During 2015–17, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service (Forest Service), carried out a study to characterize the hydrology and water chemistry in two study areas within the Daniel Boone National Forest. One study area was within the Rock Creek drainage and the other study area included the Wildcat and Addison Branch drainages. Both study areas historically were mined for coal prior to the Surface Mining Control and Reclamation Act of 1977 and contain abandoned coal mine sites that have since been the focus of remediation efforts. Synoptic surveys of streamflow and water-quality properties (water temperature, pH, specific conductance, and dissolved oxygen) of Rock Creek were done during November 2015 and May 2016, and surveys of Wildcat and Addison Branches were done during June 2016 and May 2017. Streamflow measurements were used to quantify contributions from tributaries and to compute streamflow gain and loss in designated reaches. Discrete measurements of water temperature, pH, specific conductance, and dissolved oxygen were used to evaluate conditions during a short timeframe and for comparison between study areas. Study designs for the two study areas differed because there was an operating streamgage on Rock Creek near Yamacraw, Kentucky (station number 03410590) where streamflow and water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were monitored continuously, while Addison and Wildcat Branches were ungaged. Several hydrograph separation methods were used to estimate base flow and runoff at the Rock Creek gage. These data will be used by the Forest Service to evaluate the current (2018) conditions and plan remediation efforts.
The water quality at Rock Creek was less affected by acid mine drainage (AMD) than the Wildcat or Addison Branches. Appreciable losing reaches, where water flowed underground, were identified in both study areas. All losing reaches coincided with karst topography. Streamflow increased in areas with openings to underground mine tunnels, known as portals.
Six hydrograph separation methods (Base-flow index [BFI; standard and modified], HYSEP [fixed interval, sliding interval, and local minimum], and PART) were applied to daily mean streamflow collected from August 2015 to August 2017 at station number 03410590. The hydrograph separation methods partition total streamflow into base flow and streamflow that originated from surface runoff. Base flow typically reacts slowly to precipitation infiltration and is largely sustained by groundwater discharge. The estimated daily base flow and runoff made with the different separation methods are not highly different. On average, base flow accounted for more total streamflow than surface runoff during the study period, irrespective of method.
Water temperature, pH, dissolved oxygen, specific conductance, and turbidity values were measured from July 2016 through July 2017 with a continuous monitor installed at station number 03410590. Nearly neutral pH values that ranged from 6.8 to 7.9 standard units likely limited metal solubility in the surface water. The continuous specific conductance values ranged between 30 and 259 microsiemens per centimeter at 25 degrees Celsius. The previous remediation efforts are likely continuing to improve the effect of AMD in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service ","usgsCitation":"Cherry, M.A., 2019, Streamflow gain and loss, hydrograph separation, and water quality of abandoned mine lands in the Daniel Boone National Forest, eastern Kentucky, 2015–17: U.S. Geological Survey Scientific Investigations Report 2019–5006, 36 p., https://doi.org/10.3133/sir20195006.","productDescription":"Report: viii, 36 p.;Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091989","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":363195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5006/sir20195006.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5006"},{"id":363196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78D9","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and water-quality data for selected streams in the Daniel Boone National Forest, eastern Kentucky, 2015–17"},{"id":363194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5006/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Daniel Boone National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.67437744140625,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 36.63536611993544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Weather has been recognized as a density independent factor influencing the abundance, distribution, and behavior of vertebrates. Male wild turkeys’ (Meleagris gallopavo) breeding behavior includes vocalizations and courtship displays to attract females, the phenology of which can vary with latitude. State biologists design spring turkey-hunting season frameworks centered on annual vocalization patterns to maximize hunter engagement. The Mississippi Department of Wildlife, Fisheries, and Parks has traditionally instituted a statewide, 7-week, spring harvest season. However, hunters routinely argue that different peaks in gobbling activity across the state exist. The objective of this study was to determine whether differences in peak gobbling activity existed across a latitudinal gradient of Mississippi and assess the effect of weather on gobbling. During 2008 and 2009, we conducted a statewide gobbling survey. We used generalized additive mixed models to describe the probability and frequency of gobbling activity within northern and southern regions of the state. We also investigated the effect of daily weather conditions on gobbling activity. Our results revealed an approximate 10–14-day difference in peak gobbling activity between southern and northern Mississippi. The majority of all gobbling activity occurred within the current spring harvest framework. Perhaps more importantly, gobbling activity was more prevalent on days of regionally dry conditions (i.e., less humid) according to the Spatial Synoptic Classification. Our results provide information on gobbling activity phenology relative to hunting-season dates and weather-response information. Our approach may be particularly applicable in states with relatively shorter seasons or highly variable daily weather conditions that moderate gobbling frequency.
","language":"English","publisher":"Springer","doi":"10.1007/s00484-019-01720-2","usgsCitation":"Palumbo, M.D., Vilella, F., Wang, G., Strickland, B.K., Godwin, D., Dixon, P.G., Rubin, B.D., and Lashley, M., 2019, Latitude and daily-weather effects on gobbling activity of wild turkeys in Mississippi: International Journal of Biometeorology, v. 63, no. 8, p. 1059-1067, https://doi.org/10.1007/s00484-019-01720-2.","productDescription":"9 p.","startPage":"1059","endPage":"1067","ipdsId":"IP-068111","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":370060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-89.095623,30.231767],[-89.077259,30.23168],[-89.067128,30.250199],[-89.063989,30.246299],[-89.073538,30.223318],[-89.091469,30.202305],[-89.118222,30.223343],[-89.156738,30.230699],[-89.095623,30.231767]]],[[[-88.90037,30.224576],[-88.945498,30.209646],[-88.974672,30.207391],[-88.984249,30.21032],[-88.920511,30.220578],[-88.889797,30.239665],[-88.87366,30.241748],[-88.90037,30.224576]]],[[[-88.710719,30.250799],[-88.656804,30.233956],[-88.573044,30.22264],[-88.562067,30.227476],[-88.569138,30.221357],[-88.587424,30.219154],[-88.665857,30.228847],[-88.71183,30.242662],[-88.752782,30.238803],[-88.771991,30.245523],[-88.73255,30.246322],[-88.718104,30.252931],[-88.710719,30.250799]]],[[[-88.506999,30.214348],[-88.453444,30.201236],[-88.430332,30.208548],[-88.401466,30.210172],[-88.428301,30.198511],[-88.453654,30.196584],[-88.493523,30.205945],[-88.506999,30.214348]]],[[[-90.309297,34.995694],[-88.200064,34.995634],[-88.176106,34.962519],[-88.154617,34.922392],[-88.136692,34.907592],[-88.097888,34.892202],[-88.243025,33.79568],[-88.473227,31.893856],[-88.41863,30.866528],[-88.395023,30.369425],[-88.399062,30.360744],[-88.39398,30.349307],[-88.401181,30.344382],[-88.409927,30.342115],[-88.418811,30.353911],[-88.433891,30.354652],[-88.446495,30.347753],[-88.446625,30.337671],[-88.45381,30.329626],[-88.480117,30.318345],[-88.504802,30.321472],[-88.506334,30.327398],[-88.522494,30.340092],[-88.579483,30.34419],[-88.599249,30.358933],[-88.613745,30.353108],[-88.624523,30.358713],[-88.66382,30.362099],[-88.700587,30.343689],[-88.728893,30.342671],[-88.746945,30.347622],[-88.771742,30.365403],[-88.811615,30.384907],[-88.810227,30.394698],[-88.841328,30.409598],[-88.857828,30.392898],[-88.851442,30.375355],[-88.89393,30.393398],[-88.971233,30.390798],[-89.016334,30.383898],[-89.083237,30.368097],[-89.18684,30.331197],[-89.285744,30.303097],[-89.294444,30.307596],[-89.291844,30.328096],[-89.287844,30.333196],[-89.281564,30.33198],[-89.279818,30.34979],[-89.292499,30.365635],[-89.315067,30.375408],[-89.335942,30.374016],[-89.353248,30.368795],[-89.366116,30.352169],[-89.332546,30.337895],[-89.322545,30.314896],[-89.329946,30.302896],[-89.365747,30.284896],[-89.379547,30.270396],[-89.419348,30.25432],[-89.430428,30.223218],[-89.447465,30.205098],[-89.44791,30.185352],[-89.461275,30.174745],[-89.469792,30.176278],[-89.480214,30.193751],[-89.503231,30.183051],[-89.524504,30.180753],[-89.530452,30.192197],[-89.538652,30.195797],[-89.570154,30.180297],[-89.585754,30.192096],[-89.596655,30.211796],[-89.615856,30.223195],[-89.614156,30.244595],[-89.631789,30.256924],[-89.631411,30.279662],[-89.64344,30.287682],[-89.640401,30.306755],[-89.626221,30.314255],[-89.629727,30.339287],[-89.657191,30.356515],[-89.683686,30.405873],[-89.684118,30.412646],[-89.678514,30.414012],[-89.684816,30.439511],[-89.68341,30.451793],[-89.701799,30.465115],[-89.709551,30.477853],[-89.715886,30.477797],[-89.724614,30.491902],[-89.758133,30.505404],[-89.76057,30.515761],[-89.768133,30.51502],[-89.779565,30.544345],[-89.793818,30.545935],[-89.794532,30.556554],[-89.803887,30.560581],[-89.790078,30.565333],[-89.794495,30.569653],[-89.808027,30.567998],[-89.807118,30.587337],[-89.819838,30.59534],[-89.814563,30.606152],[-89.821286,30.60713],[-89.823261,30.622803],[-89.818081,30.634019],[-89.821868,30.644024],[-89.833261,30.657516],[-89.851889,30.661199],[-89.85055,30.664781],[-89.843355,30.663699],[-89.837894,30.672514],[-89.847201,30.670038],[-89.835848,30.699555],[-89.845801,30.707314],[-89.836257,30.716185],[-89.831961,30.715384],[-89.828061,30.725018],[-89.836331,30.727197],[-89.83687,30.734661],[-89.816764,30.740076],[-89.826053,30.742322],[-89.831537,30.76761],[-89.819164,30.795229],[-89.81261,30.789876],[-89.804696,30.791624],[-89.810863,30.797379],[-89.800422,30.810425],[-89.800049,30.819078],[-89.782404,30.817975],[-89.783985,30.827385],[-89.790432,30.830985],[-89.790121,30.837983],[-89.780947,30.848542],[-89.784073,30.85527],[-89.771722,30.854677],[-89.767955,30.863858],[-89.778005,30.873411],[-89.778583,30.878903],[-89.770027,30.882254],[-89.773553,30.896862],[-89.757024,30.898947],[-89.764202,30.911906],[-89.759403,30.915134],[-89.750073,30.91293],[-89.744789,30.918933],[-89.756333,30.943498],[-89.735686,30.966573],[-89.728041,30.966518],[-89.727086,30.969707],[-89.736883,30.977122],[-89.732168,30.978088],[-89.727698,30.993329],[-89.73554,30.999715],[-89.728145,31.0023],[-89.732504,31.004831],[-89.751481,30.99969],[-91.636942,30.999416],[-91.578413,31.02403],[-91.564397,31.038965],[-91.559907,31.054119],[-91.567648,31.070186],[-91.61857,31.107328],[-91.625994,31.116896],[-91.625118,31.131879],[-91.597062,31.163492],[-91.589046,31.178586],[-91.588939,31.188959],[-91.601616,31.208573],[-91.625119,31.226071],[-91.644356,31.234414],[-91.652019,31.242691],[-91.654027,31.255753],[-91.637672,31.26768],[-91.564192,31.261633],[-91.537734,31.267369],[-91.515614,31.27821],[-91.508858,31.291644],[-91.507977,31.312943],[-91.548967,31.347255],[-91.551002,31.363645],[-91.546607,31.381198],[-91.55568,31.386413],[-91.568953,31.377629],[-91.578334,31.399067],[-91.576265,31.410498],[-91.565179,31.423447],[-91.548465,31.432668],[-91.541626,31.431706],[-91.532336,31.390275],[-91.521836,31.37517],[-91.504163,31.36495],[-91.47887,31.364955],[-91.471098,31.376917],[-91.472065,31.395925],[-91.500046,31.42052],[-91.513366,31.444396],[-91.518148,31.483483],[-91.514917,31.510113],[-91.520579,31.513207],[-91.522536,31.522078],[-91.511217,31.532612],[-91.489618,31.534266],[-91.481318,31.530666],[-91.443916,31.542466],[-91.414915,31.562166],[-91.405415,31.576466],[-91.403915,31.589766],[-91.422716,31.597065],[-91.466317,31.586066],[-91.488618,31.587466],[-91.502783,31.595727],[-91.516567,31.611818],[-91.515462,31.630372],[-91.494478,31.645013],[-91.474318,31.625365],[-91.436716,31.612665],[-91.421116,31.611565],[-91.401015,31.620365],[-91.395715,31.644165],[-91.400115,31.688164],[-91.397915,31.709364],[-91.371804,31.742948],[-91.365034,31.748184],[-91.338663,31.750005],[-91.275545,31.745515],[-91.263406,31.754468],[-91.259611,31.76129],[-91.263043,31.766995],[-91.286045,31.772062],[-91.325973,31.76151],[-91.355214,31.758063],[-91.365529,31.761628],[-91.359514,31.799362],[-91.345714,31.842861],[-91.333814,31.853261],[-91.294713,31.86046],[-91.289312,31.846861],[-91.289412,31.828661],[-91.282212,31.814762],[-91.269212,31.809162],[-91.255611,31.812662],[-91.245047,31.831447],[-91.266612,31.851161],[-91.267712,31.86266],[-91.234899,31.876863],[-91.20281,31.907959],[-91.18061,31.917959],[-91.18491,31.923759],[-91.18371,31.933158],[-91.19111,31.934158],[-91.18481,31.965557],[-91.16441,31.982557],[-91.104108,31.990357],[-91.075908,32.016828],[-91.088108,32.034455],[-91.15141,32.049255],[-91.16131,32.059755],[-91.16031,32.070354],[-91.14881,32.080154],[-91.139309,32.081754],[-91.128609,32.076554],[-91.103708,32.050255],[-91.082308,32.047555],[-91.079108,32.050255],[-91.080008,32.079154],[-91.034707,32.101053],[-91.030507,32.108153],[-91.030907,32.120552],[-91.017606,32.125153],[-91.004106,32.146152],[-91.00619,32.156957],[-91.025007,32.162552],[-91.050207,32.178451],[-91.057647,32.177354],[-91.058907,32.171251],[-91.048507,32.150152],[-91.053175,32.124237],[-91.08163,32.133992],[-91.101181,32.131136],[-91.111294,32.125036],[-91.162822,32.132694],[-91.174552,32.154978],[-91.164171,32.196888],[-91.133587,32.213432],[-91.11727,32.206668],[-91.108509,32.20815],[-91.100409,32.21785],[-91.083708,32.22645],[-91.071108,32.22605],[-91.061408,32.21865],[-91.050307,32.237949],[-91.039007,32.242349],[-91.021507,32.236149],[-91.006106,32.22405],[-91.002469,32.215812],[-90.988672,32.215812],[-90.983434,32.221305],[-90.98029,32.243601],[-90.970016,32.25168],[-90.982985,32.270294],[-90.980747,32.29141],[-90.976199,32.29645],[-90.964149,32.296872],[-90.953008,32.284043],[-90.947834,32.283486],[-90.922231,32.298639],[-90.902558,32.319587],[-90.90072,32.330379],[-90.875631,32.372434],[-90.89206,32.370579],[-90.897762,32.35436],[-90.912363,32.339454],[-90.993625,32.354047],[-91.004506,32.364744],[-90.99408,32.403862],[-90.967767,32.418279],[-90.96856,32.438084],[-90.978547,32.447032],[-90.993863,32.45085],[-91.029606,32.433542],[-91.052907,32.438442],[-91.095308,32.458741],[-91.116008,32.48314],[-91.116708,32.500139],[-91.101304,32.525599],[-91.093741,32.549128],[-91.074817,32.533467],[-91.050907,32.500139],[-91.038106,32.49044],[-91.004206,32.48214],[-90.990703,32.487749],[-90.987831,32.49419],[-90.994481,32.506331],[-91.005468,32.513842],[-91.061685,32.536448],[-91.075373,32.546718],[-91.080398,32.556442],[-91.03617,32.579556],[-91.010228,32.601927],[-91.002962,32.622555],[-91.014286,32.640482],[-91.025769,32.646573],[-91.038415,32.636443],[-91.049796,32.607188],[-91.112764,32.590186],[-91.118641,32.585139],[-91.127912,32.586493],[-91.144074,32.600613],[-91.153556,32.626181],[-91.152699,32.640757],[-91.138712,32.649774],[-91.127723,32.665343],[-91.104443,32.682434],[-91.076061,32.693751],[-91.063946,32.702926],[-91.054749,32.719229],[-91.056999,32.72558],[-91.077176,32.732534],[-91.123152,32.742798],[-91.154461,32.742339],[-91.165328,32.751301],[-91.156918,32.780343],[-91.164397,32.785821],[-91.161669,32.812465],[-91.145002,32.84287],[-91.127886,32.855059],[-91.105631,32.858396],[-91.070602,32.888659],[-91.064449,32.901064],[-91.064804,32.926464],[-91.083084,32.947909],[-91.080355,32.962794],[-91.086802,32.976266],[-91.09693,32.986412],[-91.106581,32.988938],[-91.134414,32.980533],[-91.138585,32.971352],[-91.131243,32.960928],[-91.137863,32.952756],[-91.132115,32.923122],[-91.15169,32.901935],[-91.170235,32.899391],[-91.196785,32.906784],[-91.208263,32.915354],[-91.213972,32.927198],[-91.210705,32.939845],[-91.199415,32.952314],[-91.201842,32.961212],[-91.168973,32.992132],[-91.162363,33.019684],[-91.129088,33.033554],[-91.120379,33.05453],[-91.124639,33.064127],[-91.149823,33.081603],[-91.171514,33.087818],[-91.180836,33.098364],[-91.200167,33.10693],[-91.20178,33.125121],[-91.193174,33.136734],[-91.183662,33.141691],[-91.161651,33.141781],[-91.151853,33.131802],[-91.131659,33.129101],[-91.104317,33.131598],[-91.089862,33.139655],[-91.084366,33.180856],[-91.091711,33.220813],[-91.070697,33.227302],[-91.050407,33.251202],[-91.045191,33.265404],[-91.043624,33.274636],[-91.04815,33.282796],[-91.072567,33.285885],[-91.083694,33.278557],[-91.099093,33.238173],[-91.106142,33.241799],[-91.117223,33.260685],[-91.128078,33.268502],[-91.125539,33.280255],[-91.141615,33.299539],[-91.143667,33.328398],[-91.142219,33.348989],[-91.120409,33.363809],[-91.101456,33.38719],[-91.075293,33.405966],[-91.058152,33.428705],[-91.057621,33.445341],[-91.067623,33.455104],[-91.086498,33.451576],[-91.096723,33.437603],[-91.095211,33.417488],[-91.10717,33.399078],[-91.123623,33.387526],[-91.154017,33.378914],[-91.171968,33.38103],[-91.191127,33.389634],[-91.20922,33.40629],[-91.199354,33.418321],[-91.17628,33.416979],[-91.131885,33.430063],[-91.118495,33.449116],[-91.125109,33.472842],[-91.167403,33.498304],[-91.177148,33.48617],[-91.16936,33.452629],[-91.177293,33.443638],[-91.206807,33.433846],[-91.235181,33.438972],[-91.231661,33.4571],[-91.208535,33.468606],[-91.182901,33.502379],[-91.187367,33.510552],[-91.219297,33.532364],[-91.229834,33.547047],[-91.231418,33.560593],[-91.224121,33.567369],[-91.198285,33.572061],[-91.17822,33.582607],[-91.152148,33.582721],[-91.134043,33.594489],[-91.130445,33.606034],[-91.139209,33.625658],[-91.171168,33.647766],[-91.219048,33.661503],[-91.228228,33.671326],[-91.227857,33.683073],[-91.22057,33.692923],[-91.205377,33.700819],[-91.162464,33.70684],[-91.13045,33.674522],[-91.09404,33.658351],[-91.078507,33.658283],[-91.034565,33.673018],[-91.030402,33.687766],[-91.03612,33.689113],[-91.046778,33.706313],[-91.059891,33.714816],[-91.06829,33.716477],[-91.101101,33.705007],[-91.117733,33.705342],[-91.132338,33.714246],[-91.146618,33.732456],[-91.140756,33.759735],[-91.145112,33.76734],[-91.133854,33.782814],[-91.123466,33.782106],[-91.107318,33.770619],[-91.053886,33.778701],[-91.023285,33.762991],[-91.000106,33.769165],[-90.988466,33.78453],[-91.000107,33.799549],[-91.046849,33.815365],[-91.067511,33.840443],[-91.073011,33.857449],[-91.070883,33.866714],[-91.026382,33.90798],[-91.010318,33.929352],[-91.035961,33.943758],[-91.084095,33.956179],[-91.089787,33.966004],[-91.087921,33.975335],[-91.075378,33.983586],[-91.042751,33.986811],[-91.01889,34.003151],[-91.000108,33.966428],[-90.983359,33.960186],[-90.967632,33.963324],[-90.961548,33.979945],[-90.979945,34.000106],[-90.987948,34.019038],[-90.970726,34.02162],[-90.942662,34.01805],[-90.89242,34.02686],[-90.887413,34.032505],[-90.887837,34.055403],[-90.870528,34.080516],[-90.882628,34.096615],[-90.918395,34.093054],[-90.946323,34.109374],[-90.958467,34.125105],[-90.9543,34.138498],[-90.91001,34.165508],[-90.894385,34.160953],[-90.86458,34.140555],[-90.847168,34.136884],[-90.825708,34.142011],[-90.810884,34.155903],[-90.808685,34.175878],[-90.816572,34.183023],[-90.8556,34.18688],[-90.887884,34.18198],[-90.916048,34.196916],[-90.93522,34.21905],[-90.937152,34.23411],[-90.929015,34.244541],[-90.907082,34.244492],[-90.89456,34.22438],[-90.87912,34.21545],[-90.847808,34.20653],[-90.840009,34.223077],[-90.836972,34.250104],[-90.828267,34.27365],[-90.802928,34.282465],[-90.765165,34.280524],[-90.743082,34.302257],[-90.74061,34.313469],[-90.744713,34.324872],[-90.767108,34.345264],[-90.767061,34.360271],[-90.762085,34.364754],[-90.750107,34.367919],[-90.712088,34.363805],[-90.683222,34.368817],[-90.68162,34.35291],[-90.691551,34.338618],[-90.693129,34.32257],[-90.686003,34.315771],[-90.669343,34.31302],[-90.661395,34.315398],[-90.657488,34.322231],[-90.666862,34.348569],[-90.666788,34.35582],[-90.655346,34.371846],[-90.658542,34.375705],[-90.613944,34.390723],[-90.571145,34.420319],[-90.566505,34.429528],[-90.56733,34.440383],[-90.585477,34.461247],[-90.588942,34.491097],[-90.578493,34.516296],[-90.545728,34.53775],[-90.540736,34.548085],[-90.545891,34.563257],[-90.570133,34.587457],[-90.587224,34.615732],[-90.58344,34.641389],[-90.588536,34.668646],[-90.567334,34.693371],[-90.552317,34.697087],[-90.540074,34.684981],[-90.538061,34.673232],[-90.552642,34.659707],[-90.554129,34.640871],[-90.543696,34.629559],[-90.524481,34.628504],[-90.479718,34.659934],[-90.466041,34.674312],[-90.462552,34.687576],[-90.475194,34.700826],[-90.538974,34.698783],[-90.565646,34.721053],[-90.565437,34.736536],[-90.547606,34.744367],[-90.542695,34.752626],[-90.547612,34.784589],[-90.53651,34.798572],[-90.514706,34.801768],[-90.500994,34.771187],[-90.520556,34.753388],[-90.521004,34.738612],[-90.514735,34.729656],[-90.488865,34.723731],[-90.469897,34.72703],[-90.454968,34.735557],[-90.453038,34.753352],[-90.47459,34.7932],[-90.456935,34.823383],[-90.481955,34.857805],[-90.485038,34.869252],[-90.479872,34.883264],[-90.466154,34.890989],[-90.453916,34.891122],[-90.438313,34.884581],[-90.42898,34.867168],[-90.431741,34.855051],[-90.428754,34.8414],[-90.423879,34.834606],[-90.414864,34.831846],[-90.34038,34.860357],[-90.323067,34.846391],[-90.307384,34.846195],[-90.303698,34.859704],[-90.313476,34.871698],[-90.250095,34.90732],[-90.244725,34.921031],[-90.244476,34.937596],[-90.253969,34.954988],[-90.296422,34.976346],[-90.309297,34.995694]]]]},\"properties\":{\"name\":\"Mississippi\",\"nation\":\"USA \"}}]}","volume":"63","issue":"8","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Palumbo, Matthew D.","contributorId":146265,"corporation":false,"usgs":false,"family":"Palumbo","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Guiming","contributorId":146267,"corporation":false,"usgs":false,"family":"Wang","given":"Guiming","affiliations":[],"preferred":false,"id":776890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strickland, Bronson K.","contributorId":146266,"corporation":false,"usgs":false,"family":"Strickland","given":"Bronson","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":776891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godwin, Dave","contributorId":146268,"corporation":false,"usgs":false,"family":"Godwin","given":"Dave","email":"","affiliations":[],"preferred":false,"id":776892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dixon, P. Grady","contributorId":221067,"corporation":false,"usgs":false,"family":"Dixon","given":"P.","email":"","middleInitial":"Grady","affiliations":[],"preferred":false,"id":776893,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubin, Benjamin D.","contributorId":221068,"corporation":false,"usgs":false,"family":"Rubin","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776894,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lashley, Marcus A.","contributorId":187467,"corporation":false,"usgs":false,"family":"Lashley","given":"Marcus A.","affiliations":[],"preferred":false,"id":776895,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203193,"text":"70203193 - 2019 - The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","interactions":[],"lastModifiedDate":"2019-04-26T09:55:28","indexId":"70203193","displayToPublicDate":"2019-04-25T15:26:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","docAbstract":"We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194290,"text":"sir20175118 - 2019 - Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","interactions":[],"lastModifiedDate":"2025-05-15T13:21:20.301081","indexId":"sir20175118","displayToPublicDate":"2019-04-25T11:25:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5118","displayTitle":"Geochemical and Mineralogical Maps, with Interpretation, for Soils of the Conterminous United States","title":"Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","docAbstract":"Between 2007 and 2013, the U.S. Geological Survey conducted a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental concentration. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. This report presents all the maps and statistical information for each determined element and mineral along with an interpretive section discussing the possible processes that caused the observed national-scale geochemical and mineralogical patterns. Most often, the geochemical and mineralogical patterns reflect the composition of the underlying soil parent material with some modifications caused by leaching of the more mobile elements (for example, calcium and sodium) in the humid areas of the country.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175118","usgsCitation":"Smith, D.B., Solano, Federico, Woodruff, L.G., Cannon, W.F., and Ellefsen, K.J., 2019, Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2017-5118, https://doi.org/10.3133/sir20175118. [Available as HTML.]","productDescription":"HTML Document","onlineOnly":"Y","ipdsId":"IP-069903","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":363066,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2017/5118/"},{"id":363062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5118/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.
","language":"English","publisher":"IOPscience","doi":"10.1088/1748-9326/ab1c91","usgsCitation":"Leffler, J., Beard, K.H., Kelsey, K.C., Choi, R.T., Schmutz, J.A., and Welker, J., 2019, Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland: Environmental Research Letters, v. 14, no. 8, 084030, 11 p., https://doi.org/10.1088/1748-9326/ab1c91.","productDescription":"084030, 11 p.","ipdsId":"IP-103167","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab1c91","text":"Publisher Index Page"},{"id":363281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.625,\n 69.83962194067463\n ],\n [\n -157.58789062499997,\n 72.28906720017675\n ],\n [\n -166.2890625,\n 69.80930869552193\n ],\n [\n -168.310546875,\n 66.23145747862573\n ],\n [\n -167.607421875,\n 61.270232790000634\n ],\n [\n -158.90625,\n 55.1286490684888\n ],\n [\n -151.171875,\n 56.218923189166624\n ],\n [\n -147.91992187499997,\n 59.5343180010956\n ],\n [\n -143.525390625,\n 59.44507509904714\n ],\n [\n -134.6484375,\n 53.12040528310657\n ],\n [\n -129.814453125,\n 50.84757295365389\n ],\n [\n -129.28710937499997,\n 53.38332836757156\n ],\n [\n -128.32031249999997,\n 55.62799595426723\n ],\n [\n -134.736328125,\n 59.93300042374631\n ],\n [\n -139.482421875,\n 60.673178565817715\n ],\n [\n -140.625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leffler, Josh","contributorId":215123,"corporation":false,"usgs":false,"family":"Leffler","given":"Josh","email":"","affiliations":[{"id":39181,"text":"South Dakota State","active":true,"usgs":false}],"preferred":false,"id":761682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Karen H.","contributorId":205934,"corporation":false,"usgs":false,"family":"Beard","given":"Karen","email":"","middleInitial":"H.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Katharine C.","contributorId":195397,"corporation":false,"usgs":false,"family":"Kelsey","given":"Katharine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":761684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choi, Ryan T.","contributorId":205936,"corporation":false,"usgs":false,"family":"Choi","given":"Ryan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":761681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welker, Jeffrey","contributorId":214926,"corporation":false,"usgs":false,"family":"Welker","given":"Jeffrey","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":761685,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203164,"text":"70203164 - 2019 - Calcrete uranium deposits in the Southern High Plains, USA","interactions":[],"lastModifiedDate":"2019-04-25T05:57:51","indexId":"70203164","displayToPublicDate":"2019-04-25T05:53:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Calcrete uranium deposits in the Southern High Plains, USA","docAbstract":"The Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone. Fifteen calcrete uranium occurrences are identified; two of these, the Buzzard Draw and Sulfur Springs Draw deposits, have combined in-place resources estimated at about 4 million pounds of U3O8. Ore minerals carnotite and finchite are hosted in dolomite at the Sulfur Springs Draw deposit, with accessory fluorite, celestine, smectite/illite, autunite, and strontium carbonate. Host carbonate at the Sulfur Springs Draw deposit is ∼190 ka and mineralization mobilized as recently as 3.8 ka. Ash collected near the deposit is 631 ka and erupted from the Yellowstone caldera complex. The Triassic Dockum Group that contains sandstone-hosted uranium deposits throughout the region and underlies the SHP is a potential source for uranium and vanadium. Regional uplift and dissection reintroduced oxygenated groundwater into the Dockum Group, mobilizing uranium. Additional uranium may have been contributed to groundwater by weathering of volcanic ash in Pliocene and Pleistocene host rocks. The locations of the uranium occurrences are mostly in modern drainage systems in the southeast portion of the SHP. Modelling of modern groundwater in the SHP carried out in a parallel study shows that a single fluid could form carnotite through evaporation, and that fluids of the requisite composition are more prevalent in the southern portion of the SHP. The southeastern portion of the SHP hosts more uranium occurrences due to a variety of factors including (1) upward transport of groundwater and connectivity between source and host rock, (2) higher uranium and vanadium content of groundwater, (3) higher rates of groundwater recharge in this region to drive the mineralizing system, and (4) shallower groundwater facilitating surface evaporation. Ongoing erosion of host rocks challenges preservation of deposits and may limit their size.
Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning System (GPS), yielding up to 3 cm subsidence southwest of the mainshock epicenter and horizontal motions of
Hydrocarbon reserves and technically recoverable undiscovered resources in continuous accumulations are present in Upper Ordovician strata in the Appalachian Basin Province. The province includes parts of New York, Pennsylvania, Ohio, Maryland, West Virginia, Virginia, Kentucky, Tennessee, Georgia, and Alabama. The Upper Ordovician strata are part of the previously defined Utica-Lower Paleozoic Total Petroleum System (TPS) that extends from New York and southern Canada to Tennessee. This publication presents a revision to the hydrocarbon source rocks in the TPS, a change to the name of the TPS, and changes to the geographic extent of the Utica-Lower Paleozoic TPS. The revision to the TPS recognizes the Upper Ordovician Point Pleasant Formation as a major hydrocarbon source rock in this TPS. Consequently, the name of the TPS is changed to Ordovician Point Pleasant/Utica-Lower Paleozoic TPS. The most significant modification to the boundary of the newly defined Ordovician Point Pleasant/Utica-Lower Paleozoic TPS is a westward extension in the southwesterly portion of the TPS, adding areas in Ohio, Indiana, Kentucky, and Tennessee in order to include Ordovician strata, including potential petroleum source rocks, from the subsurface to their near-surface exposure. Also, portions of the former Utica-Lower Paleozoic TPS are now excluded from the newly defined TPS in a portion of northwestern Ohio and adjacent States to eliminate overlap with the Ordovician to Devonian Composite TPS in the Michigan basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195025","collaboration":" ","usgsCitation":"Enomoto, C.B., Trippi, M.H., and Higley, D.K., 2019, Ordovician Point Pleasant/Utica-Lower Paleozoic Total Petroleum System—Revisions to the Utica-Lower Paleozoic Total Petroleum System in the Appalachian Basin Province: U.S. Geological Survey Scientific Investigations Report 2019–5025, \n6 p., https://doi.org/10.3133/sir20195025. ","productDescription":"Report: iii, 14 p.; 1 Figure","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099699","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":363034,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5025/sir20195025.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5025"},{"id":363033,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5025/coverthb.jpg"},{"id":363035,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5025/sir20195025_fig2.pdf","text":"Figure 2","size":"345 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Correlation chart of the stratigraphic units in the Ordovician Point Pleasant/Utica-Lower Paleozoic Total Petroleum System"}],"country":"United States","otherGeospatial":"Appalachian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86,\n 36\n ],\n [\n -74,\n 36\n ],\n [\n -74,\n 43\n ],\n [\n -86,\n 43\n ],\n [\n -86,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The use of single nucleotide polymorphism (SNP) arrays to generate large SNP datasets for comparison purposes have recently become an attractive alternative to other genotyping methods. Although most SNP arrays were originally developed for domestic organisms, they can be effectively applied to wild relatives to obtain large panels of SNPs. In this study, we tested the cross-species application of the Affymetrix 600K Chicken SNP array in five species of North American prairie grouse (Centrocercus and Tympanuchus genera). Two individuals were genotyped per species for a total of ten samples. A high proportion (91%) of the total 580 961 SNPs were genotyped in at least one individual (73–76% SNPs genotyped per species). Principal component analysis with autosomal SNPs separated the two genera, but failed to clearly distinguish species within genera. Gene ontology analysis identified a set of genes related to morphogenesis and development (including genes involved in feather development), which may be primarily responsible for large phenotypic differences between Centrocercus and Tympanuchus grouse. Our study provided evidence for successful cross-species application of the chicken SNP array in grouse which diverged ca. 37 mya from the chicken lineage. As far as we are aware, this is the first reported application of a SNP array in non-passerine birds, and it demonstrates the feasibility of using commercial SNP arrays in research on non-model bird species.
Adaptive management has been applied to problems with multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values regarding system response. Hydropower applications have only recently begun to emerge in the field, yet in the specific example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite of objectives outlined in a long-term adaptive management program below R.L. Harris Dam, a large, privately owned dam in Alabama. Stakeholders convened an objective-setting workshop to engage a governance structure and developed a decision support model to determine appropriate actions that optimized stakeholder values. The process led to implemented change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To account for the iterative loop of adaptive management, yearly monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data collected were incorporated into the decision model. Specific analysis of fish and macroinvertebrate population responses indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for recreationists still exists. The project led to a Federal Energy Regulatory Commission process for renewing the license to operate the dam (beginning in 2018); adaptive management could be a viable path forward to ensure stakeholder satisfaction related to new management options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191026","collaboration":"Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive Management Stakeholders","usgsCitation":"Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026.","productDescription":"Report: x, 93 p.; 4 Appendixes; 1 Table","numberOfPages":"108","onlineOnly":"Y","ipdsId":"IP-096592","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":363058,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A2.pdf","text":"Appendix A2","size":"302 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A2","linkHelpText":"– Initial Bayesian Belief Network (2005), Training Cases and Learned Networks (2005–16)"},{"id":363057,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A1.pdf","text":"Appendix A1","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A1","linkHelpText":"– Transcripts from the Adaptive Management Workshop, April 30–May 1, 2003"},{"id":363061,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_table_C2.1.pdf","text":"Table C2.1","size":"198 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Table C2.1","linkHelpText":"– Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family and taxon"},{"id":363060,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_B.pdf","text":"Appendix B","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix B","linkHelpText":"– R code used to conduct metapopulation analyses"},{"id":363056,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026.pdf","text":"Report","size":"5.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026"},{"id":363053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1026/coverthb3.jpg"},{"id":363059,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A3.pdf","text":"Appendix A3","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A3","linkHelpText":"– Charter of the R.L. Harris Stakeholders Board"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alabama Cooperative Fish and Wildlife Research Unit
School of Forestry and Wildlife Sciences
Auburn University
602 Duncan Dr.
Auburn, AL 36849–5418
Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The dynamic, multiscale nature of stream systems makes it challenging to establish basic ecological principles to guide stream fish conservation and management. For example, finer-scale instream habitat is often constrained by coarser-scale characteristics driving observed species distributions. Additionally, instream environmental variability can result in patchy species distributions within general upstream–downstream occurrence patterns (i.e., variation around a common theme). Groundwater contribution, an often-overlooked habitat characteristic in warmwater systems, has numerous influences on the instream environment and can play a role in fish habitat-use patterns and assemblage structure. We identified multiscale instream habitat characteristics associated with the occurrence probability of 20 Ozark Highland stream fishes. Fishes were surveyed using tow-barge electrofishing in 76 channel unit complexes (i.e., riffle-to-riffle habitat sequences) nested in 20 reaches of northwest Oklahoma and southwest Missouri. We used a multiscale, multispecies generalized linear mixed model to identify relationships between fish occurrence and both channel unit complex- and reach-scale variables. Stream fishes were more likely to occur in larger or deeper channel unit complexes. Fish occurrence was also associated with different levels of reach-scale groundwater contribution, bankfull width-to-depth ratio, and percent instream cover. Ten fishes, typically associated with warmer water temperatures, had lower occurrence probabilities in reaches with higher groundwater contribution, whereas Banded Sculpin Cottus carolinae and Creek Chub Semotilus atromaculatus occurrence probabilities were higher. There was no relationship between occurrence probabilities and instream cover for 11 fishes. The occurrence probabilities in relation to varying amounts of instream cover for the other nine stream fishes was dependent on bankfull width-to-depth ratio, where the direction and magnitude of the relationships varied among stream fishes. The variation in occurrence relationships can be attributed to thermal preferences, environmental interactions, and the use of multiple habitat types. Our findings demonstrate the multiscale nature of fish occurrence relationships and how conservation and management may benefit from considering this complexity when developing holistic instream habitat enhancement strategies.
","language":"English","publisher":"American Society of Ichthyologists and Herpetologists","doi":"10.1643/CE-18-099","usgsCitation":"Mollenhauer, R., Zhou, Y., and Brewer, S.K., 2019, Multiscale habitat factors explain variability in stream fish occurrence in the Ozark Highlands ecoregion, USA: Copeia, v. 107, no. 2, p. 219-231, https://doi.org/10.1643/CE-18-099.","productDescription":"13 p.","startPage":"219","endPage":"231","ipdsId":"IP-099822","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1910400390625,\n 36.465471886798134\n ],\n [\n -93.9935302734375,\n 36.465471886798134\n ],\n [\n -93.9935302734375,\n 36.99816565700228\n ],\n [\n -95.1910400390625,\n 36.99816565700228\n ],\n [\n -95.1910400390625,\n 36.465471886798134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":274327,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Yan","contributorId":274328,"corporation":false,"usgs":false,"family":"Zhou","given":"Yan","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832909,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203640,"text":"70203640 - 2019 - Tracking legacy mercury in the Hackensack River Estuary using mercury stable isotopes","interactions":[],"lastModifiedDate":"2019-06-18T12:17:25","indexId":"70203640","displayToPublicDate":"2019-04-22T09:19:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Tracking legacy mercury in the Hackensack River Estuary using mercury stable isotopes","docAbstract":"Spatial redistribution of legacy mercury (Hg) contamination in the Hackensack River estuary (New Jersey, USA) was evaluated using mercury stable isotopes. Total Hg varied from 0.06 to 3.8 µg g-1 in sediment from the tidal Hackensack River and from 15 to 154 µg g-1 near historically contaminated sites in upper Berry's Creek, a tributary of the Hackensack River. delta202Hg values for total Hg from the Berry's Creek and Hackensack River estuaries varied over a fairly narrow range (-0.44‰ to -0.21‰), but were highest for sediment from upper Berry's Creek. Isotope mixing plots show that residual legacy mercury from upper Berry's Creek is partially diluted by a low concentration and low delta202Hg pool of mercury associated with low organic matter content sediments similar to those in Newark Bay. Based on an isotope mixing model, we estimate that upper Berry’s Creek contributes 21% to 82% of the mercury in sediments in the Hackensack River estuary and its tidal tributaries, including upstream marsh habitats far from the primary source. Our results show that mercury stable isotopes can be used to track the redistribution of mercury in tidal ecosystems and highlight the potentially large areas which may be affected by legacy mercury contamination in estuaries.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2019.04.074","usgsCitation":"Reinfelder, J.R., and Janssen, S., 2019, Tracking legacy mercury in the Hackensack River Estuary using mercury stable isotopes: Journal of Hazardous Materials, v. 375, p. 121-129, https://doi.org/10.1016/j.jhazmat.2019.04.074.","productDescription":"9 p.","startPage":"121","endPage":"129","ipdsId":"IP-106971","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":364225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.34585571289062,\n 40.45948689837198\n ],\n [\n -73.6138916015625,\n 40.45948689837198\n ],\n [\n -73.6138916015625,\n 41.3757780692323\n ],\n [\n -74.34585571289062,\n 41.3757780692323\n ],\n [\n -74.34585571289062,\n 40.45948689837198\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"375","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reinfelder, John R 0000-0002-3737-604X","orcid":"https://orcid.org/0000-0002-3737-604X","contributorId":215897,"corporation":false,"usgs":false,"family":"Reinfelder","given":"John","email":"","middleInitial":"R","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":763372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","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":763371,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215100,"text":"70215100 - 2019 - Risks of hydroclimatic regime shifts across the western United States","interactions":[],"lastModifiedDate":"2020-10-07T23:57:28.018685","indexId":"70215100","displayToPublicDate":"2019-04-19T18:48:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7146,"text":"Nature Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Risks of hydroclimatic regime shifts across the western United States","docAbstract":"Paleohydrologic reconstructions of water-year streamflow for 105 sites across the western United States (West) were used to compute the likelihood (risk) of regime (wet/dry state) shifts given the length of time in a specific regime and for a specified time in the future. The spatial variability of risks was examined and indicates that regime shift risks are variable across the West. The Pacific-Northwest region is associated with low risks of regime shifts, indicating persistence controlled by prevalent low frequency variability in flow (periods above 64 years). Other areas in the West indicate higher risks compared to the Pacific-Northwest due to flow variability in the mid-to-high frequencies (periods of 32 to 16 years). Understanding risks of regime shifts provides critical information for improved management of water supplies, particularly during periods of extended low flows. The method presented here has global applicability as a decision-making framework for risk-based planning and management.
Pennsylvania has the second highest number of residential wells of any state in the Nation with approximately 2.4 million residents that depend on groundwater for their domestic water supply. Despite the widespread reliance on groundwater in rural areas of the state, publicly available data to characterize the quality of private well water are limited. In Bradford County, more than half of the residents use groundwater from private domestic-supply wells as their primary drinking source. The quality of private well water is influenced by the regional and local setting, including the surrounding soil, geology, land use, household plumbing, and well construction. The groundwater used for domestic water supply in Bradford County is obtained primarily from shallow bedrock and from unconsolidated (glacial) deposits that overlie the bedrock. Historical land use has been predominately forested, agricultural, and residential, but more recently unconventional oil/gas development has been distributed throughout the landscape. Pennsylvania is one of only two states in the Nation without statewide water-well construction standards.
To better assess the quality of groundwater used for drinking water supply in Bradford County, data for 72 domestic wells were collected and analyzed for a wide range of constituents that could be evaluated in relation to drinking water health standards, geology, land use, and other environmental factors. Groundwater samples were collected from May through August 2016 and analyzed for physical and chemical properties, including major ions, nutrients, trace elements, volatile organic compounds, ethylene and propylene glycol, alcohols, gross-alpha/beta-particle activity, uranium, radon-222, and dissolved gases. A subset of samples was analyzed for radium isotopes (radium-226 and -228) and for the isotopic composition of methane. This study was conducted by the U.S. Geological Survey in cooperation with the Northern Tier Regional Planning and Development Commission and is part of a regional effort to characterize groundwater in rural areas of Pennsylvania.
Results of the 2016 study show that groundwater quality generally met most drinking-water standards. However, a percentage of samples failed to meet maximum contaminant levels (MCLs) for total coliform bacteria (49.3 percent), Escherichia coli (8.5 percent), barium (2.8 percent), and arsenic (2.8 percent); and secondary maximum contaminant levels (SMCL) for sodium (48.6 percent), manganese (30.6 percent), gross alpha and beta activity (16.7 percent), iron (11.1 percent), pH (8.3 percent), total dissolved solids (5.6 percent), chloride (1.4 percent), and aluminum (1.4 percent). Radon-222 activities exceeded the proposed drinking-water standard of 300 picocuries per liter (pCi/L) in 70.4 percent of the samples. There were no exceedances of drinking water health standards for any volatile organic compounds, and the only detections were for three trihalomethanes in one sample.
The pH of the groundwater had a large influence on chemical characteristics and ranged from 6.18 to 9.31. Generally, the higher pH samples had higher potential for elevated concentrations of several constituents, including total dissolved solids, sodium, lithium, chloride, fluoride, boron, arsenic, and methane. For the Bradford County well-water samples, calcium/bicarbonate type waters were most abundant, with others classified as sodium/bicarbonate or mixed water types including calcium-sodium/bicarbonate, calcium-sodium/bicarbonate-chloride, sodium/bicarbonate-chloride, sodium/bicarbonate-sulfate, or sodium/chloride types. Six principal components (pH, redox, hardness, chloride-bromide, strontium-barium, and molybdenum-arsenic) explained nearly 78.3 percent of the variance in the groundwater dataset.
Groundwater from 12.5 percent of the wells had concentrations of methane greater than the Pennsylvania action level of 7 milligrams per liter (mg/L); detectable methane concentrations ranged from 0.01 to 77 mg/L. In addition, low levels of ethane (as much as 0.13 mg/L) were present in seven samples with the highest methane concentrations. The isotopic composition of methane in five of these groundwater samples was consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source. Isotopic composition from a sixth sample suggested the methane in that sample may be of microbial origin. Well-water samples with the higher methane concentrations also had higher pH values and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Four of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported for gas and oil well brines in Pennsylvania. In several other eastern Pennsylvania counties where gas drilling is absent, groundwater with comparable chloride/bromide ratios and chloride concentrations have been reported, implying a potential natural source of brine. Most of Bradford County well-water samples have chloride concentrations less than 20 mg/L, and those with higher chloride concentrations have chloride/bromide ratios that indicate anthropogenic sources (such as road-deicing salt and septic effluent) or brine. Brines that are naturally present may originate from deeper parts of the aquifer system, whereas anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land surface.
The available data for this study indicate that no one physical factor, such as the topographic setting, well depth, or altitude at the bottom of the well, was particularly useful for predicting those well locations with an elevated dissolved concentration of methane. The 2016 assessment of groundwater quality in Bradford County shows groundwater is generally of good quality, but methane and some constituents that occur in high concentration in naturally occurring brine and also in produced waters may be present at low to moderate concentrations in groundwater in various parts of the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185170","collaboration":"Prepared in cooperation with the Northern Tier Regional Planning and Development Commission","usgsCitation":"Clune, J.W., and Cravotta, C.A., III, 2019, Drinking water health standards comparison and chemical analysis of groundwater for 72 domestic wells in Bradford County, Pennsylvania, 2016 (ver 1.2, May 30, 2019): U.S. Geological Survey Scientific Investigations Report 2018–5170, 66 p., https://doi.org/10.3133/sir20185170.","productDescription":"Report: vi, 66 p.; Data Release","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098593","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":363039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5170/coverthb4.jpg"},{"id":363132,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5170/versionHist.txt","text":"Version History","size":"1.24 KB","linkFileType":{"id":2,"text":"txt"}},{"id":363047,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VRV6US","text":"USGS data release","description":"USGS data release","linkHelpText":"Compilation of Data Not Available in the National Water Information System for Domestic Wells Sampled by the U.S. Geological Survey in Bradford County, Pennsylvania, May-August 2016"},{"id":363040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5170/sir20185170.pdf","text":"Report","size":"8.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5170"}],"country":"United States","state":"Pennsylvania","county":"Bradford County ","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-76.9291,42.0024],[-76.9095,42.0025],[-76.8966,42.0026],[-76.6476,42.0019],[-76.6334,42.0017],[-76.5964,42.0013],[-76.5618,42.0009],[-76.5531,42.0008],[-76.5229,42.0005],[-76.466,41.9999],[-76.3826,41.9989],[-76.1467,41.9991],[-76.1382,41.898],[-76.1336,41.8467],[-76.1285,41.7935],[-76.1258,41.773],[-76.1219,41.7217],[-76.1171,41.6531],[-76.1959,41.648],[-76.1996,41.6467],[-76.2015,41.6435],[-76.2015,41.6426],[-76.2015,41.6408],[-76.2016,41.6353],[-76.2016,41.6344],[-76.2023,41.6335],[-76.2029,41.6322],[-76.2063,41.6145],[-76.209,41.6004],[-76.2091,41.5982],[-76.2184,41.5579],[-76.2217,41.5447],[-76.2383,41.5458],[-76.2432,41.5463],[-76.2487,41.5468],[-76.3277,41.5526],[-76.4454,41.5608],[-76.5,41.5649],[-76.5975,41.5715],[-76.6367,41.5745],[-76.6478,41.5755],[-76.6619,41.5765],[-76.679,41.578],[-76.6938,41.579],[-76.6993,41.5795],[-76.7496,41.5834],[-76.7569,41.5839],[-76.787,41.5872],[-76.7949,41.5882],[-76.8005,41.5887],[-76.8103,41.5896],[-76.8133,41.5901],[-76.8219,41.5911],[-76.8379,41.593],[-76.8747,41.5968],[-76.8747,41.599],[-76.8805,41.6363],[-76.8833,41.6681],[-76.8838,41.6717],[-76.885,41.6781],[-76.8873,41.6999],[-76.8907,41.7267],[-76.8936,41.7503],[-76.8976,41.783],[-76.8987,41.8007],[-76.8993,41.808],[-76.9022,41.8248],[-76.9022,41.8257],[-76.9051,41.8466],[-76.9162,41.918],[-76.9209,41.9507],[-76.9238,41.9711],[-76.9291,42.0024]]]},\"properties\":{\"name\":\"Bradford\",\"state\":\"PA\"}}]}","edition":"Version 1.2: May 30, 2019; Version 1.1: April 23, 2019; Version 1.0: April 19, 2019","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070