Levels of submersed aquatic vegetation (SAV) are commonly assessed using a modified garden rake. However, the utility of the rake sampling method relative to methods that are typically viewed as more definitive (and expensive) such as snorkeling and coring remains a matter of debate. This study explores whether probabilities of species detections for four SAV species varied among sampling units in a rake-biomass study and, if so, whether such variation reflected variation in species abundance. Variation in detection probabilities, when unaddressed, may yield biased estimators of percent frequency of occurrence (“occupancy”) and of occurrence-habitat associations. Biomass-driven variation in detection probabilities is important because such variation may not be explainable using covariates typically measured when sampling using the rake method. This study found substantial among-unit variation in detection probabilities, with majorities of that variation on the logit or modeling scale being associated with biomass but not with the non-biomass covariates substrate type, water depth and day of study. The study closes by exploring sampling protocols and modeling methods that may yield improved SAV occupancy estimates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquabot.2021.103375","usgsCitation":"Gray, B.R., 2021, Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass: Aquatic Botany, v. 171, 103375, 7 p., https://doi.org/10.1016/j.aquabot.2021.103375.","productDescription":"103375, 7 p.","ipdsId":"IP-123221","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436468,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM11FY","text":"USGS data release","linkHelpText":"SAS Code: Estimating probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass."},{"id":385277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"171","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":814599,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237781,"text":"70237781 - 2021 - Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","interactions":[],"lastModifiedDate":"2022-10-24T14:38:38.267353","indexId":"70237781","displayToPublicDate":"2021-03-09T09:28:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","title":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","docAbstract":"African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109499","usgsCitation":"Diaz, S.G., DeAngelis, D.L., Gaines, M.S., Purdon, A., Mole, M.A., and van Aarde, R.J., 2021, Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement: Ecological Modelling, v. 447, 109499, 27 p., https://doi.org/10.1016/j.ecolmodel.2021.109499.","productDescription":"109499, 27 p.","ipdsId":"IP-124073","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Botswana, Mozambique, South Africa","otherGeospatial":"Chobe National Park, Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.26908811865576,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -17.811236528794907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.337765815705552,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -22.153479707969097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"447","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Stephanie G.","contributorId":212228,"corporation":false,"usgs":false,"family":"Diaz","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":855618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaines, Michael S.","contributorId":298435,"corporation":false,"usgs":false,"family":"Gaines","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Purdon, Andrew","contributorId":298436,"corporation":false,"usgs":false,"family":"Purdon","given":"Andrew","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mole, Michael A.","contributorId":298438,"corporation":false,"usgs":false,"family":"Mole","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Aarde, Rudi J.","contributorId":298440,"corporation":false,"usgs":false,"family":"van Aarde","given":"Rudi","email":"","middleInitial":"J.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855622,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219424,"text":"70219424 - 2021 - UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","interactions":[],"lastModifiedDate":"2021-04-05T13:40:13.157647","indexId":"70219424","displayToPublicDate":"2021-03-09T08:18:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","docAbstract":"Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13051036","usgsCitation":"Belmonte, A., Sankey, T.T., Biedermann, J., Bradford, J., Goetz, S.J., and Kolb, T., 2021, UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence: Remote Sensing, v. 13, no. 5, 1036, 20 p., https://doi.org/10.3390/rs13051036.","productDescription":"1036, 20 p.","ipdsId":"IP-126824","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13051036","text":"Publisher Index Page"},{"id":384871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.78863525390625,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 34.5235300339023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":813495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":813496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biedermann, Joel","contributorId":256936,"corporation":false,"usgs":false,"family":"Biedermann","given":"Joel","email":"","affiliations":[{"id":51904,"text":"USDA Agricultural Research Service Southwest Watershed Research Center, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":813497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott J 0000-0002-6326-4308","orcid":"https://orcid.org/0000-0002-6326-4308","contributorId":210734,"corporation":false,"usgs":false,"family":"Goetz","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":813499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolb, Thomas","contributorId":174381,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","affiliations":[],"preferred":false,"id":813500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221171,"text":"70221171 - 2021 - Characterizing stress orientations in southern Kansas","interactions":[],"lastModifiedDate":"2021-06-04T12:44:46.202587","indexId":"70221171","displayToPublicDate":"2021-03-09T07:38:51","publicationYear":"2021","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":"Characterizing stress orientations in southern Kansas","docAbstract":"Induced seismicity predominantly occurs along faults that are optimally oriented to the local principal compressive stress direction, and the characterization of these stress orientations is an important component of understanding seismic hazards. The seismicity rate in southern Kansas rapidly increased in 2013 primarily due to the disposal of large volumes of wastewater into the Arbuckle Group. Previously, local stress orientations in this area were poorly constrained, which limited our understanding of the complex faulting and diverse earthquake mechanisms in this region. We use shear‐wave splitting and focal mechanism inversion techniques to create multiple, independent estimates of maximum horizontal stress directions (
Montane meadows are areas of high biodiversity and provide many important ecosystem services; however, degradation of 40–60% of these habitats in the Sierra Nevada region of California has left many of these areas impaired. The “pond-and-plug” meadow-restoration technique is 1 type of treatment implemented to restore montane meadows. The objectives of this technique are to re-water the meadow and promote downstream flow by increasing the water-table elevation and providing additional water storage that will promote the growth of mesic and hydric vegetation that maintains and stabilizes stream channels. However, aquatic habitat and the composition and functioning of aquatic communities in these systems post-treatment are poorly documented or understood. We evaluated: (1) fish habitat, community composition, and relative abundance among recently created ponds spanning the range of pond habitats; (2) seasonal movement and survival of fish within and among ponds; and (3) food web structure in ponds. We documented over-summer and winter survival in the fish community and short-distance movement by 1 species occupying the ponds. Mark-recapture data suggest that all fish species present are capable of surviving both summer and winter conditions when pond conditions could be most limiting. Food web structure among intensively sampled ponds was similar, with overlapping isotopic niche width for dominant taxa. However, basal resource diversity (BRD) varied among ponds, with those having higher macrophyte cover also showing greater BRD. Our findings suggest that pond-and-plug techniques can provide habitat for native fishes that are able to tolerate departures from the species thermal and dissolved oxygen optima. Future meadow treatments could benefit from short-term restoration techniques such as pond-and-plug to allow for longer-term processes to influence meadow condition over time.
Tropical mountains harbor globally significant levels of biodiversity and endemism. Climate change threatens many tropical montane species, yet little research has assessed the effects of climate change on the demographic rates of tropical species, particularly in the Afrotropics. Here, we report on the demographic rates of 21 Afrotropical bird species over 30 years in montane forests in Tanzania. We used mark–recapture analyses to model rates of population growth, recruitment, and apparent survival as functions of annual mean temperature and annual precipitation. For over one‐half of focal species, decreasing population growth rates were associated with increasing temperature. Due to the trend in temperature over time, we substituted a time covariate for the temperature covariate in top‐ranked population growth rate models. Temperature was a better explanatory covariate than time for 6 of the 12 species, or 29% of all focal species. Population growth rates were also lower for species found further below their elevational midpoint and for smaller‐bodied species. Changes in population growth rates were more closely tied to changes in recruitment than to changes in apparent survival. There were no consistent associations between demographic rates and precipitation. This study demonstrates temperature‐associated demographic impacts for 6 (29%) of 21 focal species in an Afrotropical understory bird community and highlights the need to incorporate the impacts of climate change on demographic rates into conservation planning across the tropics.
We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR1-2, to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI's performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105030","usgsCitation":"Rad, A.M., Kreitler, J.R., and Sadegh, M., 2021, Augmented normalized difference water index for improved monitoring of surface water: Environmental Modeling and Software, v. 140, 105030, 15 p., https://doi.org/10.1016/j.envsoft.2021.105030.","productDescription":"105030, 15 p.","ipdsId":"IP-121583","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":453156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105030","text":"Publisher Index Page"},{"id":385213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rad, Arash Modaresi","contributorId":257536,"corporation":false,"usgs":false,"family":"Rad","given":"Arash","email":"","middleInitial":"Modaresi","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":814522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadegh, Mojitaba","contributorId":257538,"corporation":false,"usgs":false,"family":"Sadegh","given":"Mojitaba","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814523,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255557,"text":"70255557 - 2021 - Global Changes in 20-year, 50-year and 100-year River Floods","interactions":[],"lastModifiedDate":"2024-06-24T11:35:50.509067","indexId":"70255557","displayToPublicDate":"2021-03-09T06:30:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Global Changes in 20-year, 50-year and 100-year River Floods","docAbstract":"Concepts like the 100-year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20-, 50-, and 100-year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20-, 50-, and 100-year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20-/50-year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100-year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.
Bird conservation as an endeavor engages a broad range of partners and a coordinated effort across State and Federal agencies, nongovernment organizations, universities and, at times, international partnerships. To understand information needs and respond to the many challenges in bird conservation, U.S. Geological Survey (USGS) scientists participate in Flyway committees, on Joint Venture boards and working groups, in professional organizations, and in other conservation partnerships. These activities connect USGS scientists to conservation partners with whom they work to address substantial challenges. More than one hundred USGS scientists are dedicated to the scientific study of migratory birds.
This report presents the current (2021) representative breadth of activities of USGS scientists supporting the conservation and management of migratory birds. Ninety USGS scientists contributed to the project descriptions and other information detailing the work of the USGS. The science herein is organized and presented thematically by research strengths and by management topics. The report emphasizes the geographic framework of the North American Flyway councils through which USGS engages regularly with Federal and State government agencies and others who are responsible for managing migratory bird populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1480","usgsCitation":"Pearse, A.T., Sherfy, M.H., Wimer, M., Khalil, M., and Wiltermuth, M.T., 2021, U.S. Geological Survey migratory bird science, 2020–21: U.S. Geological Survey Circular 1480, 131 p., https://doi.org/10.3133/cir1480.","productDescription":"vi, 131 p.","numberOfPages":"142","onlineOnly":"Y","ipdsId":"IP-125814","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":384209,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1480/cir1480.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"USGS Circular 1480"},{"id":384208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1480/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.24609374999999,\n 32.509761735919426\n ],\n [\n -114.697265625,\n 32.65787573695528\n ],\n [\n -110.9619140625,\n 31.203404950917395\n ],\n [\n -108.28125,\n 31.353636941500987\n ],\n [\n -108.1494140625,\n 31.690781806136822\n ],\n [\n -106.5673828125,\n 31.690781806136822\n ],\n [\n -104.80957031249999,\n 30.486550842588485\n ],\n [\n -104.3701171875,\n 29.420460341013133\n ],\n [\n -102.919921875,\n 28.844673680771795\n ],\n [\n -102.6123046875,\n 29.726222319395504\n ],\n [\n -101.3818359375,\n 29.726222319395504\n ],\n [\n -100.2392578125,\n 28.459033019728043\n ],\n [\n -99.49218749999999,\n 27.254629577800063\n ],\n [\n -99.03076171875,\n 26.45090222367262\n ],\n [\n -97.2509765625,\n 25.918526162075153\n ],\n [\n -96.7236328125,\n 27.877928333679495\n ],\n [\n -93.8671875,\n 29.152161283318915\n ],\n [\n -90.1318359375,\n 28.9023972285585\n ],\n [\n -88.61572265625,\n 28.86391842622456\n ],\n [\n -88.11035156249999,\n 29.99300228455108\n ],\n [\n -84.44091796875,\n 29.668962525992505\n ],\n [\n -83.78173828125,\n 29.7453016622136\n ],\n [\n -82.880859375,\n 28.94086176940557\n ],\n [\n -82.880859375,\n 27.371767300523047\n ],\n [\n -80.74951171875,\n 24.766784522874453\n ],\n [\n -79.541015625,\n 26.64745870265938\n ],\n [\n -81.01318359375,\n 31.39115752282472\n ],\n [\n -76.26708984375,\n 34.867904962568716\n ],\n [\n -75.08056640625,\n 35.94243575255426\n ],\n [\n -75.6298828125,\n 37.24782120155428\n ],\n [\n -71.87255859375,\n 40.88029480552824\n ],\n [\n -69.71923828125,\n 40.9964840143779\n ],\n [\n -59.765625,\n 45.767522962149876\n ],\n [\n -52.53662109375,\n 46.558860303117164\n ],\n [\n -52.14111328125,\n 48.1367666796927\n ],\n [\n -55.52490234375,\n 53.44880683542759\n ],\n [\n -62.46826171874999,\n 59.085738569819505\n ],\n [\n -64.48974609375,\n 61.2913492826376\n ],\n [\n -60.79833984374999,\n 66.72254132270653\n ],\n [\n -67.7197265625,\n 70.38523137082291\n ],\n [\n -78.1787109375,\n 73.80644709395935\n ],\n [\n -78.42041015625,\n 75.97355295343336\n ],\n [\n -74.1357421875,\n 78.67755670538617\n ],\n [\n -69.54345703125,\n 80.26454391261787\n ],\n [\n -61.17187499999999,\n 82.89698689394207\n ],\n [\n -77.34374999999999,\n 83.23642648170203\n ],\n [\n -101.6015625,\n 80.70399666821143\n ],\n [\n -124.45312499999999,\n 76.434603583513\n ],\n [\n -129.0234375,\n 71.85622888185527\n ],\n [\n -136.0546875,\n 69.65708627301174\n ],\n [\n -157.5,\n 71.41317683396566\n ],\n [\n -164.1796875,\n 70.25945200030638\n ],\n [\n -168.046875,\n 66.93006025862448\n ],\n [\n -166.9921875,\n 61.10078883158897\n ],\n [\n -165.9375,\n 53.9560855309879\n ],\n [\n -150.82031249999997,\n 58.44773280389084\n ],\n [\n -143.08593749999997,\n 58.26328705248601\n ],\n [\n -132.1875,\n 54.16243396806779\n ],\n [\n -126.21093749999999,\n 46.31658418182218\n ],\n [\n -125.15625000000001,\n 38.54816542304656\n ],\n [\n -117.24609374999999,\n 32.509761735919426\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.
Multiple studies have reported widespread browning of Northern Hemisphere lakes. Most examples are from boreal lakes that have experienced limited human influence, and browning has alternatively been attributed to changes in atmospheric deposition, climate, and land use. To determine the extent and possible causes of browning across a more geographically diverse region, we examined watercolor and dissolved organic carbon (DOC) time series in hundreds of northeastern U.S. lakes. The majority of lakes have increased in both DOC and color, but there were neither coherent spatial patterns in trends nor relationships with previously reported drivers. Color trends were more variable than DOC trends, and DOC and color trends were not strongly correlated, indicating a cause other than or in addition to increased loading of terrestrial carbon. Browning may be pronounced in regions where climate and atmospheric deposition are dominant drivers but muted in more human-dominated landscapes with a limited extent of organic soils where other disturbances predominate.
Invasive predators are known to have negative consumptive and non-consumptive effects on native species, but few examples show how the abundance of native prey may influence an established invasive predator. We compared invasive brown treesnakes (Boiga irregularis; BTS) found in caves occupied by endangered Mariana swiftlets (Aerodramus bartschi) to snakes found in nearby forests and caves without birds to quantify how the abundance of native avian prey impacts BTS abundance and behavior on Guam. From 2011 to 2017 we removed 151 BTS in caves occupied by swiftlets and never observed BTS in caves without birds. Notable locations included snakes foraging near swiftlets and in holes that allowed cave access and escape from capture. Of 43 BTS with gut contents, 27 (63%) contained swiftlets. BTS in swiftlet-occupied caves had greater fat mass compared to forests, indicating access to swiftlets may increase body condition and promote reproduction. Number of ovarian follicles was significantly greater in female snakes from swiftlet-occupied caves compared to those from ravine, but not limestone forests; evidence of male BTS being more capable of reproduction was limited (i.e., fewer non-discernible but not significantly larger testes in snakes from caves). Assuming other limiting factors are considered, altering the functional response of predators through the modification of caves or interdiction lures to exclude or hinder the largest BTS could bolster swiftlet populations by increasing nesting refugia in currently-occupied caves and facilitate recolonization of historical caves.
Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.
In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028076","usgsCitation":"Formetta, G., Over, T.M., and Stewart, E., 2021, Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom: Water Resources Research, v. 57, no. 4, e2020WR028076, 21 p., https://doi.org/10.1029/2020WR028076.","productDescription":"e2020WR028076, 21 p.","ipdsId":"IP-119682","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/529960/1/N529960PP.pdf","text":"External Repository"},{"id":384966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -5.712890625,\n 49.61070993807422\n ],\n [\n -2.28515625,\n 50.064191736659104\n ],\n [\n 1.669921875,\n 50.84757295365389\n ],\n [\n 2.3291015625,\n 52.32191088594773\n ],\n [\n 0.9228515625,\n 54.826007999094955\n ],\n [\n -0.2197265625,\n 55.85064987433714\n ],\n [\n -0.791015625,\n 57.231502991478926\n ],\n [\n -1.142578125,\n 57.938183012205315\n ],\n [\n -2.548828125,\n 58.63121664342478\n ],\n [\n -4.130859375,\n 59.153403092050375\n ],\n [\n -6.767578125,\n 58.97266715450153\n ],\n [\n -8.1298828125,\n 56.24334992410525\n ],\n [\n -7.9541015625,\n 54.521081495443596\n ],\n [\n -7.0751953125,\n 54.059387886623576\n ],\n [\n -5.888671875,\n 53.409531853086435\n ],\n [\n -5.6689453125,\n 51.56341232867588\n ],\n [\n -6.1083984375,\n 50.233151832472245\n ],\n [\n -6.064453125,\n 49.55372551347579\n ],\n [\n -5.712890625,\n 49.61070993807422\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Formetta, Giuseppe 0000-0002-0252-1462","orcid":"https://orcid.org/0000-0002-0252-1462","contributorId":210296,"corporation":false,"usgs":false,"family":"Formetta","given":"Giuseppe","email":"","affiliations":[{"id":38100,"text":"Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":813730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Elizabeth","contributorId":257050,"corporation":false,"usgs":false,"family":"Stewart","given":"Elizabeth","email":"","affiliations":[{"id":51971,"text":"UK Centre for Ecology & Hydrology","active":true,"usgs":false}],"preferred":false,"id":813732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218721,"text":"70218721 - 2021 - Prioritizing landscapes for grassland bird conservation with hierarchical community models","interactions":[],"lastModifiedDate":"2021-04-08T15:11:47.181072","indexId":"70218721","displayToPublicDate":"2021-03-06T07:56:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing landscapes for grassland bird conservation with hierarchical community models","docAbstract":"Given widespread population declines of birds breeding in North American grasslands, management that sustains wildlife while supporting rancher livelihoods is needed. However, management effects vary across landscapes, and identifying areas with the greatest potential bird response to conservation is a pressing research need.
We developed a hierarchical modeling approach to study grassland bird response to habitat factors at multiple scales and levels. We then identified areas to prioritize for implementing a bird-friendly ranching program.
Using bird survey data from grassland passerine species and 175 sites (2009–2018) across northeast Wyoming, USA, we fit hierarchical community distance sampling models and evaluated drivers of site-level density and regional-level distribution. We then created spatially-explicit predictions of bird density and distribution for the study area and predicted outcomes from pasture-scale management scenarios.
Cumulative overlap of species distributions revealed areas with greater potential community response to management. Within each species’ potential regional-level distribution, the grassland bird community generally responded negatively to cropland cover and vegetation productivity at local scales (up to 10 km of survey sites). Multiple species declined with increasing bare ground and litter cover, shrub cover, and grass height measured within sites.
We demonstrated a novel approach to multi-scale and multi-level prioritization for grassland bird conservation based on hierarchical community models and extensive population monitoring. Pasture-scale management scenarios also suggested the examined community may benefit from less bare ground cover and shorter grass height. Our approach could be extended to other bird guilds in this region and beyond.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01211-z","usgsCitation":"Monroe, A.P., Edmunds, D.R., Aldridge, C.L., Holloran, M.J., Assal, T.J., and Holloran, A., 2021, Prioritizing landscapes for grassland bird conservation with hierarchical community models: Landscape Ecology, v. 36, p. 1023-1038, https://doi.org/10.1007/s10980-021-01211-z.","productDescription":"16 p.","startPage":"1023","endPage":"1038","ipdsId":"IP-122010","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01211-z","text":"Publisher Index Page"},{"id":384245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bird Conservation Region 17","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 45.02695045318546\n ],\n [\n -107.05078125,\n 44.715513732021336\n ],\n [\n -106.69921875,\n 44.37098696297173\n ],\n [\n -106.61132812499999,\n 43.78695837311561\n ],\n [\n -106.435546875,\n 43.052833917627936\n ],\n [\n -105.57861328125,\n 42.79540065303723\n ],\n [\n -104.83154296875,\n 42.4234565179383\n ],\n [\n -104.0185546875,\n 42.52069952914966\n ],\n [\n -104.0625,\n 45.042478050891546\n ],\n [\n -107.5341796875,\n 45.02695045318546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian Pierre-Frederic 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":254952,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian","email":"amonroe@usgs.gov","middleInitial":"Pierre-Frederic","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":811526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloran, Matthew J 0000-0001-5244-770X","orcid":"https://orcid.org/0000-0001-5244-770X","contributorId":254954,"corporation":false,"usgs":false,"family":"Holloran","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":51367,"text":"Operational Conservation LLC","active":true,"usgs":false}],"preferred":false,"id":811527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assal, Timothy J","contributorId":238085,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":811528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holloran, Alison G","contributorId":254955,"corporation":false,"usgs":false,"family":"Holloran","given":"Alison G","affiliations":[{"id":51369,"text":"Audubon Rockies","active":true,"usgs":false}],"preferred":false,"id":811529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222570,"text":"70222570 - 2021 - Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","interactions":[],"lastModifiedDate":"2021-08-05T12:13:48.433134","indexId":"70222570","displayToPublicDate":"2021-03-06T07:09:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","docAbstract":"We use combined teleseismic and strong motion data sets to investigate finite-fault slip models for a double of earthquakes that occurred on August 11, 2012, in northwestern Iran near the cities of Ahar and Varzaghan. The data include teleseismic P-waveforms retrieved from broadband seismic stations located between 30°–94° from the earthquakes and local strong motion data recorded by the Iran Strong Motion Network, installed and operated by the Building and Housing Research Centre. We first invert teleseismic P-waveforms and local strong motion data separately. For the first event (12:23 UTC), the teleseismic broadband inversion yields a somewhat deeper and simpler distribution of slip than the local strong motion inversion. The strong motion inversion results in a more complex distribution because of higher frequency content but can also be influenced by complexities in the propagation path. For the second event (12:34 UTC), the slip distribution from strong motion data is more similar to the teleseismic result and shows a simple slip area with a small relative movement to the west. To resolve the differences between the results of these two data sets and obtain a better constrained slip model, we perform a joint inversion of teleseismic broadband and local strong motion data.
The joint inversion for the first event shows two asperities with a maximum slip of 3.9 m up- dip from the hypocenter and extending to the west between depths of 1 and 5 km. A second narrower high-slip area is seen just above the hypocenter from 6 to 10 km depth. The total moment for this earthquake is calculated to be Mo = 3.8 × 1025 dyn-cm (3.8 × 1018 N.m) (Mw 6.4). For the second event, the results of the joint inversion show a simple slip distribution that is mainly confined in a single patch around the hypocenter with a depth range from about 10 to 13 km and maximum slip of 1.9 m. We compute a total seismic moment of Mo = 1.6 × 1025 dyn-cm (1.6 × 1018 N.m) (Mw 6.1) for the second event. The largest stress drops for the first event occur above the hypocenter with an average stress drop over the rupture area of 120 bar (12 Mpa). For the second event, the maximum stress drop occurs at the reported focal depth with an average stress drop over the rupture area of 80 bar (8 Mpa).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2021.106688","usgsCitation":"Saltanatpouri, A., Hartzell, S.H., Rahimi, H., Rouhollahi, R., and Amiri Fard, R., 2021, Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data: Physics of the Earth and Planetary Interiors, v. 313, 106688, 15 p., https://doi.org/10.1016/j.pepi.2021.106688.","productDescription":"106688, 15 p.","ipdsId":"IP-124948","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"East Anatolian Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 42.626953125,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.65011210186371\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"313","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saltanatpouri, Atefeh","contributorId":261761,"corporation":false,"usgs":false,"family":"Saltanatpouri","given":"Atefeh","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rahimi, Habib","contributorId":261762,"corporation":false,"usgs":false,"family":"Rahimi","given":"Habib","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouhollahi, Rahmatollah","contributorId":261763,"corporation":false,"usgs":false,"family":"Rouhollahi","given":"Rahmatollah","email":"","affiliations":[{"id":53001,"text":"Babol Noshirvani University of Technology, Babol, Iran","active":true,"usgs":false}],"preferred":false,"id":820606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amiri Fard, Rouholla","contributorId":261764,"corporation":false,"usgs":false,"family":"Amiri Fard","given":"Rouholla","email":"","affiliations":[{"id":53002,"text":"International Institute of Earthquake Engineering and Seismology, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219437,"text":"70219437 - 2021 - Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","interactions":[],"lastModifiedDate":"2021-04-06T11:58:58.749804","indexId":"70219437","displayToPublicDate":"2021-03-06T06:53:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","docAbstract":"Toxic cyanobacterial blooms, often containing multiple toxins, are a serious public health issue. However, there are no known models that predict a cyanotoxin mixture (anatoxin-a, microcystin, saxitoxin). This paper presents two cyanotoxin mixture models (MIX) and compares them to two microcystin (MC) models from data collected in 2016–2017 from three recurring cyanobacterial bloom locations in Kabetogama Lake, Voyageurs National Park (Minnesota, USA). Models include those using near-real-time environmental variables (readily available) and those using additional comprehensive variables (based on laboratory analyses). Comprehensive models (R2 = 0.87 MC; R2 = 0.86 MIX) explained more variability than the environmental models (R2 = 0.58 MC; R2 = 0.57 MIX). Although neither MIX model was a better fit than the MC models, the MIX models produced no false negatives in the calibration dataset, indicating that all observations above regulatory guidelines were simulated by the MIX models. This is the first known use of Virtual Beach software for a cyanotoxin mixture model, and the methods used in this paper may be applicable to other lakes or beaches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125560","usgsCitation":"Christensen, V., Stelzer, E., Eikenberry, B., Olds, H., LeDuc, J.F., Maki, R., Norland, J.E., and Khan, E., 2021, Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk: Journal of Hazardous Materials, v. 415, 125560, 13 p., https://doi.org/10.1016/j.jhazmat.2021.125560.","productDescription":"125560, 13 p.","ipdsId":"IP-123013","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436472,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X7EO1K","text":"USGS data release","linkHelpText":"Data and model archive for multiple linear regression models for prediction of weighted cyanotoxin mixture concentrations and microcystin concentrations at three recurring bloom sites in Kabetogama Lake in Minnesota"},{"id":384883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3453369140625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.21735290928554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eikenberry, Barbara C. Scudder 0000-0001-8058-1201 beikenberry@usgs.gov","orcid":"https://orcid.org/0000-0001-8058-1201","contributorId":172148,"corporation":false,"usgs":true,"family":"Eikenberry","given":"Barbara C. Scudder","email":"beikenberry@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeDuc, Jaime F.","contributorId":190132,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":813552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":813553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":813554,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":813555,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221894,"text":"70221894 - 2021 - Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","interactions":[],"lastModifiedDate":"2021-07-13T18:35:29.258188","indexId":"70221894","displayToPublicDate":"2021-03-05T13:30:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3892,"text":"Ecohydrology & Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","docAbstract":"Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000).
In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 (Huntington and Aiken, 2013).
Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecohyd.2021.02.005","usgsCitation":"Rouhani, S., Schaaf, C.B., Huntington, T., and Choate, J., 2021, Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine: Ecohydrology & Hydrobiology, v. 21, no. 23-24, p. 256-270, https://doi.org/10.1016/j.ecohyd.2021.02.005.","productDescription":"15 p.","startPage":"256","endPage":"270","ipdsId":"IP-106391","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecohyd.2021.02.005","text":"Publisher Index Page"},{"id":387156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.73046875,\n 44.48866833139464\n ],\n [\n -67.576904296875,\n 45.57560020947802\n ],\n [\n -68.5986328125,\n 46.255846818480315\n ],\n [\n -70.15869140625,\n 46.430285240839964\n ],\n [\n -70.37841796875,\n 45.78284835197676\n ],\n [\n -69.43359375,\n 45.874712248904764\n ],\n [\n -69.60937499999999,\n 45.36758436884978\n ],\n [\n -70.11474609375,\n 45.213003555993964\n ],\n [\n -69.345703125,\n 44.6061127451739\n ],\n [\n -68.73046875,\n 44.48866833139464\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"23-24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rouhani, Shabnam","contributorId":260994,"corporation":false,"usgs":false,"family":"Rouhani","given":"Shabnam","email":"","affiliations":[{"id":52735,"text":"University of Massachusetts, Boston, MA","active":true,"usgs":false}],"preferred":false,"id":819233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Janet","contributorId":260995,"corporation":false,"usgs":false,"family":"Choate","given":"Janet","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":819236,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218748,"text":"70218748 - 2021 - Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018)","interactions":[],"lastModifiedDate":"2021-03-29T17:21:00.056125","indexId":"70218748","displayToPublicDate":"2021-03-05T07:55:31","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":273,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":4}},"title":"Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018)","docAbstract":"The National Park of American Samoa (NPSA) was surveyed for landbirds from June through July, 2018. Surveys were conducted using point-transect distance sampling methods to estimate bird densities. This information provides the second datum in the time-series of landbird monitoring for long-term trends in landbird distribution, density, and abundance within NPSA. The Ta‘ū Unit and Tutuila Unit, each on separate islands, were first surveyed in 2011 and we tested for changes in densities between each survey year.","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2284409","usgsCitation":"Judge, S., Camp, R.J., Vaivai, V., and Hart, P.J., 2021, Population density, distribution, and trends of landbirds in the National Park of American Samoa, Ta‘ū and Tutuila Units (2011–2018): Natural Resource Report, viii, 77 p., https://doi.org/10.36967/nrr-2284409.","productDescription":"viii, 77 p.","ipdsId":"IP-120277","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":384274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"American Samoa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.87310791015625,\n -14.386797246182454\n ],\n [\n -170.53253173828125,\n -14.386797246182454\n ],\n [\n -170.53253173828125,\n -14.20914185212544\n ],\n [\n -170.87310791015625,\n -14.20914185212544\n ],\n [\n -170.87310791015625,\n -14.386797246182454\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":811588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":811589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaivai, Visa","contributorId":254982,"corporation":false,"usgs":false,"family":"Vaivai","given":"Visa","affiliations":[{"id":51382,"text":"National Park Service, I&M","active":true,"usgs":false}],"preferred":false,"id":811590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":811591,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218813,"text":"70218813 - 2021 - The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","interactions":[],"lastModifiedDate":"2021-03-15T13:59:10.529639","indexId":"70218813","displayToPublicDate":"2021-03-05T07:53:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","docAbstract":"The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.
Periods of very shallow water (water depth in the order of 10 cm) occur daily on tidal flats because of the propagation of tides over very gently sloping beds, leading to distinct morphodynamical phenomena. To improve the understanding of the characteristics of velocity and suspended sediment concentration (SSC) surges and their contribution to sediment transport and local bed changes during periods of very shallow water, measurements of near-bed flow, and SSC were carried out at two cross-shore locations on an intertidal flat along the Jiangsu coast, China. Furthermore, the role of surges in local resuspension and morphological change was explored. Results indicate that flow and SSC surges occurred at both stations during very shallow water periods. On the lower intertidal flat, flood surges were erosive, while weaker surges on the middle intertidal flat were not. Surges on lower intertidal flats resulted in local resuspension and strong turbidity, contributing up to 25% of the onshore-suspended sediment flux during flood tides, even though they last only 10% of the flood duration. When surges travel across the flats, conditions change from erosional to depositional. Velocity surges on the middle intertidal flat were too weak to resuspend bed sediment, and the associated SSC surges were produced by advection.
Water supplies for millions of U.S. individuals exceed maximum contaminant levels for per- and polyfluoroalkyl substances (PFAS). Contemporary and legacy use of aqueous film forming foams (AFFF) is a major contamination source. However, diverse PFAS sources are present within watersheds, making it difficult to isolate their predominant origins. Here we examine PFAS source signatures among six adjacent coastal watersheds on Cape Cod, MA, U.S.A. using multivariate clustering techniques. A distinct signature of AFFF contamination enriched in precursors with six perfluorinated carbons (C6) was identified in watersheds with an AFFF source, while others were enriched in C4 precursors. Principal component analysis of PFAS composition in impacted watersheds showed a decline in precursor composition relative to AFFF stocks and a corresponding increase in terminal perfluoroalkyl sulfonates with < C6 but not those with ≥ C6. Prior work shows that in AFFF stocks, all extractable organofluorine (EOF) can be explained by targeted PFAS and precursors inferred using Bayesian inference on the total oxidizable precursor assay. Using the same techniques for the first time in impacted watersheds, we find that only 24%–63% of the EOF can be explained by targeted PFAS and oxidizable precursors. Our work thus indicates the presence of large non-AFFF organofluorine sources in these coastal watersheds.
Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w13050713","usgsCitation":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and De Vita, P., 2021, Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments: Water, v. 13, no. 5, 27 p., https://doi.org/10.3390/w13050713.","productDescription":"27 p.","ipdsId":"IP-120315","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13050713","text":"Publisher Index Page"},{"id":384490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Vesuvius","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.327545166015625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.75974059207392\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":812516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812519,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218517,"text":"ofr20201145 - 2021 - Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","interactions":[],"lastModifiedDate":"2021-03-05T12:53:46.034292","indexId":"ofr20201145","displayToPublicDate":"2021-03-04T15:39:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1145","displayTitle":"Estimated Total Phosphorus Loads for Selected Sites on Great Lakes Tributaries, Water Years 2014–2018","title":"Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","docAbstract":"Monthly and annual total phosphorus loads were estimated for water years 2014 through 2018 for 23 streamgaged (gaged) sites on tributaries to the Great Lakes. Processing and regression methods described by Robertson and others (2018) were used with discrete and continuous data collected during water years 2011 and 2018 to update regression models for estimating instantaneous flux with the same form of equations as published by Robertson and others (2018). Monthly and water year average fluxes for all but two of the 23 gage sites were estimated using a weighted combination of results from surrogate models (which have streamflow, turbidity, and seasonal indicators as explanatory variables) and unit-value (UV)-flow models which have only UV streamflow and seasonal indicators as explanatory variables. Two of the gage sites had extensive periods of missing turbidity records, so average flux estimates for those stations were based solely on results from UV-flow models.
For most sites, estimated loads of total phosphorus were computed and summed for water years 2014–2018. The cumulative loads were used to compute yields and flow-weighted mean concentrations for water years 2014–2018. The estimated cumulative total phosphorus loads for water years 2014–2018 ranged from 112 to 11,500 metric tons. The Maumee River site (U.S. Geological Survey gage number 04193500) had the largest estimated cumulative load for water years 2014–2018 and the third largest estimated flow-weighted mean concentration. In fact, the estimated cumulative load at the Maumee River site was more than three times larger than the second largest estimated cumulative load.
Estimated average annual total phosphorus yields and flow-weighted mean concentrations for water years 2014–2018 ranged from 0.016 metric tons per square kilometer to 0.771 metric tons per square kilometer and 0.033 milligram per liter to 0.466 milligram per liter, respectively. The Cattaraugus Creek gage site (U.S. Geological Survey gage number 04213500) had the highest estimated average annual total phosphorus yield and flow-weighted mean concentration. The average annual total phosphorus yield at the Cattaraugus Creek gage site was almost twice as large as the second largest estimated yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201145","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Koltun, G.F., 2021, Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018: U.S. Geological Survey Open-File Report 2020–1145, 13 p., https://doi.org/10.3133/ofr20201145.","productDescription":"Report: v, 13 p.; 2 Appendixes; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122090","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":383717,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145//ofr20201145_appendix_2.csv","text":"Appendix 2","size":"64.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1145/coverthb.jpg"},{"id":383713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1145"},{"id":383714,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.xlsx","text":"Appendix 1","size":"16.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383715,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.csv","text":"Appendix 1","size":"8.45 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383716,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_2.xlsx","text":"Appendix 2","size":"66.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383718,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEW32M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model archive—Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018"}],"country":"United States","state":"Illinois, Indiana, Michigan, 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U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229-1737
Lake charr Salvelinus namaycush life history and population dynamics metrics were reviewed to evaluate populations inside (n = 462) and outside (n = 24) the native range. Our goals were to create a database of metrics useful for evaluating population status and to test for large-scale patterns between metrics and latitude and lake size. An average lake charr grew from a 69-mm length at age-0 (L0) at 89 mm/year early growth rate (ω) to 50% maturity at 420 mm (L50) at age 8 (t50), and then continued to grow toward a 717-mm asymptotic length (L∞). L50 was positively correlated to ω, whereas t50 was inversely correlated to ω. Lake charr grew slower toward larger size and older age in northern latitudes and larger lakes than in southern latitudes and smaller lakes. Population density (number/ha) and yield density (kg/ha) decreased with lake size, and yield and total annual mortality (A) decreased with latitude. Native populations grew slower (ω), were heavier at 500 mm (W500), matured at shorter L50, grew to a shorter L∞, and suffered lower annual mortality A than non-native populations. Our review and database should be useful to managers and researchers for quantifying lake charr population status across the species range.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-62259-6_8","usgsCitation":"Hansen, M.J., Guy, C.S., Bronte, C.R., and Nate, N.A., 2021, Life history and population dynamics, chap. of The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management, p. 253-286, https://doi.org/10.1007/978-3-030-62259-6_8.","productDescription":"34 p.","startPage":"253","endPage":"286","ipdsId":"IP-105836","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":390397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Michael J","contributorId":260100,"corporation":false,"usgs":false,"family":"Hansen","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":824840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":824839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":824841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":824842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}