San Francisco Bay (SFB) is a eutrophic estuary that harbors both freshwater and marine toxigenic organisms that are responsible for harmful algal blooms. While there are few commercial fishery harvests within SFB, recreational and subsistence harvesting for shellfish is common. Coastal shellfish are monitored for domoic acid and paralytic shellfish toxins (PSTs), but within SFB there is no routine monitoring for either toxin. Dinophysis shellfish toxins (DSTs) and freshwater microcystins are also present within SFB, but not routinely monitored. Acute exposure to any of these toxin groups has severe consequences for marine organisms and humans, but chronic exposure to sub-lethal doses, or synergistic effects from multiple toxins, are poorly understood and rarely addressed. This study documents the occurrence of domoic acid and microcystins in SFB from 2011 to 2016, and identifies domoic acid, microcystins, DSTs, and PSTs in marine mussels within SFB in 2012, 2014, and 2015. At least one toxin was detected in 99% of mussel samples, and all four toxin suites were identified in 37% of mussels. The presence of these toxins in marine mussels indicates that wildlife and humans who consume them are exposed to toxins at both sub-lethal and acute levels. As such, there are potential deleterious impacts for marine organisms and humans and these effects are unlikely to be documented. These results demonstrate the need for regular monitoring of marine and freshwater toxins in SFB, and suggest that co-occurrence of multiple toxins is a potential threat in other ecosystems where freshwater and seawater mix.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2018.02.005","usgsCitation":"Peacock, M.B., Gibble, C.M., Senn, D.B., Cloern, J.E., and Kudela, R.M., 2018, Blurred lines: Multiple freshwater and marine algal toxins at the land-sea interface of San Francisco Bay, California: Harmful Algae, v. 73, p. 138-147, https://doi.org/10.1016/j.hal.2018.02.005.","productDescription":"10 p.","startPage":"138","endPage":"147","ipdsId":"IP-086768","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":460917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2018.02.005","text":"Publisher Index Page"},{"id":354437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.87933349609376,\n 37.31119861382921\n ],\n [\n -121.19842529296875,\n 37.31119861382921\n ],\n [\n -121.19842529296875,\n 38.30718056188316\n ],\n [\n -122.87933349609376,\n 38.30718056188316\n ],\n [\n -122.87933349609376,\n 37.31119861382921\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3a","contributors":{"authors":[{"text":"Peacock, Melissa B.","contributorId":205179,"corporation":false,"usgs":false,"family":"Peacock","given":"Melissa","email":"","middleInitial":"B.","affiliations":[{"id":37042,"text":"Northwest Indian College","active":true,"usgs":false}],"preferred":false,"id":736305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gibble, Corinne M.","contributorId":205180,"corporation":false,"usgs":false,"family":"Gibble","given":"Corinne","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":736306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Senn, David B.","contributorId":205182,"corporation":false,"usgs":false,"family":"Senn","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":736308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","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":736304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":736307,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197221,"text":"70197221 - 2018 - Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed","interactions":[],"lastModifiedDate":"2018-10-11T15:00:36","indexId":"70197221","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed","docAbstract":"The U.S. Geological Survey monthly water balance model (MWBM) was enhanced with the capability to simulate glaciers in order to make it more suitable for simulating cold region hydrology. The new model, MWBMglacier, is demonstrated in the heavily glacierized and ecologically important Copper River watershed in Southcentral Alaska. Simulated water budget components compared well to satellite‐based observations and ground measurements of streamflow, evapotranspiration, snow extent, and total water storage, with differences ranging from 0.2% to 7% of the precipitation flux. Nash Sutcliffe efficiency for simulated and observed streamflow was greater than 0.8 for six of eight stream gages. Snow extent matched satellite‐based observations with Nash Sutcliffe efficiency values of greater than 0.89 in the four Copper River ecoregions represented. During the simulation period 1949 to 2009, glacier ice melt contributed 25% of total runoff, ranging from 12% to 45% in different tributaries, and glacierized area was reduced by 6%. Statistically significant (p < 0.05) decreasing and increasing trends in annual glacier mass balance occurred during the multidecade cool and warm phases of the Pacific Decadal Oscillation, respectively, reinforcing the link between climate perturbations and glacier mass balance change. The simulations of glaciers and total runoff for a large, remote region of Alaska provide useful data to evaluate hydrologic, cryospheric, ecologic, and climatic trends. MWBM glacier is a valuable tool to understand when, and to what extent, streamflow may increase or decrease as glaciers respond to a changing climate.
","language":"English","publisher":"AGU","doi":"10.1029/2017JF004482","usgsCitation":"Valentin, M.M., Viger, R.J., Van Beusekom, A.E., Hay, L.E., Hogue, T.S., and Foks, N.L., 2018, Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed: Journal of Geophysical Research F: Earth Surface, v. 123, no. 5, p. 1116-1132, https://doi.org/10.1029/2017JF004482.","productDescription":"17 p.","startPage":"1116","endPage":"1132","ipdsId":"IP-094374","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":468839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017jf004482","text":"Publisher Index Page"},{"id":354424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-23","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3c","contributors":{"authors":[{"text":"Valentin, Melissa M.","contributorId":205172,"corporation":false,"usgs":false,"family":"Valentin","given":"Melissa","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":147818,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":736280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736284,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foks, Nathan Leon","contributorId":194012,"corporation":false,"usgs":false,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[],"preferred":false,"id":736285,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197216,"text":"70197216 - 2018 - Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","interactions":[],"lastModifiedDate":"2018-05-23T10:35:34","indexId":"70197216","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","docAbstract":"Loess is widespread over Alaska, and its accumulation has traditionally been associated with glacial periods. Surprisingly, loess deposits securely dated to the last glacial period are rare in Alaska, and paleowind reconstructions for this time period are limited to inferences from dune orientations. We report a rare occurrence of loess deposits dating to the last glacial period, ~19 ka to ~12 ka, in the Yukon-Tanana Upland. Loess in this area is very coarse grained (abundant coarse silt), with decreases in particle size moving south of the Yukon River, implying that the drainage basin of this river was the main source. Geochemical data show, however, that the Tanana River valley to the south is also a likely distal source. The occurrence of last-glacial loess with sources to both the south and north is explained by both regional, synoptic-scale winds from the northeast and opposing katabatic winds that could have developed from expanded glaciers in both the Brooks Range to the north and the Alaska Range to the south. Based on a comparison with recent climate modeling for the last glacial period, seasonality of dust transport may also have played a role in bringing about contributions from both northern and southern sources.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2018.11","usgsCitation":"Muhs, D., Pigati, J.S., Budahn, J.R., Skipp, G.L., Bettis, E.A., and Jensen, B., 2018, Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA: Quaternary Research, v. 89, no. 3, p. 797-819, https://doi.org/10.1017/qua.2018.11.","productDescription":"23 p.","startPage":"797","endPage":"819","ipdsId":"IP-086762","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":354407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150,\n 65.5\n ],\n [\n -148.75,\n 65.5\n ],\n [\n -148.75,\n 66\n ],\n [\n -150,\n 66\n ],\n [\n -150,\n 65.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3e","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budahn, James R. 0000-0001-9794-8882 jbudahn@usgs.gov","orcid":"https://orcid.org/0000-0001-9794-8882","contributorId":1175,"corporation":false,"usgs":true,"family":"Budahn","given":"James","email":"jbudahn@usgs.gov","middleInitial":"R.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":736259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skipp, Gary L. 0000-0002-9404-0980","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":201777,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736260,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bettis, E. Arthur III 0000-0002-6137-1433","orcid":"https://orcid.org/0000-0002-6137-1433","contributorId":204005,"corporation":false,"usgs":false,"family":"Bettis","given":"E.","suffix":"III","email":"","middleInitial":"Arthur","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":736261,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Britta","contributorId":184164,"corporation":false,"usgs":false,"family":"Jensen","given":"Britta","affiliations":[],"preferred":false,"id":736262,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200455,"text":"70200455 - 2018 - Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios","interactions":[],"lastModifiedDate":"2018-10-18T13:45:55","indexId":"70200455","displayToPublicDate":"2018-05-22T13:45:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios","docAbstract":"Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October–March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040–2069) and late-century (2070–2099) changes in PSF, SFE, and SFE:P relative to historical (1970–1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040–2069 and the largest for RCP 8.5 in 2070–2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century.
","language":"English","publisher":"MDPI","doi":"10.3390/w10050668","usgsCitation":"Littell, J., McAfee, S., and Hayward, G.D., 2018, Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios: Water, v. 10, no. 5, p. 1-16, https://doi.org/10.3390/w10050668.","productDescription":"Article 668; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-097141","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"links":[{"id":468739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10050668","text":"Publisher Index Page"},{"id":358537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-22","publicationStatus":"PW","scienceBaseUri":"5c10a9b9e4b034bf6a7e5403","contributors":{"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":748948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAfee, Stephanie A.","contributorId":167115,"corporation":false,"usgs":false,"family":"McAfee","given":"Stephanie A.","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":748949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayward, Gregory D.","contributorId":209846,"corporation":false,"usgs":false,"family":"Hayward","given":"Gregory","email":"","middleInitial":"D.","affiliations":[{"id":38010,"text":"US Forest Service, Alaska Region","active":true,"usgs":false}],"preferred":false,"id":748950,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197208,"text":"70197208 - 2018 - Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China","interactions":[],"lastModifiedDate":"2018-05-22T15:16:00","indexId":"70197208","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China","docAbstract":"Seven hundred and nine surface sediment samples, along with deeper sediment samples, were collected from Hebei Province along the coastal section of the Bohai Sea, China, and analyzed for grain size, concentrations of organic carbon (Corg) and heavy metals (Cu, Pb, Zn, Cr, Cd, As, and Hg). Results indicated that the average concentrations in the sediments were 16.1 mg/kg (Cu), 19.4 mg/kg (Pb), 50 mg/kg (Zn), 48.8 mg/kg (Cr), 0.1 mg/kg (Cd), 8.4 mg/kg (As), and 20.3 μg/kg (Hg). These concentrations generally met the China Marine Sediment Quality criteria. However, both pollution assessments indicated moderate to strong Cd and Hg contamination in the study area. The potential ecological risk index suggested that the combined ecological risk of the seven studied metals may be low, but that 24.5% of the sites, where sediments were more finer and higher in Corg concentration, had high ecological risk in Hg and Cd pollution.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2018.04.060","usgsCitation":"Ding, X., Ye, S., Yuan, H., and Krauss, K.W., 2018, Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China: Marine Pollution Bulletin, v. 131, no. A, p. 655-661, https://doi.org/10.1016/j.marpolbul.2018.04.060.","productDescription":"7 p.","startPage":"655","endPage":"661","ipdsId":"IP-095668","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":354398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Bohai Sea, Hebei Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 117.5,\n 38\n ],\n [\n 120,\n 38\n ],\n [\n 120,\n 40\n ],\n [\n 117.5,\n 40\n ],\n [\n 117.5,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"A","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d79e4b092d9651e1b46","contributors":{"authors":[{"text":"Ding, Xigui","contributorId":173536,"corporation":false,"usgs":false,"family":"Ding","given":"Xigui","email":"","affiliations":[{"id":27244,"text":"Qingdao Institute of Marine Geology, China","active":true,"usgs":false}],"preferred":false,"id":736210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ye, Siyuan","contributorId":146732,"corporation":false,"usgs":false,"family":"Ye","given":"Siyuan","email":"","affiliations":[{"id":16739,"text":"Qingdao Institute of Marine Geology, Shandong Province, China","active":true,"usgs":false}],"preferred":false,"id":736211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yuan, Hongming","contributorId":173534,"corporation":false,"usgs":false,"family":"Yuan","given":"Hongming","email":"","affiliations":[{"id":27244,"text":"Qingdao Institute of Marine Geology, China","active":true,"usgs":false}],"preferred":false,"id":736212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":736209,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197203,"text":"70197203 - 2018 - A framework for modeling scenario-based barrier island storm impacts","interactions":[],"lastModifiedDate":"2018-05-22T13:22:48","indexId":"70197203","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A framework for modeling scenario-based barrier island storm impacts","docAbstract":"Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2018.04.012","usgsCitation":"Mickey, R.C., Long, J., Dalyander, P.S., Plant, N.G., and Thompson, D.M., 2018, A framework for modeling scenario-based barrier island storm impacts: Coastal Engineering, v. 138, p. 98-112, https://doi.org/10.1016/j.coastaleng.2018.04.012.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-092224","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2018.04.012","text":"Publisher Index Page"},{"id":354386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","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,\n 28.67\n ],\n [\n -87,\n 28.67\n ],\n [\n -87,\n 30.67\n ],\n [\n -89,\n 30.67\n ],\n [\n -89,\n 28.67\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"138","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4a","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":202183,"corporation":false,"usgs":true,"family":"Long","given":"Joseph W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736125,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197202,"text":"70197202 - 2018 - Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","interactions":[],"lastModifiedDate":"2023-03-27T22:49:54.298718","indexId":"70197202","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","docAbstract":"Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2016-0422","usgsCitation":"Mollenhauer, R., Mouser, J.B., and Brewer, S.K., 2018, Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 10, p. 1614-1625, https://doi.org/10.1139/cjfas-2016-0422.","productDescription":"12 p.","startPage":"1614","endPage":"1625","ipdsId":"IP-079903","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468741,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/87924","text":"External Repository"},{"id":354387,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.09031466610114,\n 36.913206107808435\n ],\n [\n -95.09031466610114,\n 36.02798264227451\n ],\n [\n -93.63459116212798,\n 36.02798264227451\n ],\n [\n -93.63459116212798,\n 36.913206107808435\n ],\n [\n -95.09031466610114,\n 36.913206107808435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"75","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4c","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":735984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, Joshua B.","contributorId":205087,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua","email":"","middleInitial":"B.","affiliations":[{"id":37027,"text":"Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":735985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":735983,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196645,"text":"ofr20181065 - 2018 - Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, water year 2015","interactions":[],"lastModifiedDate":"2018-05-22T10:00:35","indexId":"ofr20181065","displayToPublicDate":"2018-05-21T16:15:00","publicationYear":"2018","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":"2018-1065","title":"Streamflow, water quality, and constituent loads and yields, Scituate Reservoir Drainage Area, Rhode Island, water year 2015","docAbstract":"Streamflow and concentrations of sodium and chloride estimated from records of specific conductance were used to calculate loads of sodium and chloride during water year (WY) 2015 (October 1, 2014, through September 30, 2015) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow and water-quality data used in the study were collected by the U.S. Geological Survey and the Providence Water Supply Board. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected at 36 sampling stations by the Providence Water Supply Board and at 14 continuous-record streamgages by the U.S. Geological Survey during WY 2015 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for WY 2015.
The largest tributary to the reservoir (the Ponaganset River, which was monitored by the U.S. Geological Survey) contributed a mean streamflow of 25 cubic feet per second to the reservoir during WY 2015. For the same time period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.38 to about 14 cubic feet per second. Together, tributaries (equipped with instrumentation capable of continuously monitoring specific conductance) transported about 1,500,000 kilograms of sodium and 2,400,000 kilograms of chloride to the Scituate Reservoir during WY 2015; sodium and chloride yields for the tributaries ranged from 8,000 to 54,000 kilograms per square mile and from 12,000 to 91,000 kilograms per square mile, respectively.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the medians of the median concentrations were the following: for chloride, 29.5 milligrams per liter; for nitrite, 0.002 milligrams per liter as nitrogen; for nitrate, 0.05 milligrams per liter as nitrogen; for orthophosphate, 0.08 milligrams per liter as phosphate; and for total coliform bacteria and Escherichia coli, 440 and 20 colony forming units per 100 milliliters, respectively. The medians of the median daily loads (and yields) of chloride, nitrite, nitrate, orthophosphate, and total coliform and Escherichia coli bacteria were 170 kilograms per day (79 kilograms per day per square mile), 14 grams per day (5.2 grams per day per square mile), 670 grams per day (190 grams per day per square mile), 640 grams per day (210 grams per day per square mile), 18,000 million colony forming units per day (7,600 million colony forming units per day per square mile), and 1,200 million colony forming units per day (810 million colony forming units per day per square mile), respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181065","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2018, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2015: U.S. Geological Survey Open-File Report 2018–1065, 28 p., https://doi.org/10.3133/ofr20181065.","productDescription":"Report: v, 28 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088040","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":354074,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2FR5","text":"USGS data release","description":"USGS data release","linkHelpText":"Water Quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2015"},{"id":354073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1065/ofr20181065.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1065"},{"id":354072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1065/coverthb2.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.61334991455077,\n 41.72546174412562\n ],\n [\n -71.59687042236328,\n 41.73571038089891\n ],\n [\n -71.57112121582031,\n 41.75978824099475\n ],\n [\n -71.5481185913086,\n 41.784369074958185\n ],\n [\n -71.5536117553711,\n 41.82173436916421\n ],\n [\n -71.55635833740234,\n 41.83145600892532\n ],\n [\n -71.5536117553711,\n 41.842199261672384\n ],\n [\n -71.55979156494139,\n 41.85652079326776\n ],\n [\n -71.5707778930664,\n 41.862146232018134\n ],\n [\n -71.59000396728514,\n 41.862146232018134\n ],\n [\n -71.60923004150389,\n 41.877485822442324\n ],\n [\n -71.61128997802734,\n 41.907642945005215\n ],\n [\n -71.6421890258789,\n 41.918373400143004\n ],\n [\n -71.66622161865234,\n 41.91173095014917\n ],\n [\n -71.66416168212889,\n 41.886687809812926\n ],\n [\n -71.66622161865234,\n 41.857543637121026\n ],\n [\n -71.67377471923828,\n 41.854986496815044\n ],\n [\n -71.6861343383789,\n 41.8744181988335\n ],\n [\n -71.69265747070311,\n 41.87262868373214\n ],\n [\n -71.70398712158203,\n 41.86674849564142\n ],\n [\n -71.72321319580077,\n 41.88719899247718\n ],\n [\n -71.73282623291016,\n 41.89435512030307\n ],\n [\n -71.75617218017578,\n 41.895888471963744\n ],\n [\n -71.77196502685547,\n 41.88771017105127\n ],\n [\n -71.76578521728516,\n 41.87237303462768\n ],\n [\n -71.77265167236328,\n 41.854986496815044\n ],\n [\n -71.76647186279297,\n 41.83145600892532\n ],\n [\n -71.7685317993164,\n 41.82122266302155\n ],\n [\n -71.76990509033202,\n 41.81457011105642\n ],\n [\n -71.75754547119139,\n 41.79358445913614\n ],\n [\n -71.74655914306639,\n 41.78129698585321\n ],\n [\n -71.7348861694336,\n 41.77464028786307\n ],\n [\n -71.71634674072264,\n 41.771567732673454\n ],\n [\n -71.7033004760742,\n 41.76081263047197\n ],\n [\n -71.69300079345702,\n 41.75620274904767\n ],\n [\n -71.6861343383789,\n 41.74185877811425\n ],\n [\n -71.6744613647461,\n 41.741346434162764\n ],\n [\n -71.6476821899414,\n 41.73775991204249\n ],\n [\n -71.63875579833983,\n 41.727511602285894\n ],\n [\n -71.61334991455077,\n 41.72546174412562\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix provides an analysis of spatially combined NEXRAD–MPE precipitation data as a function of temperature at an hourly time scale and indicates, among other results, that most precipitation in the study area occurs at moderate temperatures of 30 to 74 degrees Fahrenheit. However, when precipitation does occur, its intensity increases with temperature to about 86 degrees Fahrenheit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181061","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Spies, R.R., Over, T.M., and Ortel, T.W., 2018, Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12: U.S. Geological Survey Open-File Report 2018–1061, 30 p., https://doi.org/10.3133/ofr20181061. ","productDescription":"v, 30 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057485","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":354281,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1061/coverthb.jpg","text":"Report"},{"id":354282,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1061/ofr20181061.pdf","text":"Report","size":"5.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1061"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33,\n 41.5833\n ],\n [\n -87.8333,\n 41.5833\n ],\n [\n -87.8333,\n 42.1667\n ],\n [\n -88.33,\n 42.1667\n ],\n [\n -88.33,\n 41.5833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Brown bullhead (Ameiurus nebulosus) is a commonly used indicator species for tumor surveys at Great Lakes Areas of Concern. The “fish tumors or other deformities” is one of the beneficial use impairments at the Ashtabula River Area of Concern. In May 2016, 150 brown bullhead were collected in the lower Ashtabula River and 150 were collected in the nearby Conneaut Creek as a reference. Length, weight and external visible abnormalities were documented. Fish were euthanized, and skin lesions and liver tissue preserved for histopathological analyses. Otoliths were collected for age analyses. The percentage of bullhead with raised external lesions on lips, barbels and body surface was 34.7 percent at the Ashtabula River and 23.3 percent at Conneaut Creek. At the Ashtabula River, 26.7 percent of the bullhead collected had skin neoplasms, including papillomas, melanomas and squamous cell carcinomas, whereas at Conneaut Creek 18.6 percent had only papillomas, benign skin tumors. Liver neoplasms were observed in 7.3 percent of the bullhead from the Ashtabula River and 4.7 percent of those from Conneaut Creek. These neoplasms were observed in fish 6 years of age or older at both sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181072","usgsCitation":"Blazer, V.S., Walsh, H.L., and Braham, R.P.. 2018 Assessment of skin and liver neoplasms in brown bullhead (Ameiurus nebulosus) collected at the Ashtabula River Area of Concern and associated reference site, Ohio, in 2016: U.S. Geological Survey Open-File Report 2018-1072, 18 p., https://doi.org/10.3133/ofr20181072.","productDescription":"v, 18 p.","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094847","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":354298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1072/ofr20181072.pdf","text":"Report","size":"6.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1072"},{"id":354297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1072/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Ashtabula River","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Human impacts on rivers threaten the natural function of riverine ecosystems. This paper assesses how channel confinement affects the scour depth and spatial extent of bed disturbance and discusses the implications of these results for salmon-redd disturbance in gravel-bedded rivers. Two-dimensional hydrodynamic models of relatively confined and unconfined reaches of the Cedar River in Washington State, USA, were constructed with surveyed bathymetry and available airborne lidar data then calibrated and verified with field observations of water-surface elevation and streamflow velocity. Simulations showed greater water depths and velocities in the confined reach and greater areas of low-velocity inundation in the unconfined reach at high flows. Data on previously published scour depth of bed disturbance during high flows were compared to simulated bed shear stress to construct a probabilistic logistic-regression model of bed disturbance, which was applied to spatial patterns of simulated bed shear stress to quantify the extent of likely bed disturbance to the burial depth of sockeye and Chinook salmon redds. The disturbance depth was not observed to differ between confined and unconfined reaches; however, results indicated the spatial extent of disturbance to a given depth in the confined reach was roughly twice as large as in the unconfined reach.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2018.1457986","usgsCitation":"Christiana R. Czuba, Czuba, J.A., Magirl, C.S., Gendaszek, A.S., and Konrad, C.P., 2018, Effect of river confinement on depth and spatial extent of bed disturbance affecting salmon redds: Journal of Ecohydraulics, v. 2, no. 2, p. 1-14, https://doi.org/10.1080/24705357.2018.1457986.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-066545","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":359514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29499816894531,\n 47.357431944587034\n ],\n [\n -121.62620544433592,\n 47.357431944587034\n ],\n [\n -121.62620544433592,\n 47.58717856130287\n ],\n [\n -122.29499816894531,\n 47.58717856130287\n ],\n [\n -122.29499816894531,\n 47.357431944587034\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5befe5bde4b045bfcadf7f44","contributors":{"authors":[{"text":"Christiana R. 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These data come from multiple agencies, programs, and sources, often with their own methods and standards for data collection and organization. Coordinating standards and methods is often prohibitively time-intensive and expensive. MonitoringResources.org offers a suite of tools and resources that support coordination of monitoring efforts, cost-effective planning, and sharing of knowledge among organizations. The website was developed by the Pacific Northwest Aquatic Monitoring Partnership—a collaboration of Federal, state, tribal, local, and private monitoring programs—and the U.S. Geological Survey (USGS), with funding from the Bonneville Power Administration and USGS. It is a key component of a coordinated monitoring and information network.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183015","usgsCitation":"Bayer, J.M., Scully, R.A., and Weltzin, J.F., 2018, MonitoringResources.org—Supporting coordinated and cost-effective natural resource monitoring across organizations: U.S. Geological Survey Fact Sheet 2018–3015, 2 p., https://doi.org/10.3133/fs20183015.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-091401","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":354360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3015/fs20183015.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3015"},{"id":354359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3015/coverthb.jpg"}],"contact":"Populations of Northern Spotted Owls (Strix occidentalis caurina; hereinafter referred to as Spotted Owl) are declining throughout this subspecies’ geographic range. Evidence indicates that competition with invading populations of Barred Owls (S. varia) has contributed significantly to those declines. A pilot study in California showed that localized removal of Barred Owls coupled with conservation of suitable forest conditions can slow or even reverse population declines of Spotted Owls. It remains unknown, however, whether similar results can be obtained in areas with different forest conditions, greater densities of Barred Owls, and fewer remaining Spotted Owls. During 2015–17, we initiated a before-after-control-impact (BACI) experiment at three study areas in Oregon and Washington to determine if removal of Barred Owls can improve population trends of Spotted Owls. Each study area had at least 20 years of pre-treatment demographic data on Spotted Owls, and represented different forest conditions occupied by the two owl species in the Pacific Northwest. This report describes research accomplishments and preliminary results from the first 2.5 years (March 2015–August 2017) of the planned 5-year experiment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181086","usgsCitation":"Wiens, J.D., Dugger, K.M., Lesmeister, D.B., Dilione, K.E., and Simon, D.C., 2018, Effects of experimental removal of Barred Owls on population demography of Northern Spotted Owls in Washington and Oregon—2017 progress report: U.S. Geological Survey Open-File Report 2018–1086, 23 p., https://doi.org/10.3133/ofr20181086.","productDescription":"iv, 23 p.","onlineOnly":"Y","ipdsId":"IP-095904","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":354381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1086/ofr20181086.pdf","text":"Report","size":"2.5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1086"},{"id":354380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1086/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
The U.S. Geological Survey, in cooperation with the Southwest Florida Water Management District, initiated a study to quantify the inflows and outflows in the Floral City, Inverness, and Hernando pools of the Tsala Apopka Lake Basin in Citrus County, Florida. This study assesses hydrologic changes in pool stages, groundwater levels, spring flows, and streamflows caused by the diversion of streamflow from the Withlacoochee River to the Tsala Apopka Lake Basin through water-control structures. A surface-water/groundwater flow model was developed using hydraulic parameters for lakes, streams, the unsaturated zone, and the underlying surficial and Upper Floridan aquifers estimated using an inverse modeling calibration technique. After calibration, the model was used to assess the relation between inflows and outflows in the Tsala Apopka Lake Basin and changes in pool stages.
Simulation results using the calibrated surface-water/groundwater flow model showed that leakage rates from the pools to the Upper Floridan aquifer were largest at the deep lake cells and that these leakage rates to the Upper Floridan aquifer were the highest in the model area. Downward leakage to the Upper Floridan aquifer occurred beneath most of the extent of the Floral City, Inverness, and Hernando pools. These leakage rates depended on the lakebed leakance and the difference between lake stages and heads in the Upper Floridan aquifer. Leakage rates were higher for the Floral City pool than for the Inverness pool, and higher for the Inverness pool than for the Hernando pool. Lakebed leakance was higher for the Floral City pool than for the Hernando pool, and higher for the Hernando pool than for the Inverness pool.
Simulation results showed that the average recharge rate to the surficial aquifer was 10.3 inches per year for the 2004 to 2012 simulation period. Areas that recharge the surficial aquifer covered about 86 percent of the model area. Simulations identified areas along segments of the Withlacoochee River and within land-surface depressions that receive water from the surficial aquifer. Recharge rates were largest in physiographic regions having a deep water table. Simulated heads in the Upper Floridan aquifer indicated the general flow directions in the active flow model area were from the northeast toward the southwest and then westward toward the coast, and from the southeast toward the northwest and then westward toward the coast, consistent with flow directions inferred from the estimated potentiometric surface map for May 2010. The largest inflow in the water budget of the Upper Floridan aquifer was downward leakage from the overlying hydrogeologic unit. The largest outflow in the water budget of the Upper Floridan aquifer was spring flow.
The calibrated surface-water and groundwater flow model was used to simulate hydrologic scenarios that included changes in rainfall rates, projected increases in groundwater pumping rates for 2025 and 2035, no flow for the 2004–12 period through the eight water-control structures in the Tsala Apopka Lake Basin, and the removal of the Inglis Dam and the Inglis Bypass Spillway on Lake Rousseau. Scenario simulation results were compared to annual average calibrated water levels and flows from 2004 to 2012. Simulated declines in the Tsala Apopka Lake pool stages under the 10-percent lower rainfall scenario were about 0.8, 0.3, and 1.3 feet (ft) for the Floral City, Inverness, and Hernando pools, respectively. Simulated groundwater levels under the same scenario declined up to 5.4 ft in the surficial aquifer and up to 2.9 ft in the Upper Floridan aquifer. Under the projected increases in groundwater pumping rates for 2035 that represented an increase of 36 percent from average 2004 to 2012 pumping rates, the simulated declines in the Floral City, Inverness, and Hernando pool stages were, in downstream order, 0.02, 0.06, and 0.04 ft. The largest drawdown under the projected increases in groundwater pumping rates for 2035 was 2.1 ft in the surficial aquifer and about 1.8 ft in the Upper Floridan aquifer. A scenario of decreased rainfall by 10 percent caused greater declines in water levels and pool stages than projected increases in groundwater pumping rates. The simulation with no flow through the eight Tsala Apopka Lake water-control structures resulted in simulated declines in average pool stage of 1.8, 1.9, and 0.5 ft in the Floral City, Inverness, and Hernando pools, respectively. The simulated removal of the two water-control structures in Lake Rousseau caused flow to increase at Rainbow Springs by 28 cubic feet per second, an increase of 4.7 percent from the average calibrated flow for 2004 to 2012.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185055","collaboration":"Prepared in cooperation with the Southwest Florida Water Management District","usgsCitation":"Sepúlveda, N., Fulkerson, M., Basso, R., and Ryan, P.J., 2018, Effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida: U.S. Geological Survey Scientific Investigations Report 2018–5055, 137 p., https://doi.org/10.3133/sir20185055.","productDescription":"Report: vii, 137 p.; Data Release","numberOfPages":"150","onlineOnly":"Y","ipdsId":"IP-066230","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":354255,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8RS2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets for simulation of effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida"},{"id":354253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5055/coverthb2.jpg"},{"id":354254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5055/sir20185055.pdf","text":"Report","size":"41.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5055"}],"country":"United States","state":"Florida","county":"Citrus County","otherGeospatial":"Tsala Apopka Lake Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.81494140625,\n 28.168875180063345\n ],\n [\n -81.650390625,\n 28.168875180063345\n ],\n [\n -81.650390625,\n 29.16895060109228\n ],\n [\n -82.81494140625,\n 29.16895060109228\n ],\n [\n -82.81494140625,\n 28.168875180063345\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Operable Unit 2, Area 8, at Naval Base Kitsap, Keyport is the site of a former chrome-plating facility that released metals (primarily chromium and cadmium), chlorinated volatile organic compounds, and petroleum compounds into the local environment. To ensure long-term protectiveness, as stipulated in the Fourth Five-Year Review for the site, Naval Facilities Engineering Command Northwest collaborated with the U.S. Environmental Protection Agency, the Washington State Department of Ecology, and the Suquamish Tribe, to collect data to monitor the contamination left in place and to ensure the site does not pose a risk to human health or the environment. To support these efforts, refined information was needed on the interaction of fresh groundwater with seawater in response to the up-to 13-ft tidal fluctuations at this nearshore site adjacent to Port Orchard Bay. The information was analyzed to meet the primary objective of this investigation, which was to determine the optimal time during the semi-diurnal and the neap-spring tidal cycles to sample groundwater for freshwater contaminants in Area 8 monitoring wells.
Groundwater levels and specific conductance in five monitoring wells, along with marine water-levels (tidal levels) in Port Orchard Bay, were monitored every 15 minutes during a 3-week duration to determine how nearshore groundwater responds to tidal forcing. Time series data were collected from October 24, 2017, to November 16, 2017, a period that included neap and spring tides. Vertical profiles of specific conductance were also measured once in the screened interval of each well prior to instrument deployment to determine if a freshwater/saltwater interface was present in the well during that particular time.
The vertical profiles of specific conductance were measured only one time during an ebbing tide at approximately the top, middle, and bottom of the saturated thickness within the screened interval of each well. The landward-most well, MW8-8, was completely freshwater, while one of the most seaward wells, MW8-9, was completely saline. A distinct saltwater interface was measured in the three other shallow wells (MW8-11, MW8-12, and MW8-14), with the topmost groundwater occurring fresh underlain by higher conductivity water.
Lag times between minimum spring-tide level and minimum groundwater levels in wells ranged from about 2 to 4.5 hours in the less-than 20-ft deep wells screened across the water table, and was about 7 hours for the single 48-ft deep well screened below the water table. Those lag times were surprisingly long considering the wells are all located within 200-ft of the shoreline and the local geology is largely coarse-grained glacial outwash deposits. Various manmade subsurface features, such as slurry walls and backfilled excavations, likely influence and confuse the connectivity between seawater and groundwater.
The specific-conductance time-series data showed clear evidence of substantial saltwater intrusion into the screened intervals of most shallow wells. Unexpectedly, the intrusion was associated with the neap part of the tidal cycle around November 13–16, when relatively low barometric pressure and high southerly winds led to the highest high and low tides measured during the monitoring period. The data consistently indicated that the groundwater had the lowest specific conductance (was least mixed with seawater) during the prior neap tides around October 30, the same period when the shallow groundwater levels were lowest. Although the specific conductance response is somewhat different between wells, the data do suggest that it is the heights of the actual high-high and low-low tides, regardless of whether or not they occur during the neap or spring part of the cycle, that allows seawater intrusion into the nearshore aquifer at Area 8.
With all the data taken into consideration, the optimal time for sampling the shallow monitoring wells at Area 8 would be centered on a 2–5-hour period following the predicted low-low tide during neap tide, with due consideration of local atmospheric pressure and wind conditions that have the potential to generate tides that can be substantially higher than those predicted from lunar-solar tidal forces. The optimal time for sampling the deeper monitoring wells at Area 8 would be during the 6–8-hour period following a predicted low-low tide, also during the neap tide part of the tidal cycle. The specific time window to sample each well following a low tide can be found in table 5. Those periods are when groundwater in the wells is most fresh and least diluted by seawater intrusion. In addition to timing, consideration should be given to collecting undisturbed samples from the top of the screened interval (or top of the water table if below the top of the interval) to best characterize contaminant concentrations in freshwater. A downhole conductivity probe could be used to identify the saltwater interface, above which would be the ideal depth for sampling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181082","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2018, Analysis of groundwater response to tidal fluctuations, Operable Unit 2, Area 8, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Open-File Report 2018-1082, 20 p., https://doi.org/10.3133/ofr20181082.","productDescription":"Report: iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-095017","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":354378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1082/coverthb.jpg"},{"id":354379,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1082/ofr20181082.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1082"},{"id":358998,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JW8D5S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Groundwater and Tidal Time Series Data, Operable Unit 2, Area 8, Naval Base Kitsap, Keyport, Washington"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.64913558959962,\n 47.683072220525\n ],\n [\n -122.59180068969725,\n 47.683072220525\n ],\n [\n -122.59180068969725,\n 47.72627665811123\n ],\n [\n -122.64913558959962,\n 47.72627665811123\n ],\n [\n -122.64913558959962,\n 47.683072220525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The arroyo southwestern toad is a specialized and federally endangered amphibian endemic to the coastal plains and mountains of central and southern California and northwestern Baja California. It is largely unknown how long these toads live in natural systems, how their population demographics vary across occupied drainages, and how hydrology affects age structure. We used skeletochronology to estimate the ages of adult arroyo toads in seven occupied drainages with varying surface water hydrology in southern California. We processed 179 adult toads with age estimates between 1 and 6 years. Comparisons between skeletochronological ages and known ages of PIT tagged toads showed that skeletochronology likely underestimated toad age by up to 2 years, indicating they may live to 7 or 8 years, but nonetheless major patterns were evident. Arroyo toads showed sexual size dimorphism with adult females reaching a maximum size of 12 mm greater than males. Population age structure varied among the sites. Age structure at sites with seasonally predictable surface water was biased toward younger individuals, which indicated stable recruitment for these populations. Age structures at the ephemeral sites were biased toward older individuals with cohorts roughly corresponding to higher rainfall years. These populations are driven by surface water availability, a stochastic process, and thus more unstable. Based on our estimates of toad ages, climate predictions of extreme and prolonged drought events could mean that the number of consecutive dry years could surpass the maximum life span of toads making them vulnerable to extirpation, especially in ephemeral freshwater systems. Understanding the relationship between population demographics and hydrology is essential for predicting species resilience to projected changes in weather and rainfall patterns. The arroyo toad serves as a model for understanding potential responses to climatic and hydrologic changes in Mediterranean stream systems. We recommend development of adaptive management strategies to address these threats.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4158","usgsCitation":"Fisher, R.N., Brehme, C.S., Hathaway, S.A., Hovey, T.E., Warburton, M.L., and Stokes, D.C., 2018, Longevity and population age structure of the arroyo southwestern toad (Anaxyrus californicus) with drought implications: Ecology and Evolution, v. 8, no. 12, p. 6124-6132, https://doi.org/10.1002/ece3.4158.","productDescription":"9 p.","startPage":"6124","endPage":"6132","ipdsId":"IP-095515","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468742,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4158","text":"Publisher Index Page"},{"id":356281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.01239013671874,\n 32.579220642875676\n ],\n [\n -116.68853759765626,\n 32.579220642875676\n ],\n [\n -116.68853759765626,\n 34.04583232505719\n ],\n [\n -118.01239013671874,\n 34.04583232505719\n ],\n [\n -118.01239013671874,\n 32.579220642875676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationDate":"2018-05-20","publicationStatus":"PW","scienceBaseUri":"5b6fc450e4b0f5d57878ea4d","contributors":{"authors":[{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehme, Cheryl S. 0000-0001-8904-3354 cbrehme@usgs.gov","orcid":"https://orcid.org/0000-0001-8904-3354","contributorId":3419,"corporation":false,"usgs":true,"family":"Brehme","given":"Cheryl","email":"cbrehme@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hathaway, Stacie A. 0000-0002-4167-8059 sahathaway@usgs.gov","orcid":"https://orcid.org/0000-0002-4167-8059","contributorId":3420,"corporation":false,"usgs":true,"family":"Hathaway","given":"Stacie","email":"sahathaway@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741873,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hovey, Tim E.","contributorId":206822,"corporation":false,"usgs":false,"family":"Hovey","given":"Tim","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":741874,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warburton, Manna L.","contributorId":174875,"corporation":false,"usgs":false,"family":"Warburton","given":"Manna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":741875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stokes, Drew C.","contributorId":33836,"corporation":false,"usgs":true,"family":"Stokes","given":"Drew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":741876,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200911,"text":"70200911 - 2018 - Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","interactions":[],"lastModifiedDate":"2018-12-13T09:13:22","indexId":"70200911","displayToPublicDate":"2018-05-19T11:09:07","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","docAbstract":"Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs10050791","usgsCitation":"Pastick, N.J., Wylie, B.K., and Wu, Z., 2018, Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems: Remote Sensing, v. 10, no. 5, 15 p., https://doi.org/10.3390/rs10050791.","productDescription":"15 p.","ipdsId":"IP-097826","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468743,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050791","text":"Publisher Index Page"},{"id":359420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c92","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":751236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":751237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":751238,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196044,"text":"sim3400 - 2018 - Geologic map of the Leadville North 7.5’ quadrangle, Eagle and Lake Counties, Colorado","interactions":[],"lastModifiedDate":"2018-05-18T15:37:42","indexId":"sim3400","displayToPublicDate":"2018-05-18T16:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3400","title":"Geologic map of the Leadville North 7.5’ quadrangle, Eagle and Lake Counties, Colorado","docAbstract":"The Leadville North 7.5’ quadrangle lies at the northern end of the Upper Arkansas Valley, where the Continental Divide at Tennessee Pass creates a low drainage divide between the Colorado and Arkansas River watersheds. In the eastern half of the quadrangle, the Paleozoic sedimentary section dips generally 20–30 degrees east. At Tennessee Pass and Missouri Hill, the core of the Sawatch anticlinorium is mapped as displaying a tight hanging-wall syncline and foot-wall anticline within the basement-cored structure. High-angle, west-dipping, Neogene normal faults cut the eastern margin of the broad, Sawatch anticlinorium. Minor displacements along high-angle, east- and west-dipping Laramide reverse faults occurred in the core of the north-plunging anticlinorium along the western and eastern flanks of Missouri Hill. Within the western half of the quadrangle, Meso- and Paleoproterozoic metamorphic and igneous rocks are uplifted along the generally east-dipping, high-angle Sawatch fault system and are overlain by at least three generations of glacial deposits in the western part of the quadrangle. 10Be and 26Al cosmogenic nuclide ages of the youngest glacial deposits indicate a last glacial maximum age of about 21–22 kilo-annum and complete deglaciation by about 14 kilo-annum, supported by chronologic studies in adjacent drainages. No late Pleistocene tectonic activity is apparent within the quadrangle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3400","usgsCitation":"Ruleman, C.A., Brandt, T.R., Caffee, M.W., and Goehring, B.M., 2018, Geologic map of the Leadville North 7.5’ quadrangle, Eagle and Lake Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3400, 1:24,000, https://doi.org/10.3133/sim3400.","productDescription":"Map: 50.00 x 39.94 inches; Data release; Read Me","onlineOnly":"Y","ipdsId":"IP-085050","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":353270,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3400/sim3400_hillshade.pdf","text":"Hillshaded Map","size":"65.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3400 Hillshaded Map"},{"id":353268,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3400/sim3400.pdf","text":"Map","size":"63.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3400 Map"},{"id":353269,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3400/sim3400_georeferenced.pdf","text":"Georeferenced Map","size":"181.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3400 Georeferenced Map"},{"id":353271,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DR2TRG","text":"USGS data release","linkHelpText":"Data Release for Geologic Map of the Leadville North 7.5' Quadrangle, Eagle and Lake Counties, Colorado"},{"id":353612,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3400/coverthb1.jpg"},{"id":353272,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3400/sim3400_readme.txt","text":"Read Me","size":"8.00 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3400 Read Me"},{"id":354341,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3400/versionHist.txt","size":"4/00 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3400 Version History"}],"country":"United States","state":"Colorado","county":"Eagle County, Lake County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.375,\n 39.375\n ],\n [\n -106.25,\n 39.375\n ],\n [\n -106.25,\n 39.25\n ],\n [\n -106.375,\n 39.25\n ],\n [\n -106.375,\n 39.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
http://gec.cr.usgs.gov/
No abstract available.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-11-1849-2018","usgsCitation":"Sherwood, C.R., Aretxabaleta, A., Harris, C.K., Rinehimer, J.P., Verney, R., and Ferre, B., 2018, Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234): Geoscientific Model Development, v. 11, p. 1849-1871, https://doi.org/10.5194/gmd-11-1849-2018.","productDescription":"23 p.","startPage":"1849","endPage":"1871","ipdsId":"IP-090028","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-11-1849-2018","text":"Publisher Index Page"},{"id":413620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2018-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":865485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":865486,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Courtney K.","contributorId":19620,"corporation":false,"usgs":false,"family":"Harris","given":"Courtney","email":"","middleInitial":"K.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":865487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rinehimer, J. Paul","contributorId":140081,"corporation":false,"usgs":false,"family":"Rinehimer","given":"J.","email":"","middleInitial":"Paul","affiliations":[{"id":13381,"text":"Center for Coastal Margin Observation & Prediction, Oregon Health and Sciences University, Portland, OR, 97239","active":true,"usgs":false}],"preferred":false,"id":865488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verney, Romaric","contributorId":302800,"corporation":false,"usgs":false,"family":"Verney","given":"Romaric","email":"","affiliations":[],"preferred":false,"id":865489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferre, Benedicte","contributorId":302801,"corporation":false,"usgs":false,"family":"Ferre","given":"Benedicte","email":"","affiliations":[],"preferred":false,"id":865490,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208930,"text":"70208930 - 2018 - The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity","interactions":[],"lastModifiedDate":"2020-03-06T06:48:35","indexId":"70208930","displayToPublicDate":"2018-05-18T06:45:46","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity","docAbstract":"Genetic networks can characterize complex genetic relationships among groups of\nindividuals, which can be used to rank nodes most important to the overall connectivity\nof the system. Ranking allows scarce resources to be guided toward nodes integral\nto connectivity. The greater sage-grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on spatially discrete leks that must remain connected by genetic exchange for population persistence. We genotyped 5,950 individuals from 1,200 greater sage-grouse leks distributed across the entire species’ geographic range. We found a small-world network composed of 458 nodes connected by 14,481 edges. This network was composed of hubs—that is, nodes facilitating gene flow across the network—and spokes—that is, nodes where connectivity is served by hubs. It is within these hubs that the greatest genetic diversity was housed. Using indices of network centrality, we identified hub nodes of greatest conservation importance. We also identified keystone nodes with elevated centrality\ndespite low local population size. Hub and keystone nodes were found across the\nentire species’ contiguous range, although nodes with elevated importance to\nnetwork-wide connectivity were found more central: especially in northeastern, central,\nand southwestern Wyoming and eastern Idaho. Nodes among which genes are\nmost readily exchanged were mostly located in Montana and northern Wyoming, as\nwell as Utah and eastern Nevada. The loss of hub or keystone nodes could lead to the\ndisintegration of the network into smaller, isolated subnetworks. Protecting both hub\nnodes and keystone nodes will conserve genetic diversity and should maintain network\nconnections to ensure a resilient and viable population over time. Our analysis\nshows that network models can be used to model gene flow, offering insights into its\npattern and process, with application to prioritizing landscapes for conservation.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4056","usgsCitation":"Cross, T.B., Schwartz, M.D., Naugle, D., Fedy, B.C., Row, J.R., and Oyler-McCance, S.J., 2018, The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity: Ecology and Evolution, v. 8, no. 11, p. 5394-5412, https://doi.org/10.1002/ece3.4056.","productDescription":"19 p.","startPage":"5394","endPage":"5412","ipdsId":"IP-091562","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468745,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4056","text":"Publisher Index Page"},{"id":437891,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73N22PN","text":"USGS data release","linkHelpText":"Genetic data and genetic network attributes for rangewide Greater Sage-grouse network constructed in 2018 (ver. 2.0, December 2022)"},{"id":372986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana, Utah, 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\"}}]}","volume":"8","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":784078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":784082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naugle, David","contributorId":223090,"corporation":false,"usgs":false,"family":"Naugle","given":"David","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":784079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fedy, Brad C.","contributorId":140877,"corporation":false,"usgs":false,"family":"Fedy","given":"Brad","email":"","middleInitial":"C.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":784081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Row, Jeff R","contributorId":140874,"corporation":false,"usgs":false,"family":"Row","given":"Jeff","email":"","middleInitial":"R","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":784080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":784077,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197124,"text":"70197124 - 2018 - Do downscaled general circulation models reliably simulate historical climatic conditions?","interactions":[],"lastModifiedDate":"2018-05-18T09:43:34","indexId":"70197124","displayToPublicDate":"2018-05-18T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Do downscaled general circulation models reliably simulate historical climatic conditions?","docAbstract":"The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-17-0018.1","usgsCitation":"Bock, A.R., Hay, L.E., McCabe, G., Markstrom, S.L., and Atkinson, R.D., 2018, Do downscaled general circulation models reliably simulate historical climatic conditions?: Earth Interactions, v. 22, p. 1-22, https://doi.org/10.1175/EI-D-17-0018.1.","productDescription":"Paper 10; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-090110","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-17-0018.1","text":"Publisher Index Page"},{"id":354299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5afee6b2e4b0da30c1bfbd46","contributors":{"authors":[{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":735759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":735770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":1453,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory J.","email":"gmccabe@usgs.gov","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":735771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":140378,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":735772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, R. Dwight","contributorId":195660,"corporation":false,"usgs":false,"family":"Atkinson","given":"R.","email":"","middleInitial":"Dwight","affiliations":[],"preferred":false,"id":735773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196218,"text":"sir20105070P - 2018 - Quartz-pebble-conglomerate gold deposits","interactions":[],"lastModifiedDate":"2024-04-16T16:38:01.913558","indexId":"sir20105070P","displayToPublicDate":"2018-05-17T17:15:00","publicationYear":"2018","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":"2010-5070","chapter":"P","title":"Quartz-pebble-conglomerate gold deposits","docAbstract":"Quartz-pebble-conglomerate gold deposits represent the largest repository of gold on Earth, largely due to the deposits of the Witwatersrand Basin, which account for nearly 40 percent of the total gold produced throughout Earth’s history. This deposit type has had a controversial history in regards to genetic models. However, most researchers conclude that they are paleoplacer deposits that have been modified by metamorphism and hydrothermal fluid flow subsequent to initial sedimentation.
The deposits are found exclusively within fault-bounded depositional basins. The periphery of these basins commonly consists of granite-greenstone terranes, classic hosts for lode gold that source the detrital material infilling the basin. The gold reefs are typically located along unconformities or, less commonly, at the top of sedimentary beds. Large quartz pebbles and heavy-mineral concentrates are found associated with the gold. Deposits that formed prior to the Great Oxidation Event (circa 2.4 giga-annum [Ga]) contain pyrite, whereas younger deposits contain iron oxides. Uranium minerals and hydrocarbons are also notable features of some deposits.
Much of the gold in these types of deposits forms crystalline features that are the product of local remobilization. However, some gold grains preserve textures that are undoubtedly of detrital origin. Other heavy minerals, such as pyrite, contain growth banding that is truncated along broken margins, which indicates that they were transported into place as opposed to forming by in situ growth in a hydrothermal setting.
The ore tailings associated with these deposits commonly contain uranium-rich minerals and sulfides. Oxidation of the sulfides releases sulfuric acid and mobilizes various metals into the environment. The neutralizing potential of the tailings is minimal, since carbonate minerals are rare. The continuity of the tabular ore bodies, such as those of the Witwatersrand Basin, has allowed these mines to be the deepest in the world. The extreme depths create engineering complications and safety issues for the miners, such as rock bursts as a result of pressure release.
The richness of these deposits makes them a desirable exploration target. However, the likelihood of future discoveries is minimal. Small deposits found in the United States include those found at Nemo in the Black Hills of South Dakota and Deep Lake in the Sierra Madre of Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070P","usgsCitation":"Taylor, R.D., and Anderson, E.D., 2018, Quartz-pebble-conglomerate gold deposits: U.S. Geological Survey Scientific Investigations Report 2010–5070–P, 34 p., https://doi.org/10.3133/sir20105070P","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-086517","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":354221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/p/sir20105070p.pdf","text":"Report","size":"6.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010–5070–P"},{"id":354220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5070/p/coverthb.jpg"}],"contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS–973
Denver, CO 80225
Specific conductance (SC), soil volumetric water content (VWC), and discharge were monitored on a subsurface agricultural drain for a 2-yr period (2007–2008) to differentiate preferential flow paths from matrix flow paths. A major observation from the 2-yr period was the fast SC decrease after relatively small rainfall events, often <5 mm. A total of 25 paired rainfall–SC events were classified, with an average preferential flow onset time (from the event start) after 1.7 h and maximum preferential flow after 2.4 h. A specific conductance end-member mixing analysis (SC-EMMA) was used to determine the volume of water that infiltrated through preferential flow pathways. The SC-EMMA was used for 20 of the 25 paired rainfall–SC events; of the 20 classified events, the maximum preferential flow ranged from 11 to 75% of the total subsurface drain flow, with a mean maximum preferential flow of 31%. Overall, SC-EMMA illustrated that a significant portion of the subsurface drain discharge can be attributed to preferential flow, mainly through macropores or other largely open preferential flow pathways. The other primary mechanism, antecedent moisture conditions shifts, could only be shown for four of the 25 classified events. Specific conductance as a tracer of preferential flow was shown to be an effective tool for distinguishing preferential flow to subsurface drains. Even during relatively dry periods, the SC had a substantial decrease shortly after a rainfall event contrary to the conventional idea that macropore flow starts only after all the smaller pores are saturated and surface ponding begins to occur.
","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2017.11.0206","usgsCitation":"Smith, E.A., and Capel, P.D., 2018, Specific conductance as a tracer of preferential flow in a subsurface-drained field: Vadose Zone Journal, v. 17, no. 1, 13 p., https://doi.org/10.2136/vzj2017.11.0206.","productDescription":"13 p.","ipdsId":"IP-030091","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":468747,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2017.11.0206","text":"Publisher Index Page"},{"id":370106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","county":"Hamilton County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.5002,42.557],[-93.4996,42.4702],[-93.4641,42.4698],[-93.4642,42.3821],[-93.4636,42.2948],[-93.4643,42.2081],[-93.5844,42.2081],[-93.7009,42.2079],[-93.8179,42.2081],[-93.9319,42.2087],[-93.9324,42.2955],[-93.9324,42.3827],[-93.9323,42.4709],[-93.9727,42.4716],[-93.972,42.5566],[-93.8563,42.557],[-93.7367,42.5568],[-93.6191,42.5565],[-93.5002,42.557]]]},\"properties\":{\"name\":\"Hamilton\",\"state\":\"IA\"}}]}","volume":"17","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":709973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":709972,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70046497,"text":"70046497 - 2018 - Succession in ecological education","interactions":[],"lastModifiedDate":"2019-09-13T11:06:20","indexId":"70046497","displayToPublicDate":"2018-05-17T11:07:44","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Succession in ecological education","docAbstract":"As complex and dynamic systems, wetlands offer the opportunity to investigate and incorporate the ecological concept of succession in educational settings. For example, the well-known, classic hydrosere concept is illustrated in numerous ecology and life-science textbooks. In this chapter, the drawbacks of using the hydrosere successional concept are assessed, and two examples of using wetlands to illustrate the process of succession for educational purposes are described. In each case, the premise and approach is that students best “learn ecology by doing ecology.”
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wetland book: Structure and function, management, and methods","language":"English","publisher":"Springer","doi":"10.1007/978-90-481-9659-3_6","usgsCitation":"Middleton, B.A., and Gibson, D.J., 2018, Succession in ecological education, chap. of The wetland book: Structure and function, management, and methods, p. 47-53, https://doi.org/10.1007/978-90-481-9659-3_6.","productDescription":"7 p.","startPage":"47","endPage":"53","ipdsId":"IP-040452","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":357227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5b98a2c5e4b0702d0e842fdc","contributors":{"authors":[{"text":"Middleton, Beth A. 0000-0002-1220-2326 middletonb@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":2029,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","email":"middletonb@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":744737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibson, David J.","contributorId":140174,"corporation":false,"usgs":false,"family":"Gibson","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":517990,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}