The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (Odocoileus virginianus), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22120","usgsCitation":"Bauder, J., Anderson, C.S., Gibbs, H., Tonkovich, M., and Walter, W., 2021, Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA: Journal of Wildlife Management, v. 85, no. 8, p. 1669-1684, https://doi.org/10.1002/jwmg.22120.","productDescription":"16 p.","startPage":"1669","endPage":"1684","ipdsId":"IP-128673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-82.835118,41.708971],[-82.82572,41.72281],[-82.820409,41.724549],[-82.810487,41.720524],[-82.782719,41.694003],[-82.793069,41.664692],[-82.827011,41.633701],[-82.842099,41.628323],[-82.843602,41.647009],[-82.835118,41.708971]]],[[[-82.700208,41.61219],[-82.691123,41.611331],[-82.68015,41.61897],[-82.677772,41.617986],[-82.680669,41.594611],[-82.686033,41.587246],[-82.702027,41.585437],[-82.735766,41.600982],[-82.735707,41.603361],[-82.718802,41.619629],[-82.70731,41.619609],[-82.703438,41.617734],[-82.700208,41.61219]]],[[[-81.725583,39.215835],[-81.733357,39.205868],[-81.737085,39.193836],[-81.741533,39.189596],[-81.752754,39.184676],[-81.755754,39.180976],[-81.756254,39.177276],[-81.743565,39.141933],[-81.744838,39.130898],[-81.742153,39.116777],[-81.745453,39.098078],[-81.752353,39.089878],[-81.760753,39.084078],[-81.775554,39.078378],[-81.785554,39.078578],[-81.803055,39.083878],[-81.810655,39.083278],[-81.813855,39.079278],[-81.811655,39.059578],[-81.803355,39.047678],[-81.772854,39.026179],[-81.764253,39.015279],[-81.765153,39.002579],[-81.774062,38.993682],[-81.776723,38.985142],[-81.775734,38.980737],[-81.78182,38.964935],[-81.778845,38.955892],[-81.756975,38.937152],[-81.756131,38.933545],[-81.759995,38.925828],[-81.76976,38.92273],[-81.781248,38.924804],[-81.793372,38.930204],[-81.806137,38.942112],[-81.814235,38.946168],[-81.827354,38.945898],[-81.831516,38.943697],[-81.844486,38.928746],[-81.845312,38.910088],[-81.848653,38.901407],[-81.855971,38.892734],[-81.889233,38.874279],[-81.898541,38.874582],[-81.910312,38.879294],[-81.928,38.893492],[-81.926671,38.901311],[-81.90091,38.924338],[-81.89847,38.929603],[-81.900595,38.937671],[-81.933186,38.987659],[-81.941829,38.993295],[-81.951447,38.996032],[-81.967769,38.992955],[-81.979371,38.993193],[-81.982032,38.995697],[-81.987061,39.011978],[-81.994961,39.022478],[-82.002261,39.027878],[-82.017562,39.030078],[-82.035963,39.025478],[-82.041563,39.017878],[-82.045663,39.003778],[-82.051563,38.994378],[-82.091565,38.973778],[-82.094865,38.964578],[-82.109065,38.945579],[-82.111666,38.932579],[-82.128866,38.909979],[-82.143167,38.898079],[-82.145267,38.883479],[-82.139224,38.86502],[-82.144867,38.84048],[-82.16157,38.824632],[-82.179478,38.817376],[-82.191172,38.815137],[-82.20929,38.802672],[-82.217269,38.79568],[-82.221566,38.787187],[-82.220449,38.773739],[-82.216614,38.76835],[-82.198882,38.757725],[-82.195606,38.752441],[-82.193268,38.741182],[-82.188268,38.734082],[-82.182467,38.708782],[-82.190167,38.687382],[-82.190867,38.680383],[-82.186067,38.666783],[-82.185567,38.659583],[-82.179067,38.648883],[-82.172667,38.629684],[-82.172066,38.619284],[-82.177267,38.603784],[-82.188767,38.594984],[-82.205171,38.591719],[-82.222168,38.591384],[-82.245969,38.598483],[-82.26207,38.598183],[-82.27427,38.593683],[-82.291271,38.578983],[-82.293871,38.572683],[-82.293271,38.560283],[-82.295671,38.538483],[-82.303971,38.517683],[-82.304223,38.496308],[-82.310639,38.483172],[-82.318111,38.457876],[-82.323999,38.449268],[-82.330335,38.4445],[-82.34064,38.440948],[-82.381773,38.434783],[-82.389746,38.434355],[-82.404882,38.439347],[-82.529579,38.405182],[-82.549799,38.403202],[-82.569368,38.406258],[-82.588249,38.415489],[-82.596921,38.426705],[-82.600761,38.437425],[-82.604089,38.459841],[-82.610458,38.471457],[-82.618474,38.477089],[-82.637707,38.484449],[-82.657051,38.496816],[-82.675724,38.515504],[-82.689965,38.53592],[-82.700045,38.544336],[-82.730958,38.559264],[-82.763695,38.560399],[-82.779472,38.559023],[-82.800112,38.563183],[-82.820161,38.572703],[-82.844306,38.590862],[-82.854291,38.613454],[-82.856791,38.632878],[-82.856291,38.646078],[-82.859391,38.660378],[-82.863291,38.669277],[-82.874892,38.682827],[-82.877592,38.690177],[-82.870392,38.722077],[-82.871292,38.739376],[-82.879492,38.751476],[-82.889193,38.756076],[-82.894193,38.756576],[-82.923694,38.750076],[-82.943147,38.74328],[-82.968695,38.728776],[-82.979395,38.725976],[-83.011816,38.730057],[-83.030702,38.72572],[-83.053104,38.695831],[-83.064319,38.688976],[-83.084226,38.68109],[-83.102746,38.677316],[-83.112372,38.671685],[-83.122547,38.6592],[-83.128973,38.640231],[-83.135046,38.631719],[-83.142836,38.625076],[-83.156926,38.620547],[-83.202453,38.616956],[-83.211027,38.618578],[-83.232404,38.627569],[-83.245572,38.627936],[-83.254558,38.623403],[-83.264011,38.621535],[-83.26851,38.615104],[-83.286514,38.599241],[-83.294193,38.596588],[-83.307832,38.600824],[-83.317542,38.609242],[-83.322383,38.630615],[-83.327636,38.637489],[-83.356445,38.654009],[-83.384755,38.663171],[-83.420194,38.668428],[-83.446989,38.670143],[-83.468059,38.67547],[-83.493342,38.694187],[-83.504365,38.699256],[-83.520953,38.703045],[-83.533339,38.702105],[-83.569098,38.692842],[-83.615736,38.684145],[-83.626922,38.679387],[-83.636208,38.670584],[-83.642994,38.643273],[-83.649737,38.632753],[-83.663911,38.62793],[-83.679484,38.630036],[-83.704006,38.639724],[-83.713405,38.641591],[-83.720779,38.646704],[-83.74992,38.649613],[-83.769347,38.65522],[-83.775761,38.666748],[-83.78362,38.695641],[-83.787113,38.699489],[-83.798549,38.704668],[-83.821854,38.709575],[-83.836696,38.717857],[-83.841689,38.724264],[-83.846207,38.74229],[-83.852085,38.751433],[-83.859028,38.756793],[-83.873168,38.762418],[-83.926986,38.771562],[-83.928454,38.774583],[-83.943978,38.783616],[-83.962123,38.787384],[-83.978814,38.787104],[-84.044486,38.770572],[-84.071491,38.770475],[-84.108836,38.779247],[-84.135088,38.789485],[-84.155912,38.794909],[-84.198358,38.80092],[-84.212904,38.805707],[-84.2253,38.817665],[-84.231306,38.830552],[-84.233727,38.853576],[-84.232132,38.880483],[-84.234453,38.893226],[-84.25701,38.923208],[-84.279916,38.945168],[-84.288164,38.955789],[-84.295076,38.968295],[-84.297255,38.989694],[-84.304698,39.006455],[-84.31368,39.016981],[-84.326539,39.027463],[-84.346039,39.036963],[-84.360439,39.041362],[-84.38684,39.045162],[-84.406941,39.045662],[-84.42573,39.053059],[-84.429841,39.058262],[-84.432341,39.067561],[-84.432941,39.083961],[-84.434641,39.086861],[-84.432841,39.094261],[-84.435541,39.102261],[-84.445242,39.114461],[-84.455342,39.12036],[-84.470542,39.12146],[-84.476243,39.11916],[-84.487743,39.11076],[-84.496543,39.10026],[-84.509743,39.09366],[-84.524644,39.09216],[-84.541344,39.09916],[-84.550844,39.09936],[-84.572144,39.08206],[-84.607928,39.073238],[-84.620112,39.073457],[-84.632446,39.07676],[-84.657246,39.09546],[-84.684847,39.100459],[-84.718548,39.137059],[-84.732048,39.144458],[-84.744149,39.147458],[-84.754449,39.146658],[-84.766749,39.138558],[-84.78768,39.115297],[-84.820157,39.10548],[-84.819451,39.305152],[-84.814955,39.566251],[-84.814179,39.814212],[-84.803918,40.310094],[-84.804504,40.411555],[-84.802547,40.50181],[-84.803919,41.435531],[-84.806082,41.696089],[-84.360546,41.706621],[-83.453832,41.732647],[-83.455626,41.727445],[-83.446032,41.706847],[-83.409531,41.691247],[-83.39263,41.691947],[-83.37573,41.686647],[-83.357073,41.687763],[-83.341817,41.693518],[-83.337985,41.698682],[-83.337977,41.70341],[-83.326825,41.701562],[-83.293928,41.680846],[-83.29068,41.676794],[-83.238191,41.651167],[-83.23166,41.644218],[-83.194524,41.631008],[-83.145887,41.617904],[-83.103928,41.613558],[-83.086036,41.60668],[-83.066593,41.59534],[-83.043287,41.568205],[-83.028072,41.555656],[-82.999916,41.538534],[-82.96985,41.524327],[-82.934369,41.514353],[-82.897728,41.519241],[-82.875229,41.529684],[-82.85677,41.548262],[-82.855197,41.564114],[-82.859531,41.576371],[-82.852957,41.583327],[-82.834101,41.587587],[-82.820207,41.570664],[-82.794324,41.546486],[-82.785496,41.540675],[-82.77201,41.54058],[-82.749907,41.54647],[-82.717878,41.54193],[-82.711332,41.53695],[-82.711632,41.527201],[-82.721914,41.516677],[-82.713904,41.501697],[-82.710013,41.49759],[-82.687921,41.492324],[-82.658302,41.461878],[-82.617745,41.431833],[-82.616952,41.428425],[-82.55808,41.400005],[-82.513827,41.384257],[-82.499099,41.381541],[-82.481214,41.381342],[-82.460599,41.386316],[-82.431315,41.396866],[-82.398086,41.413945],[-82.361784,41.426644],[-82.334182,41.430243],[-82.29158,41.428442],[-82.268479,41.430842],[-82.254678,41.434441],[-82.193375,41.46454],[-82.184774,41.47404],[-82.181598,41.471634],[-82.165373,41.47444],[-82.094169,41.495039],[-82.011966,41.515639],[-81.994565,41.51444],[-81.964912,41.505446],[-81.958463,41.498642],[-81.937862,41.491443],[-81.87736,41.483445],[-81.860262,41.483841],[-81.810992,41.495592],[-81.794449,41.49663],[-81.782258,41.49605],[-81.744755,41.48715],[-81.738755,41.48855],[-81.710986,41.501734],[-81.664884,41.53143],[-81.633652,41.540458],[-81.599747,41.560649],[-81.529955,41.614374],[-81.50044,41.623448],[-81.48864,41.631348],[-81.466038,41.649148],[-81.441339,41.674074],[-81.353229,41.727743],[-81.301626,41.750543],[-81.286925,41.760243],[-81.264224,41.758143],[-81.248672,41.761291],[-81.112885,41.817571],[-81.092716,41.822988],[-81.05192,41.839557],[-81.024525,41.846469],[-81.01049,41.853962],[-80.936244,41.862352],[-80.900342,41.868912],[-80.814943,41.897694],[-80.799822,41.909749],[-80.784682,41.911525],[-80.782052,41.906635],[-80.757945,41.913352],[-80.720816,41.919744],[-80.581882,41.95761],[-80.519461,41.977513],[-80.518991,40.638801],[-80.551126,40.628847],[-80.56784,40.617552],[-80.576736,40.614224],[-80.583633,40.61552],[-80.592049,40.622496],[-80.598764,40.625263],[-80.627171,40.619936],[-80.634355,40.616095],[-80.651716,40.597744],[-80.662564,40.5916],[-80.667957,40.582496],[-80.666917,40.573664],[-80.652436,40.562544],[-80.633107,40.538705],[-80.627507,40.535793],[-80.618003,40.502049],[-80.609058,40.489506],[-80.599194,40.482566],[-80.595494,40.475266],[-80.596094,40.463366],[-80.612295,40.434867],[-80.612195,40.402667],[-80.615195,40.399867],[-80.632196,40.393667],[-80.633596,40.390467],[-80.631596,40.385468],[-80.619196,40.381768],[-80.609695,40.374968],[-80.607595,40.369568],[-80.612796,40.347668],[-80.610796,40.340868],[-80.602895,40.327869],[-80.599895,40.317669],[-80.602895,40.307069],[-80.614896,40.291969],[-80.616796,40.285269],[-80.616196,40.27227],[-80.619297,40.26517],[-80.622497,40.26177],[-80.644598,40.25127],[-80.652098,40.24497],[-80.661543,40.229798],[-80.6681,40.199671],[-80.6726,40.192371],[-80.686137,40.181607],[-80.704602,40.154823],[-80.710042,40.138311],[-80.710554,40.125271],[-80.706702,40.110872],[-80.709102,40.101472],[-80.713003,40.096872],[-80.730704,40.086472],[-80.738604,40.075672],[-80.737104,40.064972],[-80.733104,40.058772],[-80.730904,40.046672],[-80.731504,40.037472],[-80.737389,40.027593],[-80.741901,40.007929],[-80.738717,39.985113],[-80.740126,39.970793],[-80.758527,39.959241],[-80.764479,39.95025],[-80.761312,39.929098],[-80.756784,39.920586],[-80.756432,39.91393],[-80.759296,39.909466],[-80.762592,39.908906],[-80.772641,39.911306],[-80.782849,39.917162],[-80.795714,39.91969],[-80.806018,39.91713],[-80.809283,39.910314],[-80.809011,39.903226],[-80.802339,39.89161],[-80.793989,39.882787],[-80.790156,39.872252],[-80.790761,39.86728],[-80.799898,39.858912],[-80.821279,39.849982],[-80.826124,39.844238],[-80.826228,39.835802],[-80.82248,39.824971],[-80.822181,39.811708],[-80.826079,39.798584],[-80.835311,39.79069],[-80.863048,39.775197],[-80.869092,39.766364],[-80.869933,39.763555],[-80.865339,39.753251],[-80.854717,39.742592],[-80.846091,39.737812],[-80.831551,39.719475],[-80.829764,39.711839],[-80.831871,39.705655],[-80.839112,39.701033],[-80.854599,39.697473],[-80.863698,39.691724],[-80.86633,39.683167],[-80.866647,39.652616],[-80.876002,39.627084],[-80.88036,39.620706],[-80.892208,39.616756],[-80.91762,39.618703],[-80.925841,39.617396],[-80.936906,39.612616],[-80.970436,39.590127],[-80.993695,39.571253],[-81.020055,39.55541],[-81.030169,39.545211],[-81.044902,39.5363],[-81.070594,39.515991],[-81.100833,39.487175],[-81.114433,39.466275],[-81.132534,39.446275],[-81.138134,39.443775],[-81.152534,39.443175],[-81.170634,39.439175],[-81.185946,39.430731],[-81.190714,39.423562],[-81.208231,39.407147],[-81.211433,39.402031],[-81.211654,39.392977],[-81.217315,39.38759],[-81.223581,39.386062],[-81.24184,39.390276],[-81.270716,39.385914],[-81.281405,39.379258],[-81.297517,39.374378],[-81.347567,39.34577],[-81.375961,39.341697],[-81.384556,39.343449],[-81.393794,39.351706],[-81.406689,39.38809],[-81.412706,39.394618],[-81.435642,39.408474],[-81.446543,39.410374],[-81.456143,39.409274],[-81.473188,39.40017],[-81.489044,39.384074],[-81.503189,39.373242],[-81.542346,39.352874],[-81.557547,39.338774],[-81.565047,39.293874],[-81.565247,39.276175],[-81.570247,39.267675],[-81.585559,39.268747],[-81.59516,39.273387],[-81.608408,39.276043],[-81.621305,39.273643],[-81.656138,39.277355],[-81.678331,39.273755],[-81.689483,39.266043],[-81.69638,39.257035],[-81.696636,39.246123],[-81.691067,39.230139],[-81.692395,39.226443],[-81.700908,39.220844],[-81.725583,39.215835]]]]},\"properties\":{\"name\":\"Ohio\",\"nation\":\"USA \"}}]}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan M.","contributorId":288641,"corporation":false,"usgs":false,"family":"Bauder","given":"Javan M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":838159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christine S.","contributorId":288642,"corporation":false,"usgs":false,"family":"Anderson","given":"Christine","email":"","middleInitial":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, H. Lisle","contributorId":288643,"corporation":false,"usgs":false,"family":"Gibbs","given":"H. Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tonkovich, Michael J.","contributorId":288644,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Michael J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":838162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838158,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224927,"text":"70224927 - 2021 - Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement","interactions":[],"lastModifiedDate":"2021-10-05T12:14:42.13764","indexId":"70224927","displayToPublicDate":"2021-09-03T07:09:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement","docAbstract":"Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Livebearers in the family Poeciliidae are some of the most widely introduced fishes. Native poeciliid translocations within the U.S. are mostly due to deliberate stocking for mosquito control. Introductions of exotic poeciliids, those not native to the U.S., are more likely to be due to release from aquaria or escape from farms. Many of these non-natives originate from warm climate regions, contrasting with the relatively cold climates in the U.S. Thus, thermal springs may increase the possible range of these species. Our primary objective was to examine the importance of climate and thermal springs in affecting the distribution of translocated and non-native poeciliids in the U.S. This objective was addressed using a national database of poeciliid introductions. Records were dominated by a handful of states and most introductions led to established populations. While translocated mosquitofish were found across many states and climates, non-natives were found almost exclusively in warm climate states and territories (e.g., Florida, Hawaii, Puerto Rico), especially where air temperatures remained above freezing. Outside warm climate states, 46% of established non-native populations were located at thermal spring sources. These results indicate that thermal springs extend the distribution of non-natives, but were relatively unimportant for translocated poeciliids.
A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.
The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR.
A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report.
An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet.
The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data.
With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205150","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Koczot, K.M., Risley, J.C., Gronberg, J.M., Donovan, J.M., and McPherson, K.R., 2021, Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013: U.S. Geological Survey Scientific Investigations Report 2020–5150, 61 p., https://doi.org/10.3133/sir20205150.","productDescription":"Report: ix, 61 p.; 1 Figure: 16.0 x 10.0 inches; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-028665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":388739,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KFR","linkHelpText":"Archive of Merced River Basin Precipitation-Runoff Modeling System, with forecasting, climate-file preparation, and data-visualization tools"},{"id":388738,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150_fig11_sheet.pdf","text":"Figure 11 (16\" x 10\" sheet)","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Physical architecture of the Merced River Basin Precipitation-Runoff Modeling System."},{"id":388737,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5150/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Merced River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.3055419921875,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 36.88401445049676\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Airborne electromagnetic surveys provide spatially extensive resistivity information that can be useful for groundwater salinity mapping; however, the transformation from geophysical data to salinity interpretations carries uncertainty. We compare two quantitative approaches to salinity mapping recently applied to address water resource management objectives: the location of the depth to the freshwater-brine interface at Paradox Valley, Colorado, and 3D categorical mapping of fresh, brackish, and saline groundwater near oil and gas fields of the San Joaquin Valley, California. These different approaches were driven by a combination of the availability of water quality observations, the hydrogeologic setting, and study objectives.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"First international meeting for applied geoscience & energy expanded abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"First International Meeting for Applied Geoscience & Energy (IMAGE ’21)","conferenceDate":"Sep 26-Oct1, 2021","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/segam2021-3584073.1","usgsCitation":"Ball, L.B., and Minsley, B.J., 2021, Incorporating uncertainty into groundwater salinity mapping using AEM data, in First international meeting for applied geoscience & energy expanded abstracts, Sep 26-Oct1, 2021, p. 3105-3109, https://doi.org/10.1190/segam2021-3584073.1.","productDescription":"5 p.","startPage":"3105","endPage":"3109","ipdsId":"IP-128031","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":399677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ball, Lyndsay B. 0000-0002-6356-4693 lbball@usgs.gov","orcid":"https://orcid.org/0000-0002-6356-4693","contributorId":1138,"corporation":false,"usgs":true,"family":"Ball","given":"Lyndsay","email":"lbball@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841358,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237357,"text":"70237357 - 2021 - LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","interactions":[],"lastModifiedDate":"2022-10-11T15:49:16.703697","indexId":"70237357","displayToPublicDate":"2021-09-01T10:40:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","docAbstract":"Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105101","usgsCitation":"Moore, T.N., Mesman, J., Ladwig, R., Feldbauer, J., Olsson, F., Pilla, R.M., Shatwell, T., Venkiteswaran, J.J., Delany, A.D., Dugan, H., Rose, K.C., and Read, J., 2021, LakeEnsemblR: An R package that facilitates ensemble modelling of lakes: Environmental Modelling & Software, v. 143, 105101, 14 p., https://doi.org/10.1016/j.envsoft.2021.105101.","productDescription":"105101, 14 p.","ipdsId":"IP-122731","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":450973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105101","text":"Publisher Index Page"},{"id":408161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Tadhg N.","contributorId":297476,"corporation":false,"usgs":false,"family":"Moore","given":"Tadhg","email":"","middleInitial":"N.","affiliations":[{"id":64406,"text":"Dundalk Institute of Technology, Centre for Freshwater and Environmental Studies, Dundalk, Co. Louth, Ireland","active":true,"usgs":false}],"preferred":false,"id":854248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mesman, Jorrit P.","contributorId":297477,"corporation":false,"usgs":false,"family":"Mesman","given":"Jorrit P.","affiliations":[{"id":64408,"text":"University of Geneva, Department F.A. Forel for Environmental and Aquatic Sciences, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":854249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":854250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feldbauer, Johannes 0000-0002-8238-5375","orcid":"https://orcid.org/0000-0002-8238-5375","contributorId":268217,"corporation":false,"usgs":false,"family":"Feldbauer","given":"Johannes","email":"","affiliations":[{"id":55600,"text":"Technische Universität Dresden","active":true,"usgs":false}],"preferred":false,"id":854251,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olsson, Freya","contributorId":297478,"corporation":false,"usgs":false,"family":"Olsson","given":"Freya","email":"","affiliations":[{"id":64410,"text":"UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Bailrigg, Lancaster, UK","active":true,"usgs":false}],"preferred":false,"id":854252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilla, Rachel M. 0000-0001-9156-9486","orcid":"https://orcid.org/0000-0001-9156-9486","contributorId":261758,"corporation":false,"usgs":false,"family":"Pilla","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":854253,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shatwell, Tom","contributorId":297279,"corporation":false,"usgs":false,"family":"Shatwell","given":"Tom","email":"","affiliations":[{"id":64343,"text":"Helmholtz Centre for Environmental Research - UFZ, Department Lake Research, Magdeburg, Germany","active":true,"usgs":false}],"preferred":false,"id":854254,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Venkiteswaran, Jason J.","contributorId":297479,"corporation":false,"usgs":false,"family":"Venkiteswaran","given":"Jason","email":"","middleInitial":"J.","affiliations":[{"id":64411,"text":"Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854255,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delany, Austin D.","contributorId":297480,"corporation":false,"usgs":false,"family":"Delany","given":"Austin","email":"","middleInitial":"D.","affiliations":[{"id":64412,"text":"University of Wisconsin – Madison, Center for Limnology, Madison, Wisconsin, USA","active":true,"usgs":false}],"preferred":false,"id":854256,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dugan, Hilary","contributorId":150191,"corporation":false,"usgs":false,"family":"Dugan","given":"Hilary","affiliations":[{"id":17938,"text":"Center for Limnology University of Wisconsin, Madison, WI 53706, US","active":true,"usgs":false}],"preferred":false,"id":854257,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kevin C.","contributorId":174809,"corporation":false,"usgs":false,"family":"Rose","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":854258,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854259,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70224964,"text":"70224964 - 2021 - Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","interactions":[],"lastModifiedDate":"2021-10-11T15:41:58.169094","indexId":"70224964","displayToPublicDate":"2021-09-01T10:37:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","docAbstract":"Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic-terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high-frequency oxygen, temperature, and carbon dioxide (CO2) data to infer watershed connectivity in Boulder Creek, a mid-sized (1160 km2) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients (K600) were paired with high-frequency, in-situ dissolved CO2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated (ρ = −0.72, p ≪ 0.001) during the non-snowmelt period and NEP was frequently negative. Mean FCO2 during the non-snowmelt period was approximately 302 (±171) mmol C m−2 d−1 and was primarily supported by watershed CO2 inputs. During snowmelt, GPP and ER were not significantly correlated (ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non-snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher FCO2 (843 ± 338 mmol C m−2 d−1) and greater allochthonous CO2 inputs than during non-snowmelt periods, emphasizing the effects of seasonal differences in aquatic-terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006296","usgsCitation":"Reed, A.P., Stets, E.G., Murphy, S.F., and Mullins, E., 2021, Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt: Journal of Geophysical Research Biogeosciences, v. 126, no. 9, e2021JG006296, 16 p., https://doi.org/10.1029/2021JG006296.","productDescription":"e2021JG006296, 16 p.","ipdsId":"IP-113327","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450975,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006296","text":"Publisher Index Page"},{"id":436214,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P991TMNQ","text":"USGS data release","linkHelpText":"Modeled Stream Metabolism in Boulder Creek near Boulder, CO (2016 - 2018)"},{"id":390389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","otherGeospatial":"Boulder Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.43922424316406,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 39.95343802330847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Ariel P. 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":219992,"corporation":false,"usgs":true,"family":"Reed","given":"Ariel","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":824894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":824895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Emily 0000-0002-6710-0327","orcid":"https://orcid.org/0000-0002-6710-0327","contributorId":219993,"corporation":false,"usgs":true,"family":"Mullins","given":"Emily","email":"","affiliations":[],"preferred":true,"id":824896,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229691,"text":"70229691 - 2021 - Seasonal and age-related variation in daily travel distances of California Condors","interactions":[],"lastModifiedDate":"2022-03-15T14:33:38.031134","indexId":"70229691","displayToPublicDate":"2021-09-01T09:17:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and age-related variation in daily travel distances of California Condors","docAbstract":"Despite a dramatic recovery from the brink of extinction, California Condors (Gymnogyps californianus) still face significant anthropogenic threats. Although condor movement patterns across large temporal scales are understood, less is known about their movements on a fine temporal scale. We used a trajectory-based analysis of GPS telemetry data gathered from condors during 2013 to 2018 to investigate the relationship between the distances condors travel in a day, demographic characteristics (e.g., age and sex), and time of year. Most (>71.4%) daily travel distances by condors were <100 km, and, on average, condors traveled 70.1 ± 60.9 km/d (x̄ ± SD). On two occasions one condor traveled >400 km in a single day (477 km one day and 415 km the following day). The tendency for condors to travel long distances increased with age, and condors traveled longer distances during the summer and when nesting. Traveling such long distances likely exposes birds to threats across a greater variety of landscapes than would be expected for birds that moved shorter distances. Given anticipated condor range expansion and population increase, this work highlights the importance of coordinating condor conservation across the broad spatial scales at which they move.
","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/JRR-20-100","usgsCitation":"Hall, J.C., Hong, I., Poessel, S.A., Braham, M., Brandt, J., Burnett, J., and Katzner, T., 2021, Seasonal and age-related variation in daily travel distances of California Condors: Journal of Raptor Research, v. 55, no. 3, p. 388-398, https://doi.org/10.3356/JRR-20-100.","productDescription":"11 p.","startPage":"388","endPage":"398","ipdsId":"IP-113752","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":397110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Hopper Mountain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.97021484374999,\n 36.4477991295848\n ],\n [\n -121.9482421875,\n 36.31512514748051\n ],\n [\n -121.827392578125,\n 36.19995805932895\n ],\n [\n -121.695556640625,\n 36.12012758978146\n ],\n [\n -121.56372070312499,\n 35.97800618085566\n ],\n [\n -120.465087890625,\n 35.23664622093195\n ],\n [\n -120.16845703125,\n 34.43409789359469\n ],\n [\n -119.70703125,\n 34.34343606848294\n ],\n [\n -119.50927734374999,\n 34.31621838080741\n ],\n [\n -117.32299804687499,\n 33.94335994657882\n ],\n [\n -117.83935546874999,\n 35.24561909420681\n ],\n [\n -118.14697265625,\n 36.27970720524017\n ],\n [\n -118.377685546875,\n 36.76529191711624\n ],\n [\n -118.85009765625,\n 37.43997405227057\n ],\n [\n -119.454345703125,\n 38.16911413556086\n ],\n [\n -119.84985351562499,\n 38.19502155795575\n ],\n [\n -119.90478515625,\n 37.640334898059486\n ],\n [\n -119.94873046875,\n 37.413800350662896\n ],\n [\n -119.32250976562499,\n 36.80928470205937\n ],\n [\n -119.014892578125,\n 36.35052700542763\n ],\n [\n -118.85009765625,\n 35.862343734896484\n ],\n [\n -118.817138671875,\n 35.31736632923788\n ],\n [\n -119.46533203125,\n 35.209721645221386\n ],\n [\n -120.02563476562501,\n 35.77325759103725\n ],\n [\n -120.77270507812499,\n 36.659606226479696\n ],\n [\n -120.89355468749999,\n 36.94989178681327\n ],\n [\n -121.77246093750001,\n 36.94111143010769\n ],\n [\n -121.97021484374999,\n 36.4477991295848\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Jonathan C.","contributorId":202606,"corporation":false,"usgs":false,"family":"Hall","given":"Jonathan","email":"","middleInitial":"C.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":837968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hong, Insu","contributorId":288470,"corporation":false,"usgs":false,"family":"Hong","given":"Insu","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":837967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":837972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":837969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burnett, Joseph","contributorId":127741,"corporation":false,"usgs":false,"family":"Burnett","given":"Joseph","email":"","affiliations":[{"id":7132,"text":"Ventana Wildlife Society, Salinas, CA","active":true,"usgs":false}],"preferred":false,"id":837971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":837973,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228264,"text":"70228264 - 2021 - Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","interactions":[],"lastModifiedDate":"2023-06-09T14:08:15.215958","indexId":"70228264","displayToPublicDate":"2021-09-01T08:49:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","docAbstract":"Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy. Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2021.08.014","usgsCitation":"Enwright, N., Kranenburg, C.J., Patton, B., Dalyander, P., Brown, J., Piazza, S., and Cheney, W.C., 2021, Developing bare-earth digital elevation models from structure-from-motion data on barrier islands: ISPRS Journal of Photogrammetry and Remote Sensing, v. 180, p. 269-282, https://doi.org/10.1016/j.isprsjprs.2021.08.014.","productDescription":"14 p.; Data Release","startPage":"269","endPage":"282","ipdsId":"IP-127598","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450987,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2021.08.014","text":"Publisher Index Page"},{"id":436215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RA15I0","text":"USGS data release","linkHelpText":"Barrier island vegetation and elevation survey, Dauphin Island, AL, 2018-19"},{"id":395611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417857,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PX0O3"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33969116210938,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"180","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217794,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":833556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piazza, Sarai 0000-0001-6962-9008","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":220329,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cheney, Wyatt C 0000-0003-1009-8411","orcid":"https://orcid.org/0000-0003-1009-8411","contributorId":274998,"corporation":false,"usgs":false,"family":"Cheney","given":"Wyatt","email":"","middleInitial":"C","affiliations":[{"id":56693,"text":"Cheney Consulting at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":833559,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224576,"text":"70224576 - 2021 - Breeding waterbird populations have declined in south San Francisco Bay: An assessment over two decades","interactions":[],"lastModifiedDate":"2021-09-29T13:25:22.066754","indexId":"70224576","displayToPublicDate":"2021-09-01T08:17:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Breeding waterbird populations have declined in south San Francisco Bay: An assessment over two decades","docAbstract":"In south San Francisco Bay, former salt ponds now managed as wildlife habitat support large populations of breeding waterbirds. In 2006, the South Bay Salt Pond Restoration Project began the process of converting 50% to 90% of these managed pond habitats into tidal marsh. We compared American Avocet (Recurvirostra americana) and Black-necked Stilt (Himantopus mexicanus) abundance in south San Francisco Bay before (2001) and after approximately 1,300 ha of managed ponds were breached to tidal action to begin tidal marsh restoration (2019). Over the 18-year period, American Avocet abundance declined 13.5% (2,765 in 2001 vs. 2,391 in 2019), and Black-necked Stilt abundance declined 30.0% (1,184 in 2001 vs. 828 in 2019). Forster’s Tern (Sterna forsteri) abundance was 2,675 birds in 2019. In 2019, managed ponds accounted for only 25.8% of suitable habitats, yet contained 53.9%, 38.6%, and 65.6% American Avocet, Black-necked Stilt, and Forster’s Tern observations, respectively. Conversely, tidal marsh and tidal mudflats accounted for 42.9% of suitable habitats, yet contained only 18.4%, 10.3%, and 19.8% of American Avocet, Black-necked Stilt, and Forster’s Tern observations, respectively. Using a separate nest-monitoring data set, we found that nest abundance in south San Francisco Bay declined for all three species from 2005–2019. Average annual nest abundance during 2017–2019 declined 53%, 71%, and 36%, for American Avocets, Back-necked Stilts, and Forster’s Terns, respectively, compared to 2005–2007. Loss of island nesting habitat as a result of tidal marsh conversion and an increasing population of predatory California Gulls (Larus californicus) are two potential causes of these declines. All three species established nesting colonies on newly constructed islands within remaining managed ponds; however, these new colonies did not make up for the steep declines observed at other historical nesting sites. For future wetland restoration, retaining more managed ponds that contain islands suitable for nesting may help to limit further declines in breeding waterbird populations.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss3art4","usgsCitation":"Hartman, C.A., Ackerman, J.T., Schacter, C.R., Herzog, M.P., Tarjan, M., Wang, Y., Strong, C., Tertes, R., and Warnock, N., 2021, Breeding waterbird populations have declined in south San Francisco Bay: An assessment over two decades: San Francisco Estuary and Watershed Science, v. 19, no. 3, 4, 28 p., https://doi.org/10.15447/sfews.2021v19iss3art4.","productDescription":"4, 28 p.","ipdsId":"IP-120016","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450993,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss3art4","text":"Publisher Index Page"},{"id":436216,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94RYHZL","text":"USGS data release","linkHelpText":"Breeding Waterbird Populations in South San Francisco Bay 2005-2019"},{"id":389947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"south San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.12745666503905,\n 37.63380988687157\n ],\n [\n -122.23045349121094,\n 37.59954417809496\n ],\n [\n -122.28263854980467,\n 37.567984011320256\n ],\n [\n -122.33207702636717,\n 37.53042087175374\n ],\n [\n -122.13569641113281,\n 37.38707192644979\n ],\n [\n -121.981201171875,\n 37.35924242260126\n ],\n [\n -121.87202453613281,\n 37.388708634542056\n ],\n [\n -121.88713073730469,\n 37.46777358281261\n ],\n [\n -121.99905395507812,\n 37.61042389163107\n ],\n [\n -122.08419799804689,\n 37.65120864327176\n ],\n [\n -122.12745666503905,\n 37.63380988687157\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schacter, Carley Rose 0000-0001-5493-2768","orcid":"https://orcid.org/0000-0001-5493-2768","contributorId":266023,"corporation":false,"usgs":true,"family":"Schacter","given":"Carley","email":"","middleInitial":"Rose","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tarjan, Max","contributorId":266024,"corporation":false,"usgs":false,"family":"Tarjan","given":"Max","affiliations":[{"id":54860,"text":"San Francisco Bay Bird Observatory Milpitas, CA 95035 USA","active":true,"usgs":false}],"preferred":false,"id":824136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Yiwei","contributorId":203687,"corporation":false,"usgs":false,"family":"Wang","given":"Yiwei","email":"","affiliations":[{"id":17738,"text":"San Francisco Bay Bird Observatory","active":true,"usgs":false}],"preferred":false,"id":824137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strong, Cheryl","contributorId":149428,"corporation":false,"usgs":false,"family":"Strong","given":"Cheryl","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":824138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tertes, Rachel","contributorId":266025,"corporation":false,"usgs":false,"family":"Tertes","given":"Rachel","email":"","affiliations":[{"id":54861,"text":"US Fish and Wildlife Service Don Edwards San Francisco Bay National Wildlife Refuge Fremont, CA 94536 USA","active":true,"usgs":false}],"preferred":false,"id":824139,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Warnock, Nils","contributorId":64534,"corporation":false,"usgs":false,"family":"Warnock","given":"Nils","email":"","affiliations":[],"preferred":false,"id":824140,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225763,"text":"70225763 - 2021 - Hydrological control shift from river level to rainfall in the reactivated Guobu slope besides the Laxiwa hydropower station in China","interactions":[],"lastModifiedDate":"2021-11-10T13:09:25.303331","indexId":"70225763","displayToPublicDate":"2021-09-01T07:02:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Hydrological control shift from river level to rainfall in the reactivated Guobu slope besides the Laxiwa hydropower station in China","docAbstract":"Landslides are common geohazards associated with natural drivers such as precipitation, land degradation, toe erosion by rivers and wave attack, and ground shaking. On the other hand, human alterations such as inundation by water impoundment or rapid drawdown may also destabilize the surrounding slopes. The Guobu slope is an ancient rockslide on the banks of the Laxiwa hydropower station reservoir (China), which reactivated during the reservoir impoundment in 2009. We extracted three-dimensional surface displacements with azimuth and range radar interferometry using European Space Agency's Copernicus Sentinel-1 and German Aerospace Center's TerraSAR-X data during 20152019. The upper part of the Guobu rockslide is characterized by toppling and is mostly subsiding with maximum rates over 0.4 m/yr and 0.7 m/yr in the vertical and horizontal directions, respectively. During filling of the reservoir prior to 2014, there was a long-wavelength in-phase response between rising reservoir level and GPS-observed increased slope movements. After the reservoir water level stabilized from 2015 to 2019, the slide movement became seasonal and we see a correlation between rainfall and landslide movement. These observations suggest that the slide motion is now primarily controlled by rainfall. The spatiotemporal landslide displacements allow us to estimate the hydraulic diffusivity of the rock mass, to be on the order (~1.05 × 10‐7 m2/s) and the thickness of the moving rock mass (~200 m). Our results demonstrate that InSAR is a useful tool for monitoring the rockslide movement as a function of seasonal precipitation.
Two datasets containing the first complete estimates of reach-scale nutrient, water use, dissolved oxygen, and pH conditions for the Pacific drainages of the United States were created to help inform water-quality management decisions in that region. The datasets were developed using easily obtainable watershed data, most of which have not been available until recently, and the techniques that were used provide a framework for integrating watershed data to assess water-quality impairment across other large hydrologic regions in the United States. These datasets were used to summarize regional nutrient and water-use conditions within impaired water bodies and to summarize regional dissolved oxygen concentrations and pH conditions for free-flowing stream reaches. Two examples are also presented that show how the datasets can be applied to specific water-quality management issues: (1) nutrient conditions in water bodies that have recently experienced problems with harmful algal blooms; and (2) dissolved oxygen and pH conditions in stream reaches likely to be populated by steelhead trout (Oncorhynchus mykiss irideus) during their summer run. The nutrient and water-use estimates could help inform actions aimed at managing water-quality conditions in impaired water bodies while the dissolved oxygen and pH predictions could be useful as screening tools to identify water bodies experiencing potential impairment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215087","programNote":"National Water Quality Program","usgsCitation":"Wise, D.R., 2021, Using regional watershed data to assess water-quality impairment in the Pacific Drainages of the United States: U.S. Geological Survey Scientific Investigations Report 2021–5087, 29 p., https://doi.org/10.3133/sir20215087.","productDescription":"vii, 29 p.","onlineOnly":"Y","ipdsId":"IP-123766","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436221,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B3BQOW","text":"USGS data release","linkHelpText":"Reach-scale estimates of nutrient, water use, dissolved oxygen, and pH conditions in the Pacific drainages of the United States"},{"id":388699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5087/coverthb.jpg"},{"id":388700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5087/sir20215087.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5087"}],"country":"United States","state":"California, Idaho, Montana, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 49.03786794532644\n ],\n [\n -123.04687499999999,\n 48.545705491847464\n ],\n [\n -124.541015625,\n 48.48748647988415\n ],\n [\n -124.93652343749999,\n 48.28319289548349\n ],\n [\n -124.23339843749999,\n 46.76996843356982\n ],\n [\n -124.18945312500001,\n 45.42929873257377\n ],\n [\n -124.71679687499999,\n 43.83452678223682\n ],\n [\n -124.76074218749999,\n 42.5530802889558\n ],\n [\n -124.49707031249999,\n 41.47566020027821\n ],\n [\n -124.71679687499999,\n 40.34654412118006\n ],\n [\n -124.18945312500001,\n 39.639537564366684\n ],\n [\n -123.96972656249999,\n 38.993572058209466\n ],\n [\n -123.00292968749999,\n 38.03078569382294\n ],\n [\n -122.03613281249999,\n 36.27970720524017\n ],\n [\n -121.025390625,\n 35.02999636902566\n ],\n [\n -120.4541015625,\n 34.34343606848294\n ],\n [\n -118.91601562499999,\n 34.05265942137599\n ],\n [\n -117.68554687499999,\n 33.46810795527896\n ],\n [\n -117.20214843749999,\n 32.54681317351514\n ],\n [\n -116.4111328125,\n 32.62087018318113\n ],\n [\n -116.54296874999999,\n 33.211116472416855\n ],\n [\n -117.6416015625,\n 34.08906131584994\n ],\n [\n -118.0810546875,\n 34.23451236236987\n ],\n [\n -118.21289062499999,\n 35.96022296929667\n ],\n [\n -118.91601562499999,\n 36.70365959719456\n ],\n [\n -119.970703125,\n 37.92686760148135\n ],\n [\n -120.41015624999999,\n 38.61687046392973\n ],\n [\n -120.58593749999999,\n 39.470125122358176\n ],\n [\n -120.58593749999999,\n 40.27952566881291\n ],\n [\n -121.11328124999999,\n 41.07935114946899\n ],\n [\n -121.6845703125,\n 42.35854391749705\n ],\n [\n -121.59667968749999,\n 42.94033923363181\n ],\n [\n -121.46484375,\n 43.96119063892024\n ],\n [\n -121.11328124999999,\n 44.77793589631623\n ],\n [\n -120.2783203125,\n 44.33956524809713\n ],\n [\n -119.4873046875,\n 43.8028187190472\n ],\n [\n -118.69628906249999,\n 42.5530802889558\n ],\n [\n -118.0810546875,\n 41.83682786072714\n ],\n [\n -117.158203125,\n 41.07935114946899\n ],\n [\n -116.1474609375,\n 40.78054143186033\n ],\n [\n -114.2138671875,\n 41.343824581185686\n ],\n [\n -113.466796875,\n 42.00032514831621\n ],\n [\n -111.884765625,\n 42.35854391749705\n ],\n [\n -111.005859375,\n 42.4234565179383\n ],\n [\n -109.6875,\n 42.5530802889558\n ],\n [\n -109.4677734375,\n 43.26120612479979\n ],\n [\n -109.951171875,\n 44.05601169578525\n ],\n [\n -110.7861328125,\n 44.55916341529182\n ],\n [\n -112.06054687499999,\n 44.59046718130883\n ],\n [\n -112.8515625,\n 44.43377984606822\n ],\n [\n -113.73046875,\n 45.24395342262324\n ],\n [\n -114.0380859375,\n 45.460130637921004\n ],\n [\n -113.203125,\n 45.73685954736049\n ],\n [\n -112.236328125,\n 46.40756396630067\n ],\n [\n -112.19238281249999,\n 47.39834920035926\n ],\n [\n -113.64257812499999,\n 48.980216985374994\n ],\n [\n -122.958984375,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Spilogale putorius (Eastern Spotted Skunk) is a small, secretive carnivore that has substantially declined throughout the eastern United States since the mid-1900s. To better understand the current status of Eastern Spotted Skunks, we studied survival and reproduction of the S. p. putorius (Appalachian Spotted Skunk) subspecies across 4 states in the central and southern Appalachian Mountains from 2014 to 2020. Using encounter histories from 99 radio-collared Appalachian Spotted Skunks in a Kaplan–Meier known-fate survival analysis, we calculated a mean annual adult survival rate of 0.58. We did not find support for this survival rate varying by sex, predator cover (canopy cover and topographic ruggedness), or climate. Compared to estimates of survival from previous research, our data suggest that Appalachian Spotted Skunk survival is intermediate to the S. p. interrupta (Plains Spotted Skunk) and S. p. ambarvalis (Florida Spotted Skunk) subspecies of Eastern Spotted Skunk. We located 11 Appalachian Spotted Skunk natal dens and estimated mean litter size to be 2.8 juveniles per female. We used a Lefkovitch matrix to identify the most important demographic rates and found that adult survivorship had the largest impact on the population growth rate. These results provide important demographic information for future Eastern Spotted Skunk population viability analyses and can serve as a baseline for future comparative assessments of the effects of management interventions on the species.
","language":"English","publisher":"Humboldt Field Research Institute","doi":"10.1656/058.020.0sp1110","usgsCitation":"Butler, A.R., Edelman, A., Eng, R.Y., Harris, S.N., Olfenbuttel, C., Thorne, E., Ford, W., and Jachowski, D.S., 2021, Demography of the Appalachian Spotted Skunk (Spilogale putorius putorius): Southeastern Naturalist, v. 20, no. SP11, p. 95-109, https://doi.org/10.1656/058.020.0sp1110.","productDescription":"15 p.","startPage":"95","endPage":"109","ipdsId":"IP-120641","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451013,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/111968","text":"External Repository"},{"id":393589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, North Carolina, South Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.03369140625,\n 32.62087018318113\n ],\n [\n -85.10009765625,\n 32.62087018318113\n ],\n [\n -85.10009765625,\n 34.95799531086792\n ],\n [\n -87.03369140625,\n 34.95799531086792\n ],\n [\n -87.03369140625,\n 32.62087018318113\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.462890625,\n 34.542762387234845\n ],\n [\n -80.85937499999999,\n 34.542762387234845\n ],\n [\n -80.85937499999999,\n 36.527294814546245\n ],\n [\n -84.462890625,\n 36.527294814546245\n ],\n [\n -84.462890625,\n 34.542762387234845\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.11181640625,\n 36.61552763134925\n ],\n [\n -78.50830078125,\n 36.61552763134925\n ],\n [\n -78.50830078125,\n 38.92522904714054\n ],\n [\n -82.11181640625,\n 38.92522904714054\n ],\n [\n -82.11181640625,\n 36.61552763134925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"SP11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Ragheb, Erin Hewett","contributorId":270650,"corporation":false,"usgs":false,"family":"Ragheb","given":"Erin","email":"","middleInitial":"Hewett","affiliations":[],"preferred":false,"id":829653,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Butler, Andrew R.","contributorId":270595,"corporation":false,"usgs":false,"family":"Butler","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edelman, Andrew J.","contributorId":270596,"corporation":false,"usgs":false,"family":"Edelman","given":"Andrew J.","affiliations":[{"id":56182,"text":"University of West Georgia","active":true,"usgs":false}],"preferred":false,"id":829601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eng, Robin Y. Y.","contributorId":270597,"corporation":false,"usgs":false,"family":"Eng","given":"Robin","email":"","middleInitial":"Y. Y.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Stephen N.","contributorId":270598,"corporation":false,"usgs":false,"family":"Harris","given":"Stephen","email":"","middleInitial":"N.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olfenbuttel, Colleen","contributorId":270649,"corporation":false,"usgs":false,"family":"Olfenbuttel","given":"Colleen","email":"","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":829652,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Emily D.","contributorId":270599,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":829604,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":829599,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jachowski, David S.","contributorId":270600,"corporation":false,"usgs":false,"family":"Jachowski","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829605,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230258,"text":"70230258 - 2021 - An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","interactions":[],"lastModifiedDate":"2022-04-06T14:19:46.516429","indexId":"70230258","displayToPublicDate":"2021-08-31T09:11:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","title":"An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","docAbstract":"Conservation efforts need reliable information concerning the status of a species and their trends to help identify which species are in most need of assistance. We completed a comparative evaluation of the occurrence of breeding for Cascades Frog (Rana cascadae), an amphibian that is being considered for federal protection under the U.S. Endangered Species Act. Specifically, in 2018–2019 we resurveyed 67 sites that were surveyed approximately 15 y prior and fit occupancy models to quantify the distribution of R. cascadae breeding in the Cascade Range, Oregon, USA. Furthermore, we conducted a simulation exercise to assess the power of sampling designs to detect declines in R. cascadae breeding at these sites. Our analysis of field data combined with our simulation results suggests that if there was a decline in the proportion of sites used for R. cascadae breeding in Oregon, it was likely a < 20% decline across our study period. Our results confirm that while R. cascadae detection probabilities are high, methods that allow the sampling process to be explicitly modeled are necessary to reliably track the status of the species. This study demonstrates the usefulness of investing in baseline information and data quality standards to increase capacity to make similar comparisons for other species in a timeframe that meet the needs of land managers and policy makers.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Duarte, A., Pearl, C., McCreary, B., Rowe, J., and Adams, M.J., 2021, An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA: Herpetological Conservation and Biology, v. 16, no. 2, p. 361-373.","productDescription":"13 p.","startPage":"361","endPage":"373","ipdsId":"IP-127196","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":398216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398175,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol16_issue2.html"}],"country":"United States","state":"Oregon","otherGeospatial":"Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.398681640625,\n 45.537136680398596\n ],\n [\n -122.82714843749999,\n 44.88701247981298\n ],\n [\n -123.07983398437499,\n 44.07969327425713\n ],\n [\n -123.26660156249999,\n 42.58544425738491\n ],\n [\n -123.145751953125,\n 42.00848901572399\n ],\n [\n -121.61865234375,\n 42.00032514831621\n ],\n [\n -121.77246093750001,\n 42.98053954751642\n ],\n [\n -121.278076171875,\n 44.134913443750726\n ],\n [\n -121.025390625,\n 45.034714778688624\n ],\n [\n -121.124267578125,\n 45.68315803253308\n ],\n [\n -121.57470703125,\n 45.744526980468436\n ],\n [\n -121.871337890625,\n 45.729191061299915\n ],\n [\n -122.398681640625,\n 45.537136680398596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":839736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCreary, Brome 0000-0002-0313-7796 brome_mccreary@usgs.gov","orcid":"https://orcid.org/0000-0002-0313-7796","contributorId":3130,"corporation":false,"usgs":true,"family":"McCreary","given":"Brome","email":"brome_mccreary@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowe, Jennifer 0000-0002-5253-2223 jrowe@usgs.gov","orcid":"https://orcid.org/0000-0002-5253-2223","contributorId":172670,"corporation":false,"usgs":true,"family":"Rowe","given":"Jennifer","email":"jrowe@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839740,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224304,"text":"70224304 - 2021 - Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","interactions":[],"lastModifiedDate":"2021-11-16T15:44:27.13098","indexId":"70224304","displayToPublicDate":"2021-08-31T07:54:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","docAbstract":"Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971–2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971–2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.
The Black River flows through southeast Missouri and northeast Arkansas to its confluence with the White River in Arkansas. The U.S. Army Corps of Engineers operates Clearwater Dam on the Black River and a series of dams in the White River Basin primarily for flood control. In this study, the hydrology and geomorphology of the Black River are examined through an analysis of annual mean and peak discharges at streamgages, a specific stage analysis of stage and discharge at streamgages, and an examination of bathymetric data and aerial imagery. Five streamgages on the Black River were analyzed, in addition to four streamgages on Black River tributaries and one streamgage on the White River, located just downstream from the Black River confluence. The analyses indicated that regulation of discharges at the flood-control dams caused a decrease in the magnitude and variability of the peak discharges at several of the analyzed gages on the Black and White Rivers. Conversely, peak discharges on the Black River have been increasing since water year 2000, though this is not matched by an increase in peak discharges on the White River for the same time period. The specific stage analyses and the available morphologic data generally did not indicate pronounced changes in stage-discharge relations at streamgages on the Black River, with the exception of the gages nearest to Clearwater Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215067","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"LeRoy, J.Z., Huizinga, R.J., Heimann, D.C., Lindroth, E.M., and Doyle, H.F., 2021, Historical hydrologic and geomorphic conditions on the Black River and selected tributaries, Arkansas and Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5067, 72 p., https://doi.org/10.3133/sir20215067.","productDescription":"Report: ix, 72 p.; Appendix; Dataset","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114034","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":388646,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5067/images","description":"SIR 2021–5067 Images"},{"id":388645,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.xml","text":"Report","size":"298 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5067"},{"id":388644,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5067/coverthb.jpg"},{"id":388642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5067/sir20215067.pdf","text":"Report","size":"7.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5067"},{"id":388643,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5067/downloads","text":"Appendix Tables 1.0 through 1.10 (.csv and .xlsx formats)"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Black River and selected tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.51611328125,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 35.585851593232384\n ],\n [\n -89.95605468749999,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 37.317751851636906\n ],\n [\n -91.51611328125,\n 35.585851593232384\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.
Groundwater-dependent ecosystems and species (GDEs) are found throughout watersheds at locations of groundwater discharge, yet not all GDEs are the same, nor are the groundwater systems supporting them. Groundwater moves along a variety of flow paths of different lengths and with different contributing areas, ranging from shorter local flow paths with low discharge and large seasonal variability to streams, springs and wetlands to longer regional flow paths with potentially larger discharge and low seasonal variability, commonly at low basin elevations. How does this variation in physical hydrology affect the type and distribution of GDEs? Using data on hypsographic position, groundwater-dependent species distributions, groundwater pumping and streamflow from Oregon, USA, we provide a conceptual model and initial supporting evidence demonstrating that spatial variation in groundwater flow path scales, illustrated using basin hypsography, is a driver of non-random distribution of GDEs across watersheds. Further, we posit that the spatial variation in primary stressors to groundwater (e.g. pumping and climate change) will differentially affect GDEs depending on their hypsographic position. Furthermore, because of their use for irrigation and municipal water supply, regional groundwater systems and associated species are more likely to be studied and receive regulatory protection. Our initial data point to a disproportionate focus on larger discharge, lower elevation GDEs, which leads to a bias in our understanding of the full suite of biodiversity associated with groundwater discharge as well as their stressors and potential mechanisms for protection.
One of the largest historical earthquakes in the U.S. Pacific Northwest occurred on December 15, 1872 near the south end of Lake Chelan. Lack of recognized surface deformation suggested that the earthquake occurred on a blind, perhaps deep, fault. New LiDAR data revealed a NW-side-up scarp along the north side of Spencer Canyon near Entiat, Washington. Landslides triggered during the earthquake impounded small ponds in Spencer Canyon; the larger of the two landslides obliterated a portion of the scarp. Tree-ring counts show that the oldest trees on each landslide are 130 and 128 years old, and lend credence to the idea that the earthquake triggered the landslides. Trenches across the scarp exposed a NW-dipping thrust fault offsetting young soils and Mesozoic bedrock. Radiocarbon and tree ring data shows that the last fault movement was between 1856 and 1873 CE, and was most likely during the 1872 CE earthquake.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL093318","usgsCitation":"Sherrod, B.L., Blakely, R., and Weaver, C., 2021, LiDAR and paleoseismology solve earthquake mystery in the Pacific Northwest, USA: Geophysical Research Letters, v. 48, no. 16, e2021GL093318, 9 p., https://doi.org/10.1029/2021GL093318.","productDescription":"e2021GL093318, 9 p.","ipdsId":"IP-097563","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487175,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl093318","text":"Publisher Index Page"},{"id":482509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Idaho, Montana, Oregon, Washington","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -128.51232932665914,\n 52.80136491932822\n ],\n [\n -128.51232932665914,\n 42.07755395865152\n ],\n [\n -111.38444038177542,\n 42.07755395865152\n ],\n [\n -111.38444038177542,\n 52.80136491932822\n ],\n [\n -128.51232932665914,\n 52.80136491932822\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"16","noUsgsAuthors":false,"publicationDate":"2021-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blakely, Richard J.","contributorId":351509,"corporation":false,"usgs":false,"family":"Blakely","given":"Richard J.","affiliations":[{"id":36625,"text":"Emeritus","active":true,"usgs":false}],"preferred":false,"id":928753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weaver, Craig S.","contributorId":224057,"corporation":false,"usgs":false,"family":"Weaver","given":"Craig S.","affiliations":[],"preferred":false,"id":928754,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}