Accurate estimates of the age composition of populations can inform past reproductive success and future population trajectories. We examined fall age ratios (juveniles:total birds) of black brant (Branta bernicla nigricans; brant) staging at Izembek National Wildlife Refuge near the tip of the Alaska Peninsula, southwest Alaska, USA, 1963 to 2015. We also investigated variation in fall age ratios associated with sampling location, an index of flock size, survey effort, day of season, observer, survey platform (boat‐ or land‐based) and tide stage. We analyzed data using logistic regression models implemented in a Bayesian framework. Mean predicted fall age ratio controlling for survey effort, day of year, and temporal and spatial variation was 0.24 (95% CL = 0.23, 0.25). Overall trend in age ratios was −0.6% per year (95% CL = −1.3%, 0.2%), resulting in an approximate 26% decline in the proportion of juveniles over the study period. We found evidence for variation across a range of variables implying that juveniles are not randomly distributed in space and time within Izembek Lagoon. Age ratios varied by location within the study area and were highly variable among years. They decreased with the number of birds aged (an index of flock size) and increased throughout September before leveling off in early October and declining in late October. Age ratios were similar among tide stages and observers and were lower during boat‐based (offshore) than land‐based (nearshore) surveys. Our results indicate surveys should be conducted annually during early to mid‐October to ensure the entire population is present and available for sampling, and throughout Izembek Lagoon to account for spatiotemporal variation in age ratios. Sampling should include a wide range of flock sizes representative of their distribution and occur in flocks located near and off shore. Further research evaluating the cause of declining age ratios in the fall population is necessary to inform management and predict long‐term population dynamics of brant.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21388","usgsCitation":"Ward, D.H., Amundson, C.L., Stehn, R.A., and Dau, C.P., 2018, Long‐term trends in fall age ratios of black brant: Journal of Wildlife Management, v. 82, no. 2, p. 362-373, https://doi.org/10.1002/jwmg.21388.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-082174","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461053,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21388","text":"Publisher Index Page"},{"id":438037,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13578ZF","text":"USGS data release","linkHelpText":"Brant Age Ratio Model"},{"id":438036,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QIJIU2","text":"USGS data release","linkHelpText":"Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska"},{"id":352991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-27","publicationStatus":"PW","scienceBaseUri":"5afee741e4b0da30c1bfc1e7","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":732022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amundson, Courtney L. 0000-0002-0166-7224 camundson@usgs.gov","orcid":"https://orcid.org/0000-0002-0166-7224","contributorId":4833,"corporation":false,"usgs":true,"family":"Amundson","given":"Courtney","email":"camundson@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":732023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehn, Robert A.","contributorId":83986,"corporation":false,"usgs":true,"family":"Stehn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":732024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dau, Christian P.","contributorId":26185,"corporation":false,"usgs":true,"family":"Dau","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":732025,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197893,"text":"70197893 - 2018 - Seismic hazard, risk, and design for South America","interactions":[],"lastModifiedDate":"2018-10-04T13:27:16","indexId":"70197893","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismic hazard, risk, and design for South America","docAbstract":"We calculate seismic hazard, risk, and design criteria across South America using the latest data, models, and methods to support public officials, scientists, and engineers in earthquake risk mitigation efforts. Updated continental scale seismic hazard models are based on a new seismicity catalog, seismicity rate models, evaluation of earthquake sizes, fault geometry and rate parameters, and ground‐motion models. Resulting probabilistic seismic hazard maps show peak ground acceleration, modified Mercalli intensity, and spectral accelerations at 0.2 and 1 s periods for 2%, 10%, and 50% probabilities of exceedance in 50 yrs. Ground shaking soil amplification at each site is calculated by considering uniform soil that is applied in modern building codes or by applying site‐specific factors based on
The dynamics of soil organic carbon (SOC) storage and turnover are a critical component of the global carbon cycle. Mechanistic models seeking to represent these complex dynamics require detailed SOC compositions, which are currently difficult to characterize quantitatively. Here, we address this challenge by using a novel approach that combines Fourier transform infrared spectroscopy (FT-IR) and bulk carbon X-ray absorption spectroscopy (XAS) to determine the abundance of SOC functional groups, using elemental analysis (EA) to constrain the total amount of SOC. We used this SOC functional group abundance (SOC-fga) method to compare variability in SOC compositions as a function of depth across a subalpine watershed (East River, Colorado, USA) and found a large degree of variability in SOC functional group abundances between sites at different elevations. Soils at a lower elevation are predominantly composed of polysaccharides, while soils at a higher elevation have more substantial portions of carbonyl, phenolic, or aromatic carbon. We discuss the potential drivers of differences in SOC composition between these sites, including vegetation inputs, internal processing and losses, and elevation-driven environmental factors. Although numerical models would facilitate the understanding and evaluation of the observed SOC distributions, quantitative and meaningful measurements of SOC molecular compositions are required to guide such models. Comparison among commonly used characterization techniques on shared reference materials is a critical next step for advancing our understanding of the complex processes controlling SOC compositions.
","language":"English","publisher":"MDPI","doi":"10.3390/soils2010006","usgsCitation":"Hsu, H., Lawrence, C.R., Winnick, M.J., Bargar, J.R., and Maher, K., 2018, A molecular investigation of soil organic carbon composition across a subalpine catchment: Soil Systems, v. 2, no. 1, p. 1-23, https://doi.org/10.3390/soils2010006.","productDescription":"Article 6; 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-088725","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/soils2010006","text":"Publisher Index Page"},{"id":351525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-01","publicationStatus":"PW","scienceBaseUri":"5afee743e4b0da30c1bfc207","contributors":{"authors":[{"text":"Hsu, Hsiao-Tieh","contributorId":202391,"corporation":false,"usgs":false,"family":"Hsu","given":"Hsiao-Tieh","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":728306,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey R. 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":202390,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":728305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winnick, Matthew J.","contributorId":202392,"corporation":false,"usgs":false,"family":"Winnick","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":728307,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bargar, John R.","contributorId":14970,"corporation":false,"usgs":true,"family":"Bargar","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":728308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maher, Katharine","contributorId":46004,"corporation":false,"usgs":true,"family":"Maher","given":"Katharine","email":"","affiliations":[],"preferred":false,"id":728309,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196067,"text":"70196067 - 2018 - A tool for efficient, model-independent management optimization under uncertainty","interactions":[],"lastModifiedDate":"2018-03-15T15:57:07","indexId":"70196067","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A tool for efficient, model-independent management optimization under uncertainty","docAbstract":"To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2017.11.019","usgsCitation":"White, J.T., Fienen, M.N., Barlow, P.M., and Welter, D., 2018, A tool for efficient, model-independent management optimization under uncertainty: Environmental Modelling and Software, v. 100, p. 213-221, https://doi.org/10.1016/j.envsoft.2017.11.019.","productDescription":"9 p.","startPage":"213","endPage":"221","ipdsId":"IP-090477","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":352580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee742e4b0da30c1bfc1f1","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":731193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welter, Dave E.","contributorId":203342,"corporation":false,"usgs":false,"family":"Welter","given":"Dave E.","affiliations":[{"id":36603,"text":"SFWMD","active":true,"usgs":false}],"preferred":false,"id":731194,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195430,"text":"70195430 - 2018 - Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts","interactions":[],"lastModifiedDate":"2018-02-14T13:31:00","indexId":"70195430","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts","docAbstract":"Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2067","usgsCitation":"Muller, B.J., Cade, B.S., and Schwarzkoph, L., 2018, Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts: Ecosphere, v. 9, no. 1, p. 1-14, https://doi.org/10.1002/ecs2.2067.","productDescription":"Article e02067; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-092621","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2067","text":"Publisher Index Page"},{"id":351611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-05","publicationStatus":"PW","scienceBaseUri":"5afee743e4b0da30c1bfc203","contributors":{"authors":[{"text":"Muller, Benjamin J.","contributorId":202492,"corporation":false,"usgs":false,"family":"Muller","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":36457,"text":"Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Quensland, Australia","active":true,"usgs":false}],"preferred":false,"id":728565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":728564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarzkoph, Lin","contributorId":202493,"corporation":false,"usgs":false,"family":"Schwarzkoph","given":"Lin","email":"","affiliations":[{"id":36458,"text":"College of Science and Engineering, James Cook University, Townsville, Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":728566,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194887,"text":"sir20185011 - 2018 - Flood-inundation maps for the Withlacoochee River From Skipper Bridge Road to St. Augustine Road, within the City of Valdosta, Georgia, and Lowndes County, Georgia","interactions":[],"lastModifiedDate":"2018-06-11T09:26:02","indexId":"sir20185011","displayToPublicDate":"2018-01-31T10:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5011","title":"Flood-inundation maps for the Withlacoochee River From Skipper Bridge Road to St. Augustine Road, within the City of Valdosta, Georgia, and Lowndes County, Georgia","docAbstract":"Digital flood-inundation maps for a 12.6-mile reach of the Withlacoochee River from Skipper Bridge Road to St. Augustine Road (Georgia State Route 133) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey (USGS) streamgage at Withlacoochee River at Skipper Bridge Road, near Bemiss, Ga. (023177483). Real-time stage information from this streamgage can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgage at Withlacoochee River at Skipper Bridge Road, near Bemiss, Ga. (023177483), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineer-ing Center’s River Analysis System (HEC–RAS) software for the Withlacoochee River and was used to compute flood profiles for a 12.6-mile reach of the Withlacoochee River. The hydraulic model was then used to simulate 23 water-surface profiles at 1.0-foot (ft) intervals at the Withlacoochee River near the Bemiss streamgage. The profiles ranged from the National Weather Service action stage of 10.7 ft, which is 131.0 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 32.7 ft, which is 153.0 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 4.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185011","collaboration":"Prepared in cooperation with the City of Valdosta, Georgia, and Lowndes County, Georgia","usgsCitation":"Musser, J.W., 2018, Flood-inundation maps for the Withlacoochee River from Skipper Bridge Road to St. Augustine Road, within the City of Valdosta, Georgia, and Lowndes County, Georgia: U.S. Geological Survey Scientific Investigations Report 2018–5011, 15 p., https://doi.org/10.3133/sir20185011.","productDescription":"Report: viii, 18 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087876","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350785,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5011/coverthb.jpg"},{"id":350787,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71N809J","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation area for the Withlacoochee River in Lowndes County, Georgia from Skipper Bridge Road to St. Augustine Road"},{"id":350786,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5011/sir20185011.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5011"}],"country":"United States","state":"Georgia","county":"Lowndes County","city":"Valdosta","otherGeospatial":"Withlacoochee River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-83.295,31.0276],[-83.2822,31.0272],[-83.2801,31.0272],[-83.2496,31.0268],[-83.231,31.0264],[-83.2112,31.0259],[-83.2005,31.0255],[-83.1995,31.0255],[-83.1877,31.0004],[-83.1802,31.0004],[-83.1802,30.9995],[-83.1796,30.9589],[-83.1796,30.9502],[-83.1662,30.9497],[-83.1358,30.9489],[-83.0431,30.9471],[-83.0431,30.9457],[-83.0436,30.9435],[-83.0431,30.943],[-83.0436,30.9416],[-83.043,30.9403],[-83.043,30.9389],[-83.0425,30.9371],[-83.0409,30.9361],[-83.0393,30.9357],[-83.0388,30.9352],[-83.0366,30.9307],[-83.0377,30.9293],[-83.0388,30.9293],[-83.0393,30.9288],[-83.0404,30.927],[-83.0393,30.9256],[-83.0382,30.9243],[-83.0372,30.9233],[-83.0361,30.9229],[-83.0356,30.922],[-83.035,30.9215],[-83.035,30.9206],[-83.0356,30.9192],[-83.0361,30.9183],[-83.0366,30.9165],[-83.0356,30.9147],[-83.034,30.9142],[-83.0329,30.9142],[-83.0302,30.9147],[-83.0292,30.9142],[-83.0281,30.9128],[-83.0286,30.9124],[-83.0297,30.9106],[-83.0297,30.9097],[-83.0292,30.9092],[-83.0265,30.9074],[-83.0249,30.906],[-83.0238,30.9046],[-83.0217,30.9033],[-83.0212,30.9028],[-83.0207,30.9019],[-83.0207,30.901],[-83.0212,30.9005],[-83.0217,30.8996],[-83.0228,30.8969],[-83.0228,30.8959],[-83.0222,30.8941],[-83.0217,30.8918],[-83.0212,30.8905],[-83.0217,30.89],[-83.0228,30.89],[-83.0233,30.8896],[-83.0238,30.8886],[-83.0238,30.8873],[-83.0233,30.8864],[-83.0233,30.8854],[-83.0238,30.8845],[-83.0238,30.8827],[-83.0222,30.8809],[-83.0222,30.8795],[-83.0228,30.8786],[-83.0233,30.8768],[-83.0228,30.8704],[-83.0228,30.8694],[-83.0233,30.869],[-83.0233,30.8681],[-83.0238,30.8672],[-83.0254,30.8649],[-83.026,30.8626],[-83.0254,30.8612],[-83.0254,30.8603],[-83.0249,30.8589],[-83.0249,30.858],[-83.0254,30.8566],[-83.0254,30.8553],[-83.0249,30.8516],[-83.051,30.8512],[-83.0515,30.8516],[-83.0696,30.8511],[-83.0883,30.8511],[-83.0882,30.8297],[-83.1037,30.8296],[-83.1047,30.8036],[-83.1249,30.8036],[-83.126,30.8027],[-83.1265,30.8017],[-83.1271,30.7999],[-83.1281,30.7985],[-83.1287,30.7981],[-83.1297,30.7967],[-83.1297,30.7958],[-83.1303,30.7949],[-83.1313,30.7944],[-83.1313,30.7935],[-83.1318,30.7917],[-83.1318,30.7908],[-83.1324,30.7903],[-83.1329,30.7889],[-83.1329,30.7876],[-83.1334,30.7857],[-83.1334,30.7848],[-83.1329,30.7816],[-83.1334,30.7807],[-83.135,30.7784],[-83.1366,30.7757],[-83.1361,30.7725],[-83.1355,30.7711],[-83.1361,30.7688],[-83.1355,30.7679],[-83.1355,30.7661],[-83.1366,30.7656],[-83.1366,30.7647],[-83.1371,30.7643],[-83.1366,30.7638],[-83.135,30.7629],[-83.1345,30.762],[-83.1345,30.7611],[-83.1334,30.7592],[-83.1329,30.7588],[-83.1329,30.7574],[-83.1334,30.7565],[-83.1339,30.7547],[-83.1339,30.7528],[-83.1345,30.7515],[-83.1339,30.7505],[-83.1334,30.7501],[-83.1328,30.7492],[-83.1328,30.7483],[-83.1334,30.7469],[-83.1339,30.7451],[-83.135,30.7432],[-83.135,30.7419],[-83.1355,30.74],[-83.1355,30.7391],[-83.1349,30.7387],[-83.1349,30.7378],[-83.1355,30.7373],[-83.1355,30.7359],[-83.1349,30.7341],[-83.1339,30.7327],[-83.1344,30.7313],[-83.1339,30.7304],[-83.1333,30.73],[-83.1333,30.7291],[-83.1339,30.7281],[-83.1344,30.7277],[-83.1349,30.7268],[-83.1349,30.7259],[-83.1344,30.725],[-83.1333,30.7245],[-83.1307,30.7222],[-83.1344,30.7217],[-83.1344,30.709],[-83.1349,30.7026],[-83.1349,30.6884],[-83.1348,30.6824],[-83.1354,30.6797],[-83.1359,30.6582],[-83.1178,30.6578],[-83.1183,30.6445],[-83.1358,30.645],[-83.1369,30.6229],[-83.2945,30.6319],[-83.2966,30.6319],[-83.2988,30.6324],[-83.3016,30.6323],[-83.3083,30.6327],[-83.3563,30.6356],[-83.3573,30.6371],[-83.3584,30.6381],[-83.3595,30.6385],[-83.3643,30.6412],[-83.3643,30.6422],[-83.3637,30.6426],[-83.3616,30.6435],[-83.3611,30.6444],[-83.3606,30.6463],[-83.3611,30.6472],[-83.3611,30.6495],[-83.3611,30.6508],[-83.3617,30.6522],[-83.3633,30.6536],[-83.3643,30.6554],[-83.3649,30.6568],[-83.3654,30.6572],[-83.3665,30.6572],[-83.3686,30.6568],[-83.3702,30.6568],[-83.3723,30.6572],[-83.3734,30.6577],[-83.3744,30.659],[-83.3755,30.6604],[-83.376,30.6613],[-83.3766,30.6618],[-83.3787,30.6622],[-83.3803,30.6627],[-83.3824,30.664],[-83.3835,30.6645],[-83.3862,30.6663],[-83.3872,30.6672],[-83.3894,30.6677],[-83.3904,30.6677],[-83.3931,30.6686],[-83.3926,30.6699],[-83.392,30.6704],[-83.3915,30.6718],[-83.392,30.6722],[-83.3936,30.6736],[-83.3947,30.6749],[-83.3952,30.6759],[-83.3958,30.6763],[-83.3985,30.6772],[-83.4011,30.6763],[-83.4022,30.6754],[-83.4037,30.6749],[-83.4059,30.6754],[-83.4075,30.6749],[-83.4101,30.6744],[-83.4117,30.674],[-83.4128,30.674],[-83.4144,30.6744],[-83.4149,30.6749],[-83.4171,30.6772],[-83.4176,30.6776],[-83.4176,30.6785],[-83.4171,30.6794],[-83.4171,30.6804],[-83.4176,30.6854],[-83.4197,30.6881],[-83.4208,30.6886],[-83.4224,30.6886],[-83.4229,30.6881],[-83.4235,30.6867],[-83.4251,30.6863],[-83.4261,30.6863],[-83.4272,30.6867],[-83.4299,30.6881],[-83.4314,30.6885],[-83.433,30.6885],[-83.4394,30.6889],[-83.44,30.6894],[-83.4405,30.6908],[-83.441,30.6917],[-83.4437,30.6949],[-83.4464,30.6953],[-83.4485,30.6962],[-83.449,30.6971],[-83.4512,30.6999],[-83.4533,30.7022],[-83.4528,30.7035],[-83.4523,30.7044],[-83.4523,30.7058],[-83.4528,30.7076],[-83.4539,30.7095],[-83.4544,30.7108],[-83.4555,30.7117],[-83.4571,30.7127],[-83.4582,30.7136],[-83.4608,30.7149],[-83.4619,30.7158],[-83.4635,30.7163],[-83.4651,30.7172],[-83.4672,30.7181],[-83.4683,30.719],[-83.4688,30.7199],[-83.4683,30.7208],[-83.4683,30.7217],[-83.4678,30.7259],[-83.4678,30.7272],[-83.4684,30.7327],[-83.4694,30.7336],[-83.4732,30.7373],[-83.4759,30.7404],[-83.4769,30.7414],[-83.4791,30.7427],[-83.4801,30.7441],[-83.4812,30.7459],[-83.4818,30.7477],[-83.4834,30.7505],[-83.4844,30.7518],[-83.4845,30.7528],[-83.4839,30.7546],[-83.4834,30.756],[-83.4824,30.7569],[-83.4802,30.7578],[-83.4792,30.7583],[-83.4754,30.7601],[-83.4738,30.7601],[-83.4728,30.7597],[-83.4712,30.7606],[-83.4701,30.762],[-83.4696,30.7629],[-83.4691,30.7633],[-83.4675,30.7643],[-83.4659,30.7643],[-83.4632,30.7638],[-83.4622,30.7638],[-83.46,30.7643],[-83.459,30.7643],[-83.4574,30.7647],[-83.4563,30.7657],[-83.4558,30.767],[-83.4558,30.768],[-83.4553,30.7684],[-83.4542,30.7707],[-83.4548,30.7716],[-83.4569,30.7744],[-83.4575,30.7753],[-83.4591,30.7775],[-83.4591,30.7785],[-83.4585,30.7794],[-83.458,30.7812],[-83.4585,30.7821],[-83.4591,30.7844],[-83.4586,30.7853],[-83.4575,30.7862],[-83.4559,30.7867],[-83.4548,30.7867],[-83.4538,30.7872],[-83.4517,30.7895],[-83.4522,30.7904],[-83.4522,30.7913],[-83.4533,30.7927],[-83.4533,30.794],[-83.4528,30.7945],[-83.4512,30.7959],[-83.4501,30.7963],[-83.4475,30.7991],[-83.447,30.8018],[-83.4464,30.8068],[-83.447,30.8078],[-83.4481,30.8082],[-83.4486,30.8087],[-83.4507,30.8109],[-83.4507,30.8128],[-83.4502,30.816],[-83.4502,30.8169],[-83.4492,30.8173],[-83.4465,30.8178],[-83.4423,30.8169],[-83.4374,30.8142],[-83.4358,30.8142],[-83.4311,30.8151],[-83.43,30.8156],[-83.4268,30.8161],[-83.4257,30.8161],[-83.4241,30.817],[-83.4231,30.8179],[-83.4226,30.8193],[-83.422,30.8202],[-83.421,30.8211],[-83.4199,30.8225],[-83.4188,30.8234],[-83.4178,30.8239],[-83.4173,30.8252],[-83.4167,30.8257],[-83.4167,30.8266],[-83.4157,30.828],[-83.4146,30.8285],[-83.413,30.8289],[-83.4114,30.8289],[-83.4056,30.828],[-83.4029,30.8276],[-83.4008,30.8267],[-83.397,30.8253],[-83.3949,30.8253],[-83.3933,30.8258],[-83.3923,30.8272],[-83.3896,30.8276],[-83.3885,30.8281],[-83.3859,30.8286],[-83.3832,30.8286],[-83.3779,30.8313],[-83.3774,30.8318],[-83.3768,30.8332],[-83.3763,30.8354],[-83.3758,30.8364],[-83.3747,30.8377],[-83.3721,30.8387],[-83.371,30.8382],[-83.3689,30.8377],[-83.3667,30.8378],[-83.3652,30.8373],[-83.363,30.8378],[-83.3625,30.8373],[-83.3614,30.8369],[-83.3604,30.8369],[-83.3582,30.8373],[-83.3561,30.8387],[-83.354,30.8405],[-83.3508,30.8428],[-83.3513,30.8437],[-83.3513,30.8447],[-83.3508,30.8456],[-83.3503,30.846],[-83.3492,30.8483],[-83.3487,30.8511],[-83.3476,30.8533],[-83.3482,30.8547],[-83.3487,30.8552],[-83.3498,30.8556],[-83.3503,30.8565],[-83.3514,30.8575],[-83.3514,30.8584],[-83.3519,30.8597],[-83.3546,30.8616],[-83.3546,30.8625],[-83.3551,30.8647],[-83.3557,30.8657],[-83.3568,30.8666],[-83.3578,30.867],[-83.3578,30.8679],[-83.3573,30.8684],[-83.3573,30.8693],[-83.3568,30.8702],[-83.3573,30.8707],[-83.3573,30.8716],[-83.3589,30.873],[-83.3595,30.8734],[-83.361,30.8752],[-83.3632,30.8761],[-83.3653,30.878],[-83.3664,30.8807],[-83.3675,30.8816],[-83.368,30.8825],[-83.3707,30.8848],[-83.3723,30.8843],[-83.3744,30.8825],[-83.3765,30.8816],[-83.3776,30.882],[-83.3792,30.8834],[-83.3819,30.8848],[-83.3829,30.8848],[-83.3845,30.8852],[-83.3856,30.8852],[-83.3872,30.8848],[-83.3888,30.8847],[-83.3904,30.8843],[-83.392,30.8843],[-83.3931,30.8838],[-83.3947,30.8843],[-83.3968,30.8847],[-83.3979,30.8843],[-83.3989,30.8843],[-83.4016,30.8847],[-83.4027,30.8852],[-83.4043,30.887],[-83.4053,30.8874],[-83.4069,30.8892],[-83.4075,30.8897],[-83.408,30.8911],[-83.4091,30.892],[-83.4101,30.8924],[-83.4123,30.8929],[-83.4155,30.8897],[-83.4187,30.8901],[-83.4213,30.8919],[-83.4251,30.8924],[-83.4256,30.8933],[-83.4262,30.8956],[-83.4278,30.8983],[-83.4288,30.8992],[-83.4304,30.8997],[-83.4342,30.9019],[-83.4342,30.9029],[-83.4353,30.9038],[-83.4363,30.9038],[-83.439,30.9047],[-83.4395,30.9065],[-83.4401,30.9092],[-83.4401,30.9101],[-83.4406,30.9115],[-83.4406,30.9129],[-83.4412,30.9138],[-83.4412,30.9147],[-83.4407,30.917],[-83.438,30.9197],[-83.4386,30.9207],[-83.438,30.922],[-83.437,30.9257],[-83.437,30.9266],[-83.4375,30.927],[-83.438,30.928],[-83.4381,30.9289],[-83.4375,30.9307],[-83.4365,30.9325],[-83.4359,30.9335],[-83.4354,30.9348],[-83.4365,30.9367],[-83.4381,30.9389],[-83.436,30.9408],[-83.4349,30.9421],[-83.4339,30.9426],[-83.4296,30.9481],[-83.4302,30.949],[-83.4312,30.9508],[-83.4312,30.9522],[-83.4307,30.9527],[-83.4313,30.9536],[-83.4318,30.954],[-83.4328,30.954],[-83.4339,30.9545],[-83.435,30.9545],[-83.4355,30.9549],[-83.4355,30.9558],[-83.4361,30.9613],[-83.4366,30.9627],[-83.4372,30.9636],[-83.4372,30.9645],[-83.4382,30.965],[-83.4393,30.9663],[-83.4388,30.9686],[-83.4393,30.9691],[-83.4426,30.9723],[-83.4426,30.9732],[-83.442,30.9741],[-83.442,30.975],[-83.4436,30.9759],[-83.4426,30.9809],[-83.4432,30.9818],[-83.4432,30.9837],[-83.4442,30.9846],[-83.4453,30.9864],[-83.4464,30.9873],[-83.4491,30.9891],[-83.4512,30.9905],[-83.4528,30.9914],[-83.4539,30.9923],[-83.4544,30.9932],[-83.4555,30.9951],[-83.4555,30.996],[-83.4566,30.9987],[-83.4571,31.0001],[-83.4582,31.001],[-83.4587,31.0019],[-83.4598,31.0033],[-83.4609,31.0037],[-83.4614,31.0046],[-83.4636,31.0087],[-83.4641,31.0096],[-83.4641,31.0105],[-83.4647,31.0115],[-83.4657,31.0124],[-83.4663,31.0133],[-83.4668,31.0137],[-83.4663,31.0156],[-83.4663,31.0165],[-83.4668,31.0192],[-83.47,31.0229],[-83.4711,31.0247],[-83.4706,31.0256],[-83.4706,31.027],[-83.4701,31.0274],[-83.4706,31.0288],[-83.4722,31.0292],[-83.4738,31.0301],[-83.4744,31.0306],[-83.4445,31.0312],[-83.4375,31.0312],[-83.4295,31.0308],[-83.4242,31.0308],[-83.4183,31.0304],[-83.4018,31.03],[-83.3863,31.0296],[-83.3484,31.0288],[-83.3116,31.028],[-83.3068,31.0275],[-83.3057,31.0275],[-83.3003,31.0276],[-83.295,31.0276]]]},\"properties\":{\"name\":\"Lowndes\",\"state\":\"GA\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
In Autumn 2015, USA National Phenology Network (USA-NPN) staff implemented new U.S. Geological Survey (USGS) data-management policies intended to ensure that the results of Federally funded research are made available to the public. The effort aimed both to improve USA-NPN data releases and to provide a model for similar programs within the USGS. This report provides an overview of the steps taken to ensure compliance, following the USGS Science Data Lifecycle, and provides lessons learned about the data-release process for USGS program leaders and data managers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181007","usgsCitation":"Rosemartin, A., Langseth, M.L., Crimmins, T.M., and Weltzin, J.F., 2018, Development and release of phenological data products—A case study in compliance with federal open data policy: U.S. Geological Survey Open-File Report 2018–1007, 13 p., https://doi.org/10.3133/ofr20181007.","productDescription":"iv, 13 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-090322","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":350850,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1007/coverthb.jpg"},{"id":350851,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1007/ofr20181007.pdf","text":"Report","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1007"}],"contact":"Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 300
Reston, VA 20192
The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3397","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2018, Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3397, 2 sheets, various scales, https://doi.org/10.3133/sim3397.","productDescription":"2 Sheets: 18.99 x 24.04 inches and 18.99 x 23.75 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-083513","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":350545,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T151S1","linkHelpText":"Probability distribution grids of dissolved oxygen and dissolved manganese concentrations at selected thresholds in drinking water depth zones, Central Valley, California"},{"id":350705,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3397/sim3397_plate1_.pdf","text":"Plate 1","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3397","linkHelpText":" - Spatial Distribution of Predicted Probabilities for Selected Dissolved Oxygen Threshold Events"},{"id":350544,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3397/coverthb_.jpg"},{"id":350706,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3397/sim3397_plate2.pdf","text":"Plate 2","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3397","linkHelpText":" - Spatial Distribution of Predicted Probabilities for Selected Dissolved Manganese Threshold Events"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.354736328125,\n 40.17887331434696\n ],\n [\n -122.2119140625,\n 38.02213147353745\n ],\n [\n -119.46533203125,\n 34.858890491257796\n ],\n [\n -118.54248046874999,\n 34.93097858831627\n ],\n [\n -118.57543945312501,\n 35.29943548054545\n ],\n [\n -118.93798828125,\n 36.465471886798134\n ],\n [\n -119.5751953125,\n 36.94989178681327\n ],\n [\n -120.487060546875,\n 37.70120736474139\n ],\n [\n -120.92651367187499,\n 38.05674222065296\n ],\n [\n -121.11328124999999,\n 38.676933444637925\n ],\n [\n -121.47583007812501,\n 39.39375459224348\n ],\n [\n -121.53076171875,\n 39.64799732373418\n ],\n [\n -121.871337890625,\n 39.977120098439634\n ],\n [\n -122.1240234375,\n 40.212440718286466\n ],\n [\n -122.310791015625,\n 40.212440718286466\n ],\n [\n -122.354736328125,\n 40.17887331434696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Despite the wealth of global paleoclimate data available for the warm period in the middle of the Piacenzian Stage of the Pliocene Epoch (about 3.3 to 3.0 million years ago [Ma]; Dowsett and others, 2013, and references therein), the Indian Ocean has remained a region of sparse geographic coverage in terms of microfossil analysis. In an effort to characterize the surface Indian Ocean during this interval, we examined the planktic foraminifera from Ocean Drilling Program (ODP) sites 709, 716, 722, 754, 757, 758, and 763, encompassing a wide range of oceanographic conditions. We quantitatively analyzed the data for sea surface temperature (SST) estimation using both the modern analog technique (MAT) and a factor analytic transfer function. The data will contribute to the U.S. Geological Survey (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project’s global SST reconstruction and climate model SST boundary condition for the mid-Piacenzian and will become part of the PRISM verification dataset designed to ground-truth Pliocene climate model simulations (Dowsett and others, 2013).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171158","usgsCitation":"Robinson, M.M., Dowsett, H.J., and Stoll, D.K., 2018, Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763: U.S. Geological Survey Open-File Report 2017–1158, 14 p., https://doi.org/10.3133/ofr20171158.","productDescription":"iv, 14 p.","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087996","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":350488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1158/coverthb.jpg"},{"id":350489,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1158/ofr20171158.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1158"}],"otherGeospatial":"Indian Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 59.80,\n -30.93\n ],\n [\n 112.21,\n -30.93\n ],\n [\n 112.21,\n 16.62\n ],\n [\n 59.80,\n 16.62\n ],\n [\n 59.80,\n -30.93\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
926A National Center
Reston, VA 20192
A calibrated conceptual glacio-hydrological monthly water balance model (MWBMglacier) was used to evaluate future changes in water partitioning in a high-latitude glacierized watershed in Southcentral Alaska under future climate conditions. The MWBMglacier was previously calibrated and evaluated against streamflow measurements, literature values of glacier mass balance change, and satellite-based observations of snow covered area, evapotranspiration, and total water storage. Output from five global climate models representing two future climate scenarios (RCP 4.5 and RCP 8.5) was used with the previously calibrated parameters to drive the MWBMglacier at 2 km spatial resolution. Relative to the historical period 1949–2009, precipitation will increase and air temperature in the mountains will be above freezing for an additional two months per year by mid-century which significantly impacts snow/rain partitioning and the generation of meltwater from snow and glaciers. Analysis of the period 1949–2099 reveals that numerous hydrologic regime shifts already occurred or are projected to occur in the study area including glacier accumulation area, snow covered area, and forest vulnerability. By the end of the century, Copper River discharge is projected to increase by 48%, driven by 21% more precipitation and 53% more glacial melt water (RCP 8.5) relative to the historical period (1949–2009).
","language":"English","publisher":"MDPI","doi":"10.3390/w10020128","usgsCitation":"Valentin, M., Hogue, T.S., and Hay, L., 2018, Hydrologic regime changes in a high-latitude glacierized watershed under future climate conditions: Water, v. 10, no. 2, 128, 24 p., https://doi.org/10.3390/w10020128.","productDescription":"128, 24 p.","ipdsId":"IP-088012","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":469085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10020128","text":"Publisher Index Page"},{"id":429864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Copper River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148,\n 63.4\n ],\n [\n -148,\n 60.5\n ],\n [\n -140,\n 60.5\n ],\n [\n -140,\n 63.4\n ],\n [\n -148,\n 63.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Valentin, Melissa","contributorId":202218,"corporation":false,"usgs":false,"family":"Valentin","given":"Melissa","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":902997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":902998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":205020,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":902999,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194984,"text":"70194984 - 2018 - Greenhouse gas emissions from diverse Arctic Alaskan lakes are dominated by young carbon","interactions":[],"lastModifiedDate":"2018-02-01T11:43:44","indexId":"70194984","displayToPublicDate":"2018-01-29T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Greenhouse gas emissions from diverse Arctic Alaskan lakes are dominated by young carbon","docAbstract":"Climate-sensitive Arctic lakes have been identified as conduits for ancient permafrost-carbon (C) emissions and as such accelerate warming. However, the environmental factors that control emission pathways and their sources are unclear; this complicates upscaling, forecasting and climate-impact-assessment efforts. Here we show that current whole-lake CH4 and CO2 emissions from widespread lakes in Arctic Alaska primarily originate from organic matter fixed within the past 3–4 millennia (modern to 3,300 ± 70 years before the present), and not from Pleistocene permafrost C. Furthermore, almost 100% of the annual diffusive C flux is emitted as CO2. Although the lakes mostly processed younger C (89 ± 3% of total C emissions), minor contributions from ancient C sources were two times greater in fine-textured versus coarse-textured Pleistocene sediments, which emphasizes the importance of the underlying geological substrate in current and future emissions. This spatially extensive survey considered the environmental and temporal variability necessary to monitor and forecast the fate of ancient permafrost C as Arctic warming progresses.
","language":"English","publisher":"Nature","doi":"10.1038/s41558-017-0066-9","usgsCitation":"Elder, C.D., Xu, X., Walker, J., Schnell, J.L., Hinkel, K.M., Townsend-Small, A., Arp, C.D., Pohlman, J.W., Gaglioti, B., and Czimzik, C.I., 2018, Greenhouse gas emissions from diverse Arctic Alaskan lakes are dominated by young carbon: Nature Climate Change, v. 8, p. 166-171, https://doi.org/10.1038/s41558-017-0066-9.","productDescription":"6 p.","startPage":"166","endPage":"171","ipdsId":"IP-088994","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/9q0086pg","text":"External Repository"},{"id":350887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158,\n 68.5\n ],\n [\n -149,\n 68.5\n ],\n [\n -149,\n 71.5\n ],\n [\n -158,\n 71.5\n ],\n [\n -158,\n 68.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-29","publicationStatus":"PW","scienceBaseUri":"5a743584e4b0a9a2e9e25ca0","contributors":{"authors":[{"text":"Elder, Clayton D.","contributorId":201542,"corporation":false,"usgs":false,"family":"Elder","given":"Clayton","email":"","middleInitial":"D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":726347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Xiaomei","contributorId":32055,"corporation":false,"usgs":true,"family":"Xu","given":"Xiaomei","affiliations":[],"preferred":false,"id":726348,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Jennifer","contributorId":201558,"corporation":false,"usgs":false,"family":"Walker","given":"Jennifer","affiliations":[],"preferred":false,"id":726349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schnell, Jordan L.","contributorId":201543,"corporation":false,"usgs":false,"family":"Schnell","given":"Jordan","email":"","middleInitial":"L.","affiliations":[{"id":36194,"text":"Department of Earth System Science, University of California, Irvine, CA, 92697, USA.","active":true,"usgs":false}],"preferred":false,"id":726350,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hinkel, Kenneth M.","contributorId":15405,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":726351,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Townsend-Small, Amy","contributorId":201545,"corporation":false,"usgs":false,"family":"Townsend-Small","given":"Amy","email":"","affiliations":[{"id":36196,"text":"Department of Geology, University of Cincinnati, OH, 45221, USA.","active":true,"usgs":false}],"preferred":false,"id":726352,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":726353,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pohlman, John W. 0000-0002-3563-4586 jpohlman@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":145771,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","email":"jpohlman@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":726346,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaglioti, Benjamin V.","contributorId":193129,"corporation":false,"usgs":false,"family":"Gaglioti","given":"Benjamin V.","affiliations":[],"preferred":false,"id":726354,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Czimzik, Claudia I.","contributorId":199102,"corporation":false,"usgs":false,"family":"Czimzik","given":"Claudia","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":726355,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70194835,"text":"ofr20181002 - 2018 - Using a food web model to inform the design of river restoration—An example at the Barkley Bear Segment, Methow River, north-central Washington","interactions":[],"lastModifiedDate":"2018-06-06T14:13:05","indexId":"ofr20181002","displayToPublicDate":"2018-01-29T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1002","title":"Using a food web model to inform the design of river restoration—An example at the Barkley Bear Segment, Methow River, north-central Washington","docAbstract":"With the decline of Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss), habitat restoration actions in freshwater tributaries have been implemented to improve conditions for juveniles. Typically, physical (for example, hydrologic and engineering) based models are used to design restoration alternatives with the assumption that biological responses will be improved with changes to the physical habitat. Biological models rarely are used. Here, we describe simulations of a food web model, the Aquatic Trophic Productivity (ATP) model, to aid in the design of a restoration project in the Methow River, north-central Washington. The ATP model mechanistically links environmental conditions of the stream to the dynamics of river food webs, and can be used to simulate how alternative river restoration designs influence the potential for river reaches to sustain fish production. Four restoration design alternatives were identified that encompassed varying levels of side channel and floodplain reconnection and large wood addition. Our model simulations suggest that design alternatives focused on reconnecting side channels and the adjacent floodplain may provide the greatest increase in fish capacity. These results were robust to a range of discharge and thermal regimes that naturally occur in the Methow River. Our results suggest that biological models, such as the ATP model, can be used during the restoration planning phase to increase the effectiveness of restoration actions. Moreover, the use of multiple modeling efforts, both physical and biological, when evaluating restoration design alternatives provides a better understanding of the potential outcome of restoration actions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181002","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Benjamin, J.R., Bellmore, J.R., and Dombroski, Daniel, 2018, Using a food web model to inform the design of river restoration—An example at the Barkley Bear Segment, Methow River, north-central Washington: U.S. Geological Survey Open-File Report 2018–1002, 24 p., https://doi.org/10.3133/ofr20181002.","productDescription":"iv, 24 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-092102","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":350751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1002/ofr20181002.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1002"},{"id":350750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1002/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Methow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.497802734375,\n 47.646886969413\n ],\n [\n -119.02587890624999,\n 47.646886969413\n ],\n [\n -119.02587890624999,\n 49.15296965617042\n ],\n [\n -121.497802734375,\n 49.15296965617042\n ],\n [\n -121.497802734375,\n 47.646886969413\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
Flood-damage reduction in the United States has been a longstanding but elusive societal goal. The national strategy for reducing flood damage has shifted over recent decades from a focus on construction of flood-control dams and levee systems to a three-pronged strategy to (1) improve the design and operation of such structures, (2) provide more accurate and accessible flood forecasting, and (3) shift the Federal Emergency Management Agency (FEMA) National Flood Insurance Program to a more balanced, less costly flood-insurance paradigm. Expanding the availability and use of high-quality, three-dimensional (3D) elevation information derived from modern light detection and ranging (lidar) technologies to provide essential terrain data poses a singular opportunity to dramatically enhance the effectiveness of all three components of this strategy. Additionally, FEMA, the National Weather Service, and the U.S. Geological Survey (USGS) have developed tools and joint program activities to support the national strategy.
The USGS 3D Elevation Program (3DEP) has the programmatic infrastructure to produce and provide essential terrain data. This infrastructure includes (1) data acquisition partnerships that leverage funding and reduce duplicative efforts, (2) contracts with experienced private mapping firms that ensure acquisition of consistent, low-cost 3D elevation data, and (3) the technical expertise, standards, and specifications required for consistent, edge-to-edge utility across multiple collection platforms and public access unfettered by individual database designs and limitations.
High-quality elevation data, like that collected through 3DEP, are invaluable for assessing and documenting flood risk and communicating detailed information to both responders and planners alike. Multiple flood-mapping programs make use of USGS streamflow and 3DEP data. Flood insurance rate maps, flood documentation studies, and flood-inundation map libraries are products of these programs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173081","usgsCitation":"Carswell, W.J., Jr., and Lukas, Vicki, 2018, The 3D Elevation Program—Flood risk management: U.S. Geological Survey Fact Sheet 2017-3081, 6 p., https://doi.org/10.3133/fs20173081.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073000","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":350200,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3081/coverthb2.jpg"},{"id":350201,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3081/fs20173081.pdf","text":"Report","size":"4.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3081"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Population dynamics vary in space and time. Survey designs that ignore these dynamics may be inefficient and fail to capture essential spatio‐temporal variability of a process. Alternatively, dynamic survey designs explicitly incorporate knowledge of ecological processes, the associated uncertainty in those processes, and can be optimized with respect to monitoring objectives. We describe a cohesive framework for monitoring a spreading population that explicitly links animal movement models with survey design and monitoring objectives. We apply the framework to develop an optimal survey design for sea otters in Glacier Bay. Sea otters were first detected in Glacier Bay in 1988 and have since increased in both abundance and distribution; abundance estimates increased from 5 otters to >5,000 otters, and they have spread faster than 2.7 km/yr. By explicitly linking animal movement models and survey design, we are able to reduce uncertainty associated with forecasting occupancy, abundance, and distribution compared to other potential random designs. The framework we describe is general, and we outline steps to applying it to novel systems and taxa.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2120","usgsCitation":"Williams, P.J., Hooten, M., Womble, J.N., Esslinger, G.G., and Bower, M.R., 2018, Monitoring dynamic spatio-temporal ecological processes optimally: Ecology, v. 99, no. 3, p. 524-535, https://doi.org/10.1002/ecy.2120.","productDescription":"12 p.","startPage":"524","endPage":"535","ipdsId":"IP-088621","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469087,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1707.03047","text":"External Repository"},{"id":356607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-25","publicationStatus":"PW","scienceBaseUri":"5b98a30de4b0702d0e843021","contributors":{"authors":[{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":742812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":742810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Womble, Jamie N.","contributorId":198631,"corporation":false,"usgs":false,"family":"Womble","given":"Jamie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":742813,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742811,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Michael R.","contributorId":198632,"corporation":false,"usgs":false,"family":"Bower","given":"Michael","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":742814,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221602,"text":"70221602 - 2018 - Spatial and temporal variability in growth of giant gartersnakes: Plasticity, precipitation, and prey","interactions":[],"lastModifiedDate":"2021-06-25T11:47:22.00398","indexId":"70221602","displayToPublicDate":"2018-01-25T06:40:24","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variability in growth of giant gartersnakes: Plasticity, precipitation, and prey","docAbstract":"The growth rate of reptiles is plastic and often varies among individuals, populations, and years in response to environmental conditions. For an imperiled species, the growth rate of individual animals is an important component of demographic models, and changes in individual growth rates might precede changes in abundance. We analyzed a long-term dataset on the growth of Giant Gartersnakes (Thamnophis gigas) to characterize spatial and temporal variability and evaluate potential environmental predictors of growth. We collected data on the growth in snout–vent length (SVL) of Giant Gartersnakes over 22 yr (1995–2016) from eight sites distributed throughout the Sacramento Valley of California, USA. The von Bertalanffy growth curves indicated male Giant Gartersnakes grew faster toward shorter, asymptotic SVL than did females. Nearly equal variability in growth was attributable to differences among years and among sites. From 2003–2016 we collected data on precipitation, temperature, and the abundance of fish and anuran prey at each site and used these variables as predictors in growth models of Giant Gartersnakes. Snake growth was positively related to the amount of precipitation that fell during the prior water year and the abundance of anurans at a site. Fish and frog abundance interacted to affect snake growth: at low abundances of one prey type, the other positively affected growth, but the slope of this relationship decreased as alternative prey abundance increased. Our results highlight the plasticity of growth in this threatened snake species, point to potential environmental drivers of growth, and provide valuable data for demographic modeling efforts.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/17-055","usgsCitation":"Rose, J.P., Halstead, B., Wylie, G.D., and Casazza, M.L., 2018, Spatial and temporal variability in growth of giant gartersnakes: Plasticity, precipitation, and prey: Journal of Herpetology, v. 52, no. 1, p. 40-49, https://doi.org/10.1670/17-055.","productDescription":"10 p.","startPage":"40","endPage":"49","ipdsId":"IP-086235","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":386725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.78320312499999,\n 38.27268853598095\n ],\n [\n -120.38818359374997,\n 38.27268853598095\n ],\n [\n -120.38818359374997,\n 40.863679665481676\n ],\n [\n -122.78320312499999,\n 40.863679665481676\n ],\n [\n -122.78320312499999,\n 38.27268853598095\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":818254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Glenn D. 0000-0002-7061-6658 glenn_wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":3052,"corporation":false,"usgs":true,"family":"Wylie","given":"Glenn","email":"glenn_wylie@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818256,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194197,"text":"sir20175143 - 2018 - Simulated hydrologic response to climate change during the 21st century in New Hampshire","interactions":[],"lastModifiedDate":"2022-02-08T15:23:52.993636","indexId":"sir20175143","displayToPublicDate":"2018-01-24T09:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5143","title":"Simulated hydrologic response to climate change during the 21st century in New Hampshire","docAbstract":"The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services and the Department of Health and Human Services, has developed a hydrologic model to assess the effects of short- and long-term climate change on hydrology in New Hampshire. This report documents the model and datasets developed by using the model to predict how climate change will affect the hydrologic cycle and provide data that can be used by State and local agencies to identify locations that are vulnerable to the effects of climate change in areas across New Hampshire.
Future hydrologic projections were developed from the output of five general circulation models for two future climate scenarios. The scenarios are based on projected future greenhouse gas emissions and estimates of land-use and land-cover change within a projected global economic framework. An evaluation of the possible effect of projected future temperature on modeling of evapotranspiration is summarized to address concerns regarding the implications of the future climate on model parameters that are based on climate variables. The results of the model simulations are hydrologic projections indicating increasing streamflow across the State with large increases in streamflow during winter and early spring and general decreases during late spring and summer. Wide spatial variability in changes to groundwater recharge is projected, with general decreases in the Connecticut River Valley and at high elevations in the northern part of the State and general increases in coastal and lowland areas of the State. In general, total winter snowfall is projected to decrease across the State, but there is a possibility of increasing snow in some locations, particularly during November, February, and March. The simulated future changes in recharge and snowfall vary by watershed across the State. This means that each area of the State could experience very different changes, depending on topography or other factors. Therefore, planning for infrastructure and public safety needs to be flexible in order to address the range of possible outcomes indicated by the various model simulations. The absolute magnitude and timing of the daily streamflows, especially the larger floods, are not considered to be reliably simulated compared to changes in frequency and duration of daily streamflows and changes in accumulated monthly and seasonal streamflow volumes.
Simulated current and future streamflow, groundwater recharge, and snowfall datasets include simulated data derived from the five general circulation models used in this study for a current reference time period and two future time periods. Average monthly streamflow time series datasets are provided for 27 streamgages in New Hampshire. Fourteen of the 27 streamgages associated with daily streamflow time series showed a good calibration. Average monthly groundwater recharge and snowfall time series for the same reference time period and two future time periods are also provided for each of the 467 hydrologic response units that compose the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175143","collaboration":"Prepared in cooperation with the New Hampshire Department of Environmental Services and Department of Health and Human Services","usgsCitation":"Bjerklie, D.M., and Sturtevant, Luke, 2018, Simulated hydrologic response to climate change during the 21st century in New Hampshire: U.S. Geological Survey Scientific Investigations Report 2017–5143, 53 p., https://doi.org/10.3133/sir20175143.","productDescription":"Report: viii, 53 p.; 4 Tables; Data release","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074537","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":395616,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0KJZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Thirty- and ninety-year data sets of streamflow, groundwater recharge, and snowfall simulating potential hydrologic response to climate change in the 21st century in New Hampshire"},{"id":350514,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table4.csv","text":"Table 4","size":"10.8 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamflow percent change"},{"id":350513,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table3.csv","text":"Table 3","size":"6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamgages in New Hampshire"},{"id":350512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5143/sir20175143.pdf","text":"Report","size":"14.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5143"},{"id":350511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5143/coverthb.jpg"},{"id":350515,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table5.csv","text":"Table 5","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Mean monthly streamflow percent change"},{"id":350516,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table6.csv","text":"Table 6","size":"10.3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Mean monthly streamflow percent change standard deviation"}],"country":"United States","state":"New Hampshire","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-72.4521,43.161414],[-72.452556,43.172117],[-72.443405,43.179729],[-72.45028,43.192485],[-72.437719,43.20275],[-72.4405,43.219049],[-72.433796,43.232999],[-72.438937,43.24424],[-72.438693,43.252905],[-72.435221,43.258483],[-72.421583,43.263442],[-72.41545,43.271374],[-72.407842,43.282892],[-72.401666,43.303395],[-72.395462,43.312994],[-72.410353,43.331675],[-72.400981,43.345775],[-72.390103,43.356926],[-72.403949,43.358098],[-72.413377,43.362741],[-72.415978,43.376531],[-72.413154,43.384302],[-72.403811,43.391935],[-72.395659,43.438541],[-72.390567,43.451225],[-72.3925,43.467364],[-72.382951,43.476],[-72.381723,43.480091],[-72.380894,43.493394],[-72.384773,43.500259],[-72.396305,43.508062],[-72.398563,43.513435],[-72.394218,43.5274],[-72.389097,43.528266],[-72.380383,43.54088],[-72.382783,43.562459],[-72.37944,43.574069],[-72.373126,43.579419],[-72.349926,43.587726],[-72.328514,43.600805],[-72.328232,43.606839],[-72.3327,43.610313],[-72.334401,43.61925],[-72.33236,43.62507],[-72.327236,43.630534],[-72.32966,43.634648],[-72.314083,43.64281],[-72.31402,43.656158],[-72.304322,43.669507],[-72.303092,43.678078],[-72.30602,43.683061],[-72.305326,43.69577],[-72.299715,43.706558],[-72.292215,43.711333],[-72.27118,43.734138],[-72.264245,43.734158],[-72.232713,43.748286],[-72.218099,43.765729],[-72.205193,43.770952],[-72.2053,43.784474],[-72.195552,43.791492],[-72.190754,43.800807],[-72.184847,43.804698],[-72.183333,43.808177],[-72.18857,43.821153],[-72.182203,43.834032],[-72.182864,43.845109],[-72.187916,43.856126],[-72.184788,43.863393],[-72.182956,43.865335],[-72.167476,43.86915],[-72.173576,43.87967],[-72.170604,43.886388],[-72.160819,43.887223],[-72.151324,43.901704],[-72.121002,43.918956],[-72.118013,43.923292],[-72.116767,43.933923],[-72.118985,43.943225],[-72.117839,43.946828],[-72.105875,43.94937],[-72.098689,43.95766],[-72.100543,43.962478],[-72.090357,43.965409],[-72.104972,43.96995],[-72.110945,43.966959],[-72.114273,43.967513],[-72.111756,43.984943],[-72.116985,43.99448],[-72.103765,44.002837],[-72.105292,44.012663],[-72.102475,44.014882],[-72.098897,44.015477],[-72.093384,44.01045],[-72.090059,44.009903],[-72.090504,44.012736],[-72.095193,44.016666],[-72.0951,44.021831],[-72.09203,44.024459],[-72.084871,44.021308],[-72.082432,44.022154],[-72.081357,44.028529],[-72.075004,44.032789],[-72.079397,44.039531],[-72.078989,44.042886],[-72.06215,44.049931],[-72.068405,44.054021],[-72.067612,44.058034],[-72.057173,44.058646],[-72.048289,44.069136],[-72.051602,44.075193],[-72.042088,44.077008],[-72.036641,44.073999],[-72.031898,44.076241],[-72.048781,44.087141],[-72.046235,44.089538],[-72.03429,44.090138],[-72.031878,44.093359],[-72.03124,44.100101],[-72.039674,44.103371],[-72.042943,44.097636],[-72.048334,44.096905],[-72.052391,44.101088],[-72.054831,44.110137],[-72.052342,44.119891],[-72.041948,44.125653],[-72.037506,44.124708],[-72.033703,44.131541],[-72.041983,44.137165],[-72.042867,44.151288],[-72.040167,44.157023],[-72.042387,44.160817],[-72.047593,44.161801],[-72.053021,44.167903],[-72.057496,44.179444],[-72.066166,44.189773],[-72.064577,44.196949],[-72.058987,44.202114],[-72.058605,44.208215],[-72.053233,44.216876],[-72.053582,44.22604],[-72.047889,44.238493],[-72.050112,44.244046],[-72.059782,44.256018],[-72.061174,44.263377],[-72.05874,44.270005],[-72.064544,44.267997],[-72.067774,44.270976],[-72.065434,44.277235],[-72.053355,44.290501],[-72.046302,44.291983],[-72.033465,44.301878],[-72.033136,44.320365],[-72.029061,44.322398],[-72.01913,44.320383],[-72.009977,44.321951],[-71.988306,44.329768],[-71.984617,44.336243],[-71.98112,44.3375],[-71.945163,44.337744],[-71.935395,44.33577],[-71.92911,44.337577],[-71.917434,44.346535],[-71.906909,44.348284],[-71.872472,44.336628],[-71.852628,44.340873],[-71.833261,44.350136],[-71.814351,44.354541],[-71.812206,44.357356],[-71.816157,44.367559],[-71.812424,44.372532],[-71.815251,44.374594],[-71.814388,44.381932],[-71.800316,44.384276],[-71.803488,44.39189],[-71.793924,44.399271],[-71.778613,44.399799],[-71.761966,44.407027],[-71.756091,44.406401],[-71.749533,44.401955],[-71.743104,44.401657],[-71.735923,44.410062],[-71.715087,44.41049],[-71.699434,44.416069],[-71.67995,44.427908],[-71.679933,44.434062],[-71.66183,44.440293],[-71.653348,44.460499],[-71.645068,44.460545],[-71.640404,44.464186],[-71.647864,44.469976],[-71.64589,44.475141],[-71.639312,44.477836],[-71.632795,44.48389],[-71.627655,44.484207],[-71.622089,44.481387],[-71.617614,44.485715],[-71.609568,44.484348],[-71.59948,44.486455],[-71.594303,44.500749],[-71.586972,44.498526],[-71.586648,44.502873],[-71.577643,44.502692],[-71.577068,44.504041],[-71.583233,44.508268],[-71.594259,44.52168],[-71.582505,44.524403],[-71.574456,44.53366],[-71.573083,44.53798],[-71.575193,44.540859],[-71.596804,44.553424],[-71.598116,44.555412],[-71.596137,44.560898],[-71.59017,44.565694],[-71.569599,44.562777],[-71.559846,44.564119],[-71.557972,44.570451],[-71.552629,44.569543],[-71.548728,44.571873],[-71.5533,44.576924],[-71.5532,44.580683],[-71.544922,44.579278],[-71.537724,44.584785],[-71.536251,44.588441],[-71.553447,44.593451],[-71.556014,44.601383],[-71.553873,44.607069],[-71.55656,44.616988],[-71.55576,44.624119],[-71.551722,44.627598],[-71.554634,44.632197],[-71.562124,44.63658],[-71.562636,44.639505],[-71.558859,44.640122],[-71.558571,44.644373],[-71.566144,44.653863],[-71.570235,44.650483],[-71.575145,44.650612],[-71.57571,44.654574],[-71.586578,44.659478],[-71.584574,44.665351],[-71.585645,44.669277],[-71.581983,44.673533],[-71.596304,44.679083],[-71.594224,44.683815],[-71.598042,44.692818],[-71.59436,44.695996],[-71.600162,44.698919],[-71.59975,44.705318],[-71.604912,44.70815],[-71.613094,44.718933],[-71.618355,44.72261],[-71.617431,44.72805],[-71.624922,44.729032],[-71.62518,44.743978],[-71.626909,44.747224],[-71.631109,44.748689],[-71.631883,44.752463],[-71.617941,44.755883],[-71.614238,44.758664],[-71.611767,44.764345],[-71.604615,44.767738],[-71.596035,44.775422],[-71.596949,44.778987],[-71.592966,44.782776],[-71.580005,44.78548],[-71.573247,44.791882],[-71.571706,44.79483],[-71.573129,44.797947],[-71.569216,44.808813],[-71.572864,44.810383],[-71.5755,44.816058],[-71.567907,44.823832],[-71.562256,44.824632],[-71.557672,44.834421],[-71.552218,44.837775],[-71.556805,44.848808],[-71.548345,44.85553],[-71.550176,44.861609],[-71.545901,44.866134],[-71.534588,44.869698],[-71.529154,44.873559],[-71.528889,44.876928],[-71.512292,44.890246],[-71.51387,44.894648],[-71.501088,44.904433],[-71.495844,44.90498],[-71.49392,44.910923],[-71.500788,44.914535],[-71.515189,44.927317],[-71.516949,44.939704],[-71.514843,44.958741],[-71.516223,44.964569],[-71.52237,44.966308],[-71.527163,44.973668],[-71.531605,44.976023],[-71.538592,44.988182],[-71.53698,44.994177],[-71.530091,44.999656],[-71.514609,45.003957],[-71.507767,45.00817],[-71.487565,45.000936],[-71.479611,45.002905],[-71.476168,45.009054],[-71.464555,45.013637],[-71.502487,45.013367],[-71.500069,45.014212],[-71.499945,45.026323],[-71.494009,45.034345],[-71.491085,45.043671],[-71.49315,45.045772],[-71.500874,45.04511],[-71.505222,45.048791],[-71.505091,45.051465],[-71.500545,45.051943],[-71.497738,45.054751],[-71.496105,45.065082],[-71.498399,45.069629],[-71.489145,45.072308],[-71.486345,45.078503],[-71.480219,45.081316],[-71.480897,45.08303],[-71.471382,45.084199],[-71.467447,45.086851],[-71.464837,45.093023],[-71.449257,45.104522],[-71.445613,45.113367],[-71.440577,45.114464],[-71.428828,45.123881],[-71.426755,45.129672],[-71.437216,45.142333],[-71.433179,45.149166],[-71.42675,45.153257],[-71.423616,45.161096],[-71.424616,45.165872],[-71.419058,45.170488],[-71.414853,45.184908],[-71.408777,45.18797],[-71.405636,45.198139],[-71.39781,45.203553],[-71.403267,45.215348],[-71.415553,45.218001],[-71.417233,45.221293],[-71.44288,45.234799],[-71.443883,45.237061],[-71.438546,45.239004],[-71.433014,45.237656],[-71.429326,45.234228],[-71.420335,45.232719],[-71.402638,45.242589],[-71.394422,45.241216],[-71.391901,45.237216],[-71.385629,45.233214],[-71.37763,45.244203],[-71.363013,45.248205],[-71.357253,45.253336],[-71.356835,45.257175],[-71.363218,45.266429],[-71.360664,45.269835],[-71.353446,45.268695],[-71.347622,45.272125],[-71.344029,45.271167],[-71.336392,45.273066],[-71.331733,45.279969],[-71.320922,45.282324],[-71.314318,45.287033],[-71.309008,45.287238],[-71.301107,45.296563],[-71.284396,45.302434],[-71.28074,45.295188],[-71.27232,45.296694],[-71.264939,45.293446],[-71.266754,45.29123],[-71.262136,45.276098],[-71.250393,45.269191],[-71.245503,45.26887],[-71.239346,45.261925],[-71.236271,45.261126],[-71.231122,45.249712],[-71.221994,45.253543],[-71.220634,45.251121],[-71.2118,45.250457],[-71.203033,45.254302],[-71.198276,45.254257],[-71.194878,45.250515],[-71.183785,45.244932],[-71.180905,45.239858],[-71.173367,45.246348],[-71.162845,45.250332],[-71.148165,45.242412],[-71.13943,45.242958],[-71.131953,45.245423],[-71.127962,45.253672],[-71.124517,45.25527],[-71.119914,45.262287],[-71.120112,45.265738],[-71.116332,45.272322],[-71.107339,45.278612],[-71.105691,45.282498],[-71.109349,45.282222],[-71.110743,45.284576],[-71.105151,45.294635],[-71.097772,45.301906],[-71.085564,45.305476],[-71.076914,45.246912],[-71.059004,45.004918],[-71.037518,44.755607],[-71.012749,44.340784],[-70.992842,43.916269],[-70.989067,43.79244],[-70.982083,43.715043],[-70.972716,43.570255],[-70.957234,43.561358],[-70.955017,43.554239],[-70.950838,43.551026],[-70.955252,43.540887],[-70.962153,43.541036],[-70.963531,43.536756],[-70.95822,43.531586],[-70.957214,43.524994],[-70.954066,43.52261],[-70.956856,43.512719],[-70.954755,43.509802],[-70.957958,43.508041],[-70.959185,43.499351],[-70.969572,43.486201],[-70.967968,43.480783],[-70.974245,43.47742],[-70.970946,43.4739],[-70.964542,43.473262],[-70.961428,43.469696],[-70.96045,43.466592],[-70.9669,43.450458],[-70.96164,43.443039],[-70.96115,43.438321],[-70.968782,43.434891],[-70.968359,43.429283],[-70.971039,43.425606],[-70.982898,43.419332],[-70.986812,43.414264],[-70.986677,43.403541],[-70.982565,43.39778],[-70.982876,43.394808],[-70.98739,43.393457],[-70.987649,43.389521],[-70.985205,43.386745],[-70.985965,43.380023],[-70.974156,43.362925],[-70.974863,43.357969],[-70.967229,43.343777],[-70.960439,43.341048],[-70.956528,43.334691],[-70.953034,43.333257],[-70.93711,43.337367],[-70.932735,43.33676],[-70.930783,43.329569],[-70.916421,43.320279],[-70.912004,43.319821],[-70.91246,43.308289],[-70.907405,43.304782],[-70.90231,43.304872],[-70.900386,43.301358],[-70.907405,43.293582],[-70.906005,43.291682],[-70.896304,43.285282],[-70.886504,43.282783],[-70.882804,43.273183],[-70.86323,43.265109],[-70.858207,43.256286],[-70.855082,43.255191],[-70.852015,43.256808],[-70.843302,43.254321],[-70.839213,43.251224],[-70.841059,43.249699],[-70.838678,43.242931],[-70.817865,43.237911],[-70.815453,43.229023],[-70.811852,43.228306],[-70.80964,43.225407],[-70.813119,43.217252],[-70.816903,43.214604],[-70.820763,43.19978],[-70.819344,43.193036],[-70.827201,43.189485],[-70.828301,43.186685],[-70.823501,43.174585],[-70.828301,43.168985],[-70.829101,43.157886],[-70.8338,43.146886],[-70.8268,43.127086],[-70.78388,43.100867],[-70.779098,43.095887],[-70.766398,43.092688],[-70.756397,43.079988],[-70.741897,43.077388],[-70.737897,43.073488],[-70.708896,43.074989],[-70.704696,43.070989],[-70.703799,43.059574],[-70.71363,43.056006],[-70.71355,43.042077],[-70.718936,43.03235],[-70.730426,43.025392],[-70.734363,43.013307],[-70.743793,43.008027],[-70.749969,42.991689],[-70.756701,42.991337],[-70.761474,42.986681],[-70.765222,42.975349],[-70.7718,42.968064],[-70.769673,42.964419],[-70.771729,42.961321],[-70.775597,42.957213],[-70.780383,42.955798],[-70.793996,42.93989],[-70.797806,42.930037],[-70.798153,42.920926],[-70.805971,42.916549],[-70.810069,42.909549],[-70.810999,42.892375],[-70.81586,42.88625],[-70.817296,42.87229],[-70.830795,42.868918],[-70.848625,42.860939],[-70.886136,42.88261],[-70.902768,42.88653],[-70.914886,42.886564],[-70.930799,42.884589],[-70.9665,42.868989],[-71.031201,42.859089],[-71.044401,42.848789],[-71.047501,42.844089],[-71.064201,42.806289],[-71.132503,42.821389],[-71.165603,42.808689],[-71.186104,42.790689],[-71.181803,42.73759],[-71.223904,42.746689],[-71.245504,42.742589],[-71.267905,42.72589],[-71.278929,42.711258],[-71.294205,42.69699],[-71.981402,42.713294],[-72.458519,42.726853],[-72.461001,42.733209],[-72.473071,42.745916],[-72.477615,42.761245],[-72.484878,42.76554],[-72.491122,42.772465],[-72.497949,42.772918],[-72.50069,42.767657],[-72.507985,42.764414],[-72.513105,42.763822],[-72.516082,42.765949],[-72.514836,42.771436],[-72.508372,42.77461],[-72.508858,42.779919],[-72.515838,42.78856],[-72.542784,42.808482],[-72.54855,42.842021],[-72.557247,42.853019],[-72.554232,42.860038],[-72.556214,42.86695],[-72.552834,42.884968],[-72.540708,42.889379],[-72.532777,42.896076],[-72.530218,42.911576],[-72.52443,42.915575],[-72.527431,42.943148],[-72.534554,42.949894],[-72.532186,42.954945],[-72.518422,42.96317],[-72.492597,42.967648],[-72.481706,42.973985],[-72.473827,42.972045],[-72.461627,42.982906],[-72.465335,42.989558],[-72.46294,42.996943],[-72.456936,43.001306],[-72.448714,43.001169],[-72.443762,43.006245],[-72.444635,43.010566],[-72.457035,43.017285],[-72.462397,43.02556],[-72.460252,43.040671],[-72.465896,43.047505],[-72.467363,43.052648],[-72.463812,43.057404],[-72.445202,43.071352],[-72.435316,43.083536],[-72.435191,43.086622],[-72.443051,43.100841],[-72.440587,43.106145],[-72.433129,43.112637],[-72.432972,43.119655],[-72.442933,43.130192],[-72.44078,43.131472],[-72.440905,43.135793],[-72.451986,43.138924],[-72.45689,43.146558],[-72.45714,43.148493],[-72.451802,43.153486],[-72.4521,43.161414]]]},\"properties\":{\"name\":\"New Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Digital flood-inundation maps for a 9.5-mile reach of the Patoka River in and near the city of Jasper, southwestern Indiana (Ind.), from the streamgage near County Road North 175 East, downstream to State Road 162, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Patoka River at Jasper, Ind. (station number 03375500). The Patoka streamgage is located at the upstream end of the 9.5-mile river reach. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, although flood forecasts and stages for action and minor, moderate, and major flood stages are not currently (2017) available at this site (JPRI3).
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Patoka River at Jasper, Ind., streamgage and the documented high-water marks from the flood of April 30, 2017. The calibrated hydraulic model was then used to compute five water-surface profiles for flood stages referenced to the streamgage datum ranging from 15 feet (ft), or near bankfull, to 19 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging [lidar] data having a 0.98 ft vertical accuracy and 4.9 ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these flood-inundation maps, along with real-time stage from the USGS streamgage at the Patoka River at Jasper, Ind., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175138","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Fowler, K.K., 2018, Flood-inundation maps for the Patoka River in and near Jasper, southwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5138, 11 p., https://doi.org/10.3133/sir20175138.","productDescription":"Report: vii, 11 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086512","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":350479,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5138/coverthb.jpg"},{"id":350480,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5138/sir20175138.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5138"},{"id":350481,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7862DX0","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Datasets and Surface-Water Hydraulic Model for the Patoka River in and near Jasper, Southwest Indiana, Flood-inundation Study"}],"country":"United States","state":"Indiana","city":"Jasper","otherGeospatial":"Patoka River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.95,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.425\n ],\n [\n -86.95,\n 38.425\n ],\n [\n -86.95,\n 38.360839624761944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
Interannual differences in the water quality of Anvil Lake, Wisconsin, were examined to determine how water level and climate affect the hydrodynamics and trophic state of shallow lakes, and their importance compared to anthropogenic changes in the watershed. Anvil Lake is a relatively pristine seepage lake with hydrology dominated by precipitation, evaporation, and groundwater exchange enabling the typically subtle effects of water level and climate to be evaluated. Groundwater and hydrodynamic models were used to describe lake water and phosphorus budgets and how its hydrodynamics are affected by water level and air temperature. Decreases in water level are expected to cause Anvil Lake and other shallow lakes to stratify fewer days, and have warmer bottom temperatures and more deep-mixing events. Increasing air temperatures should cause these lakes to have shorter ice cover, longer summer stratification periods, and warmer bottom temperatures. How water level affects water quality depends on how nutrient loading and lake volume vary: during drier, low-water years, lakes with large interannual changes in loading should have better water quality, whereas lakes with small changes in loading should degrade slightly. Anthropogenic changes in Anvil Lake's watershed over the past ∼100 yr were about 1.5 times the effects of changes in water level when levels were low, but the effects were similar when levels were high. Climate warming is expected to increase productivity in shallow lakes because warmer air temperatures will likely increase bottom temperatures increasing sediment phosphorus release and deep-mixing events enabling this phosphorus to reach the epilimnion.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2017.1412374","usgsCitation":"Robertson, D.M., Juckem, P.F., Dantoin, E.D., and Winslow, L., 2018, Effects of water level and climate on the hydrodynamics and water quality of Anvil Lake, Wisconsin, a shallow seepage lake: Lake and Reservoir Management, v. 34, no. 3, p. 211-231, https://doi.org/10.1080/10402381.2017.1412374.","productDescription":"21 p.","startPage":"211","endPage":"231","ipdsId":"IP-082880","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":438051,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7F18WXW","text":"USGS data release","linkHelpText":"MODFLOW-NWT model data sets used to evaluate the changes in hydrodynamics of Anvil Lake, Wisconsin"},{"id":361735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Anvil Lake","volume":"34","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dantoin, Eric D. 0000-0002-8561-2924 edantoin@usgs.gov","orcid":"https://orcid.org/0000-0002-8561-2924","contributorId":2278,"corporation":false,"usgs":true,"family":"Dantoin","given":"Eric","email":"edantoin@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winslow, Luke A. 0000-0002-8602-5510","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":211187,"corporation":false,"usgs":false,"family":"Winslow","given":"Luke A.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":758740,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207054,"text":"70207054 - 2018 - Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model","interactions":[],"lastModifiedDate":"2019-12-04T15:12:07","indexId":"70207054","displayToPublicDate":"2018-01-22T15:07:05","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model","docAbstract":"We present results from 20-year “high-resolution” regional climate model simulations of precipitation change for the sub-tropical island of Puerto Rico. The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) operating at a 2-km grid resolution is nested inside the Regional Spectral Model (RSM) at 10-km grid resolution, which in turn is forced at the lateral boundaries by the Community Climate System Model (CCSM4). At this resolution, the climate change experiment allows for deep convection in model integrations, which is an important consideration for sub-tropical regions in general, and on islands with steep precipitation gradients in particular that strongly influence local ecological processes and the provision of ecosystem services. Projected precipitation change for this region of the Caribbean is simulated for the mid-twenty-first century (2041–2060) under the RCP8.5 climate-forcing scenario relative to the late twentieth century (1986–2005). The results show that by the mid-twenty-first century, there is an overall rainfall reduction over the island for all seasons compared to the recent climate but with diminished mid-summer drought (MSD) in the northwestern parts of the island. Importantly, extreme rainfall events on sub-daily and daily time scales also become slightly less frequent in the projected mid-twenty-first-century climate over most regions of the island.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-2130-x","usgsCitation":"Bhardwaj, A., Misra, V., Mishra, A., Adrienne Wootten, Boyles, R.P., Bowden, J., and Terando, A.J., 2018, Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model: Climatic Change, v. 147, no. 1-2, p. 133-147, https://doi.org/10.1007/s10584-017-2130-x.","productDescription":"15 p.","startPage":"133","endPage":"147","ipdsId":"IP-077134","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":369913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.4066162109375,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 17.814071002942764\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2018-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhardwaj, Amit","contributorId":221025,"corporation":false,"usgs":false,"family":"Bhardwaj","given":"Amit","email":"","affiliations":[],"preferred":false,"id":776645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Misra, Vasubandhu","contributorId":63520,"corporation":false,"usgs":true,"family":"Misra","given":"Vasubandhu","email":"","affiliations":[],"preferred":false,"id":776646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mishra, A.","contributorId":53129,"corporation":false,"usgs":true,"family":"Mishra","given":"A.","email":"","affiliations":[],"preferred":false,"id":776647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adrienne Wootten","contributorId":127631,"corporation":false,"usgs":false,"family":"Adrienne Wootten","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":776648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":776649,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bowden, J.H.","contributorId":174320,"corporation":false,"usgs":false,"family":"Bowden","given":"J.H.","email":"","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":776650,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":776651,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198929,"text":"70198929 - 2018 - Temperate and tropical forest canopies are already functioning beyond their thermal thresholds for photosynthesis","interactions":[],"lastModifiedDate":"2018-08-27T14:25:23","indexId":"70198929","displayToPublicDate":"2018-01-22T14:25:06","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Temperate and tropical forest canopies are already functioning beyond their thermal thresholds for photosynthesis","docAbstract":"Tropical tree species have evolved under very narrow temperature ranges compared to temperate forest species. Studies suggest that tropical trees may be more vulnerable to continued warming compared to temperate species, as tropical trees have shown declines in growth and photosynthesis at elevated temperatures. However, regional and global vegetation models lack the data needed to accurately represent such physiological responses to increased temperatures, especially for tropical forests. To address this need, we compared instantaneous photosynthetic temperature responses of mature canopy foliage, leaf temperatures, and air temperatures across vertical canopy gradients in three forest types: tropical wet, tropical moist, and temperate deciduous. Temperatures at which maximum photosynthesis occurred were greater in the tropical forests canopies than the temperate canopy (30 ± 0.3 °C vs. 27 ± 0.4 °C). However, contrary to expectations that tropical species would be functioning closer to threshold temperatures, photosynthetic temperature optima was exceeded by maximum daily leaf temperatures, resulting in sub-optimal rates of carbon assimilation for much of the day, especially in upper canopy foliage (>10 m). If trees are unable to thermally acclimate to projected elevated temperatures, these forests may shift from net carbon sinks to sources, with potentially dire implications to climate feedbacks and forest community composition.
","language":"English","publisher":"MDPI","doi":"10.3390/f9010047","usgsCitation":"Mau, A.C., Reed, S.C., Wood, T.E., and Cavaleri, M.A., 2018, Temperate and tropical forest canopies are already functioning beyond their thermal thresholds for photosynthesis: Forests, v. 9, no. 1, Article 47; 24 p., https://doi.org/10.3390/f9010047.","productDescription":"Article 47; 24 p.","ipdsId":"IP-094050","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f9010047","text":"Publisher Index Page"},{"id":356799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-22","publicationStatus":"PW","scienceBaseUri":"5b98a30de4b0702d0e843023","contributors":{"authors":[{"text":"Mau, Alida C.","contributorId":207291,"corporation":false,"usgs":false,"family":"Mau","given":"Alida","email":"","middleInitial":"C.","affiliations":[{"id":37512,"text":"School of Forest Resources & Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":743455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Tana E.","contributorId":202372,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[{"id":36399,"text":"International Institute of Tropical Forestry, USDA Forest Service, Rio Piedras, PR","active":true,"usgs":false}],"preferred":false,"id":743458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":743456,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248916,"text":"70248916 - 2018 - VS2DRTI: Simulating heat and reactive solute transport in variably saturated porous media","interactions":[],"lastModifiedDate":"2023-09-26T11:47:36.049733","indexId":"70248916","displayToPublicDate":"2018-01-19T06:44:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"VS2DRTI: Simulating heat and reactive solute transport in variably saturated porous media","docAbstract":"Variably saturated groundwater flow, heat transport, and solute transport are important processes in environmental phenomena, such as the natural evolution of water chemistry of aquifers and streams, the storage of radioactive waste in a geologic repository, the contamination of water resources from acid-rock drainage, and the geologic sequestration of carbon dioxide. Up to now, our ability to simulate these processes simultaneously with fully coupled reactive transport models has been limited to complex and often difficult-to-use models. To address the need for a simple and easy-to-use model, the VS2DRTI software package has been developed for simulating water flow, heat transport, and reactive solute transport through variably saturated porous media. The underlying numerical model, VS2DRT, was created by coupling the flow and transport capabilities of the VS2DT and VS2DH models with the equilibrium and kinetic reaction capabilities of PhreeqcRM. Flow capabilities include two-dimensional, constant-density, variably saturated flow; transport capabilities include both heat and multicomponent solute transport; and the reaction capabilities are a complete implementation of geochemical reactions of PHREEQC. The graphical user interface includes a preprocessor for building simulations and a postprocessor for visual display of simulation results. To demonstrate the simulation of multiple processes, the model is applied to a hypothetical example of injection of heated waste water to an aquifer with temperature-dependent cation exchange. VS2DRTI is freely available public domain software.
Introduction
California’s recent drought (2012–2016) has implications throughout the state for natural resource management and adaptation planning and has generated many discussions about drought characterization and recovery. This study characterizes drought conditions with two indices describing deficits in natural water supply and increases in landscape stress developed on the basis of water balance modeling, at a fine spatial scale to assess the variation in conditions across the entire state, and provides an in-depth evaluation for the Russian River basin in northern California to address local resource management by developing extreme drought scenarios for consideration in planning and adaptation.
Methods
We employed the USGS Basin Characterization Model to characterize drought on the basis of water supply (a measure of recharge plus runoff) and landscape stress (climatic water deficit). These were applied to the state and to the Russian River basin where antecedent soil moisture conditions were evaluated and extreme drought scenarios were developed and run through a water management and reservoir operations model to further explore impacts on water management.
Results
Drought indices indicated that as of the end of water year 2016 when reservoirs were full, additional water supply and landscape replenishment of up to three average years of precipitation in some locations was needed to return to normal conditions. Antecedent soil conditions in the Russian River were determined to contribute to very different water supply results for different years and were necessary to understand to anticipate proper watershed response to climate. Extreme drought scenarios manifested very different kinds of drought and recovery and characterization helps to guide the management response to drought.
Conclusions
These scenarios and indices illustrate how droughts differ with regard to water supply and landscape stress and how long warm droughts recover much more slowly than short very dry droughts due to the depletion of water in the soil and unsaturated zone that require filling before runoff can occur. Recognition of ongoing conditions and likelihood of recovery provides tools and information for a range of resource managers to cope with drought conditions.
Gas hydrate is a naturally occurring, ice-like substance that forms when water and gas combine under high pressure and at moderate temperatures. Methane is the most common gas present in gas hydrate, although other gases may also be included in hydrate structures, particularly in areas close to conventional oil and gas reservoirs. Gas hydrate is widespread in ocean-bottom sediments at water depths greater than 300–500 meters (m; 984–1,640 feet [ft]) and is also present in areas with permanently frozen ground (permafrost). Several countries are evaluating gas hydrate as a possible energy resource in deepwater or permafrost settings. Gas hydrate is also under investigation to determine how environmental change may affect these deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173080","usgsCitation":"Ruppel, C.D., 2018, Gas hydrate in nature: U.S. Geological Survey Fact Sheet 2017–3080, 4 p., https://doi.org/10.3133/fs20173080.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081102","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":350446,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20173079","text":"Fact Sheet 2017–3079"},{"id":350442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3080/coverthb.jpg"},{"id":350445,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://energy.usgs.gov/OilGas/UnconventionalOilGas/GasHydrates.aspx","text":"U.S. Geological Survey’s Energy Resources Program gas hydrates site"},{"id":350444,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://woodshole.er.usgs.gov/project-pages/hydrates/index.html","text":"Overview of the U.S. Geological Survey’s Gas Hydrates Project"},{"id":350443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3080/fs20173080.pdf","text":"Report","size":"1.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3080"}],"contact":"Coastal and Marine Geology Program Coordinator
Energy Resources Program Coordinator
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Armenia is a landlocked country located in the mountainous Caucasus region between Asia and Europe. It shares borders with the countries of Georgia on the north, Azerbaijan on the east, Iran on the south, and Turkey and Azerbaijan on the west. The Ararat Basin is a transboundary basin in Armenia and Turkey. The Ararat Basin (or Ararat Valley) is an intermountain depression that contains the Aras River and its tributaries, which also form the border between Armenia and Turkey and divide the basin into northern and southern regions. The Ararat Basin also contains Armenia’s largest agricultural and fish farming zone that is supplied by high-quality water from wells completed in the artesian aquifers that underlie the basin. Groundwater constitutes about 40 percent of all water use, and groundwater provides 96 percent of the water used for drinking purposes in Armenia. Since 2000, groundwater withdrawals and consumption in the Ararat Basin of Armenia have increased because of the growth of aquaculture and other uses. Increased groundwater withdrawals caused decreased springflow, reduced well discharges, falling water levels, and a reduction of the number of flowing artesian wells in the southern part of Ararat Basin in Armenia.
In 2016, the U.S. Geological Survey and the U.S. Agency for International Development (USAID) began a cooperative study in Armenia to share science and field techniques to increase the country’s capabilities for groundwater study and modeling. The purpose of this report is to describe the hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia based on data collected in 2016 and previous hydrogeologic studies. The study area includes the Ararat Basin in Armenia. This report was completed through a partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships effort through the Advanced Science and Partnerships for Integrated Resource Development program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter.
The hydrogeologic framework of the Ararat Basin includes several basin-fill stratigraphic units consisting of interbedded dense clays, gravels, sands, volcanic basalts, and andesite deposits. Previously published cross sections and well lithologic logs were used to map nine general hydrogeologic units. Hydrogeologic units were mapped based on lithology and water-bearing potential. Water-level data measured in the water-bearing hydrogeologic units 2, 4, 6, and 8 in 2016 were used to create potentiometric surface maps. In hydrogeologic unit 2, the estimated direction of groundwater flow is from the west to north in the western part of the basin (away from the Aras River) and from north to south (toward the Aras River) in the eastern part of the basin. In hydrogeologic unit 4, the direction of groundwater flow is generally from west to east and north to south (toward the Aras River) except in the western part of the basin where groundwater flow is toward the north or northwest. Hydrogeologic unit 6 has the same general pattern of groundwater flow as unit 4. Hydrogeologic unit 8 is the deepest of the water-bearing units and is confined in the basin. Groundwater flow generally is from the south to north (away from the Aras River) in the western part of the basin and from west to east and north to south (toward the Aras River) elsewhere in the basin.
In addition to water levels, personnel from Armenia’s Hydrogeological Monitoring Center also measured specific conductance at 540 wells and temperature at 2,470 wells in the Ararat Basin using U.S. Geological Survey protocols in 2016. The minimum specific conductance was 377 microsiemens per centimeter (μS/cm), the maximum value was 4,000 μS/cm, and the mean was 998 μS/cm. The maximum water temperature was 24.2 degrees Celsius. An analysis between water temperature and well depth indicated no relation; however, spatially, most wells with cooler water temperatures were within the 2016 pressure boundary or in the western part of the basin. Wells with generally warmer water temperatures were in the eastern part of the basin.
Samples were collected from four groundwater sites and one surface-water site by the U.S. Geological Survey in 2016. The stable-isotope values were similar for all five sites, indicating similar recharge sources for the sampled wells. The Hrazdan River sample was consistent with the groundwater samples, indicating the river could serve as a source of recharge to the Ararat artesian aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175163","usgsCitation":"Valder, J.F., Carter, J.M., Medler, C.J., Thompson, R.F., and Anderson, M.T., 2018, Hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia: U.S. Geological Survey Scientific Investigations Report 2017–5163, 40 p., https://doi.org/10.3133/sir20175163.","productDescription":"Report: viii, 40 p.; Tables","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-088554","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350454,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table6.xls","text":"Table 6. Historical water-level and well yield data from various dates ranging from 1981 to 2013 in the Ararat Basin, Armenia","size":"96 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 6"},{"id":350430,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5163/coverthb.jpg"},{"id":350452,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table5.xlsx","text":"Table 5. Historical water-level data from 2007 in the Ararat Basin, Armenia, provided to the U.S. Geological Survey by Armenian partners","size":"200 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 5"},{"id":350451,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table4.xls","text":"Table 4. Hydrologic data provided to the U.S. Geological Survey from the 2016 well inventory conducted in the Ararat Basin, Armenia, by Armenian partners","size":"808 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 4"},{"id":350434,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table1.xlsx","text":"Table 1 Lithologic descriptions, land-surface elevations, geologic layer thicknesses, and hydrogeologic units of the Ararat Basin, Armenia","size":"792 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 1"},{"id":350432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5163"}],"country":"Armenia","otherGeospatial":"Ararat Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 43.75,\n 39.75\n ],\n [\n 44.8,\n 39.75\n ],\n [\n 44.8,\n 40.25\n ],\n [\n 43.75,\n 40.25\n ],\n [\n 43.75,\n 39.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center, South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Anthropogenic alteration of the form and concentration of nitrogen (N) in aquatic ecosystems is widespread. Understanding availability and uptake of different N sources at the base of aquatic food webs is critical to establishment of effective nutrient management programs. Stable isotopes of N (14N, 15N) are often used to trace the sources of N fueling aquatic primary production, but effective use of this approach requires obtaining a reliable isotopic ratio for phytoplankton. In this study, we tested the use of flow cytometry to isolate phytoplankton from bulk particulate organic matter (POM) in a portion of the Sacramento River, California, during river-scale nutrient manipulation experiments that involved halting wastewater discharges high in ammonium (NH4+). Field samples were collected using a Lagrangian approach, allowing us to measure changes in phytoplankton N source in the presence and absence of wastewater-derived NH4+. Comparison of δ15N-POM and δ15N-phytoplankton (δ15N-PHY) revealed that their δ15N values followed broadly similar trends. However, after 3 days of downstream travel in the presence of wastewater treatment plant (WWTP) effluent, δ15N-POM and δ15N-PHY in the Sacramento River differed by as much as 7 ‰. Using a stable isotope mixing model approach, we estimated that in the presence of effluent between 40 and 90 % of phytoplankton N was derived from NH4+ after 3 days of downstream transport. An apparent gradual increase over time in the proportion of NH4+ in the phytoplankton N pool suggests that either very low phytoplankton growth rates resulted in an N turnover time that exceeded the travel time sampled during this study, or a portion of the phytoplankton community continued to access nitrate even in the presence of elevated NH4+ concentrations.
","language":"English","publisher":"EGU","doi":"10.5194/bg-15-353-2018","usgsCitation":"Schmidt, C.M., Kraus, T.E., Young, M.B., and Kendall, C., 2018, Use of flow cytometry and stable isotope analysis to determine phytoplankton uptake of wastewater derived ammonium in a nutrient-rich river: Biogeosciences, v. 15, p. 353-367, https://doi.org/10.5194/bg-15-353-2018.","productDescription":"15 p.","startPage":"353","endPage":"367","ipdsId":"IP-085879","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":469092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-15-353-2018","text":"Publisher Index Page"},{"id":351289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.74774169921875,\n 38.1151107557172\n ],\n [\n -121.40029907226562,\n 38.1151107557172\n ],\n [\n -121.40029907226562,\n 38.63939998171362\n ],\n [\n -121.74774169921875,\n 38.63939998171362\n ],\n [\n -121.74774169921875,\n 38.1151107557172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-17","publicationStatus":"PW","scienceBaseUri":"5a7c1e75e4b00f54eb2292ee","contributors":{"authors":[{"text":"Schmidt, Calla M. 0000-0003-2120-9877","orcid":"https://orcid.org/0000-0003-2120-9877","contributorId":201956,"corporation":false,"usgs":false,"family":"Schmidt","given":"Calla","email":"","middleInitial":"M.","affiliations":[{"id":16849,"text":"University of San Francisco","active":true,"usgs":false}],"preferred":false,"id":727266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Megan B. 0000-0002-0229-4108 mbyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0229-4108","contributorId":3315,"corporation":false,"usgs":true,"family":"Young","given":"Megan","email":"mbyoung@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":727267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Carol 0000-0002-0247-3405 ckendall@usgs.gov","orcid":"https://orcid.org/0000-0002-0247-3405","contributorId":1462,"corporation":false,"usgs":true,"family":"Kendall","given":"Carol","email":"ckendall@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":727268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}