Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.
For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.
Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.
The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165024","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities, Alaska Department of Natural Resources, and U.S. Army Corps of Engineers","usgsCitation":"Curran, J.H., Barth, N.A., Veilleux, A.G., and Ourso, R.T., 2016, Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012: U.S. Geological Survey Scientific Investigations Report 2016–5024, 47 p., https://dx.doi.org/10.3133/sir20165024.","productDescription":"Report: vi, 47 p.; 3 Tables; 1 Appendix; Companion File; Database","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","ipdsId":"IP-068358","costCenters":[{"id":114,"text":"Alaska Science 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4210 University Drive
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http://alaska.usgs.gov
Long-term fish sampling data from the San Francisco Estuary were combined with detailed three dimensional hydrodynamic modeling to investigate the relationship between historical fish catch and hydrodynamic complexity. Delta Smelt catch data at 45 stations from the Fall Midwater Trawl (FMWT) survey in the vicinity of Suisun Bay were used to develop a quantitative catch-based station index. This index was used to rank stations based on historical Delta Smelt catch. The correlations between historical Delta Smelt catch and 35 quantitative metrics of environmental complexity were evaluated at each station. Eight metrics of environmental conditions were derived from FMWT data and 27 metrics were derived from model predictions at each FMWT station. To relate the station index to conceptual models of Delta Smelt habitat, the metrics were used to predict the station ranking based on the quantified environmental conditions. Salinity, current speed, and turbidity metrics were used to predict the relative ranking of each station for Delta Smelt catch. Including a measure of the current speed at each station improved predictions of the historical ranking for Delta Smelt catch relative to similar predictions made using only salinity and turbidity. Current speed was also found to be a better predictor of historical Delta Smelt catch than water depth. The quantitative approach developed using the FMWT data was validated using the Delta Smelt catch data from the San Francisco Bay Study. Complexity metrics in Suisun Bay were-evaluated during 2010 and 2011. This analysis indicated that a key to historical Delta Smelt catch is the overlap of low salinity, low maximum velocity, and low Secchi depth regions. This overlap occurred in Suisun Bay during 2011, and may have contributed to higher Delta Smelt abundance in 2011 than in 2010 when the favorable ranges of the metrics did not overlap in Suisun Bay.
","language":"English","publisher":"University of California at Davis John Muir Institute of the Environment and the Delta Stewardship Council","doi":"10.15447/sfews.2016v14iss1art3","usgsCitation":"Bever, A.J., MacWilliams, M.L., Herbold, B., Brown, L.R., and Feyrer, F.V., 2016, Linking hydrodynamic complexity to delta smelt (Hypomesus transpacificus) distribution in the San Francisco Estuary, USA: San Francisco Estuary and Watershed Science, v. 14, no. 1, p. 1-27, https://doi.org/10.15447/sfews.2016v14iss1art3.","productDescription":"27 p.","startPage":"1","endPage":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063936","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":471146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2016v14iss1art3","text":"Publisher Index Page"},{"id":325464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 38.1734326790354\n ],\n [\n -121.6680908203125,\n 38.14103736644331\n ],\n [\n -121.64611816406249,\n 38.089174937729794\n ],\n [\n -121.66259765625001,\n 38.035112420612975\n ],\n [\n -121.67495727539061,\n 37.99724489645483\n ],\n [\n -121.75048828124999,\n 38.00049145082287\n ],\n [\n -121.86447143554686,\n 38.01888587738773\n ],\n [\n -121.96197509765625,\n 38.034030762875844\n ],\n [\n -122.05535888671875,\n 38.037275688165614\n ],\n [\n -122.09793090820311,\n 38.031867399480674\n ],\n [\n -122.14187622070311,\n 38.024295124443995\n ],\n [\n -122.19680786132812,\n 38.034030762875844\n ],\n [\n -122.21054077148438,\n 38.04700960141592\n ],\n [\n -122.22702026367188,\n 38.06647354576796\n ],\n [\n -122.2119140625,\n 38.0729603768343\n ],\n [\n -122.14462280273436,\n 38.052416771864834\n ],\n [\n -122.12265014648438,\n 38.064311140919\n ],\n [\n -122.08694458007812,\n 38.09782123329514\n ],\n [\n -122.07046508789062,\n 38.129155479572624\n ],\n [\n -122.05261230468751,\n 38.23386541556983\n ],\n [\n -122.02926635742188,\n 38.25004423627535\n ],\n [\n -122.00042724609374,\n 38.23170796744926\n ],\n [\n -121.99630737304688,\n 38.182068998322094\n ],\n [\n -121.97708129882812,\n 38.155077102180655\n ],\n [\n -121.87408447265625,\n 38.134556577054134\n ],\n [\n -121.761474609375,\n 38.1334763895322\n ],\n [\n -121.6845703125,\n 38.1734326790354\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-23","publicationStatus":"PW","scienceBaseUri":"5790a183e4b030378fb4743b","contributors":{"authors":[{"text":"Bever, Aaron J.","contributorId":173009,"corporation":false,"usgs":false,"family":"Bever","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":27140,"text":"Delta Modeling Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":642984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacWilliams, Michael L.","contributorId":173010,"corporation":false,"usgs":false,"family":"MacWilliams","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":27140,"text":"Delta Modeling Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":642985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herbold, Bruce","contributorId":51223,"corporation":false,"usgs":false,"family":"Herbold","given":"Bruce","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":642986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":5901,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":642987,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169069,"text":"70169069 - 2016 - Seasonal response of ghrelin, growth hormone, and insulin-like growth factor I in the free-ranging Florida manatee (Trichechus manatus latirostris)","interactions":[],"lastModifiedDate":"2016-07-17T23:41:24","indexId":"70169069","displayToPublicDate":"2016-03-16T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2653,"text":"Mammalian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal response of ghrelin, growth hormone, and insulin-like growth factor I in the free-ranging Florida manatee (Trichechus manatus latirostris)","docAbstract":"Seasonal changes in light, temperature, and food availability stimulate a physiological response in an animal. Seasonal adaptations are well studied in Arctic, Sub-Arctic, and hibernating mammals; however, limited studies have been conducted in sub-tropical species. The Florida manatee (Trichechus manatus latirostris), a sub-tropical marine mammal, forages less during colder temperatures and may rely on adipose stores for maintenance energy requirements. Metabolic hormones, growth hormone (GH), insulin-like growth factor (IGF)-I, and ghrelin influence growth rate, accretion of lean and adipose tissue. They have been shown to regulate seasonal changes in body composition. The objective of this research was to investigate manatee metabolic hormones in two seasons to determine if manatees exhibit seasonality and if these hormones are associated with seasonal changes in body composition. In addition, age related differences in these metabolic hormones were assessed in multiple age classes. Concentrations of GH, IGF-I, and ghrelin were quantified in adult manatee serum using heterologous radioimmunoassays. Samples were compared between short (winter) and long (summer) photoperiods (n = 22 male, 20 female) and by age class (adult, juvenile, and calf) in long photoperiods (n = 37). Short photoperiods tended to have reduced GH (p = 0.08), greater IGF-I (p = 0.01), and greater blubber depth (p = 0.03) compared with long photoperiods. No differences were observed in ghrelin (p = 0.66). Surprisingly, no age related differences were observed in IGF-I or ghrelin concentrations (p > 0.05). However, serum concentrations of GH tended (p = 0.07) to be greater in calves and juveniles compared with adults. Increased IGF-I, greater blubber thickness, and reduced GH during short photoperiod suggest a prioritization for adipose deposition. Whereas, increased GH, reduced blubber thickness, and decreased IGF-I in long photoperiod suggest prioritization of lean tissue accretion. Hormone profiles in conjunction with difference in body composition between photoperiods indicate seasonal adjustments in manatee nutrient partitioning priorities.
\nThe presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets, and often have different spatial and temporal patterns. Here we use remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. We found marine stratus to be persistent from May through September across the years 2001-2012. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening, and dissipated by the following early afternoon. We present a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to our ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve our understanding of cloud-ecosystem interactions, species distributions and coastal ecohydrology.
","language":"English","publisher":"American Meterorological Society","doi":"10.1175/EI-D-15-0033.1","usgsCitation":"Rastogi, B., Williams, A.P., Fischer, D.T., Iacobellis, S.F., McEachern, K., Carvalho, L., Jones, C.L., Baguskas, S.A., and Still, C.J., 2016, Spatial and temporal patterns of cloud cover and fog inundation in coastal California: Ecological implications: Earth Interactions, v. 20, Paper 15; 19 p., https://doi.org/10.1175/EI-D-15-0033.1.","productDescription":"Paper 15; 19 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073097","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-15-0033.1","text":"Publisher Index Page"},{"id":318897,"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 -120.45959472656249,\n 33.880677127838844\n ],\n [\n -120.45959472656249,\n 34.08564930273551\n ],\n [\n -119.32662963867188,\n 34.08564930273551\n ],\n [\n -119.32662963867188,\n 33.880677127838844\n ],\n [\n -120.45959472656249,\n 33.880677127838844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-24","publicationStatus":"PW","scienceBaseUri":"56ea759ce4b0f59b85d8979f","contributors":{"authors":[{"text":"Rastogi, Bharat","contributorId":167577,"corporation":false,"usgs":false,"family":"Rastogi","given":"Bharat","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, A. Park","contributorId":88456,"corporation":false,"usgs":true,"family":"Williams","given":"A.","email":"","middleInitial":"Park","affiliations":[],"preferred":false,"id":622756,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischer, Douglas T.","contributorId":167578,"corporation":false,"usgs":false,"family":"Fischer","given":"Douglas","email":"","middleInitial":"T.","affiliations":[{"id":24759,"text":"Strategic Environmental Consulting; University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":622758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iacobellis, Sam F.","contributorId":11502,"corporation":false,"usgs":true,"family":"Iacobellis","given":"Sam","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":622757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEachern, Kathryn 0000-0003-2631-8247 kathryn_mceachern@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-8247","contributorId":146324,"corporation":false,"usgs":true,"family":"McEachern","given":"Kathryn","email":"kathryn_mceachern@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":622754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carvalho, Leila","contributorId":167579,"corporation":false,"usgs":false,"family":"Carvalho","given":"Leila","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":622759,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Charles Leslie","contributorId":27790,"corporation":false,"usgs":true,"family":"Jones","given":"Charles","email":"","middleInitial":"Leslie","affiliations":[],"preferred":false,"id":622760,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baguskas, Sara A.","contributorId":167580,"corporation":false,"usgs":false,"family":"Baguskas","given":"Sara","email":"","middleInitial":"A.","affiliations":[{"id":24760,"text":"University of California, Santa Barbara and Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":622761,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Still, Christopher J.","contributorId":167581,"corporation":false,"usgs":false,"family":"Still","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":24761,"text":"University of California, Santa Barbara; Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622762,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170130,"text":"70170130 - 2016 - Estimating abundance in the presence of species uncertainty","interactions":[],"lastModifiedDate":"2016-09-19T15:52:34","indexId":"70170130","displayToPublicDate":"2016-03-16T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating abundance in the presence of species uncertainty","docAbstract":"1.N-mixture models have become a popular method for estimating abundance of free-ranging animals that are not marked or identified individually. These models have been used on count data for single species that can be identified with certainty. However, co-occurring species often look similar during one or more life stages, making it difficult to assign species for all recorded captures. This uncertainty creates problems for estimating species-specific abundance and it can often limit life stages to which we can make inference.
\n2.We present a new extension of N-mixture models that accounts for species uncertainty. In addition to estimating site-specific abundances and detection probabilities, this model allows estimating probability of correct assignment of species identity. We implement this hierarchical model in a Bayesian framework and provide all code for running the model in BUGS-language programs.
\n3.We present an application of the model on count data from two sympatric freshwater fishes, the brook stickleback (Culaea inconstans) and the ninespine stickleback (Pungitius pungitius), ad illustrate implementation of covariate effects (habitat characteristics). In addition, we used a simulation study to validate the model and illustrate potential sample size issues. We also compared, for both real and simulated data, estimates provided by our model to those obtained by a simple N-mixture model when captures of unknown species identification were discarded. In the latter case, abundance estimates appeared highly biased and very imprecise, while our new model provided unbiased estimates with higher precision.
\n4.This extension of the N-mixture model should be useful for a wide variety of studies and taxa, as species uncertainty is a common issue. It should notably help improve investigation of abundance and vital rate characteristics of organisms’ early life stages, which are sometimes more difficult to identify than adults.
\nThe Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2.
\nFollowing on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models.
\nFinally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-12-663-2016","usgsCitation":"Haywood, A.M., Dowsett, H.J., Dolan, A.M., Rowley, D., Abe-Ouchi, A., Otto-Bliesner, B., Chandler, M.A., Hunter, S.J., Lunt, D.J., Pound, M., and Salzmann, U., 2016, The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design: Climate of the Past, v. 12, no. 3, p. 663-675, https://doi.org/10.5194/cp-12-663-2016.","productDescription":"13 p.","startPage":"663","endPage":"675","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060058","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-12-663-2016","text":"Publisher Index Page"},{"id":325565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-16","publicationStatus":"PW","scienceBaseUri":"5793444de4b0eb1ce79e8c1d","contributors":{"authors":[{"text":"Haywood, Alan M.","contributorId":86663,"corporation":false,"usgs":true,"family":"Haywood","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":643339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dolan, Aisling M.","contributorId":30117,"corporation":false,"usgs":true,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowley, David","contributorId":173099,"corporation":false,"usgs":false,"family":"Rowley","given":"David","email":"","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":643342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abe-Ouchi, Ayako","contributorId":94942,"corporation":false,"usgs":true,"family":"Abe-Ouchi","given":"Ayako","email":"","affiliations":[],"preferred":false,"id":643343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Otto-Bliesner, Bette","contributorId":58171,"corporation":false,"usgs":true,"family":"Otto-Bliesner","given":"Bette","affiliations":[],"preferred":false,"id":643344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandler, Mark A.","contributorId":101768,"corporation":false,"usgs":true,"family":"Chandler","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":643345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Stephen J.","contributorId":55711,"corporation":false,"usgs":true,"family":"Hunter","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643346,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lunt, Daniel J.","contributorId":101168,"corporation":false,"usgs":true,"family":"Lunt","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643347,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pound, Matthew","contributorId":173100,"corporation":false,"usgs":false,"family":"Pound","given":"Matthew","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643357,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Salzmann, Ulrich","contributorId":173101,"corporation":false,"usgs":false,"family":"Salzmann","given":"Ulrich","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643358,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70164510,"text":"sir20165022 - 2016 - Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida","interactions":[],"lastModifiedDate":"2019-12-30T14:41:27","indexId":"sir20165022","displayToPublicDate":"2016-03-15T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5022","title":"Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida","docAbstract":"To address concerns about the effects of water-resource management practices and rising sea level on saltwater intrusion, the U.S. Geological Survey in cooperation with the Broward County Environmental Planning and Community Resilience Division, initiated a study to examine causes of saltwater intrusion and predict the effects of future alterations to the hydrologic system on salinity distribution in eastern Broward County, Florida. A three-dimensional, variable-density solute-transport model was calibrated to conditions from 1970 to 2012, the period for which data are most complete and reliable, and was used to simulate historical conditions from 1950 to 2012. These types of models are typically difficult to calibrate by matching to observed groundwater salinities because of spatial variability in aquifer properties that are unknown, and natural and anthropogenic processes that are complex and unknown; therefore, the primary goal was to reproduce major trends and locally generalized distributions of salinity in the Biscayne aquifer. The methods used in this study are relatively new, and results will provide transferable techniques for protecting groundwater resources and maximizing groundwater availability in coastal areas. The model was used to (1) evaluate the sensitivity of the salinity distribution in groundwater to sea-level rise and groundwater pumping, and (2) simulate the potential effects of increases in pumping, variable rates of sea-level rise, movement of a salinity control structure, and use of drainage recharge wells on the future distribution of salinity in the aquifer.
\nResults from the simulation of historical conditions indicate that the model generally represents the observed greater westward extent of elevated salinity in the central part of the intruded area relative to the northern and southernmost parts of the intruded area. Results of sensitivity testing indicate that the extent of elevated salinity is most sensitive to pumping in areas where the source of saltwater is largely offshore, from the Atlantic Ocean, and is most sensitive to sea-level rise in areas where the source of salinity is downward leakage of brackish water from canals.
\nSimulations of future scenarios indicate that increases in pumping near the existing interface may cause the interface to advance and decreases in pumping may cause it to retreat. Climatic effects, such as periods of prolonged drought or high precipitation, may augment or counteract long-term effects of changes in pumping on aquifer salinity at well fields. With increasing rates of sea-level rise, the freshwater-saltwater interface advances progressively inland, and flow-averaged salinities at well fields near the existing interface increase commensurately. Hypothetical southeastward (downstream) re-positioning of the existing G–54 salinity-control structure may prevent the interface from moving northwestward along and near the North New River canal, but beneficial effects are localized. Implementation of freshwater recharge wells in the city of Hallandale Beach may also have only a localized freshening effect in the aquifer and little appreciable effect on the freshwater-saltwater interface or on concentrations of salinity at well fields.
\nModel accuracy and use are limited by uncertainty in the physical properties and boundary conditions of the system, uncertainty in historical and future conditions, and generalizations made in the mathematical relationships used to describe the physical processes of groundwater flow and transport. Because of these limitations, model results should be considered in relative rather than absolute terms. Nonetheless, model results do provide useful information on the relative scale of response of the system to changes in pumping distribution, sea-level rise, and mitigation activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165022","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Community Resilience Division","usgsCitation":"Hughes, J.D., Sifuentes, D.F., and White, J.T., 2016, Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida: U.S. Geological Survey Scientific Investigations Report 2016–5022, 114 p., https://dx.doi.org/10.3133/sir20165022.","productDescription":"Report: x, 114 p.; Data Release","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056536","costCenters":[{"id":269,"text":"FLWSC-Ft. 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Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
http://fl.water.usgs.gov/
Exfoliation of rock deteriorates cliffs through the formation and subsequent opening of fractures, which in turn can lead to potentially hazardous rockfalls. Although a number of mechanisms are known to trigger rockfalls, many rockfalls occur during periods when likely triggers such as precipitation, seismic activity and freezing conditions are absent. It has been suggested that these enigmatic rockfalls may occur due to solar heating of rock surfaces, which can cause outward expansion. Here we use data from 3.5 years of field monitoring of an exfoliating granite cliff in Yosemite National Park in California, USA, to assess the magnitude and temporal pattern of thermally induced rock deformation. From a thermodynamic analysis, we find that daily, seasonal and annual temperature variations are sufficient to drive cyclic and cumulative opening of fractures. Application of fracture theory suggests that these changes can lead to further fracture propagation and the consequent detachment of rock. Our data indicate that the warmest times of the day and year are particularly conducive to triggering rockfalls, and that cyclic thermal forcing may enhance the efficacy of other, more typical rockfall triggers.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ngeo2686","collaboration":"National Park Service","usgsCitation":"Collins, B.D., and Stock, G.M., 2016, Rockfall triggering by cyclic thermal stressing of exfoliation fractures: Nature Geoscience, v. 9, p. 395-400, https://doi.org/10.1038/ngeo2686.","productDescription":"7 p.","startPage":"395","endPage":"400","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064900","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":323258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.66308593749999,\n 37.496652341233364\n ],\n [\n -119.57244873046874,\n 37.49556277942662\n ],\n [\n -119.57382202148439,\n 37.53368798315969\n ],\n [\n -119.43923950195312,\n 37.536954951447285\n ],\n [\n -119.388427734375,\n 37.554376365024865\n ],\n [\n -119.36920166015624,\n 37.63054716639914\n ],\n [\n -119.32388305664064,\n 37.63380988687157\n ],\n [\n -119.25659179687499,\n 37.72836644908416\n ],\n [\n -119.26895141601562,\n 37.74248523826606\n ],\n [\n -119.20166015625,\n 37.80001858607365\n ],\n [\n -119.20028686523438,\n 37.89002800137122\n ],\n [\n -119.23599243164062,\n 37.91061711049342\n ],\n [\n -119.31289672851562,\n 37.9593578107923\n ],\n [\n -119.32113647460936,\n 37.97451499202459\n ],\n [\n -119.31015014648438,\n 38.04484662140698\n ],\n [\n -119.34722900390625,\n 38.08593231319764\n ],\n [\n -119.42825317382811,\n 38.11943249695316\n ],\n [\n -119.44198608398438,\n 38.098901948321256\n ],\n [\n -119.46533203125,\n 38.098901948321256\n ],\n [\n -119.47219848632812,\n 38.13023573104302\n ],\n [\n -119.50241088867188,\n 38.158316657442\n ],\n [\n -119.50790405273436,\n 38.135636748588574\n ],\n [\n -119.57382202148439,\n 38.161556068786886\n ],\n [\n -119.58755493164061,\n 38.187466178077905\n ],\n [\n -119.62326049804688,\n 38.1680344597114\n ],\n [\n -119.62600708007812,\n 38.15291731872143\n ],\n [\n -119.7015380859375,\n 38.13239618602294\n ],\n [\n -119.73724365234375,\n 38.09998264736481\n ],\n [\n -119.81414794921875,\n 38.09674050228651\n ],\n [\n -119.83886718750001,\n 38.089174937729794\n ],\n [\n -119.87457275390625,\n 38.05566088242076\n ],\n [\n -119.88693237304688,\n 37.98642201062662\n ],\n [\n -119.88555908203126,\n 37.89327929625019\n ],\n [\n -119.82650756835938,\n 37.89219554724437\n ],\n [\n -119.827880859375,\n 37.83798775896512\n ],\n [\n -119.871826171875,\n 37.81195385919268\n ],\n [\n -119.87594604492188,\n 37.791337175930686\n ],\n [\n -119.85260009765624,\n 37.738141282210385\n ],\n [\n -119.7784423828125,\n 37.73922729512254\n ],\n [\n -119.77981567382812,\n 37.7109857819458\n ],\n [\n -119.7674560546875,\n 37.68708070686609\n ],\n [\n -119.81277465820312,\n 37.67947293019486\n ],\n [\n -119.81277465820312,\n 37.665342132088284\n ],\n [\n -119.761962890625,\n 37.669690356567855\n ],\n [\n -119.71389770507814,\n 37.65338320128765\n ],\n [\n -119.72625732421874,\n 37.642509774448754\n ],\n [\n -119.72351074218749,\n 37.62945956107554\n ],\n [\n -119.69604492187499,\n 37.62837193983584\n ],\n [\n -119.69467163085936,\n 37.58267747761301\n ],\n [\n -119.70428466796875,\n 37.579412513438385\n ],\n [\n -119.70016479492188,\n 37.54893261064111\n ],\n [\n -119.66308593749999,\n 37.496652341233364\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-28","publicationStatus":"PW","scienceBaseUri":"57594231e4b04f417c256989","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":637686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Greg M.","contributorId":88593,"corporation":false,"usgs":true,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637687,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70158609,"text":"sir20155141 - 2016 - Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana","interactions":[],"lastModifiedDate":"2016-03-18T09:27:57","indexId":"sir20155141","displayToPublicDate":"2016-03-15T09:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5141","title":"Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana","docAbstract":"The U.S. Geological Survey (USGS) collected data and simulated groundwater flow to increase understanding of the hydrology and the effects of drainage alterations on the water table in the vicinity of Great Marsh, near Beverly Shores and Town of Pines, Indiana. Prior land-management practices have modified drainage and caused changes in the distribution of open water, streams and ditches, and groundwater abundance and flow paths.
\nCollected hydrologic data indicate that the majority of water entering Great Marsh flows from the southern dune ridge beneath Town of Pines, Indiana. Groundwater flow is intercepted by Brown Ditch in the eastern portion of the study area and Derby Ditch in the western portion of the study area. A smaller amount of groundwater from the northern dune ridge beneath Beverly Shores also contributed water to Great Marsh. Continuous groundwater-level data collected indicate that the predominant north-south groundwater-flow gradients vary during the course of the year due to increased levels of precipitation or during periods of drainage obstructions. Continuous surface-water discharge and surface-water elevation were measured at three USGS streamgages, one each on Brown, Kintzele and Derby Ditches. The monthly mean discharge statistics indicate that during the period of record— June 2012 to September 2013—streamflow in Kintzele Ditch was lowest during July 2012 and highest during April 2013. In Derby Ditch, streamflow also was lowest during July 2012 and highest during April 2013.
\nPeriods of relatively high and low groundwater levels during August 1982, March 2013, and April 2014 were examined and simulated by using MODFLOW and companion software. Results from the simulation of conditions during March 2013 include that nearly 100 percent of all water entering the area simulating Town of Pines is from recharge. Of all the water simulated to enter the eastern and western portions of Great Marsh, nearly 20 and 18 percent, respectively, flows from Town of Pines to the western and eastern portions of Great Marsh. The dune ridges beneath Town of Pines and to a lesser extent beneath Beverly Shores are a major source of recharge to the surficial aquifer and Great Marsh.
\nResults from the simulation of the conditions of April 2014 include that, despite increases in the amount of water entering Great Marsh due to a beaver-dam-modified hydrologic condition, there is still virtually zero simulated groundwater flow from Great Marsh to Town of Pines. The volume of water simulated to be entering the zone representing Beverly Shores decreased by 0.43 cubic foot per second from the results of the March 2013 simulation. This simulated difference in water budgets can be attributed to increased simulated recharge in Great Marsh and Town of Pines. Effects of the inclusion of the beaver dam included the increase of the simulated water table and simulated inundated area upstream of the beaver dam due to the effects of ponding surface water.
\nResults from the simulation scenario that includes six proposed pool-riffle control structures in Brown Ditch under the hydrologic conditions of March 2013 indicate areas inundated by water are larger, including areas just to the north of the entrance of Brown Ditch into Great Marsh, and areas north of the confluence of Brown and Kintzele Ditches.
\nResults from the scenario simulating the increase of the Lake Michigan water level to the historical high of May 31, 1998, showed inundated areas of Great Marsh south of Beverly Shores enlarged on both sides of Lakeshore County Road with the greatest enlargement simulated to be southeast of the intersection of Lakeshore County Road and Beverly Drive. For the scenario simulating the decrease of the Lake Michigan water level to the historical low of December 23, 2007, results show little change from the original March 2013 inundated area.
\nThe results of this study can be used by water-resource managers to understand how surrounding ditches affect water levels in Great Marsh and other inland wetlands and residential areas. The groundwater model developed can be applied to answer questions about how alterations to the drainage system in the area affects water levels in the public and residential areas surrounding Great Marsh. The modeling methods developed in this study provide a template for other studies of groundwater flow and groundwater/surface-water interactions within the shallow surficial aquifer in northern Indiana, and in similar hydrologic settings that include surficial sand aquifers in coastal areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155141","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lampe, D.C., 2015, Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana: U.S. Geological Survey Scientific Investigations Report 2015– 5141, 97 p., https://dx.doi.org/10.3133/sir20155141.","productDescription":"xi, 97 p.","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055857","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":318807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5141/coverthb.jpg"},{"id":318808,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5141/sir20155141.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5141"}],"country":"United States","state":"Indiana","otherGeospatial":"Brown Ditch Watershed, Indiana Dunes National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1,\n 41.65\n ],\n [\n -87.1,\n 41.73\n ],\n [\n -86.9,\n 41.73\n ],\n [\n -86.9,\n 41.65\n ],\n [\n -87.1,\n 41.65\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
Phone: (317) 290-3333
http://in.water.usgs.gov/
Drought conditions during the study period of January 1, 2009, to September 30, 2013, caused a reduction in surface-water releases from water-supply storage infrastructure of the Rio Grande Project, which led to changes in surface-water and groundwater (conjunctive) use in downstream agricultural alluvial valleys. Surface water and groundwater in the agriculturally dominated alluvial Rincon and Mesilla Valleys were investigated in this study to measure the influence of drought and subsequent change in conjunctive water use on quantity and quality of these water resources. In 2013, the U.S. Geological Survey, in cooperation with the New Mexico Environment Department and the New Mexico Interstate Stream Commission, began a study to (1) calculate dissolved-solids loads over the study period at streamgages in the study area where data are available, (2) assess the temporal variability of dissolved-solids loads at and between each streamgage where data are available, and (3) relate the spatiotemporal variability of shallow groundwater data (groundwater levels and quality) within the alluvial valleys of the study area to spatiotemporal variability of surface-water data over the study period. This assessment included the calculation of surface-water dissolved-solids loads at streamgages as well as a mass-balance approach to measure the change in salt load between these streamgages. Bimodal surface-water discharge data led to a temporally-dynamic volumetric definition of release and nonrelease seasons. Continuous surface-water discharge and water-quality data from three streamgages on the Rio Grande were used to calculate daily dissolved-solids loads over the study period, and the results were aggregated annually and seasonally. Results show the majority of dissolved-solids loading occurs during release season; however, decreased duration of the release season over the 5-year study period has resulted in a decrease of the total annual loads at each streamgage. Calculation of the change of salt loads using a mass-balance approach was applied between streamgages. Results from these calculations suggest differing responses to releases in the Rincon and Mesilla Valleys over the period of study; there is a decreasing sink of salt in the Rincon Valley whereas there is an increasing sink of salt in the Mesilla Valley. Daily groundwater-level and water-quality data from shallow wells within the two alluvial valleys show spatial heterogeneity of water quality over the study period. Mass-balance salt-loading trends during the study period are similar to previous trends during the 1950s drought as well as a wet period in the 1980s. The similarity of salt-loading trends from the 1950s, 1980s, and 2000s independent of the climate indicates salt loading in this hydrologic setting may be driven by water-use practices rather than a single climatic variable.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165006","collaboration":"Prepared in cooperation with the New Mexico Environment Department and the New Mexico Interstate Stream Commission","usgsCitation":"Driscoll, J.M., and Sherson, L.R., 2016, Variability of surface-water quantity and quality and shallow groundwater levels and quality within the Rio Grande Project area, New Mexico and Texas, 2009–13: U.S. Geological Survey Scientific Investigations Report 2016–5006, 33 p., https://dx.doi.org/10.3133/sir20165006.","productDescription":"vi, 33 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065706","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318886,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5006/coverthb.jpg"},{"id":318887,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5006/sir20165006.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5006"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.46826171874999,\n 31.076460800121122\n ],\n [\n -107.46826171874999,\n 33.367237465838315\n ],\n [\n -105.6060791015625,\n 33.367237465838315\n ],\n [\n -105.6060791015625,\n 31.076460800121122\n ],\n [\n -107.46826171874999,\n 31.076460800121122\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
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This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).
Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4 wk), whereas corn conditions were more closely related to longer-range (8–12 wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2015.12.065","usgsCitation":"Otkin, J.A., Anderson, M.C., Hain, C., Svoboda, M., Johnson, D., Mueller, R., Tadesse, T., Wardlow, B.D., and Brown, J.F., 2016, Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought: Agricultural and Forest Meteorology, v. 218-219, p. 230-242, https://doi.org/10.1016/j.agrformet.2015.12.065.","productDescription":"13 p.","startPage":"230","endPage":"242","ipdsId":"IP-071141","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471152,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2015.12.065","text":"Publisher Index Page"},{"id":342143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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barred owls (Strix varia) is causing rapid declines in populations of northern spotted owls (Strix occidentalis caurina), and that the longterm persistence of spotted owls may be in question without additional management intervention. A pilot study in California showed that lethal removal of barred owls in combination with habitat conservation may be able to slow or even reverse population declines of spotted owls at local scales, but it remains unknown whether similar results can be obtained in larger areas with different forest conditions and where barred owls are more abundant. In 2015, we implemented a before-after-controlimpact (BACI) experimental design on two study areas in Oregon and Washington with at least 20 years of pre-treatment demographic data on spotted owls to determine if removal of barred owls can improve population trends of spatially associated spotted owls. Here we provide an overview of our research accomplishments and preliminary results in Oregon and Washington in 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161041","usgsCitation":"Wiens, J.D., Dugger, K.M., Lewicki, K.E., and Simon, D.C., 2016, Effects of experimental removal of barred owls on population demography of northern spotted owls in Washington and Oregon—2015 progress report: U.S. Geological Survey Open-File Report 2016-1041, 16 p., https://dx.doi.org/10.3133/ofr20161041.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072911","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":318862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1041/ofr20161041.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1041"},{"id":318861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1041/coverthb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.31054687499999,\n 49.023461463214126\n ],\n [\n -117.04833984375001,\n 49.023461463214126\n ],\n [\n -117.02636718749999,\n 46.31658418182218\n ],\n [\n -116.91650390625,\n 46.164614496897094\n ],\n [\n -116.89453125,\n 46.07323062540838\n ],\n [\n -117.02636718749999,\n 45.9511496866914\n ],\n [\n -116.71874999999999,\n 45.82879925192134\n ],\n [\n -116.49902343749999,\n 45.72152152227954\n ],\n [\n -116.52099609375,\n 45.537136680398596\n ],\n [\n -116.65283203124999,\n 45.30580259943578\n ],\n [\n -116.69677734375,\n 45.1510532655634\n ],\n [\n -116.8505859375,\n 44.98034238084973\n ],\n [\n -116.8505859375,\n 44.91813929958515\n ],\n [\n -117.04833984375001,\n 44.68427737181225\n ],\n [\n -117.158203125,\n 44.55916341529184\n ],\n [\n -117.20214843749999,\n 44.38669150215206\n ],\n [\n -117.158203125,\n 44.26093725039923\n ],\n [\n -116.91650390625,\n 44.213709909702054\n ],\n [\n -116.806640625,\n 44.071800467511565\n ],\n [\n -116.93847656250001,\n 43.929549935614595\n ],\n [\n -117.04833984375001,\n 43.73935207915473\n ],\n [\n -117.04833984375001,\n 41.96765920367816\n ],\n [\n -124.25537109375,\n 41.96765920367816\n ],\n [\n -124.45312499999999,\n 42.19596877629178\n ],\n [\n -124.49707031249999,\n 42.42345651793833\n ],\n [\n -124.43115234375,\n 42.569264372193864\n ],\n [\n -124.51904296875,\n 42.68243539838623\n ],\n [\n -124.60693359374999,\n 42.81152174509788\n ],\n [\n -124.43115234375,\n 43.229195113965005\n ],\n [\n -124.51904296875,\n 43.34116005412307\n ],\n [\n -124.29931640625,\n 43.40504748787035\n ],\n [\n -124.21142578125,\n 43.73935207915473\n ],\n [\n -124.21142578125,\n 44.402391829093915\n ],\n [\n -124.12353515624999,\n 44.762336674810996\n ],\n [\n -124.01367187499999,\n 45.19752230305685\n ],\n [\n -123.99169921875,\n 45.61403741135093\n ],\n [\n -124.01367187499999,\n 45.9511496866914\n ],\n [\n -123.99169921875,\n 46.13417004624326\n ],\n [\n -124.1455078125,\n 46.240651955001695\n ],\n [\n -124.16748046874999,\n 46.66451741754235\n ],\n [\n -124.21142578125,\n 46.93526088057719\n ],\n [\n -124.25537109375,\n 47.234489635299184\n ],\n [\n -124.43115234375,\n 47.39834920035926\n ],\n [\n -124.47509765625,\n 47.635783590864854\n ],\n [\n -124.73876953125,\n 47.945786463687185\n ],\n [\n -124.73876953125,\n 48.268569112964336\n ],\n [\n -124.73876953125,\n 48.516604348867475\n ],\n [\n -124.34326171874999,\n 48.45835188280866\n ],\n [\n -123.94775390625,\n 48.268569112964336\n ],\n [\n -123.59619140625001,\n 48.25394114463431\n ],\n [\n -123.33251953125,\n 48.25394114463431\n ],\n [\n -123.1787109375,\n 48.4146186174932\n ],\n [\n -123.31054687499999,\n 48.69096039092549\n ],\n [\n -123.06884765625,\n 48.76343113791796\n ],\n [\n -123.11279296875001,\n 48.850258199721495\n ],\n [\n -123.31054687499999,\n 49.023461463214126\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov
Linking marine epizootics to a specific aetiology is notoriously difficult. Recent diagnostic successes show that marine disease diagnosis requires both modern, cutting-edge technology (e.g. metagenomics, quantitative real-time PCR) and more classic methods (e.g. transect surveys, histopathology and cell culture). Here, we discuss how this combination of traditional and modern approaches is necessary for rapid and accurate identification of marine diseases, and emphasize how sole reliance on any one technology or technique may lead disease investigations astray. We present diagnostic approaches at different scales, from the macro (environment, community, population and organismal scales) to the micro (tissue, organ, cell and genomic scales). We use disease case studies from a broad range of taxa to illustrate diagnostic successes from combining traditional and modern diagnostic methods. Finally, we recognize the need for increased capacity of centralized databases, networks, data repositories and contingency plans for diagnosis and management of marine disease.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2015.0207","usgsCitation":"Burge, C.A., Friedman, C., Getchell, R.G., House, M., Lafferty, K.D., Mydlarz, L.D., Prager, K.C., Sutherland, K.P., Renault, T., Kiryu, I., and Vega-Thurber, R., 2016, Complementary approaches to diagnosing marine diseases: a union of the modern and the classic: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 371, p. 1-11, https://doi.org/10.1098/rstb.2015.0207.","productDescription":"Article 20150207; 11 p.","startPage":"1","endPage":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071582","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2015.0207","text":"Publisher Index Page"},{"id":318858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"371","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-05","publicationStatus":"PW","scienceBaseUri":"56e7d299e4b0f59b85d64006","contributors":{"authors":[{"text":"Burge, Colleen A.","contributorId":34814,"corporation":false,"usgs":true,"family":"Burge","given":"Colleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":622715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Carolyn S.","contributorId":13890,"corporation":false,"usgs":true,"family":"Friedman","given":"Carolyn S.","affiliations":[],"preferred":false,"id":622716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Getchell, Rodman G.","contributorId":32416,"corporation":false,"usgs":true,"family":"Getchell","given":"Rodman","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":622717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"House, Marcia","contributorId":167561,"corporation":false,"usgs":false,"family":"House","given":"Marcia","email":"","affiliations":[{"id":7059,"text":"Northwest Indian Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":622718,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mydlarz, Laura D.","contributorId":167562,"corporation":false,"usgs":false,"family":"Mydlarz","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":622719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prager, Katherine C.","contributorId":8366,"corporation":false,"usgs":true,"family":"Prager","given":"Katherine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":622720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sutherland, Kathryn P.","contributorId":167563,"corporation":false,"usgs":false,"family":"Sutherland","given":"Kathryn","email":"","middleInitial":"P.","affiliations":[{"id":24752,"text":"Rollins College","active":true,"usgs":false}],"preferred":false,"id":622721,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Renault, Tristan","contributorId":167564,"corporation":false,"usgs":false,"family":"Renault","given":"Tristan","email":"","affiliations":[{"id":24753,"text":"Ifremer, De´partement Ressources Biologiques et Environnement, rue de l’Ile d’Yeu","active":true,"usgs":false}],"preferred":false,"id":622722,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kiryu, Ikunari","contributorId":167565,"corporation":false,"usgs":false,"family":"Kiryu","given":"Ikunari","email":"","affiliations":[{"id":24754,"text":"National Research Institute of Aquaculture, Fisheries Research Agency","active":true,"usgs":false}],"preferred":false,"id":622723,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vega-Thurber, Rebecca","contributorId":167566,"corporation":false,"usgs":false,"family":"Vega-Thurber","given":"Rebecca","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622724,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70169058,"text":"70169058 - 2016 - What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment","interactions":[],"lastModifiedDate":"2021-01-07T18:48:40.164069","indexId":"70169058","displayToPublicDate":"2016-03-14T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2381,"text":"Journal of Marine Systems","active":true,"publicationSubtype":{"id":10}},"title":"What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment","docAbstract":"Continental shelves worldwide are subject to intense bottom trawling that causes sediment to be resuspended. The widely used traditional concepts of modern sedimentary transport systems on the shelf rely only on estimates for naturally driven sediment resuspension such as through storm waves, bottom currents, and gravity-driven flows but they overlook a critical anthropogenic factor. The strong influence of bottom trawling on a source-to-sink sediment budget is explored on the NW Iberian shelf. Use of Automated Information System vessel tracking data provides for a high-resolution vessel track reconstruction and the accurate calculation of the spatial distribution of bottom trawling intensity and associated resuspended sediment load. The mean bottom trawling-induced resuspended sediment mass for the NW Iberian shelf is 13.50 Mt yr− 1, which leads to a six-fold increase in off-shelf sediment transport when compared to natural resuspension mechanisms. The source-to-sink budget analysis provides evidence that bottom trawling causes a rapid erosion of the fine sediment on human time scales. Combining global soft sediment distribution data of the shelves with worldwide bottom trawling intensity estimates we show that the bottom trawling-induced resuspended sediment mass amounts to approximately the same mass of all sediment entering the shelves through rivers. Spatial delineations between natural and anthropogenic sediment resuspension areas are presented to aid in marine management questions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jmarsys.2015.12.007","usgsCitation":"Oberle, F.K., Storlazzi, C.D., and Hanebuth, T.J., 2016, What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment: Journal of Marine Systems, v. 159, p. 109-119, https://doi.org/10.1016/j.jmarsys.2015.12.007.","productDescription":"11 p.","startPage":"109","endPage":"119","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063932","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":318859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"159","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e7d29be4b0f59b85d6401a","contributors":{"authors":[{"text":"Oberle, Ferdinand K. J. 0000-0001-8871-3619","orcid":"https://orcid.org/0000-0001-8871-3619","contributorId":167559,"corporation":false,"usgs":false,"family":"Oberle","given":"Ferdinand","email":"","middleInitial":"K. J.","affiliations":[{"id":24749,"text":"University of Bremen","active":true,"usgs":false}],"preferred":false,"id":622712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":622711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanebuth, Till J.J.","contributorId":167560,"corporation":false,"usgs":false,"family":"Hanebuth","given":"Till","email":"","middleInitial":"J.J.","affiliations":[{"id":24750,"text":"Coastal Carolina University","active":true,"usgs":false}],"preferred":false,"id":622713,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193158,"text":"70193158 - 2016 - Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales","interactions":[],"lastModifiedDate":"2017-11-16T16:07:49","indexId":"70193158","displayToPublicDate":"2016-03-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales","docAbstract":"The loss of historic ecosystem conditions has led forest managers to implement woodland and savanna ecosystem restoration on a landscape scale (≥10,000 ha) in the Ozark Plateau of Arkansas. Managers are attempting to restore and conserve these ecosystems through the reintroduction of disturbance, mainly short-rotation early-growing-season prescribed fire. Short-rotation early-growing season prescribed fire in the Ozarks typically occurs immediately before bud-break, through bud-break, and before leaf-out, and fire events occur on a three-to five-year interval. We examined short-rotation early-growing season prescribed fire as a restoration tool on vegetation characteristics. We collected vegetation measurements at 70 locations annually from 2011 to 2012 in and around the White Rock Ecosystem Restoration Area (WRERA), Ozark-St. Francis National Forest, Arkansas, and used generalized linear models to investigate the impact and efficacy of prescribed fire on vegetation structure. We found the number of large shrubs (>5 cm base diameter) decreased and small shrubs (<5 cm ground diameter) increased with prescribed fire severity. We found that horizontal understory cover from ground level to 1 m in height increased with time-since-prescribed-fire and woody ground cover decreased with the number of prescribed fire treatments. Using LANDFIRE datasets at the landscape scale, we found that since the initiation of a short-rotation early-growing season prescribed fire management regime, forest canopy cover has not reverted to levels characteristic of woodlands and savannas or reached restoration objectives over large areas. Without greater reductions in forest canopy cover and increases in forest-canopy cover heterogeneity, advanced regeneration will be limited in success, and woodland and savanna conditions will not return soon or to the extent desired.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Pittman, H.T., and Krementz, D.G., 2016, Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 3, p. 233-242.","productDescription":"10 p.","startPage":"233","endPage":"242","ipdsId":"IP-059541","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.seafwa.org/publications/journal/?id=402058"},{"id":349022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Ozark Highlands","volume":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fd6be4b06e28e9c24d6e","contributors":{"authors":[{"text":"Pittman, H. Tyler","contributorId":200530,"corporation":false,"usgs":false,"family":"Pittman","given":"H.","email":"","middleInitial":"Tyler","affiliations":[],"preferred":false,"id":722562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718106,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70162298,"text":"sir20155181 - 2016 - Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota","interactions":[],"lastModifiedDate":"2016-03-14T11:52:52","indexId":"sir20155181","displayToPublicDate":"2016-03-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5181","title":"Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota","docAbstract":"Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water; however, streamgages cannot be installed at every location where streamflow information is needed. Therefore, methods for estimating streamflow at ungaged stream locations need to be developed. This report presents a statewide study to develop methods to estimate the structure of historical daily streamflow at ungaged stream locations in Minnesota. Historical daily mean streamflow at ungaged locations in Minnesota can be estimated by transferring streamflow data at streamgages to the ungaged location using the QPPQ method. The QPPQ method uses flow-duration curves at an index streamgage, relying on the assumption that exceedance probabilities are equivalent between the index streamgage and the ungaged location, and estimates the flow at the ungaged location using the estimated flow-duration curve. Flow-duration curves at ungaged locations can be estimated using recently developed regression equations that have been incorporated into StreamStats (http://streamstats.usgs.gov/), which is a U.S. Geological Survey Web-based interactive mapping tool that can be used to obtain streamflow statistics, drainage-basin characteristics, and other information for user-selected locations on streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155181","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Lorenz, D.L., and Ziegeweid, J.R., 2016, Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5181, 18 p., https://dx.doi.org/10.3133/sir20155181.","productDescription":"iv, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068751","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":318797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5181/coverthb.jpg"},{"id":318798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5181/sir20155181.pdf","text":"Report","size":"2.91 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\"}}]}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
A 15-year estimated Trophic State Index (eTSI) for Michigan inland lakes is available, and it spans seven datasets, each representing 1 to 3 years of data from 1999 to 2013. On average, 3,000 inland lake eTSI values are represented in each of the datasets by a process that relates field-measured Secchi-disk transparency (SDT) to Landsat satellite imagery to provide eTSI values for unsampled inland lakes. The correlation between eTSI values and field-measured Trophic State Index (TSI) values from SDT was strong as shown by R2 values from 0.71 to 0.83. Mean eTSI values ranged from 42.7 to 46.8 units, which when converted to estimated SDT (eSDT) ranged from 8.9 to 12.5 feet for the datasets. Most eTSI values for Michigan inland lakes are in the mesotrophic TSI class. The Environmental Protection Agency (EPA) Level III Ecoregions were used to illustrate and compare the spatial distribution of eTSI classes for Michigan inland lakes. Lakes in the Northern Lakes and Forests, North Central Hardwood Forests, and Southern Michigan/Northern Indiana Drift Plains ecoregions are predominantly in the mesotrophic TSI class. The Huron/Erie Lake Plains and Eastern Corn Belt Plains ecoregions, had predominantly eutrophic class lakes and also the highest percent of hypereutrophic lakes than other ecoregions in the State. Data from multiple sampling programs—including data collected by volunteers with the Cooperative Lakes Monitoring Program (CLMP) through the Michigan Department of Environmental Quality (MDEQ), and the 2007 National Lakes Assessment (NLA)—were compiled to compare the distribution of lake TSI classes between each program. The seven eTSI datasets are available for viewing and download with eSDT from the Michigan Lake Water Clarity Interactive Map Viewer at http://mi.water.usgs.gov/projects/RemoteSensing/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165023","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Fuller, L.M., and Jodoin, R.S., 2016, Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013: U.S. Geological Survey Scientific Investigations Report 2016–5023, 16 p., https://dx.doi.org/10.3133/sir20165023.","productDescription":"vii, 16 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067016","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":318806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5023/sir20165023.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911-5991
http://mi.water.usgs.gov/
Understanding the distribution of coal-bearing geologic units in Antarctica provides information that can be used in sedimentary, geomorphological, paleontological, and climatological studies. This report is a digital compilation of information on Antarctica’s coal-bearing geologic units found in the literature. It is intended to be used in small-scale spatial geographic information system (GIS) investigations and as a visual aid in the discussion of Antarctica’s coal resources or in other coal-based geologic investigations. Instead of using spatially insignificant point markers to represent large coal-bearing areas, this dataset uses polygons to represent actual coal-bearing lithologic units. Specific locations of coal deposits confirmed from the literature are provided in the attribution for the coal-bearing unit polygons. Coal-sample-location data were used to confirm some reported coal-bearing geology. The age and extent of the coal deposits indicated in the literature were checked against geologic maps ranging from local scale at 1:50,000 to Antarctic continental scale at 1:5,000,000; if satisfactory, the map boundaries were used to generate the polygons for the coal-bearing localities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161031","usgsCitation":"Merrill, M.D., 2016, GIS representation of coal-bearing areas in Antarctica: U.S. Geological Survey Open-File Report 2016–1031, \n3 p., https://dx.doi.org/10.3133/ofr20161031.","productDescription":"Report: iii, 3 p.; Zipped Shapefiles; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063245","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318710,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2016/1031/ofr20161031.zip","text":"Zipped Shapefiles and metadata","size":"1.10 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 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U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
Floodplains can be viewed as complex adaptive systems (Levin, 1998) because they are comprised of many different biophysical components, such as morphological features, soil groups and vegetation communities as well as being sites of key biogeochemical processing (Stanford et al., 2005). Interactions and feedbacks among the biophysical components often result in additional phenomena occuring over a range of scales, often in the absence of any controlling factors (sensu Hallet, 1990). This emergence of new biophysical features and rates of processing can lead to alternative stable states which feed back into floodplain adaptive cycles (cf. Hughes, 1997; Stanford et al., 2005). Interactions between different biophysical components, feedbacks, self emergence and scale are all key properties of complex adaptive systems (Levin, 1998; Phillips, 2003; Murray et al., 2014) and therefore will influence the manner in which we study and view spatial patterns. Measuring the spatial patterns of floodplain biophysical components is a prerequisite to examining and understanding these ecosystems as complex adaptive systems. Elucidating relationships between pattern and process, which are intrinsically linked within floodplains (Ward et al., 2002), is dependent upon an understanding of spatial pattern. This knowledge can help river scientists determine the major drivers, controllers and responses of floodplain structure and function, as well as the consequences of altering those drivers and controllers (Hughes and Cass, 1997; Whited et al., 2007). Interactions and feedbacks between physical, chemical and biological components of floodplain ecosystems create and maintain a structurally diverse and dynamic template (Stanford et al., 2005). This template influences subsequent interactions between components that consequently affect system trajectories within floodplains (sensu Bak et al., 1988). Constructing and evaluating models used to predict floodplain ecosystem responses to natural and anthropogenic disturbances therefore require quantification of spatial pattern (Asselman and Middelkoop, 1995; Walling and He, 1998). Quantifying these patterns also provides insights into the spatial and temporal domains of structuring processes as well as enabling the detection of self-emergent phenomena, environmental constraints or anthropogenic interference (Turner et al., 1990; Holling, 1992; De Jager and Rohweder, 2012). Thus, quantifying spatial pattern is an important building block on which to examine floodplains as complex adaptive systems (Levin, 1998). Approaches to measuring spatial pattern in floodplains must be cognisant of scale, self-emergent phenomena, spatial organisation, and location. Fundamental problems may arise when patterns observed at a site or transect scale are scaled-up to infer processes and patterns over entire floodplain surfaces (Wiens, 2002; Thorp et al., 2008). Likewise, patterns observed over the entire spatial extent of a landscape can mask important variation and detail at finer scales (Riitters et al., 2002). Indeed, different patterns often emerge at different scales (Turner et al., 1990) because of hierarchical structuring processes (O'Neill et al., 1991). Categorising data into discrete, homogeneous and predefined spatial units at a particular scale (e.g. polygons) creates issues and errors associated with scale and subjective classification (McGarigal et al., 2009; Cushman et al., 2010). These include, loss of information within classified ‘patches’, as well as the ability to detect the emergence of new features that do not fit the original classification scheme. Many of these issues arise because floodplains are highly heterogeneous and have complex spatial organizations (Carbonneau et al., 2012; Legleiter, 2013). As a result, the scale and location at which measurements are made can influence the observed spatial patterns; and patterns may not be scale independent or applicable in different geomorp
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River science: Research and management for the 21st century","language":"English","publisher":"John Wiley & Sons, Ltd","doi":"10.1002/9781118643525.ch6","isbn":"978-1-119-99434-3","usgsCitation":"Scown, M.W., Thoms, M.C., and De Jager, N.R., 2016, Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6, chap. of River science: Research and management for the 21st century, p. 103-131, https://doi.org/10.1002/9781118643525.ch6.","productDescription":"29 p.","startPage":"103","endPage":"131","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056619","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":321675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5746ccbee4b07e28b662dcf0","contributors":{"editors":[{"text":"Gilvear, David J.","contributorId":169613,"corporation":false,"usgs":false,"family":"Gilvear","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630282,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Greenwood, Malcolm T.","contributorId":169614,"corporation":false,"usgs":false,"family":"Greenwood","given":"Malcolm","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":630283,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"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":630284,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Wood, Paul J.","contributorId":169615,"corporation":false,"usgs":false,"family":"Wood","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630285,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Scown, Murray W.","contributorId":145709,"corporation":false,"usgs":false,"family":"Scown","given":"Murray","email":"","middleInitial":"W.","affiliations":[{"id":24492,"text":"Riverine Landscapes Research Laboratory, University of New England, Armidale, Australia","active":true,"usgs":false}],"preferred":false,"id":622119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":622120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":622118,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169027,"text":"ofr20161036 - 2016 - Assessing the socioeconomic impact and value of open geospatial information","interactions":[],"lastModifiedDate":"2016-05-23T09:05:39","indexId":"ofr20161036","displayToPublicDate":"2016-03-10T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1036","title":"Assessing the socioeconomic impact and value of open geospatial information","docAbstract":"The production and accessibility of geospatial information including Earth observation is changing greatly both technically and in terms of human participation. Advances in technology have changed the way that geospatial data are produced and accessed, resulting in more efficient processes and greater accessibility than ever before. Improved technology has also created opportunities for increased participation in the gathering and interpretation of data through crowdsourcing and citizen science efforts. Increased accessibility has resulted in greater participation in the use of data as prices for Government-produced data have fallen and barriers to access have been reduced.
\nThe increase in participation in the production and in the use of data, defined as data democracy for this workshop, are having great impacts on economics and more generally on society.
\nThere is also a strong drive by governments around the world, as shown by the G8 Declaration in June 2013, to make public sector information and scientific data more widely accessible. These are respectively termed “open data” and “open research data.”
\nThis report summarizes discussion at the Workshop on Assessing the Impact and Value of Open Geospatial Information held at George Washington University in Washington, D.C. in October 2014. Workshop participants examined the consequences of expanding data democracy with a focus on its socioeconomic impacts. Evaluations were presented of state-of-the-art methods to assess these socioeconomic impacts, which included position papers and remarks by discussants. The workshop included discussions about the following topics: (1) increased and expanded information sources; (2) societal impacts, including approaches to economics assessments; (3) constraints to open access, including the demands for return on investment, specifications of intellectual property rights, and privacy issues; and (4) learning from the experiences of other data-rich domains, such as environmental management, internet businesses, health, and transportation.
\nThe workshop was a working meeting with strong participant engagement, leading to recommendations for action. The meeting included five topic-driven sessions and keynote presentations. Precirculated position papers for each panel session facilitated preparation and remarks by discussants. After the position papers are updated following the discussants’ remarks, it is planned to submit them for publication.
\nThe workshop included 68 participants coming from international organizations, the U.S. public and private sectors, nongovernmental organizations, and academia. Participants included policy makers and analysts, financial analysts, economists, information scientists, geospatial practitioners, and other discipline experts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161036","collaboration":"Prepared in cooperation with the Socioeconomic Benefits Community","usgsCitation":"Pearlman, Francoise, Pearlman, Jay, Bernknopf, Richard, Coote, Andrew, Craglia, Massimo, Friedl, Lawrence, Gallo, Jason, Hertzfeld, Henry, Jolly, Claire, Macauley, Molly, Shapiro, Carl, and Smart, Alan, 2016, Assessing the socioeconomic impact and value of open geospatial information: U.S. Geological Survey Open-File Report 2016–1036, 36 p., https://dx.doi.org/10.3133/ofr20161036.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1036/coverthb.jpg"},{"id":318803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1036/ofr20161036.pdf","text":"Report","size":"5.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1036"}],"otherGeospatial":"Global","contact":"U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
http://www.usgs.gov
Recent growth in the wind energy industry has increased concerns about its impacts on wildlife populations. Direct impacts of wind energy include bird and bat collisions with turbines whereas indirect impacts include changes in wildlife habitat and behavior. Although many species may withstand these effects, species that are long-lived with low rates of reproduction, have specialized habitat preferences, or are attracted to turbines may be more prone to declines in population abundance. We developed a prioritization system to identify the avian species most likely to experience population declines from wind facilities based on their current conservation status and their expected risk from turbines. We developed 3 metrics of turbine risk that incorporate data on collision fatalities at wind facilities, population size, life history, species’ distributions relative to turbine locations, number of suitable habitat types, and species’ conservation status. We calculated at least 1 measure of turbine risk for 428 avian species that breed in the United States. We then simulated 100,000 random sets of cutoff criteria (i.e., the metric values used to assign species to different priority categories) for each turbine risk metric and for conservation status. For each set of criteria, we assigned each species a priority score and calculated the average priority score across all sets of criteria. Our prioritization system highlights both species that could potentially experience population decline caused by wind energy and species at low risk of population decline. For instance, several birds of prey, such as the long-eared owl, ferruginous hawk, Swainson’s hawk, and golden eagle, were at relatively high risk of population decline across a wide variety of cutoff values, whereas many passerines were at relatively low risk of decline. This prioritization system is a first step that will help researchers, conservationists, managers, and industry target future study and management activity.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS One","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0150813","usgsCitation":"Beston, J.A., Diffendorfer, J., Loss, S., and Johnson, D.H., 2016, Prioritizing avian species for their risk of population-level consequences from wind energy development: PLoS ONE, v. 11, no. 3, https://doi.org/10.1371/journal.pone.0150813.","startPage":"Article e0150813","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057769","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0150813","text":"Publisher Index Page"},{"id":318849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56e7e0c0e4b0f59b85d6aabc","contributors":{"authors":[{"text":"Beston, Julie A. jbeston@usgs.gov","contributorId":5673,"corporation":false,"usgs":true,"family":"Beston","given":"Julie","email":"jbeston@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":622671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loss, Scott","contributorId":131107,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":622673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622674,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70157168,"text":"ds69GG - 2016 - Assessment of undiscovered hydrocarbon resources of sub-Saharan Africa","interactions":[],"lastModifiedDate":"2016-06-08T09:28:17","indexId":"ds69GG","displayToPublicDate":"2016-03-10T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"69","chapter":"GG","title":"Assessment of undiscovered hydrocarbon resources of sub-Saharan Africa","docAbstract":"The main objective of the U.S. Geological Survey’s (USGS) National and Global Petroleum Assessment Project is to assess the potential for undiscovered, technically recoverable oil and natural gas resources of the United States and the world (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). The USGS updated assessments that were completed during the USGS World Petroleum Assessment 2000 (U.S. Geological Survey World Energy Assessment Team, 2000) and conducted new assessments in areas around the world that were not previously examined (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). These assessments used the latest geology-based assessment methodology for conventional oil and gas resources. The new assessments are available at the USGS website, (http://energy.usgs.gov/OilGas/AssessmentsData/WorldPetroleumAssessment.aspx).
\nAs part of this project, the USGS assessed 13 geologic provinces located in sub-Saharan Africa (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). Coastal provinces were extended offshore to water depths ranging from 2,000 to 4,000 meters (m). Within these 13 geologic provinces 18 assessment units (figs. 1, 2) were identified.
\nThe west Africa provinces are (1) the Senegal, containing the passive-margin Senegal Basin of Middle Jurassic to Holocene age; (2) the West African Coastal, characterized by rift, passive-margin, and transform tectonics; (3) the Gulf of Guinea, characterized by transform tectonics; (4) the Niger Delta, containing more than 9,100 m of sedimentary rock and recent sediments; (5) the West-Central Coastal, which contains the Aptian salt basin, is dominated by both rift and sag tectonics, and includes the Congo Basin; and (6) the Orange River Coastal, containing more than 7,000 m of syn-rift and post-rift sedimentary rock. The West African Coastal Province was assessed for the first time, whereas the other five west Africa provinces were reassessed for the 2012 World Oil and Wandrey Gas Resource Assessment (fig. 1 of U.S. Geological Survey World Conventional Resources Assessment Team, 2012). More than 275 new oil and gas fields have been discovered in the six west Africa provinces (IHS Energy, 2008, 2009) since the USGS World Petroleum Assessment in 2000 (U.S. Geological Survey World Energy Assessment Team, 2000). These provinces were assessed because of increased energy exploration activity and new oil and gas discoveries within the provinces.
\nSeven provinces not assessed as part of the World Petroleum Assessment 2000 (U.S. Geological Survey World Energy Assessment Team, 2000) were assessed by the USGS as part of the World Assessment 2012 (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). These provinces are (1) the Chad Province, containing Cretaceous and Cenozoic-age lacustrine, continental, and minor marine rocks; (2) the Sud Province, containing Cretaceous and Paleogene age lacustrine, continental, and minor marine rocks; (3) the South Africa Coastal Province, which contains rift, transform, and passive-margin rocks; (4) the Mozambique Coastal Province, containing rift, drift, and passive-margin rocks; (5) the Morondava Province, which contains failed rift, drift, and passive-margin rocks; (6) the Tanzania Coastal Province, containing rift, drift, and passive-margin rocks; and (7) the Seychelles Province, which contains rift and drift rocks. At the time of this assessment 157 oil and gas fields had been discovered in the seven provinces (IHS Energy, 2009). These provinces were assessed because of increased interest and new oil and gas discoveries within the provinces.
\nThe assessment was geology-based and used the total petroleum system (TPS) concept. The geologic elements of a TPS are hydrocarbon source rocks (source rock maturation and hydrocarbon generation and migration), reservoir rocks (quality and distribution), and traps where hydrocarbon accumulates. Using these geologic criteria, 16 conventional total petroleum systems and 18 assessment units in the 13 provinces were defined. The undiscovered, technically recoverable oil and gas resources were assessed for all assessment units.
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2016 - Changing regional emissions of airborne pollutants reflected in the chemistry of snowpacks and wetfall in the Rocky Mountain region, USA, 1993–2012","interactions":[],"lastModifiedDate":"2018-02-13T10:27:49","indexId":"70174947","displayToPublicDate":"2016-03-10T02:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3728,"text":"Water, Air, & Soil Pollution","onlineIssn":"1573-2932","printIssn":"0049-6979","active":true,"publicationSubtype":{"id":10}},"title":"Changing regional emissions of airborne pollutants reflected in the chemistry of snowpacks and wetfall in the Rocky Mountain region, USA, 1993–2012","docAbstract":"Wintertime precipitation sample data from 55 Snowpack sites and 17 National Atmospheric Deposition Program (NADP)/National Trends Network Wetfall sites in the Rocky Mountain region were examined to identify long-term trends in chemical concentration, deposition, and precipitation using Regional and Seasonal Kendall tests. The Natural Resources Conservation Service snow-telemetry (SNOTEL) network provided snow-water-equivalent data from 33 sites located near Snowpack- and NADP Wetfall-sampling sites for further comparisons. Concentration and deposition of ammonium, calcium, nitrate, and sulfate were tested for trends for the period 1993–2012. Precipitation trends were compared between the three monitoring networks for the winter seasons and downward trends were observed for both Snowpack and SNOTEL networks, but not for the NADP Wetfall network. The dry-deposition fraction of total atmospheric deposition, relative to wet deposition, was shown to be considerable in the region. Potential sources of regional airborne pollutant emissions were identified from the U.S. Environmental Protection Agency 2011 National Emissions Inventory, and from long-term emissions data for the period 1996–2013. Changes in the emissions of ammonia, nitrogen oxides, and sulfur dioxide were reflected in significant trends in snowpack and wetfall chemistry. In general, ammonia emissions in the western USA showed a gradual increase over the past decade, while ammonium concentrations and deposition in snowpacks and wetfall showed upward trends. Emissions of nitrogen oxides and sulfur dioxide declined while regional trends in snowpack and wetfall concentrations and deposition of nitrate and sulfate were downward.
","language":"English","publisher":"Springer","doi":"10.1007/s11270-016-2784-4","usgsCitation":"Ingersoll, G.P., Miller, D.C., Morris, K.H., McMurray, J.A., Port, G.M., and Caruso, B., 2016, Changing regional emissions of airborne pollutants reflected in the chemistry of snowpacks and wetfall in the Rocky Mountain region, USA, 1993–2012: Water, Air, & Soil Pollution, v. 227, p. 1-18, https://doi.org/10.1007/s11270-016-2784-4.","productDescription":"Article 94; 18 p.","startPage":"1","endPage":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075003","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":325574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Montana, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 36\n ],\n [\n -117,\n 47.5\n ],\n [\n -107,\n 47.5\n ],\n [\n -107,\n 36\n ],\n [\n -117,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"227","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-29","publicationStatus":"PW","scienceBaseUri":"57934442e4b0eb1ce79e8bdb","contributors":{"authors":[{"text":"Ingersoll, George P. gpingers@usgs.gov","contributorId":1469,"corporation":false,"usgs":true,"family":"Ingersoll","given":"George","email":"gpingers@usgs.gov","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Debra C.","contributorId":173088,"corporation":false,"usgs":false,"family":"Miller","given":"Debra","email":"","middleInitial":"C.","affiliations":[{"id":27147,"text":"U.S. Forest Service, Rocky Mountain Region, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":643271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morris, Kristi H.","contributorId":173089,"corporation":false,"usgs":false,"family":"Morris","given":"Kristi","email":"","middleInitial":"H.","affiliations":[{"id":27148,"text":"National Park Service, Air Resources Division, Denver, CO","active":true,"usgs":false}],"preferred":false,"id":643272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McMurray, Jill A.","contributorId":173090,"corporation":false,"usgs":false,"family":"McMurray","given":"Jill","email":"","middleInitial":"A.","affiliations":[{"id":27149,"text":"U.S. Forest Service, Northern and Intermountain Regions, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":643273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Port, Garrett M. gport@usgs.gov","contributorId":5158,"corporation":false,"usgs":true,"family":"Port","given":"Garrett","email":"gport@usgs.gov","middleInitial":"M.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caruso, Brian bcaruso@usgs.gov","contributorId":173087,"corporation":false,"usgs":true,"family":"Caruso","given":"Brian","email":"bcaruso@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643269,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70164560,"text":"ofr20161014 - 2016 - The effect of suspended sediment and color on ultraviolet spectrophotometric nitrate sensors","interactions":[],"lastModifiedDate":"2016-05-26T09:12:19","indexId":"ofr20161014","displayToPublicDate":"2016-03-08T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1014","title":"The effect of suspended sediment and color on ultraviolet spectrophotometric nitrate sensors","docAbstract":"Four commercially available ultraviolet nitrate spectrophotometric sensors were evaluated by the U.S. Geological Survey Hydrologic Instrumentation Facility (HIF) to determine the effects of suspended sediment concentration (SSC) and colored dissolved organic matter (CDOM) on sensor accuracy. The evaluated sensors were: the Hach NITRATAX plus sc (5-millimeters (mm) path length), Hach NITRATAX plus sc (2 mm), S::CAN Spectro::lyser (5 mm), and the Satlantic SUNA V2 (5 mm). A National Institute of Standards and Technology-traceable nitrate-free sediment standard was purchased and used to create the turbid environment, and an easily made filtered tea solution was used for the CDOM test. All four sensors performed well in the test that evaluated the effect of suspended sediment on accuracy. The Hach 5 mm, Hach 2 mm, and the SUNA V2 met their respective manufacturer accuracy specifications up to concentrations of 4,500 milligrams per liter (mg/L) SSC. The S::CAN failed to meet its accuracy specifications when the SSC concentrations exceeded 4,000 mg/L. Test results from the effect of CDOM on accuracy indicated a significant skewing of data from all four sensors and showed an artificial elevation of measured nitrate to varying amounts. Of the four sensors tested, the Satlantic SUNA V2’s accuracy was affected the least in the CDOM test. The nitrate concentration measured by the SUNA V2 was approximately 24 percent higher than the actual concentration when estimated total organic carbon values exceeded 44 mg/L. Measured nitrate concentration falsely increased 49 percent when measured by the Hach 5 mm, and 75 percent when measured by the Hach 2 mm. The S::CAN’s reported nitrate concentration increased 96 percent. Path length plays an important role in the sensor’s ability to compensate measurements for matrix interferences, but does not solely determine how well a sensor can handle all interferences. The sensor’s proprietary algorithms also play a key role in matrix interference compensation. The sensors’ ability to compensate for CDOM varied significantly during the tests, even among the three with 5-mm path lengths. Results of this evaluation suggest that the proprietary algorithms of the nitrate analyzers are more effective compensating for suspended sediment, and less effective compensating for CDOM (color) when sensor path length remains constant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161014","usgsCitation":"Snazelle, T.T., 2016, The effect of suspended sediment and color on ultraviolet spectrophotometric nitrate sensors: U.S. Geological Survey Open-File Report, 2016−1014, 10 p., https://dx.doi.org/10.3133/ofr20161014.","productDescription":"Report: v,10 p.; Tables: 2-4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064543","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":318658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1014/ofr20161014.pdf","text":"Report","size":"1.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1014"},{"id":318676,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1014/table/ofr20161014_table4.xlsx","text":"Table 4 - Nitrate measurements by four ultraviolet sensors in water with a 5-mg-NL concentration with varying concentrations ofChief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
http://water.usgs.gov/hif/
An increase of groundwater withdrawals from the surficial aquifer system on Jekyll Island, Georgia, prompted an investigation of hydrologic conditions and water quality by the U.S. Geological Survey during October 2012 through December 2013. The study demonstrated the importance of rainfall as the island’s main source of recharge to maintain freshwater resources by replenishing the water table from the effects of hydrologic stresses, primarily evapotranspiration and pumping. Groundwater-flow directions, recharge, and water quality of the water-table zone on the island were investigated by installing 26 shallow wells and three pond staff gages to monitor groundwater levels and water quality in the water-table zone. Climatic data from Brunswick, Georgia, were used to calculate potential maximum recharge to the water-table zone on Jekyll Island. A weather station located on the island provided only precipitation data. Additional meteorological data from the island would enhance potential evapotranspiration estimates for recharge calculations.
\nGroundwater levels and specific-conductance measurements showed the dependence of freshwater resources on rainfall to recharge the water-table zone of the surficial aquifer system and to influence groundwater flow on Jekyll Island. The unseasonably dry conditions during November 2012 to April 2013 induced saline water infiltration to the water-table zone from the marshland separating the Jekyll River from the island. A strong correlation (R2 = 0.97) of specific conductance to chloride concentration in water samples from wells installed in the water-table zone provided support for the determination of seasonal directions of groundwater flow by confirming salinity changes in the water-table zone. Unseasonably wet conditions during the late spring to August caused groundwater-flow reversals in some areas. The high dependence of the water-table zone in the surficial aquifer system on precipitation to replenish the aquifer with freshwater underscored the importance of monitoring groundwater levels, water quality, and water use to identify aquifer-discharge conditions that have the potential to promote seawater encroachment and degrade freshwater resources on Jekyll Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161017","collaboration":"Prepared in cooperation with the Jekyll Island Authority","usgsCitation":"Gordon, D.W., and Torak, L.J., 2016, Hydrologic conditions, recharge, and baseline water quality of the surficial aquifer system at Jekyll Island, Georgia, 2012–13: U.S. Geological Survey Open-File Report 2016–1017, 34 p., https://dx.doi.org/10.3133/ofr20161017.","productDescription":"Report: viii, 34 p.; Appendixes: 1-3","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055404","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":318637,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1017/coverthb.jpg"},{"id":318641,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1017/ofr20161017_appendix3.xlsx","text":"Appendix 3. Groundwater-Level Measurements Made onDirector, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/