This report is the earliest Government publication to furnish estimates covering 2016 nonfuel mineral industry data. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70180197","usgsCitation":"U.S. Geological Survey, 2017, Mineral commodity summaries 2017: U.S. Geological Survey, 202 p., https://doi.org/10.3133/70180197.","productDescription":"202 p.","numberOfPages":"202","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":334527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":335372,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/","text":"Mineral Commodity Summaries Index Page","linkFileType":{"id":5,"text":"html"},"description":"Link to page with all USGS Mineral Commodities Summaries"}],"publishedDate":"2017-01-31","noUsgsAuthors":false,"publicationDate":"2017-01-31","publicationStatus":"PW","scienceBaseUri":"589aeab1e4b0efcedb72d23d","contributors":{"authors":[{"text":"Ober, Joyce A. 0000-0003-1608-5611 jober@usgs.gov","orcid":"https://orcid.org/0000-0003-1608-5611","contributorId":394,"corporation":false,"usgs":true,"family":"Ober","given":"Joyce","email":"jober@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":662110,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70198051,"text":"70198051 - 2017 - Paleomagnetism and 40Ar/39Ar geochronology of the Plio-Pleistocene Boring Volcanic Field: Implications for the geomagnetic polarity time scale and paleosecular variation","interactions":[],"lastModifiedDate":"2018-07-16T12:04:02","indexId":"70198051","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Paleomagnetism and 40Ar/39Ar geochronologyof the Plio-Pleistocene Boring Volcanic Field: Implications for the geomagnetic polarity time scale and paleosecular variation","title":"Paleomagnetism and 40Ar/39Ar geochronology of the Plio-Pleistocene Boring Volcanic Field: Implications for the geomagnetic polarity time scale and paleosecular variation","docAbstract":"Paleomagnetic directions and 40Ar/39Ar ages have been determined for samples of lava flows from the same outcrops, where possible, for 84 eruptive units ranging in age from 3200 ka to 60 ka within the Boring Volcanic Field (BVF) of the Pacific Northwest, USA. This study expands upon our previous results for the BVF, and compares the combined results with the current geomagnetic polarity time scale (GPTS). Lava flows with transitional directions were found within the BVF at the Matuyama-Brunhes and Jaramillo-Matuyama polarity boundaries, and replicate ages corresponding to these and other boundaries have been newly ascertained. Although the BVF data generally agree with GPTS chronozone boundaries, they indicate that onset of the Gauss-Matuyama transition and Olduvai subchron occurred significantly earlier than given in the current time scale calibration. Additional comparisons show that the BVF results are consistent with recent statistical models of geomagnetic paleosecular variation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2016.07.008","usgsCitation":"Hagstrum, J.T., Fleck, R.J., Evarts, R., and Calvert, A.T., 2017, Paleomagnetism and 40Ar/39Ar geochronology of the Plio-Pleistocene Boring Volcanic Field: Implications for the geomagnetic polarity time scale and paleosecular variation: Physics of the Earth and Planetary Interiors, v. 262, p. 101-115, https://doi.org/10.1016/j.pepi.2016.07.008.","productDescription":"15 p.","startPage":"101","endPage":"115","ipdsId":"IP-076015","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.pepi.2016.07.008","text":"Publisher Index Page"},{"id":355622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"262","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e766e4b060350a15d2ad","contributors":{"authors":[{"text":"Hagstrum, Jonathan T. 0000-0002-0689-280X jhag@usgs.gov","orcid":"https://orcid.org/0000-0002-0689-280X","contributorId":3474,"corporation":false,"usgs":true,"family":"Hagstrum","given":"Jonathan","email":"jhag@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleck, Robert J. 0000-0002-3149-8249 fleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3149-8249","contributorId":1048,"corporation":false,"usgs":true,"family":"Fleck","given":"Robert","email":"fleck@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evarts, Russell C.","contributorId":206202,"corporation":false,"usgs":false,"family":"Evarts","given":"Russell C.","affiliations":[],"preferred":false,"id":739781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calvert, Andrew T. 0000-0001-5237-2218 acalvert@usgs.gov","orcid":"https://orcid.org/0000-0001-5237-2218","contributorId":2694,"corporation":false,"usgs":true,"family":"Calvert","given":"Andrew","email":"acalvert@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":739782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187565,"text":"70187565 - 2017 - The use of data-mining techniques for developing effective decisionsupport systems: A case study of simulating the effects ofclimate change on coastal salinity intrusion","interactions":[],"lastModifiedDate":"2017-05-09T09:46:33","indexId":"70187565","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesNumber":"408","title":"The use of data-mining techniques for developing effective decisionsupport systems: A case study of simulating the effects ofclimate change on coastal salinity intrusion","docAbstract":"Natural-resource managers and stakeholders face difficult challenges when managing interactions between natural and societal systems. Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. The availability of freshwater in coastal streams can be threatened by saltwater intrusion. Even though the collective interests and computer skills of the community of managers, scientists and other stakeholders are quite varied, there is an overarching need for equal access by all to the scientific knowledge needed to make the best possible decisions. This paper describes a decision support system, PRISM-2, developed to evaluate salinity intrusion due to potential climate change along the South Carolina coast in southeastern USA. The decision support system is disseminated as a spreadsheet application and integrates the output of global circulation models, watershed models and salinity intrusion models with real-time databases for simulation, graphical user interfaces, and streaming displays of results. The results from PRISM-2 showed that a 31-cm and 62-cm increase in sea level reduced the daily availability of freshwater supply to a coastal municipal intake by 4% and 12% of the time, respectively. Future climate change projections by a global circulation model showed a seasonal change in salinity intrusion events from the summer to the fall for the majority of events.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Integrated environmental modelling to solve real world problems: Methods, vision and challenges","language":"English","publisher":"Geological Society of London","doi":"10.1144/SP408.8","usgsCitation":"Conrads, P., and Edwin Roehl, J., 2017, The use of data-mining techniques for developing effective decisionsupport systems: A case study of simulating the effects ofclimate change on coastal salinity intrusion, chap. of Integrated environmental modelling to solve real world problems: Methods, vision and challenges, p. 222-234, https://doi.org/10.1144/SP408.8.","productDescription":"13 p.","startPage":"222","endPage":"234","ipdsId":"IP-042501","costCenters":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":340987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.2548828125,\n 32.48196313217176\n ],\n [\n -81.15600585937499,\n 32.37996146435729\n ],\n [\n -81.123046875,\n 32.25926542645933\n ],\n [\n -81.05712890625,\n 32.045332838858506\n ],\n [\n -80.9912109375,\n 31.93351676190369\n ],\n [\n -80.804443359375,\n 31.85889704445453\n ],\n [\n -80.5517578125,\n 32.12910537866883\n ],\n [\n -80.299072265625,\n 32.33355894864106\n ],\n [\n -80.068359375,\n 32.47269502206151\n ],\n [\n -79.716796875,\n 32.58384932565662\n ],\n [\n -79.4970703125,\n 32.76880048488168\n ],\n [\n -79.07958984375,\n 32.98102014898148\n ],\n [\n -79.013671875,\n 33.201924189778936\n ],\n [\n -78.848876953125,\n 33.422272258866045\n ],\n [\n -78.717041015625,\n 33.62376800118811\n ],\n [\n -78.33251953125,\n 33.715201644740844\n ],\n [\n -78.870849609375,\n 34.14363482031264\n ],\n [\n -79.1015625,\n 34.05265942137599\n ],\n [\n -79.552001953125,\n 33.76088200086917\n ],\n [\n -79.771728515625,\n 33.38558626887102\n ],\n [\n -80.1123046875,\n 33.128351191631566\n ],\n [\n -80.694580078125,\n 32.88881315761995\n ],\n [\n -81.2548828125,\n 32.48196313217176\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-16","publicationStatus":"PW","scienceBaseUri":"5912d537e4b0e541a03d4521","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":694578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwin Roehl, Jr.","contributorId":191874,"corporation":false,"usgs":false,"family":"Edwin Roehl","given":"Jr.","affiliations":[],"preferred":false,"id":694579,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70180534,"text":"70180534 - 2017 - Spatial variability of Chinook salmon spawning distribution and habitat preferences","interactions":[],"lastModifiedDate":"2017-11-22T10:26:57","indexId":"70180534","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability of Chinook salmon spawning distribution and habitat preferences","docAbstract":"We investigated physical habitat conditions associated with the spawning sites of Chinook Salmon Oncorhynchus tshawytscha and the interannual consistency of spawning distribution across multiple spatial scales using a combination of spatially continuous and discrete sampling methods. We conducted a census of aquatic habitat in 76 km of the upper main-stem Yakima River in Washington and evaluated spawning site distribution using redd survey data from 2004 to 2008. Interannual reoccupation of spawning areas was high, ranging from an average Pearson’s correlation of 0.62 to 0.98 in channel subunits and 10-km reaches, respectively. Annual variance in the interannual correlation of spawning distribution was highest in channel units and subunits, but it was low at reach scales. In 13 of 15 models developed for individual years (2004–2008) and reach lengths (800 m, 3 km, 6 km), stream power and depth were the primary predictors of redd abundance. Multiple channels and overhead cover were patchy but were important secondary and tertiary predictors of reach-scale spawning site selection. Within channel units and subunits, pool tails and thermal variability, which may be associated with hyporheic exchange, were important predictors of spawning. We identified spawning habitat preferences within reaches and channel units that are relevant for salmonid habitat restoration planning. We also identified a threshold (i.e., 2-km reaches) beyond which interannual spawning distribution was markedly consistent, which may be informative for prioritizing habitat restoration or conservation. Management actions may be improved through enhanced understanding of spawning habitat preferences and the consistency with which Chinook Salmon reoccupy spawning areas at different spatial scales.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1254112","usgsCitation":"Cram, J.M., Torgersen, C.E., Klett, R.S., Pess, G.R., May, D., Pearsons, T.N., and Dittman, A.H., 2017, Spatial variability of Chinook salmon spawning distribution and habitat preferences: Transactions of the American Fisheries Society, v. 146, no. 2, p. 206-221, https://doi.org/10.1080/00028487.2016.1254112.","productDescription":"16 p.","startPage":"206","endPage":"221","ipdsId":"IP-079878","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":334422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.19705200195312,\n 46.97556750833867\n ],\n [\n -121.19705200195312,\n 47.253135632244216\n ],\n [\n -120.55709838867188,\n 47.253135632244216\n ],\n [\n -120.55709838867188,\n 46.97556750833867\n ],\n [\n -121.19705200195312,\n 46.97556750833867\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-19","publicationStatus":"PW","scienceBaseUri":"5891b0a5e4b072a7ac1298e1","contributors":{"authors":[{"text":"Cram, Jeremy M.","contributorId":178956,"corporation":false,"usgs":false,"family":"Cram","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":661780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":661779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klett, Ryan S.","contributorId":178957,"corporation":false,"usgs":false,"family":"Klett","given":"Ryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":661781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pess, George R.","contributorId":13501,"corporation":false,"usgs":false,"family":"Pess","given":"George","email":"","middleInitial":"R.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":661782,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"May, Darran","contributorId":178958,"corporation":false,"usgs":false,"family":"May","given":"Darran","email":"","affiliations":[],"preferred":false,"id":661783,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearsons, Todd N.","contributorId":178959,"corporation":false,"usgs":false,"family":"Pearsons","given":"Todd","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":661784,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dittman, Andrew H.","contributorId":178960,"corporation":false,"usgs":false,"family":"Dittman","given":"Andrew","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":661785,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70180629,"text":"70180629 - 2017 - Potential distribution of the viral haemorrhagic septicaemia virus in the Great Lakes region","interactions":[],"lastModifiedDate":"2017-01-31T10:34:42","indexId":"70180629","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2286,"text":"Journal of Fish Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Potential distribution of the viral haemorrhagic septicaemia virus in the Great Lakes region","docAbstract":"Viral haemorrhagic septicaemia virus (VHSV) genotype IVb has been responsible for large-scale fish mortality events in the Great Lakes of North America. Anticipating the areas of potential VHSV occurrence is key to designing epidemiological surveillance and disease prevention strategies in the Great Lakes basin. We explored the environmental features that could shape the distribution of VHSV, based on remote sensing and climate data via ecological niche modelling. Variables included temperature measured during the day and night, precipitation, vegetation, bathymetry, solar radiation and topographic wetness. VHSV occurrences were obtained from available reports of virus confirmation in laboratory facilities. We fit a Maxent model using VHSV-IVb reports and environmental variables under different parameterizations to identify the best model to determine potential VHSV occurrence based on environmental suitability. VHSV reports were generated from both passive and active surveillance. VHSV occurrences were most abundant near shore sites. We were, however, able to capture the environmental signature of VHSV based on the environmental variables employed in our model, allowing us to identify patterns of VHSV potential occurrence. Our findings suggest that VHSV is not at an ecological equilibrium and more areas could be affected, including areas not in close geographic proximity to past VHSV reports.
","language":"English","publisher":"Wiley","doi":"10.1111/jfd.12490","usgsCitation":"Escobar, L.E., Kurath, G., Escobar-Dodero, J., Craft, M.E., and Phelps, N.B., 2017, Potential distribution of the viral haemorrhagic septicaemia virus in the Great Lakes region: Journal of Fish Diseases, v. 40, no. 1, p. 11-28, https://doi.org/10.1111/jfd.12490.","productDescription":"18 p.","startPage":"11","endPage":"28","ipdsId":"IP-072867","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":334415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.35107421874999,\n 40.64730356252251\n ],\n [\n -92.35107421874999,\n 47.264320080254805\n ],\n [\n -75.4541015625,\n 47.264320080254805\n ],\n [\n -75.4541015625,\n 40.64730356252251\n ],\n [\n -92.35107421874999,\n 40.64730356252251\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-13","publicationStatus":"PW","scienceBaseUri":"5891b0a3e4b072a7ac1298db","contributors":{"authors":[{"text":"Escobar, Luis E.","contributorId":178962,"corporation":false,"usgs":false,"family":"Escobar","given":"Luis","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":661794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurath, Gael 0000-0003-3294-560X gkurath@usgs.gov","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":2629,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","email":"gkurath@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":661793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Escobar-Dodero, Joaquim","contributorId":178963,"corporation":false,"usgs":false,"family":"Escobar-Dodero","given":"Joaquim","email":"","affiliations":[],"preferred":false,"id":661796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craft, Meggan E.","contributorId":168372,"corporation":false,"usgs":false,"family":"Craft","given":"Meggan","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":661795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phelps, Nicholas B.D.","contributorId":95803,"corporation":false,"usgs":true,"family":"Phelps","given":"Nicholas","email":"","middleInitial":"B.D.","affiliations":[],"preferred":false,"id":661797,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70180542,"text":"70180542 - 2017 - Linking dominant Hawaiian tree species to understory development in recovering pastures via impacts on soils and litter","interactions":[],"lastModifiedDate":"2018-01-04T08:31:26","indexId":"70180542","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Linking dominant Hawaiian tree species to understory development in recovering pastures via impacts on soils and litter","docAbstract":"Large areas of tropical forest have been cleared and planted with exotic grass species for use as cattle pasture. These often remain persistent grasslands after grazer removal, which is problematic for restoring native forest communities. It is often hoped that remnant and/or planted trees can jump-start forest succession; however, there is little mechanistic information on how different canopy species affect community trajectories. To investigate this, I surveyed understory communities, exotic grass biomass, standing litter pools, and soil properties under two dominant canopy trees—Metrosideros polymorpha (‘ōhi‘a) and Acacia koa (koa)—in recovering Hawaiian forests. I then used structural equation models (SEMs) to elucidate direct and indirect effects of trees on native understory. Native understory communities developed under ‘ōhi‘a, which had larger standing litter pools, lower soil nitrogen, and lower exotic grass biomass than koa. This pattern was variable, potentially due to historical site differences and/or distance to intact forest. Koa, in contrast, showed little understory development. Instead, data suggest that increased soil nitrogen under koa leads to high grass biomass that stalls native recruitment. SEMs suggested that indirect effects of trees via litter and soils were as or more important than direct effects for determining native cover. It is suggested that diverse plantings which incorporate species that have high carbon to nitrogen ratios may help ameliorate the negative indirect effects of koa on natural understory regeneration.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.12377","usgsCitation":"Yelenik, S.G., 2017, Linking dominant Hawaiian tree species to understory development in recovering pastures via impacts on soils and litter: Restoration Ecology, v. 25, no. 1, p. 42-52, https://doi.org/10.1111/rec.12377.","productDescription":"11 p.","startPage":"42","endPage":"52","ipdsId":"IP-072291","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":334419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.36178588867188,\n 19.75571800093756\n ],\n [\n -155.36178588867188,\n 19.922358302239935\n ],\n [\n -155.17845153808594,\n 19.922358302239935\n ],\n [\n -155.17845153808594,\n 19.75571800093756\n ],\n [\n -155.36178588867188,\n 19.75571800093756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-20","publicationStatus":"PW","scienceBaseUri":"5891b0a5e4b072a7ac1298df","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":661786,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70178937,"text":"ofr20161203 - 2017 - Noble gas isotopes in mineral springs and wells within the Cascadia forearc, Washington, Oregon, and California","interactions":[],"lastModifiedDate":"2017-01-31T09:53:14","indexId":"ofr20161203","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1203","title":"Noble gas isotopes in mineral springs and wells within the Cascadia forearc, Washington, Oregon, and California","docAbstract":"This U.S. Geological Survey report presents laboratory analyses along with field notes for an exploratory study to document the relative abundance of noble gases in mineral springs and water wells within the Cascadia forearc of Washington, Oregon, and California (fig. 1). This report describes 14 samples collected in 2014 and 2015 and complements a previous report that describes 9 samples collected in 2012 and 2013 (McCrory and others, 2014b). Estimates of the depth to the underlying Juan de Fuca oceanic plate beneath sample sites are derived from the McCrory and others (2012) slab model. Some of the springs have been previously sampled for chemical analyses (Mariner and others, 2006), but none of the springs or wells currently has publicly available noble gas data. The helium and neon isotope values and ratios presented below are used to determine the sources and mixing history of these mineral and well waters (for example, McCrory and others, 2016).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161203","usgsCitation":"McCrory, P.A., Constantz, J.E., and Hunt, A.G., 2017, Noble gas isotopes in mineral springs and wells within the Cascadia forearc, Washington, Oregon, and California: U.S. Geological Survey Open-File Report 2016–1203, 58 p., https://doi.org/10.3133/ofr20161203.","productDescription":"Report: vii, 58 p; Companion File","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075367","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":334305,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1203/coverthb.jpg"},{"id":334306,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1203/ofr20161203.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1203"},{"id":334307,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2016/1203/ofr20161203_NobleGasData.xlsx","text":"Noble Gas Data","size":"14 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016–1203 Noble Gas Data"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascadia Forearc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132,\n 40\n ],\n [\n -132,\n 52\n ],\n [\n -120,\n 52\n ],\n [\n -120,\n 40\n ],\n [\n -132,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif. Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
This report describes an R package called smwrGraphs, which consists of a collection of graphing functions for hydrologic data within R, a programming language and software environment for statistical computing. The functions in the package have been developed by the U.S. Geological Survey to create high-quality graphs for publication or presentation of hydrologic data that meet U.S. Geological Survey graphics guidelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161188","usgsCitation":"Lorenz, D.L., and Diekoff, A.L., 2017, smwrGraphs—An R package for graphing hydrologic data, version 1.1.2: U.S. Geological Survey Open-File Report 2016–1188, 17 p., https://doi.org/10.3133/ofr20161188. [Supersedes USGS Open-File Report 2015–1202.]","productDescription":"Report: iii, 17 p.; Appendixes: 1–9","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-054442","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":334283,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1188/ofr20161188.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1188"},{"id":334282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1188/coverthb.jpg"},{"id":334284,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1188/downloads","text":"Appendixes 1–9","size":"3.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1188 Appendixes 1–9","linkHelpText":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
Glacier mass balance measurements help to provide an understanding of the behavior of glaciers and their response to local and regional climate. In 2005 the United States Geological Survey established a surface mass balance monitoring program on Sperry Glacier, Montana, USA. This project is the first quantitative study of mass changes of a glacier in the US northern Rocky Mountains and continues to the present. The following paper describes the methods used during the first 11 years of measurements and reports the associated results. From 2005 to 2015, Sperry Glacier had a cumulative mean mass balance loss of 4.37 m w.e. (water equivalent). The mean winter, summer, and annual glacier-wide mass balances were 2.92, −3.41, and −0.40 m w.e. yr−1 respectively. We derive these cumulative and mean results from an expansive data set of snow depth, snow density, and ablation measurements taken at selected points on the glacier. These data allow for the determination of mass balance point values and a time series of seasonal and annual glacier-wide mass balances for all 11 measurement years. We also provide measurements of glacier extent and accumulation areas for select years. All data have been submitted to the World Glacier Monitoring Service and are available at doi:10.5904/wgms-fog-2016-08. This foundational work provides valuable insight about Sperry Glacier and supplies additional data to the worldwide record of glaciers measured using the glaciological method. Future research will focus on the processes that control accumulation and ablation patterns across the glacier. Also we plan to examine the uncertainties related to our methods and eventually quantify a more robust estimate of error associated with our results.
","language":"English","publisher":"Copernicus","publisherLocation":"Katlenberg-Lindau, Germany","doi":"10.5194/essd-9-47-2017","usgsCitation":"Clark, A., Fagre, D.B., Peitzsch, E.H., Reardon, B.A., and Harper, J.T., 2017, Glaciological measurements and mass balances from Sperry Glacier, Montana, USA, years 2005–2015: Earth System Science Data, v. 9, p. 47-61, https://doi.org/10.5194/essd-9-47-2017.","productDescription":"15 p.","startPage":"47","endPage":"61","ipdsId":"IP-078667","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-9-47-2017","text":"Publisher Index Page"},{"id":334294,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Sperry Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.77098083496094,\n 48.613618785872504\n ],\n [\n -113.77098083496094,\n 48.63693581952899\n ],\n [\n -113.74583244323729,\n 48.63693581952899\n ],\n [\n -113.74583244323729,\n 48.613618785872504\n ],\n [\n -113.77098083496094,\n 48.613618785872504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-23","publicationStatus":"PW","scienceBaseUri":"58905eefe4b072a7ac0cad2b","contributors":{"authors":[{"text":"Clark, Adam 0000-0002-8863-1434 amclark@usgs.gov","orcid":"https://orcid.org/0000-0002-8863-1434","contributorId":177529,"corporation":false,"usgs":true,"family":"Clark","given":"Adam","email":"amclark@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":661436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":661437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":661438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reardon, Blase A.","contributorId":178872,"corporation":false,"usgs":false,"family":"Reardon","given":"Blase","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":661550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harper, Joel T.","contributorId":173392,"corporation":false,"usgs":false,"family":"Harper","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":16951,"text":"Department of Geosciences, University of Montana, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":661440,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177941,"text":"fs20163094 - 2017 - The 3D Elevation Program—Landslide recognition, hazard assessment, and mitigation support","interactions":[],"lastModifiedDate":"2017-02-13T11:19:31","indexId":"fs20163094","displayToPublicDate":"2017-01-27T14:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3094","title":"The 3D Elevation Program—Landslide recognition, hazard assessment, and mitigation support","docAbstract":"The U.S. Geological Survey (USGS) Landslide Hazards Program conducts landslide hazard assessments, pursues landslide investigations and forecasts, provides technical assistance to respond to landslide emergencies, and engages in outreach. All of these activities benefit from the availability of high-resolution, three-dimensional (3D) elevation information in the form of light detection and ranging (lidar) data and interferometric synthetic aperture radar (IfSAR) data. Research on landslide processes addresses critical questions of where and when landslides are likely to occur as well as their size, speed, and effects. This understanding informs the development of methods and tools for hazard assessment and situational awareness used to guide efforts to avoid or mitigate landslide impacts. Such research is essential for the USGS to provide improved information on landslide potential associated with severe storms, earthquakes, volcanic activity, coastal wave erosion, and wildfire burn areas.
Decisionmakers in government and the private sector increasingly depend on information the USGS provides before, during, and following disasters so that communities can live, work, travel, and build safely. The USGS 3D Elevation Program (3DEP) provides the programmatic infrastructure to generate and supply lidar-derived superior terrain data to address landslide applications and a wide range of other urgent needs nationwide. By providing data to users, 3DEP reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163094","usgsCitation":"Lukas, Vicki, and Carswell, W.J., Jr., 2017, The 3D Elevation Program—Landslide recognition, hazard assessment, and mitigation support: U.S. Geological Survey Fact Sheet 2016–3094, 2 p., https://doi.org/10.3133/fs20163094.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073045","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":333830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3094/coverthb.jpg"},{"id":333831,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3094/fs20163094.pdf","text":"Report","size":"643 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3094"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
511 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3dep@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
The U.S. Geological Survey completed hydrologic and hydraulic analyses of Cromwell Brook and the Sieur de Monts tributary in Acadia National Park, Maine, to better understand causes of flooding in complex hydrologic and hydraulic environments, like those in the Great Meadow wetland and Sieur de Monts Spring area. Regional regression equations were used to compute peak flows with from 2 to 100-year recurrence intervals at seven locations. Light detection and ranging data were adjusted for bias caused by dense vegetation in the Great Meadow wetland; and then combined with local ground surveys used to define the underwater topography and hydraulic structures in the study area. Hydraulic modeling was used to evaluate flood response in the study area to a variety of hydrologic and hydraulic scenarios.
Hydraulic modeling indicates that enlarging the culvert at Park Loop Road could help mitigate flooding near the Sieur de Monts Nature Center that is caused by streamflows with large recurrence intervals; however, hydraulic modeling also indicates that the Park Loop Road culvert does not aggravate flooding near the Nature Center caused by the more frequent high intensity rainstorms. That flooding is likely associated with overland flow resulting from (1) quick runoff from the steep Dorr Mountain hitting the lower gradient Great Meadow wetland area and (2) poor drainage aggravated by beaver dams holding water in the wetland.
Rapid geomorphic assessment data collected in June 2015 and again in April 2016 indicate that Cromwell Brook has evidence of aggradation, degradation, and channel widening throughout the drainage basin. Two of five reference cross sections developed for this report also indicate channel aggradation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lombard, P.J., 2017, Hydrologic and hydraulic analyses of Great Meadow wetland, Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2016–5159, 39 p., https://doi.org/10.3133/sir20165159.","productDescription":"viii, 39 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-077064","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"links":[{"id":333754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5159/sir20165159.pdf","text":"Report","size":"7.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5159"},{"id":333753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5159/coverthb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park, Mount Desert Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.43795776367188,\n 44.22158376545796\n ],\n [\n -68.43795776367188,\n 44.44554600843547\n ],\n [\n -68.16329956054688,\n 44.44554600843547\n ],\n [\n -68.16329956054688,\n 44.22158376545796\n ],\n [\n -68.43795776367188,\n 44.22158376545796\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
196 Whitten Road
Augusta, ME 04330
Or visit our Web site at:
http://newengland.water.usgs.gov
The U.S. Geological Survey (USGS) has made significant progress toward the rapid estimation of shaking and shakingrelated losses through their Did You Feel It? (DYFI), ShakeMap, ShakeCast, and PAGER products. However, quantitative estimates of the extent and severity of secondary hazards (e.g., landsliding, liquefaction) are not currently included in scenarios and real-time post-earthquake products despite their significant contributions to hazard and losses for many events worldwide. We are currently running parallel global statistical models for landslides and liquefaction developed with our collaborators in testing mode, but much work remains in order to operationalize these systems. We are expanding our efforts in this area by not only improving the existing statistical models, but also by (1) exploring more sophisticated, physics-based models where feasible; (2) incorporating uncertainties; and (3) identifying and undertaking research and product development to provide useful landslide and liquefaction estimates and their uncertainties. Although our existing models use standard predictor variables that are accessible globally or regionally, including peak ground motions, topographic slope, and distance to water bodies, we continue to explore readily available proxies for rock and soil strength as well as other susceptibility terms. This work is based on the foundation of an expanding, openly available, case-history database we are compiling along with historical ShakeMaps for each event. The expected outcome of our efforts is a robust set of real-time secondary hazards products that meet the needs of a wide variety of earthquake information users. We describe the available datasets and models, developments currently underway, and anticipated products.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 16th World Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":" 16th World Conference on Earthquake Engineering","conferenceDate":"January 9-13, 2017","conferenceLocation":"Santiago, Chile","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Allstadt, K.E., Thompson, E.M., Hearne, M., Nowicki Jessee, M., Zhu, J., Wald, D.J., and Tanyas, H., 2017, Integrating landslide and liquefaction hazard and loss estimates with existing USGS real-time earthquake information products, in Proceedings of the 16th World Conference on Earthquake Engineering, Santiago, Chile, January 9-13, 2017, 13 p.","productDescription":"13 p.","ipdsId":"IP-080338","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76f57e4b08b1644ddfaf4","contributors":{"authors":[{"text":"Allstadt, Kate E. 0000-0003-4977-5248 kallstadt@usgs.gov","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":167684,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"kallstadt@usgs.gov","middleInitial":"E.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":725403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":725404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hearne, Mike 0000-0002-8225-2396 mhearne@usgs.gov","orcid":"https://orcid.org/0000-0002-8225-2396","contributorId":4659,"corporation":false,"usgs":true,"family":"Hearne","given":"Mike","email":"mhearne@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":725405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nowicki Jessee, M. Anna","contributorId":196186,"corporation":false,"usgs":false,"family":"Nowicki Jessee","given":"M. Anna","affiliations":[],"preferred":false,"id":725406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, J.","contributorId":6289,"corporation":false,"usgs":true,"family":"Zhu","given":"J.","email":"","affiliations":[],"preferred":false,"id":725407,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":725408,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tanyas, Hakan","contributorId":167686,"corporation":false,"usgs":false,"family":"Tanyas","given":"Hakan","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":707641,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70180266,"text":"70180266 - 2017 - Nutrient processes at the stream-lake interface for a channelized versus unmodified stream mouth","interactions":[],"lastModifiedDate":"2025-05-14T18:36:52.488165","indexId":"70180266","displayToPublicDate":"2017-01-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient processes at the stream-lake interface for a channelized versus unmodified stream mouth","docAbstract":"Inorganic forms of nitrogen and phosphorous impact freshwater lakes by stimulating primary production and affecting water quality and ecosystem health. Communities around the world are motivated to sustain and restore freshwater resources and are interested in processes controlling nutrient inputs. We studied the environment where streams flow into lakes, referred to as the stream-lake interface (SLI), for a channelized and unmodified stream outlet. Channelization is done to protect infrastructure or recreational beach areas. We collected hydraulic and nutrient data for surface water and shallow groundwater in two SLIs to develop conceptual models that describe characteristics that are representative of these hydrologic features. Water, heat, and solute transport models were used to evaluate hydrologic conceptualizations and estimate mean residence times of water in the sediment. A nutrient mass balance model is developed to estimate net rates of adsorption and desorption, mineralization, and nitrification along subsurface flow paths. Results indicate that SLIs are dynamic sources of nutrients to lakes and that the common practice of channelizing the stream at the SLI decreases nutrient concentrations in pore water discharging along the lakeshore. This is in contrast to the unmodified SLI that forms a barrier beach that disconnects the stream from the lake and results in higher nutrient concentrations in pore water discharging to the lake. These results are significant because nutrient delivery through pore water seepage at the lakebed from the natural SLI contributes to nearshore algal communities and produces elevated concentrations of inorganic nutrients in the benthic zone where attached algae grow.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR019538","usgsCitation":"Niswonger, R.G., Naranjo, R.C., Smith, D., Constantz, J., Allander, K.K., Rosenberry, D.O., Neilson, B., Rosen, M.R., and Stonestrom, D.A., 2017, Nutrient processes at the stream-lake interface for a channelized versus unmodified stream mouth: Water Resources Research, v. 53, no. 1, p. 237-256, https://doi.org/10.1002/2016WR019538.","productDescription":"20 p.","startPage":"237","endPage":"256","ipdsId":"IP-077507","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":334057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-11","publicationStatus":"PW","scienceBaseUri":"588b1976e4b0ad67323f97da","contributors":{"authors":[{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":152462,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":661003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":661004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David 0000-0002-9543-800X","orcid":"https://orcid.org/0000-0002-9543-800X","contributorId":169280,"corporation":false,"usgs":true,"family":"Smith","given":"David","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":661005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Constantz, James E. 0000-0002-4062-2096 jconstan@usgs.gov","orcid":"https://orcid.org/0000-0002-4062-2096","contributorId":1962,"corporation":false,"usgs":true,"family":"Constantz","given":"James E.","email":"jconstan@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":661006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allander, Kip K. 0000-0002-3317-298X kalland@usgs.gov","orcid":"https://orcid.org/0000-0002-3317-298X","contributorId":2290,"corporation":false,"usgs":true,"family":"Allander","given":"Kip","email":"kalland@usgs.gov","middleInitial":"K.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":661007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":661008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neilson, Bethany","contributorId":178798,"corporation":false,"usgs":false,"family":"Neilson","given":"Bethany","affiliations":[],"preferred":false,"id":661009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":661010,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stonestrom, David A. 0000-0001-7883-3385 dastones@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-3385","contributorId":2280,"corporation":false,"usgs":true,"family":"Stonestrom","given":"David","email":"dastones@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":661011,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70180250,"text":"70180250 - 2017 - Comparison of climate envelope models developed using expert-selected variables versus statistical selection","interactions":[],"lastModifiedDate":"2017-01-26T13:37:44","indexId":"70180250","displayToPublicDate":"2017-01-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of climate envelope models developed using expert-selected variables versus statistical selection","docAbstract":"Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.11.016","usgsCitation":"Brandt, L.A., Benscoter, A., Harvey, R.G., Speroterra, C., Bucklin, D., Romanach, S.S., Watling, J.I., and Mazzotti, F., 2017, Comparison of climate envelope models developed using expert-selected variables versus statistical selection: Ecological Modelling, v. 345, p. 10-20, https://doi.org/10.1016/j.ecolmodel.2016.11.016.","productDescription":"11 p.","startPage":"10","endPage":"20","ipdsId":"IP-079574","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":438442,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J101BT","text":"USGS data release","linkHelpText":"Data for comparison of climate envelope models developed using expert-selected variables versus statistical selection"},{"id":334065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"345","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"588b1976e4b0ad67323f97de","contributors":{"authors":[{"text":"Brandt, Laura A.","contributorId":146646,"corporation":false,"usgs":false,"family":"Brandt","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":660921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benscoter, Allison 0000-0003-4205-3808 abenscoter@usgs.gov","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":178750,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","email":"abenscoter@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":660922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harvey, Rebecca G.","contributorId":149719,"corporation":false,"usgs":false,"family":"Harvey","given":"Rebecca","email":"","middleInitial":"G.","affiliations":[{"id":12558,"text":"University of Florida, Gainesville","active":true,"usgs":false}],"preferred":false,"id":660923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Speroterra, Carolina","contributorId":178751,"corporation":false,"usgs":false,"family":"Speroterra","given":"Carolina","email":"","affiliations":[],"preferred":false,"id":660924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bucklin, David N.","contributorId":58963,"corporation":false,"usgs":true,"family":"Bucklin","given":"David N.","affiliations":[],"preferred":false,"id":660925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":660920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Watling, James I.","contributorId":175275,"corporation":false,"usgs":false,"family":"Watling","given":"James","email":"","middleInitial":"I.","affiliations":[{"id":27555,"text":"John Carroll University","active":true,"usgs":false}],"preferred":false,"id":660926,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":660927,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70180171,"text":"70180171 - 2017 - Macroclimatic change expected to transform coastal wetland ecosystems this century","interactions":[],"lastModifiedDate":"2017-02-02T11:00:41","indexId":"70180171","displayToPublicDate":"2017-01-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Macroclimatic change expected to transform coastal wetland ecosystems this century","docAbstract":"Coastal wetlands, existing at the interface between land and sea, are highly vulnerable to climate change. Macroclimate (for example, temperature and precipitation regimes) greatly influences coastal wetland ecosystem structure and function. However, research on climate change impacts in coastal wetlands has concentrated primarily on sea-level rise and largely ignored macroclimatic drivers, despite their power to transform plant community structure and modify ecosystem goods and services. Here, we model wetland plant community structure based on macroclimate using field data collected across broad temperature and precipitation gradients along the northern Gulf of Mexico coast. Our analyses quantify strongly nonlinear temperature thresholds regulating the potential for marsh-to-mangrove conversion. We also identify precipitation thresholds for dominance by various functional groups, including succulent plants and unvegetated mudflats. Macroclimate-driven shifts in foundation plant species abundance will have large effects on certain ecosystem goods and services. Based on current and projected climatic conditions, we project that transformative ecological changes are probable throughout the region this century, even under conservative climate scenarios. Coastal wetland ecosystems are functionally similar worldwide, so changes in this region are indicative of potential future changes in climatically similar regions globally.","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/nclimate3203","usgsCitation":"Gabler, C., Osland, M.J., Grace, J.B., Stagg, C.L., Day, R.H., Hartley, S.B., Enwright, N.M., From, A., McCoy, M., and McLeod, J.L., 2017, Macroclimatic change expected to transform coastal wetland ecosystems this century: Nature Climate Change, v. 7, p. 142-147, https://doi.org/10.1038/nclimate3203.","productDescription":"6 p.","startPage":"142","endPage":"147","ipdsId":"IP-071500","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":333901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-23","publicationStatus":"PW","scienceBaseUri":"5889c797e4b0ba3b075e05cf","contributors":{"authors":[{"text":"Gabler, Christopher A.","contributorId":178709,"corporation":false,"usgs":false,"family":"Gabler","given":"Christopher A.","affiliations":[{"id":34767,"text":"School of Earth, Environmental, and Marine Sciences, University of Texas Rio Grande Valley, Brownsville, Texas","active":true,"usgs":false}],"preferred":false,"id":660608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":660607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":660609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stagg, Camille L. 0000-0002-1125-7253 staggc@usgs.gov","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":4111,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","email":"staggc@usgs.gov","middleInitial":"L.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":660610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day, Richard H. 0000-0002-5959-7054 dayr@usgs.gov","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":2427,"corporation":false,"usgs":true,"family":"Day","given":"Richard","email":"dayr@usgs.gov","middleInitial":"H.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":660611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","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":660612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Enwright, Nicholas M. 0000-0002-7887-3261 enwrightn@usgs.gov","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":4880,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","email":"enwrightn@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":660613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"From, Andrew 0000-0002-6543-2627 froma@usgs.gov","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":169668,"corporation":false,"usgs":true,"family":"From","given":"Andrew","email":"froma@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":660614,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCoy, Meagan L.","contributorId":178710,"corporation":false,"usgs":false,"family":"McCoy","given":"Meagan L.","affiliations":[],"preferred":false,"id":660615,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McLeod, Jennie L.","contributorId":149006,"corporation":false,"usgs":false,"family":"McLeod","given":"Jennie","email":"","middleInitial":"L.","affiliations":[{"id":17617,"text":"McLeod Consulting, U.S. Geological Survey, National Wetlands Research Center, Lafayette, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":660616,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70181027,"text":"70181027 - 2017 - Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA","interactions":[],"lastModifiedDate":"2017-02-11T15:47:02","indexId":"70181027","displayToPublicDate":"2017-01-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA","docAbstract":"Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9020105","usgsCitation":"Vanderhoof, M.K., Distler, H., Mendiola, D.A., and Lang, M., 2017, Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA: Remote Sensing, v. 9, no. 105, rs9020105; 25 p., https://doi.org/10.3390/rs9020105.","productDescription":"rs9020105; 25 p.","ipdsId":"IP-079678","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":461783,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9020105","text":"Publisher Index Page"},{"id":335163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Delmarva Peninsula, Upper Choptank River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.1,\n 38.5\n ],\n [\n -76.1,\n 39.1\n ],\n [\n -75.5,\n 39.1\n ],\n [\n -75.5,\n 38.5\n ],\n [\n -76.1,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"105","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-25","publicationStatus":"PW","scienceBaseUri":"589ffecde4b099f50d3e042a","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":663370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Distler, Hayley 0000-0001-5006-1360 hdistler@usgs.gov","orcid":"https://orcid.org/0000-0001-5006-1360","contributorId":179359,"corporation":false,"usgs":true,"family":"Distler","given":"Hayley","email":"hdistler@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":663371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendiola, Di Ana","contributorId":179360,"corporation":false,"usgs":false,"family":"Mendiola","given":"Di","email":"","middleInitial":"Ana","affiliations":[],"preferred":false,"id":663372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan","contributorId":156431,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":663373,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70180205,"text":"70180205 - 2017 - A carbon balance model for the great dismal swamp ecosystem","interactions":[],"lastModifiedDate":"2017-02-08T10:30:06","indexId":"70180205","displayToPublicDate":"2017-01-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1183,"text":"Carbon Balance and Management","active":true,"publicationSubtype":{"id":10}},"title":"A carbon balance model for the great dismal swamp ecosystem","docAbstract":"Carbon storage potential has become an important consideration for land management and planning in the United States. The ability to assess ecosystem carbon balance can help land managers understand the benefits and tradeoffs between different management strategies. This paper demonstrates an application of the Land Use and Carbon Scenario Simulator (LUCAS) model developed for local-scale land management at the Great Dismal Swamp National Wildlife Refuge. We estimate the net ecosystem carbon balance by considering past ecosystem disturbances resulting from storm damage, fire, and land management actions including hydrologic inundation, vegetation clearing, and replanting.
We modeled the annual ecosystem carbon stock and flow rates for the 30-year historic time period of 1985–2015, using age-structured forest growth curves and known data for disturbance events and management activities. The 30-year total net ecosystem production was estimated to be a net sink of 0.97 Tg C. When a hurricane and six historic fire events were considered in the simulation, the Great Dismal Swamp became a net source of 0.89 Tg C. The cumulative above and below-ground carbon loss estimated from the South One and Lateral West fire events totaled 1.70 Tg C, while management activities removed an additional 0.01 Tg C. The carbon loss in below-ground biomass alone totaled 1.38 Tg C, with the balance (0.31 Tg C) coming from above-ground biomass and detritus.
Natural disturbances substantially impact net ecosystem carbon balance in the Great Dismal Swamp. Through alternative management actions such as re-wetting, below-ground biomass loss may have been avoided, resulting in the added carbon storage capacity of 1.38 Tg. Based on two model assumptions used to simulate the peat system, (a burn scar totaling 70 cm in depth, and the soil carbon accumulation rate of 0.36 t C/ha−1/year−1 for Atlantic white cedar), the total soil carbon loss from the South One and Lateral West fires would take approximately 1740 years to re-amass. Due to the impractical time horizon this presents for land managers, this particular loss is considered permanent. Going forward, the baseline carbon stock and flow parameters presented here will be used as reference conditions to model future scenarios of land management and disturbance.
Submerged aquatic vegetation is generally thought to attenuate waves, but this interaction remains poorly characterized in shallow-water field settings with locally generated wind waves. Better quantification of wave–vegetation interaction can provide insight to morphodynamic changes in a variety of environments and also is relevant to the planning of nature-based coastal protection measures. Toward that end, an instrumented transect was deployed across a Zostera marina (common eelgrass) meadow in Chincoteague Bay, Maryland/Virginia, U.S.A., to characterize wind-wave transformation within the vegetated region. Field observations revealed wave-height reduction, wave-period transformation, and wave-energy dissipation with distance into the meadow, and the data informed and calibrated a spectral wave model of the study area. The field observations and model results agreed well when local wind forcing and vegetation-induced drag were included in the model, either explicitly as rigid vegetation elements or implicitly as large bed-roughness values. Mean modeled parameters were similar for both the explicit and implicit approaches, but the spectral performance of the explicit approach was poor compared to the implicit approach. The explicit approach over-predicted low-frequency energy within the meadow because the vegetation scheme determines dissipation using mean wavenumber and frequency, in contrast to the bed-friction formulations, which dissipate energy in a variable fashion across frequency bands. Regardless of the vegetation scheme used, vegetation was the most important component of wave dissipation within much of the study area. These results help to quantify the influence of submerged aquatic vegetation on wave dynamics in future model parameterizations, field efforts, and coastal-protection measures.
","language":"English","publisher":"ASLO","doi":"10.1002/lno.10456","usgsCitation":"Nowacki, D.J., Beudin, A., and Ganju, N., 2017, Spectral wave dissipation by submerged aquatic vegetation in a back-barrier estuary: Limnology and Oceanography, v. 62, no. 2, p. 736-753, https://doi.org/10.1002/lno.10456.","productDescription":"18 p.","startPage":"736","endPage":"753","ipdsId":"IP-074582","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470116,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10456","text":"Publisher Index Page"},{"id":333910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-11","publicationStatus":"PW","scienceBaseUri":"5889c79ae4b0ba3b075e05db","contributors":{"authors":[{"text":"Nowacki, Daniel J. 0000-0002-7015-3710 dnowacki@usgs.gov","orcid":"https://orcid.org/0000-0002-7015-3710","contributorId":174586,"corporation":false,"usgs":true,"family":"Nowacki","given":"Daniel","email":"dnowacki@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":660424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beudin, Alexis 0000-0001-9525-9450 abeudin@usgs.gov","orcid":"https://orcid.org/0000-0001-9525-9450","contributorId":5751,"corporation":false,"usgs":true,"family":"Beudin","given":"Alexis","email":"abeudin@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":660425,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":140088,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","email":"nganju@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":660426,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177879,"text":"ofr20161171 - 2017 - Water quality and bed sediment quality in the Albemarle Sound, North Carolina, 2012–14","interactions":[],"lastModifiedDate":"2017-01-23T11:15:32","indexId":"ofr20161171","displayToPublicDate":"2017-01-23T11:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1171","title":"Water quality and bed sediment quality in the Albemarle Sound, North Carolina, 2012–14","docAbstract":"The Albemarle Sound region was selected in 2012 as one of two demonstration sites in the Nation to test and improve the design of the National Water Quality Monitoring Council’s National Monitoring Network (NMN) for U.S. Coastal Waters and Tributaries. The goal of the NMN for U.S. Coastal Waters and Tributaries is to provide information about the health of our oceans, coastal ecosystems, and inland influences on coastal waters for improved resource management. The NMN is an integrated, multidisciplinary, and multi-organizational program using multiple sources of data and information to augment current monitoring programs.
This report presents and summarizes selected water-quality and bed sediment-quality data collected as part of the demonstration project conducted in two phases. The first phase was an occurrence and distribution study to assess nutrients, metals, pesticides, cyanotoxins, and phytoplankton communities in the Albemarle Sound during the summer of 2012 at 34 sites in Albemarle Sound, nearby sounds, and various tributaries. The second phase consisted of monthly sampling over a year (March 2013 through February 2014) to assess seasonality in a more limited set of constituents including nutrients, cyanotoxins, and phytoplankton communities at a subset (eight) of the sites sampled in the first phase. During the summer of 2012, few constituent concentrations exceeded published water-quality thresholds; however, elevated levels of chlorophyll a and pH were observed in the northern embayments and in Currituck Sound. Chlorophyll a, and metals (copper, iron, and zinc) were detected above a water-quality threshold. The World Health Organization provisional guideline based on cyanobacterial density for high recreational risk was exceeded in approximately 50 percent of water samples collected during the summer of 2012. Cyanobacteria capable of producing toxins were present, but only low levels of cyanotoxins below human health benchmarks were detected. Finally, 12 metals in surficial bed sediments were detected at levels above a published sediment-quality threshold. These metals included chromium, mercury, copper, lead, arsenic, nickel, and cadmium. Sites with several metal concentrations above the respective thresholds had relatively high concentrations of organic carbon or fine sediment (silt plus clay), or both and were predominantly located in the western and northwestern parts of the Albemarle Sound.
Results from the second phase were generally similar to those of the first in that relatively few constituents exceeded a water-quality threshold, both pH and chlorophyll a were detected above the respective water-quality thresholds, and many of these elevated concentrations occurred in the northern embayments and in Currituck Sound. In contrast to the results from phase one, the cyanotoxin, microcystin was detected at more than 10 times the water-quality threshold during a phytoplankton bloom on the Chowan River at Mount Gould, North Carolina in August of 2013. This was the only cyanotoxin concentration measured during the entire study that exceeded a respective water-quality threshold.
The information presented in this report can be used to improve understanding of water-quality conditions in the Albemarle Sound, particularly when evaluating causal and response variables that are indicators of eutrophication. In particular, this information can be used by State agencies to help develop water-quality criteria for nutrients, and to understand factors like cyanotoxins that may affect fisheries and recreation in the Albemarle Sound region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161171","usgsCitation":"Moorman, M.C., Fitzgerald, S.A., Gurley, L.N., Rhoni-Aref, Ahmed, and Loftin, K.A., 2017, Water quality and bed sediment quality in the Albemarle Sound, North Carolina, 2012–14: U.S. Geological Survey Open-File Report 2016–1171, 46 p., https://doi.org/10.3133/ofr20161171. ","productDescription":"Report: viii, 46 p.; Appendixes 1-4; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063224","costCenters":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":333448,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1171/coverthb.jpg"},{"id":333449,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1171/ofr20161171.pdf","text":"Report","size":"4.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1171"},{"id":333450,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1171/downloads/ofr20161171_appendix1.xls","text":"Appendix 1 - ","size":"262 KB (xls)","linkHelpText":"Quality Control Results"},{"id":333451,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1171/downloads/ofr20161171_appendix2.xls","text":"Appendix 2 - ","size":"287 KB (xls)","linkHelpText":"Chemical, Biological and Physical Results for Samples Collected in the Albemarle Sound and Tributaries, 2012"},{"id":333453,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1171/downloads/ofr20161171_appendix4.xls","text":"Appendix 4 - ","size":"57.5 KB (xls)","linkHelpText":"Constituents in Bed Sediment Samples Collected in the Albemarle Sound and Tributaries, 2012"},{"id":333454,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7057D2V","text":"USGS data release","description":"USGS data release","linkHelpText":"Associated Data for Water Quality and Bed Sediment Quality in the Albemarle Sound, North Carolina, 2012–14"},{"id":333452,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1171/downloads/ofr20161171_appendix3.xls","text":"Appendix 3 - ","size":"268 KB (xls)","linkHelpText":"Chemical, Biological and Physical Results for Samples Collected in the Albemarle Sound and Tributaries, 2013–14"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Albemarle Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.43212890625,\n 35.740939184355575\n ],\n [\n -75.62850952148436,\n 35.7286770448517\n ],\n [\n -75.76171875,\n 35.761000430333475\n ],\n [\n -75.78369140625,\n 35.753199435570316\n ],\n [\n -75.81939697265625,\n 35.745397676018534\n ],\n [\n -75.85235595703125,\n 35.73202145196925\n ],\n [\n -75.860595703125,\n 35.71752800693265\n ],\n [\n -75.87432861328125,\n 35.7019167328534\n ],\n [\n -75.89218139648438,\n 35.6907639509368\n ],\n [\n -75.89630126953125,\n 35.68407153314097\n ],\n [\n -75.8990478515625,\n 35.669569368624195\n ],\n [\n -75.9100341796875,\n 35.6517169333161\n ],\n [\n -75.94573974609375,\n 35.63832498777989\n ],\n [\n -75.97183227539062,\n 35.633860507136994\n ],\n [\n -75.98419189453125,\n 35.63051198300061\n ],\n [\n -75.99105834960938,\n 35.61600009092947\n ],\n [\n -76.01577758789062,\n 35.60036895434144\n ],\n [\n -76.06521606445311,\n 35.59701902776685\n ],\n [\n -76.11328125,\n 35.60595185369916\n ],\n [\n -76.13525390624999,\n 35.62381451392674\n ],\n [\n -76.1627197265625,\n 35.639441068973944\n ],\n [\n -76.17782592773438,\n 35.652832827451654\n ],\n [\n -76.19842529296875,\n 35.661759419295045\n ],\n [\n -76.22314453125,\n 35.67737855391475\n ],\n [\n -76.22451782226562,\n 35.6907639509368\n ],\n [\n -76.28219604492188,\n 35.68853320738875\n ],\n [\n -76.32476806640625,\n 35.68295607559029\n ],\n [\n -76.38381958007812,\n 35.6807251137039\n ],\n [\n -76.41265869140625,\n 35.679609609368576\n ],\n [\n -76.42776489257812,\n 35.68630240145625\n ],\n [\n -76.4593505859375,\n 35.69299463209881\n ],\n [\n -76.47033691406249,\n 35.69522525087309\n ],\n [\n -76.50054931640625,\n 35.708607653285505\n ],\n [\n -76.53213500976562,\n 35.72756221127198\n ],\n [\n -76.57196044921874,\n 35.735365718650236\n ],\n [\n -76.61590576171875,\n 35.745397676018534\n ],\n [\n -76.64337158203125,\n 35.74874138089811\n ],\n [\n -76.66122436523438,\n 35.753199435570316\n ],\n [\n -76.68731689453125,\n 35.762114795721\n ],\n [\n -76.72027587890625,\n 35.7688006602384\n ],\n [\n -76.7449951171875,\n 35.77214338175441\n ],\n [\n -76.7669677734375,\n 35.764343479667176\n ],\n [\n -76.78482055664062,\n 35.75431391021905\n ],\n [\n -76.80130004882812,\n 35.735365718650236\n ],\n [\n -76.827392578125,\n 35.708607653285505\n ],\n [\n -76.849365234375,\n 35.67849408943879\n ],\n [\n -76.86859130859375,\n 35.65952786487723\n ],\n [\n -76.88369750976561,\n 35.63832498777989\n ],\n [\n -76.904296875,\n 35.631628173298445\n ],\n [\n -76.9427490234375,\n 35.63832498777989\n ],\n [\n -77.01690673828125,\n 35.66622234103479\n ],\n [\n -77.09381103515624,\n 35.71418299132001\n ],\n [\n -77.10617065429688,\n 35.73982452242507\n ],\n [\n -77.135009765625,\n 35.78328477203738\n ],\n [\n -77.14599609375,\n 35.807790334456776\n ],\n [\n -77.16659545898438,\n 35.83451505415075\n ],\n [\n -77.17758178710938,\n 35.85121343450061\n ],\n [\n -77.18582153320312,\n 35.879036255232734\n ],\n [\n -77.22015380859375,\n 35.90017506551962\n ],\n [\n -77.23800659179688,\n 35.90128747814897\n ],\n [\n -77.29156494140625,\n 35.91685961322499\n ],\n [\n -77.35885620117186,\n 35.93910020359144\n ],\n [\n -77.40554809570311,\n 35.9635576265708\n ],\n [\n -77.42477416992188,\n 36.00356252895066\n ],\n [\n -77.43026733398438,\n 36.03133177633187\n ],\n [\n -77.42477416992188,\n 36.060201412392914\n ],\n [\n -77.42477416992188,\n 36.075742215627\n ],\n [\n -77.45086669921875,\n 36.081291758402664\n ],\n [\n -77.46047973632811,\n 36.097938036628065\n ],\n [\n -77.45635986328125,\n 36.13898378070337\n ],\n [\n -77.45773315429688,\n 36.15007354140755\n ],\n [\n -77.46734619140625,\n 36.17003115949951\n ],\n [\n -77.47970581054688,\n 36.18998369654791\n ],\n [\n -77.49069213867188,\n 36.20328256455562\n ],\n [\n -77.51541137695312,\n 36.184441834883\n ],\n [\n -77.55523681640625,\n 36.16448788632064\n ],\n [\n -77.58270263671875,\n 36.15783544102625\n ],\n [\n -77.62939453125,\n 36.16227046727883\n ],\n [\n -77.65411376953125,\n 36.17446549576358\n ],\n [\n -77.66510009765625,\n 36.20106624342554\n ],\n [\n -77.64312744140625,\n 36.234304470709226\n ],\n [\n -77.63763427734375,\n 36.24205802497115\n ],\n [\n -77.62664794921875,\n 36.25756282630298\n ],\n [\n -77.61566162109375,\n 36.27638594874375\n ],\n [\n -77.61428833007812,\n 36.296311367952875\n ],\n [\n -77.61016845703125,\n 36.30627216957992\n ],\n [\n -77.61978149414062,\n 36.3106987841827\n ],\n [\n -77.64724731445311,\n 36.311805398567465\n ],\n [\n -77.68707275390625,\n 36.32065774818664\n ],\n [\n -77.73651123046875,\n 36.34389038251925\n ],\n [\n -77.78457641601562,\n 36.359374956015856\n ],\n [\n -77.82989501953125,\n 36.358269017192455\n ],\n [\n -77.87521362304688,\n 36.36490441440569\n ],\n [\n -77.93701171875,\n 36.37153924584892\n ],\n [\n -77.98370361328125,\n 36.38922936340128\n ],\n [\n -78.02627563476562,\n 36.41354670392873\n ],\n [\n -78.02215576171875,\n 36.43785643398897\n ],\n [\n -77.97683715820312,\n 36.45442688547802\n ],\n [\n -77.92327880859375,\n 36.474306755095235\n ],\n [\n -77.90679931640625,\n 36.50522086338427\n ],\n [\n -77.90542602539062,\n 36.516258626036624\n ],\n [\n -77.9315185546875,\n 36.527294814546245\n ],\n [\n -77.94387817382811,\n 36.54163950596125\n ],\n [\n -77.92190551757812,\n 36.54494944148322\n ],\n [\n -77.87933349609375,\n 36.53722603815998\n ],\n [\n -77.8436279296875,\n 36.52508770278463\n ],\n [\n -77.80517578125,\n 36.532812318439724\n ],\n [\n -77.75161743164062,\n 36.548259235289144\n ],\n [\n -77.71453857421875,\n 36.55708465923294\n ],\n [\n -77.64312744140625,\n 36.58134937787054\n ],\n [\n -77.57171630859375,\n 36.57252672584645\n ],\n [\n -77.552490234375,\n 36.54715598643371\n ],\n [\n -77.51678466796875,\n 36.52950186333475\n ],\n [\n -77.48245239257812,\n 36.535019209789\n ],\n [\n -77.431640625,\n 36.53391577198655\n ],\n [\n -77.36572265625,\n 36.535019209789\n ],\n [\n -77.354736328125,\n 36.55046568575947\n ],\n [\n -77.35061645507812,\n 36.5670120564234\n ],\n [\n -77.35061645507812,\n 36.58355488335723\n ],\n [\n -77.3272705078125,\n 36.62765175890193\n ],\n [\n -77.310791015625,\n 36.649690741415014\n ],\n [\n -77.2833251953125,\n 36.65189429289055\n ],\n [\n -77.26409912109375,\n 36.640875904982344\n ],\n [\n -77.25173950195312,\n 36.63316209558658\n ],\n [\n -77.21878051757811,\n 36.630958008194106\n ],\n [\n -77.20367431640625,\n 36.63316209558658\n ],\n [\n -77.17208862304686,\n 36.64307970866717\n ],\n [\n -77.135009765625,\n 36.65299604498243\n ],\n [\n -77.135009765625,\n 36.65850456897558\n ],\n [\n -77.08694458007812,\n 36.667317387570925\n ],\n [\n -77.04986572265625,\n 36.6551995018735\n ],\n [\n -77.0306396484375,\n 36.667317387570925\n ],\n [\n -77.02789306640625,\n 36.676129197108104\n ],\n [\n -76.99356079101562,\n 36.693749788636545\n ],\n [\n -76.9757080078125,\n 36.69485094156225\n ],\n [\n -76.86859130859375,\n 36.69485094156225\n ],\n [\n -76.81915283203125,\n 36.681636065615216\n ],\n [\n -76.78207397460936,\n 36.66070786821854\n ],\n [\n -76.75735473632812,\n 36.66070786821854\n ],\n [\n -76.72988891601562,\n 36.67062193440137\n ],\n [\n -76.7230224609375,\n 36.68493999749423\n ],\n [\n -76.7010498046875,\n 36.701457527917896\n ],\n [\n -76.68869018554686,\n 36.70806354647625\n ],\n [\n -76.66259765625,\n 36.705861603381145\n ],\n [\n -76.61865234374999,\n 36.68824378744832\n ],\n [\n -76.60491943359375,\n 36.679433365517774\n ],\n [\n -76.56509399414062,\n 36.677230602346214\n ],\n [\n -76.53213500976562,\n 36.67833199181616\n ],\n [\n -76.5252685546875,\n 36.691547435472636\n ],\n [\n -76.48681640625,\n 36.69485094156225\n ],\n [\n -76.46484375,\n 36.686041276581925\n ],\n [\n -76.44012451171875,\n 36.68383870263707\n ],\n [\n -76.4208984375,\n 36.69485094156225\n ],\n [\n -76.4208984375,\n 36.70696258281522\n ],\n [\n -76.42364501953124,\n 36.730079507078415\n ],\n [\n -76.42776489257812,\n 36.746587336189386\n ],\n [\n -76.4044189453125,\n 36.75098882435508\n ],\n [\n -76.38381958007812,\n 36.73448194195683\n ],\n [\n -76.38107299804688,\n 36.71466899719828\n ],\n [\n -76.38519287109375,\n 36.686041276581925\n ],\n [\n -76.3385009765625,\n 36.68934501922664\n ],\n [\n -76.22726440429688,\n 36.68273739201066\n ],\n [\n -76.22177124023438,\n 36.701457527917896\n ],\n [\n -76.201171875,\n 36.70916449436405\n ],\n [\n -76.16958618164062,\n 36.70916449436405\n ],\n [\n -76.16546630859375,\n 36.731180139463724\n ],\n [\n -76.16546630859375,\n 36.74438649742862\n ],\n [\n -76.15310668945312,\n 36.76199144012652\n ],\n [\n -76.13937377929688,\n 36.772992477693414\n ],\n [\n -76.12701416015624,\n 36.79059085468796\n ],\n [\n -76.08306884765625,\n 36.772992477693414\n ],\n [\n -76.0638427734375,\n 36.779592342620816\n ],\n [\n -76.05422973632812,\n 36.79059085468796\n ],\n [\n -76.02401733398438,\n 36.79498981749727\n ],\n [\n -75.98693847656249,\n 36.787291466820015\n ],\n [\n -75.87432861328125,\n 36.779592342620816\n ],\n [\n -75.8441162109375,\n 36.697053200100335\n ],\n [\n -75.80017089843749,\n 36.61883440069682\n ],\n [\n -75.80017089843749,\n 36.542742833547834\n ],\n [\n -75.77545166015625,\n 36.46768069827346\n ],\n [\n -75.7562255859375,\n 36.38922936340128\n ],\n [\n -75.728759765625,\n 36.295204533693536\n ],\n [\n -75.65872192382812,\n 36.184441834883\n ],\n [\n -75.59280395507812,\n 36.05687084084707\n ],\n [\n -75.52413940429688,\n 35.917971791312816\n ],\n [\n -75.48568725585938,\n 35.84564769832635\n ],\n [\n -75.43212890625,\n 35.740939184355575\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/
Rock-detention structures are used as restoration treatments to engineer ephemeral stream channels of southeast Arizona, USA, to reduce streamflow velocity, limit erosion, retain sediment, and promote surface-water infiltration. Structures are intended to aggrade incised stream channels, yet little quantified evidence of efficacy is available. The goal of this 3-year study was to characterize the geomorphic impacts of rock-detention structures used as a restoration strategy and develop a methodology to predict the associated changes. We studied reaches of two ephemeral streams with different watershed management histories: one where thousands of loose-rock check dams were installed 30 years prior to our study, and one with structures constructed at the beginning of our study. The methods used included runoff, sediment transport, and geomorphic modelling and repeat terrestrial laser scanner (TLS) surveys to map landscape change. Where discharge data were not available, event-based runoff was estimated using KINEROS2, a one-dimensional kinematic-wave runoff and erosion model. Discharge measurements and estimates were used as input to a two-dimensional unsteady flow-and-sedimentation model (Nays2DH) that combined a gridded flow, transport, and bed and bank simulation with geomorphic change. Through comparison of consecutive DEMs, the potential to substitute uncalibrated models to analyze stream restoration is introduced. We demonstrate a new approach to assess hydraulics and associated patterns of aggradation and degradation resulting from the construction of check-dams and other transverse structures. Notably, we find that stream restoration using rock-detention structures is effective across vastly different timescales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.01.017","usgsCitation":"Norman, L.M., Sankey, J.B., Dean, D.J., Caster, J.J., DeLong, S.B., Henderson-DeLong, W., and Pelletier, J.D., 2017, Quantifying geomorphic change at ephemeral stream restoration sites using a coupled-model approach: Geomorphology, v. 283, p. 1-16, https://doi.org/10.1016/j.geomorph.2017.01.017.","productDescription":"16 p.","startPage":"1","endPage":"16","ipdsId":"IP-078626","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2017.01.017","text":"Publisher Index Page"},{"id":335960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Bone Creek subwatershed, Turkey Pen subwatershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.478759765625,\n 31.80289258670676\n ],\n [\n -109.31396484375,\n 31.80289258670676\n ],\n [\n -109.31396484375,\n 31.910204597744382\n ],\n [\n -109.478759765625,\n 31.910204597744382\n ],\n [\n -109.478759765625,\n 31.80289258670676\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.81634521484375,\n 31.5504526754715\n ],\n [\n -110.65155029296875,\n 31.5504526754715\n ],\n [\n -110.65155029296875,\n 31.7141494098071\n ],\n [\n -110.81634521484375,\n 31.7141494098071\n ],\n [\n -110.81634521484375,\n 31.5504526754715\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"283","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58aeb13ae4b01ccd54f9ee14","chorus":{"doi":"10.1016/j.geomorph.2017.01.017","url":"http://dx.doi.org/10.1016/j.geomorph.2017.01.017","publisher":"Elsevier BV","authors":"Norman Laura M., Sankey Joel B., Dean David, Caster Joshua, DeLong Stephen, DeLong Whitney, Pelletier Jon D.","journalName":"Geomorphology","publicationDate":"4/2017","publiclyAccessibleDate":"1/20/2017"},"contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":670207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":670208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":670209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caster, Joshua J. 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":131114,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":670210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":670211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Henderson-DeLong, Whitney","contributorId":182018,"corporation":false,"usgs":false,"family":"Henderson-DeLong","given":"Whitney","email":"","affiliations":[],"preferred":false,"id":670212,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pelletier, Jon D.","contributorId":22657,"corporation":false,"usgs":false,"family":"Pelletier","given":"Jon","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":670213,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178520,"text":"ofr20161195 - 2017 - Assessment of ecosystem response to a temporary water level drawdown and subsequent refilling at Topock Marsh, Arizona—July 2011–October 2014","interactions":[],"lastModifiedDate":"2017-01-23T08:34:23","indexId":"ofr20161195","displayToPublicDate":"2017-01-20T15:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1195","title":"Assessment of ecosystem response to a temporary water level drawdown and subsequent refilling at Topock Marsh, Arizona—July 2011–October 2014","docAbstract":"Topock Marsh is a 1,637-hectare (4,045-acre) wetland adjacent to the Colorado River near Needles, California, and a main feature of Havasu National Wildlife Refuge (NWR). The U.S. Fish and Wildlife Service, in cooperation with the Bureau of Reclamation, began construction of an infrastructure improvement project in 2010 to increase the efficiency of water use and to help protect the habitats and species found within the Havasu NWR. During construction, normal water delivery from the Colorado River into Topock Marsh through the Inlet Canal was restricted, which resulted in unusually low water elevations in 2011. The U.S. Geological Survey, commissioned by the U.S. Fish and Wildlife Service, undertook the investigation of the water quality and aquatic flora and fauna during the low water conditions. Subsequently, water elevations in the marsh returned to more normal elevations after the new concrete-lined Fire Break Canal became fully operational in January 2012.
The U.S. Geological Survey made 11 field trips to the Havasu NWR between July 2011 and October 2014 to assess the effects of the temporary low water conditions and the change of inflow location (from the Inlet Canal to the Fire Break Canal) on water quality and aquatic habitat. The following conditions were monitored: water quality, sediment and plant chemistry, phytoplankton, zooplankton, aquatic macro-invertebrates, and emergent and submerged aquatic vegetation (SAV). Water-quality and biota data collected during 2013–14 were then compared with data collected during the 2011–12 low water period.
Once the new Fire Break Canal became operational and Colorado River water flowed regularly into the marsh, concentrations of several water quality parameters decreased (for example, specific conductance, total dissolved solids, turbidity, chlorophyll a, and total and organic nitrogen), and phytoplankton abundance was reduced at the upstream sampling stations (TP-3, TP-2, and TP-6); the water flow pushed water with higher concentrations of these components downstream (measured at TP-8). The upstream sampling locations in 2013–14 had decreased turbidity, therefore more SAV biomass accumulated, especially in shallow areas with water depths of ≤1.0 meter (≤3.3 feet). However, the furthest downstream station had higher turbidity caused by both the suspension of autochthonous sediment and high phytoplankton density and biovolume. This higher turbidity resulted in minimal SAV growth, especially in the deeper water (>1.0 meter [>3.3 feet]). Emergent vegetation not only survived the low water conditions of 2011, but expanded its areal coverage and subsequently thrived in the higher water elevations.
Overall, no immediate critically negative consequences were detected for aquatic fauna or flora that could be attributd unequivocally to the effect of low water levels. Concentrations of nutrient and trace elements in all water samples were below wildlife toxicity thresholds as established by Arizona Department of Environmental Quality. Three nonnative species were discovered shortly after the Fire Break Canal went into operation. Of the three, gizzard shad (Dorosoma cepedianum) and Eurasian watermilfoil (Myriophyllum spicatum) increased substantially in numbers from 2011–14, but quagga mussels (Dreissena bugensis) did not increase. Future monitoring will determine the long-term impact of the new flow regime
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161195","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service–Region 2–National Wildlife Refuge System, the Havasu National Wildlife Refuge, and the Desert Landscape Conservation Cooperative","usgsCitation":"Daniels, J.S., and Haegele, J.C., 2017, Assessment of ecosystem response to a temporary water level drawdown and subsequent refilling at Topock Marsh, Arizona—July 2011–October 2014: U.S. Geological Survey Open-File Report 2016–1195, 93 p., https://doi.org/10.3133/ofr20161195.","productDescription":"Report: vi, 92 p.; Appendixes 1-2","onlineOnly":"Y","ipdsId":"IP-051540","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":333180,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix2_2014_Topock_Marsh_Fish_Survey_AGFD.pdf","text":"Appendix 2-2014 Topock Marsh Fish Survey AGFD","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195 Appendix 2 2014"},{"id":333171,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1195/coverthb2.jpg"},{"id":333179,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix2_2013_Topock_Marsh_Fish_Survey_AGFD.pdf","text":"Appendix 2-2013 Topock Marsh Fish Survey AGFD","size":"28.0 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195 Appendix 2 2013"},{"id":333175,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix 1-Reclamation_longterm_WQ_data_1983-2015.xlsx","text":"Appendix 1-Reclamation longterm WQ data 1983-2015","size":"120 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1195 Appendix 1"},{"id":333172,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195.pdf","text":"Report","size":"4.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195"},{"id":333181,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix2_2015_Topock_Marsh_Fish_Survey_AGFD.pdf","text":"Appendix 2-2015 Topock Marsh Fish Survey AGFD","size":"120 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195 Appendix 2 2015"},{"id":333176,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix2_2010-2011-Topock_Marsh_Fish_Surveys_AGFD.pdf","text":"Appendix 2-2010-2011 Topock Marsh Fish Surveys AGFD","size":"20.0 kb","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195 Appendix 2 2010-2011"},{"id":333178,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1195/ofr20161195_Appendix2_2012_Topock_Marsh_Fish_Survey_AGFD.pdf","text":"Appendix 2-2012 Topock Marsh Fish Survey AGFD","size":"24.0 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1195 Appendix 2 2012"}],"country":"United States","state":"Arizona","otherGeospatial":"Topock Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.5625,\n 34.844444\n ],\n [\n -114.5625,\n 34.733333\n ],\n [\n -114.466667,\n 34.733333\n ],\n [\n -114.466667,\n 34.844444\n ],\n [\n -114.5625,\n 34.844444\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Petroleum production data are usually stored in a format that makes it easy to determine the year and month production started, if there are any breaks, and when production ends. However, in some cases, you may want to compare production runs where the start of production for all wells starts at month one regardless of the year the wells started producing. This report describes the JAVA program the U.S. Geological Survey developed to examine water-to-oil and water-to-gas ratios in the form of month 1, month 2, and so on with the objective of estimating quantities of water and proppant used in low-permeability petroleum production. The text covers the data used by the program, the challenges with production data, the program logic for checking the quality of the production data, and the program logic for checking the completeness of the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161204","usgsCitation":"Varela, B.A., Haines, S.S., and Gianoutsos, N.J., 2017, Data cleaning methodology for monthly water-to-oil and water-to-gas production ratios in continuous resource assessments: U.S. Geological Survey Open-File Report 2016–1204, 11 p., https://doi.org/10.3133/ofr20161204.","productDescription":"Report: ii, 11 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-069302","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333339,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1204/ofr20161204.pdf","text":"Report","size":"4.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1204"},{"id":333343,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9VG7","text":"USGS Data Release","description":"OFR 2016-1204 USGS Data Release","linkHelpText":" Data cleaning methodology source code—Creating water-to-oil and water-to-gas ratios in sequence from start of production using the IHS PIDM database"},{"id":333337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1204/coverthb2.jpg"}],"contact":"Center Director, USGS Central Energy Resources Science Center
Box 25046, Mail Stop 939
Denver, CO 80225
Since the original publication of the Death Valley regional groundwater flow system (DVRFS) numerical model in 2004, more information on the regional groundwater flow system in the form of new data and interpretations has been compiled. Cooperators such as the Bureau of Land Management, National Park Service, U.S. Fish and Wildlife Service, the Department of Energy, and Nye County, Nevada, recognized a need to update the existing regional numerical model to maintain its viability as a groundwater management tool for regional stakeholders. The existing DVRFS numerical flow model was converted to MODFLOW-2005, updated with the latest available data, and recalibrated. Five main data sets were revised: (1) recharge from precipitation varying in time and space, (2) pumping data, (3) water-level observations, (4) an updated regional potentiometric map, and (5) a revision to the digital hydrogeologic framework model.
The resulting DVRFS version 2.0 (v. 2.0) numerical flow model simulates groundwater flow conditions for the Death Valley region from 1913 to 2003 to correspond to the time frame for the most recently published (2008) water-use data. The DVRFS v 2.0 model was calibrated by using the Tikhonov regularization functionality in the parameter estimation and predictive uncertainty software PEST. In order to assess the accuracy of the numerical flow model in simulating regional flow, the fit of simulated to target values (consisting of hydraulic heads and flows, including evapotranspiration and spring discharge, flow across the model boundary, and interbasin flow; the regional water budget; values of parameter estimates; and sensitivities) was evaluated. This evaluation showed that DVRFS v. 2.0 simulates conditions similar to DVRFS v. 1.0. Comparisons of the target values with simulated values also indicate that they match reasonably well and in some cases (boundary flows and discharge) significantly better than in DVRFS v. 1.0.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165150","collaboration":"Prepared in cooperation with the Bureau of Land Management, National Park Service, U.S. Department of Energy National Nuclear Security Administration (Interagency Agreement DE–AI52–01NV13944), and Office of Civilian Radioactive Waste Management (Interagency Agreement DE–AI28–02RW12167), U.S. Fish and Wildlife Service, and Nye County, Nevada","usgsCitation":"Belcher, W.R., Sweetkind, D.S., Faunt, C.C., Pavelko, M.T., and Hill, M.C., 2017, An update of the Death Valley regional groundwater flow system transient model, Nevada and California: U.S. Geological Survey Scientific Investigations Report 2016-5150, 74 p., 1 pl. https://doi.org/10.3133/sir20165150","productDescription":"Report: x, 74 p.; Plate: 18 x 26 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-045053","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":333413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5150/coverthb.jpg"},{"id":333414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5150/sir20165150.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5150 Report PDF"},{"id":333415,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5150/sir20165150_plate.pdf","text":"Plate 1","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5150 Plate 1"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley regional groundwater flow system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118,\n 35\n ],\n [\n -118,\n 38.25\n ],\n [\n -115,\n 38.25\n ],\n [\n -115,\n 35\n ],\n [\n -118,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nevada.usgs.gov/