxiliary Water System (AWS) details (e.g., water conveyance method, diffuser type) for 60 hydroelectric sites in the region. Findings indicate that nearly 4 out of every 10 fishway in the region is a fish lift and approximately 1 out of every 4 is a Denil ladder. The remainder are a mix of vertical slot fishways, pool and weirs, and Ice Harbor fishways. Furthermore, over half of all AWS systems use floor diffusers to discharge the auxiliary (or attraction) water into the entrance of a fishway, whereas only 14% use wall diffusers.\n\nSecond, limited experiments on a conventional AWS with live, actively migrating fish were conducted at the USGS Easter Ecological Science Center (EESC) S.O. Conte Research Laboratory (Conventional Auxiliary Water System Experiments Section). This study determined how water velocity through a wall diffuser, without turning vanes or timber baffles to distribute the flow, affects the behavior and passage of adult American shad, a conservative surrogate species for migratory fish on the East Coast. Two gross diffuser velocity treatments were examined, 0.5 ft/s and 1.0 ft/s. These wall diffuser velocities represented current (0.5 ft/s) and past (1.0 ft/s) design criteria guidelines set forth by the USFWS North Atlantic-Appalachian Region (Rojas 2020; USFWS 2019). Six trials with a total of 151 American Shad were conducted in June of 2019 for the two treatments. \n\nNo differences in American shad passage efficiency were discovered between the two treatments, while approximately 3 in every 4 attempts were successful at passing the diffuser. While these results may appear to indicate that the generally accepted gross wall diffuser velocity criteria for American shad of 0.5 ft/s could be safely increased to 1.0 ft/s, further analysis is warranted. Furthermore, it is unknown how other migratory and resident fish species that traverse these structures would be impacted by such a change. \n\nStudying the wall diffuser hydraulics led to an important AWS observation. Without turning vanes or timber baffles in this study, doubling the diffuser area was insufficient at producing the type of flow field change one may expect by halving the gross diffuser velocity. Instead, the flow fields throughout each treatments study area were similar, which led to similar results in shad performance. This not only highlights the importance of installing flow guidance devices like turning vanes, but also to the importance of properly maintaining them, which can be costly.\n\nThird, more expansive experiments on the novel EP were conducted in the spring of 2019 and 2021 (Fishway Entrance Palisade Experiments). The goal of this study was to determine how adult American shad responded to a variety of conditions at a full-scale EP. A total of six treatments were examined by changing the average auxiliary channel velocity between 1.0 and 5.0 ft/s in intervals of 1.0 ft/s and by inserting/removing an entrance gate at the opening of the fishway. Thirty trials with a total of 1,273 shad were conducted over the two years.\n\nIn all treatments, at least ~7 out of every 10 fish successfully passed the EP diffuser and swam into the entrance channel within the 3.5-hour long trial, highlighting the general effectiveness of the novel AWS technology. In both study years, lower velocities through the EP diffuser led to increased shad performance, though performance peaked for the 2 ft/s velocity treatment. This treatment condition represents an approximate six-fold increase in gross diffuser velocity relative to conventional auxiliary water systems, which in turn presents opportunities for cost savings (e.g., reduction in diffuser size).\n\nShad performance, in general, was worse in 2019 than in 2021, potentially due to the different run timing when our trials were conducted (2019 trials occurred near the end of the migration season, unlike in 2021). Treatments in 2019 had approximately a 20% reduction in entrance efficiency by the trial end, including a 16.7% drop for the 3 ft/s velocity treatment in 2019 relative to 2021 (the only carryover treatment between years). \n\nLastly, adding an entrance gate caused a significant delay to entry. The time to 25% entry raised ~20 minutes from the near instantaneous 25% entry that was reported for the other treatments conducted in the same year (2021). Though by the end of the 3.5-hour trial, the overall entrance efficiency nearly matched those of the other 2021 treatments.\n\nThe fourth and final component of the EP project was an economic analysis that focused on the cost of attraction and environmental flows (Modeling Power Generation Losses Due to Environmental and Fish Passage Attraction Flows at a Run-Of-River Hydroelectric Operation in the Northeast). The study assessed the economic impact of meeting environmental flow requirements at a representative hydroelectric facility and fish lift in the Northeast. An initial finding of the study was that there is a paucity of published data on the costs of meeting attraction and environmental flows. This is due, in part, to the proprietary nature of this data. To explore the costs associated with these flows, three types of environmental flows were assessed: upstream fishway attraction flows, downstream fishway attraction flows, and habitat maintenance minimum flows. A physics-based model was developed and calibrated with three years of hourly generation and flow data as inputs. Gage flow inputs were adjusted and used to calculate power generated. To address hydrologic variability, the model was executed to simulate 30 years of historical flows.\n\nResults indicate that both interannual and seasonal climatic factors impact the costs of meeting environmental flow requirements. Generation potential is most strongly curtailed during dry years in terms of maximizing the capacity factor (the percent of time a plant generates at capacity). Dry years, and especially dry summers, have the most significant costs associated with mitigation flows. Of the three types of flows, habitat flows are most costly in terms of power production, followed by upstream attraction flows. Downstream attraction flows are least costly. This finding is the likely result of differences in both flow rates and duration of the seasonal requirement for each flow. Overall, environmental flows represented a 2-12% loss in annual generation, but losses during a dry summer can reach over 20%.","language":"English","publisher":"U.S. Department of Energy","doi":"10.2172/1905243","usgsCitation":"Mulligan, K., Palmer, R., Towler, B., Haro, A., Lake, B., Rojas, M., and Lotter, E., 2022, Fishway Entrance Palisade: Final Technical Report, 23 p., https://doi.org/10.2172/1905243.","productDescription":"23 p.","ipdsId":"IP-138003","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448800,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1905243","text":"External Repository"},{"id":411632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.05182598949801,\n 44.89319311674552\n ],\n [\n -68.3175817931259,\n 47.33465807108087\n ],\n [\n -69.24621769928491,\n 47.283640086042396\n ],\n [\n -70.6255546394362,\n 45.53467504444376\n ],\n [\n -73.37060956424577,\n 44.92914333096371\n ],\n [\n -83.12438010438365,\n 34.6176223177726\n ],\n [\n -80.40129683431417,\n 31.8360293402377\n ],\n [\n -75.74355199471707,\n 35.10791041480914\n ],\n [\n -75.21833415636709,\n 38.125898555273295\n ],\n [\n -72.87164643954584,\n 40.72488283550473\n ],\n [\n -69.8736057821464,\n 41.750002105411085\n ],\n [\n -70.47472444522607,\n 43.094355406979275\n ],\n [\n -67.05182598949801,\n 44.89319311674552\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mulligan, Kevin 0000-0002-3534-4239 kmulligan@usgs.gov","orcid":"https://orcid.org/0000-0002-3534-4239","contributorId":177024,"corporation":false,"usgs":true,"family":"Mulligan","given":"Kevin","email":"kmulligan@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmer, Richard","contributorId":202903,"corporation":false,"usgs":false,"family":"Palmer","given":"Richard","affiliations":[],"preferred":false,"id":859309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Towler, Brett","contributorId":141164,"corporation":false,"usgs":false,"family":"Towler","given":"Brett","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":859310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haro, Alexander 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":139198,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lake, Bjorn","contributorId":300039,"corporation":false,"usgs":false,"family":"Lake","given":"Bjorn","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":859312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rojas, Marcia","contributorId":300040,"corporation":false,"usgs":false,"family":"Rojas","given":"Marcia","email":"","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":859313,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lotter, Elizabeth","contributorId":300041,"corporation":false,"usgs":false,"family":"Lotter","given":"Elizabeth","email":"","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":859314,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228674,"text":"70228674 - 2022 - A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level","interactions":[],"lastModifiedDate":"2022-02-16T15:22:51.655089","indexId":"70228674","displayToPublicDate":"2022-02-14T09:19:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5542,"text":"Advances in Statistical Climatology, Meteorology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level","docAbstract":"Robust, proxy-based reconstructions of relative sea-level (RSL) change are critical to distinguishing the processes that drive spatial and temporal sea-level variability. The relationships between individual proxies and RSL can be complex and are often poorly represented by traditional methods that assume Gaussian likelihood distributions. We develop a new statistical framework to estimate past RSL change based on nonparametric, empirical modern distributions of proxies in relation to RSL, applying the framework to corals and mangroves as an illustrative example. We validate our model by comparing its skill in reconstructing RSL and rates of change to two previous RSL models using synthetic time-series datasets based on Holocene sea-level data from South Florida. The new framework results in lower bias, better model fit, and greater accuracy and precision than the two previous RSL models. We also perform sensitivity tests using sea-level scenarios based on two periods of interest – meltwater pulses (MWPs) and the Holocene – to analyze the sensitivity of the statistical reconstructions to the quantity and precision of proxy data; we define high-precision indicators, such as mangroves and the reef-crest coral Acropora palmata, with 2σ vertical uncertainties within ± 3 m and lower-precision indicators, such as Orbicella spp., with 2σ vertical uncertainties within ± 10 m. For reconstructing rapid rates of change in RSL of up to ∼ 40 m kyr−1, such as those that may have characterized MWPs during deglacial periods, we find that employing the nonparametric model with 5 to 10 high-precision data points per kiloyear enables us to constrain rates to within ± 3 m kyr−1 (1σ). For reconstructing RSL with rates of up to ∼ 15 m kyr−1, as observed during the Holocene, we conclude that employing the model with 5 to 10 high-precision (or a combination of high- and low-precision) data points per kiloyear enables precise estimates of RSL within
Volcano seismology has been fundamental to our current understanding of crustal magma migration and eruption. The increasing availability of portable seismic networks with the creative use of seismic sources and ambient noise has led to a better understanding of the volcanic structure of many volcanoes and is producing increasingly detailed images of the volcanic subsurface. The past decade (2010-2020) has seen advances in our understanding of seismic sources under and surrounding volcanoes through precise locations, and through analysis of source mechanisms from seismic signals that are more varied and smaller in magnitude, reaching beyond traditional techniques. In tandem with continued research on fundamental physics-based understanding of volcano-seismic sources, new advances in computational analyses including machine learning methods will push our understanding of volcanic processes into the future. Incorporation of multidisciplinary geophysical observations (especially infrasound) has become commonplace, and our understanding of infrasound propagation and sources will feed back into our ability to monitor ongoing eruptions and surficial mass movements. Open-source codes will permit widespread evaluation and adoption of new methodologies for volcano-seismic analysis and inversion. Combined with quantitative and conceptual source models using improved structural constraints, these new methodologies will better characterize the range of volcano-seismic signal evolution scenarios and hold promise for creating better short-term forecasts. Finally, permanent instrumentation is available on an expanding range of volcanoes, and open data policies are increasingly making these data available to the scientific community in near real time.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01530-2","usgsCitation":"Thelen, W., Matoza, R., and Hotovec-Ellis, A.J., 2022, Trends in volcano seismology: 2010 to 2020 and beyond: Bulletin of Volcanology, v. 84, 26, 10 p., https://doi.org/10.1007/s00445-022-01530-2.","productDescription":"26, 10 p.","ipdsId":"IP-133090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467200,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/94s4g2sc","text":"External Repository"},{"id":419345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","noUsgsAuthors":false,"publicationDate":"2022-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":879113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matoza, Robin","contributorId":268788,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":879114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":879115,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228383,"text":"ofr20221003 - 2022 - Annotated bibliography of scientific research on pygmy rabbits published from 1990 to 2020","interactions":[],"lastModifiedDate":"2022-02-11T12:04:51.904289","indexId":"ofr20221003","displayToPublicDate":"2022-02-10T15:10:00","publicationYear":"2022","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":"2022-1003","displayTitle":"Annotated Bibliography of Scientific Research on Pygmy Rabbits Published from 1990 to 2020","title":"Annotated bibliography of scientific research on pygmy rabbits published from 1990 to 2020","docAbstract":"Integrating recent scientific knowledge into management decisions supports effective natural resource management and can lead to better resource outcomes. However, finding and accessing scientific knowledge can be time consuming and costly. To assist in this process, the U.S. Geological Survey (USGS) is creating a series of annotated bibliographies on topics of management concern for western lands. Previously published reports introduced a methodology for preparing annotated bibliographies to facilitate the integration of recent, peer-reviewed science into resource management decisions. Therefore, relevant text from those efforts is reproduced here to frame the presentation. Sagebrush ecosystems throughout North America face management challenges including habitat loss and fragmentation. Brachylagus idahoensis (pygmy rabbits) are a sagebrush-obligate species that has experienced population declines and range contraction in recent decades. A disjunct population of pygmy rabbits in the Columbia Basin in Washington was listed as federally endangered in 2003. Due to their specialized habitat requirements and low dispersal ability, pygmy rabbits are a high priority for managers throughout their range. We compiled and summarized peer-reviewed journal articles, data products, and formal technical reports (such as U.S. Forest Service General Technical Reports and U.S. Geological Survey Open-File Reports) on pygmy rabbits published between January 1, 1990 and December 31, 2020. We first conducted a structured search of three reference databases and three government databases using the phrase “pygmy rabbit” or “Brachylagus idahoensis.” We refined the initial list of products by removing (1) duplicates, (2) products not written in English, (3) publications that were not focused on North America, (4) publications that were not published as research, data products, or scientific review articles in peer-reviewed journals or as formal technical reports, and (5) products for which pygmy rabbits were not a research focus or for which the study did not present new data or findings about pygmy rabbits. We summarized each product using a consistent structure (background, objectives, methods, location, findings, and implications) and identified the management topics (for example, captive breeding, habitat characteristics, and population estimates) addressed by each product. We also noted which publications included new publicly available geospatial data. The review process for this annotated bibliography included an initial internal colleague review of each summary, requesting input on each summary from an author of the original publication, and a formal peer review. Our initial searches resulted in 2,285 total products, of which 105 met our criteria for inclusion. Sensitive/rare wildlife, behavior or demographics, site-scale habitat characteristics, habitat selection, and effects distances or spatial scale were the management topics most commonly addressed. The online version of this bibliography, Science for Resource Managers, will be searchable by topic, location, and year; it will include links to each original publication, where available. The studies compiled and summarized here may inform planning and management actions that seek to maintain and restore sagebrush landscapes and associated native species across the western United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221003","usgsCitation":"Kleist, N.J., Willems, J.S., Bencin, H.L., Foster, A.C., McCall, L.E., Meineke, J.K., Poor, E.E., and Carter, S.K., 2022, Annotated bibliography of scientific research on pygmy rabbits published from 1990 to 2020: U.S. Geological Survey Open-File Report 2022–1003, 75 p., https://doi.org/10.3133/ofr20221003.","productDescription":"viii, 75 p.","onlineOnly":"Y","ipdsId":"IP-127323","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":395704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1003/coverthb.jpg"},{"id":395705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1003/ofr20221003.pdf","text":"Report","size":"1.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1003"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 59th annual report of the River Master of the Delaware River. It covers the 2012 River Master report year, the period from December 1, 2011 to November 30, 2012.
During the report year, precipitation in the upper Delaware River Basin was 43.35 inches or 97 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high through late May, declined from then until mid-September, decreasing below 80 percent of combined capacity in late August, increased in late October, and decreased slightly in November 2012. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 52 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on October 25, 2012, to increase discharge mitigation releases from the Neversink Reservoir due to potential impacts from Hurricane Sandy.
The quality of water in the Delaware River estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211095","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2011–November 30, 2012: U.S. Geological Survey Open-File Report 2021–1095, 101 p., https://doi.org/10.3133/ofr20211095.","productDescription":"x, 101 p.","numberOfPages":"101","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123829","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":395538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1095/coverthb.jpg"},{"id":395539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1095/ofr20211095.pdf","text":"Report","size":"4.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1095"},{"id":501528,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112444.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey, New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.66259765625,\n 39.67337039176558\n ],\n [\n -73.65234375,\n 39.67337039176558\n ],\n [\n -73.65234375,\n 42.52069952914966\n ],\n [\n -76.66259765625,\n 42.52069952914966\n ],\n [\n -76.66259765625,\n 39.67337039176558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Delaware River Master
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120 Route 209 South
Milford, PA 18337
A major advancement in the use of radio telemetry has been the development of automated radio tracking systems (ARTS), which allow animal movements to be tracked continuously. A new ARTS approach is the use of a network of simple radio receivers (nodes) that collect radio signal strength (RSS) values from animal-borne radio transmitters. However, the use of RSS-based localization methods in wildlife tracking research is new, and analytical approaches critical for determining high-quality location data have lagged behind technological developments. We present an analytical approach to optimize RSS-based localization estimates for a node network designed to track fine-scale animal movements in a localized area. Specifically, we test the application of analytical filters (signal strength, distance among nodes) to data from real and simulated node networks that differ in the density and configuration of nodes. We evaluate how different filters and network configurations (density and regularity of node spacing) may influence the accuracy of RSS-based localization estimates. Overall, the use of signal strength and distance-based filters resulted in a 3- to 9-fold increase in median accuracy of location estimates over unfiltered estimates, with the most stringent filters providing location estimates with a median accuracy ranging from 28 to 73 m depending on the configuration and spacing of the node network. We found that distance filters performed significantly better than RSS filters for networks with evenly spaced nodes, but the advantage diminished when nodes were less uniformly spaced within a network. Our results not only provide analytical approaches to greatly increase the accuracy of RSS-based localization estimates, as well as the computer code to do so, but also provide guidance on how to best configure node networks to maximize the accuracy and capabilities of such systems for wildlife tracking studies.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8561","usgsCitation":"Paxton, K.L., Baker, K.M., Crytser, Z., Guinto, R.M., Brinck, K., Rogers, H., and Paxton, E.H., 2022, Optimizing trilateration estimates for tracking fine-scale movement of wildlife using automated radio telemetry networks: Ecology and Evolution, v. 12, no. 2, e8561, 12 p., https://doi.org/10.1002/ece3.8561.","productDescription":"e8561, 12 p.","ipdsId":"IP-133962","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":448836,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8561","text":"External Repository"},{"id":435973,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LQWIE","text":"USGS data release","linkHelpText":"Guam automated radio telemetry network test data 2021"},{"id":396012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.83379364013672,\n 13.54721129739022\n ],\n [\n 144.95773315429685,\n 13.54721129739022\n ],\n [\n 144.95773315429685,\n 13.660666140853907\n ],\n [\n 144.83379364013672,\n 13.660666140853907\n ],\n [\n 144.83379364013672,\n 13.54721129739022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":835023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Kayla M","contributorId":279515,"corporation":false,"usgs":false,"family":"Baker","given":"Kayla","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":835030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crytser, Zia","contributorId":279506,"corporation":false,"usgs":false,"family":"Crytser","given":"Zia","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":835025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guinto, Ray Mark Provido 0000-0001-5481-9781","orcid":"https://orcid.org/0000-0001-5481-9781","contributorId":279507,"corporation":false,"usgs":true,"family":"Guinto","given":"Ray","email":"","middleInitial":"Mark Provido","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":835026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":835027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Haldre","contributorId":279510,"corporation":false,"usgs":false,"family":"Rogers","given":"Haldre","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":835028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":835029,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228746,"text":"70228746 - 2022 - A novel approach for directly incorporating disease into fish stock assessment: A case study with seroprevalence data","interactions":[],"lastModifiedDate":"2022-03-28T16:49:12.186131","indexId":"70228746","displayToPublicDate":"2022-02-10T06:49:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A novel approach for directly incorporating disease into fish stock assessment: A case study with seroprevalence data","docAbstract":"Disastrous floods in the twin cities of Nogales, Arizona, USA, and Nogales, Sonora, Mexico (collectively referred to as Ambos Nogales) occur annually in response to monsoonal summer rains. Flood-related hazards include property damage, impairment to sewage systems, sewage discharge, water contamination, erosion, and loss of life. Flood risk, particularly in Nogales, Sonora, is amplified by informal, “squatter” settlements in the watershed floodplain and associated development and infrastructure. The expected increase in precipitation intensity, resulting from climate change, poses further risk to flooding therein. We explore binational community perceptions of flooding, preferences for watershed management, and potential actions to address flooding and increase socio-ecological resilience in Ambos Nogales using standardized questionnaires and interviews to collect data about people and their preferences. We conducted 25 semi-structured interviews with local subject matter experts and gathered survey responses from community members in Ambos Nogales. Though survey response was limited, expected frequencies were high enough to conduct Chi-squared tests of independence to test for statistically significant relationships between survey variables. Results showed that respondents with previous experience with flooding corresponded with their level of concern about future floods. Additionally, respondents perceived greater flood-related risks from traveling across town and damage to vehicles than from inundation or damages to their homes or neighborhoods. Binationally, women respondents felt less prepared for future floods than men. On both sides of the border, community members and local experts agreed that Ambos Nogales lacks adequate preparation for future floods. To increase preparedness, they recommended flood risk education and awareness campaigns, implementation of green infrastructure, additional stormwater infrastructure (such as drainage systems), enhanced flood early warning systems, and reduction of flood flows through regulations to reduce the expansion of hard surfaces. This study contributes systematic collection of information about flood risk perceptions across an international border, including novel data regarding risks related to climate change and gender-based assessments of flood risk. Our finding of commonalities across both border communities, in perceptions of flood risk and in the types of risk reduction solutions recommended by community members, provides clear directions for flood risk education, outreach, and preparedness, as well as measures to enhance cross-border cooperation.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11069-022-05225-x","usgsCitation":"Freimund, C.A., Garfin, G.M., Norman, L., Fisher, L., and Buizer, J., 2022, Flood resilience in paired US–Mexico border cities: A study of binational risk perceptions: Natural Hazards, v. 112, p. 1247-1271, https://doi.org/10.1007/s11069-022-05225-x.","productDescription":"25 p.","startPage":"1247","endPage":"1271","ipdsId":"IP-126099","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-022-05225-x","text":"Publisher Index Page"},{"id":396437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Sonora","city":"Nogales","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5606689453125,\n 30.840931139029916\n ],\n [\n -110.3466796875,\n 30.840931139029916\n ],\n [\n -110.3466796875,\n 32.194208672875384\n ],\n [\n -111.5606689453125,\n 32.194208672875384\n ],\n [\n -111.5606689453125,\n 30.840931139029916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Freimund, Christopher A.","contributorId":280033,"corporation":false,"usgs":false,"family":"Freimund","given":"Christopher","email":"","middleInitial":"A.","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garfin, Gregg M.","contributorId":205905,"corporation":false,"usgs":false,"family":"Garfin","given":"Gregg","email":"","middleInitial":"M.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":835921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":835920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Larry A.","contributorId":280034,"corporation":false,"usgs":false,"family":"Fisher","given":"Larry A.","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buizer, James","contributorId":280035,"corporation":false,"usgs":false,"family":"Buizer","given":"James","email":"","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":835923,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228396,"text":"70228396 - 2022 - Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data","interactions":[],"lastModifiedDate":"2022-04-04T11:13:24.753751","indexId":"70228396","displayToPublicDate":"2022-02-09T08:26:22","publicationYear":"2022","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":"Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data","docAbstract":"The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14040807","usgsCitation":"Dahal, D., Pastick, N.J., Boyte, S., Parajuli, S., Oimoen, M.J., and Megard, L.J., 2022, Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data: Remote Sensing, v. 14, no. 4, Article: 807, 21 p. ; 3 Data Releases, https://doi.org/10.3390/rs14040807.","productDescription":"Article: 807, 21 p. ; 3 Data Releases","ipdsId":"IP-135991","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448849,"rank":6,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14040807","text":"Publisher Index Page"},{"id":486325,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14VQEGO","text":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2025"},{"id":435974,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1Y5TZBM","text":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024"},{"id":397952,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GC5JVG","text":"USGS data release","description":"USGS data release","linkHelpText":"Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2020"},{"id":397951,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVGRH8","text":"USGS data release","description":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1"},{"id":395764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397950,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FG6X9Q","text":"USGS data release","description":"USGS data release","linkHelpText":"Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.865234375,\n 30.031055426540206\n ],\n [\n -103.3154296875,\n 32.65787573695528\n ],\n [\n -103.6669921875,\n 34.74161249883172\n ],\n [\n -100.6787109375,\n 36.63316209558658\n ],\n [\n -100.72265625,\n 36.98500309285596\n ],\n [\n -101.29394531249999,\n 37.64903402157866\n ],\n [\n -103.4033203125,\n 39.198205348894795\n ],\n [\n -104.2822265625,\n 40.713955826286046\n ],\n [\n -102.7880859375,\n 43.004647127794435\n ],\n [\n -102.3046875,\n 47.84265762816538\n ],\n [\n -103.53515625,\n 48.980216985374994\n ],\n [\n -121.201171875,\n 49.009050809382046\n ],\n [\n -121.5087890625,\n 46.92025531537451\n ],\n [\n -122.25585937500001,\n 43.96119063892024\n ],\n [\n -122.25585937500001,\n 41.27780646738183\n ],\n [\n -123.662109375,\n 39.605688178320804\n ],\n [\n -124.01367187499999,\n 38.95940879245423\n ],\n [\n -121.86035156249999,\n 36.38591277287651\n ],\n [\n -120.58593749999999,\n 34.70549341022544\n ],\n [\n -120.673828125,\n 33.97980872872457\n ],\n [\n -117.99316406249999,\n 32.91648534731439\n ],\n [\n -117.2900390625,\n 32.69486597787505\n ],\n [\n -114.9609375,\n 32.54681317351514\n ],\n [\n -110.74218749999999,\n 31.27855085894653\n ],\n [\n -108.06152343749999,\n 31.466153715024294\n ],\n [\n -108.06152343749999,\n 31.952162238024975\n ],\n [\n -105.29296874999999,\n 30.977609093348686\n ],\n [\n -104.19433593749999,\n 29.611670115197377\n ],\n [\n -102.919921875,\n 28.998531814051795\n ],\n [\n -101.865234375,\n 30.031055426540206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":834192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":834193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":834194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":275653,"corporation":false,"usgs":false,"family":"Parajuli","given":"Sujan","affiliations":[{"id":56871,"text":"KBR Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oimoen, Michael J. 0000-0003-3611-6227","orcid":"https://orcid.org/0000-0003-3611-6227","contributorId":275654,"corporation":false,"usgs":false,"family":"Oimoen","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":56871,"text":"KBR Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Megard, Logan J. 0000-0002-0150-4521","orcid":"https://orcid.org/0000-0002-0150-4521","contributorId":275655,"corporation":false,"usgs":false,"family":"Megard","given":"Logan","email":"","middleInitial":"J.","affiliations":[{"id":56872,"text":"C2G Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":834197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228381,"text":"70228381 - 2022 - Shoaling wave shape estimates from field observations and derived bedload sediment rates","interactions":[],"lastModifiedDate":"2022-02-09T16:23:50.789606","indexId":"70228381","displayToPublicDate":"2022-02-08T10:12:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Shoaling wave shape estimates from field observations and derived bedload sediment rates","docAbstract":"The shoaling transformation from generally linear deep-water waves to asymmetric shallow-water waves modifies wave shapes and causes near-bed orbital velocities to become asymmetrical, contributing to net sediment transport. In this work, we used two methods to estimate the asymmetric wave shape from data at three sites. The first method converted wave measurements made at the surface to idealized near-bottom wave-orbital velocities using a set of empirical equations: the “parameterized” waveforms. The second method involved direct measurements of velocities and pressure made near the seabed: the “direct” waveforms. Estimates from the two methods were well correlated at all three sites (Pearson’s correlation coefficient greater than 0.85). Both methods were used to drive bedload-transport calculations that accounted for asymmetric waves, and the results were compared with a traditional excess-stress formulation and field estimates of bedload transport derived from ripple migration rates based on sonar imagery. The cumulative bedload transport from the parameterized waveform was 25% greater than the direct waveform, mainly because the parameterized waveform did not account for negative skewness. Calculated transport rates were comparable to rates estimated from ripple migration except during the largest event, when calculated rates were as much as 100 times greater, which occurred during high period waves.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse10020223","usgsCitation":"Kalra, T., Suttles, S.E., Sherwood, C.R., Warner, J.C., Aretxabaleta, A., and Leavitt, G.R., 2022, Shoaling wave shape estimates from field observations and derived bedload sediment rates: Journal of Marine Science and Engineering, v. 10, no. 2, 223, 27 p., https://doi.org/10.3390/jmse10020223.","productDescription":"223, 27 p.","ipdsId":"IP-130494","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020223","text":"Publisher Index Page"},{"id":395674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Massachusetts, New York","otherGeospatial":"Fire Island, Martha Vineyard, Matanzas Inlet","geographicExtents":"{\n \"type\": 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-73.1744384765625,\n 40.61890405098613\n ],\n [\n -73.1744384765625,\n 40.63206312461566\n ],\n [\n -73.19744110107422,\n 40.63206312461566\n ],\n [\n -73.19744110107422,\n 40.61890405098613\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":834048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suttles, Steven E. 0000-0002-4119-8370 ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":192272,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven","email":"ssuttles@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leavitt, Gibson Robert Scott 0000-0001-5362-9150","orcid":"https://orcid.org/0000-0001-5362-9150","contributorId":275364,"corporation":false,"usgs":true,"family":"Leavitt","given":"Gibson","email":"","middleInitial":"Robert Scott","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232989,"text":"70232989 - 2022 - Empirical map-based nonergodic models of site response in the greater Los Angeles area","interactions":[],"lastModifiedDate":"2022-07-15T13:43:13.704946","indexId":"70232989","displayToPublicDate":"2022-02-08T08:31:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Empirical map-based nonergodic models of site response in the greater Los Angeles area","docAbstract":"We develop empirical estimates of site response at seismic stations in the Los Angeles area using recorded ground motions from 414 M 3–7.3 earthquakes in southern California. The data are from a combination of the Next Generation Attenuation‐West2 project, the 2019 Ridgecrest earthquakes, and about 10,000 newly processed records. We estimate site response using an iterative mixed‐effects residuals partitioning approach, accounting for azimuthal variations in anelastic attenuation and potential bias due to spatial clusters of colocated earthquakes. This process yields site response for peak ground acceleration, peak ground velocity, and pseudospectral acceleration relative to a 760 m/s shear‐wave velocity (
The U.S. Geological Survey and U.S. Army Corps of Engineers collected high-resolution multibeam sonar data during 2016–18 to compute stage-area and stage-capacity tables for the U.S. part of Koocanusa Reservoir in Lincoln County, northwestern Montana. Koocanusa Reservoir is a transboundary reservoir extending about 48 miles from Libby Dam upstream to the U.S. international boundary with Canada and another 42 miles within Canada to near Wardner, British Columbia. The upstream extent of the reservoir within Canada, where much of the sedimentation was previously documented, was not included in this study. Previously developed stage-area and stage-capacity tables were developed for the entire reservoir and could not be directly compared to the stage-area and stage-capacity tables from this study. Two discrete stage-area and stage-capacity values from the original survey (unknown survey date prior to 1980) were available for parts of the reservoir within the United States at the normal full-pool and normal minimum-pool elevations (2,459 and 2,287 U.S. survey feet above the National Geodetic Vertical Datum of 1929, respectively). At the normal full-pool elevation, the stage-area relation resulted in a 0.06-percent increase in surface-water acreage. Conversely, a 0.03-percent decrease in storage capacity at the normal full-pool elevation occurred. At the normal-minimum-pool elevation, the stage-area relation showed a 1.21-percent decrease in surface water from 14,487 to 14,314 acres. The usable storage capacity, defined as the volume of water between the normal full-pool and normal minimum-pool elevations, decreased by 0.39 percent (15,353 acre-feet). Results from this study indicate that a relatively minimal amount of sedimentation has occurred since initial filling in Koocanusa Reservoir for parts of the reservoir within the United States. Updated stage-area and stage-capacity tables for the entire reservoir will require additional bathymetric and topographic surveys for the parts of Koocanusa Reservoir within Canada.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3485","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Fosness, R.L., and Dudunake, T.J., 2022, Bathymetric map, surface area, and stage-capacity for the U.S. part of Lake Koocanusa, Lincoln County, Montana, 2016–18: U.S. Geological Survey Scientific Investigations Map 3485, scale 1:100,000, https://doi.org/10.3133/sim3485.","productDescription":"1 Plate: 30.00 × 35.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-121814","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":395469,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOPNSN","text":"USGS data release","description":"USGS data release","linkHelpText":"U.S. Geological Survey and U.S. Army Corps of Engineers bathymetric survey of Koocanusa Reservoir, Lincoln County, Montana, 2016–2018 (ver. 2.0, August 2021)"},{"id":501890,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112443.htm","linkFileType":{"id":5,"text":"html"}},{"id":395468,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3485/sim3485.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3485"},{"id":395467,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3485/coverthb.jpg"}],"country":"United States","state":"Montana","county":"Lincoln County","otherGeospatial":"Koocanusa Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.367431640625,\n 48.38544219115483\n ],\n [\n -115.08453369140625,\n 48.38544219115483\n ],\n [\n -115.08453369140625,\n 49.00004203215395\n ],\n [\n -115.367431640625,\n 49.00004203215395\n ],\n [\n -115.367431640625,\n 48.38544219115483\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director , Idaho Water Science Center
U.S. Geological Survey
230 Collins Road Boise, Idaho 83702-4520
It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.
This report addresses system characterization of Planet’s SuperDove and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5.8 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 3-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SuperDove has a band-to-band geometric performance in the range of −1.701 m (−0.567 pixel) to 1.173 m (0.391 pixel) in easting and −4.950 m (−1.650 pixels) to 6.051 m (2.017 pixels) in northing, an image-to-image geometric performance of −1.17 m (−0.39 pixel) to 23.45 m (7.82 pixels) in easting and −10.61 m (−3.54 pixels) to −4.43 m (−1.48 pixels) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.043 to 0.020 in offset and 0.812 to 1.246 in slope, and a spatial performance in the range of 3.59 to 3.70 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.005 to 0.008.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030F","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on Planet’s SuperDove, chap. F of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 19 p., https://doi.org/10.3133/ofr20211030F.","productDescription":"iv, 19 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-126679","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":395388,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/f/Images"},{"id":395385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/f/coverthb.jpg"},{"id":395387,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/f/ofr20211030f.XML","size":"67.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1030–F XML"},{"id":395386,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/f/ofr20211030f.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–F"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Since they were first introduced to the United States more than 50 years ago, invasive carp have rapidly colonized rivers of the Mississippi River Basin, with detrimental effects on native aquatic species. Their continued range expansion, and potential for subsequent invasion of the Great Lakes, has led to increased concern for the susceptibility of as-yet uncompromised lotic and lentic systems in the central United States. Because invasive carp eggs and larvae must drift in the river current for the first several days following spawning, numerical drift modeling has emerged as a useful technique for determining whether certain river systems and reaches have the potential to support suspension-to-hatching survival of invasive carp eggs, a critical first step in recruitment. Here we use one such numerical modeling approach, the Fluvial Egg Drift Simulator (FluEgg), to estimate bighead carp (Hypophthalmichthys nobilis) egg hatching success and larval retention in a 47.8-kilometer (km) reach of the multi-thread St. Croix River, Minnesota and Wisconsin, United States. We explore three approaches for obtaining the hydraulic data required by FluEgg, parameterizing the model with either (a) field hydraulic data collected within the main channel during a high-flow event, or hydraulic data output from a one-dimensional hydrodynamic model with both (b) steady, and (c) unsteady flows. We find that the three approaches, along with the range of water temperatures and discharge used in simulations, produce vastly different predictions of streamwise transport and in-river egg hatching probability (0% for field data, 0 to 96% for steady-state hydraulic modeling, and 1.8 to 65% for unsteady modeling). However, all FluEgg simulations, regardless of the source of hydraulic data, predicted that no larvae reach the gas bladder inflation stage within the study reach where nursery habitat is abundant. Overall, these results indicate that the lower St. Croix River is suitable for invasive carp spawning and egg suspension until hatching for a range of discharge and water temperatures. These results highlight the role of complex channel hydraulics and morphology, particularly multi-thread reaches, and their inclusion in ecohydraulic-suitability modeling to determine susceptibility of river systems for invasive carp reproduction. Our work also emphasizes the scientific value of multi-dimensional hydrodynamic models that can capture the spatial heterogeneity of flow fields in geomorphically complex rivers. This work may help to guide management efforts based on the targeted monitoring and control and improve invasive carp egg and larvae sampling efficiency.
","language":"English","publisher":"Public Library of Science (PLOS)","doi":"10.1371/journal.pone.0263052","usgsCitation":"Kasprak, A., Jackson, P.R., Lindroth, E.M., Lund, J.W., and Ziegeweid, J.R., 2022, The role of hydraulic and geomorphic complexity in predicting invasive carp spawning potential: St. Croix River, Minnesota and Wisconsin, United States: PLoS ONE, v. 17, no. 2, e0263052, 25 p., https://doi.org/10.1371/journal.pone.0263052.","productDescription":"e0263052, 25 p.","ipdsId":"IP-128244","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448898,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0263052","text":"Publisher Index Page"},{"id":435980,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93K0UUI","text":"USGS data release","linkHelpText":"Bathymetric, water velocity, and water temperature data on the St. Croix River between St. Croix Falls, Wisconsin, and Stillwater, Minnesota, June 19-22, 2018"},{"id":395554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.84408569335938,\n 44.98714175309689\n ],\n [\n -92.59552001953125,\n 44.98714175309689\n ],\n [\n -92.59552001953125,\n 45.42062422307843\n ],\n [\n -92.84408569335938,\n 45.42062422307843\n ],\n [\n -92.84408569335938,\n 44.98714175309689\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":833274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindroth, Evan M. 0000-0002-9746-4359 elindroth@usgs.gov","orcid":"https://orcid.org/0000-0002-9746-4359","contributorId":264885,"corporation":false,"usgs":true,"family":"Lindroth","given":"Evan","email":"elindroth@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lund, J. William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":211157,"corporation":false,"usgs":true,"family":"Lund","given":"J.","email":"","middleInitial":"William","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833278,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264967,"text":"70264967 - 2022 - Evaluation of electrical and electromagnetic geophysical techniques to inspect earthen dam and levee structures in Arkansas","interactions":[],"lastModifiedDate":"2025-03-27T15:34:18.439831","indexId":"70264967","displayToPublicDate":"2022-02-03T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9128,"text":"Journal of Environmental and Engineering Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of electrical and electromagnetic geophysical techniques to inspect earthen dam and levee structures in Arkansas","docAbstract":"Within the state of Arkansas there is an increasing number of aging dams and levees that have little to no documentation concerning their construction or composition. Surface geophysical surveys offer a non-intrusive method for investigating these structures: To describe their lithologic makeup, to evaluate the materials that they were constructed upon, and to identify potential flow paths through them. Techniques such as electrical resistivity tomography, seismic refraction, and electromagnetic induction have all been used to image dams and levees and require additional information from geologic outcrops, geotechnical borings, or drill cores in order to make informed geologic interpretations of the geophysical models. These geologic models then allow the owners of these structures to make more informed decisions about their operation and maintenance. Between 2011 and 2018, the U.S. Geological Survey conducted geophysical and geotechnical investigations of three earthen structures within the state of Arkansas. Electrical and electromagnetic geophysical data were used to develop lithologic models of these structures and the underlying geology. Self-potential surveys were utilized to detect the movement of water through these structures indicating possible seepage pathways. Geotechnical methods such as electric and hydraulic direct-push well logs and cores acted as both a control on the geophysical interpretations and a confirmation of anomalies. This integrated approach detected the lack of an impermeable core within a levee, imaged a change in lithology of the bedrock forming the seal beneath a gravity dam, and identified a potential seepage feature within the core of an earthen dam. These results further support that this method of extending known lithologic features via surface and borehole geophysics is a useful approach for characterizing earthen water control structures.","language":"English","publisher":"GeoScienceWorld","doi":"10.32389/JEEG20-063","usgsCitation":"Adams, R.F., Miller, B., Kress, W., Ikard, S., Payne, J.D., and Killion, W., 2022, Evaluation of electrical and electromagnetic geophysical techniques to inspect earthen dam and levee structures in Arkansas: Journal of Environmental and Engineering Geophysics, v. 26, no. 4, p. 287-303, https://doi.org/10.32389/JEEG20-063.","productDescription":"17 p.","startPage":"287","endPage":"303","ipdsId":"IP-116502","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":483950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Science Center","active":true,"usgs":true}],"preferred":true,"id":932119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":212256,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Payne, Jason D. 0000-0003-4294-7924","orcid":"https://orcid.org/0000-0003-4294-7924","contributorId":257453,"corporation":false,"usgs":true,"family":"Payne","given":"Jason","email":"","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932122,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Killion, Walter 0000-0002-5653-8489","orcid":"https://orcid.org/0000-0002-5653-8489","contributorId":214713,"corporation":false,"usgs":true,"family":"Killion","given":"Walter","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932123,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229525,"text":"70229525 - 2022 - Human-in-the-Loop segmentation of earth surface imagery","interactions":[],"lastModifiedDate":"2022-03-10T21:47:50.876493","indexId":"70229525","displayToPublicDate":"2022-02-02T15:44:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Human-in-the-Loop segmentation of earth surface imagery","docAbstract":"Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EA002085","usgsCitation":"Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C.S., Brown, J., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C., Warrick, J.A., and Wernette, P., 2022, Human-in-the-Loop segmentation of earth surface imagery: Earth and Space Science, v. 9, e2021EA002085, 31 p., https://doi.org/10.1029/2021EA002085.","productDescription":"e2021EA002085, 31 p.","ipdsId":"IP-132726","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":680,"text":"Woods Hole Science Center","active":false,"usgs":true}],"links":[{"id":448911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Several abiotic factors affect nest incubation, and thus nest success, but tides and gas exchange are two of the most important. The effects of tides on nest success have been well documented in regard to overwash and inundation events. However, the possible effect of tidally-driven gas exchange has received little attention. The incursion and retreat of the saltwater wedge may cause substantial movement of gases through the beach during the tidal cycle. This study quantifies the differences in tidally-driven gas exchange among beach types and shoreline elevation levels. Carbon dioxide (CO2) efflux was used as a means of measuring gas movement through the beach to examine tidal effects across different beach zones and among different beach types. CO2 efflux was measured throughout the tidal cycle at three distinct beaches (accreting, eroding, and nourished) at Cape San Blas, Florida. There was a general pattern of CO2 efflux rising and falling throughout the tidal cycle on each beach and a difference in the CO2 efflux observed among beaches and beach zones. Efflux patterns at the nourished and eroding beaches were similar, but the nourished beach exhibited a decreased and dampened CO2 efflux pattern throughout the course of the tidal cycle. Analyses of the hatchling turtle emergence success data from 2011 to 2014 for the three beaches found that emergence success differed among the three beaches. The highest emergence success was on the nourished beach, which exhibited a relatively consistent efflux pattern. These results suggest that tidally-driven gas exchange may have implications on nest incubation and survival and are a consideration in beach restoration management and best practices for coastline conservation.
","language":"English","publisher":"Coastal Education and Research Foundation, Inc.","doi":"10.2112/JCOASTRES-D-21-00082.1","usgsCitation":"Goforth, K., and Carthy, R., 2022, Tidally-driven gas exchange in beaches: Implications for sea turtle nest success: Journal of Coastal Research, v. 38, no. 3, p. 523-537, https://doi.org/10.2112/JCOASTRES-D-21-00082.1.","productDescription":"15 p.","startPage":"523","endPage":"537","ipdsId":"IP-130877","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Gulf County","otherGeospatial":"Eglin Air Force Base–CSB, and Rish Park, Salinas Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.40572058069057,\n 29.74177252032031\n ],\n [\n -85.37633878087428,\n 29.664358173736517\n ],\n [\n -85.3489157677126,\n 29.658826431928226\n ],\n [\n -85.32541032785983,\n 29.663507352511033\n ],\n [\n -85.35087455436684,\n 29.69243951475036\n ],\n [\n -85.37927696085592,\n 29.739645420414227\n ],\n [\n -85.40572058069057,\n 29.74177252032031\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goforth, K.M.","contributorId":341648,"corporation":false,"usgs":false,"family":"Goforth","given":"K.M.","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908740,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227958,"text":"70227958 - 2022 - Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland","interactions":[],"lastModifiedDate":"2022-02-02T15:10:24.469421","indexId":"70227958","displayToPublicDate":"2022-02-02T09:04:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland","docAbstract":"Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3590","usgsCitation":"Pau, S., Nippert, J., Slapikas, R., Griffith, D.M., Bachle, S., Helliker, B., O’Connor, R., Riley, W.J., Still, C.J., and Zaricor, M., 2022, Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland: Ecology, v. 103, no. 2, e03590, https://doi.org/10.1002/ecy.3590.","productDescription":"e03590","ipdsId":"IP-123791","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":395269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"editors":[{"text":"Borer, Elizabeth T.","contributorId":45049,"corporation":false,"usgs":false,"family":"Borer","given":"Elizabeth","email":"","middleInitial":"T.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":832742,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Pau, Stephanie","contributorId":190208,"corporation":false,"usgs":false,"family":"Pau","given":"Stephanie","email":"","affiliations":[],"preferred":false,"id":832703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nippert, Jesse","contributorId":273240,"corporation":false,"usgs":false,"family":"Nippert","given":"Jesse","affiliations":[],"preferred":false,"id":832704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slapikas, Ryan","contributorId":273467,"corporation":false,"usgs":false,"family":"Slapikas","given":"Ryan","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":832741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffith, Daniel Mark 0000-0001-7463-4004","orcid":"https://orcid.org/0000-0001-7463-4004","contributorId":271033,"corporation":false,"usgs":true,"family":"Griffith","given":"Daniel","email":"","middleInitial":"Mark","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":832705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bachle, Seton","contributorId":273242,"corporation":false,"usgs":false,"family":"Bachle","given":"Seton","email":"","affiliations":[],"preferred":false,"id":832706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helliker, Brent","contributorId":273243,"corporation":false,"usgs":false,"family":"Helliker","given":"Brent","email":"","affiliations":[],"preferred":false,"id":832707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Connor, Rory","contributorId":273244,"corporation":false,"usgs":false,"family":"O’Connor","given":"Rory","affiliations":[],"preferred":false,"id":832708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riley, William J. 0000-0002-4615-2304","orcid":"https://orcid.org/0000-0002-4615-2304","contributorId":194645,"corporation":false,"usgs":false,"family":"Riley","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832709,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Still, Christopher J.","contributorId":167581,"corporation":false,"usgs":false,"family":"Still","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":24761,"text":"University of California, Santa Barbara; 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Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a “gain–loss” approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3).
We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year−1. Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source.
The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.
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bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":883878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frid, Leonardo","contributorId":196604,"corporation":false,"usgs":false,"family":"Frid","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":883879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rayfield, Bronwyn 0000-0003-1768-1300","orcid":"https://orcid.org/0000-0003-1768-1300","contributorId":203690,"corporation":false,"usgs":false,"family":"Rayfield","given":"Bronwyn","email":"","affiliations":[{"id":36690,"text":"Apex Resource Management Solutions","active":true,"usgs":false}],"preferred":false,"id":883880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Colin","contributorId":197531,"corporation":false,"usgs":false,"family":"Daniel","given":"Colin","affiliations":[],"preferred":false,"id":883881,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":883882,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marvin, Dave","contributorId":330032,"corporation":false,"usgs":false,"family":"Marvin","given":"Dave","email":"","affiliations":[{"id":78770,"text":"Salo Sciences","active":true,"usgs":false}],"preferred":false,"id":883883,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241786,"text":"70241786 - 2022 - The role of monitoring and research in the Greater Yellowstone Ecosystem in framing our understanding of the effects of disease on amphibians","interactions":[],"lastModifiedDate":"2023-03-27T12:13:51.160574","indexId":"70241786","displayToPublicDate":"2022-02-02T07:12:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"The role of monitoring and research in the Greater Yellowstone Ecosystem in framing our understanding of the effects of disease on amphibians","docAbstract":"Emerging infectious disease threatens amphibian biodiversity worldwide, including in landscapes that are protected from many anthropogenic stressors. We summarized data from studies in the Greater Yellowstone Ecosystem (GYE), one of the largest and most complete temperate-zone ecosystems on Earth, to assess the current state of knowledge about ranaviruses and the novel amphibian chytrid fungus (Bd) in this landscape, and to provide insight into future threats and conservation strategies. Our comprehension of these amphibian diseases in the GYE is based on >20 years of monitoring, surveys, population studies, and opportunistic observations of mortality events. Research indicates that local species are affected differently, depending on temperature, community structure, and location in the GYE. Bd has not been linked to die-offs in the GYE but evidence for ongoing reductions in survival contributes to foundational data about the effects of this pathogen in North America. Localized mortality events attributed to, or consistent with, disease from ranaviruses, are widespread in the GYE, but there is less information on how ranaviruses affect amphibian vital rates. The significance of disease in the long-term persistence of amphibians in the GYE is linked to anticipated changes in climate, especially drought. Additionally, expected increases in visitor use, and its associated impacts, have the potential to exacerbate the effects of disease. Long-term information from this large, intact landscape helps to frame our understanding of the effects of disease on amphibians and provides data that can contribute to management decisions, mitigation strategies, and forecasting efforts.
Groundwater pumping can cause reductions in streamflow (“streamflow depletion”) that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management-focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion-related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.