To better understand the effects of climate change on streamflow, the hydrologic response to both temperature and precipitation needs to be examined at the mesoscale. New York State provides a hydrologically diverse mesoscale region, where sub-regional clusters of watersheds may respond differently to changes in temperature and in seasonal precipitation rates. Connections between streamflow and climate were examined for 97 gaging stations across the state and surrounding areas, for a historical period of 56 years of daily average streamflow. Gages were grouped into clusters if their mean annual discharge rates were strongly correlated to one another. Within each cluster, sharp temporal changes in discharge, or change points, were identified. These change points clustered both spatially and by flow regime, with low, medium, and high flows increasing around 1970 for much of the state consistent with other studies in the region. A step increase in Catskill low flows in 2003 coincides with increases in summer precipitation, and is consistent with a positive correlation between summer precipitation and annual low flows. Our results support previous studies that have shown that streamflow at this mesoscale is strongly tied to precipitation, and the strength of that connection is modulated by land cover, geographic position, and seasonal moisture conditions. Across the state, the winter-spring center of volume date has moved earlier along with increasing January streamflow rates, the result of warmer winter temperatures and an increased proportion of precipitation as rain. The transition to a post-1970s pluvial period also coincided with more frequent peak over threshold flows statewide, and this wetter period has continued to the present day.
High natural variability in the condition of fish communities in headwater streams complicates detection of long-term responses to changes in water quality. As a result, little is known about the impacts and recovery of fishes from acid deposition in streams of New York. Twenty-one fish metrics from annual electrofishing surveys at 13 streams sites in the Catskill and Adirondack mountains were assessed to quantify temporal variability and identify effective monitoring strategies for detecting change in headwater stream fish assemblages. Metrics included the density and biomass of Brook Trout populations and entire fish communities using length-, area-, and effort-based standardization techniques. Linear mixed models were used to estimate changes in coefficients of variation (CV) for different classes of metrics and standardization techniques, and a simulation-based power analysis was conducted to assess differences in statistical power for each metric with various sampling designs. Metric CV varied significantly as a result of standardization technique and whether metrics were calculated for the entire community or Brook Trout only. The sampling effort necessary to detect a 30% change with power of 0.80 was strongly correlated with CV (R2 = 0.77). The number of sampling events at the 13 sites needed to detect this change ranged from 1 to >60, suggesting metric selection can strongly affect statistical power and the resources necessary to detect change. Thus, metric selection is a complex decision that must consider study objectives and biological relevance, in addition to natural variability and statistical power. However, adequate statistical power was achieved at relatively small sample sizes using certain metrics, indicating that fish communities in headwater streams can be a valuable component of long-term assessments of acidification impacts and recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.01.055","usgsCitation":"George, S.D., Baldigo, B.P., and Stich, D.S., 2019, Temporal variability in stream fish assemblage metrics and implications for long-term monitoring: Ecological Indicators, v. 101, p. 661-669, https://doi.org/10.1016/j.ecolind.2019.01.055.","productDescription":"9 p.","startPage":"661","endPage":"669","ipdsId":"IP-097687","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":467653,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2019.01.055","text":"Publisher Index Page"},{"id":363416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Center","active":true,"usgs":true}],"preferred":true,"id":761804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stich, Daniel S.","contributorId":139212,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":12606,"text":"University of Maine, Dept of Plant, Soil, & Envir Sciences","active":true,"usgs":false}],"preferred":false,"id":761805,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208521,"text":"70208521 - 2019 - A federal-state partnership for mapping Florida's coast and seafloor ","interactions":[],"lastModifiedDate":"2020-02-14T06:54:01","indexId":"70208521","displayToPublicDate":"2019-05-01T06:52:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3891,"text":"Coastal Sediments","active":true,"publicationSubtype":{"id":10}},"title":"A federal-state partnership for mapping Florida's coast and seafloor ","docAbstract":"The Florida Coastal Mapping Program, a partnership of state and federal agencies, has a goal of having modern, consistent, high- resolution sea-floor data for all of Florida’s coastal zone in the next decade to support a myriad of coastal zone science and management applications. One of the early steps in the implementation process is to prioritize and justify mapping needs. This is accomplished by holding six regional workshops across the state during which stakeholders and partners are introduced to a newly developed geospatial prioritization tool. Users independently utilize the tool to populate grid cells for a given region, and the result is a cumulative perspective on sea floor mapping priorities to inform future mapping initiatives. The tool also allows participants to indicate their primary use of sea floor mapping data and types of additional data required for their science or management need.","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0184","usgsCitation":"Hapke, C.J., Druyor, R., Baumstark, R.D., Kramer, P., Fitos, E., Fredericks, X., and Fetherston-Resch, E.H., 2019, A federal-state partnership for mapping Florida's coast and seafloor : Coastal Sediments, p. 2150-2158, https://doi.org/10.1142/9789811204487_0184.","productDescription":"9 p.","startPage":"2150","endPage":"2158","ipdsId":"IP-105639","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Ethiopia","interactions":[],"lastModifiedDate":"2019-12-04T18:01:01","indexId":"70202916","displayToPublicDate":"2019-04-30T18:00:37","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Revisiting Herto: New evidence of Homo sapiens from Ethiopia","docAbstract":"Localities in the radiometrically dated Upper Herto Member of Ethiopia’s Bouri Formation continue to produce new data that complement and extend initial reports of fossils and artifacts published in 2003. Results of these revisits are reported here and include the in situ recovery of artifacts from the same sediments containing hominid fossils. We evaluate the absolute and relative temporal placement of the Herto discoveries in light of new field observations and in the context of new data from the Omo Kibish Formation of southern Ethiopia. The Upper Herto Member stone tool assemblage includes artifacts normally typologically attributed to both Acheulean and Middle Stone Age industries or technocomplexes. These discoveries and other aspects of continuing Herto research help to elucidate the timing and patterns of technological, behavioral, and anatomical change in Africa during the evolution of our species.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Modern Human Origins and Dispersal","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Kerns Verlag","isbn":"9783935751308","usgsCitation":"Sahle, Y., Beyene, Y., Defleur, A., Asfaw, B., WoldeGabriel, G., Hart, W.K., Morgan, L.E., Renne, P.R., Carlson, J., and White, T., 2019, Revisiting Herto: New evidence of Homo sapiens from Ethiopia, chap. 3 of Modern Human Origins and Dispersal, p. 73-104.","productDescription":"32 p.","startPage":"73","endPage":"104","ipdsId":"IP-102180","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":369938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":362677,"type":{"id":15,"text":"Index Page"},"url":"https://kernsverlag.com/en/modern-human-origins-and-dispersal"}],"country":"Ethiopia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[37.90607,14.95943],[38.51295,14.50547],[39.0994,14.74064],[39.34061,14.53155],[40.02625,14.51959],[40.8966,14.11864],[41.1552,13.77333],[41.59856,13.45209],[42.00975,12.86582],[42.35156,12.54223],[42,12.1],[41.66176,11.6312],[41.73959,11.35511],[41.75557,11.05091],[42.31414,11.0342],[42.55493,11.10511],[42.77685,10.92688],[42.55876,10.57258],[42.92812,10.02194],[43.29699,9.54048],[43.67875,9.18358],[46.94834,7.99688],[47.78942,8.003],[44.9636,5.00162],[43.66087,4.95755],[42.76967,4.25259],[42.12861,4.23413],[41.85508,3.91891],[41.1718,3.91909],[40.76848,4.25702],[39.85494,3.83879],[39.55938,3.42206],[38.89251,3.50074],[38.67114,3.61607],[38.43697,3.58851],[38.12092,3.59861],[36.85509,4.44786],[36.15908,4.44786],[35.81745,4.77697],[35.81745,5.33823],[35.29801,5.506],[34.70702,6.59422],[34.25032,6.82607],[34.0751,7.22595],[33.56829,7.71334],[32.95418,7.78497],[33.2948,8.35458],[33.8255,8.37916],[33.97498,8.68456],[33.96162,9.58358],[34.25745,10.63009],[34.73115,10.91017],[34.83163,11.31896],[35.26049,12.08286],[35.86363,12.57828],[36.27022,13.56333],[36.42951,14.42211],[37.59377,14.2131],[37.90607,14.95943]]]},\"properties\":{\"name\":\"Ethiopia\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sahle, Yonatan","contributorId":214635,"corporation":false,"usgs":false,"family":"Sahle","given":"Yonatan","email":"","affiliations":[{"id":39092,"text":"1DFG Center for Advanced Studies: Words, Bones, Genes, Tools. University of Tübingen, Germany","active":true,"usgs":false}],"preferred":false,"id":760452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beyene, Yonas","contributorId":214636,"corporation":false,"usgs":false,"family":"Beyene","given":"Yonas","email":"","affiliations":[{"id":39093,"text":"2French Center for Ethiopian Studies, Addis Ababa, Ethiopia","active":true,"usgs":false}],"preferred":false,"id":760453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Defleur, Alban","contributorId":214637,"corporation":false,"usgs":false,"family":"Defleur","given":"Alban","email":"","affiliations":[{"id":39094,"text":"3CNRS UMR 5276 - Lyon Geology Laboratory, France","active":true,"usgs":false}],"preferred":false,"id":760454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asfaw, Berhane","contributorId":214638,"corporation":false,"usgs":false,"family":"Asfaw","given":"Berhane","email":"","affiliations":[{"id":39095,"text":"4Rift Valley Research Service, Addis Ababa, Ethiopia","active":true,"usgs":false}],"preferred":false,"id":760455,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"WoldeGabriel, Giday","contributorId":214639,"corporation":false,"usgs":false,"family":"WoldeGabriel","given":"Giday","email":"","affiliations":[{"id":39096,"text":"5Los Alamos National Laboratory, Los Alamos, NM, USA","active":true,"usgs":false}],"preferred":false,"id":760456,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hart, William K","contributorId":214640,"corporation":false,"usgs":false,"family":"Hart","given":"William","email":"","middleInitial":"K","affiliations":[{"id":39097,"text":"6Department of Geology and Environmental Earth Science, Miami University, Oxford, OH, USA","active":true,"usgs":false}],"preferred":false,"id":760457,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760451,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Renne, Paul R.","contributorId":214641,"corporation":false,"usgs":false,"family":"Renne","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":39098,"text":"8Berkeley Geochronology Center, Berkeley; Department of Earth & Planetary Science, University of California, Berkeley, CA, USA","active":true,"usgs":false}],"preferred":false,"id":760458,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carlson, Joshua","contributorId":214642,"corporation":false,"usgs":false,"family":"Carlson","given":"Joshua","email":"","affiliations":[{"id":39099,"text":"9Human Evolution Research Center, and Department of Integrative Biology, The University of California, Berkeley, CA, USA","active":true,"usgs":false}],"preferred":false,"id":760459,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"White, Tim D","contributorId":214643,"corporation":false,"usgs":false,"family":"White","given":"Tim D","affiliations":[{"id":39099,"text":"9Human Evolution Research Center, and Department of Integrative Biology, The University of California, Berkeley, CA, USA","active":true,"usgs":false}],"preferred":false,"id":760460,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70203313,"text":"70203313 - 2019 - Evaluation of ground motion models for USGS seismic hazard forecasts: Induced and tectonic earthquakes in the Central and Eastern U.S.","interactions":[],"lastModifiedDate":"2019-05-02T15:52:52","indexId":"70203313","displayToPublicDate":"2019-04-30T15:42:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of ground motion models for USGS seismic hazard forecasts: Induced and tectonic earthquakes in the Central and Eastern U.S.","docAbstract":"Ground motion model (GMM) selection and weighting introduces a significant source of uncertainty in United States Geological Survey (USGS) seismic hazard models. The increase in moderate moment magnitude induced earthquakes (Mw 4 to 5.8) in Oklahoma and Kansas since 2009, due to increased wastewater injection related to oil and gas production (Keranen et al., 2013; 2014; Weingarten et al., 2015; McNamara et al., 2015a), provides useful near-source (< 40 km) instrumental ground-motion observations for comparisons between central and eastern US (CEUS) induced (Rennolet et al., 2017) and tectonic (Goulet et al., 2014) earthquakes. In this study, we evaluate over 50 GMMs using two well-established probabilistic scoring methods: log likelihood (LLH) (Scherbaum et al., 2004; 2009) and multivariate LLH (MLLH) (Mak et al., 2017). The LLH approach compares the mean and standard deviation (σ) of the observed and modeled ground motions. The MLLH approach advances the LLH method by considering the variability (φ,τ) of multiple correlated variables namely intra- (within) and inter- (between) event residuals.\n \nFor the probabilistic scoring GMM evaluation methods (LLH, MLLH), we compute horizontal component peak ground acceleration (PGA) and 1s period pseudo spectral acceleration (PSA1.0) total residuals using GMM software (nshmp-haz) recently implemented by the USGS National Seismic Hazard Model Project (NSHMP). We observe from LLH and MLLH scores that: 1) newer GMMs with lower standard deviations (σ,φ,τ) score better than older GMMs with higher published uncertainty; 2) 2014 CEUS GMMs score better for CEUS tectonic earthquakes than induced earthquakes; 3) NGA-West2, G17 and A15 GMMs score well for CEUS induced earthquake ground motions; and 4) NGA-East GMMs score well for CEUS tectonic earthquake ground motions. We also use the LLH and MLLH scores to evaluate GMM weights applied in past USGS seismic hazard forecasts and to inform weighting of GMMs in future seismic hazard forecasts.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180106","usgsCitation":"McNamara, D.E., Petersen, M.D., Thompson, E.M., Powers, P.M., Shumway, A., Hoover, S.M., Moschetti, M.P., and Wolin, E., 2019, Evaluation of ground motion models for USGS seismic hazard forecasts: Induced and tectonic earthquakes in the Central and Eastern U.S.: Bulletin of the Seismological Society of America, v. 109, no. 1, p. 322-335, https://doi.org/10.1785/0120180106.","productDescription":"14 p.","startPage":"322","endPage":"335","ipdsId":"IP-103404","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":363496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"109","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":762096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762099,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762100,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolin, Emily 0000-0003-1610-1191 ewolin@usgs.gov","orcid":"https://orcid.org/0000-0003-1610-1191","contributorId":198778,"corporation":false,"usgs":true,"family":"Wolin","given":"Emily","email":"ewolin@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":762101,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203992,"text":"70203992 - 2019 - Kinetic study on clogging of a geothermal pumping well triggered by mixing-induced biogeochemical reactions","interactions":[],"lastModifiedDate":"2019-06-26T13:18:43","indexId":"70203992","displayToPublicDate":"2019-04-30T13:13:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Kinetic study on clogging of a geothermal pumping well triggered by mixing-induced biogeochemical reactions","docAbstract":"The sustainability of ground-source geothermal systems can be severely impacted by microbially mediated clogging processes. Biofouling of water wells by hydrous ferric oxide is a widespread problem. Although the mechanisms and critical environmental factors associated with clogging development are widely recognized, effects of mixing processes within the wells and time scales for clogging processes are not well characterized. Here we report insights from a joint hydrological, geochemical, and metagenomics characterization of a geothermal doublet in which hydrous ferric oxide and hydrous manganese oxide deposits had formed as a consequence of mixing shallow groundwater containing dissolved oxygen and nitrate with deeper, anoxic groundwater containing dissolved iron (FeII) and manganese (MnII). Metagenomics identify distinct bacteria consortia in the pumping well oxic and anoxic zones, including autotrophic iron-oxidizing bacteria. Batch mixing experiments and geochemical kinetics modeling of the associated reactions indicate that FeII and MnII oxidation are slow compared to the residence time of water in the pumping well; however, adsorption of FeII and MnII by accumulated hydrous ferric oxide and hydrous manganese oxide in the well bore and pump riser provides “infinite” time for surface-catalyzed oxidation and a convenient source of energy for iron-oxidizing bacteria, which colonize the surfaces and also catalyze oxidation. Thus, rapid clogging is caused by mixing-induced redox reactions and is exacerbated by microbial activity on accumulated hydrous oxide surfaces.
","language":"English","publisher":"American Chemical Society Publications","doi":"10.1021/acs.est.9b00453","usgsCitation":"Burte, L., Cravotta, C.A., Bethencourt, L., Farasin, J., Pedrot, M., Dufresne, A., Gerard, M., Baranger, C., Le Borgne, T., and Aquilina, L., 2019, Kinetic study on clogging of a geothermal pumping well triggered by mixing-induced biogeochemical reactions: Environmental Science & Technology, v. 53, no. 10, p. 5848-5857, https://doi.org/10.1021/acs.est.9b00453.","productDescription":"10 p.","startPage":"5848","endPage":"5857","ipdsId":"IP-104719","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":467656,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://insu.hal.science/insu-02123861","text":"External Repository"},{"id":365077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France","city":"Orleans","volume":"53","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Burte, Luc","contributorId":216592,"corporation":false,"usgs":false,"family":"Burte","given":"Luc","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":216591,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles","suffix":"III","email":"","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bethencourt, Lorine","contributorId":216593,"corporation":false,"usgs":false,"family":"Bethencourt","given":"Lorine","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765126,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farasin, Julien","contributorId":216594,"corporation":false,"usgs":false,"family":"Farasin","given":"Julien","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765127,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pedrot, Mathieu","contributorId":216595,"corporation":false,"usgs":false,"family":"Pedrot","given":"Mathieu","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765128,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dufresne, Alexis","contributorId":216596,"corporation":false,"usgs":false,"family":"Dufresne","given":"Alexis","email":"","affiliations":[{"id":39482,"text":"Ecobio-UMR 6553, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765129,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gerard, Marie-Francoise","contributorId":216597,"corporation":false,"usgs":false,"family":"Gerard","given":"Marie-Francoise","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765130,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baranger, Catherine","contributorId":216598,"corporation":false,"usgs":false,"family":"Baranger","given":"Catherine","email":"","affiliations":[{"id":39483,"text":"Antea Group, ZAC du Moulin 803 boulevard Duhamel du Monceau, Olivet, France","active":true,"usgs":false}],"preferred":false,"id":765131,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Le Borgne, Tanguy","contributorId":216599,"corporation":false,"usgs":false,"family":"Le Borgne","given":"Tanguy","email":"","affiliations":[{"id":39481,"text":"OSUR-UMR6118 Géosciences Rennes, Université de Rennes 1 and Centre National de la Recherche Scientifique, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":765132,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Aquilina, Luc 0000-0001-9875-6436","orcid":"https://orcid.org/0000-0001-9875-6436","contributorId":215171,"corporation":false,"usgs":false,"family":"Aquilina","given":"Luc","email":"","affiliations":[{"id":39190,"text":"Université de Rennes","active":true,"usgs":false}],"preferred":false,"id":765133,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70204709,"text":"70204709 - 2019 - Permafrost collapse is accelerating carbon release","interactions":[],"lastModifiedDate":"2019-08-12T10:49:42","indexId":"70204709","displayToPublicDate":"2019-04-30T10:48:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost collapse is accelerating carbon release","docAbstract":"This much is clear: the Arctic is warming fast, and frozen soils are starting to thaw, often for the first time in thousands of years. But how this happens is as murky as the mud that oozes from permafrost when ice melts.
As the temperature of the ground rises above freezing, microorganisms break down organic matter in the soil. Greenhouse gases — including carbon dioxide, methane and nitrous oxide — are released into the atmosphere, accelerating global warming. Soils in the permafrost region hold twice as much carbon as the atmosphere does — almost 1,600 billion tonnes1.
What fraction of that will decompose? Will it be released suddenly, or seep out slowly? We need to find out.
Current models of greenhouse-gas release and climate assume that permafrost thaws gradually from the surface downwards. Deeper layers of organic matter are exposed over decades or even centuries, and some models are beginning to track these slow changes.
But models are ignoring an even more troubling problem. Frozen soil doesn’t just lock up carbon — it physically holds the landscape together. Across the Arctic and Boreal regions, permafrost is collapsing suddenly as pockets of ice within it melt. Instead of a few centimetres of soil thawing each year, several metres of soil can become destabilized within days or weeks. The land can sink and be inundated by swelling lakes and wetlands.
Abrupt thawing of permafrost is dramatic to watch. Returning to field sites in Alaska, for example, we often find that lands that were forested a year ago are now covered with lakes2. Rivers that once ran clear are thick with sediment. Hillsides can liquefy, sometimes taking sensitive scientific equipment with them.
This type of thawing is a serious problem for communities living around the Arctic (see ‘Arctic permafrost’). Roads buckle, houses become unstable. Access to traditional foods is changing, because it is becoming dangerous to travel across the land to hunt. Families cannot reach lines of game traps that have supported them for generations.
","language":"English","publisher":"Nature","doi":"10.1038/d41586-019-01313-4","usgsCitation":"Turetsky, M.R., Abbott, B., Jones, M., Walter Anthony, K., Olefeldt, D., Schuur, E.A., Koven, C., McGuire, A., Grosse, G., Kuhry, P., Gustaf Hugelius, Lawrence, D.M., Gibson, C., and Sannel, A.B., 2019, Permafrost collapse is accelerating carbon release: Nature, v. 569, p. 32-34, https://doi.org/10.1038/d41586-019-01313-4.","productDescription":"3 p.","startPage":"32","endPage":"34","ipdsId":"IP-105217","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/d41586-019-01313-4","text":"Publisher Index Page"},{"id":366474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic","volume":"569","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Turetsky, Merritt R.","contributorId":169398,"corporation":false,"usgs":false,"family":"Turetsky","given":"Merritt","email":"","middleInitial":"R.","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":768155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abbott, Benjamin W.","contributorId":218049,"corporation":false,"usgs":false,"family":"Abbott","given":"Benjamin W.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":768154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Miriam 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":201994,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":768153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walter Anthony, Katey","contributorId":192911,"corporation":false,"usgs":false,"family":"Walter Anthony","given":"Katey","affiliations":[],"preferred":false,"id":768156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olefeldt, David","contributorId":169408,"corporation":false,"usgs":false,"family":"Olefeldt","given":"David","affiliations":[{"id":32365,"text":"Department of Renewable Resources, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":768157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schuur, Edward A.","contributorId":218050,"corporation":false,"usgs":false,"family":"Schuur","given":"Edward","email":"","middleInitial":"A.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":768158,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koven, Charles","contributorId":218051,"corporation":false,"usgs":false,"family":"Koven","given":"Charles","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":768159,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, A.D.","contributorId":199633,"corporation":false,"usgs":false,"family":"McGuire","given":"A.D.","email":"","affiliations":[],"preferred":false,"id":768160,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":768183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kuhry, Peter","contributorId":9513,"corporation":false,"usgs":true,"family":"Kuhry","given":"Peter","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":768184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gustaf Hugelius","contributorId":199595,"corporation":false,"usgs":false,"family":"Gustaf Hugelius","affiliations":[],"preferred":false,"id":768185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lawrence, David M.","contributorId":105206,"corporation":false,"usgs":false,"family":"Lawrence","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":768186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gibson, Carolyn","contributorId":218061,"corporation":false,"usgs":false,"family":"Gibson","given":"Carolyn","email":"","affiliations":[],"preferred":false,"id":768187,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sannel, A. B. K.","contributorId":38450,"corporation":false,"usgs":false,"family":"Sannel","given":"A.","email":"","middleInitial":"B. K.","affiliations":[],"preferred":false,"id":768188,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70203503,"text":"70203503 - 2019 - Overview of future USGS Gulf of Mexico buoyant storage assessment project","interactions":[],"lastModifiedDate":"2019-05-21T08:59:22","indexId":"70203503","displayToPublicDate":"2019-04-28T08:51:59","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Overview of future USGS Gulf of Mexico buoyant storage assessment project","docAbstract":"The United States Geological Survey (USGS) is a member of a U.S. Department of Energy-funded partnership headed by the University of Texas Bureau of Economic Geology that is working to assess the feasibility of offshore geologic carbon dioxide (CO2) storage in the Gulf of Mexico. The role of the USGS is to assess the buoyant geologic CO2 storage resource of the western half of the offshore Gulf of Mexico (GoM). Buoyant CO2 storage is the CO2 held in place by a top and lateral seal (either a sealing formation or a sealing fault), that creates a column of CO2 in communication across pore space in a geologic reservoir. This assessment will be similar to the USGS assessment of onshore buoyant geologic CO2 storage [1] and will employ a modified version of existing USGS methodology [2] to assess the buoyant CO2 storage capacity in the GoM.
River networks have the potential to permanently remove nitrogen through denitrification. Few studies have measured denitrification rates within an entire river network or assessed how land use affect rates at larger spatial scales. We sampled 108 sites throughout the network of the Fox River watershed, Wisconsin, to determine if land use influence sediment denitrification rates, and to identify zones of elevated sediment denitrification rates (hot spots) within the river network. Partial least squares regression models identified variables from four levels of organization (river bed sediment, water column, riparian zone, and watershed) that best predicted denitrification rates throughout the river network. Nitrate availability was the most important predictor of denitrification rates, while land cover was not always a good predictor of local-scale nitrate concentrations. Thus, land cover and denitrification rate were not strongly related across the Fox River watershed. A direct relationship between denitrification rate and watershed land cover occurred only in the Wolf River sub-watershed, the least anthropogenically disturbed of the sub-watersheds. Denitrification hot spots were located throughout the river network, regardless of watershed land use, with hot spot location being determined primarily by nitrate availability. In the Fox River watershed, when nitrate was abundant, river bed sediment character influenced denitrification rate, with higher denitrification rates at sites with fine, organic sediments. These findings suggest that denitrification occurring throughout an entire river network, from headwater streams to larger rivers, can help reduce nitrogen loads to downstream water bodies.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-019-00565-6","usgsCitation":"Kreiling, R., Richardson, W.B., Bartsch, L., Thoms, M.C., and Christensen, V.G., 2019, Denitrification in the river network of a mixed land use watershed: Unpacking the complexities: Biogeochemistry, v. 143, p. 327-346, https://doi.org/10.1007/s10533-019-00565-6.","productDescription":"20 p.","startPage":"327","endPage":"346","numberOfPages":"20","ipdsId":"IP-096829","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437480,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93RTNVY","text":"USGS data release","linkHelpText":"Great Lakes Restoration Initiative Project 49 Fox River Basin 2016 and 2017 Data"},{"id":374060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.8736572265625,\n 43.45690646829029\n ],\n [\n -87.6873779296875,\n 43.45690646829029\n ],\n [\n -87.6873779296875,\n 45.79816953017265\n ],\n [\n -89.8736572265625,\n 45.79816953017265\n ],\n [\n -89.8736572265625,\n 43.45690646829029\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationDate":"2019-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca 0000-0002-9295-4156 rkreiling@usgs.gov","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":147679,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","email":"rkreiling@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":787294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Victoria G. 0000-0003-4166-7461 vglenn@usgs.gov","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":2354,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","email":"vglenn@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787295,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215492,"text":"70215492 - 2019 - Pleistocene hydrothermal activity on Brokeoff volcano and in the Maidu volcanic center, Lassen Peak area, northeast California: Evolution of magmatic-hydrothermal systems on stratovolcanoes","interactions":[],"lastModifiedDate":"2020-10-21T15:55:55.367862","indexId":"70215492","displayToPublicDate":"2019-04-26T10:52:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7166,"text":"Geopshere","active":true,"publicationSubtype":{"id":10}},"title":"Pleistocene hydrothermal activity on Brokeoff volcano and in the Maidu volcanic center, Lassen Peak area, northeast California: Evolution of magmatic-hydrothermal systems on stratovolcanoes","docAbstract":"Partially eroded stratovolcanoes worldwide, notably Mounts Rainier and Adams in the Cascades and several volcanoes in Japan, record episodic periods of eruption and geothermal activity that produce zones of hydrothermal alteration. The partly eroded core of late Pleistocene Brokeoff volcano on the south side of Lassen Peak exposes the upper 1 km of multiple ancient (ca. 410–300 ka) magmatic-hydrothermal alteration zones in a 3.5 by 5 km area that allows characterization of the three-dimensional hydrothermal evolution of the volcano. Both acid- and neutral-pH hydrothermal solutions produced distinctive alteration mineral assemblages in close proximity. Early hydrothermal activity is characterized by alunite-rich alteration that is temporally and spatially related to shallow intrusions in the center of the volcano. Younger acid alteration and a large area of neutral-pH alteration formed along the volcano’s flanks. The neutral-pH alteration is vertically zoned over 1000 m from shallow zeolite ± adularia through intermediate argillic (smectite-pyrite ± illite) to deep propylitic (chlorite-calcite-albite-illite) alteration. Pleistocene alteration is partly overprinted by surficial, steam-heated alteration related to Lassen’s modern hydrothermal activity. A large (∼3.5 km2), shallow (≤300 m), ca. 1.5 Ma alunite-rich magmatic-hydrothermal alteration zone is exposed on the northeast flank of the nearby Maidu volcanic center.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02049.1","usgsCitation":"John, D.A., Lee, R.G., Breit, G.N., Dilles, J.H., Calvert, A.T., Muffler, L.P., and Clynne, M.A., 2019, Pleistocene hydrothermal activity on Brokeoff volcano and in the Maidu volcanic center, Lassen Peak area, northeast California: Evolution of magmatic-hydrothermal systems on stratovolcanoes: Geopshere, v. 15, no. 3, p. 946-982, https://doi.org/10.1130/GES02049.1.","productDescription":"37 p.","startPage":"946","endPage":"982","ipdsId":"IP-100523","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467668,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02049.1","text":"Publisher Index Page"},{"id":437481,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PZQGJG","text":"USGS data release","linkHelpText":"Geochemical and Geochronological Data for Hydrothermal Systems on Brokeoff Volcano and in the Maidu Volcanic Center, Lassen Peak area, northeastern California"},{"id":379596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Lassen Peak area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.01965332031249,\n 39.52522954427751\n ],\n [\n -120.2947998046875,\n 39.52522954427751\n ],\n [\n -120.2947998046875,\n 41.10832999732831\n ],\n [\n -122.01965332031249,\n 41.10832999732831\n ],\n [\n -122.01965332031249,\n 39.52522954427751\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":802450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Robert G.","contributorId":243516,"corporation":false,"usgs":false,"family":"Lee","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":802451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breit, George N. 0000-0003-2188-6798 gbreit@usgs.gov","orcid":"https://orcid.org/0000-0003-2188-6798","contributorId":1480,"corporation":false,"usgs":true,"family":"Breit","given":"George","email":"gbreit@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":802452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dilles, John H.","contributorId":243517,"corporation":false,"usgs":false,"family":"Dilles","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":802453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calvert, Andrew T. 0000-0001-5237-2218 acalvert@usgs.gov","orcid":"https://orcid.org/0000-0001-5237-2218","contributorId":2694,"corporation":false,"usgs":true,"family":"Calvert","given":"Andrew","email":"acalvert@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muffler, L.J. Patrick 0000-0001-6638-7218 pmuffler@usgs.gov","orcid":"https://orcid.org/0000-0001-6638-7218","contributorId":3322,"corporation":false,"usgs":true,"family":"Muffler","given":"L.J.","email":"pmuffler@usgs.gov","middleInitial":"Patrick","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802455,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802456,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203366,"text":"70203366 - 2019 - Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","interactions":[],"lastModifiedDate":"2019-05-09T08:56:24","indexId":"70203366","displayToPublicDate":"2019-04-26T09:52:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2328,"text":"Journal of Glaciology","active":true,"publicationSubtype":{"id":10}},"title":"Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","docAbstract":"Surface debris covers much of the western portion of the McMurdo Ice Shelf and has a strong influence on the local surface albedo and energy balance. Differential ablation between debris-covered and debris-free areas creates an unusual heterogeneous surface of topographically low, high-ablation, and topographically raised (‘pedestalled’), low-ablation areas. Analysis of Landsat and MODIS satellite imagery from 1999 to 2018, alongside field observations from the 2016/2017 austral summer, shows that pedestalled relict lakes (‘pedestals’) form when an active surface meltwater lake that develops in the summer, freezes-over in winter, resulting in the lake-bottom debris being masked by a high-albedo, superimposed, ice surface. If this ice surface fails to melt during a subsequent melt season, it experiences reduced surface ablation relative to the surrounding debris-covered areas of the ice shelf. We propose that this differential ablation, and resultant hydrostatic and flexural readjustments of the ice shelf, causes the former supraglacial lake surface to become increasingly pedestalled above the lower topography of the surrounding ice shelf. Consequently, meltwater streams cannot flow onto these pedestalled features, and instead divert around them. We suggest that the development of pedestals has a significant influence on the surface-energy balance, hydrology and flexure of the ice shelf.
During 2015–17, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service (Forest Service), carried out a study to characterize the hydrology and water chemistry in two study areas within the Daniel Boone National Forest. One study area was within the Rock Creek drainage and the other study area included the Wildcat and Addison Branch drainages. Both study areas historically were mined for coal prior to the Surface Mining Control and Reclamation Act of 1977 and contain abandoned coal mine sites that have since been the focus of remediation efforts. Synoptic surveys of streamflow and water-quality properties (water temperature, pH, specific conductance, and dissolved oxygen) of Rock Creek were done during November 2015 and May 2016, and surveys of Wildcat and Addison Branches were done during June 2016 and May 2017. Streamflow measurements were used to quantify contributions from tributaries and to compute streamflow gain and loss in designated reaches. Discrete measurements of water temperature, pH, specific conductance, and dissolved oxygen were used to evaluate conditions during a short timeframe and for comparison between study areas. Study designs for the two study areas differed because there was an operating streamgage on Rock Creek near Yamacraw, Kentucky (station number 03410590) where streamflow and water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were monitored continuously, while Addison and Wildcat Branches were ungaged. Several hydrograph separation methods were used to estimate base flow and runoff at the Rock Creek gage. These data will be used by the Forest Service to evaluate the current (2018) conditions and plan remediation efforts.
The water quality at Rock Creek was less affected by acid mine drainage (AMD) than the Wildcat or Addison Branches. Appreciable losing reaches, where water flowed underground, were identified in both study areas. All losing reaches coincided with karst topography. Streamflow increased in areas with openings to underground mine tunnels, known as portals.
Six hydrograph separation methods (Base-flow index [BFI; standard and modified], HYSEP [fixed interval, sliding interval, and local minimum], and PART) were applied to daily mean streamflow collected from August 2015 to August 2017 at station number 03410590. The hydrograph separation methods partition total streamflow into base flow and streamflow that originated from surface runoff. Base flow typically reacts slowly to precipitation infiltration and is largely sustained by groundwater discharge. The estimated daily base flow and runoff made with the different separation methods are not highly different. On average, base flow accounted for more total streamflow than surface runoff during the study period, irrespective of method.
Water temperature, pH, dissolved oxygen, specific conductance, and turbidity values were measured from July 2016 through July 2017 with a continuous monitor installed at station number 03410590. Nearly neutral pH values that ranged from 6.8 to 7.9 standard units likely limited metal solubility in the surface water. The continuous specific conductance values ranged between 30 and 259 microsiemens per centimeter at 25 degrees Celsius. The previous remediation efforts are likely continuing to improve the effect of AMD in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service ","usgsCitation":"Cherry, M.A., 2019, Streamflow gain and loss, hydrograph separation, and water quality of abandoned mine lands in the Daniel Boone National Forest, eastern Kentucky, 2015–17: U.S. Geological Survey Scientific Investigations Report 2019–5006, 36 p., https://doi.org/10.3133/sir20195006.","productDescription":"Report: viii, 36 p.;Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091989","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":363195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5006/sir20195006.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5006"},{"id":363196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78D9","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and water-quality data for selected streams in the Daniel Boone National Forest, eastern Kentucky, 2015–17"},{"id":363194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5006/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Daniel Boone National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.67437744140625,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 36.63536611993544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Weather has been recognized as a density independent factor influencing the abundance, distribution, and behavior of vertebrates. Male wild turkeys’ (Meleagris gallopavo) breeding behavior includes vocalizations and courtship displays to attract females, the phenology of which can vary with latitude. State biologists design spring turkey-hunting season frameworks centered on annual vocalization patterns to maximize hunter engagement. The Mississippi Department of Wildlife, Fisheries, and Parks has traditionally instituted a statewide, 7-week, spring harvest season. However, hunters routinely argue that different peaks in gobbling activity across the state exist. The objective of this study was to determine whether differences in peak gobbling activity existed across a latitudinal gradient of Mississippi and assess the effect of weather on gobbling. During 2008 and 2009, we conducted a statewide gobbling survey. We used generalized additive mixed models to describe the probability and frequency of gobbling activity within northern and southern regions of the state. We also investigated the effect of daily weather conditions on gobbling activity. Our results revealed an approximate 10–14-day difference in peak gobbling activity between southern and northern Mississippi. The majority of all gobbling activity occurred within the current spring harvest framework. Perhaps more importantly, gobbling activity was more prevalent on days of regionally dry conditions (i.e., less humid) according to the Spatial Synoptic Classification. Our results provide information on gobbling activity phenology relative to hunting-season dates and weather-response information. Our approach may be particularly applicable in states with relatively shorter seasons or highly variable daily weather conditions that moderate gobbling frequency.
","language":"English","publisher":"Springer","doi":"10.1007/s00484-019-01720-2","usgsCitation":"Palumbo, M.D., Vilella, F., Wang, G., Strickland, B.K., Godwin, D., Dixon, P.G., Rubin, B.D., and Lashley, M., 2019, Latitude and daily-weather effects on gobbling activity of wild turkeys in Mississippi: International Journal of Biometeorology, v. 63, no. 8, p. 1059-1067, https://doi.org/10.1007/s00484-019-01720-2.","productDescription":"9 p.","startPage":"1059","endPage":"1067","ipdsId":"IP-068111","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":370060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"63","issue":"8","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Palumbo, Matthew D.","contributorId":146265,"corporation":false,"usgs":false,"family":"Palumbo","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, 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Grady","contributorId":221067,"corporation":false,"usgs":false,"family":"Dixon","given":"P.","email":"","middleInitial":"Grady","affiliations":[],"preferred":false,"id":776893,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubin, Benjamin D.","contributorId":221068,"corporation":false,"usgs":false,"family":"Rubin","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776894,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lashley, Marcus A.","contributorId":187467,"corporation":false,"usgs":false,"family":"Lashley","given":"Marcus A.","affiliations":[],"preferred":false,"id":776895,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203193,"text":"70203193 - 2019 - The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","interactions":[],"lastModifiedDate":"2019-04-26T09:55:28","indexId":"70203193","displayToPublicDate":"2019-04-25T15:26:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","docAbstract":"We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194290,"text":"sir20175118 - 2019 - Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","interactions":[],"lastModifiedDate":"2025-05-15T13:21:20.301081","indexId":"sir20175118","displayToPublicDate":"2019-04-25T11:25:00","publicationYear":"2019","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":"2017-5118","displayTitle":"Geochemical and Mineralogical Maps, with Interpretation, for Soils of the Conterminous United States","title":"Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","docAbstract":"Between 2007 and 2013, the U.S. Geological Survey conducted a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental concentration. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. This report presents all the maps and statistical information for each determined element and mineral along with an interpretive section discussing the possible processes that caused the observed national-scale geochemical and mineralogical patterns. Most often, the geochemical and mineralogical patterns reflect the composition of the underlying soil parent material with some modifications caused by leaching of the more mobile elements (for example, calcium and sodium) in the humid areas of the country.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175118","usgsCitation":"Smith, D.B., Solano, Federico, Woodruff, L.G., Cannon, W.F., and Ellefsen, K.J., 2019, Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2017-5118, https://doi.org/10.3133/sir20175118. 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U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.
","language":"English","publisher":"IOPscience","doi":"10.1088/1748-9326/ab1c91","usgsCitation":"Leffler, J., Beard, K.H., Kelsey, K.C., Choi, R.T., Schmutz, J.A., and Welker, J., 2019, Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland: Environmental Research Letters, v. 14, no. 8, 084030, 11 p., https://doi.org/10.1088/1748-9326/ab1c91.","productDescription":"084030, 11 p.","ipdsId":"IP-103167","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab1c91","text":"Publisher Index Page"},{"id":363281,"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 -140.625,\n 69.83962194067463\n ],\n [\n -157.58789062499997,\n 72.28906720017675\n ],\n [\n -166.2890625,\n 69.80930869552193\n ],\n [\n -168.310546875,\n 66.23145747862573\n ],\n [\n -167.607421875,\n 61.270232790000634\n ],\n [\n -158.90625,\n 55.1286490684888\n ],\n [\n -151.171875,\n 56.218923189166624\n ],\n [\n -147.91992187499997,\n 59.5343180010956\n ],\n [\n -143.525390625,\n 59.44507509904714\n ],\n [\n -134.6484375,\n 53.12040528310657\n ],\n [\n -129.814453125,\n 50.84757295365389\n ],\n [\n -129.28710937499997,\n 53.38332836757156\n ],\n [\n -128.32031249999997,\n 55.62799595426723\n ],\n [\n -134.736328125,\n 59.93300042374631\n ],\n [\n -139.482421875,\n 60.673178565817715\n ],\n [\n -140.625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leffler, Josh","contributorId":215123,"corporation":false,"usgs":false,"family":"Leffler","given":"Josh","email":"","affiliations":[{"id":39181,"text":"South Dakota State","active":true,"usgs":false}],"preferred":false,"id":761682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Karen H.","contributorId":205934,"corporation":false,"usgs":false,"family":"Beard","given":"Karen","email":"","middleInitial":"H.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Katharine C.","contributorId":195397,"corporation":false,"usgs":false,"family":"Kelsey","given":"Katharine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":761684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choi, Ryan T.","contributorId":205936,"corporation":false,"usgs":false,"family":"Choi","given":"Ryan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":761681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welker, Jeffrey","contributorId":214926,"corporation":false,"usgs":false,"family":"Welker","given":"Jeffrey","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":761685,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203164,"text":"70203164 - 2019 - Calcrete uranium deposits in the Southern High Plains, USA","interactions":[],"lastModifiedDate":"2019-04-25T05:57:51","indexId":"70203164","displayToPublicDate":"2019-04-25T05:53:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Calcrete uranium deposits in the Southern High Plains, USA","docAbstract":"The Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone. Fifteen calcrete uranium occurrences are identified; two of these, the Buzzard Draw and Sulfur Springs Draw deposits, have combined in-place resources estimated at about 4 million pounds of U3O8. Ore minerals carnotite and finchite are hosted in dolomite at the Sulfur Springs Draw deposit, with accessory fluorite, celestine, smectite/illite, autunite, and strontium carbonate. Host carbonate at the Sulfur Springs Draw deposit is ∼190 ka and mineralization mobilized as recently as 3.8 ka. Ash collected near the deposit is 631 ka and erupted from the Yellowstone caldera complex. The Triassic Dockum Group that contains sandstone-hosted uranium deposits throughout the region and underlies the SHP is a potential source for uranium and vanadium. Regional uplift and dissection reintroduced oxygenated groundwater into the Dockum Group, mobilizing uranium. Additional uranium may have been contributed to groundwater by weathering of volcanic ash in Pliocene and Pleistocene host rocks. The locations of the uranium occurrences are mostly in modern drainage systems in the southeast portion of the SHP. Modelling of modern groundwater in the SHP carried out in a parallel study shows that a single fluid could form carnotite through evaporation, and that fluids of the requisite composition are more prevalent in the southern portion of the SHP. The southeastern portion of the SHP hosts more uranium occurrences due to a variety of factors including (1) upward transport of groundwater and connectivity between source and host rock, (2) higher uranium and vanadium content of groundwater, (3) higher rates of groundwater recharge in this region to drive the mineralizing system, and (4) shallower groundwater facilitating surface evaporation. Ongoing erosion of host rocks challenges preservation of deposits and may limit their size.
Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning System (GPS), yielding up to 3 cm subsidence southwest of the mainshock epicenter and horizontal motions of