Wind energy development is rapidly expanding in North America, often accompanied by requirements to survey potential facility locations for existing wildlife. Within the USA, golden eagles (Aquila chrysaetos) are among the most high-profile species of birds that are at risk from wind turbines. To minimize golden eagle fatalities in areas proposed for wind development, modified point count surveys are usually conducted to estimate use by these birds. However, it is not always clear what drives variation in the relationship between on-site point count data and actual use by eagles of a wind energy project footprint. We used existing GPS-GSM telemetry data, collected at 15 min intervals from 13 golden eagles in 2012 and 2013, to explore the relationship between point count data and eagle use of an entire project footprint. To do this, we overlaid the telemetry data on hypothetical project footprints and simulated a variety of point count sampling strategies for those footprints. We compared the time an eagle was found in the sample plots with the time it was found in the project footprint using a metric we called “error due to sampling”. Error due to sampling for individual eagles appeared to be influenced by interactions between the size of the project footprint (20, 40, 90 or 180 km2) and the sampling type (random, systematic or stratified) and was greatest on 90 km2 plots. However, use of random sampling resulted in lowest error due to sampling within intermediate sized plots. In addition sampling intensity and sampling frequency both influenced the effectiveness of point count sampling. Although our work focuses on individual eagles (not the eagle populations typically surveyed in the field), our analysis shows both the utility of simulations to identify specific influences on error and also potential improvements to sampling that consider the context-specific manner that point counts are laid out on the landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2018.01.024","usgsCitation":"Sur, M., Belthoff, J.R., Bjerre, E.R., Millsap, B.A., and Katzner, T., 2018, The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles: Ecological Indicators, v. 88, p. 126-133, https://doi.org/10.1016/j.ecolind.2018.01.024.","productDescription":"8 p.","startPage":"126","endPage":"133","ipdsId":"IP-081663","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468711,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2018.01.024","text":"Publisher Index Page"},{"id":354710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.5667,\n 33.4333\n ],\n [\n -115.3833,\n 33.4333\n ],\n [\n -115.3833,\n 36.1333\n ],\n [\n -118.5667,\n 36.1333\n ],\n [\n -118.5667,\n 33.4333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1ad","contributors":{"authors":[{"text":"Sur, Maitreyi","contributorId":191354,"corporation":false,"usgs":false,"family":"Sur","given":"Maitreyi","email":"","affiliations":[],"preferred":false,"id":737147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":737148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bjerre, Emily R.","contributorId":205390,"corporation":false,"usgs":false,"family":"Bjerre","given":"Emily","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":737149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Millsap, Brian A.","contributorId":205391,"corporation":false,"usgs":false,"family":"Millsap","given":"Brian","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":737150,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":737146,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197403,"text":"70197403 - 2018 - Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration","interactions":[],"lastModifiedDate":"2018-07-03T11:10:56","indexId":"70197403","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration","docAbstract":"Assessing nutrient stores in avian species is important for understanding the extent to which body condition influences success or failure in life‐history events. We evaluated predictive models using morphometric characteristics to estimate total body lipids (TBL) and total body protein (TBP), based on traditional proximate analyses, in spring migrating lesser snow geese (Anser caerulescens caerulescens) and Ross's geese (A. rossii). We also compared performance of our lipid model with a previously derived predictive equation for TBL developed for nesting lesser snow geese. We used external and internal measurements on 612 lesser snow and 125 Ross's geese collected during spring migration in 2015 and 2016 within the Central and Mississippi flyways to derive and evaluate predictive models. Using a validation data set, our best performing lipid model for snow geese better predicted TBL (root mean square error [RMSE] of 23.56) compared with a model derived from nesting individuals (RMSE = 48.60), suggesting the importance of season‐specific models for accurate lipid estimation. Models that included body mass and abdominal fat deposit best predicted TBL determined by proximate analysis in both species (lesser snow goose, R2 = 0.87, RMSE = 23.56: Ross's geese, R2 = 0.89, RMSE = 13.75). Models incorporating a combination of external structural measurements in addition to internal muscle and body mass best predicted protein values (R2 = 0.85, RMSE = 19.39 and R2 = 0.85, RMSE = 7.65, lesser snow and Ross's geese, respectively), but protein models including only body mass and body size were also competitive and provided extended utility to our equations for field applications. Therefore, our models indicated the importance of specimen dissection and measurement of the abdominal fat pad to provide the most accurate lipid estimates and provide alternative dissection‐free methods for estimating protein.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.867","usgsCitation":"Webb, E.B., Fowler, D.N., Woodall, B.A., and Vrtiska, M.P., 2018, Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration: Wildlife Society Bulletin, v. 42, no. 2, p. 295-303, https://doi.org/10.1002/wsb.867.","productDescription":"9 p.","startPage":"295","endPage":"303","ipdsId":"IP-086754","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499991,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b506db9d3c3242bc8767f8d690d7f8c1","text":"External Repository"},{"id":354662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Nebraska, South Dakota","volume":"42","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d6fe4b092d9651e1ae0","contributors":{"authors":[{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fowler, Drew N.","contributorId":205356,"corporation":false,"usgs":false,"family":"Fowler","given":"Drew","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":737053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodall, Brendan A.","contributorId":205358,"corporation":false,"usgs":false,"family":"Woodall","given":"Brendan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":737054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vrtiska, Mark P.","contributorId":54008,"corporation":false,"usgs":true,"family":"Vrtiska","given":"Mark","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":737055,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197445,"text":"70197445 - 2018 - Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon","interactions":[],"lastModifiedDate":"2018-06-12T11:03:14","indexId":"70197445","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5708,"text":"Arcata Fisheries Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"TR 2018-33","title":"Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon","docAbstract":"An area of great importance to resource management and conservation biology in the Klamath Basin is balancing water usage against the life history requirements of threatened Coho Salmon. One tool for addressing this topic is a freshwater dynamics model to forecast Coho Salmon productivity based on environmental inputs. Constructing such a forecasting tool requires local data to quantify the unique life history processes of Coho Salmon inhabiting this region. Here, we describe analytical methods for estimating a series of sub-models, each capturing a different life history process, which will eventually be synchronized as part of a freshwater dynamics model for Klamath River Coho Salmon. Specifically, we draw upon extensive population monitoring data collected in the basin to estimate models of freshwater productivity, overwinter survival, and migration patterns. Our models of freshwater productivity indicated that high summer temperatures and high winter flows can both adversely affect smolt production and that such relationships are more likely in tributaries with naturally regulated flows due to substantial intraannual environmental variation. Our models of overwinter survival demonstrated extensive variability in survival among years, but not among rearing locations, and demonstrated that a substantial proportion (~ 20%) of age-0+ fish emigrate from some rearing sites in the winter. Our models of migration patterns indicated that many age-0+ fish redistribute in the basin during the summer and winter. Further, we observed that these redistributions can entail long migrations in the mainstem where environmental stressors likely play a role in cueing refuge entry. Finally, our models of migration patterns indicated that changes in discharge are important in cueing the seaward migration of smolts, but that the nature of this behavioral response can differ dramatically between tributaries with naturally and artificially regulated flows. Collectively, these analyses demonstrate that environmental variation interacts with most phases of the freshwater life history of Klamath River Coho Salmon and that anthropogenic environmental variation can have a particularly large bearing on productivity.
","language":"English","publisher":"U.S. Fish and Wildlife Service, Arcata Fish and Wildlife Office","usgsCitation":"Manhard, C.V., Som, N.A., Perry, R.W., Faukner, J., and Soto, T., 2018, Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon: Arcata Fisheries Technical Report TR 2018-33, x, 74 p.","productDescription":"x, 74 p.","numberOfPages":"84","ipdsId":"IP-088669","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354702,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/arcata/fisheries/reports/technical/2018/EstimatingFreshwaterProductivityOverwinterSurvivalandMigrationPatternsofKlamathRiverCohoSalmon.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1ab","contributors":{"authors":[{"text":"Manhard, Christopher V.","contributorId":203911,"corporation":false,"usgs":false,"family":"Manhard","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":36754,"text":"U.S. Fish and Wildlife Service, California Cooperative Fish and Wildlife Research Unit, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA","active":true,"usgs":false}],"preferred":false,"id":737180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":737181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faukner, Jimmy","contributorId":205405,"corporation":false,"usgs":false,"family":"Faukner","given":"Jimmy","email":"","affiliations":[{"id":37098,"text":"Yurok Tribal Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soto, Toz","contributorId":205406,"corporation":false,"usgs":false,"family":"Soto","given":"Toz","email":"","affiliations":[{"id":37099,"text":"Karuk Tribe Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737183,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197446,"text":"70197446 - 2018 - Estimation of stream conditions in tributaries of the Klamath River, northern California","interactions":[],"lastModifiedDate":"2018-06-12T11:11:00","indexId":"70197446","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5708,"text":"Arcata Fisheries Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"TR 2018-32","title":"Estimation of stream conditions in tributaries of the Klamath River, northern California","docAbstract":"Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge.\n\nObserved temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams.\n\nKlamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.","language":"English","publisher":"U.S. Fish and Wildlife Service. Arcata Fish and Wildlife Office","usgsCitation":"Manhard, C.V., Som, N.A., Jones, E.C., and Perry, R.W., 2018, Estimation of stream conditions in tributaries of the Klamath River, northern California: Arcata Fisheries Technical Report TR 2018-32, vi, 28 p.","productDescription":"vi, 28 p.","numberOfPages":"34","ipdsId":"IP-088667","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354703,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/arcata/fisheries/reports/technical/2018/EstimationofStreamConditionsinTributariesoftheKlamathRiverNorthernCalifornia.pdf"}],"country":"United States","state":"California","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.667,\n 41\n ],\n [\n -122.3333,\n 41\n ],\n [\n -122.3333,\n 42\n ],\n [\n -123.667,\n 42\n ],\n [\n -123.667,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1a9","contributors":{"authors":[{"text":"Manhard, Christopher V.","contributorId":203911,"corporation":false,"usgs":false,"family":"Manhard","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":36754,"text":"U.S. Fish and Wildlife Service, California Cooperative Fish and Wildlife Research Unit, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA","active":true,"usgs":false}],"preferred":false,"id":737185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":737186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Edward C. 0000-0001-7255-1475 ejones@usgs.gov","orcid":"https://orcid.org/0000-0001-7255-1475","contributorId":203917,"corporation":false,"usgs":true,"family":"Jones","given":"Edward","email":"ejones@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737184,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197407,"text":"70197407 - 2018 - Movement behavior preceding autumn mortality for white-tailed deer in central New York","interactions":[],"lastModifiedDate":"2018-06-01T09:08:06","indexId":"70197407","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Movement behavior preceding autumn mortality for white-tailed deer in central New York","docAbstract":"A common yet largely untested assumption in the theory of animal movements is that increased rates and a wider range of movements, such as occurs during breeding, make animals more vulnerable to mortality. We examined mortality among 34 white-tailed deer (Odocoileus virginianus) wearing GPS collars during the autumn breeding season of 2006 and 2007 in a heavily hunted, forest-agricultural landscape of central New York state. We evaluated whether individuals having higher rates of movement incurred higher rates of mortality and whether mortality risk was higher when deer were in less familiar areas. We used a Cox proportional hazards model to analyze how mortality risk changes with movement rates measured over 3 time periods: < 1 day, up to 2 weeks prior to death, and 3–4 weeks prior to death. Overall, deer increased their movement rates as autumn progressed, males more so than females. However, deer that died moved at a slower rate relative to surviving deer up to 2 weeks prior to death (ß = -2.22 ± 0.81; 95% confidence interval [CI] = -3.91 to -0.51) and a slower rate on their day of death compared to deer that survived (ß = -1.77 ± 0.73; 95% CI = -3.19 to -0.33). Site familiarity was not significantly related to mortality risk. Deer were equally likely to die within their 50% core use area as elsewhere within their autumn home range. We hypothesize that increased sociality associated with breeding may make animals more vulnerable to harvest mortality. Our findings contradict general assumptions about the influences of movement behavior on mortality risk, suggesting that patterns may be sensitive to the spatiotemporal context of the movement analysis.","language":"English","doi":"10.1093/jmammal/gyy023","usgsCitation":"Whitman, B.J., Porter, W.F., Dechen Quinn, A.C., Williams, D.M., Frair, J.L., Underwood, H.B., and Crawford, J.C., 2018, Movement behavior preceding autumn mortality for white-tailed deer in central New York: Journal of Mammalogy, v. 99, no. 3, p. 675-683, https://doi.org/10.1093/jmammal/gyy023.","productDescription":"9 p.","startPage":"675","endPage":"683","ipdsId":"IP-091546","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyy023","text":"Publisher Index Page"},{"id":354660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","county":"Cortland, Madison, Oneida, Onondaga","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-75.8896,42.7238],[-75.8728,42.5016],[-75.8636,42.4139],[-76.0195,42.4075],[-76.0196,42.4129],[-76.1258,42.4107],[-76.2549,42.4082],[-76.266,42.6221],[-76.2745,42.7712],[-76.2752,42.7803],[-76.2784,42.7857],[-76.2968,42.8079],[-76.3554,42.8502],[-76.4498,42.8464],[-76.4585,42.9391],[-76.4644,43],[-76.4657,43.0059],[-76.4927,43.0052],[-76.4994,43.0766],[-76.4997,43.1002],[-76.4991,43.1003],[-76.4972,43.1039],[-76.4929,43.1067],[-76.486,43.1058],[-76.4809,43.1054],[-76.4765,43.1045],[-76.4741,43.1073],[-76.4754,43.1164],[-76.48,43.1277],[-76.4852,43.139],[-76.4852,43.1422],[-76.484,43.1445],[-76.4803,43.1454],[-76.4759,43.1473],[-76.4734,43.1496],[-76.4754,43.1591],[-76.4808,43.2273],[-76.3243,43.2325],[-76.318,43.2321],[-76.3137,43.2316],[-76.3105,43.2312],[-76.3074,43.2308],[-76.3023,43.2294],[-76.2966,43.2258],[-76.2903,43.2199],[-76.2864,43.2136],[-76.2819,43.205],[-76.2775,43.2032],[-76.2737,43.2041],[-76.2719,43.2064],[-76.2657,43.2133],[-76.2632,43.2183],[-76.2632,43.2219],[-76.2665,43.2278],[-76.269,43.2337],[-76.2735,43.2382],[-76.2716,43.2396],[-76.2666,43.2401],[-76.2628,43.2383],[-76.2603,43.2369],[-76.2571,43.2351],[-76.2526,43.2293],[-76.2444,43.2193],[-76.2374,43.2162],[-76.2292,43.2117],[-76.2191,43.2076],[-76.2128,43.2072],[-76.2091,43.2099],[-76.2072,43.2141],[-76.2029,43.2168],[-76.2016,43.2164],[-76.1985,43.2164],[-76.1972,43.2173],[-76.1966,43.2187],[-76.1985,43.2209],[-76.2017,43.2245],[-76.2036,43.2286],[-76.2062,43.235],[-76.2081,43.2382],[-76.2107,43.2436],[-76.2108,43.2518],[-76.2103,43.2677],[-76.2072,43.2723],[-76.2028,43.2723],[-76.1965,43.271],[-76.1908,43.2674],[-76.1825,43.2592],[-76.1686,43.2488],[-76.161,43.2461],[-76.1509,43.243],[-76.1302,43.2395],[-76.1264,43.2368],[-76.122,43.2359],[-76.1163,43.235],[-76.1119,43.2314],[-76.1062,43.2277],[-76.103,43.2259],[-76.0999,43.2255],[-76.0961,43.2251],[-76.0873,43.2215],[-76.074,43.2174],[-76.0696,43.217],[-76.0684,43.2147],[-76.0708,43.2134],[-76.0734,43.2133],[-76.0753,43.2147],[-76.0772,43.2156],[-76.0797,43.2147],[-76.0809,43.2133],[-76.0815,43.2115],[-76.0815,43.2101],[-76.0809,43.2092],[-76.0808,43.206],[-76.0808,43.2047],[-76.0802,43.2033],[-76.0764,43.2024],[-76.0714,43.2024],[-76.0651,43.2029],[-76.0601,43.2029],[-76.0576,43.2029],[-76.0551,43.2039],[-76.0513,43.2052],[-76.0463,43.2044],[-76.0431,43.2035],[-76.04,43.2021],[-76.0374,43.1994],[-76.0343,43.1962],[-76.0311,43.194],[-76.0255,43.1931],[-76.0192,43.1917],[-76.0141,43.1899],[-76.0097,43.1872],[-76.0078,43.1836],[-76.0053,43.1827],[-76.0015,43.1822],[-75.9971,43.1832],[-75.9902,43.1855],[-75.9877,43.1868],[-75.9858,43.1864],[-75.9846,43.1837],[-75.9839,43.181],[-75.9808,43.18],[-75.977,43.1805],[-75.9726,43.1819],[-75.96,43.1824],[-75.9588,43.1819],[-75.9594,43.1801],[-75.9594,43.1756],[-75.9575,43.1738],[-75.9499,43.1733],[-75.9424,43.1752],[-75.9368,43.177],[-75.9336,43.1775],[-75.9317,43.177],[-75.9286,43.1739],[-75.9292,43.1693],[-75.9285,43.1657],[-75.9273,43.1639],[-75.9241,43.1625],[-75.8845,43.1572],[-75.864,43.3205],[-75.8861,43.3259],[-75.8163,43.4845],[-75.7576,43.471],[-75.5349,43.4212],[-75.4599,43.4548],[-75.2628,43.5452],[-75.1103,43.6154],[-75.0977,43.497],[-75.0785,43.3327],[-75.0898,43.3295],[-75.0924,43.3263],[-75.0943,43.3241],[-75.1006,43.3236],[-75.105,43.3159],[-75.1082,43.3118],[-75.1113,43.3105],[-75.1183,43.3109],[-75.1195,43.31],[-75.1208,43.3082],[-75.1214,43.3064],[-75.1252,43.306],[-75.1271,43.3082],[-75.134,43.3078],[-75.1429,43.3042],[-75.1473,43.2997],[-75.1517,43.2874],[-75.1613,43.2637],[-75.1607,43.2592],[-75.1588,43.2578],[-75.1557,43.2574],[-75.1544,43.256],[-75.1532,43.2528],[-75.1525,43.2501],[-75.1507,43.2487],[-75.1481,43.2505],[-75.1456,43.2537],[-75.1431,43.2546],[-75.1381,43.2514],[-75.1299,43.2468],[-75.1199,43.2404],[-75.1092,43.2372],[-75.0998,43.2331],[-75.0904,43.2317],[-75.0847,43.2321],[-75.0804,43.233],[-75.0766,43.2344],[-75.0734,43.2344],[-75.0722,43.2321],[-75.0703,43.228],[-75.1525,43.1245],[-75.1803,43.0969],[-75.22,43.0542],[-75.2121,42.9991],[-75.2054,42.9377],[-75.2019,42.8836],[-75.2019,42.8804],[-75.2119,42.88],[-75.2451,42.8783],[-75.2445,42.8714],[-75.2433,42.861],[-75.2427,42.8542],[-75.2433,42.8451],[-75.2427,42.8401],[-75.244,42.8364],[-75.2421,42.8355],[-75.2415,42.8319],[-75.2422,42.8287],[-75.2447,42.8273],[-75.2459,42.8255],[-75.2466,42.8228],[-75.2466,42.8196],[-75.2453,42.8164],[-75.2447,42.8137],[-75.2479,42.8046],[-75.2504,42.7878],[-75.2523,42.7828],[-75.2567,42.7787],[-75.263,42.7746],[-75.268,42.7715],[-75.2742,42.7619],[-75.2793,42.7524],[-75.2818,42.751],[-75.2868,42.7492],[-75.2893,42.7465],[-75.2905,42.7429],[-75.3174,42.7415],[-75.426,42.7362],[-75.4272,42.7439],[-75.4372,42.7435],[-75.5676,42.7371],[-75.6413,42.7352],[-75.7779,42.7273],[-75.8154,42.7254],[-75.8896,42.7238]]]},\"properties\":{\"name\":\"Cortland\",\"state\":\"NY\"}}]}","volume":"99","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-16","publicationStatus":"PW","scienceBaseUri":"5b155d6ee4b092d9651e1adc","contributors":{"authors":[{"text":"Whitman, Brigham J.","contributorId":205351,"corporation":false,"usgs":false,"family":"Whitman","given":"Brigham","email":"","middleInitial":"J.","affiliations":[{"id":37087,"text":"Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":737047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Porter, W. F.","contributorId":205352,"corporation":false,"usgs":false,"family":"Porter","given":"W.","email":"","middleInitial":"F.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":737048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dechen Quinn, Amy C.","contributorId":205353,"corporation":false,"usgs":false,"family":"Dechen Quinn","given":"Amy","email":"","middleInitial":"C.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":737049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, David M.","contributorId":205354,"corporation":false,"usgs":false,"family":"Williams","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":737050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frair, Jacqueline L.","contributorId":140184,"corporation":false,"usgs":false,"family":"Frair","given":"Jacqueline","email":"","middleInitial":"L.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":737051,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Underwood, H. Brian 0000-0002-2064-9128 hbunderw@usgs.gov","orcid":"https://orcid.org/0000-0002-2064-9128","contributorId":140185,"corporation":false,"usgs":true,"family":"Underwood","given":"H.","email":"hbunderw@usgs.gov","middleInitial":"Brian","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":737046,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crawford, Joanne C.","contributorId":205355,"corporation":false,"usgs":false,"family":"Crawford","given":"Joanne","email":"","middleInitial":"C.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":737052,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197378,"text":"70197378 - 2018 - Exposure-related effects of Zequanox on juvenile lake sturgeon (Acipenser fulvescens) and lake trout (Salvelinus namaycush)","interactions":[],"lastModifiedDate":"2018-05-31T14:55:13","indexId":"70197378","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exposure-related effects of Zequanox on juvenile lake sturgeon (Acipenser fulvescens) and lake trout (Salvelinus namaycush)","title":"Exposure-related effects of Zequanox on juvenile lake sturgeon (Acipenser fulvescens) and lake trout (Salvelinus namaycush)","docAbstract":"The environmental fate, persistence, and non-target animal impacts of traditional molluscicides for zebra, Dreissena polymorpha, and quagga, D. bugensis, mussel control led to the development of the biomolluscicide Zequanox. Although previous research has demonstrated the specificity of Zequanox, one study indicated sensitivity of salmonids and lake sturgeon, Acipenser fulvescens, following non-label compliant exposures to Zequanox. This study was conducted to evaluate sublethal and lethal impacts of Zequanox exposure on juvenile lake sturgeon and lake trout, Salvelinus namaycush, following applications that were conducted in a manner consistent with the Zequanox product label. Fish were exposed to 50 or 100 mg/L of Zequanox as active ingredient for 8 h and then held for 33 d to evaluate latent impacts. No acute mortality was observed in either species; however, significant latent mortality (P < 0.01, df = 9; 46.2%) was observed in lake trout that were exposed to the highest dose of Zequanox. Statistically significant (P < 0.03, df = 9), but biologically minimal differences were observed in the weight (range 20.17 to 21.49 g) of surviving lake sturgeon at the termination of the 33 d post-exposure observation period. Statistically significant (P < 0.05, df = 9) and biologically considerable differences were observed in the weight (range 6.19 to 9.55 g) of surviving lake trout at the termination of the 33 d post-exposure observation period. Histologic evaluation of lake trout gastrointestinal tracts suggests that the mode of action in lake trout is different from the mode of action that induces zebra and quagga mussel mortality. Further research could determine the sensitivity of other salmonid species to Zequanox and determine if native fish will avoid Zequanox treated water.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2018.9.2.09","collaboration":".","usgsCitation":"Luoma, J.A., Severson, T.J., Wise, J.K., and Barbour, M., 2018, Exposure-related effects of Zequanox on juvenile lake sturgeon (Acipenser fulvescens) and lake trout (Salvelinus namaycush): Management of Biological Invasions, v. 9, no. 2, p. 163-175, https://doi.org/10.3391/mbi.2018.9.2.09.","productDescription":"13 p.","startPage":"163","endPage":"175","ipdsId":"IP-090152","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468716,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2018.9.2.09","text":"Publisher Index Page"},{"id":437888,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23ZGT","text":"USGS data release","linkHelpText":"Exposure-related effects of Zequanox on juvenile lake sturgeon (Acipenser fulvescens) and lake trout (Salvelinus namaycush) Data"},{"id":354644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d71e4b092d9651e1af2","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barbour, Matthew 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736926,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197365,"text":"sir20185064 - 2018 - Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016","interactions":[],"lastModifiedDate":"2018-06-06T14:16:17","indexId":"sir20185064","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5064","title":"Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016","docAbstract":"This report identifies water-level trends in wells and provides a conceptual framework that explains the hydrologic stresses and factors causing the trends in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Water levels in 79 wells were analyzed for trends between 1966 and 2016. The magnitude and duration of water-level responses to hydrologic stresses were analyzed graphically, statistically, and with water-level models.
The conceptual framework consists of multiple stress-specific conceptual models to explain water-level responses to the following hydrologic stresses: recharge, evapotranspiration, pumping, nuclear testing, and wellbore equilibration. Dominant hydrologic stresses affecting water-level trends in each well were used to categorize trends as nonstatic, transient, or steady state.
The conceptual framework of water-level responses to hydrologic stresses and trend analyses provide a comprehensive understanding of the PMOV basin and vicinity. The trend analysis links water-level fluctuations in wells to hydrologic stresses and potential factors causing the trends. Transient and steady-state trend categorizations can be used to determine the appropriate water-level data for groundwater studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185064","collaboration":"Prepared in cooperation with the Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-NA0001654","usgsCitation":"Jackson, T.R., and Fenelon, J.M., 2018, Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016: U.S. Geological Survey Scientific Investigations Report 2018-5064, 89 p., https://doi.org/10.3133/sir20185064.","productDescription":"ix, 89 p.","numberOfPages":"104","onlineOnly":"Y","ipdsId":"IP-086316","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":354612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77942XB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data for conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley Groundwater Basin, Nevada, 1966-2016"},{"id":354610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5064/coverthb.jpg"},{"id":354611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5064/sir20185064.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5064"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 38\n ],\n [\n -117,\n 38\n ],\n [\n -117,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
Elevated concentrations of dissolved-solids (salinity) including calcium, sodium, sulfate, and chloride, among others, in the Colorado River cause substantial problems for its water users. Previous efforts to reduce dissolved solids in upper Colorado River basin (UCRB) streams often focused on reducing suspended-sediment transport to streams, but few studies have investigated the relationship between suspended sediment and salinity, or evaluated which watershed characteristics might be associated with this relationship. Are there catchment properties that may help in identifying areas where control of suspended sediment will also reduce salinity transport to streams? A random forests classification analysis was performed on topographic, climate, land cover, geology, rock chemistry, soil, and hydrologic information in 163 UCRB catchments. Two random forests models were developed in this study: one for exploring stream and catchment characteristics associated with stream sites where dissolved solids increase with increasing suspended-sediment concentration, and the other for predicting where these sites are located in unmonitored reaches. Results of variable importance from the exploratory random forests models indicate that no simple source, geochemical process, or transport mechanism can easily explain the relationship between dissolved solids and suspended sediment concentrations at UCRB monitoring sites. Among the most important watershed characteristics in both models were measures of soil hydraulic conductivity, soil erodibility, minimum catchment elevation, catchment area, and the silt component of soil in the catchment. Predictions at key locations in the basin were combined with observations from selected monitoring sites, and presented in map-form to give a complete understanding of where catchment sediment control practices would also benefit control of dissolved solids in streams.
","language":"English","publisher":"MDPI","doi":"10.3390/w10060676","usgsCitation":"Tillman, F.D., Anning, D., Heilman, J.A., Buto, S.G., and Miller, M.P., 2018, Managing salinity in Upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models: Water, v. 10, no. 6, Article 676; , https://doi.org/10.3390/w10060676.","productDescription":"Article 676; ","ipdsId":"IP-082147","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":460911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10060676","text":"Publisher Index Page"},{"id":354625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112,\n 36.5\n ],\n [\n -106,\n 36.5\n ],\n [\n -106,\n 44\n ],\n [\n -112,\n 44\n ],\n [\n -112,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-24","publicationStatus":"PW","scienceBaseUri":"5b155d71e4b092d9651e1af4","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anning, David W. 0000-0002-4470-3387","orcid":"https://orcid.org/0000-0002-4470-3387","contributorId":202783,"corporation":false,"usgs":true,"family":"Anning","given":"David W.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heilman, Julian A. 0000-0002-2987-4057 jahr@usgs.gov","orcid":"https://orcid.org/0000-0002-2987-4057","contributorId":202192,"corporation":false,"usgs":true,"family":"Heilman","given":"Julian","email":"jahr@usgs.gov","middleInitial":"A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buto, Susan G. 0000-0002-1107-9549 sbuto@usgs.gov","orcid":"https://orcid.org/0000-0002-1107-9549","contributorId":1057,"corporation":false,"usgs":true,"family":"Buto","given":"Susan","email":"sbuto@usgs.gov","middleInitial":"G.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736917,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736918,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197406,"text":"ofr20181091 - 2018 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico","interactions":[{"subject":{"id":70197406,"text":"ofr20181091 - 2018 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico","indexId":"ofr20181091","publicationYear":"2018","noYear":false,"title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},"predicate":"SUPERSEDED_BY","object":{"id":70206191,"text":"sir20195120 - 2020 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico","indexId":"sir20195120","publicationYear":"2020","noYear":false,"title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},"id":1}],"supersededBy":{"id":70206191,"text":"sir20195120 - 2020 - Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico","indexId":"sir20195120","publicationYear":"2020","noYear":false,"title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},"lastModifiedDate":"2021-04-13T21:07:54.430093","indexId":"ofr20181091","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1091","title":"Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico","docAbstract":"**September 28, 2018: The purpose of a USGS Open-file report (OFR) is dissemination of information that must be released immediately to fill a public need or information that is not sufficiently refined to warrant publication in one of the other USGS series. As part of that refinement process, an error was discovered in one of the input data sets of the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) that this OFR was based upon. The error involved the assignment of storage properties to “phantom cells.”
Phantom cells are required for most variants of MODFLOW that use a structured finite-difference grid when individual stratigraphic layers are represented as separate layers. Using phantom cells is a common practice that allows separate model layers to be maintained without having to combine stratigraphic layers into equivalent model layers or to use an unstructured grid. Typically, phantom cell horizontal hydraulic conductivities and storage properties are set to a small number and vertical hydraulic conductivities are set to a number large enough to allow vertical flow between the vertically adjacent layers.
In the RGTIHM, the specific storage properties of the phantom cells for the upper (RGTIHM layers 3 and 4), middle (RGTIHM layers 5 and 6), and lower (RGTIHM layers 7 and 8) members of the Santa Fe Group were inadvertently assigned a value of 1 feet-1. The revision of these specific storage values to a small number (1.0 x 10-09 feet-1) required additional trial-and-error model calibration and a new sensitivity analysis. After calibration, the overall model fit remained similar to the fit described in the OFR, but the fit for many individual features such as project water available for diversions at the American Canal and Acequia Madre improved due to the reduction in flow coming from lower layers. Overall, there is still an average net depletion of groundwater flow, and the conclusions of the report are not changed. The revised average annual groundwater flow depletion simulated for the period 1953-2014 is -1,480 acre-feet/year for the entire model region, and -3,660 acre-feet/year for the portion of the model in the United States. The final version of the model will be the basis of the USGS Scientific Investigations Report that will supersede this OFR. An updated Model Archive of RGTIHM is available upon request to the USGS California Water Science Center.
The corrected version of the model WAS the basis for the USGS Scientific Investigations Report that SUPERSEDED this Open-File Report.**
Changes in population, agricultural development and practices (including shifts to more water-intensive crops), and climate variability are increasing demands on available water resources, particularly groundwater, in one of the most productive agricultural regions in the Southwest—the Rincon and Mesilla Valley parts of Rio Grande Valley, Doña Ana and Sierra Counties, New Mexico, and El Paso County, Texas. The goal of this study was to produce an integrated hydrological simulation model to help evaluate water-management strategies, including conjunctive use of surface water and groundwater for historical conditions, and to support long-term planning for the Rio Grande Project. This report describes model construction and applications by the U.S. Geological Survey, working in cooperation and collaboration with the Bureau of Reclamation.
This model, the Rio Grande Transboundary Integrated Hydrologic Model, simulates the most important natural and human components of the hydrologic system, including selected components related to variations in climate, thereby providing a reliable assessment of surface-water and groundwater conditions and processes that can inform water users and help improve planning for future conditions and sustained operations of the Rio Grande Project (RGP) by the Bureau of Reclamation. Model development included a revision of the conceptual model of the flow system, construction of a Transboundary Rio Grande Watershed Model (TRGWM) water-balance model using the Basin Characterization Model (BCM), and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model (referred to as One Water). The hydrologic models were developed for and calibrated to historical conditions of water and land use, and parameters were adjusted so that simulated values closely matched available measurements (calibration). The calibrated model was then used to assess the use and movement of water in the Rincon Valley, Mesilla Basin, and northern part of the Conejos-Médanos Basin, with the entire region referred to as the “Transboundary Rio Grande” or TRG. These tools provide a means to understand hydrologic system response to the evolution of water use in the region, its availability, and potential operational constraints of the RGP.
The conceptual model identified surface-water and groundwater inflows and outflows that included the movement and use of water both in natural and in anthropogenic systems. The groundwater-flow system is characterized by a layered geologic sedimentary sequence combined with the effects of groundwater pumping, operation of the RGP, natural runoff and recharge, and the application of irrigation water at the land surface that is captured and reused in an extensive network of canals and drains as part of the conjunctive use of water in the region.
Historical groundwater-level fluctuations followed a cyclic pattern that were aligned with climate cycles, which collectively resulted in alternating periods of wet or dry years. Periods of drought that persisted for one or more years are associated with low surface-water availability that resulted in higher rates of groundwater-level decline. Rates of groundwater-level decline also increased during periods of agricultural intensification, which necessitated increasing use of groundwater as a source of irrigation water. Agriculture in the area was initially dominated by alfalfa and cotton, but since 1970 more water-intensive pecan orchards and vegetable production have become more common. Groundwater levels substantially declined in subregions where drier climate combined with increased demand, resulting in periods of reduced streamflows.
Most of the groundwater was recharged in the Rio Grande Valley floor, and most of the pumpage and aquifer storage depletion was in Mesilla Basin agricultural subregions. A cyclic imbalance between inflows and outflows resulted in the modeled cyclic depletion (groundwater withdrawals in excess of natural recharge) of the groundwater basin during the 75-year simulation period of 1940–2014. Changes in groundwater storage can vary considerably from year to year, depending on land use, pumpage, and climate conditions. Climatic drivers of wet and dry years can greatly affect all inflows, outflows, and water use. Although streamflow and, to a minor extent, precipitation during inter-decadal wet-year periods replenished the groundwater historically, contemporary water use and storage depletion could have reduced the effects of these major recharge events. The average net groundwater flow-rate deficit for 1953–2014 was estimated to be about 8,990 acre-feet per year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181091","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hanson, R.T., Ritchie, A.B., Boyce, S.E., Galanter, A.E., Ferguson, I.A., Flint, L.E., and Henson, W.R., 2018, Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico: U.S Geological Survey Open-File Report 2018–1091, 185 p., https://doi.org/10.3133/ofr20181091.","productDescription":"Report: x, 185 p.; Dataset; Data release; Errata","numberOfPages":"200","onlineOnly":"Y","ipdsId":"IP-071162","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354790,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1091/ofr20181091.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":354791,"rank":2,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.3133/ofr20181091","linkHelpText":"- This Open-File report (OFR) was superseded by USGS Scientific Investigations report (SIR) SIR 2019-5120. The final model archive will be available on the national USGS archive site."},{"id":357946,"rank":4,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/of/2018/1091/erratum.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"}},{"id":363155,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9NYND","linkHelpText":"Digital hydrologic and geospatial data for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},{"id":354795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1091/coverthb_.jpg"}],"country":"Mexico, United States","state":"New Mexico, Northern Chihuahua, Texas","otherGeospatial":"Rio Grande","publicComments":"This Open-File report (OFR) will be superseded by a USGS Scientific Investigations report (SIR) once the USGS Techniques and Methods report (T&M) documenting the numerical code is published. Once the SIR is released, the final model archive will be available on the national USGS archive site. For the interim archive for this model, please contact CaWSC for directions on downloading 916-278-3026.","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Aeolian processes are important drivers of ecosystem dynamics in drylands, and important feedbacks exist among aeolian—hydrological processes and vegetation. The trapping of wind‐borne sediments by vegetation canopies may result in changes in soil properties beneath the vegetation, which, in turn, can alter hydrological and biogeochemical processes. Despite the relevance of aeolian transport to ecosystem dynamics, the interactions between aeolian transport and vegetation in shaping dryland landscapes where sediment distribution is altered by relatively rapid changes in vegetation composition such as shrub encroachment, are not well understood. Here, we used a computational fluid dynamics modelling framework to investigate the sediment trapping efficiencies of vegetation canopies commonly found in a shrub‐grass ecotone in the Chihuahuan Desert (New Mexico, USA) and related the results to spatial heterogeneity in soil texture and infiltration measured in the field. The vegetation structures were created using a computer‐aided design software, with inherent canopy porosities, which were derived using Light Detection and Ranging (LiDAR) measurements of plant canopies. Results show that considerable heterogeneity in infiltration and soil grain size distribution exist between the microsites, with higher infiltration and coarser soil texture under shrubs. Numerical simulations further indicate that the differential trapping of canopies might contribute to the observed heterogeneity in soil texture. In the early stages of encroachment, the shrub canopies, by trapping coarser particles more efficiently, might maintain higher infiltration rates leading to faster development of the microsites with enhanced ecological productivity, which might provide positive feedbacks to shrub encroachment.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1986","usgsCitation":"Gonzales, H.B., Ravi, S., Li, J., and Sankey, J.B., 2018, Ecohydrological implications of aeolian sediment trapping by sparse vegetation in drylands: Ecohydrology, v. 11, no. 7, p. 1-11, https://doi.org/10.1002/eco.1986.","productDescription":"e1986; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-093901","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":354650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-30","publicationStatus":"PW","scienceBaseUri":"5b155d73e4b092d9651e1afc","contributors":{"authors":[{"text":"Gonzales, Howell B.","contributorId":202737,"corporation":false,"usgs":false,"family":"Gonzales","given":"Howell","email":"","middleInitial":"B.","affiliations":[{"id":36520,"text":"Department of Earth and Environmental Science, Temple University","active":true,"usgs":false}],"preferred":false,"id":736877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ravi, Sujith","contributorId":202738,"corporation":false,"usgs":false,"family":"Ravi","given":"Sujith","email":"","affiliations":[{"id":36520,"text":"Department of Earth and Environmental Science, Temple University","active":true,"usgs":false}],"preferred":false,"id":736878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Junran","contributorId":202740,"corporation":false,"usgs":false,"family":"Li","given":"Junran","email":"","affiliations":[{"id":36521,"text":"Department of Geosciences, University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":736879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":736876,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194985,"text":"sir20175158 - 2018 - Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada","interactions":[],"lastModifiedDate":"2018-10-01T06:58:00","indexId":"sir20175158","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5158","title":"Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada","docAbstract":"The U.S. Geological Survey developed a groundwater-flow model for the uppermost principal aquifer systems in the Williston Basin in parts of Montana, North Dakota, and South Dakota in the United States and parts of Manitoba and Saskatchewan in Canada as part of a detailed assessment of the groundwater availability in the area. The assessment was done because of the potential for increased demands and stresses on groundwater associated with large-scale energy development in the area. As part of this assessment, a three-dimensional groundwater-flow model was developed as a tool that can be used to simulate how the groundwater-flow system responds to changes in hydrologic stresses at a regional scale.
The three-dimensional groundwater-flow model was developed using the U.S. Geological Survey’s numerical finite-difference groundwater model with the Newton-Rhapson solver, MODFLOW–NWT, to represent the glacial, lower Tertiary, and Upper Cretaceous aquifer systems for steady-state (mean) hydrological conditions for 1981‒2005 and for transient (temporally varying) conditions using a combination of a steady-state period for pre-1960 and transient periods for 1961‒2005. The numerical model framework was constructed based on existing and interpreted hydrogeologic and geospatial data and consisted of eight layers. Two layers were used to represent the glacial aquifer system in the model; layer 1 represented the upper one-half and layer 2 represented the lower one-half of the glacial aquifer system. Three layers were used to represent the lower Tertiary aquifer system in the model; layer 3 represented the upper Fort Union aquifer, layer 4 represented the middle Fort Union hydrogeologic unit, and layer 5 represented the lower Fort Union aquifer. Three layers were used to represent the Upper Cretaceous aquifer system in the model; layer 6 represented the upper Hell Creek hydrogeologic unit, layer 7 represented the lower Hell Creek aquifer, and layer 8 represented the Fox Hills aquifer. The numerical model was constructed using a uniform grid with square cells that are about 1 mile (1,600 meters) on each side with a total of about 657,000 active cells.
Model calibration was completed by linking Parameter ESTimation (PEST) software with MODFLOW–NWT. The PEST software uses statistical parameter estimation techniques to identify an optimum set of input parameters by adjusting individual model input parameters and assessing the differences, or residuals, between observed (measured or estimated) data and simulated values. Steady-state model calibration consisted of attempting to match mean simulated values to measured or estimated values of (1) hydraulic head, (2) hydraulic head differences between model layers, (3) stream infiltration, and (4) discharge to streams. Calibration of the transient model consisted of attempting to match simulated and measured temporally distributed values of hydraulic head changes, stream base flow, and groundwater discharge to artesian flowing wells. Hydraulic properties estimated through model calibration included hydraulic conductivity, vertical hydraulic conductivity, aquifer storage, and riverbed hydraulic conductivity in addition to groundwater recharge and well skin.
The ability of the numerical model to accurately simulate groundwater flow in the Williston Basin was assessed primarily by its ability to match calibration targets for hydraulic head, stream base flow, and flowing well discharge. The steady-state model also was used to assess the simulated potentiometric surfaces in the upper Fort Union aquifer, the lower Fort Union aquifer, and the Fox Hills aquifer. Additionally, a previously estimated regional groundwater-flow budget was compared with the simulated steady-state groundwater-flow budget for the Williston Basin. The simulated potentiometric surfaces typically compared well with the estimated potentiometric surfaces based on measured hydraulic head data and indicated localized groundwater-flow gradients that were topographically controlled in outcrop areas and more generalized regional gradients where the aquifers were confined. The differences between the measured and simulated (residuals) hydraulic head values for 11,109 wells were assessed, which indicated that the steady-state model generally underestimated hydraulic head in the model area. This underestimation is indicated by a positive mean residual of 11.2 feet for all model layers. Layer 7, which represents the lower Hell Creek aquifer, is the only layer for which the steady-state model overestimated hydraulic head. Simulated groundwater-level changes for the transient model matched within plus or minus 2.5 feet of the measured values for more than 60 percent of all measurements and to within plus or minus 17.5 feet for 95 percent of all measurements; however, the transient model underestimated groundwater-level changes for all model layers. A comparison between simulated and estimated base flows for the steady-state and transient models indicated that both models overestimated base flow in streams and underestimated annual fluctuations in base flow.
The estimated and simulated groundwater budgets indicate the model area received a substantial amount of recharge from precipitation and stream infiltration. The steady-state model indicated that reservoir seepage was a larger component of recharge in the Williston Basin than was previously estimated. Irrigation recharge and groundwater inflow from outside the Williston Basin accounted for a relatively small part of total groundwater recharge when compared with recharge from precipitation, stream infiltration, and reservoir seepage. Most of the estimated and simulated groundwater discharge in the Williston Basin was to streams and reservoirs. Simulated groundwater withdrawal, discharge to reservoirs, and groundwater outflow in the Williston Basin accounted for a smaller part of total groundwater discharge.
The transient model was used to simulate discharge to 571 flowing artesian wells within the model area. Of the 571 established flowing artesian wells simulated by the model, 271 wells did not flow at any time during the simulation because hydraulic head was always below the land-surface altitude. As hydraulic head declined throughout the simulation, 68 of these wells responded by ceasing to flow by the end of 2005. Total mean simulated discharge for the 571 flowing artesian wells was 55.1 cubic feet per second (ft3/s), and the mean simulated flowing well discharge for individual wells was 0.118 ft3/s. Simulated discharge to individual flowing artesian wells increased from 0.039 to 0.177 ft3/s between 1961 and 1975 and decreased to 0.102 ft3/s by 2005. The mean residual for 34 flowing wells with measured discharge was 0.014 ft3/s, which indicates the transient model overestimated discharge to flowing artesian wells in the model area.
Model limitations arise from aspects of the conceptual model and from simplifications inherent in the construction and calibration of a regional-scale numerical groundwater-flow model. Simplifying assumptions in defining hydraulic parameters in space and hydrologic stresses and time-varying observational data in time can limit the capabilities of this tool to simulate how the groundwater-flow system responds to changes in hydrologic stresses, particularly at the local scale; nevertheless, the steady-state model adequately simulated flow in the uppermost principal aquifer systems in the Williston Basin based on the comparison between the simulated and estimated groundwater-flow budget, the comparison between simulated and estimated potentiometric surfaces, and the results of the calibration process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175158","collaboration":"Water Availability and Use Science Program","usgsCitation":"Davis, K.W., and Long, A.J., 2018, Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2017–5158, 70 p., https://doi.org/10.3133/sir20175158.","productDescription":"Report: ix, 70; Appendixes 1-2; Data Release","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080007","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":354478,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01CZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to assess groundwater availability in the uppermost principal aquifer systems of the Williston structural basin, United States and Canada"},{"id":354477,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158.pdf","text":"Report","size":"97.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5158"},{"id":354510,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_1.xlsx","text":"Appendix Table 1","size":"1.77 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 1"},{"id":354511,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_2.xlsx","text":"Appendix Table 2","size":"25.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 2"},{"id":354476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5158/coverthb2.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.3359375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Rd.
Rapid City, SD 57702
The right‐lateral San Gregorio‐Hosgri fault system (SGHF) extends mainly offshore for about 400 km along the central California coast and is a major structure in the distributed transform margin of western North America. We mapped a poorly known 64‐km‐long section of the SGHF offshore Big Sur between Piedras Blancas and Point Sur using high‐resolution bathymetry, seismic reflection, and marine magnetic data. In this region, the SGHF is characterized by multiple strands, step overs, scarps and lineaments, shutter ridges, deflected drainages, and other geomorphic features consistent with strike‐slip faulting. Analysis of offset shelfbreak gullies suggest a lateral slip rate of about 3.35 mm/year. Vertical slip rates range as high as 0.8 to 1.2 mm/year. Lateral slip combined with high vertical slip rates result in a northwest decrease in shelf width, a northward increase in shelf and upper slope gradient, and progressive incision of submarine canyon heads. The SGHF between Point Sur and Piedras Blancas trends ~321° and forms a 105‐km‐long transpressive bend (the Big Sur Bend) between more north trending transtensional fault sections to the south (120 km long, ~336° trend) and north (180 km long, ~337° trend). This transpressional bend and SGHF splay faults have had a significant role in shaping the modern geomorphology of the central California coast, controlling or influencing the locations of mountainous uplifts, large coastal headlands, embayments, and rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017TC004724","usgsCitation":"Johnson, S.Y., Watt, J., Hartwell, S., and Kluesner, J.W., 2018, Neotectonics of the Big Sur Bend, San Gregorio‐Hosgri fault system, central California: Tectonics, v. 37, no. 7, p. 1930-1954, https://doi.org/10.1029/2017TC004724.","productDescription":"25 p.","startPage":"1930","endPage":"1954","ipdsId":"IP-088681","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468715,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017tc004724","text":"Publisher Index Page"},{"id":355620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Gregorio‐Hosgri fault","volume":"37","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e578e4b060350a15d1b1","contributors":{"authors":[{"text":"Johnson, Samuel Y. 0000-0001-7972-9977 sjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":2607,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel","email":"sjohnson@usgs.gov","middleInitial":"Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watt, Janet 0000-0002-4759-3814 jwatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":146222,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","email":"jwatt@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartwell, Stephen 0000-0002-3522-7526 shartwell@usgs.gov","orcid":"https://orcid.org/0000-0002-3522-7526","contributorId":146221,"corporation":false,"usgs":true,"family":"Hartwell","given":"Stephen","email":"shartwell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739772,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739773,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198335,"text":"70198335 - 2018 - Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data","interactions":[],"lastModifiedDate":"2018-07-30T16:11:03","indexId":"70198335","displayToPublicDate":"2018-05-30T15:39:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data","docAbstract":"This study uses an airborne Light Detection and Ranging (LiDAR) survey, historical aerial photography and historical climate data to describe the character and dynamics of the Nogahabara Sand Dunes, a sub-Arctic dune field in interior Alaska’s discontinuous permafrost zone. The Nogahabara Sand Dunes consist of a 43-km2 area of active transverse and barchanoid dunes within a 3200-km2 area of vegetated dune and sand sheet deposits. The average dune height in the active portion of the dune field is 5.8 m, with a maximum dune height of 28 m. Dune spacing is variable with average crest-to-crest distances for select transects ranging from 66–132 m. Between 1952 and 2015, dunes migrated at an average rate of 0.52 m a−1. Dune movement was greatest between 1952 and 1978 (0.68 m a−1) and least between 1978 and 2015 (0.43 m a−1). Dunes migrated predominantly to the southeast; however, along the dune field margin, net migration was towards the edge of the dune field regardless of heading. Better constraining the processes controlling dune field dynamics at the Nogahabara dunes would provide information that can be used to model possible reactivation of more northerly dune fields and sand sheets in response to climate change, shifting fire regimes and permafrost thaw.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/rs10050792","usgsCitation":"Baughman, C., Jones, B.M., Bodony, K.L., Mann, D.H., Larsen, C.F., Himmelstoss, E., and Smith, J., 2018, Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data: Remote Sensing, v. 10, no. 5, p. 1-19, https://doi.org/10.3390/rs10050792.","productDescription":"Article 792; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-082492","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468719,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050792","text":"Publisher Index Page"},{"id":356010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5b6fc444e4b0f5d57878ea3b","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":741101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":741124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodony, Karin L.","contributorId":206563,"corporation":false,"usgs":false,"family":"Bodony","given":"Karin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":741125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mann, Daniel H.","contributorId":67010,"corporation":false,"usgs":true,"family":"Mann","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":741126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larsen, Christopher F.","contributorId":147408,"corporation":false,"usgs":false,"family":"Larsen","given":"Christopher","email":"","middleInitial":"F.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":741127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Himmelstoss, Emily A. ehimmelstoss@usgs.gov","contributorId":2508,"corporation":false,"usgs":true,"family":"Himmelstoss","given":"Emily A.","email":"ehimmelstoss@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":741128,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Jeremy","contributorId":62919,"corporation":false,"usgs":true,"family":"Smith","given":"Jeremy","affiliations":[],"preferred":false,"id":741129,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70200590,"text":"70200590 - 2018 - The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska","interactions":[],"lastModifiedDate":"2018-10-25T11:50:24","indexId":"70200590","displayToPublicDate":"2018-05-30T11:50:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska","docAbstract":"The youngest part of the Farewell terrane in interior Alaska (USA) is the enigmatic Devonian–Cretaceous Mystic subterrane. New U-Pb detrital zircon, fossil, geochemical, neodymium isotopic, and petrographic data illuminate the origin of the rocks of this subterrane. The Devonian–Permian Sheep Creek Formation yielded youngest detrital zircons of Devonian age, major detrital zircon age probability peaks between ca. 460 and 405 Ma, and overall age spectra like those from the underlying Dillinger subterrane. Samples are sandstones rich in sedimentary lithic clasts, and differ from approximately coeval strata to the east that have abundant volcanic lithic clasts and late Paleozoic detrital zircons. The Permian Mount Dall conglomerate has mainly carbonate and chert clasts and yielded youngest detrital zircons of latest Pennsylvanian age. Permian quartz-carbonate sandstone in the northern Farewell terrane yielded abundant middle to late Permian detrital zircons.
Late Triassic–Early Jurassic mafic igneous rocks occur in the central and eastern Mystic subterrane. New whole-rock geochemical and isotopic data indicate that magmas were rift related and derived from subcontinental mantle. Triassic and Jurassic strata have detrital zircon age spectra much like those of the Sheep Creek Formation, with major age populations between ca. 430 and 410 Ma. These rocks include conglomerate with clasts of carbonate ± chert and youngest detrital zircons of Late Triassic age and quartz-carbonate sandstone with youngest detrital zircons of Early Jurassic age. Lithofacies indicating highly productive oceanographic conditions (upwelling?) bracket the main part of the Mystic succession: Upper Devonian bedded barite and phosphatic Upper Devonian and Lower Jurassic rocks.
The youngest part of the Mystic subterrane consists of Lower Cretaceous (Valanginian–Aptian) limestone, calcareous sandstone, and related strata. These rocks are partly coeval with the oldest parts of the Kahiltna assemblage, an overlap succession exposed along the southern margin of the Farewell terrane.
Our findings support previous models suggesting that the Farewell terrane was proximal to the Alexander-Wrangellia-Peninsular composite terrane during the late Paleozoic, and further suggest that such proximity continued into (or recurred during) the Late Triassic–Early Jurassic. But middle to late Permian detrital zircons in northern Farewell require another source; the Yukon-Tanana terrane is one possibility.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01588.1","usgsCitation":"Dumoulin, J.A., Jones, J.V., Box, S.E., Bradley, D., Ayuso, R.A., and O’Sullivan, P.B., 2018, The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska: Geosphere, v. 14, no. 4, p. 1501-1543, https://doi.org/10.1130/GES01588.1.","productDescription":"43 p.","startPage":"1501","endPage":"1543","ipdsId":"IP-095640","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":468720,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01588.1","text":"Publisher Index Page"},{"id":437889,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7765DN7","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Detrital Zircon Grains, Whole Rock Major and Trace-element Geochemistry, and Whole Rock Isotopic Data from Selected Rocks from the Western Alaska Range, Medfra area, and Livengood area, Alaska"},{"id":358807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"14","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-30","publicationStatus":"PW","scienceBaseUri":"5c10a9abe4b034bf6a7e53b3","contributors":{"authors":[{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":749662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":749664,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Sullivan, Paul B.","contributorId":193544,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749665,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203040,"text":"70203040 - 2018 - Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico","interactions":[],"lastModifiedDate":"2019-04-15T10:46:45","indexId":"70203040","displayToPublicDate":"2018-05-30T09:29:48","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico","docAbstract":"We present a case study of gas hydrate quantification using dense short-offset multichannel seismic (MCS) and sparse long-offset ocean-bottom-seismometer (OBS) data in lease block Green Canyon 955 (GC955), Gulf of Mexico (GOM), where the presence of gas hydrate was interpreted using logging while drilling (LWD) data acquired by the GOM Gas Hydrate Joint Industry Project Leg II expedition. We use frequency-domain full-waveform inversion (FWI) of seven OBS gathers to invert for a P-wave velocity model of an approximately 7 km long MCS profile connecting two LWD sites, GC955-H and GC955-Q. We build the starting model for FWI using traveltime inversion (TI) of the MCS and OBS data. In addition, we use the TI model for depth migrating the MCS stack. At the LWD sites, we constrain the hydrate saturation (Sgh) using sonic and resistivity logs. Unfortunately, as is typical of seismic quantification problems, the FWI model resolution is not sufficient to extrapolate the LWD-based Sgh. Therefore, we apply Backus averaging to the sonic log, at 60 m wavelength, bringing it within approximately 8% of the FWI model and make the assumption that averaging the sonic log is same as redistributing the gas hydrate within the Backus wavelength. In this manner, instead of Sgh, the FWI model is able to estimate the total gas hydrate volume. In the end, we use the FWI model and the migrated stack to constrain the locations and bulk volumes of free gas and gas hydrate. Our results demonstrate that with careful processing, reasonable estimates on locations and bulk volumes of submarine gas hydrate accumulations can be achieved even with sparse seismic data that are not adequate for amplitude-based assessments.","language":"English","publisher":"SEG","doi":"10.1190/geo2017-0414.1","usgsCitation":"Wang, J., Jaiswal, P., Haines, S.S., Hart, P.E., and Wu, S., 2018, Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico: Geophysics, v. 83, no. 4, p. B167-B181, https://doi.org/10.1190/geo2017-0414.1.","productDescription":"15 p.","startPage":"B167","endPage":"B181","ipdsId":"IP-065223","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":362942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Jiliang","contributorId":214827,"corporation":false,"usgs":false,"family":"Wang","given":"Jiliang","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":760908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Priyank","contributorId":214828,"corporation":false,"usgs":false,"family":"Jaiswal","given":"Priyank","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":760909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":760910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wu, Shiguo","contributorId":214829,"corporation":false,"usgs":false,"family":"Wu","given":"Shiguo","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":760911,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197338,"text":"70197338 - 2018 - Diel habitat selection of largemouth bass following woody structure installation in Table Rock Lake, Missouri","interactions":[],"lastModifiedDate":"2018-05-30T11:44:47","indexId":"70197338","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Diel habitat selection of largemouth bass following woody structure installation in Table Rock Lake, Missouri","docAbstract":"Largemouth bass Micropterus salmoides (Lacepède) use of installed habitat structure was evaluated in a large Midwestern USA reservoir to determine whether or not these structures were used in similar proportion to natural habitats. Seventy largemouth bass (>380 mm total length) were surgically implanted with radio transmitters and a subset was relocated monthly during day and night for one year. The top habitat selection models (based on Akaike's information criterion) suggest largemouth bass select 2–4 m depths during night and 4–7 m during day, whereas littoral structure selection was similar across diel periods. Largemouth bass selected boat docks at twice the rate of other structures. Installed woody structure was selected at similar rates to naturally occurring complex woody structure, whereas both were selected at a higher rate than simple woody structure. The results suggest the addition of woody structure may concentrate largemouth bass and mitigate the loss of woody habitat in a large reservoir.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12266","usgsCitation":"Harris, J., Paukert, C.P., Bush, S., Allen, M., and Siepker, M., 2018, Diel habitat selection of largemouth bass following woody structure installation in Table Rock Lake, Missouri: Fisheries Management and Ecology, v. 25, no. 2, p. 107-115, https://doi.org/10.1111/fme.12266.","productDescription":"9 p.","startPage":"107","endPage":"115","ipdsId":"IP-083719","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Table Rock Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2352294921875,\n 36.0624217151089\n ],\n [\n -92.3895263671875,\n 36.0624217151089\n ],\n [\n -92.3895263671875,\n 36.83566824724438\n ],\n [\n -94.2352294921875,\n 36.83566824724438\n ],\n [\n -94.2352294921875,\n 36.0624217151089\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-25","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b14","contributors":{"authors":[{"text":"Harris, J.M.","contributorId":42751,"corporation":false,"usgs":true,"family":"Harris","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":736806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":147821,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":736743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bush, S.C.","contributorId":205298,"corporation":false,"usgs":false,"family":"Bush","given":"S.C.","email":"","affiliations":[],"preferred":false,"id":736807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, M.J.","contributorId":205302,"corporation":false,"usgs":false,"family":"Allen","given":"M.J.","email":"","affiliations":[],"preferred":false,"id":736808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siepker, Michael","contributorId":145583,"corporation":false,"usgs":false,"family":"Siepker","given":"Michael","email":"","affiliations":[],"preferred":false,"id":736809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196064,"text":"pp1837A - 2018 - Geochemistry of groundwater in the eastern Snake River Plain aquifer, Idaho National Laboratory and vicinity, eastern Idaho","interactions":[],"lastModifiedDate":"2023-04-14T16:55:56.536311","indexId":"pp1837A","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837","chapter":"A","title":"Geochemistry of groundwater in the eastern Snake River Plain aquifer, Idaho National Laboratory and vicinity, eastern Idaho","docAbstract":"Nuclear research activities at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) in eastern Idaho produced radiochemical and chemical wastes that were discharged to the subsurface, resulting in detectable concentrations of some waste constituents in the eastern Snake River Plain (ESRP) aquifer. These waste constituents may pose risks to the water quality of the aquifer. In order to understand these risks to water quality the U.S. Geological Survey, in cooperation with the DOE, conducted a study of groundwater geochemistry to improve the understanding of hydrologic and chemical processes in the ESRP aquifer at and near the INL and to understand how these processes affect waste constituents in the aquifer.
Geochemistry data were used to identify sources of recharge, mixing of water, and directions of groundwater flow in the ESRP aquifer at the INL. The geochemistry data were analyzed from 167 sample sites at and near the INL. The sites included 150 groundwater, 13 surface-water, and 4 geothermal-water sites. The data were collected between 1952 and 2012, although most data collected at the INL were collected from 1989 to 1996. Water samples were analyzed for all or most of the following: field parameters, dissolved gases, major ions, dissolved metals, isotope ratios, and environmental tracers.
Sources of recharge identified at the INL were regional groundwater, groundwater from the Little Lost River (LLR) and Birch Creek (BC) valleys, groundwater from the Lost River Range, geothermal water, and surface water from the Big Lost River (BLR), LLR, and BC. Recharge from the BLR that may have occurred during the last glacial epoch, or paleorecharge, may be present at several wells in the southwestern part of the INL. Mixing of water at the INL primarily included mixing of surface water with groundwater from the tributary valleys and mixing of geothermal water with regional groundwater. Additionally, a zone of mixing between tributary valley water and regional groundwater, trending southwesterly, extended from near the northeastern boundary of the INL to the southern boundary of the INL. Groundwater flow directions for regional groundwater were southwesterly, and flow directions for tributary groundwater were southeasterly upon entering the ESRP, but eventually began to flow southwesterly in a direction parallel with regional groundwater.
Several discrepancies were identified from comparison of sources of recharge determined from geochemistry data and backward particle tracking with a groundwater-flow model. Some discrepancies observed in the particle tracking results included representation of recharge from BC near the north INL boundary, groundwater from the BC valley not extending far enough south, regional groundwater that extends too far west in the southern part of the INL, and no representation of recharge from geothermal water in model layer 1 or recharge from the BLR in the southwestern part of the INL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837A","collaboration":"DOE/ID-22246Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Groundwater quality in the approximately 7,820-square-kilometer (km2) Monterey-Salinas Shallow Aquifer (MS-SA) study unit was investigated from October 2012 to May 2013 as part of the second phase of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in the central coast region of California in the counties of Santa Cruz, Monterey, and San Luis Obispo. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in cooperation with the U.S. Geological Survey and the Lawrence Livermore National Laboratory.
The MS-SA study was designed to provide a statistically robust assessment of untreated-groundwater quality in the shallow aquifer systems. The assessment was based on water-quality samples collected by the U.S. Geological Survey from 100 groundwater sites and 70 household tap sites, along with ancillary data such as land use and well-construction information. The shallow aquifer systems were defined by the depth interval of wells associated with domestic supply. The MS-SA study unit consisted of four study areas—Santa Cruz (210 km2), Pajaro Valley (360 km2), Salinas Valley (2,000 km2), and Highlands (5,250 km2).
This study had two primary components: the status assessment and the understanding assessment. The first primary component of this study—the status assessment—assessed the quality of the groundwater resource indicated by data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally present inorganic constituents, such as major ions and trace elements. The status assessment is intended to characterize the quality of groundwater resources in the shallow aquifer system of the MS-SA study unit, not the treated drinking water delivered to consumers by water purveyors. As opposed to the public wells, however, water from private wells, which often tap the shallow aquifer, is usually consumed without any treatment. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, measures of location, geologic factors, groundwater age, and geochemical conditions of the shallow aquifer. An additional component of this study was a comparison of MS-SA water-quality results to those of the GAMA Monterey Bay and Salinas Valley Groundwater Basins study unit. This study unit covered much of the same areal extent as the MS-SA, but assessed the deeper, public drinking-water aquifer system.
Relative concentrations (sample concentration divided by the benchmark concentration) were used to evaluate concentrations of constituents in groundwater samples relative to water-quality benchmarks for those constituents that have Federal or California benchmarks, such as maximum contaminant levels. For organic and special-interest constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.1 and less than or equal to 1.0; or low, less than or equal to 0.1. For inorganic constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.5 and less than or equal to 1.0; or low, less than or equal to 0.5. A relative concentration greater than 1.0 indicates that the concentration was greater than a benchmark. Aquifer-scale proportions were used to quantify regional-scale groundwater quality. The aquifer-scale proportions are the areal percentages of the shallow aquifer system where relative concentrations for a given constituent or class of constituents were high, moderate, or low.
Inorganic constituents were measured at high and moderate relative concentrations more frequently than organic constituents. In the MS-SA study unit, inorganic constituents with benchmarks were detected at high relative concentrations in 51 percent of the study unit. The greatest proportions of high relative concentrations of trace elements and radioactive constituents were in the Highlands and Santa Cruz study areas, whereas high relative concentrations of nutrients were most often detected in the Salinas Valley and Pajaro Valley study areas and salinity indicators were most often detected in the Highlands and Salinas Valley study areas. The trace elements detected at high relative concentrations were arsenic, boron, iron, manganese, molybdenum, selenium, and strontium. The radioactive constituents detected at high relative concentrations were adjusted gross alpha radioactivity and uranium. The nutrient detected at high relative concentrations was nitrate plus nitrite. The salinity indicators detected at high relative concentrations were chloride, sulfate, and total dissolved solids.
Organic constituents (VOCs and pesticides) were not detected at high relative concentrations in any of the study areas. The fumigant 1,2-dichloropropane was detected at moderate relative concentrations. The VOC chloroform and the pesticide simazine were the only organic constituents detected in more than 10 percent of samples. The constituents of special interest NDMA (N-nitrosodimethylamine) and perchlorate were detected at high relative concentrations in the MS-SA study unit.
Selected constituents were evaluated with explanatory factors to identify potential sources or processes that could explain their presence and distribution. Trace elements and radioactive constituents came from natural sources and were not elevated by anthropogenic sources or processes, except for selenium and the radioactive constituent uranium. Arsenic, manganese, iron, selenium, and uranium concentrations were all influenced by oxidation-reduction conditions.
Unlike other trace elements and radioactive constituents, uranium and selenium can be affected by agricultural practices. Uranium and selenium can be released from aquifer sediments as a result of irrigation recharge water interacting with bicarbonate systems.
Nitrate can be strongly affected by anthropogenic sources. Nitrate concentrations were significantly higher in modern groundwater, indicating recent inputs of nitrate to the shallow aquifer system. Nitrate was positively correlated with agricultural land use, indicating that irrigation-return water could be leaching nitrogen fertilizer and naturally present nitrate to elevate nitrate concentrations in shallow groundwater.
The salinity indicators total dissolved solids, chloride, and sulfate all had natural sources in the MS-SA study unit, primarily marine sediments. Concentrations of the constituents were elevated as a result of evaporative concentration of irrigation water or precipitation. Sulfate concentrations were significantly correlated to agricultural land use, indicating that agricultural land-use practices are a contributing source of sulfate to groundwater.
The samples with most of the detections of VOCs were from sites in the Pajaro Valley and northern part of the Salinas Valley. Most of the samples with pesticide detections were from sites in the Salinas Valley study area. The herbicide simazine was positively correlated to the percentage of agricultural land use, and its concentrations were higher in modern groundwater than in pre-modern groundwater.
Perchlorate, similar to nitrate, has natural and anthropogenic sources. Correlations of perchlorate to dissolved oxygen, nitrate, and percentage of agricultural land use indicated that the irrigation-return water could be leaching naturally present perchlorate, as well as perchlorate from historical applications of Chilean nitrate fertilizer, to increase perchlorate concentrations in groundwater.
The quality of the water in the shallow aquifer system from this study was compared with the quality of water in the public drinking-water aquifer in a previous GAMA (MS-PA) study in the same area. The shallow system was more oxic and had more sites with modern groundwater than the public drinking-water aquifer, which was more anoxic and had sites with more pre-modern groundwater. Arsenic and selenium were found at high relative concentrations in a greater proportion of the shallow system. Manganese and iron were found at high relative concentrations in a greater proportion of the public drinking-water aquifer. Uranium was found at higher relative concentrations in a greater proportion of the shallow system. Concentrations of arsenic, iron, manganese, and molybdenum are not likely to change much as groundwater percolates from the shallow system to the public drinking-water aquifer because there are no anthropogenic sources affecting these constituents. Uranium and selenium concentrations in the public drinking-water aquifer could be affected by the higher concentrations in the shallow system because of irrigation-return water, however.
Nitrate and salinity indicators had concentrations that were much higher in the shallow system than the deeper public drinking-water aquifer. High concentrations of these constituents in the shallow system could lead to increased concentrations in the public drinking-water aquifer in parts of the study units because of land-use practices, such as irrigated agriculture.
Organic constituents were detected more frequently in the public drinking-water aquifer than in the shallow system, possibly because more of the sites sampled in the public drinking-water aquifer were in urban areas compared to the sites sampled for the shallow system or because sources of contamination have decreased as a result of changes in use at the land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185057","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Burton, C.A., and Wright, M.T., 2018, Status and understanding of groundwater quality in the Monterey-Salinas Shallow Aquifer study unit, 2012–13: California GAMA Priority Basin Project (ver. 1.1, September 2018): U.S. Geological Survey Scientific Investigations Report 2018–5057, 116 p., https://doi.org/10.3133/sir20185057.","productDescription":"Report: x, 116 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-056428","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":357554,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_versionhist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2018-5057"},{"id":354600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5057/coverthb.jpg"},{"id":354601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_v1.1.pdf","text":"Report","size":"38.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5057"}],"country":"United States","state":"California","otherGeospatial":"Monterey-Salinas Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.904296875,\n 36.53170884914869\n ],\n [\n -121.761474609375,\n 36.527294814546245\n ],\n [\n -121.58569335937501,\n 36.49638952000399\n ],\n [\n -121.5142822265625,\n 36.40359962073253\n ],\n [\n -121.4813232421875,\n 36.363798554158635\n ],\n [\n -121.33300781249999,\n 36.33282808737917\n ],\n [\n -121.234130859375,\n 36.27085020723902\n ],\n [\n -121.17370605468749,\n 36.19995805932895\n ],\n [\n -121.08581542968751,\n 36.10237644873644\n ],\n [\n -121.01989746093749,\n 36.02688935430189\n ],\n [\n -120.94299316406249,\n 35.96911507577482\n ],\n [\n -120.88256835937499,\n 35.89795019335754\n ],\n [\n -120.80566406250001,\n 35.79999392988527\n ],\n [\n -120.89080810546875,\n 35.66622234103479\n ],\n [\n -120.8551025390625,\n 35.61488368245436\n ],\n [\n -120.6683349609375,\n 35.46961797120201\n ],\n [\n -120.56945800781249,\n 35.37113502280101\n ],\n [\n -120.465087890625,\n 35.37113502280101\n ],\n [\n -120.2838134765625,\n 35.37113502280101\n ],\n [\n -120.12451171875,\n 35.37561413174875\n ],\n [\n -120.0146484375,\n 35.44277092585766\n ],\n [\n -119.94323730468749,\n 35.46514408578589\n ],\n [\n -119.99267578124999,\n 35.53222622770337\n ],\n [\n -120.10253906249999,\n 35.59925232772949\n ],\n [\n -120.2838134765625,\n 35.755428369259626\n ],\n [\n -120.421142578125,\n 35.88905007936091\n ],\n [\n -120.50903320312501,\n 35.97356075349624\n ],\n [\n -120.673828125,\n 36.09349937380574\n ],\n [\n -120.80566406250001,\n 36.22211876039103\n ],\n [\n -120.94299316406249,\n 36.359374956015856\n ],\n [\n -121.06933593749999,\n 36.46988944681576\n ],\n [\n -121.55273437499999,\n 36.83127162140714\n ],\n [\n -121.61315917968749,\n 36.88840804313823\n ],\n [\n -121.72302246093749,\n 36.9806150652861\n ],\n [\n -121.84936523437499,\n 37.020098201368114\n ],\n [\n -121.9976806640625,\n 37.03325468997236\n ],\n [\n -122.06909179687501,\n 37.01132594307015\n ],\n [\n -122.1075439453125,\n 36.96306042436515\n ],\n [\n -122.0855712890625,\n 36.92793899776678\n ],\n [\n -121.9976806640625,\n 36.94989178681327\n ],\n [\n -121.92626953124999,\n 36.96306042436515\n ],\n [\n -121.86584472656251,\n 36.94989178681327\n ],\n [\n -121.8438720703125,\n 36.88401445049676\n ],\n [\n -121.8109130859375,\n 36.80928470205937\n ],\n [\n -121.8109130859375,\n 36.760891249565624\n ],\n [\n -121.83288574218749,\n 36.69044623523481\n ],\n [\n -121.8438720703125,\n 36.641977814705946\n ],\n [\n -121.8878173828125,\n 36.602299135790446\n ],\n [\n -121.9317626953125,\n 36.602299135790446\n ],\n [\n -121.915283203125,\n 36.56260003738545\n ],\n [\n -121.904296875,\n 36.53170884914869\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: September 2018; Version 1.0: May 2018","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1966","usgsCitation":"Brewer, S.K., Worthington, T., Mollenhauer, R., Stewart, D., McManamay, R., Guertault, L., and Moore, D., 2018, Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions: Ecohydrology, v. 11, no. 7, p. 1-26, https://doi.org/10.1002/eco.1966.","productDescription":"e1966; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-083229","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468724,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1435332","text":"External Repository"},{"id":354585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b16","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":736736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Worthington, Thomas","contributorId":205274,"corporation":false,"usgs":false,"family":"Worthington","given":"Thomas","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollenhauer, Robert","contributorId":205275,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, David","contributorId":205276,"corporation":false,"usgs":false,"family":"Stewart","given":"David","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":736739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":736740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guertault, Lucie","contributorId":205278,"corporation":false,"usgs":false,"family":"Guertault","given":"Lucie","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Desiree","contributorId":205279,"corporation":false,"usgs":false,"family":"Moore","given":"Desiree","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736742,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197340,"text":"70197340 - 2018 - Green‐wave surfing increases fat gain in a migratory ungulate","interactions":[],"lastModifiedDate":"2018-07-03T11:15:21","indexId":"70197340","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Green‐wave surfing increases fat gain in a migratory ungulate","docAbstract":"Each spring, migratory herbivores around the world track or ‘surf’ green waves of newly emergent vegetation to distant summer or wet‐season ranges. This foraging tactic may help explain the great abundance of migratory herbivores on many seasonal landscapes. However, the underlying fitness benefits of this life‐history strategy remain poorly understood. A fundamental prediction of the green‐wave hypothesis is that migratory herbivores obtain fitness benefits from surfing waves of newly emergent vegetation more closely than their resident counterparts. Here we evaluate whether this behavior increases body‐fat levels – a critically important correlate of reproduction and survival for most ungulates – in elk Cervus elaphus of the Greater Yellowstone Ecosystem. Using satellite imagery and GPS tracking data, we found evidence that migrants (n = 23) indeed surfed the green wave, occupying sites 12.7 days closer to peak green‐up than residents (n = 16). Importantly, individual variation in surfing may help account for up to 6 kg of variation in autumn body‐fat levels. Our findings point to a pathway for anthropogenic changes to the green wave (e.g. climate change) or migrants’ ability to surf it (e.g. development) to impact migratory populations. To explore this possibility, we evaluated potential population‐level consequences of constrained surfing with a heuristic model. If green‐wave surfing deteriorates by 5–15 days from observed, our model predicts up to a 20% decrease in pregnancy rates, a 2.5% decrease in population growth, and a 30% decrease in abundance over 50 years. By linking green‐wave surfing to fitness and illustrating potential effects on population growth, our study provides new insights into the evolution of migratory behavior and the prospects for the persistence of migratory ungulate populations in a changing world.
","language":"English","publisher":"Wiley","doi":"10.1111/oik.05227","usgsCitation":"Middleton, A., Merkle, J., McWhirter, D.E., Cook, J.G., Cook, R.C., White, P., and Kauffman, M., 2018, Green‐wave surfing increases fat gain in a migratory ungulate: Oikos, v. 127, no. 7, p. 1060-1068, https://doi.org/10.1111/oik.05227.","productDescription":"9 p.","startPage":"1060","endPage":"1068","ipdsId":"IP-084519","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":460913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/oik.05227","text":"Publisher Index Page"},{"id":354565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.09374999999999,\n 42.50450285299051\n ],\n [\n -107.6220703125,\n 42.50450285299051\n ],\n [\n -107.6220703125,\n 44.99588261816546\n ],\n [\n -111.09374999999999,\n 44.99588261816546\n ],\n [\n -111.09374999999999,\n 42.50450285299051\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-22","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b10","contributors":{"authors":[{"text":"Middleton, Arthur D.","contributorId":99440,"corporation":false,"usgs":true,"family":"Middleton","given":"Arthur D.","affiliations":[],"preferred":false,"id":736764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":736765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McWhirter, Douglas E.","contributorId":90623,"corporation":false,"usgs":true,"family":"McWhirter","given":"Douglas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":736766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, John G.","contributorId":12903,"corporation":false,"usgs":true,"family":"Cook","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":736767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Rachel C.","contributorId":19064,"corporation":false,"usgs":true,"family":"Cook","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":736768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, P.J.","contributorId":194049,"corporation":false,"usgs":false,"family":"White","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":736769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":false,"id":736745,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197346,"text":"70197346 - 2018 - Influence of climate on alpine stream chemistry and water sources","interactions":[],"lastModifiedDate":"2018-07-03T11:14:16","indexId":"70197346","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Influence of climate on alpine stream chemistry and water sources","docAbstract":"The resilience of alpine/subalpine watersheds may be viewed as the resistance of streamflow or stream chemistry to change under varying climatic conditions, which is governed by the relative size (volume) and transit time of surface and subsurface water sources. Here, we use end‐member mixing analysis in Andrews Creek, an alpine stream in Rocky Mountain National Park, Colorado, from water year 1994 to 2015, to explore how the partitioning of water sources and associated hydrologic resilience change in response to climate. Our results indicate that four water sources are significant contributors to Andrews Creek, including snow, rain, soil water, and talus groundwater. Seasonal patterns in source‐water contributions reflected the seasonal hydrologic cycle, which is driven by the accumulation and melting of seasonal snowpack. Flushing of soil water had a large effect on stream chemistry during spring snowmelt, despite making only a small contribution to streamflow volume. Snow had a large influence on stream chemistry as well, contributing large amounts of water with low concentrations of weathering products. Interannual patterns in end‐member contributions reflected responses to drought and wet periods. Moderate and significant correlations exist between annual end‐member contributions and regional‐scale climate indices (the Palmer Drought Severity Index, the Palmer Hydrologic Drought Index, and the Modified Palmer Drought Severity Index). From water year 1994 to 2015, the percent contribution from the talus‐groundwater end member to Andrews Creek increased an average of 0.5% per year (p < 0.0001), whereas the percent contributions from snow plus rain decreased by a similar amount (p = 0.001). Our results show how water and solute sources in alpine environments shift in response to climate variability and highlight the role of talus groundwater and soil water in providing hydrologic resilience to the system.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13124","usgsCitation":"Foks, S., Stets, E.G., Singha, K., and Clow, D.W., 2018, Influence of climate on alpine stream chemistry and water sources: Hydrological Processes, v. 32, no. 13, p. 1993-2008, https://doi.org/10.1002/hyp.13124.","productDescription":"16 p.","startPage":"1993","endPage":"2008","ipdsId":"IP-090691","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":468725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13124","text":"Publisher Index Page"},{"id":354581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","volume":"32","issue":"13","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-29","publicationStatus":"PW","scienceBaseUri":"5b155d74e4b092d9651e1b0c","contributors":{"authors":[{"text":"Foks, Sydney 0000-0002-7668-9735","orcid":"https://orcid.org/0000-0002-7668-9735","contributorId":205290,"corporation":false,"usgs":true,"family":"Foks","given":"Sydney","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736781,"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":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736784,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198026,"text":"70198026 - 2018 - The thermophysical properties of the Bagnold Dunes, Mars: Ground truthing orbital data","interactions":[],"lastModifiedDate":"2018-07-16T11:15:27","indexId":"70198026","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5718,"text":"Journal of Geophysical Research: Planets","onlineIssn":"2169-9100","active":true,"publicationSubtype":{"id":10}},"title":"The thermophysical properties of the Bagnold Dunes, Mars: Ground truthing orbital data","docAbstract":"We compare the thermophysical properties and particle sizes derived from the Mars Science Laboratory rover's Ground Temperature Sensor of the Bagnold dunes, specifically Namib dune, to those derived orbitally from Thermal Emission Imaging System, ultimately linking these measurements to ground truth particle sizes determined from Mars Hand Lens Imager images. In general, we find that all three datasets report consistent particle sizes for the Bagnold dunes (~110–350 μm and are within measurement and model uncertainties), indicating that particle sizes of homogeneous materials inferred from temperature measurements and thermophysical models are reliable. Furthermore, we examine the effects of two physical characteristics that could influence the modeled thermal inertia and particle sizes, including (1) fine‐scale (centimeter to meter scale) ripples and (2) thin layering of indurated/armored materials. To first order, we find that small‐scale ripples and thin (approximately centimeter scale) layers do not significantly affect the determination of bulk thermal inertia from orbital thermal data using a single nighttime temperature. Modeling of a layer of coarse or indurated material reveals that a thin layer (< ~5 mm; similar to what was observed by the Curiosity rover) would not significantly change the observed thermal properties of the surface and would be dominated by the properties of the underlying material. Thermal inertia and particle sizes of relatively homogeneous materials derived from nighttime orbital data should be considered as reliable, as long as there are no significant subpixel anisothermality effects (e.g., lateral mixing of multiple thermophysically distinct materials).
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017JE005501","usgsCitation":"Edwards, C., Piqueux, S., Hamilton, V.E., Fergason, R.L., Herkenhoff, K., Vasavada, A.R., Bennett, K.A., Sacks, L., Lewis, K., and Smith, M.D., 2018, The thermophysical properties of the Bagnold Dunes, Mars: Ground truthing orbital data: Journal of Geophysical Research: Planets, v. 123, no. 5, p. 1307-1326, https://doi.org/10.1029/2017JE005501.","productDescription":"15 p.","startPage":"1307","endPage":"1326","ipdsId":"IP-085162","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":468722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017je005501","text":"Publisher Index Page"},{"id":355551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Bagnold Dunes, Mars","volume":"123","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-31","publicationStatus":"PW","scienceBaseUri":"5b46e584e4b060350a15d1c0","contributors":{"authors":[{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":739692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piqueux, Sylvain","contributorId":56986,"corporation":false,"usgs":false,"family":"Piqueux","given":"Sylvain","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":739693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamilton, Victoria E.","contributorId":206169,"corporation":false,"usgs":false,"family":"Hamilton","given":"Victoria","email":"","middleInitial":"E.","affiliations":[{"id":37270,"text":"Southwest Research Institute, Boulder, Colo.","active":true,"usgs":false}],"preferred":false,"id":739694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herkenhoff, Kenneth E. 0000-0002-3153-6663","orcid":"https://orcid.org/0000-0002-3153-6663","contributorId":206170,"corporation":false,"usgs":true,"family":"Herkenhoff","given":"Kenneth E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739695,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vasavada, Ashwin R.","contributorId":200409,"corporation":false,"usgs":false,"family":"Vasavada","given":"Ashwin","email":"","middleInitial":"R.","affiliations":[],"preferred":true,"id":739696,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bennett, Kristen A.","contributorId":206171,"corporation":false,"usgs":false,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":739697,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sacks, Leah","contributorId":206172,"corporation":false,"usgs":false,"family":"Sacks","given":"Leah","email":"","affiliations":[{"id":37271,"text":"Carelton College, Northfield, Minn.","active":true,"usgs":false}],"preferred":false,"id":739698,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lewis, Kevin","contributorId":195296,"corporation":false,"usgs":false,"family":"Lewis","given":"Kevin","affiliations":[],"preferred":false,"id":739699,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Michael D.","contributorId":206173,"corporation":false,"usgs":false,"family":"Smith","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":739700,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70198755,"text":"70198755 - 2018 - The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska","interactions":[],"lastModifiedDate":"2022-04-22T16:31:43.931967","indexId":"70198755","displayToPublicDate":"2018-05-29T10:07:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska","docAbstract":"Wetlands are critical terrestrial ecosystems in Alaska, covering ~177,000 km2, an area greater than all the wetlands in the remainder of the United States. To assess the relative influence of changing climate, atmospheric carbon dioxide (CO2) concentration, and fire regime on carbon balance in wetland ecosystems of Alaska, a modeling framework that incorporates a fire disturbance model and two biogeochemical models was used. Spatially explicit simulations were conducted at 1‐km resolution for the historical period (1950–2009) and future projection period (2010–2099). Simulations estimated that wetland ecosystems of Alaska lost 175 Tg carbon (C) in the historical period. Ecosystem C storage in 2009 was 5,556 Tg, with 89% of the C stored in soils. The estimated loss of C as CO2 and biogenic methane (CH4) emissions resulted in wetlands of Alaska increasing the greenhouse gas forcing of climate warming. Simulations for the projection period were conducted for six climate change scenarios constructed from two climate models forced under three CO2 emission scenarios. Ecosystem C storage averaged among climate scenarios increased 3.94 Tg C/yr by 2099, with variability among the simulations ranging from 2.02 to 4.42 Tg C/yr. These increases were driven primarily by increases in net primary production (NPP) that were greater than losses from increased decomposition and fire. The NPP increase was driven by CO2 fertilization (~5% per 100 parts per million by volume increase) and by increases in air temperature (~1% per °C increase). Increases in air temperature were estimated to be the primary cause for a projected 47.7% mean increase in biogenic CH4 emissions among the simulations (~15% per °C increase). Ecosystem CO2 sequestration offset the increase in CH4 emissions during the 21st century to decrease the greenhouse gas forcing of climate warming. However, beyond 2100, we expect that this forcing will ultimately increase as wetland ecosystems transition from being a sink to a source of atmospheric CO2 because of (1) decreasing sensitivity of NPP to increasing atmospheric CO2, (2) increasing availability of soil C for decomposition as permafrost thaws, and (3) continued positive sensitivity of biogenic CH4 emissions to increases in soil temperature.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1755","usgsCitation":"Lyu, Z., Genet, H., He, Y., Zhuang, Q., McGuire, A.D., Bennett, A., Breen, A., Clein, J., Euskirchen, E.S., Johnson, K., Kurkowski, T., Pastick, N.J., Rupp, T.S., Wylie, B.K., and Zhu, Z., 2018, The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska: Ecological Applications, v. 28, no. 6, p. 1377-1395, https://doi.org/10.1002/eap.1755.","productDescription":"19 p.","startPage":"1377","endPage":"1395","ipdsId":"IP-091563","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":490053,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1457077","text":"External Repository"},{"id":356617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-141.0007667541504,69.64681951728146],[-141.064453125,69.80172356231073],[-141.767578125,69.96043926902489],[-142.734375,70.1478274118401],[-143.26171875,70.25945200030638],[-144.99755859375,70.1925497583889],[-146.14013671875,70.21487465331137],[-147.43652343749997,70.32613725493573],[-148.40332031249997,70.51024068514326],[-149.47998046875,70.63448406630856],[-149.85351562499997,70.63448406630856],[-150.64453125,70.59802116106809],[-151.611328125,70.61261423801925],[-151.8310546875,70.7724429742589],[-152.02880859375,70.90226826757711],[-152.666015625,71.0098110139634],[-153.52294921875,71.05979781529196],[-154.31396484375,70.95969716686398],[-154.70947265625,71.20191973293133],[-155.41259765625,71.3219146980122],[-156.46728515625,71.45515260247822],[-157.10449218749997,71.34301347171373],[-157.58789062499997,71.21607526596131],[-157.91748046875,70.99550574822297],[-158.62060546875,70.9883492241249],[-159.30175781249997,70.98119010476937],[-159.98291015625,70.94535555009823],[-160.51025390625,70.73622993891799],[-160.90576171875,70.58341752317065],[-161.5869140625,70.45150843439349],[-161.78466796875,70.45885925640687],[-162.5537109375,70.34092679475283],[-163.05908203125,70.09552886456429],[-163.388671875,69.81689109911446],[-163.564453125,69.51914693717981],[-164.02587890625,69.15474044269264],[-164.90478515625,69.03714171275197],[-165.69580078124997,68.97416358340674],[-166.26708984375,69.01354605132325],[-166.376953125,68.80004113882613],[-166.66259765624997,68.5443150407769],[-167.1240234375,68.39918004344189],[-166.61865234375,68.17155518732503],[-166.5087890625,67.99110834539984],[-165.87158203125,67.8672645403614],[-164.61914062499997,67.47492238478702],[-164.24560546874997,67.23806155909902],[-164.091796875,67.02458758377148],[-163.10302734375,66.87834504307976],[-163.05908203125,66.73990169639414],[-162.66357421875,66.58321725728175],[-162.333984375,66.48697584176404],[-162.3779296875,66.34632215978135],[-163.2568359375,66.31103501145373],[-163.19091796875,66.55700652350038],[-163.76220703124997,66.73990169639414],[-164.46533203125,66.69647781801481],[-165.30029296875,66.55700652350038],[-166.1572265625,66.41674787052298],[-166.9482421875,66.24916310923315],[-167.3876953125,66.01801815922045],[-168.42041015625,65.56754970214311],[-168.02490234375,65.33017791526855],[-167.45361328125,65.18303007291382],[-166.9921875,64.86760781632728],[-166.7724609375,64.49172504435471],[-165.69580078124997,64.24459476798195],[-164.59716796875,64.29229248039543],[-164.13574218749997,64.26368374017731],[-163.49853515625,64.18724867664994],[-162.5537109375,64.27322328178595],[-161.82861328124997,64.24459476798195],[-161.52099609375,63.93737246791484],[-163.125,63.68524808030715],[-163.93798828125,63.41119772365924],[-164.5751953125,63.32254947641308],[-165.41015625,63.15435519659187],[-165.78369140625,62.75472592723178],[-166.26708984375,62.42090322195164],[-166.5966796875,62.155240711732425],[-166.48681640625,61.616843178481375],[-166.2451171875,61.23853141060282],[-165.849609375,60.8663124746226],[-166.5966796875,60.56537850464181],[-167.34375,60.468050120874615],[-167.80517578125,60.20707506634915],[-167.8271484375,60.09771842541544],[-167.89306640624997,59.93300042374631],[-167.03613281249997,59.712097173322924],[-166.640625,59.61221219518693],[-165.95947265624997,59.60109549032134],[-165.47607421874997,59.74532608213611],[-165.10253906249997,59.95501026206206],[-164.794921875,59.987997631212224],[-164.72900390624997,59.7563950493563],[-164.46533203125,59.54545678424146],[-163.8720703125,59.54545678424146],[-163.14697265625,59.60109549032134],[-162.26806640625,59.62332522313024],[-162.20214843749997,59.17592824927136],[-162.35595703125,58.81374171570782],[-162.57568359375,58.6769376725869],[-162.09228515625,58.39019698411526],[-161.455078125,58.39019698411526],[-160.68603515625,58.44773280389084],[-160.1806640625,58.516651799363785],[-159.71923828125,58.58543569119917],[-159.43359375,58.47072082411973],[-159.08203125,58.33256713195789],[-158.55468749999997,58.286395482881034],[-158.44482421874997,58.619777025081675],[-157.60986328125,58.52812515905843],[-158.00537109375,58.00809779306888],[-158.48876953125,57.468589192089354],[-159.49951171875,56.84897198026975],[-161.0595703125,56.389583525613055],[-164.9267578125,54.97761367069628],[-165.9814453125,54.470037612805754],[-168.55224609375,53.73571574532637],[-173.232421875,52.92215137976296],[-175.62744140624997,52.3755991766591],[-177.86865234375,52.13348804077147],[-178.9453125,50.98609893339354],[-178.00048828125,51.440312757160115],[-176.8359375,51.467696956223364],[-175.36376953125,51.7406361640977],[-171.826171875,52.119998657638156],[-167.62939453124997,52.9883372533954],[-166.728515625,53.186287573913305],[-165.9375,53.553362785528094],[-165.30029296875,53.76170183021049],[-164.3115234375,54.149567212540525],[-163.89404296875,54.29088164657006],[-163.3447265625,54.18815548107151],[-162.26806640625,54.07228265560388],[-162.09228515625,54.3549556895541],[-161.89453125,54.7246201949245],[-161.0595703125,54.80068486732233],[-160.400390625,54.67383096593114],[-159.19189453125,54.61025498157912],[-159.14794921875,55.07836723201515],[-158.79638671875,55.429013452407396],[-157.58789062499997,55.825973254619015],[-155.7421875,55.541064956111036],[-154.62158203125,56.01066647040695],[-153.47900390625,56.43820369358165],[-151.45751953125,57.397624055000456],[-151.4794921875,58.07787626787517],[-151.45751953125,58.75680543225761],[-149.74365234374997,59.38917842312835],[-148.51318359375,59.63443457494949],[-146.689453125,59.355596110016315],[-144.51416015625,59.75086102411168],[-144.3109130859375,59.87239799228177],[-143.8330078125,59.968758992382334],[-143.0694580078125,60.031929699115615],[-141.5533447265625,59.842055288480076],[-140.9051513671875,59.68160832698723],[-140.020751953125,59.478568831926395],[-139.1693115234375,59.234986238722],[-138.82873535156247,59.09138238455909],[-138.3233642578125,58.96983560365735],[-138.1146240234375,58.862064179600374],[-138.076171875,58.722598828043374],[-136.9775390625,58.19387126497797],[-136.56005859375,57.7862326105289],[-135.966796875,57.33838126552897],[-136.03271484375,57.052681978717494],[-135.81298828125,56.92099675839107],[-134.571533203125,55.8845546603819],[-134.2034912109375,55.56592203025787],[-133.8958740234375,55.263468250921285],[-133.7530517578125,55.06264118216743],[-133.6102294921875,54.64523407607479],[-133.2421875,54.635697306063854],[-130.6171417236328,54.70637513489091],[-130.62950134277344,54.72422365048395],[-130.62606811523438,54.73651472417763],[-130.65765380859375,54.762274228176494],[-130.62950134277344,54.78247406031503],[-130.5663299560547,54.79237225560392],[-130.49697875976562,54.82877675365454],[-130.42282104492188,54.87423625974835],[-130.34591674804688,54.91569803760518],[-130.27244567871094,54.97288463122321],[-130.18661499023438,55.062247951730015],[-130.18043518066406,55.091729515360875],[-130.15090942382812,55.12393783348962],[-130.14747619628906,55.14160209881279],[-130.10284423828125,55.19219635238084],[-129.97169494628906,55.28146181651345],[-129.97581481933594,55.30022902025666],[-130.02044677734375,55.33890835596374],[-130.0396728515625,55.45043679812318],[-130.0884246826172,55.496749338303694],[-130.12825012207028,55.58144971869657],[-130.10971069335938,55.68223010941079],[-130.14816284179688,55.71473455012689],[-130.15296936035156,55.7649857705176],[-130.12550354003906,55.80475427021683],[-130.0843048095703,55.82134464477078],[-130.00465393066406,55.90573012454021],[-130.00465393066406,55.9130425993163],[-130.0190734863281,55.912657766599715],[-130.00259399414062,56.00605986001467],[-130.10421752929688,56.12297419573329],[-130.24635314941406,56.09693875609652],[-130.3479766845703,56.12794955397159],[-130.42556762695312,56.14134155069025],[-130.4674530029297,56.24373146827144],[-130.55740356445312,56.249454174583384],[-130.5677032470703,56.25479459547735],[-130.62400817871094,56.2685236855868],[-130.78262329101562,56.36715174252849],[-131.08612060546875,56.40668363558357],[-131.16989135742188,56.44883107459549],[-131.473388671875,56.551913918713375],[-131.58119201660156,56.61204220477141],[-131.8352508544922,56.59843662755775],[-131.85997009277344,56.702620872371355],[-131.89979553222656,56.75347577609789],[-131.87232971191406,56.805765643008264],[-132.12432861328122,56.87374615531272],[-132.0467376708984,57.04521234171931],[-132.3687744140625,57.09149987857074],[-132.2472381591797,57.211056900559335],[-132.3680877685547,57.347273783306676],[-132.55210876464844,57.49516565182901],[-132.65853881835938,57.61562391374733],[-132.75466918945312,57.69680911844304],[-132.8693389892578,57.83853792318956],[-133.06983947753906,58.00082136594698],[-133.17283630371094,58.15404059343076],[-133.34518432617188,58.27628739957773],[-133.45985412597656,58.38731772556939],[-133.37608337402344,58.430481925680034],[-133.70567321777344,58.611194853078764],[-133.83956909179685,58.730440812979516],[-134.25979614257812,58.861354043320055],[-134.3360137939453,58.92414471817596],[-134.3140411376953,58.962755708753306],[-134.4060516357422,58.978683427688686],[-134.38133239746094,59.03878841190553],[-134.44656372070312,59.08820785301446],[-134.48501586914062,59.13121539881386],[-134.56329345703125,59.130510792073984],[-134.67933654785156,59.191757369765085],[-134.70130920410156,59.24973478117606],[-134.95742797851562,59.279914277804906],[-135.02883911132812,59.34649517787861],[-134.9897003173828,59.3877798237848],[-135.10093688964844,59.42622028594434],[-135.07827758789062,59.45275367774563],[-135.0274658203125,59.47473269180728],[-135.03021240234375,59.564245132658975],[-135.11810302734372,59.62367244601488],[-135.15586853027344,59.625061301654334],[-135.2190399169922,59.6632323288228],[-135.23345947265625,59.69650975428769],[-135.252685546875,59.69789559656873],[-135.36048889160156,59.73598378851403],[-135.4779052734375,59.79821644465919],[-135.94894409179688,59.6632323288228],[-136.1927032470703,59.63998787256213],[-136.34788513183594,59.60109549032134],[-136.25038146972656,59.56633207991906],[-136.24076843261716,59.55972296971678],[-136.24076843261716,59.52387204745182],[-136.3066864013672,59.46461714320982],[-136.36642456054688,59.4496126517294],[-136.47628784179688,59.46566371970234],[-136.46804809570312,59.28552611855346],[-136.49620056152344,59.27465233689575],[-136.4900207519531,59.26096748461385],[-136.5840911865234,59.166075318301345],[-136.8285369873047,59.16009179641602],[-136.8793487548828,59.13544273484683],[-137.28240966796875,59.0009698708429],[-137.449951171875,58.908900972391415],[-137.52548217773438,58.906418795609426],[-137.5000762939453,58.985760051467075],[-137.54127502441406,59.10478272378236],[-137.60787963867188,59.24376590151355],[-138.62617492675778,59.76746035005358],[-138.66600036621094,59.80961318716828],[-138.6797332763672,59.84481485969105],[-138.70582580566406,59.90650046741583],[-139.05258178710938,59.994179105518434],[-139.19952392578125,60.08950200748712],[-139.0711212158203,60.3187885497516],[-139.07386779785156,60.35243208301854],[-139.69253540039062,60.33544473468298],[-139.97955322265625,60.181818669034776],[-140.4595184326172,60.30858669066228],[-140.5199432373047,60.22003701633967],[-141.00128173828125,60.3058656567224],[-141.0007667541504,69.64681951728146]]]}},{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-173.07586669921875,60.72157115165579],[-173.155517578125,60.69469537287745],[-173.15277099609375,60.64356945377967],[-173.08135986328125,60.61123754937553],[-173.04016113281247,60.58157148491742],[-173.08135986328125,60.53972302275651],[-173.089599609375,60.511343283202464],[-173.05938720703125,60.4788788301667],[-172.98248291015625,60.468050120874615],[-172.94677734374997,60.43689744859958],[-172.8424072265625,60.403001945865476],[-172.78472900390625,60.373144671593685],[-172.7105712890625,60.329667021005825],[-172.6611328125,60.3187885497516],[-172.5897216796875,60.309266913738156],[-172.49908447265625,60.31606836555203],[-172.4139404296875,60.3187885497516],[-172.35076904296875,60.3187885497516],[-172.30682373046872,60.29021531318375],[-172.2381591796875,60.29021531318375],[-172.17498779296875,60.30518536282736],[-172.2381591796875,60.333745513303114],[-172.34527587890625,60.378575303227215],[-172.364501953125,60.40164539086417],[-172.43041992187497,60.40571488624096],[-172.4798583984375,60.39757538658664],[-172.57598876953125,60.41249624776229],[-172.6556396484375,60.43689744859958],[-172.77374267578122,60.4788788301667],[-172.83416748046875,60.50052541051131],[-172.89459228515625,60.550527811064846],[-172.8863525390625,60.588316165776824],[-172.91656494140625,60.62606036274505],[-172.98797607421875,60.658377412327326],[-173.01544189453125,60.69469537287745],[-173.07586669921875,60.72157115165579]]]}},{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-171.650390625,63.809167882566385],[-171.793212890625,63.82128765261384],[-171.80419921875,63.73147780336167],[-171.8426513671875,63.65601144183318],[-171.8865966796875,63.54365806976644],[-171.859130859375,63.42594585479083],[-171.7877197265625,63.34966546248425],[-171.62841796875,63.32501562217765],[-171.474609375,63.28306240110864],[-171.353759765625,63.29540792564745],[-171.2548828125,63.33980806067484],[-171.1395263671875,63.38413977217118],[-171.002197265625,63.389061297647125],[-170.760498046875,63.34966546248425],[-170.57373046875,63.32501562217765],[-170.41992187499997,63.27812271092345],[-170.343017578125,63.1989725264735],[-170.3594970703125,63.156835740093236],[-170.2496337890625,63.156835740093236],[-170.145263671875,63.156835740093236],[-170.0408935546875,63.14194929585152],[-169.9090576171875,63.087300267152735],[-169.8321533203125,63.03753005973634],[-169.7991943359375,62.990169510232555],[-169.8101806640625,62.95522304515911],[-169.74975585937497,62.922735326966595],[-169.617919921875,62.91523303947614],[-169.54650878906247,62.9502272814474],[-169.4915771484375,62.97270150065472],[-169.508056640625,62.99765260346662],[-169.4970703125,63.04251090966805],[-169.43664550781247,63.08978654472616],[-169.34326171874997,63.11712157280328],[-169.178466796875,63.13946747896222],[-169.1070556640625,63.14443090047572],[-168.958740234375,63.104699747121074],[-168.760986328125,63.112153479825004],[-168.67309570312497,63.203925767041305],[-168.662109375,63.26576978358972],[-168.7115478515625,63.3348780927218],[-168.92578125,63.366907787681754],[-169.07958984374997,63.366907787681754],[-169.25537109375,63.37183226679281],[-169.420166015625,63.376755901872734],[-169.5245361328125,63.389061297647125],[-169.6124267578125,63.43331707559086],[-169.705810546875,63.46278300222105],[-169.8211669921875,63.46523712749102],[-169.947509765625,63.48976680530999],[-170.0079345703125,63.59011870211632],[-170.0958251953125,63.658448979940175],[-170.2386474609375,63.704722429433225],[-170.4638671875,63.73390885572919],[-170.5902099609375,63.721751503619956],[-170.7659912109375,63.6779417467744],[-171.2164306640625,63.648697570849286],[-171.474609375,63.6779417467744],[-171.54052734375,63.75334975181205],[-171.650390625,63.809167882566385]]]}},{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-170.40618896484375,57.022794415389725],[-170.3155517578125,57.043718234032625],[-170.22216796875,57.119841130872615],[-170.1947021484375,57.14518072479997],[-170.11505126953125,57.18985535714817],[-170.08209228515625,57.227042992549855],[-170.07110595703125,57.271618718194446],[-170.189208984375,57.23893512461504],[-170.2386474609375,57.22852971878346],[-170.32928466796875,57.22852971878346],[-170.3704833984375,57.22406936030381],[-170.49407958984375,57.20473490715757],[-170.41992187499997,57.12878649751151],[-170.364990234375,57.11387635258491],[-170.42266845703125,57.06910989239133],[-170.46112060546875,57.033257797376066],[-170.40618896484375,57.022794415389725]]]}},{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-169.8321533203125,56.62904228542147],[-169.8211669921875,56.60486209416893],[-169.7991943359375,56.586716786451156],[-169.71405029296875,56.565536245992064],[-169.71405029296875,56.551913918713375],[-169.63165283203125,56.51707901932375],[-169.56024169921875,56.515563731608296],[-169.5025634765625,56.553427752820355],[-169.43115234375,56.58369172128337],[-169.43664550781247,56.626020608371924],[-169.56024169921875,56.63055303322322],[-169.6783447265625,56.62450967912138],[-169.8321533203125,56.62904228542147]]]}}]}","volume":"28","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-25","publicationStatus":"PW","scienceBaseUri":"5b98a2bbe4b0702d0e842fcf","contributors":{"authors":[{"text":"Lyu, Zhou","contributorId":204525,"corporation":false,"usgs":false,"family":"Lyu","given":"Zhou","email":"","affiliations":[],"preferred":false,"id":742858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Genet, Helene","contributorId":198686,"corporation":false,"usgs":false,"family":"Genet","given":"Helene","email":"","affiliations":[],"preferred":false,"id":742859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"He, Yujie","contributorId":207136,"corporation":false,"usgs":false,"family":"He","given":"Yujie","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":742860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhuang, Qianlai","contributorId":207137,"corporation":false,"usgs":false,"family":"Zhuang","given":"Qianlai","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":742861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":742854,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Alec","contributorId":198680,"corporation":false,"usgs":false,"family":"Bennett","given":"Alec","email":"","affiliations":[],"preferred":false,"id":742862,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Breen, Amy","contributorId":198679,"corporation":false,"usgs":false,"family":"Breen","given":"Amy","affiliations":[],"preferred":false,"id":742863,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Clein, Joy","contributorId":207138,"corporation":false,"usgs":false,"family":"Clein","given":"Joy","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":742864,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Euskirchen, Eugenie S.","contributorId":207139,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugenie","email":"","middleInitial":"S.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":742865,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Kristofer","contributorId":195181,"corporation":false,"usgs":false,"family":"Johnson","given":"Kristofer","affiliations":[],"preferred":false,"id":742866,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kurkowski, Tom","contributorId":198681,"corporation":false,"usgs":false,"family":"Kurkowski","given":"Tom","affiliations":[],"preferred":false,"id":742867,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":742855,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rupp, T. Scott","contributorId":195180,"corporation":false,"usgs":false,"family":"Rupp","given":"T.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":742868,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":742856,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":742857,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70271466,"text":"70271466 - 2018 - Dust on a Hawaiian volcano: A regional model using field measurements to estimate transport and deposition","interactions":[],"lastModifiedDate":"2025-09-16T15:05:26.892848","indexId":"70271466","displayToPublicDate":"2018-05-29T10:00:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Dust on a Hawaiian volcano: A regional model using field measurements to estimate transport and deposition","docAbstract":"The western slopes of Hawaii's Mauna Kea volcano are mantled by fine-grained soils, the record of volcanic airfall and eolian deposition. Where exposed, strong winds transport this sediment across West Hawaii, affecting tourism and local communities with decreased air and water quality. Operations on US Army's Ke'amuku Maneuver Area (KMA) have the potential to increase dust flux from these deposits. The USGS established 18 ground monitoring sites and sampling locations surrounding KMA. For over 3 years, each station measured vertical and horizontal dust flux, while co-located anemometers measured wind speed and direction. We used these datasets to develop a parsimonious regional model for dust supply and transport to assess whether KMA is a net dust sink or source.
We found that dust transport is most highly correlated with threshold wind speeds of 8 m/s. We used this value as the regional average threshold wind speed for dust entrainment. Using a model that partitions measured horizontal dust flux into inward- and outward-directed components, we estimate that KMA is currently a net dust sink. Geochemical analysis of dust samples illustrates that local organics and carbonate make up 64% of dust mass, the remainder being volcanic silt and fine sand. Measured vertical dust deposition rates of 0.006 mm/yr are similar to 0.004 mm/yr of deposition predicted from taking the divergence of dust across KMA's boundary. These rates are low compared with pre-historic rates of ~0.2–0.3 mm/yr, from radiocarbon dating of buried soils.
KMA's soils record persistent deposition over millennia, at rates that imply episodic dust storms. Such events created a soil-mantled landscape in the middle of a largely Pleistocene rocky landscape. A substantial portion of fine-grained soils in other leeward Hawaiian Island landscapes may have formed from similar eolian deposition, and not direct weathering of parent rock. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4433","usgsCitation":"Douglas, M.M., Stock, J.D., Bishaw, K., Cerovski-Darriau, C., and Bedford, D., 2018, Dust on a Hawaiian volcano: A regional model using field measurements to estimate transport and deposition: Earth Surface Processes and Landforms, v. 43, no. 13, p. 2794-2807, https://doi.org/10.1002/esp.4433.","productDescription":"14 p.","startPage":"2794","endPage":"2807","ipdsId":"IP-089070","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.43611220977434,\n 20.37037201678426\n ],\n [\n -156.43611220977434,\n 18.704375414661897\n ],\n [\n -154.52622863370635,\n 18.704375414661897\n ],\n [\n -154.52622863370635,\n 20.37037201678426\n ],\n [\n -156.43611220977434,\n 20.37037201678426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"13","noUsgsAuthors":false,"publicationDate":"2018-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas, Madison M; 0000-0002-0762-4719","orcid":"https://orcid.org/0000-0002-0762-4719","contributorId":361469,"corporation":false,"usgs":false,"family":"Douglas","given":"Madison","middleInitial":"M;","affiliations":[{"id":86294,"text":"Caltech/USGS","active":true,"usgs":false}],"preferred":false,"id":948864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bishaw, Kai'ena; II","contributorId":361470,"corporation":false,"usgs":false,"family":"Bishaw","given":"Kai'ena;","suffix":"II","affiliations":[{"id":86297,"text":"Hawaii Cooperative Studies Unit, University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":948866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":948867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedford, David","contributorId":361471,"corporation":false,"usgs":true,"family":"Bedford","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948868,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197299,"text":"70197299 - 2018 - Substrate and flow characteristics associated with White Sturgeon recruitment in the Columbia River Basin","interactions":[],"lastModifiedDate":"2018-05-29T11:39:20","indexId":"70197299","displayToPublicDate":"2018-05-29T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5211,"text":"Heliyon","active":true,"publicationSubtype":{"id":10}},"title":"Substrate and flow characteristics associated with White Sturgeon recruitment in the Columbia River Basin","docAbstract":"A study was conducted to identify habitat characteristics associated with age 0+ White Sturgeon (Acipenser transmontanus Richardson, 1863) recruitment in three reaches of the Columbia River Basin: Skamania reach (consistent recruitment), John Day reach (intermittent/inconsistent recruitment), and Kootenai reach (no recruitment). Our modeling approach involved numerous steps. First, we collected information about substrate, embeddedness, and hydrodynamics in each reach. Second, we developed a set of spatially explicit predictor variables. Third, we built two habitat (probability) models with Skamania reach training data where White Sturgeon recruitment was consistent. Fourth, we created spawning maps of each reach by populating the habitat models with in-reach physical metrics (substrate, embeddedness, and hydrodynamics). Fifth, we examined model accuracy by overlaying spawning locations in Skamania and Kootenai reaches with habitat predictions obtained from probability models. Sixth, we simulated how predicted habitat changed in each reach after manipulating physical conditions to more closely match Skamania reach. Model verification confirmed White Sturgeon generally spawned in locations with higher model probabilities in Skamania and Kootenai reaches, indicating the utility of extrapolating the models. Model simulations revealed significant gains in White Sturgeon habitat in all reaches when spring flow increased, gravel/cobble composition increased, or embeddedness decreased. The habitat models appear well suited to assist managers when identifying reach-specific factors limiting White Sturgeon recruitment in the Columbia River Basin or throughout its range.","language":"English","publisher":"Elsevier","doi":"10.1016/j.heliyon.2018.e00629","usgsCitation":"Hatten, J.R., Parsley, M., Barton, G., Batt, T., and Fosness, R.L., 2018, Substrate and flow characteristics associated with White Sturgeon recruitment in the Columbia River Basin: Heliyon, v. 4, no. 5, e00629; 28 p., https://doi.org/10.1016/j.heliyon.2018.e00629.","productDescription":"e00629; 28 p.","ipdsId":"IP-090113","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.heliyon.2018.e00629","text":"Publisher Index Page"},{"id":354516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Oregon, Washington","otherGeospatial":"Columbia River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.94775390625,\n 45.120052841530544\n ],\n [\n -114.01611328125,\n 45.120052841530544\n ],\n [\n -114.01611328125,\n 49.32512199104001\n ],\n [\n -123.94775390625,\n 49.32512199104001\n ],\n [\n -123.94775390625,\n 45.120052841530544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d76e4b092d9651e1b24","contributors":{"authors":[{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":736576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsley, Michael 0000-0003-0097-6364 mparsley@usgs.gov","orcid":"https://orcid.org/0000-0003-0097-6364","contributorId":205229,"corporation":false,"usgs":true,"family":"Parsley","given":"Michael","email":"mparsley@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":736577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barton, Gary gbarton@usgs.gov","contributorId":205230,"corporation":false,"usgs":true,"family":"Barton","given":"Gary","email":"gbarton@usgs.gov","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batt, Thomas","contributorId":205231,"corporation":false,"usgs":false,"family":"Batt","given":"Thomas","affiliations":[{"id":37060,"text":"No longer with USGS but using USGS WFRC affiliation on MS","active":true,"usgs":false}],"preferred":false,"id":736579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fosness, Ryan L. 0000-0003-4089-2704 rfosness@usgs.gov","orcid":"https://orcid.org/0000-0003-4089-2704","contributorId":2703,"corporation":false,"usgs":true,"family":"Fosness","given":"Ryan","email":"rfosness@usgs.gov","middleInitial":"L.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736580,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}