High-water marks provide valuable data for understanding recent and historical flood events. The proper collection and recording of high-water mark data from perishable and preserved evidence informs flood assessments, research, and water resource management. Given the high cost of flooding in developed areas, experienced hydrographers, using the best available techniques, can contribute high-quality data toward efforts such as public education of flood risk, flood inundation mapping, flood frequency computations, indirect streamflow measurement, and hazard assessments.
This manual presents guidance for skilled high-water mark identification, including marks left behind in natural and man-made environments by tranquil and rapid flowing water. This manual also presents pitfalls and challenges associated with various types of flood evidence that help hydrographers identify the best high-water marks and assess the uncertainty associated with a given mark. Proficient high-water mark data collection contributes to better understanding of the flooding process and reduces risk through greater ability to estimate flood probability.
The U.S. Geological Survey, operating the Nation’s premier water data collection network, encourages readers of this manual to familiarize themselves with the art and science of high-water mark collection. The U.S. Geological survey maintains a national database at http://water.usgs.gov/floods/FEV/ that includes high-water mark information for many flood events, and local U.S. Geological Survey Water Science Centers can provide information to interested readers about participation in data collection and flood documentation efforts as volunteers or observers.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Surface-water techniques in Book 3: Applications of Hydraulics","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm3A24","usgsCitation":"Koenig, T.A., Bruce, J.L., O’Connor, J.E., McGee, B.D., Holmes, R.R., Jr., Hollins, Ryan, Forbes, B.T., Kohn, M.S., Schellekens, M.F., Martin, Z.W., and Peppler, M.C., 2016, Identifying and preserving high-water mark data: U.S. Geological Survey Techniques and Methods, book 3, chap. A24, 47 p., https://dx.doi.org/10.3133/tm3A24.","productDescription":"viii, 47 p.","numberOfPages":"60","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-071434","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":358400,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://www.youtube.com/watch?v=uZYRQLMcVOA","text":"Video","description":"YouTube Video","linkHelpText":"A USGS guide for finding and interpreting high-water marks"},{"id":318665,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a24/coverthb.jpg"},{"id":318666,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a24/tm3a24.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 3–A24"},{"id":318667,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/03/a24/tm3a24_stn_high_water_mark_form.pdf","text":"High-Water Mark Form","size":"283 kB","linkFileType":{"id":1,"text":"pdf"},"description":"High-Water Mark Form"},{"id":346112,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20171105","text":"OFR 2017–1105","description":"OFR 2017–1105"}],"publicComments":"This report is Chapter 24 of Section A: Surface-water techniques in Book 3: Applications of Hydraulics.","contact":"Chief, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/osw/
Common raven (Corvus corax; hereafter, raven) population abundance in the sagebrush steppe of the American West has increased threefold during the previous four decades, largely as a result of unintended resource subsidies from human land-use practices. This is concerning because ravens frequently depredate nests of species of conservation concern, such as greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse). Grazing by livestock in sagebrush ecosystems is common practice on most public lands, but associations between livestock and ravens are poorly understood. The primary objective of this study was to identify the effects of livestock on raven occurrence while accounting for landscape characteristics within human-altered sagebrush steppe habitat, particularly in areas occupied by breeding sage-grouse. Using data from southeastern Idaho collected during spring and summer across 3 yr, we modeled raven occurrence as a function of the presence of livestock while accounting for multiple landscape covariates, including land cover features, topographical features, and proximity to sage-grouse lek sites (breeding grounds), as well as site-level anthropogenic features. While accounting for landscape characteristics, we found that the odds of raven occurrence increased 45.8% in areas where livestock were present. In addition, ravens selected areas near sage-grouse leks, with the odds of occurrence decreasing 8.9% for every 1-km distance, increase away from the lek. We did not find an association between livestock use and distance to lek. We also found that ravens selected sites with relatively lower elevation containing increased amounts of cropland, wet meadow, and urbanization. Limiting raven access to key anthropogenic subsidies and spatially segregating livestock from sage-grouse breeding areas would likely reduce exposure of predatory ravens to sage-grouse nests and chicks.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1203","usgsCitation":"Coates, P.S., Brussee, B.E., Howe, K., Gustafson, K.B., Casazza, M.L., and Delehanty, D., 2016, Landscape characteristics and livestock presence influence common ravens: Relevance to greater sage-grouse conservation: Ecosphere, v. 7, no. 2, https://doi.org/10.1002/ecs2.1203.","productDescription":"e01203; 20 p.","startPage":"e01203","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052300","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1203","text":"Publisher Index Page"},{"id":318663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Oneida County, Power County","city":"Holbrook","otherGeospatial":"Curlew National Grassland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.71560668945311,\n 42.101788731521644\n ],\n [\n -112.71560668945311,\n 42.25495072629938\n ],\n [\n -112.58720397949219,\n 42.25495072629938\n ],\n [\n -112.58720397949219,\n 42.101788731521644\n ],\n [\n -112.71560668945311,\n 42.101788731521644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"56dea628e4b015c306fb51d9","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howe, Kristy khowe@usgs.gov","contributorId":167379,"corporation":false,"usgs":true,"family":"Howe","given":"Kristy","email":"khowe@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delehanty, David J.","contributorId":86683,"corporation":false,"usgs":true,"family":"Delehanty","given":"David J.","affiliations":[],"preferred":false,"id":622099,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169006,"text":"70169006 - 2016 - Variation of energy and carbon fluxes from a restored temperate freshwater wetland and implications for carbon market verification protocols","interactions":[],"lastModifiedDate":"2016-12-16T10:57:16","indexId":"70169006","displayToPublicDate":"2016-03-07T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Variation of energy and carbon fluxes from a restored temperate freshwater wetland and implications for carbon market verification protocols","docAbstract":"Temperate freshwater wetlands are among the most productive terrestrial ecosystems, stimulating interest in using restored wetlands as biological carbon sequestration projects for greenhouse gas reduction programs. In this study, we used the eddy covariance technique to measure surface energy carbon fluxes from a constructed, impounded freshwater wetland during two annual periods that were 8 years apart: 2002–2003 and 2010–2011. During 2010–2011, we measured methane (CH4) fluxes to quantify the annual atmospheric carbon mass balance and its concomitant influence on global warming potential (GWP). Peak growing season fluxes of latent heat and carbon dioxide (CO2) were greater in 2002–2003 compared to 2010–2011. In 2002, the daily net ecosystem exchange reached as low as −10.6 g C m−2 d−1, which was greater than 3 times the magnitude observed in 2010 (−2.9 g C m−2 d−1). CH4 fluxes during 2010–2011 were positive throughout the year and followed a strong seasonal pattern, ranging from 38.1 mg C m−2 d−1 in the winter to 375.9 mg C m−2 d−1 during the summer. The results of this study suggest that the wetland had reduced gross ecosystem productivity in 2010–2011, likely due to the increase in dead plant biomass (standing litter) that inhibited the generation of new vegetation growth. In 2010–2011, there was a net positive GWP (675.3 g C m−2 yr−1), and when these values are evaluated as a sustained flux, the wetland will not reach radiative balance even after 500 years.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Hoboken, NJ","doi":"10.1002/2015JG003083","usgsCitation":"Anderson, F., Bergamaschi, B.A., Sturtevant, C., Knox, S., Hastings, L., Windham-Myers, L., Detto, M., Hestir, E.L., Drexler, J.Z., Miller, R., Matthes, J., Verfaillie, J., Baldocchi, D., Snyder, R.L., and Fujii, R., 2016, Variation of energy and carbon fluxes from a restored temperate freshwater wetland and implications for carbon market verification protocols: Journal of Geophysical Research: Biogeosciences, v. 121, no. 3, p. 777-795, https://doi.org/10.1002/2015JG003083.","productDescription":"19 p.","startPage":"777","endPage":"795","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060580","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jg003083","text":"Publisher Index 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Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%–85% of permafrost carbon release can still be avoided if human emissions are actively reduced.
","language":"English","publisher":"Institute of Physics and IOP Pub.","publisherLocation":"Bristol, U.K.","doi":"10.1088/1748-9326/11/3/034014","usgsCitation":"Abbott, B.W., Jeremy B. Jones, Schuur, E.A., Chapin, F., Bowden, W.B., Bret-Harte, M.S., Epstein, H.E., Flannigan, M.D., Harms, T.K., Hollingsworth, T.N., Mack, M.C., McGuire, A.D., Natali, S.M., Adrian V. 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Limited information has been reported regarding the prevalence, pathogenicity, and genetic diversity of AMPV-4. To assess the intercontinental dispersal of this viral agent, we sequenced the fusion gene of 58 APMV-4 isolates collected in the United States, Japan and the Ukraine and compared them to all available sequences on GenBank. With only a single exception the phylogenetic clades of APMV-4 sequences were monophyletic with respect to their continents of origin (North America, Asia and Europe). Thus, we detected limited evidence for recent intercontinental dispersal of APMV-4 in this study.
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Change in body mass also varied by age–sex cohort and month for all 5 species and by September–January rainfall for all except green-winged teal. The random effect of year nested in period, and sometimes interacting with other factors, improved models in many cases. Results indicate that improved habitat conditions in the Central Valley have resulted in increased winter body mass of dabbling ducks, especially those that feed primarily on seeds, and this increase was greater in regions where area of post-harvest flooding of rice and other crops, and wetland area, has increased. Conservation programs that continue to promote post-harvest flooding and other agricultural practices that benefit wintering waterfowl and continue to restore and conserve wetlands would likely help maintain body condition of wintering dabbling ducks in the Central Valley of California.
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One example is the Mojave desert tortoise (Gopherus agassizii), a species often at the center of conflicts over public land development. For this species and others on public lands, a better understanding of their habitat needs can help minimize negative impacts and facilitate protection or restoration of habitat. We used radio-telemetry to track 46 neonate and juvenile tortoises in the Eastern Mojave Desert, California, USA, to quantify habitat at tortoise locations and paired random points to assess habitat selection. Tortoise locations near burrows were more likely to be under canopy cover and had greater coverage of perennial plants (especially creosote [Larrea tridentata]), more coverage by washes, a greater number of small-mammal burrows, and fewer white bursage (Ambrosia dumosa) than random points. Active tortoise locations away from burrows were closer to washes and perennial plants than were random points. Our results can help planners locate juvenile tortoises and avoid impacts to habitat critical for this life stage. Additionally, our results provide targets for habitat protection and restoration and suggest that diverse and abundant small-mammal populations and the availability of creosote bush are vital for juvenile desert tortoises in the Eastern Mojave Desert.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington D.C.","doi":"10.1002/jwmg.1054","usgsCitation":"Todd, B.D., Halstead, B., Chiquoine, L.P., Peaden, J.M., Buhlmann, K., Tuberville, T.D., and Nafus, A., 2016, Habitat selection by juvenile Mojave Desert tortoises: Journal of Wildlife Management, v. 80, no. 4, p. 720-728, https://doi.org/10.1002/jwmg.1054.","productDescription":"9 p.","startPage":"720","endPage":"728","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070685","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471178,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/0q26w9jk","text":"External Repository"},{"id":319335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eastern Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.43310546875,\n 36.37706783983682\n ],\n [\n -114.6533203125,\n 35.003003395276714\n ],\n [\n -114.60937499999999,\n 34.867904962568716\n ],\n [\n -114.45556640625,\n 34.69646117272349\n ],\n [\n -114.378662109375,\n 34.44315867450577\n ],\n [\n -114.093017578125,\n 34.298068350990846\n ],\n [\n -114.2138671875,\n 34.21634468843465\n ],\n [\n -114.466552734375,\n 34.19817309627726\n ],\n [\n -114.90600585937499,\n 34.243594729697406\n ],\n [\n -115.19165039062501,\n 34.243594729697406\n ],\n [\n -115.224609375,\n 34.016241889667015\n ],\n [\n -115.09277343749999,\n 33.770015152780125\n ],\n [\n -115.30151367187501,\n 33.568861182555565\n ],\n [\n -115.55419921875,\n 33.46810795527896\n ],\n [\n -115.7080078125,\n 33.348884792201694\n ],\n [\n -115.90576171874999,\n 33.44977658311846\n ],\n [\n -116.004638671875,\n 33.53223722395908\n ],\n [\n -116.114501953125,\n 33.53223722395908\n ],\n [\n -116.47705078125,\n 33.87041555094183\n ],\n [\n -116.72973632812499,\n 34.00713506435885\n ],\n [\n -116.993408203125,\n 34.02534773814796\n ],\n [\n -117.35595703124999,\n 34.17090836352573\n ],\n [\n -118.17993164062499,\n 34.17999758688084\n ],\n [\n -118.5205078125,\n 34.334364487026306\n ],\n [\n -118.93798828125,\n 34.84085858477277\n ],\n [\n -118.828125,\n 35.11990857099681\n ],\n [\n -118.114013671875,\n 35.137879119634185\n ],\n [\n -117.94921874999999,\n 35.4159149234562\n ],\n [\n -117.87231445312499,\n 35.7019167328534\n ],\n [\n -117.87231445312499,\n 35.84453450421662\n ],\n [\n -117.94921874999999,\n 35.98689628443789\n ],\n [\n -118.004150390625,\n 36.10237644873644\n ],\n [\n -118.05908203124999,\n 36.29741818650811\n ],\n [\n -118.05908203124999,\n 36.54494944148322\n ],\n [\n -117.80639648437499,\n 36.50963615733049\n ],\n [\n -117.61962890624999,\n 36.33282808737917\n ],\n [\n -117.366943359375,\n 36.34167804918315\n ],\n [\n -117.26806640625,\n 36.500805317604794\n ],\n [\n -117.01538085937499,\n 36.58024660149866\n ],\n [\n -116.64184570312501,\n 36.5184659896759\n ],\n [\n -116.43310546875,\n 36.37706783983682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-06","publicationStatus":"PW","scienceBaseUri":"56f50fc9e4b0f59b85e1eb66","contributors":{"authors":[{"text":"Todd, Brian D","contributorId":167777,"corporation":false,"usgs":false,"family":"Todd","given":"Brian","email":"","middleInitial":"D","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":623472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":623471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chiquoine, Lindsay P.","contributorId":167778,"corporation":false,"usgs":false,"family":"Chiquoine","given":"Lindsay","email":"","middleInitial":"P.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":623473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peaden, J. Mark","contributorId":167779,"corporation":false,"usgs":false,"family":"Peaden","given":"J.","email":"","middleInitial":"Mark","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":623474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhlmann, Kurt A.","contributorId":167780,"corporation":false,"usgs":false,"family":"Buhlmann","given":"Kurt A.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":623475,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tuberville, Tracey D.","contributorId":95823,"corporation":false,"usgs":true,"family":"Tuberville","given":"Tracey","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":623476,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nafus, Aleta","contributorId":167781,"corporation":false,"usgs":false,"family":"Nafus","given":"Aleta","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":623477,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70177902,"text":"70177902 - 2016 - Assessing spring direct mortality to avifauna from wind energy facilities in the Dakotas","interactions":[],"lastModifiedDate":"2016-10-26T12:37:45","indexId":"70177902","displayToPublicDate":"2016-03-06T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spring direct mortality to avifauna from wind energy facilities in the Dakotas","docAbstract":"The Northern Great Plains (NGP) contains much of the remaining temperate grasslands, an ecosystem that is one of the most converted and least protected in the world. Within the NGP, the Prairie Pothole Region (PPR) provides important habitat for >50% of North America's breeding waterfowl and many species of shorebirds, waterbirds, and grassland songbirds. This region also has high wind energy potential, but the effects of wind energy developments on migratory and resident bird and bat populations in the NGP remains understudied. This is troubling considering >2,200 wind turbines are actively generating power in the region and numerous wind energy projects have been proposed for development in the future. Our objectives were to estimate avian and bat fatality rates for wind turbines situated in cropland- and grassland-dominated landscapes, document species at high risk to direct mortality, and assess the influence of habitat variables on waterfowl mortality at 2 wind farms in the NGP. From 10 March to 7 June 2013–2014, we completed 2,398 searches around turbines for carcasses at the Tatanka Wind Farm (TAWF) and the Edgeley-Kulm Wind Farm (EKWF) in South Dakota and North Dakota. During spring, we found 92 turbine-related mortalities comprising 33 species and documented a greater diversity of species (n = 30) killed at TAWF (predominately grassland) than at EKWF (n = 9; predominately agricultural fields). After accounting for detection rates, we estimated spring mortality of 1.86 (SE = 0.22) deaths/megawatt (MW) at TAWF and 2.55 (SE = 0.51) deaths/MW at EKWF. Waterfowl spring (Mar–Jun) fatality rates were 0.79 (SE = 0.11) and 0.91 (SE = 0.10) deaths/MW at TAWF and EKWF, respectively. Our results suggest that future wind facility siting decisions consider avoiding grassland habitats and locate turbines in pre-existing fragmented and converted habitat outside of high densities of breeding waterfowl and major migration corridors.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.1051","usgsCitation":"Graff, B.J., Jenks, J., Stafford, J.D., Jensen, K.C., and Grovenburg, T.W., 2016, Assessing spring direct mortality to avifauna from wind energy facilities in the Dakotas: Journal of Wildlife Management, v. 80, no. 4, p. 736-745, https://doi.org/10.1002/jwmg.1051.","productDescription":"10 p.","startPage":"736","endPage":"745","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066411","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":330409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","county":"Dickey County, LaMoure County, McIntosh County, McPherson County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.876708984375,\n 45.54867850352087\n ],\n [\n -99.876708984375,\n 46.64189395892872\n ],\n [\n -98.0859375,\n 46.64189395892872\n ],\n [\n -98.0859375,\n 45.54867850352087\n ],\n [\n -99.876708984375,\n 45.54867850352087\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-06","publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a79","contributors":{"authors":[{"text":"Graff, Brianna J.","contributorId":176317,"corporation":false,"usgs":false,"family":"Graff","given":"Brianna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenks, Jonathan A.","contributorId":51591,"corporation":false,"usgs":true,"family":"Jenks","given":"Jonathan A.","affiliations":[],"preferred":false,"id":652167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stafford, Joshua D. jstafford@usgs.gov","contributorId":4267,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":652092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jensen, Kent C.","contributorId":66530,"corporation":false,"usgs":false,"family":"Jensen","given":"Kent","email":"","middleInitial":"C.","affiliations":[{"id":16687,"text":"Department of Natural Resource Management, South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":652168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grovenburg, Troy W.","contributorId":57712,"corporation":false,"usgs":true,"family":"Grovenburg","given":"Troy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":652169,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170211,"text":"70170211 - 2016 - Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use","interactions":[],"lastModifiedDate":"2016-08-04T15:35:05","indexId":"70170211","displayToPublicDate":"2016-03-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use","docAbstract":"Shared, trusted, timely data are essential elements for the cooperation needed to optimize economic, ecologic, and public safety concerns related to water. The Open Water Data Initiative (OWDI) will provide a fully scalable platform that can support a wide variety of data from many diverse providers. Many of these will be larger, well-established, and trusted agencies with a history of providing well-documented, standardized, and archive-ready products. However, some potential partners may be smaller, distributed, and relatively unknown or untested as data providers. The data these partners will provide are valuable and can be used to fill in many data gaps, but can also be variable in quality or supplied in nonstandardized formats. They may also reflect the smaller partners' variable budgets and missions, be intermittent, or of unknown provenance. A challenge for the OWDI will be to convey the quality and the contextual “fitness” of data from providers other than the most trusted brands. This article reviews past and current methods for documenting data quality. Three case studies are provided that describe processes and pathways for effective data-sharing and publication initiatives. They also illustrate how partners may work together to find a metadata reporting threshold that encourages participation while maintaining high data integrity. And lastly, potential governance is proposed that may assist smaller partners with short- and long-term participation in the OWDI.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12406","usgsCitation":"Larsen, S., Hamilton, S., Lucido, J., Garner, B.D., and Young, D., 2016, Supporting diverse data providers in the open water data initiative: Communicating water data quality and fitness of use: Journal of the American Water Resources Association, v. 52, no. 4, p. 859-872, https://doi.org/10.1111/1752-1688.12406.","productDescription":"14 p.","startPage":"859","endPage":"872","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069137","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":471179,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12406","text":"Publisher Index Page"},{"id":320019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-06","publicationStatus":"PW","scienceBaseUri":"570f6dbde4b0ef3b7ca356aa","contributors":{"authors":[{"text":"Larsen, Sara","contributorId":168563,"corporation":false,"usgs":false,"family":"Larsen","given":"Sara","email":"","affiliations":[{"id":25336,"text":"Western States Water Council","active":true,"usgs":false}],"preferred":false,"id":626478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamilton, Stuart","contributorId":168564,"corporation":false,"usgs":false,"family":"Hamilton","given":"Stuart","affiliations":[{"id":25337,"text":"Aquatic Informatics","active":true,"usgs":false}],"preferred":false,"id":626479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":626477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garner, Bradley D. 0000-0002-6912-5093 bdgarner@usgs.gov","orcid":"https://orcid.org/0000-0002-6912-5093","contributorId":2133,"corporation":false,"usgs":true,"family":"Garner","given":"Bradley","email":"bdgarner@usgs.gov","middleInitial":"D.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":626480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Dwane","contributorId":168541,"corporation":false,"usgs":false,"family":"Young","given":"Dwane","affiliations":[{"id":25326,"text":"U.S. Environmental Protection Agency, 1200 Pennsylvania Ave., NW, Washington, DC, USA 20460","active":true,"usgs":false}],"preferred":false,"id":626481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177886,"text":"70177886 - 2016 - Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","interactions":[],"lastModifiedDate":"2017-01-17T19:17:22","indexId":"70177886","displayToPublicDate":"2016-03-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","docAbstract":"Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (ETo); and parameters, differential temperature (dT), and maximum ET scalar (Kmax), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (ETo and LST) and two key parameters (Kmax and dT).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.02.026","usgsCitation":"Chen, M., Senay, G.B., Singh, R.K., and Verdin, J.P., 2016, Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites: Journal of Hydrology, v. 536, p. 384-399, https://doi.org/10.1016/j.jhydrol.2016.02.026.","productDescription":"16 p.","startPage":"384","endPage":"399","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071555","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.02.026","text":"Publisher Index Page"},{"id":330417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"536","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7b","chorus":{"doi":"10.1016/j.jhydrol.2016.02.026","url":"http://dx.doi.org/10.1016/j.jhydrol.2016.02.026","publisher":"Elsevier BV","authors":"Chen Mingshi, Senay Gabriel B., Singh Ramesh K., Verdin James P.","journalName":"Journal of Hydrology","publicationDate":"5/2016","auditedOn":"4/1/2016","publiclyAccessibleDate":"2/23/2016"},"contributors":{"authors":[{"text":"Chen, Mingshi mchen@usgs.gov","contributorId":4204,"corporation":false,"usgs":true,"family":"Chen","given":"Mingshi","email":"mchen@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":652237,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","interactions":[{"subject":{"id":70135103,"text":"ds892 - 2014 - DOI/GTN-P climate and active-layer data acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge","indexId":"ds892","publicationYear":"2014","noYear":false,"title":"DOI/GTN-P climate and active-layer data acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge"},"predicate":"SUPERSEDED_BY","object":{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","indexId":"ds977","publicationYear":"2016","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014"},"id":1},{"subject":{"id":70168397,"text":"ds977 - 2016 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","indexId":"ds977","publicationYear":"2016","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014"},"predicate":"SUPERSEDED_BY","object":{"id":70176575,"text":"ds1021 - 2017 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015","indexId":"ds1021","publicationYear":"2017","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015"},"id":2}],"supersededBy":{"id":70176575,"text":"ds1021 - 2017 - DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015","indexId":"ds1021","publicationYear":"2017","noYear":false,"title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2015"},"lastModifiedDate":"2017-02-09T12:51:41","indexId":"ds977","displayToPublicDate":"2016-03-04T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"977","title":"DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014","docAbstract":"This report provides data collected by the climate monitoring array of the U.S. Department of the Interior on Federal lands in Arctic Alaska over the period August 1998 to July 2014; this array is part of the Global Terrestrial Network for Permafrost (DOI/GTN-P). In addition to presenting data, this report also describes monitoring, data collection, and quality-control methods. The array of 16 monitoring stations spans lat 68.5°N. to 70.5°N. and long 142.5°W. to 161°W., an area of approximately 150,000 square kilometers. Climate summaries are presented along with quality-controlled data. Data collection is ongoing and includes the following climate- and permafrost-related variables: air temperature, wind speed and direction, ground temperature, soil moisture, snow depth, rainfall totals, up- and downwelling shortwave radiation, and atmospheric pressure. These data were collected by the U.S. Geological Survey in close collaboration with the Bureau of Land Management and the U.S. Fish and Wildlife Service.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds977","usgsCitation":"Urban, F.E., and Clow, G.D., 2016, DOI/GTN-P Climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2014: U.S. Geological Survey Data Series 977, https://dx.doi.org/10.3133/ds977.","productDescription":"HTML Document; 17 chapters","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069148","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":318058,"rank":19,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Ikpikpuk/Ikpikpuk.html","text":"Ikpikpuk","description":"DS 977 Ikpikpuk"},{"id":318055,"rank":16,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/DrewPoint/DrewPoint.html","text":"Drew Point","description":"DS 977 Drew Point"},{"id":318045,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Lake145/Lake145.html","text":"Lake 145","description":"DS 977 Lake 145"},{"id":318054,"rank":15,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/CamdenBay/CamdenBay.html","text":"Camden Bay","description":"DS 977 Camden Bay"},{"id":318047,"rank":9,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Niguanak/Niguanak.html","text":"Niguanak","description":"DS 977 Niguanak"},{"id":318051,"rank":13,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Awuna1/Awuna1.html","text":" Awuna1","description":"DS 977 Awuna1"},{"id":318056,"rank":17,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/EastTeshekpuk/EastTeshekpuk.html","text":"East Teshekpuk","description":"DS 977 East Teshekpuk"},{"id":318011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0977/images/coverthb.jpg"},{"id":318042,"rank":5,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Inigok/Inigok.html","text":"Inigok","description":"DS 977 Inigok"},{"id":318046,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/MarshCreek/MarshCreek.html","text":"Marsh Creek","description":"DS 977 Marsh Creek"},{"id":318048,"rank":10,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Piksiksak/Piksiksak.html","text":"Piksiksak","description":"DS 977 Piksiksak"},{"id":318049,"rank":11,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/RedSheepCreek/RedSheepCreek.html","text":"Red Sheep Creek","description":"DS 977 Red Sheep Creek"},{"id":318043,"rank":6,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Koluktak/Koluktak.html","text":"Koluktak","description":"DS 977 Koluktak"},{"id":318053,"rank":14,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Awuna2/Awuna2.html","text":" Awuna2","description":"DS 977 Awuna2"},{"id":318057,"rank":18,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/FishCreek/FishCreek.html","text":"Fish Creek","description":"DS 977 Fish Creek"},{"id":318050,"rank":12,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/SouthMeade/SouthMeade.html","text":"South Meade","description":"DS 977 South Meade"},{"id":318012,"rank":2,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/introduction.html","text":"Introduction","description":"DS 977 Introduction"},{"id":318013,"rank":3,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Tunalik/Tunalik.html","text":"Tunalik","description":"DS 977 Tunalik"},{"id":318014,"rank":4,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/ds/0977/Umiat/Umiat.html","text":"Umiat","description":"DS 977 Umiat"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.037109375,\n 66.08936427047088\n ],\n [\n -163.037109375,\n 71.66366293141732\n ],\n [\n -140.712890625,\n 71.66366293141732\n ],\n [\n -140.712890625,\n 66.08936427047088\n ],\n [\n -163.037109375,\n 66.08936427047088\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
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Box 25046, Mail Stop 980
Denver, CO 80225
Mangrove forests grow on saline, periodically flooded soils of the tropical and subtropical coasts. The tree species that comprise the mangrove are halophytes that have suites of traits that confer differing levels of tolerance of salinity, aridity, inundation and extremes of temperature. Here we review how climate change and elevated levels of atmospheric CO2 will influence mangrove forests. Tolerance of salinity and inundation in mangroves is associated with the efficient use of water for photosynthetic carbon gain which unpins anticipated gains in productivity with increasing levels of CO2. We review evidence of increases in productivity with increasing CO2, finding that enhancements in growth appear to be similar to trees in non-mangrove habitats and that gains in productivity with elevated CO2 are likely due to changes in biomass allocation. High levels of trait plasticity are observed in some mangrove species, which potentially facilitates their responses to climate change. Trait plasticity is associated with broad tolerance of salinity, aridity, low temperatures and nutrient availability. Because low temperatures and aridity place strong limits on mangrove growth at the edge of their current distribution, increasing temperatures over time and changing rainfall patterns are likely to have an important influence on the distribution of mangroves. We provide a global analysis based on plant traits and IPCC scenarios of changing temperature and aridity that indicates substantial global potential for mangrove expansion.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Tree physiology: Adaptations and responses in a changing environment","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-27422-5_7","usgsCitation":"Lovelock, C.E., Krauss, K.W., Osland, M.J., Reef, R., and Ball, M.C., 2016, The physiology of mangrove trees with changing climate, chap. of Tree physiology: Adaptations and responses in a changing environment, v. 6, p. 149-179, https://doi.org/10.1007/978-3-319-27422-5_7.","productDescription":"31 p.","startPage":"149","endPage":"179","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060844","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":319976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"570e1c37e4b0ef3b7ca24c4c","contributors":{"editors":[{"text":"Meinzer, Frederick C.","contributorId":168571,"corporation":false,"usgs":false,"family":"Meinzer","given":"Frederick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":626533,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Niinemets, Ulo","contributorId":168572,"corporation":false,"usgs":false,"family":"Niinemets","given":"Ulo","email":"","affiliations":[],"preferred":false,"id":626534,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Lovelock, Catherine E.","contributorId":64787,"corporation":false,"usgs":true,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":626520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":626518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osland, Michael J. 0000-0001-9902-8692 mosland@usgs.gov","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":3080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","email":"mosland@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":626519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reef, Ruth","contributorId":44826,"corporation":false,"usgs":true,"family":"Reef","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":626521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ball, Marilyn C.","contributorId":7981,"corporation":false,"usgs":true,"family":"Ball","given":"Marilyn","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":626522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70164631,"text":"sir20165020 - 2016 - Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013","interactions":[],"lastModifiedDate":"2016-03-09T17:48:45","indexId":"sir20165020","displayToPublicDate":"2016-03-03T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5020","title":"Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013","docAbstract":"The Upper Black Squirrel Creek Basin is located about 25 kilometers east of Colorado Springs, Colorado. The primary aquifer is a productive section of unconsolidated deposits that overlies bedrock units of the Denver Basin and is a critical resource for local water needs, including irrigation, domestic, and commercial use. The primary aquifer also serves an important regional role by the export of water to nearby communities in the Colorado Springs area. Changes in land use and development over the last decade, which includes substantial growth of subdivisions in the Upper Black Squirrel Creek Basin, have led to uncertainty regarding the potential effects to water quality throughout the basin. In response, the U.S. Geological Survey, in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, and Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County conducted an assessment of groundwater quality and groundwater age with an emphasis on characterizing nitrate in the groundwater.
\nGroundwater-quality samples were collected from 50 randomly selected wells between May and June 2013. The samples were analyzed for major ions, nutrients, dissolved gases, tritium (3H), chlorofluorocarbons (CFC-11, CFC-12, and CFC-113), and fuel products (such as benzene, toluene, ethylbenzene, and xylenes). None of the groundwater samples exceeded the U.S. Environmental Protection Agency (EPA) National Primary Drinking Water Regulations for primary maximum contaminant levels (MCL) for major ions. Secondary maximum contaminant levels, which are not health concerns and affect mainly taste, color, or odor of the water, were observed in rare instances for pH (2 samples), chloride (1 sample), iron (3 samples), and manganese (8 samples). The secondary maximum contaminant level for total dissolved solids was also exceeded for two samples.
\nNitrate (nitrite plus nitrate as nitrogen in groundwater) was elevated above the estimated background concentration of natural recharge waters of 1 milligram per liter (mg/L) in 44 of the 50 wells sampled and showed a median concentration of 5.4 mg/L. Nitrate concentrations were above the MCL of 10 mg/L in 5 of the 50 wells sampled and above half of the EPA MCL (5 mg/L) in 27 of the 50 wells sampled, which included samples above the MCL. Dissolved-oxygen concentrations exceeded 0.5 mg/L in 95 percent of reported values (40 of 42 samples) and exceeded 2.0 mg/L in 90 percent of reported values (38 of 42 samples). The oxidized conditions observed in most areas indicate that nitrate from fertilizers and animal or human waste was geochemically stable and could persist in the groundwater for decades or perhaps longer. A historical analysis of median nitrate concentrations over nearly three decades showed an increase in nitrate of approximately 1 mg/L from 4.3 to 5.4 mg/L, although the increase was not determined to be significantly different using nonparametric statistical methods.
\nMajor-ion data indicate that groundwater representative of the primary aquifer was classified as calcium-sodium bicarbonate type water. Other water samples from wells located mainly along the periphery of the primary aquifer had cation-anion compositions consistent with distinct water sources, including groundwater contributions from the underlying bedrock aquifers. The areas with differentiable water sources were located mainly where alluvial deposits were thin and geologic contacts to the underlying bedrock aquifers were relatively shallow.
\nNitrate concentrations in the groundwater were evaluated for relations to land use. An agricultural region was defined using a sequence of land satellite imagery. Groundwater flow directions interpreted from median water-table elevations measured from 2000 to 2013 were used in conjunction with cropland locations to define the agricultural region boundaries by encompassing potential pathways of nitrate transport in the groundwater from nitrogen-based fertilizers. A statistically significant higher median nitrate concentration was observed for areas inside the agricultural region (6.7 mg/L) compared to areas outside the agricultural region (2.3 mg/L), although median concentrations in both areas were below the MCL (10 mg/L). Median nitrate concentration was also significantly greater in land parcels with septic use (4.9 mg/L) compared to nonseptic parcels (1.7 mg/L). In general, agriculture or septic use was identified as the primary source of nitrate, depending on location, while commercial, county, grazing, and residential land uses were generally secondary sources of nitrate.
\nApparent groundwater ages were estimated from chlorofluorocarbons (CFC-11, CFC-12, and CFC-113) and tritium (3H) data using models that assumed piston flow and binary mixing (dilution of a young component with old, tracer-free water). The mean and median groundwater ages were about 30 years and the standard deviation was 6 years, indicating that most groundwater in the primary aquifer was “young” water that had recharged to the aquifer over the last few decades (post-1950s). The median fraction of young water was about 71 percent, and the standard deviation was 29 percent. The remaining water predated the 1950s, which may have originated from deeper geologic formations or may represent slow moving groundwater within the primary aquifer. Some of the oldest groundwater ages (older than 30 years) were observed in the upper reaches of the aquifer to the northwest where the primary aquifer is thin and intersects bedrock, supporting the hypothesis of geochemically distinct groundwater entering the primary aquifer from below. Groundwater that had reached the central part of the aquifer from upgradient areas of the basin was variable in age because of differences in flow paths and travel velocities. The groundwater age analysis showed that current (2013) land-use practices could affect water quality over decades to come, and that responses to remedial actions could be slow, especially for constituents, such as nitrate, that are stable under oxidized conditions.
\nFuel products (including acetone, benzene, diisopropyl ether, ethylbenzene, methyl acetate, methyl tertiary butyl ether (MTBE), methyl tert-pentyl ether, m- + p-xylene, o-xylene, tert-amyl alcohol, tert-butyl alcohol, tert-butyl ethyl ether, and toluene) were analyzed in groundwater from 49 of the 50 wells. Water from seven sites had detections for fuel compounds; all concentrations were below MCL. The results provided assurance of water quality and a valuable baseline to evaluate future trends of fuel constituents as the region is further developed.
\nProbability maps were developed from logistic regression models to examine the likelihood that nitrate concentrations in groundwater exceeded specified levels. Susceptibility analysis examined relations between mid-level (5.0 mg/L) nitrate concentrations and climatic, hydrologic, and geologic variables; the significant variables were identified as depth to groundwater, soil organic matter, and soil water storage to 25-centimeter (cm) depth. The vulnerability assessments included natural factors driving susceptibility but also human factors related to land use and septic use. Vulnerability to low-level (2.5 mg/L) nitrate was related to depth to groundwater, septic zoning, and soil organic matter. The results highlighted that septic zoning affected low-level nitrate concentrations. Vulnerability to mid-level (5.0 mg/L) nitrate was examined using all 50 samples and also with two data outliers removed, which showed relatively high nitrate concentrations but also anomalous water chemistry or were located beyond the primary study area. Vulnerability to mid-level (5.0 mg/L) nitrate using all 50 samples was related to depth to groundwater, land use, septic use within a 500-meter (m) radius, soil water storage to a 25-cm depth, soil organic matter, and whether a location was within the agricultural region. The mid-level (5.0 mg/L) vulnerability model using 48 samples (two outliers removed) produced the best overall fit and was related to the same variables as when using all samples except septic use. The results for mid-level vulnerability provided additional support that septic use was associated with low levels of nitrate in the groundwater. Soil properties and land use were identified as the main drivers of moderate nitrate concentrations. Probabilities of exceeding low-level nitrate concentrations were high in most areas with the lowest probabilities usually to the northwest along thin geologic deposits in the upper part of the basin.
\nThe results of this investigation offer the foundational information needed for developing best management practices to mitigate nitrate contamination, basic concepts on water quality to aid public education, and information to guide regulatory measures if policy makers determine this is warranted. Science-based decision making will require continued monitoring and analysis of water quality in the future.
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Box 25046, Mail Stop 415
Denver, CO 80225
The Sauvie Island 7.5' quadrangle is situated in the Puget-Willamette Lowland northwest of downtown Portland, Oreg. This lowland, which extends from Puget Sound to west-central Oregon, is a complex structural and topographic trough between the Coast Range and the Cascade Range. Since late Eocene time, the Cascade Range has been the locus of a discontinuously active volcanic arc associated with underthrusting of oceanic lithosphere beneath the North American continent along the Cascadia Subduction Zone. The Coast Range, which occupies the fore-arc position within the Cascadia arc-trench system, consists of a complex assemblage of Eocene to Miocene volcanic and marine sedimentary rocks.
\nThe Sauvie Island quadrangle lies along the southwest margin of the Portland Basin, a 2,000-km2 topographic and structural depression. The basin boundary is an abrupt topographic break at the base of the Tualatin Mountains, which separates the Portland and Tualatin Basins. The Tualatin Mountains are underlain by lava flows of the Miocene Columbia River Basalt Group that have been folded into an asymmetric anticline. Oligocene marine sedimentary rocks, not exposed at the surface, are inferred to underlie the basalt flows. The abrupt basin boundary marks the location of the northwest-striking Portland Hills Fault Zone, which is probably an active structure.
\nThe Columbia River flows west and north through the Portland Basin at nearly sea level. The Willamette River enters the Columbia near the southeast corner of the map area. Seismic-reflection profiles and lithologic logs of water wells show as much as 550 m of late Miocene and younger sediments in the deepest part of the basin east of the quadrangle. Deposits exposed at the surface consist chiefly of Holocene and late Pleistocene fluvial and eolian sediments and man-made fill.
\nThis map contributes to a U.S. Geological Survey program to improve the geologic database for the Portland region of the Pacific Northwest urban corridor. The map and ancillary data will support assessments of seismic risk, ground-failure hazards, and resource availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3349","usgsCitation":"Evarts, R.C., O'Connor, J.E., and Cannon, C.M., 2016, Geologic map of the Sauvie Island quadrangle, Multnomah and Columbia Counties, Oregon, and Clark County, Washington: U.S. Geological Survey Scientific Investigations Map 3349, scale 1:24,000, pamphlet 34 p., https://dx.doi.org/10.3133/sim3349.","productDescription":"Pamphlet: iv, 34 p.; 1 Plate: 40.00 x 34.00 inches; Database; Metadata; Read Me; Shape Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-049408","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":318448,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_db.zip","size":"4.8 MB","linkFileType":{"id":6,"text":"zip"}},{"id":318443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3349/coverthb.jpg"},{"id":318449,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3349/metadata/"},{"id":318447,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_shp.zip","text":"Shape Files","size":"3.3 MB","linkFileType":{"id":6,"text":"zip"}},{"id":318445,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_pamphlet.pdf","text":"Pamphlet","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3349 Pamphlet PDF"},{"id":318446,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_readme.pdf","size":"177 KB","linkFileType":{"id":2,"text":"txt"}},{"id":318444,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3349/sim3349_sheet1.pdf","text":"Sheet 1","size":"74 MB","description":"SIM 3349 Map PDF"},{"id":399013,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104040.htm"}],"scale":"24000","country":"United States","state":"Oregon, Washington","county":"Clark County, Columbia County, Multnomah County","otherGeospatial":"Sauvie Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.875,\n 45.625\n ],\n [\n -122.875,\n 45.75\n ],\n [\n -122.75,\n 45.75\n ],\n [\n -122.75,\n 45.625\n ],\n [\n -122.875,\n 45.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
http://geomaps.wr.usgs.gov/gmeg/
The Landscape Conservation Cooperatives (LCCs) are a network of partnerships throughout North America that are tasked with integrating science and management to support more effective delivery of conservation at a landscape scale. In order to achieve this integration, some LCCs have adopted the approach of providing their partners with better scientific information in an effort to facilitate more effective and coordinated conservation decisions. Taking this approach has led many LCCs to begin funding research to provide the information for improved decision making. To ensure that funding goes to research projects with the highest likelihood of leading to more integrated broad scale conservation, some LCCs have also developed approaches for prioritizing which information needs will be of most benefit to their partnerships. We describe two case studies in which decision analytic tools were used to quantitatively assess the relative importance of information for decisions made by partners in the Plains and Prairie Potholes LCC. The results of the case studies point toward a few valuable lessons in terms of using these tools with LCCs. Decision analytic tools tend to help shift focus away from research oriented discussions and toward discussions about how information is used in making better decisions. However, many technical experts do not have enough knowledge about decision making contexts to fully inform the latter type of discussion. When assessed in the right decision context, however, decision analyses can point out where uncertainties actually affect optimal decisions and where they do not. This helps technical experts understand that not all research is valuable in improving decision making. But perhaps most importantly, our results suggest that decision analytic tools may be more useful for LCCs as way of developing integrated objectives for coordinating partner decisions across the landscape, rather than simply ranking research priorities.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"Washington, D.C.","doi":"10.3996/032015-JFWM-023","usgsCitation":"Post van der Burg, M., Cullinane Thomas, C., Holcombe, T.R., and Nelson, R., 2016, Benefits and limitations of using decision analytic tools to assess uncertainty and prioritize Landscape Conservation Cooperative information needs: Journal of Fish and Wildlife Management, v. 7, no. 1, p. 280-290, https://doi.org/10.3996/032015-JFWM-023.","productDescription":"11 p.","startPage":"280","endPage":"290","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064346","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/032015-jfwm-023","text":"Publisher Index Page"},{"id":319622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-01","publicationStatus":"PW","scienceBaseUri":"56fceacae4b0a6037df29cdf","contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cullinane Thomas, Catherine 0000-0001-8168-1271 ccullinanethomas@usgs.gov","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":141097,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine","email":"ccullinanethomas@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":625612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":625613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Richard D.","contributorId":55338,"corporation":false,"usgs":true,"family":"Nelson","given":"Richard D.","affiliations":[],"preferred":false,"id":625614,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160030,"text":"cir1419 - 2016 - Get your science used—Six guidelines to improve your products","interactions":[],"lastModifiedDate":"2016-03-04T08:27:06","indexId":"cir1419","displayToPublicDate":"2016-03-03T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1419","title":"Get your science used—Six guidelines to improve your products","docAbstract":"Natural scientists, like many other experts, face challenges when communicating to people outside their fields of expertise. This is especially true when they try to communicate to those whose background, knowledge, and experience are far distant from that field of expertise.
\nAt a recent workshop, experts in risk communication offered insights into the communication challenges of probabilistic hazard products, suggested tips, and shared their strategies for making products that a targeted audience can understand and use. Although the workshop was held to broaden the understanding and use of the U.S. Geological Survey (USGS) National Seismic Hazard Maps (NSHM), the workshop outcomes presented in this report can benefit anyone who develops products based on technical information.
\nOn the basis of research and practice into how people think, use tools, make decisions, and understand scientific/technical information, the social and behavioral scientists, marketers, and social-impact designers at the NSHM workshop provided remarkably consistent guidelines to develop successful science products, such as text, maps, and other products. In this report the guidelines are numbered for clarity, but they can be applied repeatedly, piecemeal, or out of order to fit each project and your resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1419","usgsCitation":"Perry, S.C., Blanpied, M.L., Burkett, E.R., Campbell, N.M., Carlson, A., Cox, D.A., Driedger, C.L., Eisenman, D.P., Fox-Glassman, K.T., Hoffman, S., Hoffman, S.M., Jaiswal, K.S., Jones, L.M., Luco, N., Marx, S.M., McGowan, S.M., Mileti, D.S., Moschetti, M.P., Ozman, D., Pastor, E., Petersen, M.D., Porter, K.A., Ramsey, D.W., Ritchie, L.A., Fitzpatrick, J.K., Rukstales, K.S., Sellnow, T.S., Vaughon, W.L., Wald, D.J., Wald, L.A., Wein, A., and Zarcadoolas, C., 2016, Get your science used—Six guidelines to improve your products: U.S. Geological Survey Circular 1419, 37 p., https://dx.doi.org/10.3133/cir1419.","productDescription":"iv, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064533","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":318527,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1419/coverthb.jpg"},{"id":318528,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1419/c1419.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1419"}],"contact":"Staff, Science Application for Risk Reduction (SAFRR)
U.S. Geological Survey
525 S. Wilson Avenue
Pasadena, CA 91106
http://www.usgs.gov/natural_hazards/safrr/
The invasion by winter-annual grasses (AGs) such as Bromus tectorum into sagebrush steppe throughout the western USA is a classic example of a biological invasion with multiple, interacting climate, soil and biotic factors driving the invasion, although few studies have examined all components together. Across a 6000-km2 area of the northern Great Basin, we conducted a field assessment of 100 climate, soil, and biotic (functional group abundances, diversity) factors at each of 90 sites that spanned an invasion gradient ranging from 0 to 100 % AG cover. We first determined which biotic and abiotic factors had the strongest correlative relationships with AGs and each resident functional group. We then used regression and structural equation modeling to explore how multiple ecological factors interact to influence AG abundance. Among biotic interactions, we observed negative relationships between AGs and biodiversity, perennial grass cover, resident species richness, biological soil crust cover and shrub density, whereas perennial and annual forb cover, tree cover and soil microbial biomass had no direct linkage to AG. Among abiotic factors, AG cover was strongly related to climate (increasing cover with increasing temperature and aridity), but had weak relationships with soil factors. Our structural equation model showed negative effects of perennial grasses and biodiversity on AG cover while integrating the negative effects of warmer climate and positive influence of belowground processes on resident functional groups. Our findings illustrate the relative importance of biotic interactions and climate on invasive abundance, while soil properties appear to have stronger relationships with resident biota than with invasives.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-016-3583-8","usgsCitation":"Bansal, S., and Sheley, R.L., 2016, Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions: Oecologia, v. 181, no. 2, p. 543-557, https://doi.org/10.1007/s00442-016-3583-8.","productDescription":"15 p.","startPage":"543","endPage":"557","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070148","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.53125,\n 43.14909399920127\n ],\n [\n -119.53125,\n 43.77109381775651\n ],\n [\n -118.5205078125,\n 43.77109381775651\n ],\n [\n -118.5205078125,\n 43.14909399920127\n ],\n [\n -119.53125,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"181","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"56d9602ce4b015c306f726b4","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":621776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheley, Roger L.","contributorId":167296,"corporation":false,"usgs":false,"family":"Sheley","given":"Roger","email":"","middleInitial":"L.","affiliations":[{"id":24676,"text":"USDA-ARS, Burns Oregon","active":true,"usgs":false}],"preferred":false,"id":621777,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168792,"text":"70168792 - 2016 - Quantitative framework for preferential flow initiation and partitioning","interactions":[],"lastModifiedDate":"2016-03-03T10:38:45","indexId":"70168792","displayToPublicDate":"2016-03-03T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative framework for preferential flow initiation and partitioning","docAbstract":"A model for preferential flow in macropores is based on the short-range spatial distribution of soil matrix infiltrability. It uses elementary areas at two different scales. One is the traditional representative elementary area (REA), which includes a sufficient heterogeneity to typify larger areas, as for measuring field-scale infiltrability. The other, called an elementary matrix area (EMA), is smaller, but large enough to represent the local infiltrability of soil matrix material, between macropores. When water is applied to the land surface, each EMA absorbs water up to the rate of its matrix infiltrability. Excess water flows into a macropore, becoming preferential flow. The land surface then can be represented by a mesoscale (EMA-scale) distribution of matrix infiltrabilities. Total preferential flow at a given depth is the sum of contributions from all EMAs. Applying the model, one case study with multi-year field measurements of both preferential and diffuse fluxes at a specific depth was used to obtain parameter values by inverse calculation. The results quantify the preferential–diffuse partition of flow from individual storms that differed in rainfall amount, intensity, antecedent soil water, and other factors. Another case study provided measured values of matrix infiltrability to estimate parameter values for comparison and illustrative predictions. These examples give a self-consistent picture from the combination of parameter values, directions of sensitivities, and magnitudes of differences caused by different variables. One major practical use of this model is to calculate the dependence of preferential flow on climate-related factors, such as varying soil wetness and rainfall intensity.
","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2015.05.0079","usgsCitation":"Nimmo, J.R., 2016, Quantitative framework for preferential flow initiation and partitioning: Vadose Zone Journal, v. 15, no. 2, https://doi.org/10.2136/vzj2015.05.0079.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069590","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":318537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-08","publicationStatus":"PW","scienceBaseUri":"56d96034e4b015c306f726de","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":621772,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70168798,"text":"70168798 - 2016 - A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags","interactions":[],"lastModifiedDate":"2017-01-12T11:06:16","indexId":"70168798","displayToPublicDate":"2016-03-03T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags","docAbstract":"Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.1727","usgsCitation":"Letcher, B., Hocking, D., O'Neil, K., Whiteley, A.R., Nislow, K., and O’Donnell, M., 2016, A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags: PeerJ, v. 4, e1727: 26 p., https://doi.org/10.7717/peerj.1727.","productDescription":"e1727: 26 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072906","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":471182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.1727","text":"Publisher Index Page"},{"id":318531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-29","publicationStatus":"PW","scienceBaseUri":"56d96027e4b015c306f726ad","contributors":{"authors":[{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":621791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hocking, Daniel 0000-0003-1889-9184 dhocking@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-9184","contributorId":149618,"corporation":false,"usgs":true,"family":"Hocking","given":"Daniel","email":"dhocking@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neil, Kyle","contributorId":82491,"corporation":false,"usgs":true,"family":"O'Neil","given":"Kyle","affiliations":[],"preferred":false,"id":621793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":621794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nislow, Keith H.","contributorId":60106,"corporation":false,"usgs":true,"family":"Nislow","given":"Keith H.","affiliations":[],"preferred":false,"id":621795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174238,"text":"70174238 - 2016 - Mountain building on Io driven by deep faulting","interactions":[],"lastModifiedDate":"2016-07-06T14:10:22","indexId":"70174238","displayToPublicDate":"2016-03-03T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Mountain building on Io driven by deep faulting","docAbstract":"Jupiter’s volcanic moon Io possesses some of the highest relief in the Solar System: massive, isolated mountain blocks that tower up to 17 km above the surrounding plains. These mountains are likely to result from pervasive compressive stresses induced by subsidence of the surface beneath the near-continual emplacement of volcanic material. The stress state that results from subsidence and warming of Io’s lithosphere has been investigated in detail1, 2, 3, 4; however, the mechanism of orogenesis itself and its effect on regional tectonism and volcanism has not been firmly established. Here we present viscoelastic–plastic finite element simulations demonstrating that Io’s mountains form along deep-seated thrust faults that initiate at the base of the lithosphere and propagate upward. We show that faulting fundamentally alters the stress state of Io’s lithosphere by relieving the large volcanism-induced subsidence stresses. Notably, in the upper portion of the lithosphere, stresses become tensile (near-zero differential stress). A number of processes are therefore altered post-faulting, including magma transport through the lithosphere, interactions with tidal stresses and potentially the localization of mountain formation by thermoelastic stresses. We conclude that Io’s mountains form by a unique orogenic mechanism, compared with tectonic processes operating elsewhere in the Solar System.
","language":"English","publisher":"Nature Pub. Group","doi":"10.1038/ngeo2711","usgsCitation":"Bland, M.T., and McKinnon, W., 2016, Mountain building on Io driven by deep faulting: Nature Geoscience, v. 9, p. 429-432, https://doi.org/10.1038/ngeo2711.","productDescription":"4 p.","startPage":"429","endPage":"432","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068183","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":324762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-16","publicationStatus":"PW","scienceBaseUri":"577e2bb1e4b0ef4d2f445a2d","contributors":{"authors":[{"text":"Bland, Michael T. 0000-0001-5543-1519 mbland@usgs.gov","orcid":"https://orcid.org/0000-0001-5543-1519","contributorId":146287,"corporation":false,"usgs":true,"family":"Bland","given":"Michael","email":"mbland@usgs.gov","middleInitial":"T.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":641566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKinnon, William B.","contributorId":146288,"corporation":false,"usgs":false,"family":"McKinnon","given":"William B.","affiliations":[{"id":16661,"text":"Washington University in Saint Louis","active":true,"usgs":false}],"preferred":false,"id":641567,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168796,"text":"70168796 - 2016 - First documented case of snake fungal disease in a free-ranging wild snake in Louisiana","interactions":[],"lastModifiedDate":"2020-12-21T16:07:36.50284","indexId":"70168796","displayToPublicDate":"2016-03-03T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"First documented case of snake fungal disease in a free-ranging wild snake in Louisiana","docAbstract":"Snake fungal disease (SFD) is a recently documented mycotic disease characterized by scabs or crusty scales, subcutaneous nodules, abnormal molting, cloudiness of the eyes (not associated with molting), and localized thickening or crusting of the skin. SFD has been documented in many species in the Eastern and Midwestern United States within the last decade. SFD has proven lethal in many snakes, and the disease is recognized as an emerging threat to wild snake populations. Here, we describe the first documented case of SFD in Louisiana in a free-ranging wild snake.
","language":"English","publisher":"Eagle Hill Publications","doi":"10.1656/058.015.0111","usgsCitation":"Glorioso, B.M., Waddle, J., Green, D.E., and Lorch, J.M., 2016, First documented case of snake fungal disease in a free-ranging wild snake in Louisiana: Southeastern Naturalist, v. 15, no. 1, p. 4-6, https://doi.org/10.1656/058.015.0111.","productDescription":"3 p.","startPage":"4","endPage":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067103","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":318530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Cypress Island Preserve, Lake Martin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.9251823425293,\n 30.200778435288452\n ],\n [\n -91.9251823425293,\n 30.23089119743091\n ],\n [\n -91.88827514648438,\n 30.23089119743091\n ],\n [\n -91.88827514648438,\n 30.200778435288452\n ],\n [\n -91.9251823425293,\n 30.200778435288452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56d96030e4b015c306f726ba","contributors":{"authors":[{"text":"Glorioso, Brad M. 0000-0002-5400-7414 gloriosob@usgs.gov","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":4241,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","email":"gloriosob@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":621781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":149048,"corporation":false,"usgs":true,"family":"Waddle","given":"J. Hardin","email":"waddleh@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":621782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, David E. 0000-0002-7663-1832 degreen@usgs.gov","orcid":"https://orcid.org/0000-0002-7663-1832","contributorId":3715,"corporation":false,"usgs":true,"family":"Green","given":"David","email":"degreen@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":621783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":621784,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178106,"text":"70178106 - 2016 - Circumpolar biodiversity monitoring program (CBMP): Coastal expert workshop meeting report","interactions":[],"lastModifiedDate":"2016-12-14T13:39:46","indexId":"70178106","displayToPublicDate":"2016-03-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Circumpolar biodiversity monitoring program (CBMP): Coastal expert workshop meeting report","docAbstract":"The Coastal Expert Workshop, which took place in Ottawa, Canada from March 1 to 3, 2016, initiated the development of the Arctic Coastal Biodiversity Monitoring Plan (Coastal Plan). Meeting participants, including northern residents, representatives from industry, non-governmental organisations (NGOs), academia, and government regulators and agencies from across the circumpolar Arctic, discussed current biodiversity monitoring efforts, key issues facing biodiversity in Arctic coastal areas, and collectively identified monitoring indicators, or Focal Ecosystem Components (FECs). On February 29, the day before the workshop, a full day was allocated to Traditional Knowledge (TK) holders to meet and elucidate how this important knowledge can be included in the process of building the Coastal Plan and monitoring biodiversity in Arctic coastal areas, along with scientific data and variables.
This document provides 1) background information about the Circumpolar Biodiversity Monitoring Programme and the Coastal Expert Monitoring Group, 2) overviews on workshop presentations and breakout sessions, and 3) details regarding outcomes of the workshop that will inform the drafting of the Coastal Plan.
","conferenceTitle":"Circumpolar biodiversity monitoring program (CBMP) coastal expert workshop meeting","conferenceDate":"Feb. 29- March 3, 2016","conferenceLocation":"Ottawa, Canada","language":"English","publisher":"Conservation of Arctic Flora and Fauna (CAFF)","usgsCitation":"Anderson, R., McLennan, D., Thomson, L., Wegeberg, S., Pettersvik Arvnes, M., Sergienko, L., Behe, C., Moss-Davies, P., Fritz, S., Christensen, T.K., and Price, C., 2016, Circumpolar biodiversity monitoring program (CBMP): Coastal expert workshop meeting report, Circumpolar biodiversity monitoring program (CBMP) coastal expert workshop meeting, Ottawa, Canada, Feb. 29- March 3, 2016, p. 1-52.","productDescription":"53 p.","startPage":"1","endPage":"52","ipdsId":"IP-080363","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":330673,"type":{"id":15,"text":"Index Page"},"url":"https://caff.is/coastal/coastal-monitoring-publications/388-circumpolar-biodiversity-monitoring-program-cbmp-coastal-expert-workshop-meeting"},{"id":332130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585268e2e4b0e2663625ec86","contributors":{"authors":[{"text":"Anderson, Rebecca 0000-0001-6988-6311 rdanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-6988-6311","contributorId":5925,"corporation":false,"usgs":true,"family":"Anderson","given":"Rebecca","email":"rdanderson@usgs.gov","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":652776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McLennan, Donald","contributorId":176567,"corporation":false,"usgs":false,"family":"McLennan","given":"Donald","email":"","affiliations":[],"preferred":false,"id":652777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomson, Laura","contributorId":176568,"corporation":false,"usgs":false,"family":"Thomson","given":"Laura","email":"","affiliations":[],"preferred":false,"id":652778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wegeberg, Susse","contributorId":176569,"corporation":false,"usgs":false,"family":"Wegeberg","given":"Susse","email":"","affiliations":[],"preferred":false,"id":652779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pettersvik Arvnes, Maria","contributorId":176570,"corporation":false,"usgs":false,"family":"Pettersvik Arvnes","given":"Maria","email":"","affiliations":[],"preferred":false,"id":652780,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sergienko, Liudmila","contributorId":176571,"corporation":false,"usgs":false,"family":"Sergienko","given":"Liudmila","email":"","affiliations":[],"preferred":false,"id":652781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Behe, Carolina","contributorId":176572,"corporation":false,"usgs":false,"family":"Behe","given":"Carolina","email":"","affiliations":[],"preferred":false,"id":652782,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moss-Davies, Pitseolak","contributorId":176573,"corporation":false,"usgs":false,"family":"Moss-Davies","given":"Pitseolak","email":"","affiliations":[],"preferred":false,"id":652783,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fritz, Stacey","contributorId":176574,"corporation":false,"usgs":false,"family":"Fritz","given":"Stacey","email":"","affiliations":[],"preferred":false,"id":652784,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christensen, Thomas K.","contributorId":69381,"corporation":false,"usgs":false,"family":"Christensen","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":6963,"text":"Department of Bioscience, Aarhus University","active":true,"usgs":false}],"preferred":false,"id":652785,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Price, Courtney","contributorId":176575,"corporation":false,"usgs":false,"family":"Price","given":"Courtney","email":"","affiliations":[],"preferred":false,"id":652786,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70176640,"text":"70176640 - 2016 - Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin","interactions":[],"lastModifiedDate":"2021-02-17T22:43:18.006221","indexId":"70176640","displayToPublicDate":"2016-03-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin","docAbstract":"Over the last few years, canyons along the northern U.S. Atlantic continental margin have been the focus of intensive research examining canyon evolution, submarine geohazards, benthic ecology and deep-sea coral habitat. New high-resolution multibeam bathymetry and Remotely Operated Vehicle (ROV) dives in the major shelf-breaching and minor slope canyons, provided the opportunity to investigate the size of, and processes responsible for, canyon wall failures. The canyons cut through thick Late Cretaceous to Recent mixed siliciclastic and carbonate-rich lithologies which impart a primary control on the style of failures observed. Broad-scale canyon morphology across much of the margin can be correlated to the exposed lithology. Near vertical walls, sedimented benches, talus slopes, and canyon floor debris aprons were present in most canyons. The extent of these features depends on canyon wall cohesion and level of internal fracturing, and resistance to biological and chemical erosion. Evidence of brittle failure over different spatial and temporal scales, physical abrasion by downslope moving flows, and bioerosion, in the form of burrows and surficial scrape marks provide insight into the modification processes active in these canyons. The presence of sessile fauna, including long-lived, slow growing corals and sponges, on canyon walls, especially those affected by failure provide a critical, but as yet, poorly understood chronological record of geologic processes within these systems.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Submarine mass movements and their consequences: 7th international symposium part II","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-20979-1_10","usgsCitation":"Chaytor, J., Demopoulos, A., ten Brink, U., Baxter, C.D., Quattrini, A.M., and Brothers, D., 2016, Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin, chap. 10 of Submarine mass movements and their consequences: 7th international symposium part II, p. 103-113, https://doi.org/10.1007/978-3-319-20979-1_10.","productDescription":"11 p.","startPage":"103","endPage":"113","ipdsId":"IP-063805","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488132,"rank":0,"type":{"id":41,"text":"Open Access 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A recovery factor represents the percentage of an oil reservoir’s original oil in place estimated to be recoverable by the application of a miscible CO2-EOR method. The USGS estimates were calculated for 2,018 clastic and 1,681 carbonate candidate reservoirs in the “Significant Oil and Gas Fields of the United States Database” prepared by Nehring Associates, Inc. (2012).
\nThis report presents distributions of estimated recovery factors organized by plays in seven U.S. regions. The distributional parameters for plays containing at least three candidate reservoirs are presented in tables, and parameters for plays containing at least six candidate reservoirs are presented in boxplots. Over all the reservoirs evaluated, 90 percent of the recovery-factor estimates for clastic reservoirs fell within the range from 8.7 to 16.2 percent, and the median value of the distribution was 9.5 percent. Similarly, 90 percent of the recovery-factor estimates for carbonate reservoirs were within the range from 11.8 to 27.5 percent, and the median value of the distribution was 13.6 percent. Both distributions were right skewed.
\nThe retention factor is the percentage of injected CO2 that is naturally retained in the reservoir. Retention factors were also estimated in this study. For clastic reservoirs, 90 percent of the estimated retention factors were between 21.7 and 32.1 percent, and for carbonate reservoirs, 90 percent were between 23.7 and 38.2 percent. The respective median values were 22.9 for clastic reservoirs and 26.1 for carbonate reservoirs. Both distributions were right skewed. The recovery and retention factors that were calculated are consistent with the corresponding factors reported in the literature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151239","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2016, Play-level distributions of estimates of recovery factors for a miscible carbon dioxide enhanced oil recovery method used in oil reservoirs in the conterminous United States: U.S. Geological Survey Open-File Report 2015–1239, 36 p., https://dx.doi.org/10.3133/ofr20151239.","productDescription":"vii, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067608","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1239/ofr20151239.pdf","text":"Report","size":"2.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1239"},{"id":318492,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1239/coverthb.jpg"}],"country":"United 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\"}}]}\n","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
MS 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/GeneralInfo/
ScienceCenters/Eastern.aspx
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in northeastern Arizona because of continued water requirements for industrial and municipal use by a growing population and because of low precipitation in the arid climate of the Black Mesa area. Precipitation in the area typically is between 6 and 14 inches per year.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from January 2012 to September 2013. The monitoring program includes measurements of (1) groundwater withdrawals, (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2012, total groundwater withdrawals were 4,010 acre-ft, industrial withdrawals were 1,370 acre-ft, and municipal withdrawals were 2,640 acre-ft. Total withdrawals during 2012 were about 45 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline. From 2011 to 2012 total withdrawals decreased by 10 percent; industrial withdrawals decreased by approximately 1 percent, and total municipal withdrawals decreased by 15 percent.
From 2012 to 2013, annually measured water levels in the Black Mesa area declined in 6 of 16 wells that were available for comparison in the unconfined areas of the N aquifer, and the median change was 0.8 feet. Water levels declined in 5 of 16 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was 0.3 feet. From the prestress period (prior to 1965) to 2013, the median water-level change for 34 wells in both the confined and unconfined areas was -13.5 feet; the median water-level changes were -0.8 feet for 16 wells measured in the unconfined areas and -51.0 feet for 16 wells measured in the confined area.
Spring flow was measured at four springs in 2013; Burro, Unnamed Spring near Dennehotso, Moenkopi School, and Pasture Canyon Springs. Flow fluctuated during the period of record for Burro and Unnamed Springs near Dennehotso, but a decreasing trend was apparent at Moenkopi School Spring and Pasture Canyon Spring. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Unnamed Spring near Dennehotso has fluctuated for the period of record at each spring. Trend analysis for discharge at Moenkopi School and Pasture Canyon Springs showed a decreasing trend.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2013), Dinnebito Wash near Sand Springs 09401110 (1993 to 2013), Polacca Wash near Second Mesa 09400568 (1994 to 2013), and Pasture Canyon Springs 09401265 (2004 to 2013). Median winter flows (November through February) from these sites for each water year were used as an index of the amount of groundwater discharge. For the period of record of each streamflow-gaging station, the median winter flows have generally remained constant, which suggests no change in groundwater discharge.
In 2013, water samples collected from 12 wells and 4 springs in the Black Mesa area were analyzed for selected chemical constituents, and the results were compared with previous analyses. Concentrations of dissolved solids, chloride, and sulfate have varied at all 12 wells for the period of record, but neither increasing nor decreasing trends over time were found. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 13 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly since the early 1980s. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record with no increasing or decreasing trend in the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151221","collaboration":"Prepared in cooperation with the Bureau of Indian Affairs and the Arizona Department of Water Resources","usgsCitation":"Macy, J.P., and Truini, Margot, 2016, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2012–2013: U.S. Geological Survey Open-File Report 2015–1221, 43 p., https://dx.doi.org/10.3133/ofr20151221.","productDescription":"vi, 43 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2012-01-01","ipdsId":"IP-059312","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":318423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1221/coverthb.jpg"},{"id":318424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1221/ofr20151221.pdf","text":"Report","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1221 PDF"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.3,\n 35.3\n ],\n [\n -111.3,\n 37\n ],\n [\n -109.3,\n 37\n ],\n [\n -109.3,\n 35.3\n ],\n [\n -111.3,\n 35.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
http://az.water.usgs.gov/