The seismic spectrum can be constructed by assuming a Brune spectral model and estimating the parameters of seismic moment (M0), corner frequency (fc), and high-frequency site attenuation (κ). Using seismic data collected during the 2010–2011 Canterbury, New Zealand, earthquake sequence, we apply the non-linear least-squares Gauss–Newton method, a deterministic downhill optimization technique, to simultaneously determine the M0, fc, and κ for each event-station pair. We fit the Brune spectral acceleration model to Fourier-transformed S-wave records following application of path and site corrections to the data. For each event, we solve for a single M0 and fc, while any remaining residual kappa,
The trend of decreasing permeability with depth was estimated in the fractured-rock terrain of the upper Potomac River basin in the eastern USA using model calibration on 200 water-level observations in wells and 12 base-flow observations in subwatersheds. Results indicate that permeability at the 1–10 km scale (for groundwater flowpaths) decreases by several orders of magnitude within the top 100 m of land surface. This depth range represents the transition from the weathered, fractured regolith into unweathered bedrock. This rate of decline is substantially greater than has been observed by previous investigators that have plotted in situ wellbore measurements versus depth. The difference is that regional water levels give information on kilometer-scale connectivity of the regolith and adjacent fracture networks, whereas in situ measurements give information on near-hole fractures and fracture networks. The approach taken was to calibrate model layer-to-layer ratios of hydraulic conductivity (LLKs) for each major rock type. Most rock types gave optimal LLK values of 40–60, where each layer was twice a thick as the one overlying it. Previous estimates of permeability with depth from deeper data showed less of a decline at <300 m than the regional modeling results. There was less certainty in the modeling results deeper than 200 m and for certain rock types where fewer water-level observations were available. The results have implications for improved understanding of watershed-scale groundwater flow and transport, such as for the timing of the migration of pollutants from the water table to streams.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1483-y","usgsCitation":"Sanford, W.E., 2017, Estimating regional-scale permeability–depth relations in a fractured-rock terrain using groundwater-flow model calibration: Hydrogeology Journal, v. 25, no. 2, p. 405-419, https://doi.org/10.1007/s10040-016-1483-y.","productDescription":"15 p.","startPage":"405","endPage":"419","ipdsId":"IP-076752","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":352927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"5afee8c4e4b0da30c1bfc4a6","contributors":{"authors":[{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":692987,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70182452,"text":"sir20165176 - 2017 - Trends in the quality of water in New Jersey streams, water years 1971–2011","interactions":[],"lastModifiedDate":"2017-02-27T10:45:09","indexId":"sir20165176","displayToPublicDate":"2017-02-27T10:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5176","title":"Trends in the quality of water in New Jersey streams, water years 1971–2011","docAbstract":"In a study conducted by the U.S. Geological Survey in cooperation with the New Jersey Department of Environmental Protection and the Delaware River Basin Commission, trend tests were conducted on selected water-quality characteristics measured at stations on streams in New Jersey during selected periods over water years 1971‒2011. Tests were conducted on 3 nutrients (total nitrogen, filtered nitrate plus nitrite, and total phosphorus) at 28 water-quality stations. At 4 of these stations, tests were also conducted on 3 measures of major ions (specific conductance, filtered chloride, and total dissolved solids).
Two methods were used to identify trends—Weighted Regressions on Time, Discharge, and Season (WRTDS) models and seasonal rank-sum tests. For this report, the use of WRTDS models included the use of the WRTDS Bootstrap Test (WBT). WRTDS models identified trends in flow-normalized annual concentrations and flow-normalized annual fluxes over water years 1980‒2011 and 2000‒11 for each nutrient, filtered chloride, and total dissolved solids. WRTDS models were developed for each nutrient at the 20 or 21 stations at which streamflow was measured or estimated. Trends in nutrient concentration were reported for these stations; trends in nutrient fluxes were reported only for 15–17 of these stations.
The results of WRTDS models for water years 1980‒2011 identified more stations with downward trends in concentrations of either total nitrogen or total phosphorus than upward trends. For total nitrogen, there were downward trends at 9 stations and an upward trend at 1 station. For total phosphorus, there were downward trends at 8 stations and an upward trend at 1 station. For filtered nitrate plus nitrite, there were downward trends at 6 stations and upward trends at 6 stations. The result of the trend test in flux for a selected nutrient at a selected station (downward trend, no trend, or upward trend) usually matched the trend result in concentration.
Seasonal rank-sum tests, the second method used, identified step trends in water-quality measured in different decades—1970s, 1980s, 1990s, and 2000s. Tests were conducted on all nutrients at 28 stations and on all measures of major ions at the 4 selected stations. Results of seasonal rank-sum tests between the 1980s and the 2000s identified more stations with downward trends in concentrations of total nitrogen (14) than stations with upward trends (2) and more stations with downward trends in concentrations of total phosphorus (18) than stations with upward trends (1).
A combined dataset of trend results for concentrations over water years 1980‒2011 was created from the results of the two tests for the period. Results of WRTDS models were included in this combined dataset, if available. Otherwise, the results of the seasonal rank-sum tests between water-quality characteristics measured in the 1980s and 2000s were included.
Trend results over water years 1980‒2011 in the combined dataset show that few of the 28 stations had upward trends in concentrations of either total nitrogen or total phosphorus. There were only 2 stations with upward trends in total nitrogen concentration and 1 station with an upward trend in total phosphorus concentration. Results for filtered nitrate plus nitrite show about the same number of stations with upward trends (9) as stations with downward trends (7). Results for all measures of major ions show upward trends at the four stations tested.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165176","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection and the Delaware River Basin Commission","usgsCitation":"Hickman, R.E., and Hirsch, R.M., 2017, Trends in the quality of water in New Jersey streams, water years 1971–2011: U.S. Geological Survey Scientific Investigations Report 2016–5176, 58 p., https://doi.org/10.3133/sir20165176.","productDescription":"Report: vi, 58 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066358","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":336067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5176/coverthb.jpg"},{"id":336068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5176/sir20165176.pdf","text":"Report","size":"7.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5176"},{"id":336069,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NS0RZ3","text":"USGS data release","description":"USGS data release","linkHelpText":"Input data and results of WRTDS models and seasonal rank-sum tests to determine trends in the quality of water in New Jersey streams, water years 1971-2011"}],"country":"United States","state":"New 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U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
http://nj.usgs.gov/
The processes that affect water chemistry as the water flows from recharge areas through breccia-pipe uranium deposits in the Grand Canyon region of the southwestern United States are not well understood. Pigeon Spring had elevated uranium in 1982 (44 μg/L), compared to other perched springs (2.7–18 μg/L), prior to mining operations at the nearby Pigeon Mine. Perched groundwater springs in an area around the Pigeon Mine were sampled between 2009 and 2015 and compared with material from the Pigeon Mine to better understand the geochemistry and hydrology of the area. Two general groups of perched groundwater springs were identified from this study; one group is characterized by calcium sulfate type water, low uranium activity ratio 234U/238U (UAR) values, and a mixture of water with some component of modern water, and the other group by calcium-magnesium sulfate type water, higher UAR values, and radiocarbon ages indicating recharge on the order of several thousand years ago. Multivariate statistical principal components analysis of Pigeon Mine and spring samples indicate Cu, Pb, As, Mn, and Cd concentrations distinguished mining-related leachates from perched groundwater springs. The groundwater potentiometric surface indicates that perched groundwater at Pigeon Mine would likely flow toward the northwest away from Pigeon Spring. The geochemical analysis of the water, sediment and rock samples collected from the Snake Gulch area indicate that the elevated uranium at Pigeon Spring is likely related to a natural source of uranium upgradient from the spring and not likely related to the Pigeon Mine.
","language":"English","publisher":"Springer","publisherLocation":"Berlin","doi":"10.1007/s10040-016-1494-8","usgsCitation":"Beisner, K.R., Paretti, N.V., Tillman, F.D., Naftz, D.L., Bills, D.J., Walton-Day, K., and Gallegos, T.J., 2017, Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA): Hydrogeology Journal, v. 25, no. 2, p. 539-556, https://doi.org/10.1007/s10040-016-1494-8.","productDescription":"18 p.","startPage":"539","endPage":"556","ipdsId":"IP-076753","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-016-1494-8","text":"Publisher Index Page"},{"id":336246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Pigeon Spring","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.641667,\n 36.916667\n ],\n [\n -112.175,\n 36.916667\n ],\n [\n -112.175,\n 36.566667\n ],\n [\n -112.641667,\n 36.566667\n ],\n [\n -112.641667,\n 36.916667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-23","publicationStatus":"PW","scienceBaseUri":"58b548bce4b01ccd54fddfa2","chorus":{"doi":"10.1007/s10040-016-1494-8","url":"http://dx.doi.org/10.1007/s10040-016-1494-8","publisher":"Springer Nature","authors":"Beisner Kimberly R., Paretti Nicholas V., Tillman Fred D., Naftz David L., Bills Donald J., Walton-Day Katie, Gallegos Tanya J.","journalName":"Hydrogeology Journal","publicationDate":"11/23/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"11/23/2016"},"contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bills, Donald J. 0000-0001-8955-3370 djbills@usgs.gov","orcid":"https://orcid.org/0000-0001-8955-3370","contributorId":177439,"corporation":false,"usgs":true,"family":"Bills","given":"Donald","email":"djbills@usgs.gov","middleInitial":"J.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473 tgallegos@usgs.gov","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":2206,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya","email":"tgallegos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":673398,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70182734,"text":"70182734 - 2017 - Nonnative trout invasions combined with climate change threaten persistence of isolated cutthroat trout populations in the southern Rocky Mountains","interactions":[],"lastModifiedDate":"2020-02-19T13:15:14","indexId":"70182734","displayToPublicDate":"2017-02-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Nonnative trout invasions combined with climate change threaten persistence of isolated cutthroat trout populations in the southern Rocky Mountains","docAbstract":"Effective conservation of Cutthroat Trout Oncorhynchus clarkii lineages native to the Rocky Mountains will require estimating effects of multiple stressors and directing management toward the most important ones. Recent\nanalyses have focused on the direct and indirect effects of a changing climate on contemporary ranges, which are much reduced from historic ranges owing to past habitat loss and nonnative trout invasions. However, nonnative trout continue to invade Cutthroat Trout populations in the southern Rocky Mountains. Despite management to isolate and protect these native populations, nonnatives still surmount barriers or are illegally stocked above them. We used data on the incidence of invasions by nonnative Brook Trout (BT) Salvelinus fontinalis and the rate of their\ninvasion upstream to simulate effects on a set of 309 conservation populations of Colorado River Cutthroat Trout (CRCT) O. c. pleuriticus isolated in headwater stream fragments. A previously developed Bayesian network model was used to compare direct and indirect effects of climate change (CC) alone on population persistence versus the added effects of BT invasions. Although CC alone is predicted to extirpate only one CRCT population by 2080, BT invasions and CC together are predicted to completely extirpate 122 populations (39% of the total) if managers do\nnot intervene. Another 113 populations (37%) will be at risk of extirpation after CC and invasions, primarily owing to stochastic risks in short stream fragments that are similar under CC alone. Overall, invasions and CC will\nreduce the number of stream fragments that are long enough to buffer CRCT populations against negative genetic consequences and stochastic disturbances by 48, a decrease of 38% compared to CC alone. High priorities are (1) research to estimate how CC and human factors alter the incidence and rate of BT invasions and (2) management to prevent new illegal introductions, repair inadequate barriers, and monitor and address new invasions.","language":"English","publisher":"Taylor & Francis ","doi":"10.1080/02755947.2016.1264507","usgsCitation":"Roberts, J., Fausch, K.D., Hooten, M., and Peterson, D.P., 2017, Nonnative trout invasions combined with climate change threaten persistence of isolated cutthroat trout populations in the southern Rocky Mountains: North American Journal of Fisheries Management, v. 37, no. 2, p. 314-325, https://doi.org/10.1080/02755947.2016.1264507.","productDescription":"12 p. ","startPage":"314","endPage":"325","ipdsId":"IP-074344","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":336322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Utah, Wyoming ","otherGeospatial":"Upper Colorado River basin ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.57763671875,\n 42.56117285531808\n ],\n [\n -110.90698242187499,\n 41.30257109430557\n ],\n [\n -111.104736328125,\n 39.342794408952365\n ],\n [\n -111.983642578125,\n 37.09023980307208\n ],\n [\n -111.4453125,\n 36.62434536776987\n ],\n [\n -110.028076171875,\n 36.1733569352216\n ],\n [\n -108.270263671875,\n 36.03133177633187\n ],\n [\n -106.89697265625,\n 37.23907530202184\n ],\n [\n -106.666259765625,\n 38.788345355085625\n ],\n [\n -106.74316406249999,\n 39.64799732373418\n ],\n [\n -107.55615234375,\n 41.00477542222947\n ],\n [\n -107.73193359375,\n 42.06560675405716\n ],\n [\n -109.27001953125,\n 42.56117285531808\n ],\n [\n -109.57763671875,\n 42.56117285531808\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-27","publicationStatus":"PW","scienceBaseUri":"58b69a3ee4b01ccd54ff3f7c","chorus":{"doi":"10.1080/02755947.2016.1264507","url":"http://dx.doi.org/10.1080/02755947.2016.1264507","publisher":"Informa UK Limited","authors":"Roberts James J., Fausch Kurt D., Hooten Mevin B., Peterson Douglas P.","journalName":"North American Journal of Fisheries Management","publicationDate":"2/27/2017"},"contributors":{"authors":[{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":673489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fausch, Kurt D. 0000-0001-5825-7560","orcid":"https://orcid.org/0000-0001-5825-7560","contributorId":184071,"corporation":false,"usgs":false,"family":"Fausch","given":"Kurt","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":673490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":673491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Douglas P.","contributorId":145877,"corporation":false,"usgs":false,"family":"Peterson","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":673492,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179177,"text":"pp1832 - 2017 - Eruptive history, geochronology, and post-eruption structural evolution of the late Eocene Hall Creek Caldera, Toiyabe Range, Nevada","interactions":[],"lastModifiedDate":"2017-02-24T11:19:42","indexId":"pp1832","displayToPublicDate":"2017-02-24T00:11:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1832","title":"Eruptive history, geochronology, and post-eruption structural evolution of the late Eocene Hall Creek Caldera, Toiyabe Range, Nevada","docAbstract":"The magmatic, tectonic, and topographic evolution of what is now the northern Great Basin remains controversial, notably the temporal and spatial relation between magmatism and extensional faulting. This controversy is exemplified in the northern Toiyabe Range of central Nevada, where previous geologic mapping suggested the presence of a caldera that sourced the late Eocene (34.0 mega-annum [Ma]) tuff of Hall Creek. This region was also inferred to be the locus of large-magnitude middle Tertiary extension (more than 100 percent strain) localized along the Bernd Canyon detachment fault, and to be the approximate location of a middle Tertiary paleodivide that separated east and west-draining paleovalleys. Geologic mapping, 40Ar/39Ar dating, and geochemical analyses document the geologic history and extent of the Hall Creek caldera, define the regional paleotopography at the time it formed, and clarify the timing and kinematics of post-caldera extensional faulting. During and after late Eocene volcanism, the northern Toiyabe Range was characterized by an east-west trending ridge in the area of present-day Mount Callaghan, probably localized along a Mesozoic anticline. Andesite lava flows erupted around 35–34 Ma ponded hundreds of meters thick in the erosional low areas surrounding this structural high, particularly in the Simpson Park Mountains. The Hall Creek caldera formed ca. 34.0 Ma during eruption of the approximately 400 cubic kilometers (km3) tuff of Hall Creek, a moderately crystal-rich rhyolite (71–77 percent SiO2) ash-flow tuff. Caldera collapse was piston-like with an intact floor block, and the caldera filled with thick (approximately 2,600 meters) intracaldera tuff and interbedded breccia lenses shed from the caldera walls. The most extensive exposed megabreccia deposits are concentrated on or close to the caldera floor in the southwestern part of the caldera. Both silicic and intermediate post-caldera lavas were locally erupted within 400 thousand years of the main eruption, and for the next approximately 10 million years sedimentary rocks and distal tuffs sourced from calderas farther west ponded in the caldera basin surrounding low areas nearby. Patterns of tuff deposition indicate that the area was characterized by east-west trending paleovalleys and ridges in the late Eocene and Oligocene, which permitted tuffs to disperse east-west but limited their north-south extent. Although a low-angle fault contact of limited extent separates Cambrian and Ordovician strata in the southwestern part of the study area, there is no evidence that this fault cuts overlying Tertiary rocks. Total extensional strain across the caldera is on the order of 15 percent, and there is no evidence for progressive tilting of 34–25 Ma rocks that would indicate protracted Eocene–Oligocene extension. The caldera appears to have been tilted as an intact block after 25 Ma, probably during the middle Miocene extensional faulting well documented to the north and south of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1832","collaboration":"Prepared in cooperation with the Nevada Bureau of Mines and Geology","usgsCitation":"Colgan, J.P., and Henry, C.D., 2017, Eruptive history, geochronology, and post-eruption structural evolution of the late Eocene Hall Creek Caldera, Toiyabe Range, Nevada: U.S. Geological Survey Professional Paper 1832, 44 p., https://doi.org/10.3133/pp1832.","productDescription":"Report: viii, 43 p.; Figure; Data release","onlineOnly":"Y","ipdsId":"IP-075500","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":336053,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/pp/1832/pp1832_figure_4.pdf","text":"Figure 4","size":"888 kB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1832 Figure 4"},{"id":336052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1832/pp1832.pdf","text":"Report","size":"5.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1832"},{"id":336051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1832/coverthb.jpg"},{"id":336166,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JD4TX8","text":" Geochemical and geochronologic data from the Hall Creek caldera, Toiyabe Range, Nevada"}],"country":"United States","state":"Nevada","otherGeospatial":"Late Eocene Hall Creek Caldera, Toiyabe Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.033333,\n 39.8375\n ],\n [\n -116.733333,\n 39.8375\n ],\n [\n -116.733333,\n 39.708333\n ],\n [\n -117.033333,\n 39.708333\n ],\n [\n -117.033333,\n 39.8375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The establishment and invasion of non-native plant species have the ability to alter the composition of native species and functioning of ecological systems with financial costs resulting from mitigation and loss of ecological services. Spatially documenting invasions has applications for management and theory, but the utility of maps is challenged by availability and uncertainty of data, and the reliability of extrapolating mapped data in time and space. The extent and resolution of projections also impact the ability to inform invasive species science and management. Early invasive species maps were coarse-grained representations that underscored the phenomena, but had limited capacity to direct management aside from development of watch lists for priorities for prevention and containment. Integrating mapped data sets with fine-resolution environmental variables in the context of species-distribution models allows a description of species-environment relationships and an understanding of how, why, and where invasions may occur. As with maps, the extent and resolution of models impact the resulting insight. Models of cheatgrass (Bromus tectorum) across a variety of spatial scales and grain result in divergent species-environment relationships. New data can improve models and efficiently direct further inventories. Mapping can target areas of greater model uncertainty or the bounds of modeled distribution to efficiently refine models and maps. This iterative process results in dynamic, living maps capable of describing the ongoing process of species invasions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mapping across academia","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Netherlands","doi":"10.1007/978-94-024-1011-2","isbn":"978-94-024-1009-9 ","usgsCitation":"Barnett, D.T., Jarnevich, C.S., Chong, G.W., Stohlgren, T.J., Kumar, S., and Holcombe, T.R., 2017, Ecology and space: A case study in mapping harmful invasive species, chap. of Mapping across academia, p. 63-81, https://doi.org/10.1007/978-94-024-1011-2.","productDescription":"19 p.","startPage":"63","endPage":"81","ipdsId":"IP-035081","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":336224,"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":"58b15438e4b01ccd54fc5e97","contributors":{"editors":[{"text":"Brunn, Stanley D.","contributorId":182523,"corporation":false,"usgs":false,"family":"Brunn","given":"Stanley","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":671705,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Dodge, Martin","contributorId":182524,"corporation":false,"usgs":false,"family":"Dodge","given":"Martin","email":"","affiliations":[],"preferred":false,"id":671706,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Barnett, David T.","contributorId":182406,"corporation":false,"usgs":false,"family":"Barnett","given":"David","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":671365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":671363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chong, Geneva W. 0000-0003-3883-5153 geneva_chong@usgs.gov","orcid":"https://orcid.org/0000-0003-3883-5153","contributorId":419,"corporation":false,"usgs":true,"family":"Chong","given":"Geneva","email":"geneva_chong@usgs.gov","middleInitial":"W.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":671361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":671364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Sunil","contributorId":182407,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","email":"","affiliations":[],"preferred":false,"id":671366,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":671362,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182156,"text":"ds1039 - 2017 - Coastal single-beam bathymetry data collected in 2015 from the Chandeleur Islands, Louisiana","interactions":[],"lastModifiedDate":"2017-02-24T08:26:44","indexId":"ds1039","displayToPublicDate":"2017-02-23T17:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1039","title":"Coastal single-beam bathymetry data collected in 2015 from the Chandeleur Islands, Louisiana","docAbstract":"As part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring Program, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey around the Chandeleur Islands, Louisiana, in June 2015. The goal of the program is to provide long-term data on Louisiana’s barrier islands and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report, along with (1) USGS bathymetry data collected in 2013 as a part of the Barrier Island Evolution Research project covering the northern Chandeleur Islands, and (2) data collected in 2014 in collaboration with the Louisiana CPRA Barrier Island Comprehensive Monitoring Program around Breton Island, will be used to assess bathymetric change since 2006‒2007 as well as serve as a bathymetric control in supporting modeling of future changes in response to restoration and storm impacts. The survey area encompasses approximately 435 square kilometers of nearshore and back-barrier environments around Hewes Point, the Chandeleur Islands, and Curlew and Grand Gosier Shoals. This Data Series serves as an archive of processed single-beam bathymetry data, collected in the nearshore of the Chandeleur Islands, Louisiana, from June 17‒24, 2015, during USGS Field Activity Number 2015-317-FA. Geographic information system data products include a 200-meter-cell-size interpolated bathymetry grid, trackline maps, and xyz point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1039","usgsCitation":"Stalk, C.A., DeWitt, N.T., Bernier, J.C., Kindinger, J.G., Flocks, J.G., Miselis, J.L., Locker, S.D., Kelso, K.W., and Tuten, T.M., 2017, Coastal single-beam bathymetry data collected in 2015 from the Chandeleur Islands, Louisiana: U.S. Geological Survey Data Series 1039, https://doi.org/10.3133/ds1039.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076323","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":335883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1039/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1039 HTML"},{"id":335882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1039/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.116667,\n 30.116667\n ],\n [\n -88.75,\n 30.116667\n ],\n [\n -88.75,\n 29.533333\n ],\n [\n -89.116667,\n 29.533333\n ],\n [\n -89.116667,\n 30.116667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
The U.S. Geological Survey (USGS), in cooperation with Gwinnett County Department of Water Resources, established a Long-Term Trend Monitoring (LTTM) program in 1996. The LTTM program is a comprehensive, long-term, water-quantity and water-quality monitoring program designed to document and analyze the hydrologic and water-quality conditions of selected watersheds in Gwinnett County, Georgia. Water-quality monitoring initially began in six watersheds and currently [2016] includes 13 watersheds.
As part of the LTTM program, streamflow, precipitation, water temperature, specific conductance, and turbidity were measured every 15 minutes for water years 2001–15 at 12 of the 13 watershed monitoring stations and for water years 2010–15 at the other watershed. In addition, discrete water-quality samples were collected seasonally from May through October (summer) and November through April (winter), including one base-flow and three stormflow event composite samples, during the study period. Samples were analyzed for nutrients (nitrogen and phosphorus), total organic carbon, trace elements (total lead and total zinc), total dissolved solids, and total suspended sediment (total suspended solids and suspended-sediment concentrations). The sampling scheme was designed to identify variations in water quality both hydrologically and seasonally.
The 13 watersheds were characterized for basin slope, population density, land use for 2012, and the percentage of impervious area from 2000 to 2014. Several droughts occurred during the study period—water years 2002, 2007–08, and 2011–12. Watersheds with the highest percentage of impervious areas had the highest runoff ratios, which is the portion of precipitation that occurs as runoff. Watershed base-flow indexes, the ratio of base-flow runoff to total runoff, were inversely correlated with watershed impervious area.
Flood-frequency estimates were computed for 13 streamgages in the study area that have 10 or more years of annual peak flow data through water year 2015, using the expected moments algorithm to fit a Pearson Type III distribution to logarithms of annual peak flows. Kendall’s tau nonparametric test was used to determine the statistical significance of trends in the annual peak flows, with none of the 13 streamgages exhibiting significant trends.
A comparison of base-flow and stormflow water-quality samples indicates that turbidity and concentrations of total ammonia plus organic nitrogen, total nitrogen, total phosphorus, total organic carbon, total lead, total zinc, total suspended solids, and suspended-sediment concentrations increased with increasing discharge at all watersheds. Specific conductance decreased during stormflow at all watersheds, and total dissolved solids concentrations decreased during stormflow at a few of the watersheds. Total suspended solids and suspended-sediment concentrations typically were two orders of magnitude higher in stormflow samples, turbidities were about 1.5 orders of magnitude higher, total phosphorus and total zinc were about one order of magnitude higher, and total ammonia plus organic nitrogen, total nitrogen, total organic carbon, and total lead were about twofold higher than in base-flow samples.
Seasonality and long-term trends were identified for the period water years 2001–15 for 10 constituents—total nitrogen, total nitrate plus nitrite, total phosphorus, dissolved phosphorus, total organic carbon, total suspended solids, suspended-sediment concentration, total lead, total zinc, and total dissolved solids. Seasonal patterns were present in most watersheds for all constituents except total dissolved solids, and the watersheds had fairly similar patterns of higher concentrations in the summer and lower concentrations in the winter. A linear long-term trend analysis of residual concentrations from the flow-only load estimation model (without time-trend terms) identified significant trends in 67 of the 130 constituent-watershed combinations. Seventy percent of the significant trends were negative. Total organic carbon and total dissolved solids had predominantly positive trends. Total phosphorus, total suspended solids, suspended-sediment concentration, total lead, and total zinc had only negative trends. The other three constituents exhibited fewer trends, both positive and negative.
Streamwater loads were estimated annually for the 13-year period water years 2003–15 for the same 10 constituents in the trend analysis. Loads were estimated using a regression-model-based approach developed by the USGS for the Gwinnett County LTTM program that accommodates the use of storm-event composited samples. Concentrations were modeled as a function of discharge, base flow, time, season, and turbidity to improve model predictions and reduce errors in load estimates. Total suspended solids annual loads have been identified in Gwinnett County’s Watershed Protection Plan for target performance criterion.
Although the amount of annual runoff was the primary factor in variations in annual loads, climatic conditions (classified as dry, average, or wet) affected annual loads beyond what was attributed to climatic-related variations in annual runoff. Significant negative trends in loads were estimated for the combined area of the watersheds for all constituents except dissolved phosphorus, total organic carbon, and total dissolved solids. The trend analysis indicated that total suspended solids and suspended-sediment concentration loads in the study area were decreasing by 57,000 and 87,000 pounds per day per year, respectively.
Variations in constituent yields between watersheds appeared to be related to various watershed characteristics. Suspended sediment (as either total suspended solids or suspended-sediment concentrations), along with constituents transported predominately in solid phase (total phosphorus, total organic carbon, total lead, and total zinc), and total dissolved solids typically had higher yields from watersheds that had high percentages of impervious areas or high basin slope. High total nitrogen yields were also associated with watersheds with high percentages of impervious areas. Low total nitrogen, total suspended solids, total lead, and total zinc yields appeared to be associated with watersheds that had a low percentage of high-density development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175012","collaboration":"Prepared in cooperation with the Gwinnett County Department of Water Resources","usgsCitation":"Aulenbach, B.T., Joiner, J.K., and Painter, J.A., 2017, Hydrology and water quality in 13 watersheds in Gwinnett County, Georgia, 2001–15: U.S. Geological Survey Scientific Investigations Report 2017– 5012, 82 p., https://doi.org/10.3133/sir20175012. 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South Atlantic Water Science Center
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http://www.usgs.gov/water/southatlantic/
Tsunami evacuation planning in coastal communities is typically focused on local events where at-risk individuals must move on foot in a matter of minutes to safety. Less attention has been placed on distant tsunamis, where evacuations unfold over several hours, are often dominated by vehicle use and are managed by public safety officials. Traditional traffic simulation models focus on estimating clearance times but often overlook the influence of varying population demand, alternative modes, background traffic, shadow evacuation, and traffic management alternatives. These factors are especially important for island communities with limited egress options to safety. We use the coastal community of Balboa Island, California (USA), as a case study to explore the range of potential clearance times prior to wave arrival for a distant tsunami scenario. We use a first-in–first-out queuing simulation environment to estimate variations in clearance times, given varying assumptions of the evacuating population (demand) and the road network over which they evacuate (supply). Results suggest clearance times are less than wave arrival times for a distant tsunami, except when we assume maximum vehicle usage for residents, employees, and tourists for a weekend scenario. A two-lane bridge to the mainland was the primary traffic bottleneck, thereby minimizing the effect of departure times, shadow evacuations, background traffic, boat-based evacuations, and traffic light timing on overall community clearance time. Reducing vehicular demand generally reduced clearance time, whereas improvements to road capacity had mixed results. Finally, failure to recognize non-residential employee and tourist populations in the vehicle demand substantially underestimated clearance time.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-016-2655-8","usgsCitation":"Henry, K., Wood, N.J., and Frazier, T.G., 2017, Influence of road network and population demand assumptions in evacuation modeling for distant tsunamis: Natural Hazards, v. 85, no. 3, p. 1665-1687, https://doi.org/10.1007/s11069-016-2655-8.","productDescription":"23 p.","startPage":"1665","endPage":"1687","ipdsId":"IP-075676","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":343280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Balboa Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.9019045829773,\n 33.603056937190075\n ],\n [\n -117.88405179977417,\n 33.603056937190075\n ],\n [\n -117.88405179977417,\n 33.609883739325674\n ],\n [\n -117.9019045829773,\n 33.609883739325674\n ],\n [\n -117.9019045829773,\n 33.603056937190075\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"595ca914e4b0d1f9f054ca16","contributors":{"authors":[{"text":"Henry, Kevin 0000-0001-9314-2531 khenry@usgs.gov","orcid":"https://orcid.org/0000-0001-9314-2531","contributorId":176934,"corporation":false,"usgs":true,"family":"Henry","given":"Kevin","email":"khenry@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":703287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":703288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frazier, Tim G.","contributorId":64793,"corporation":false,"usgs":true,"family":"Frazier","given":"Tim","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":703289,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70179709,"text":"sir20165177 - 2017 - Characterization of streamflow, suspended sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, May 2014–December 2015","interactions":[],"lastModifiedDate":"2017-02-21T15:20:47","indexId":"sir20165177","displayToPublicDate":"2017-02-21T14:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5177","title":"Characterization of streamflow, suspended sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, May 2014–December 2015","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board and the Galveston Bay Estuary Program, collected streamflow and water-quality data at USGS streamflow-gaging stations in the lower Trinity River watershed from May 2014 to December 2015 to characterize and improve the current understanding of the quantity and quality of freshwater inflow entering Galveston Bay from the Trinity River. Continuous streamflow records at four USGS streamflow-gaging stations were compared to quantify differences in streamflow magnitude between upstream and downstream reaches of the lower Trinity River. Water-quality conditions were characterized from discrete nutrient and sediment samples collected over a range of hydrologic conditions at USGS streamflow-gaging station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), approximately 4 river miles upstream from where the Trinity River enters Galveston Bay.
Based on streamflow records, annual mean outflow from Livingston Dam into the lower Trinity River was 2,240 cubic feet per second (ft3/s) in 2014 and 22,400 ft3/s in 2015, the second lowest and the highest, respectively, during the entire period of record (1966–2015). During this study, only about 54 percent of the total volume measured at upstream sites was accounted for at the Wallisville site as the Trinity River enters Galveston Bay. This difference in water volumes between upstream sites and the Wallisville site indicates that at high flows a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River. These findings indicate that water likely flows into wetlands and water bodies surrounding the main channel of the Trinity River before reaching the Wallisville site and is being stored or discharged through other channels that flow directly into Galveston Bay.
To characterize suspended-sediment concentrations and loads in Trinity River inflow to Galveston Bay, a regression model was developed to estimate suspended-sediment concentrations by using acoustic backscatter data as a surrogate. The model yielded an adjusted coefficient of determination value of 0.92 and a root mean square error of 1.65 milligrams per liter (mg/L). The mean absolute percentage error between measured and estimated suspended-sediment concentration was 35 percent. During this study, estimated suspended-sediment concentrations ranged from 2 to 701 mg/L, with a mean of 97 mg/L. Suspended-sediment concentrations varied in response to changes in discharge, with peak suspended-sediment concentrations occurring 1 to 2 days before the peak discharge for each event. The total suspended-sediment load at the Wallisville site during May 2014–December 2015 was approximately 2,200,000 tons, with a minimum monthly suspended-sediment load of 100 tons in October 2014 and a maximum monthly load of 441,000 tons in November 2015.
Results from nutrient samples collected at the Wallisville site indicate that total nitrogen and total phosphorus concentrations fluctuated at a similar rate, with the highest nutrient concentrations occurring during periods of high flow corresponding to releases from Lake Livingston. The mean concentrations of total nitrogen and total phosphorus were approximately 75 percent higher during high flow releases than during periods of low flow, overshadowing variations in nutrient concentrations caused by seasonality at the Wallisville site.
Results from the study indicate nutrient delivery to Galveston Bay from the main channel of the Trinity River is likely controlled primarily by high-flow releases from Lake Livingston. For most samples collected at the Wallisville site, organic nitrogen was the predominant form of nitrogen; however, when discharge increased because of releases from Lake Livingston, the percentage of organic nitrogen typically decreased and the percentage of nitrate increased. The concentrations of total phosphorus also increased during high-flow events, likely as a result of suspended sediment within Lake Livingston releases and mobilization of sediment particles in the river channel and flood plain during these periods of high flow. The predominant source of phosphorous to Galveston Bay from the Trinity River is in particulate form closely tied to suspended-sediment concentrations. The changes in nutrient concentration and composition caused by releases from Lake Livingston during this study indicate the reservoir may play an important role in the delivery of nutrients into Galveston Bay. Further study is required to better understand the processes in Lake Livingston influencing the characteristics of nutrient and sediment inflow to Galveston Bay. With phosphorous concentrations correlated to suspended-sediment concentrations (coefficient of determination value of 0.75) and with the concentrations of nutrients changing as the discharge changes, the diversion of water and suspended sediment into surrounding wetlands and channels outside of the main channel of the Trinity River may play a large role in regulating nutrient inputs into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165177","isbn":"978-1-4113-4107-4","collaboration":"Prepared in cooperation with the Texas Water Development Board and the Galveston Bay Estuary Program ","usgsCitation":"Lucena, Zulimar, and Lee, M.T., 2017, Characterization of streamflow, suspended sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, May 2014–December 2015: U.S. Geological Survey Scientific Investigations Report 2016–5177, 38 p., https://doi.org/10.3133/sir20165177.\n","productDescription":"vii, 37 p.","numberOfPages":"49","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077707","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":335588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5177/sir20165177.pdf","text":"Report","size":"11.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5177"},{"id":335587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5177/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay, Trinity River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.15533447265625,\n 29.7596087873038\n ],\n [\n -94.58953857421875,\n 29.7596087873038\n ],\n [\n -94.58953857421875,\n 30.982318643027536\n ],\n [\n -95.15533447265625,\n 30.982318643027536\n ],\n [\n -95.15533447265625,\n 29.7596087873038\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, X 78754
https://tx.usgs.gov/
Disentangling the role that multiple interacting factors have on species responses to shifting climate poses a significant challenge. However, our ability to do so is of utmost importance to predict the effects of climate change on species distributions. We examined how populations of three species of wetland-breeding amphibians, which varied in life history requirements, responded to a six-year period of extremely variable precipitation. This interval was punctuated by both extensive drought and heavy precipitation and flooding, providing a natural experiment to measure community responses to environmental perturbations. We estimated occurrence dynamics using a discrete hidden Markov modeling approach that incorporated information regarding habitat state and predator–prey interactions. This approach allowed us to measure how metapopulation dynamics of each amphibian species was affected by interactions among weather, wetland hydroperiod, and co-occurrence with fish predators. The pig frog, a generalist, proved most resistant to perturbations, with both colonization and persistence being unaffected by seasonal variation in precipitation or co-occurrence with fishes. The ornate chorus frog, an ephemeral wetland specialist, responded positively to periods of drought owing to increased persistence and colonization rates during periods of low-rainfall. Low probabilities of occurrence of the ornate chorus frog in long-duration wetlands were driven by interactions with predators due to low colonization rates when fishes were present. The mole salamander was most sensitive to shifts in water availability. In our study area, this species never occurred in short-duration wetlands and persistence probabilities decreased during periods of drought. At the same time, negative effects occurred with extreme precipitation because flooding facilitated colonization of fishes to isolated wetlands and mole salamanders did not colonize wetlands once fishes were present. We demonstrate that the effects of changes in water availability depend on interactions with predators and wetland type and are influenced by the life history of each of our species. The dynamic species occurrence modeling approach we used offers promise for other systems when the goal is to disentangle the complex interactions that determine species responses to environmental variability.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1442","usgsCitation":"Davis, C.L., Miller, D.A., Walls, S.C., Barichivich, W.J., Riley, J.W., and Brown, M.E., 2017, Species interactions and the effects of climate variability on a wetland amphibian metacommunity: Ecological Applications, v. 27, no. 1, p. 285-296, https://doi.org/10.1002/eap.1442.","productDescription":"12 p.","startPage":"285","endPage":"296","ipdsId":"IP-070788","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":335901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"St. Marks National Wildlife Refuge","volume":"27","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"58ad5fc0e4b01ccd54f8b50f","contributors":{"authors":[{"text":"Davis, Courtney L.","contributorId":181922,"corporation":false,"usgs":false,"family":"Davis","given":"Courtney","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":670047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David A.W. davidmiller@usgs.gov","contributorId":4043,"corporation":false,"usgs":true,"family":"Miller","given":"David","email":"davidmiller@usgs.gov","middleInitial":"A.W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":670048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":2310,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":670046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barichivich, William J. 0000-0003-1103-6861 wbarichivich@usgs.gov","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":3697,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","email":"wbarichivich@usgs.gov","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"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":670049,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":670050,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Mary E. 0000-0002-5580-137X","orcid":"https://orcid.org/0000-0002-5580-137X","contributorId":181924,"corporation":false,"usgs":true,"family":"Brown","given":"Mary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":670051,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177979,"text":"70177979 - 2017 - Amphibian dynamics in constructed ponds on a wildlife refuge: developing expected responses to hydrological restoration","interactions":[],"lastModifiedDate":"2017-02-21T14:33:38","indexId":"70177979","displayToPublicDate":"2017-02-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Amphibian dynamics in constructed ponds on a wildlife refuge: developing expected responses to hydrological restoration","docAbstract":"Management actions are based upon predictable responses. To form expected responses to restoration actions, I estimated habitat relationships and trends (2002–2015) for four pond-breeding amphibians on a wildlife refuge (Montana, USA) where changes to restore historical hydrology to the system greatly expanded (≥8 times) the flooded area of the primary breeding site for western toads (Anaxyrus boreas). Additional restoration actions are planned for the near future, including removing ponds that provide amphibian habitat. Multi-season occupancy models based on data from 15 ponds sampled during 7 years revealed that the number of breeding subpopulations increased modestly for Columbia spotted frogs (Rana luteiventris) and was stationary for long-toed salamanders (Ambystoma macrodactylum) and Pacific treefrogs (Pseudacris regilla). For these three species, pond depth was the characteristic that was associated most frequently with occupancy or changes in colonization and extinction. In contrast, a large decrease in colonization by western toads explained the decline from eight occupied ponds in 2002 to two ponds in 2015. This decline occurred despite an increase in wetland area and the colonization of a newly created pond. These changes highlight the challenges of managing for multiple species and how management responses can be unpredictable, possibly reducing the efficacy of targeted actions.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-016-2979-0","usgsCitation":"Hossack, B.R., 2017, Amphibian dynamics in constructed ponds on a wildlife refuge: developing expected responses to hydrological restoration: Hydrobiologia, v. 790, no. 1, p. 23-33, https://doi.org/10.1007/s10750-016-2979-0.","productDescription":"11 p.","startPage":"23","endPage":"33","ipdsId":"IP-070337","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":335893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"790","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-09","publicationStatus":"PW","scienceBaseUri":"58ad5fc1e4b01ccd54f8b517","chorus":{"doi":"10.1007/s10750-016-2979-0","url":"http://dx.doi.org/10.1007/s10750-016-2979-0","publisher":"Springer Nature","authors":"Hossack Blake R.","journalName":"Hydrobiologia","publicationDate":"9/9/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/9/2016"},"contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":652472,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188559,"text":"70188559 - 2017 - Landslide kinematics and their potential controls from hourly to decadal timescales: Insights from integrating ground-based InSAR measurements with structural maps and long-term monitoring data","interactions":[],"lastModifiedDate":"2017-06-15T13:36:22","indexId":"70188559","displayToPublicDate":"2017-02-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Landslide kinematics and their potential controls from hourly to decadal timescales: Insights from integrating ground-based InSAR measurements with structural maps and long-term monitoring data","docAbstract":"Knowledge of kinematics is rudimentary for understanding landslide controls and is increasingly valuable with greater spatiotemporal coverage. However, characterizing landslide-wide kinematics is rare, especially at broadly ranging timescales. We used highly detailed kinematic data obtained using photogrammetry and field mapping during the 1980s and 1990s and our 4.3-day ground-based InSAR survey during 2010 to study kinematics of the large, persistently moving Slumgullion landslide. The landslide was segregated into 11 kinematic elements using the 1980s–1990s data and the InSAR survey revealed most of these elements within a few hours. Averages of InSAR-derived displacement point measures within each element agreed well with higher quality in situ observations; averaging was deemed necessary because adverse look angles for the radar coupled with tree cover on the landslide introduced error in the InSAR results. We found that the landslide moved during 2010 at about half its 1985–1990 speed, but slowing was most pronounced at the landslide head. Gradually decreased precipitation and increased temperature between the periods likely resulted in lower groundwater levels and consequent slowing of the landslide. We used GPS survey results and limit-equilibrium modeling to analyze changing stability of the landslide head from observed thinning and found that its stability increased between the two periods, which would result in its slowing, and the consequent slowing of the entire landslide. Additionally, InSAR results suggested movement of kinematic element boundaries in the head region and our field mapping verified that they moved and changed character, likely because of the long-term increasing head stability. On an hourly basis, InSAR results were near error bounds but suggested landslide acceleration in response to seemingly negligible rainfall. Pore-pressure diffusion modeling suggested that rainfall infiltration affected frictional strength only to shallow depths along the landslide's marginal faults, highlighting their importance in controlling landslide stability. Hourly results also suggested that motion propagated along the 3.9-km length of the active landslide, even following sub-millimeter displacements, while strengthening of landslide shear boundaries during faster movement was likely critical in regulating the landslide's motion. Hence, detailed kinematic characterizations obtained from traditional and emerging approaches helped to reveal that mechanisms controlling landslide movement and evolution over decades also are critical to sub-millimeter movement on a nearly continuous basis.
","language":"English","doi":"10.1016/j.geomorph.2017.02.011","usgsCitation":"Schulz, W.H., Coe, J.A., Ricci, P., Smoczyk, G.M., Shurtleff, B.L., and Panosky, J., 2017, Landslide kinematics and their potential controls from hourly to decadal timescales: Insights from integrating ground-based InSAR measurements with structural maps and long-term monitoring data: Geomorphology, v. 285, p. 121-136, https://doi.org/10.1016/j.geomorph.2017.02.011.","productDescription":"16 p. 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}\n }\n ]\n}","volume":"285","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59439c94e4b062508e31a9b3","contributors":{"authors":[{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ricci, P.P","contributorId":192964,"corporation":false,"usgs":false,"family":"Ricci","given":"P.P","affiliations":[],"preferred":false,"id":698339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smoczyk, Gregory M. 0000-0002-6591-4060 gsmoczyk@usgs.gov","orcid":"https://orcid.org/0000-0002-6591-4060","contributorId":5239,"corporation":false,"usgs":true,"family":"Smoczyk","given":"Gregory","email":"gsmoczyk@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shurtleff, Brett L","contributorId":192965,"corporation":false,"usgs":false,"family":"Shurtleff","given":"Brett","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":698341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Panosky, J","contributorId":192966,"corporation":false,"usgs":false,"family":"Panosky","given":"J","affiliations":[],"preferred":false,"id":698342,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182146,"text":"70182146 - 2017 - Trophic interactions and consumption rates of subyearling Chinook Salmon and nonnative juvenile American Shad in Columbia River reservoirs","interactions":[],"lastModifiedDate":"2017-02-17T10:20:07","indexId":"70182146","displayToPublicDate":"2017-02-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries 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During August we also examined the diet and consumption rates of juvenile American Shad Alosa sapidissima, a potential competitor of subyearling Chinook Salmon. Subyearling Chinook Salmon consumed Daphnia in July but switched to feeding on smaller juvenile American Shad in August. We captured no juvenile American Shad in July, but in August juvenile American Shad consumed cyclopoid and calanoid copepods. Stomach evacuation rates for subyearling Chinook Salmon were high during both sample periods (0.58 h−1 in July, 0.51 h−1 in August), and daily ration estimates were slightly higher than values reported in the literature for other subyearlings. By switching from planktivory to piscivory, subyearling Chinook Salmon gained greater growth opportunity. While past studies have shown that juvenile American Shad reduce zooplankton availability for Chinook Salmon subyearlings, our work indicates that they also become important prey after Daphnia abundance declines. The diet and consumption data here can be used in future bioenergetics modeling to estimate the growth of subyearling Chinook Salmon in lower Columbia River reservoirs.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1264997","usgsCitation":"Haskell, C.A., Beauchamp, D.A., and Bollins, S.M., 2017, Trophic interactions and consumption rates of subyearling Chinook Salmon and nonnative juvenile American Shad in Columbia River reservoirs: Transactions of the American Fisheries Society, v. 146, no. 2, p. 291-298, https://doi.org/10.1080/00028487.2016.1264997.","productDescription":"Report: 7 p.","startPage":"291","endPage":"298","ipdsId":"IP-077398","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":335804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.89791870117186,\n 46.616431255357995\n ],\n [\n -119.88006591796874,\n 46.63387921452026\n ],\n [\n -119.893798828125,\n 46.67111414362431\n ],\n [\n -119.89517211914061,\n 46.68666038407398\n ],\n [\n -119.90272521972655,\n 46.71256084516054\n ],\n [\n -119.90341186523436,\n 46.73374289048227\n ],\n [\n -119.92538452148438,\n 46.75397574126115\n ],\n [\n -119.91714477539061,\n 46.778433231125035\n ],\n [\n -119.93362426757811,\n 46.79206816192883\n ],\n [\n -119.95765686035155,\n 46.78783700126104\n ],\n [\n -119.97482299804686,\n 46.77231989968773\n ],\n [\n -119.99885559082028,\n 46.740331163960654\n ],\n [\n -120.0043487548828,\n 46.713973240259\n ],\n [\n -119.99610900878905,\n 46.670642975602824\n ],\n [\n -119.97276306152344,\n 46.64566519258491\n ],\n [\n -119.96520996093749,\n 46.624448583576246\n ],\n [\n -119.94117736816406,\n 46.60039303734549\n ],\n [\n -119.89791870117186,\n 46.616431255357995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.4170684814453,\n 46.66687348356156\n ],\n [\n -117.43423461914061,\n 46.66074749832068\n ],\n [\n -117.4383544921875,\n 46.65037886496509\n ],\n [\n -117.4376678466797,\n 46.630107159317205\n ],\n [\n -117.41500854492188,\n 46.59756227061819\n ],\n [\n -117.37724304199219,\n 46.53949896024682\n ],\n [\n -117.34565734863281,\n 46.496974231448355\n ],\n [\n -117.30857849121092,\n 46.452050911230394\n ],\n [\n -117.2515869140625,\n 46.40756396630067\n ],\n [\n -117.19047546386719,\n 46.40188216826328\n ],\n [\n -117.08541870117188,\n 46.39951457775959\n ],\n [\n -117.06344604492189,\n 46.41655893628349\n ],\n [\n -117.08473205566406,\n 46.44826620185314\n ],\n [\n -117.11769104003906,\n 46.4903562824648\n ],\n [\n -117.16163635253906,\n 46.52721745699692\n ],\n [\n -117.23854064941406,\n 46.5635815342227\n ],\n [\n -117.27081298828125,\n 46.594731356000686\n ],\n [\n -117.32299804687499,\n 46.628221033128646\n ],\n [\n -117.35870361328124,\n 46.64425101077151\n ],\n [\n -117.3896026611328,\n 46.66074749832068\n ],\n [\n -117.4170684814453,\n 46.66687348356156\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a819b7e4b025c46429afc6","contributors":{"authors":[{"text":"Haskell, Craig A. 0000-0002-3604-1758 chaskell@usgs.gov","orcid":"https://orcid.org/0000-0002-3604-1758","contributorId":3458,"corporation":false,"usgs":true,"family":"Haskell","given":"Craig","email":"chaskell@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":669788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauchamp, David A. 0000-0002-3592-8381 fadave@usgs.gov","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":4205,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","email":"fadave@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":669789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bollins, Stephen M","contributorId":181851,"corporation":false,"usgs":false,"family":"Bollins","given":"Stephen","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":669790,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188377,"text":"70188377 - 2017 - Gravitational body forces focus North American intraplate earthquakes","interactions":[],"lastModifiedDate":"2017-06-07T14:43:49","indexId":"70188377","displayToPublicDate":"2017-02-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Gravitational body forces focus North American intraplate earthquakes","docAbstract":"Earthquakes far from tectonic plate boundaries generally exploit ancient faults, but not all intraplate faults are equally active. The North American Great Plains exemplify such intraplate earthquake localization, with both natural and induced seismicity generally clustered in discrete zones. Here we use seismic velocity, gravity and topography to generate a 3D lithospheric density model of the region; subsequent finite-element modelling shows that seismicity focuses in regions of high-gravity-derived deviatoric stress. Furthermore, predicted principal stress directions generally align with those observed independently in earthquake moment tensors and borehole breakouts. Body forces therefore appear to control the state of stress and thus the location and style of intraplate earthquakes in the central United States with no influence from mantle convection or crustal weakness necessary. These results show that mapping where gravitational body forces encourage seismicity is crucial to understanding and appraising intraplate seismic hazard.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ncomms14314","usgsCitation":"Levandowski, W.B., Zellman, M., and Briggs, R.W., 2017, Gravitational body forces focus North American intraplate earthquakes: Nature Communications, v. 8, Article 14314: 9 p., https://doi.org/10.1038/ncomms14314.","productDescription":"Article 14314: 9 p.","ipdsId":"IP-073321","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms14314","text":"Publisher Index Page"},{"id":342259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 45.5\n ],\n [\n -94.5,\n 45.5\n ],\n [\n -94.5,\n 36.5\n ],\n [\n -105.5,\n 36.5\n ],\n [\n -105.5,\n 45.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-17","publicationStatus":"PW","scienceBaseUri":"593910ace4b0764e6c5e8852","contributors":{"authors":[{"text":"Levandowski, William Brower 0000-0003-4903-5012 wlevandowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4903-5012","contributorId":5729,"corporation":false,"usgs":true,"family":"Levandowski","given":"William","email":"wlevandowski@usgs.gov","middleInitial":"Brower","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zellman, Mark","contributorId":167020,"corporation":false,"usgs":false,"family":"Zellman","given":"Mark","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":697457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697458,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70180894,"text":"ofr20171009 - 2017 - A methodology for modeling barrier island storm-impact scenarios","interactions":[],"lastModifiedDate":"2017-03-29T14:44:29","indexId":"ofr20171009","displayToPublicDate":"2017-02-16T12:00:00","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":"2017-1009","title":"A methodology for modeling barrier island storm-impact scenarios","docAbstract":"A methodology for developing a representative set of storm scenarios based on historical wave buoy and tide gauge data for a region at the Chandeleur Islands, Louisiana, was developed by the U.S. Geological Survey. The total water level was calculated for a 10-year period and analyzed against existing topographic data to identify when storm-induced wave action would affect island morphology. These events were categorized on the basis of the threshold of total water level and duration to create a set of storm scenarios that were simulated, using a high-fidelity, process-based, morphologic evolution model, on an idealized digital elevation model of the Chandeleur Islands. The simulated morphological changes resulting from these scenarios provide a range of impacts that can help coastal managers determine resiliency of proposed or existing coastal structures and identify vulnerable areas within those structures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171009","usgsCitation":"Mickey, R.C., Long, J.W., Plant, N.G., Thompson, D.M., and Dalyander, P.S., 2017, A methodology for modeling barrier island storm-impact scenarios (ver. 1.1, March 2017): U.S. Geological Survey Open-File Report 2017–1009, 17 p., https://doi.org/10.3133/ofr20171009.","productDescription":"iv, 17 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":337876,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1009/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":334864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1009/coverthb2.jpg"},{"id":334865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1009/ofr20171009.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1009"},{"id":334866,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72F7KJK","text":"USGS data release","description":"USGS data release","linkHelpText":"Storm-Impact Scenario XBeach Model Input and Results"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.116667,\n 31\n ],\n [\n -86.95,\n 31\n ],\n [\n -86.95,\n 28.583333\n ],\n [\n -89.116667,\n 28.583333\n ],\n [\n -89.116667,\n 31\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted February 16, 2017; Version 1.1: March 29, 2017","contact":"Director, St. Petersburg Coastal and Marine Science Center
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Our main objectives are to (1) learn if pore-evolution models developed from marine mudrocks can be directly applied to lacustrine mudrocks, (2) investigate what controls the different pore types and sizes of Chang 7 organic matter (OM)-rich argillaceous mudstones of the Upper Triassic Yanchang Formation, and (3) describe the texture, fabric, mineralogy, and thermal maturity variation in the Chang 7 mudstones. Lacustrine mudstones from nine cored wells along a depositional dip in the southeastern Ordos Basin, China, were investigated. Helium porosimetry, nitrogen adsorption, and field-emission scanning electron microscopy of Ar-ion milled samples were applied. Measured average total porosity of samples from a proximal to distal transect (
Digital flood-inundation maps for a 4.1-mile reach of the Big Blue River at Shelbyville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The floodinundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at https://water. usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Blue River at Shelbyville, Ind. (station number 03361500). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata. usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps/, which also forecasts flood hydrographs at this site (SBVI3). Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Big Blue River at Shelbyville, Ind., streamgage. The calibrated hydraulic model was then used to compute 12 water-surface profiles for flood stages referenced to the streamgage datum and ranging from 9.0 feet, or near bankfull, to 19.4 feet, the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foot horizontal resolution) to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at the Big Blue River at Shelbyville, Ind., and forecasted stream stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165166","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2017, Flood-inundation maps for the Big Blue River at Shelbyville, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5166, 11 p., https://doi.org/10.3133/sir20165166.","productDescription":"Report: vi, 11 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077203","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":335209,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7WH2N48","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":" Big Blue River at Shelbyville, Indiana, flood-inundation geospatial datasets"},{"id":335207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5166/coverthb.jpg"},{"id":335208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5166/sir20165166.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5166"}],"country":"United States","state":"Indiana","city":"Shelbyville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8248519897461,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.504305605954634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
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Indianapolis, IN 46278–1996
Glacier hypsometry provides a first‐order approach for assessing a glacier's response to climate forcings. We couple the Randolph Glacier Inventory to a suite of in situ observations and climate model output to examine potential change for the ∼27,000 glaciers in Alaska and northwest Canada through the end of the 21st century. By 2100, based on Representative Concentration Pathways (RCPs) 4.5–8.5 forcings, summer temperatures are predicted to increase between +2.1 and +4.6°C, while solid precipitation (snow) is predicted to decrease by −6 to −11%, despite a +9 to +21% increase in total precipitation. Snow is predicted to undergo a pronounced decrease in the fall, shifting the start of the accumulation season back by ∼1 month. In response to these forcings, the regional equilibrium line altitude (ELA) may increase by +105 to +225 m by 2100. The mass balance sensitivity to this increase is highly variable, with the most substantive impact for glaciers with either limited elevation ranges (often small (<1 km2) glaciers, which account for 80% of glaciers in the region) or those with top‐heavy geometries, like icefields. For more than 20% of glaciers, future ELAs, given RCP 6.0 forcings, will exceed the maximum elevation of the glacier, resulting in their eventual demise, while for others, accumulation area ratios will decrease by >60%. Our results highlight the first‐order control of hypsometry on individual glacier response to climate change, and the variability that hypsometry introduces to a regional response to a coherent climate perturbation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016EF000479","usgsCitation":"Mcgrath, D., Sass, L., O’Neel, S., Arendt, A.A., and Kienholz, C., 2017, Hypsometric control on glacier mass balance sensitivity in Alaska and northwest Canada: Earth's Future, v. 5, no. 3, p. 324-336, https://doi.org/10.1002/2016EF000479.","productDescription":"13 p.","startPage":"324","endPage":"336","ipdsId":"IP-080924","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":470065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016ef000479","text":"Publisher Index Page"},{"id":369088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, Yukon","otherGeospatial":"Gulf of Alaska watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.7734375,\n 60.108670463036\n ],\n [\n -130.869140625,\n 51.781435604431195\n ],\n [\n -123.134765625,\n 55.02802211299252\n ],\n [\n -123.04687499999999,\n 56.36525013685606\n ],\n [\n -128.671875,\n 66.75724984139227\n ],\n [\n -136.669921875,\n 66.44310650816469\n ],\n [\n -162.7734375,\n 60.108670463036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mcgrath, Daniel 0000-0002-9462-6842 dmcgrath@usgs.gov","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":145635,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","email":"dmcgrath@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":774886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":774887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neel, Shad 0000-0002-9185-0144 soneel@usgs.gov","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":166740,"corporation":false,"usgs":true,"family":"O’Neel","given":"Shad","email":"soneel@usgs.gov","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":774888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arendt, Anthony A.","contributorId":200572,"corporation":false,"usgs":false,"family":"Arendt","given":"Anthony","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kienholz, C.","contributorId":146539,"corporation":false,"usgs":false,"family":"Kienholz","given":"C.","email":"","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":774890,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182097,"text":"70182097 - 2017 - Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape","interactions":[],"lastModifiedDate":"2017-11-22T17:02:42","indexId":"70182097","displayToPublicDate":"2017-02-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1460,"text":"Ecological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape","docAbstract":"Introduction
Climate change is expected to impose significant tension on the geographic distribution of tree species. Yet, tree species range shifts may be delayed by their long life spans, capacity to withstand long periods of physiological stress, and dispersal limitations. Wildfire could theoretically break this biological inertia by killing forest canopies and facilitating species redistribution under changing climate. We investigated the capacity of wildfire to modulate climate-induced tree redistribution across a montane landscape in the central Rocky Mountains under three climate scenarios (contemporary and two warmer future climates) and three wildfire scenarios (representing historical, suppressed, and future fire regimes).
Methods
Distributions of four common tree species were projected over 90 years by pairing a climate niche model with a forest landscape simulation model that simulates species dispersal, establishment, and mortality under alternative disturbance regimes and climate scenarios.
Results
Three species (Douglas-fir, lodgepole pine, subalpine fir) declined in abundance over time, due to climate-driven contraction in area suitable for establishment, while one species (ponderosa pine) was unable to exploit climate-driven expansion of area suitable for establishment. Increased fire frequency accelerated declines in area occupied by Douglas-fir, lodgepole pine, and subalpine fir, and it maintained local abundance but not range expansion of ponderosa pine.
Conclusions
Wildfire may play a larger role in eliminating these conifer species along trailing edges of their distributions than facilitating establishment along leading edges, in part due to dispersal limitations and interspecific competition, and future populations may increasingly depend on persistence in locations unfavorable for their establishment.
Nutria (Myocaster coypus), invasive, semi-aquatic rodents native to South America, were introduced into Maryland near Blackwater National Wildlife Refuge (BNWR) in 1943. Irruptive population growth, expansion, and destructive feeding habits resulted in the destruction of thousands of acres of emergent marshes at and surrounding BNWR. In 2002, a partnership of federal, state and private entities initiated an eradication campaign to protect remaining wetlands from further damage and facilitate the restoration of coastal wetlands throughout the Chesapeake Bay region. Program staff removed nearly 14,000 nutria from five infested watersheds in a systematic trapping and hunting program between 2002 and 2014. As part of ongoing surveillance activities, the Chesapeake Bay Nutria Eradication Project uses a variety of tools to detect and remove nutria. Project staff developed a floating raft, or monitoring platform, to determine site occupancy. These platforms are placed along waterways and checked periodically for evidence of nutria visitation. We evaluated the effectiveness of monitoring platforms and three associated detection methods: hair snares, presence of scat, and trail cameras. Our objectives were to (1) determine if platform placement on land or water influenced nutria visitation rates, (2) determine if the presence of hair snares influenced visitation rates, and (3) determine method-specific detection probabilities. Our analyses indicated that platforms placed on land were 1.5–3.0 times more likely to be visited than those placed in water and that platforms without snares were an estimated 1.7–3.7 times more likely to be visited than those with snares. Although the presence of snares appears to have discouraged visitation, seasonal variation may confound interpretation of these results. Scat was the least effective method of determining nutria visitation, while hair snares were as effective as cameras. Estimated detection probabilities provided by occupancy modeling were 0.73 for hair snares, 0.71 for cameras and 0.40 for scat. We recommend the use of hair snares on monitoring platforms as they are the most cost-effective and reliable detection method available at this time. Future research should focus on determining the cause for the observed decrease in nutria visits after snares were applied.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1312-1","usgsCitation":"Pepper, M.A., Herrmann, V., Hines, J.E., Nichols, J.D., and Kendrot, S.R., 2017, Evaluation of nutria (Myocastor coypus) detection methods in Maryland, USA: Biological Invasions, v. 19, no. 3, p. 831-841, https://doi.org/10.1007/s10530-016-1312-1.","productDescription":"11 p.","startPage":"831","endPage":"841","ipdsId":"IP-080917","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":335674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Wicomico River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.7723617553711,\n 38.272419002497735\n ],\n [\n -75.6748580932617,\n 38.272419002497735\n ],\n [\n -75.6748580932617,\n 38.347580040410506\n ],\n [\n -75.7723617553711,\n 38.347580040410506\n ],\n [\n -75.7723617553711,\n 38.272419002497735\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"58a6c828e4b025c464286258","contributors":{"authors":[{"text":"Pepper, Margaret A.","contributorId":181781,"corporation":false,"usgs":false,"family":"Pepper","given":"Margaret","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":669510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, Valentine","contributorId":181782,"corporation":false,"usgs":false,"family":"Herrmann","given":"Valentine","email":"","affiliations":[],"preferred":false,"id":669511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":669509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":669512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendrot, Stephen R","contributorId":181783,"corporation":false,"usgs":false,"family":"Kendrot","given":"Stephen","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":669513,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182111,"text":"70182111 - 2017 - Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy","interactions":[],"lastModifiedDate":"2018-03-26T13:53:02","indexId":"70182111","displayToPublicDate":"2017-02-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy","docAbstract":"Models of dune erosion depend on a set of assumptions that dictate the predicted evolution of dunes throughout the duration of a storm. Lidar observations made before and after Hurricane Sandy at over 800 profiles with diverse dune elevations, widths, and volumes are used to quantify specific dune erosion model parameters including the dune face slope, which controls dune avalanching, and the trajectory of the dune toe, which controls dune migration. Wave‐impact models of dune erosion assume a vertical dune face and erosion of the dune toe along the foreshore beach slope. Observations presented here show that these assumptions are not always valid and require additional testing if these models are to be used to predict coastal vulnerability for decision‐making purposes. Observed dune face slopes steepened by 43% yet did not become vertical faces, and only 50% of the dunes evolved along a trajectory similar to the foreshore beach slope. Observations also indicate that dune crests were lowered during dune erosion. Moreover, analysis showed a correspondence between dune lowering and narrower beaches, smaller dune volumes, and/or longer wave impact.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL071991","usgsCitation":"Overbeck, J.R., Long, J.W., and Stockdon, H.F., 2017, Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy: Geophysical Research Letters, v. 44, no. 2, p. 937-945, https://doi.org/10.1002/2016GL071991.","productDescription":"9 p.","startPage":"937","endPage":"945","ipdsId":"IP-082655","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":335723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.618896484375,\n 37.735969208590504\n ],\n [\n -71.707763671875,\n 37.735969208590504\n ],\n [\n -71.707763671875,\n 40.90520969727358\n ],\n [\n -75.618896484375,\n 40.90520969727358\n ],\n [\n -75.618896484375,\n 37.735969208590504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"58a6c826e4b025c464286252","chorus":{"doi":"10.1002/2016gl071991","url":"http://dx.doi.org/10.1002/2016gl071991","publisher":"Wiley-Blackwell","authors":"Overbeck J. R., Long J. W., Stockdon H. F.","journalName":"Geophysical Research Letters","publicationDate":"1/27/2017","auditedOn":"2/8/2017","publiclyAccessibleDate":"1/27/2017"},"contributors":{"authors":[{"text":"Overbeck, Jacquelyn R.","contributorId":181813,"corporation":false,"usgs":false,"family":"Overbeck","given":"Jacquelyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":669638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":669637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":669639,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178565,"text":"70178565 - 2017 - Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone","interactions":[],"lastModifiedDate":"2017-02-24T10:34:07","indexId":"70178565","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone","docAbstract":"Layers of strong geologic contrast within the unsaturated zone can control recharge and contaminant transport to underlying aquifers. Slow diffuse flow in certain geologic layers, and rapid preferential flow in others, complicates the prediction of vertical and lateral fluxes. A simple model is presented, designed to use limited geological site information to predict these critical subsurface processes in response to a sustained infiltration source. The model is developed and tested using site-specific information from the Idaho National Laboratory in the Eastern Snake River Plain (ESRP), USA, where there are natural and anthropogenic sources of high-volume infiltration from floods, spills, leaks, wastewater disposal, retention ponds, and hydrologic field experiments. The thick unsaturated zone overlying the ESRP aquifer is a good example of a sharply stratified unsaturated zone. Sedimentary interbeds are interspersed between massive and fractured basalt units. The combination of surficial sediments, basalts, and interbeds determines the water fluxes through the variably saturated subsurface. Interbeds are generally less conductive, sometimes causing perched water to collect above them. The model successfully predicts the volume and extent of perching and approximates vertical travel times during events that generate high fluxes from the land surface. These developments are applicable to sites having a thick, geologically complex unsaturated zone of substantial thickness in which preferential and diffuse flow, and perching of percolated water, are important to contaminant transport or aquifer recharge.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1496-6","usgsCitation":"Nimmo, J.R., Creasey, K.M., Perkins, K., and Mirus, B.B., 2017, Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone: Hydrogeology Journal, v. 25, no. 2, p. 421-444, https://doi.org/10.1007/s10040-016-1496-6.","productDescription":"24 p.","startPage":"421","endPage":"444","ipdsId":"IP-065100","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":335550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.333333,\n 44.083333\n ],\n [\n -112.333333,\n 44.083333\n ],\n [\n -112.333333,\n 43.25\n ],\n [\n -113.333333,\n 43.25\n ],\n [\n -113.333333,\n 44.083333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-26","publicationStatus":"PW","scienceBaseUri":"58a576bee4b057081a24ed30","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creasey, Kaitlyn M kcreasey@usgs.gov","contributorId":5799,"corporation":false,"usgs":true,"family":"Creasey","given":"Kaitlyn","email":"kcreasey@usgs.gov","middleInitial":"M","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":654386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":654387,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}