We surveyed for Least Bell’s Vireos (LBVI) (Vireo bellii pusillus) and Southwestern Willow Flycatchers (SWFL) (Empidonax traillii extimus) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2016. Surveys were done from March 30 to July 11 (LBVI) and from May 18 to July 30 (SWFL). We found 142 LBVI territories, at least 106 of which were occupied by pairs. Six additional transient LBVIs were detected. Of 20 banded LBVIs detected in the survey area, 9 had been given full color-band combinations prior to 2016, although we were unable to determine the exact color combination of 1 female LBVI. Seven other LBVIs with single (natal) federal bands were recaptured and banded in 2016. Four vireos with single dark blue federal bands indicating that they were banded as nestlings on the lower San Luis Rey River could not be recaptured for identification.
Three SFWL territories were observed in the survey area in 2016. Two territories were occupied by pairs and one by a male of unknown breeding status. Both pairs attempted to nest at least once, and both pairs were successful, fledging three young each. Nesting began in early June and continued into July. Brown-Headed Cowbird (Molothrus ater) eggs were not observed in either nest. An additional 12 transient Willow Flycatchers of unknown subspecies were detected in 2016.
Two of the five resident SWFLs were originally banded as nestlings on Marine Corps Base Camp Pendleton. One male and one female were banded as nestlings on Camp Pendleton in 2009 and 2011, respectively. One natal male of unknown breeding status, originally banded as a nestling on the middle San Luis Rey River in 2015, was recaptured and given a unique color combination in 2016. This male was later detected on Marine Corps Base Camp Pendleton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1065","usgsCitation":"Allen, L.D., Howell, S.L., and Kus, B.E., 2017, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego, southern California—2016 data summary: U.S. Geological Survey Data Series 1065, 11 p., https://doi.org/10.3133/ds1065.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-087469","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1065/coverthb.jpg"},{"id":346298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1065/ds1065.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1065"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Middle San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3020553588867,\n 33.244430403478276\n ],\n [\n -117.158203125,\n 33.244430403478276\n ],\n [\n -117.158203125,\n 33.324504165325195\n ],\n [\n -117.3020553588867,\n 33.324504165325195\n ],\n [\n -117.3020553588867,\n 33.244430403478276\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2017.09.057","usgsCitation":"Grinde, A.R., Hiemi, G.J., Sturtevant, B.R., Panci, H., Thogmartin, W.E., and Wolter, P., 2017, Importance of scale, land cover, and weather on the abundance of bird species in a managed forest: Forest Ecology and Management, v. 405, p. 295-308, https://doi.org/10.1016/j.foreco.2017.09.057.","productDescription":"14 p.","startPage":"295","endPage":"308","ipdsId":"IP-083833","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/239","text":"Publisher Index Page"},{"id":352524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Chippewa National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.71588134765625,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 46.80193957664001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"405","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7ebe4b0da30c1bfc3b3","contributors":{"authors":[{"text":"Grinde, Alexis R.","contributorId":196778,"corporation":false,"usgs":false,"family":"Grinde","given":"Alexis","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hiemi, Gerald J.","contributorId":196780,"corporation":false,"usgs":false,"family":"Hiemi","given":"Gerald","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturtevant, Brian R.","contributorId":190143,"corporation":false,"usgs":false,"family":"Sturtevant","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panci, Hannah","contributorId":196779,"corporation":false,"usgs":false,"family":"Panci","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":711558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":711556,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolter, Peter","contributorId":196781,"corporation":false,"usgs":false,"family":"Wolter","given":"Peter","affiliations":[],"preferred":false,"id":711561,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191169,"text":"70191169 - 2017 - Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection","interactions":[],"lastModifiedDate":"2017-09-28T11:54:17","indexId":"70191169","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection","docAbstract":"Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.08.017","usgsCitation":"Jarnevich, C.S., Talbert, M., Morisette, J.T., Aldridge, C.L., Brown, C., Kumar, S., Manier, D.J., Talbert, C., and Holcombe, T.R., 2017, Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection: Ecological Modelling, v. 363, p. 48-56, https://doi.org/10.1016/j.ecolmodel.2017.08.017.","productDescription":"9 p.","startPage":"48","endPage":"56","ipdsId":"IP-073503","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8486328125,\n 31.615965936476076\n ],\n [\n -101.90917968749999,\n 31.615965936476076\n ],\n [\n -101.90917968749999,\n 49.06666839558117\n ],\n [\n -124.8486328125,\n 49.06666839558117\n ],\n [\n -124.8486328125,\n 31.615965936476076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"363","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a26e4b05fe04cc020f6","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":711395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":711396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":711397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Cynthia 0000-0001-8486-7119","orcid":"https://orcid.org/0000-0001-8486-7119","contributorId":196741,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia","affiliations":[],"preferred":false,"id":711398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":711399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manier, Daniel J. 0000-0002-1105-1327 manierd@usgs.gov","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":127553,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","email":"manierd@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711401,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711402,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70191174,"text":"70191174 - 2017 - Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed","interactions":[],"lastModifiedDate":"2017-09-28T13:23:41","indexId":"70191174","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed","docAbstract":"The Carson River is a vital water resource for local municipalities and migratory birds travelling the Pacific Flyway. Historic mining practices that used mercury (Hg) to extract gold from Comstock Lode ore has left much of the river system heavily contaminated with Hg, a practice that continues in many parts of the world today. Between 1998 and 2013, the United States Geological Survey (USGS) collected and analyzed Carson River water for Hg and methylmercury (MeHg) concentrations resulting in a sixteen year record of unfiltered total mercury (uf.THg), filtered (dissolved) Hg (f.THg), total methylmercury (uf.MeHg), filtered MeHg (f.MeHg), and particulate-bound THg (p.THg) and MeHg (p.MeHg) concentrations. This represents one of the longest continuous records of Hg speciation data for any riverine system, thereby providing a unique opportunity to evaluate long-term trends in concentrations and annual loads. During the period of analysis, uf.THg concentration and load trended downward at rates of −0.85% and −1.8% per year, respectively. Conversely, the f.THg concentration increased at a rate of 1.7% per year between 1998 and 2005, and 4.9% per year between 2005 and 2013. Trends in flow-normalized partition coefficients for both Hg and MeHg suggest a statistically significant shift from the particulate to the filtered phase. The upwardly accelerating f.THg concentration and observed shift from the solid phase to the aqueous phase among the pools of Hg and MeHg within the river water column signals an increased risk of deteriorating ecological conditions in the lower basin with respect to Hg contamination. More broadly, the 16-year trend analysis, completed 140 years after the commencement of major Hg releases to the Carson River, provides a poignant example of the ongoing legacy left behind by gold and silver mining techniques that relied on Hg amalgamation, and a cautionary tale for regions still pursuing the practice in other countries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2017.07.090","usgsCitation":"Morway, E.D., Thodal, C.E., and Marvin-DiPasquale, M.C., 2017, Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed: Environmental Pollution, v. 229, p. 1006-1018, https://doi.org/10.1016/j.envpol.2017.07.090.","productDescription":"13 p.","startPage":"1006","endPage":"1018","ipdsId":"IP-081017","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":469496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2017.07.090","text":"Publisher Index Page"},{"id":346163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Carson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.72763061523436,\n 39.143907559644944\n ],\n [\n -119.14535522460938,\n 39.143907559644944\n ],\n [\n -119.14535522460938,\n 39.34067026099156\n ],\n [\n -119.72763061523436,\n 39.34067026099156\n ],\n [\n -119.72763061523436,\n 39.143907559644944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"229","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a24e4b05fe04cc020ef","contributors":{"authors":[{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thodal, Carl E. 0000-0003-0782-3280 cethodal@usgs.gov","orcid":"https://orcid.org/0000-0003-0782-3280","contributorId":2292,"corporation":false,"usgs":true,"family":"Thodal","given":"Carl","email":"cethodal@usgs.gov","middleInitial":"E.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":711419,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190107,"text":"sir20175087 - 2017 - A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents","interactions":[],"lastModifiedDate":"2017-09-27T16:05:02","indexId":"sir20175087","displayToPublicDate":"2017-09-27T00:00:00","publicationYear":"2017","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":"2017-5087","title":"A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents","docAbstract":"Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175087","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"McShane, R.R., Driscoll, K.P., and Sando, Roy, 2017, A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents: U.S. Geological Survey Scientific Investigations Report 2017–5087, 19 p., https://doi.org/10.3133/sir20175087.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-083112","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":346073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5087/coverthb.jpg"},{"id":346074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5087/sir20175087.pdf","text":"Report","size":"678 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5087"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Surface-water supplies are important sources of drinking water for residents in the Triangle area of North Carolina, which is located within the upper Cape Fear and Neuse River Basins. Since 1988, the U.S. Geological Survey and a consortium of local governments have tracked water-quality conditions and trends in several of the area’s water-supply lakes and streams. This report summarizes data collected through this cooperative effort, known as the Triangle Area Water Supply Monitoring Project, during October 2013 through September 2014 (water year 2014) and October 2014 through September 2015 (water year 2015). Major findings for this period include:
Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Dauphin Island is a 26-km-long barrier island located southwest of Mobile Bay, Alabama, in the north-central Gulf of Mexico. The island contains sandy beaches, dunes, maritime forests, freshwater ponds and intertidal wetlands, providing habitat for many endangered and threatened species. Dauphin Island also provides protection for and maintains estuarine conditions within Mississippi Sound, supporting oyster habitat and seagrasses. Wetland marshes along the Alabama mainland are protected by the island from wave-induced erosion during storms approaching from the Gulf of Mexico. Over the years, the island has been eroded by storms, most recently by Hurricane Ivan (2004) and Hurricane Katrina (2005) (Ivan/Katrina), which breached the island along its narrowest extent and caused damage to infrastructure. Along with storms producing significant episodic change, long-term beach erosion has exposed numerous pine tree stumps in the shoreface. The stumps are remnants of past maritime forests and reflect the consistent landward retreat of the island.
Island change has prompted the State of Alabama to evaluate restoration alternatives to increase island resilience and sustainability by protecting and preserving the natural habitat, and by understanding the processes that influence shoreline change. Under a grant from the National Fish and Wildlife Foundation, restoration alternatives are being developed that will allow the State to make decisions on engineering and ecological restoration designs based on scientific analysis of likely outcomes and tradeoffs between impacts to stakeholder interests. Science-based assessment of the coastal zone requires accurate and up-to-date baseline data to provide a valid image of present conditions and to support modeling of coastal processes. Bathymetric elevation measurements are essential to this requirement. In August 2015, the U.S. Army Corps of Engineers and the U.S. Geological Survey conducted single beam and multibeam bathymetric surveys around Dauphin Island using a variety of shallow draft vessels and equipment. More than 95 square kilometers of seafloor was imaged. The data were integrated into a seamless digital elevation model to provide a high-resolution bathymetric map of the seafloor extending 9.5 kilometers seaward from the island’s eastern end and approximately 2 km along the rest of the island on the gulf and sound sides. Water depths range from 0.3 to 15.0 meters (m), with depths greater than 10.0 m constrained to the Mobile ship channel on the extreme eastern flank of the coverage.
To measure seafloor change, two periods of historic hydrographic survey data were acquired from the National Oceanic and Atmospheric Administration National Centers for Environmental Information data archive. The two timeframes (1987–1988 and 2005–2007) were selected for their completeness of spatial coverage and because they encompass a period of significant storm impacts to the island. These timeframes were compared to each other and with the 2015 dataset to monitor elevation gain (sediment accretion) and elevation loss (sediment erosion) over time. Sediment dynamics is by far the most significant driver of nearshore elevation change in this area. The Mississippi-Alabama inner shelf is a passive margin, and other influences on elevation change (for example, tectonic adjustment, Holocene subsidence, and eustatic sea-level rise) are neither significant nor variable enough over this time period to have an imprint.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171112","usgsCitation":"Flocks, J.G., DeWitt, N.T., and Stalk, C.A., 2018, Analysis of seafloor change around Dauphin Island, Alabama, 1987–2015 (ver. 1.1, February 2018):St. Petersburg Science Center
U.S. Geological Survey
600 4th Street, South
St Petersburg, FL 33701
The maps and graphs in this summary describe national streamflow conditions for water year 2016 (October 1, 2015, to September 30, 2016) in the context of streamflow ranks relative to the 87-year period of 1930–2016, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey’s (USGS) National Streamflow Network. The period of 1930–2016 was used because the number of streamgages before 1930 was too small to provide representative data for computing statistics for most regions of the country.
In the summary, reference is made to the term “runoff,” which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a specified period was uniformly distributed on it. Runoff quantifies the magnitude of water flowing through the Nation’s rivers and streams in measurement units that can be compared from one area to another.
In all the graphics, a rank of 1 indicates the highest flow of all years analyzed and 87 indicates the lowest flow of all years. Rankings of streamflow are grouped into much below normal, below normal, normal, above normal, and much above normal based on percentiles of flow (less than 10 percent, 10–24 percent, 25–75 percent, 76–90 percent, and greater than 90 percent, respectively). Some of the data used to produce the maps and graphs are provisional and subject to change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173063","usgsCitation":"Jian, Xiaodong, Wolock, D.M., Lins, H.F., and Brady, S.J., 2017, Streamflow of 2016—Water year summary: U.S. Geological Survey Fact Sheet 2017–3063, 6 p., https://doi.org/10.3133/fs20173063.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-088111","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":346071,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3063/coverthb.jpg"},{"id":346072,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3063/fs20173063.pdf","text":"Fact Sheet","size":"524 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3063"}],"country":"United 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U.S. Geological Survey
415 National Center
Reston, VA 20192
In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Some upstream stakeholders have expressed concerns about the dynamics of sedimentation and flood flows in the transition zone between major rivers and Grand Lake O’ the Cherokees. To relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission, the hydraulic models for these rivers require high-resolution bathymetric data along the river channels. In support of the Federal Energy Regulatory Commission relicensing process, the U.S. Geological Survey, in cooperation with the Grand River Dam Authority, performed bathymetric surveys of (1) the Neosho River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, (2) the Spring River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, and (3) the Elk River from Noel, Missouri, to the Oklahoma State Highway 10 bridge near Grove, Oklahoma. The Neosho River and Spring River bathymetric surveys were performed from October 26 to December 14, 2016; the Elk River bathymetric survey was performed from February 27 to March 21, 2017. Only areas inundated during those periods were surveyed.
The bathymetric surveys covered a total distance of about 76 river miles and a total area of about 5 square miles. Greater than 1.4 million bathymetric-survey data points were used in the computation and interpolation of bathymetric-survey digital elevation models and derived contours at 1-foot (ft) intervals. The minimum bathymetric-survey elevation of the Neosho River was 709.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 34.22 ft. The minimum bathymetric-survey elevation of the Spring River was 714.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 29.22 ft. The minimum bathymetric-survey elevation of the Elk River was 715.62 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 27.78 ft.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175101","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., Ashworth, C.E., and Smith, S.J., 2017, Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17 (ver. 1.1, October 2017): U.S. Geological Survey Scientific Investigations Report 2017–5101, 59 p., https://doi.org/10.3133/sir20175101.","productDescription":"Report: 59 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087073","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346091,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71N7ZMS","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17"},{"id":348038,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5101/coverthb2.jpg"},{"id":348039,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5101/sir20175101_ver1.1.pdf","text":"Report","size":"75.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5101"},{"id":348040,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5101/versionHist.txt","text":"Version History","size":"1.68 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Neosho River, Spring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1,\n 36.8\n ],\n [\n -94.5667,\n 36.8\n ],\n [\n -94.5667,\n 37.0333\n ],\n [\n -95.1,\n 37.0333\n ],\n [\n -95.1,\n 36.8\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.7,\n 36.6667\n ],\n [\n -94.4667,\n 36.6667\n ],\n [\n -94.4667,\n 36.5333\n ],\n [\n -94.7,\n 36.5333\n ],\n [\n -94.7,\n 36.6667\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 26, 2017; Version 1.1: October 25, 2017","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
This field manual provides general guidance for identifying and collecting high-water marks and is meant to be used by field personnel as a quick reference. The field manual describes purposes for collecting and documenting high-water marks along with the most common types of high-water marks. The manual provides a list of suggested field equipment, describes rules of thumb and best practices for finding high-water marks, and describes the importance of evaluating each high-water mark and assigning a numeric uncertainty value as part of the flagging process. The manual also includes an appendix of photographs of a variety of high-water marks obtained from various U.S. Geological Survey field investigations along with general comments about the logic for the assigned uncertainty values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171105","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Koenig, T.A, 2017, Field manual for identifying and preserving high-water mark data: U.S. Geological Survey Open-File Report 2017–1105, 67 p., https://doi.org/10.3133/ofr20171105.","productDescription":"x, 67 p.","numberOfPages":"80","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-087515","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":346094,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1105/ofr20171105.pdf","text":"Report","size":"4.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1105"},{"id":346095,"rank":3,"type":{"id":21,"text":"Referenced Work"},"url":"https://doi.org/10.3133/tm3A24","text":"T & M 3–A24","description":"T & M 3–A24"},{"id":346093,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1105/coverthb2.jpg"}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 13 bridges at 8 highway crossings of the Missouri and Mississippi Rivers in the greater St. Louis, Missouri, area from May 23 to 27, 2016. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,640 to 1,970 feet longitudinally and extending laterally across the active channel from bank to bank during low to moderate flood flow conditions. These bathymetric surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were observed at most surveyed piers. The observed scour holes at the surveyed bridges were examined with respect to shape and depth.
The frontal slope values determined for scour holes observed in the current (2016) study generally are similar to recommended values in the literature and to values determined for scour holes in previous bathymetric surveys. Several of the structures had piers that were skewed to primary approach flow, as indicated by the scour hole being longer on the side of the pier with impinging flow, and some amount of deposition on the leeward side, as typically has been observed at piers skewed to approach flow; however, at most skewed piers in the current (2016) study, the scour hole was deeper on the leeward side of the pier. At most of these piers, the angled approach flow was the result of a deflection or contraction of flow caused by a spur dike near the pier, which may affect flow differently than for a simple skew. At structure A6500 (site 33), the wide face of the pier footing and seal course would behave as a complex foundation, for which scour is computed differently.
Previous bathymetric surveys exist for all the sites examined in this study. A previous survey in October 2010 at most of the sites had similar flow conditions and similar results to the 2016 surveys. A survey during flood conditions in August 2011 at the sites on the Missouri River and in May 2009 at structures A4936 and A1850 (site 35) on the Mississippi River did not always indicate more substantial scour during flood conditions. At structure A6500 (site 33) on the Mississippi River, a previous survey in 2009 was part of a habitat assessment before construction of the bridge and provides unique insight into the effects of the construction of that bridge on the channel in this reach. Substantial scour was observed near the right pier, and the riprap blanket surrounding the left pier seems to limit scour near that pier. Multiple additional surveys have been completed at structures A4936 and A1850 (site 35) on the Mississippi River, and the results of these surveys also are presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175076","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2017, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, May 23–27, 2016: U.S. Geological Survey Scientific Investigations Report 2017–5076, 102 p., https://doi.org/10.3133/sir20175076.","productDescription":"Report: x, 102 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086447","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":346078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS Data Release","linkHelpText":"Bathymetry and Velocity Data from Surveys at Highway Bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":346076,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5076/coverthb.jpg"},{"id":346077,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5076/sir20175076.pdf","text":"Report","size":"17.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5076"}],"country":"United States","state":"Missouri","city":"St. Louis","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.69419860839844,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.42293213401053\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Faulted terrace risers are semi-planar features commonly used to constrain Quaternary slip rates along strike-slip faults. These landforms are difficult to date directly and therefore their ages are commonly bracketed by age estimates of the adjacent upper and lower terrace surfaces. However, substantial differences in the ages of the upper and lower terrace surfaces (a factor of 2.4 difference observed globally) produce large uncertainties in the slip-rate estimate. In this investigation, we explore how the full range of displacements and bounding ages from multiple faulted terrace risers can be combined to yield a more accurate fault slip rate. We use 0.25-m cell size digital terrain models derived from airborne lidar data to analyze three sites where terrace risers are offset right-laterally by the Honey Lake fault in NE California, USA. We use ages for locally extensive subhorizontal surfaces to bracket the time of riser formation: an upper surface is the bed of abandoned Lake Lahontan having an age of 15.8 ± 0.6 ka and a lower surface is a fluvial terrace abandoned at 4.7 ± 0.1 ka. We estimate lateral offsets of the risers ranging between 6.6 and 28.3 m (median values), a greater than fourfold difference in values. The amount of offset corresponds to the riser's position relative to modern stream meanders: the smallest offset is in a meander cutbank position, whereas the larger offsets are in straight channel or meander point-bar positions. Taken in isolation, the individual terrace-riser offsets yield slip rates ranging from 0.3 to 7.1 mm/a. However, when the offset values are collectively assessed in a probabilistic framework, we find that a uniform (linear) slip rate of 1.6 mm/a (1.4–1.9 mm/a at 95% confidence) can satisfy the data, within their respective uncertainties. This investigation demonstrates that integrating observations of multiple offset elements (crest, midpoint, and base) from numerous faulted and dated terrace risers at closely spaced sites can refine slip-rate estimates on strike-slip faults.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.08.021","usgsCitation":"Gold, R.D., Briggs, R.W., Crone, A.J., and DuRoss, C., 2017, Refining fault slip rates using multiple displaced terrace risers-An example from the Honey Lake fault, NE California, USA: Earth and Planetary Science Letters, v. 477, p. 134-146, https://doi.org/10.1016/j.epsl.2017.08.021.","productDescription":"13 p.","startPage":"134","endPage":"146","ipdsId":"IP-088635","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":461397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2017.08.021","text":"Publisher Index Page"},{"id":346047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Honey Lake Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1333,\n 40.0417\n ],\n [\n -120.1,\n 40.0417\n ],\n [\n -120.1,\n 40.0583\n ],\n [\n -120.1333,\n 40.0583\n ],\n [\n -120.1333,\n 40.0417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"477","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15aae4b017cf314041b0","contributors":{"authors":[{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711116,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crone, Anthony J. 0000-0002-3006-406X crone@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-406X","contributorId":790,"corporation":false,"usgs":true,"family":"Crone","given":"Anthony","email":"crone@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711117,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711118,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191064,"text":"70191064 - 2017 - Coming to terms about describing Golden Eagle reproduction","interactions":[],"lastModifiedDate":"2017-11-22T16:43:46","indexId":"70191064","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Coming to terms about describing Golden Eagle reproduction","docAbstract":"Clearly defined terms are essential for reporting and understanding research findings, and inconsistent terminology can complicate efforts to compare findings from different studies. In this article, we reiterate and clarify recommended terms for describing Golden Eagle (Aquila chrysaetos) territory occupancy and reproduction. Several authors have provided recommendations for reporting data on raptor reproduction, but our literature review showed that authors continue to use different, often ambiguous and undefined, terms. The inconsistent use of terminology by researchers has been continued and expanded by lawmakers, regulators, and managers, perpetuating confusion. We recommend that authors clearly define and reference all terminology that they use, and we caution against use of the term “active” to describe a nest or nesting territory, because it is tainted with a history of inconsistent use. We provide a glossary of recommended terms for Golden Eagles and other large, long-lived raptors.
","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-16-46.1","usgsCitation":"Steenhof, K., Kochert, M.N., McIntyre, C.L., and Brown, J.L., 2017, Coming to terms about describing Golden Eagle reproduction: Journal of Raptor Research, v. 51, no. 3, p. 378-390, https://doi.org/10.3356/JRR-16-46.1.","productDescription":"13 p.","startPage":"378","endPage":"390","ipdsId":"IP-073063","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469501,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-16-46.1","text":"Publisher Index Page"},{"id":346046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15a9e4b017cf314041ac","contributors":{"authors":[{"text":"Steenhof, Karen karen_steenhof@usgs.gov","contributorId":30585,"corporation":false,"usgs":true,"family":"Steenhof","given":"Karen","email":"karen_steenhof@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":711079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kochert, Michael N. 0000-0002-4380-3298 mkochert@usgs.gov","orcid":"https://orcid.org/0000-0002-4380-3298","contributorId":3037,"corporation":false,"usgs":true,"family":"Kochert","given":"Michael","email":"mkochert@usgs.gov","middleInitial":"N.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":711078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McIntyre, Carol L.","contributorId":196673,"corporation":false,"usgs":false,"family":"McIntyre","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":20307,"text":"US National Park Service","active":true,"usgs":false}],"preferred":false,"id":711080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Jessi L.","contributorId":44817,"corporation":false,"usgs":false,"family":"Brown","given":"Jessi","email":"","middleInitial":"L.","affiliations":[{"id":13184,"text":"Program in Ecology, Evolution and Conservation Biology, University of Nevada","active":true,"usgs":false}],"preferred":false,"id":711081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191053,"text":"70191053 - 2017 - Projecting impacts of climate change on water availability using artificial neural network techniques","interactions":[],"lastModifiedDate":"2017-09-25T11:54:31","indexId":"70191053","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2501,"text":"Journal of Water Resources Planning and Management","active":true,"publicationSubtype":{"id":10}},"title":"Projecting impacts of climate change on water availability using artificial neural network techniques","docAbstract":"Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.
Linear-based data-worth analysis is an efficient and straightforward method for identifying the most important data for model forecasts.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12561","usgsCitation":"Leaf, A.T., 2017, Using models to identify the best data: An example from northern Wisconsin: Groundwater, v. 55, no. 5, p. 641-645, https://doi.org/10.1111/gwat.12561.","productDescription":"5 p.","startPage":"641","endPage":"645","ipdsId":"IP-087331","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":346051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.00387573242188,\n 46.29761098988109\n ],\n [\n -90.4119873046875,\n 46.29761098988109\n ],\n [\n -90.4119873046875,\n 46.68995749641134\n ],\n [\n -91.00387573242188,\n 46.68995749641134\n ],\n [\n -91.00387573242188,\n 46.29761098988109\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-17","publicationStatus":"PW","scienceBaseUri":"59ca15ace4b017cf314041ba","contributors":{"authors":[{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711043,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190108,"text":"sim3385 - 2017 - Concentration and trend of 1,4-dioxane in wells sampled during 2002–2017 in the vicinity of the Tucson International Airport Area Superfund Site, Arizona","interactions":[],"lastModifiedDate":"2017-12-14T16:17:26","indexId":"sim3385","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3385","title":"Concentration and trend of 1,4-dioxane in wells sampled during 2002–2017 in the vicinity of the Tucson International Airport Area Superfund Site, Arizona","docAbstract":"Industrial activities causing extensive groundwater contamination led to the listing of the Tucson International Airport Area (TIAA) as a Superfund Site in 1983. Early groundwater investigations identified volatile organic compounds (VOCs), including the chlorinated solvents trichloroethylene (TCE) and perchloroethylene (PCE), in wells in the area. Several responsible parties were identified and cleanup activities began in the late 1980s. In 2002, the compound 1,4-dioxane was discovered in wells in the area and has since been detected in measurable concentrations throughout the site. The U.S. Environmental Protection Agency (USEPA) classifies 1,4-dioxane as a likely human carcinogen.
The purpose of this map is to present 1,4-dioxane concentrations in wells sampled from 2002 through mid-2017 in the TIAA Superfund Site area to indicate both the current status and trends in 1,4-dioxane groundwater contamination. This map includes data from wells in the commercial and residential community in the TIAA and does not include data from wells in suspected or confirmed source areas, such as Air Force Plant 44 and Tucson International Airport, or from wells within treatment facilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3385","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Tillman, Fred, 2017, Concentration and trend of 1,4-dioxane in wells sampled during 2002–2017 in the vicinity of the Tucson International Airport Area Superfund Site: U.S. Geological Survey Scientific Investigations Map 3385, scale 1:7,500, 1 sheet, https://doi.org/10.3133/sim3385.","productDescription":"Map: 30 x 40 inches; Appendix A","onlineOnly":"N","ipdsId":"IP-087761","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":345617,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/3385/sim3385_appendixA.xlsx","text":"Appendix A","size":"128 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIM 3385"},{"id":345615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3385/sim.3385.pdf","text":"Map","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3385"},{"id":345614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3385/coverthb_.jpg"}],"country":"United States","state":"Arizona","city":"Tucson","otherGeospatial":"Tucson International Airport Area Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.236572265625,\n 31.813396010784928\n ],\n [\n -110.71197509765624,\n 31.813396010784928\n ],\n [\n -110.71197509765624,\n 32.127942397192314\n ],\n [\n -111.236572265625,\n 32.127942397192314\n ],\n [\n -111.236572265625,\n 31.813396010784928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Director,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The Lake Creek–Boundary Creek fault, previously mapped in Miocene bedrock as an oblique thrust on the north flank of the Olympic Mountains, poses a significant earthquake hazard. Mapping using 2015 light detection and ranging (lidar) confirms 2004 lidar mapping of postglacial (
Attribution of the causes of trends in nutrient loading is often limited to correlation, qualitative reasoning, or references to the work of others. This paper represents efforts to improve causal attribution of water-quality changes. The Red River of the North basin provides a regional test case because of international interest in the reduction of total phosphorus loads and the availability of long-term total phosphorus data and ancillary geospatial data with the potential to explain changes in water quality over time. The objectives of the study are to investigate structural equation modeling methods for application to water-quality problems and to test causal hypotheses related to the drivers of total phosphorus loads over the period 1970 to 2012. Multiple working hypotheses that explain total phosphorus loads and methods for estimating missing ancillary data were developed, and water-quality related challenges to structural equation modeling (including skewed data and scaling issues) were addressed. The model indicates that increased precipitation in season 1 (November–February) or season 2 (March–June) would increase total phosphorus loads in the basin. The effect of agricultural practices on total phosphorus loads was significant, although the effect is about one-third of the effect of season 1 precipitation. The structural equation model representing loads at six sites in the basin shows that climate and agricultural practices explain almost 60% of the annual total phosphorus load in the Red River of the North basin. The modeling process and the unexplained variance highlight the need for better ancillary long-term data for causal assessments.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2017.04.0131","usgsCitation":"Ryberg, K.R., 2017, Structural equation model of total phosphorus loads in the Red River of the North Basin, USA and Canada: Journal of Environmental Quality, v. 46, no. 5, p. 1072-1080, https://doi.org/10.2134/jeq2017.04.0131.","productDescription":"9 p.","startPage":"1072","endPage":"1080","ipdsId":"IP-075962","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":469503,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2017.04.0131","text":"Publisher Index Page"},{"id":346042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Red River of the North Basin","volume":"46","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15a7e4b017cf314041a4","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711096,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190274,"text":"ofr20171110 - 2017 - Low-flow characteristics of streams in South Carolina","interactions":[],"lastModifiedDate":"2017-09-25T11:16:46","indexId":"ofr20171110","displayToPublicDate":"2017-09-22T15:30:00","publicationYear":"2017","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":"2017-1110","title":"Low-flow characteristics of streams in South Carolina","docAbstract":"An ongoing understanding of streamflow characteristics of the rivers and streams in South Carolina is important for the protection and preservation of the State’s water resources. Information concerning the low-flow characteristics of streams is especially important during critical flow periods, such as during the historic droughts that South Carolina has experienced in the past few decades.
Between 2008 and 2016, the U.S. Geological Survey, in cooperation with the South Carolina Department of Health and Environmental Control, updated low-flow statistics at 106 continuous-record streamgages operated by the U.S. Geological Survey for the eight major river basins in South Carolina. The low-flow frequency statistics included the annual minimum 1-, 3-, 7-, 14-, 30-, 60-, and 90-day mean flows with recurrence intervals of 2, 5, 10, 20, 30, and 50 years, depending on the length of record available at the streamflow-gaging station. Computations of daily mean flow durations for the 5-, 10-, 25-, 50-, 75-, 90-, and 95-percent probability of exceedance also were included.
This report summarizes the findings from publications generated during the 2008 to 2016 investigations. Trend analyses for the annual minimum 7-day average flows are provided as well as trend assessments of long-term annual precipitation data. Statewide variability in the annual minimum 7-day average flow is assessed at eight long-term (record lengths from 55 to 78 years) streamgages. If previous low-flow statistics were available, comparisons with the updated annual minimum 7-day average flow, having a 10-year recurrence interval, were made. In addition, methods for estimating low-flow statistics at ungaged locations near a gaged location are described.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171110","collaboration":"Prepared in cooperation with the South Carolina Department of Health and Environmental Control","usgsCitation":"Feaster, T.D., and Guimaraes, W.B., 2017, Low-flow characteristics of streams in South Carolina: U.S. Geological Survey Open-File Report 2017–1110, 161 p., https://doi.org/10.3133/ofr20171110.","productDescription":"vi, 161 p.","numberOfPages":"172","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084824","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":346012,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1110/ofr20171110.pdf","text":"Report","size":"8.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Increasing reliance on non-renewable mineral resources reinforces the need for identifying potential supply constraints before they occur. The US National Science and Technology Council recently released a report that outlines a methodology for screening potentially critical minerals based on three indicators: supply risk (R), production growth (G), and market dynamics (M). This early-warning screening was initially applied to 78 minerals across the years 1996 to 2013 and identified a subset of minerals as “potentially critical” based on the geometric average of these indicators—designated as criticality potential (C). In this study, the screening methodology has been updated to include data for 2014, as well as to incorporate revisions and modifications to the data, where applicable. Overall, C declined in 2014 for the majority of minerals examined largely due to decreases in production concentration and price volatility. However, the results vary considerably across minerals, with some minerals, such as gallium, recording increases for all three indicators. In addition to assessing magnitudinal changes, this analysis also examines the significance of the change relative to historical variation for each mineral. For example, although mined nickel’s R declined modestly in 2014 in comparison to that of other minerals, it was by far the largest annual change recorded for mined nickel across all years examined and is attributable to Indonesia’s ban on the export of unprocessed minerals. Based on the 2014 results, 20 minerals with the highest C values have been identified for further study including the rare earths, gallium, germanium, rhodium, tantalum, and tungsten.
","language":"English","publisher":"Springer","doi":"10.1007/s13563-017-0119-6","usgsCitation":"McCullough, E.A., and Nassar, N., 2017, Assessment of critical minerals: Updated application of an early-warning screening methodology: Mineral Economics, v. 30, no. 3, p. 257-272, https://doi.org/10.1007/s13563-017-0119-6.","productDescription":"16 p.","startPage":"257","endPage":"272","ipdsId":"IP-090603","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":469507,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s13563-017-0119-6","text":"External Repository"},{"id":345990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-19","publicationStatus":"PW","scienceBaseUri":"59c4cf95e4b017cf313d3cab","contributors":{"authors":[{"text":"McCullough, Erin A. 0000-0002-9072-7021 emccullough@usgs.gov","orcid":"https://orcid.org/0000-0002-9072-7021","contributorId":196629,"corporation":false,"usgs":true,"family":"McCullough","given":"Erin","email":"emccullough@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":710953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nassar, Nedal 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":196630,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":710954,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191024,"text":"70191024 - 2017 - Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations","interactions":[],"lastModifiedDate":"2022-04-22T16:00:33.117943","indexId":"70191024","displayToPublicDate":"2017-09-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations","docAbstract":"This study presents new data-driven, annual estimates of the division of precipitation into the recharge, quick-flow runoff, and evapotranspiration (ET) water budget components for 2000-2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick-flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick-flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick-flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first-order estimates of these quantities across the CONUS, even where field measurements are sparse.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12546","usgsCitation":"Reitz, M., Sanford, W.E., Senay, G., and Cazenas, J., 2017, Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations: Journal of the American Water Resources Association, v. 53, no. 4, p. 961-983, https://doi.org/10.1111/1752-1688.12546.","productDescription":"23 p.","startPage":"961","endPage":"983","ipdsId":"IP-086069","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":345986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n 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Branch","active":true,"usgs":true}],"preferred":false,"id":710980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":710981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","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":710982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cazenas, J.","contributorId":196649,"corporation":false,"usgs":true,"family":"Cazenas","given":"J.","email":"","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":710983,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190099,"text":"sir20175086 - 2017 - Multiple-source tracking: Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14","interactions":[],"lastModifiedDate":"2017-09-21T14:16:10","indexId":"sir20175086","displayToPublicDate":"2017-09-20T16:00:00","publicationYear":"2017","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":"2017-5086","title":"Multiple-source tracking: Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14","docAbstract":"The South Fork Little River (SFLR) and the North Fork Little River (NFLR) are two major headwater tributaries that flow into the Little River just south of Hopkinsville, Kentucky. Both tributaries are included in those water bodies in Kentucky and across the Nation that have been reported with declining water quality. Each tributary has been listed by the Kentucky Energy and Environment Cabinet—Kentucky Division of Water in the 303(d) List of Waters for Kentucky Report to Congress as impaired by nutrients, pathogens, and sediment for contact recreation from point and nonpoint sources since 2002. In 2009, the Kentucky Energy and Environment Cabinet—Kentucky Division of Water developed a pathogen total maximum daily load (TMDL) for the Little River Basin including the SFLR and NFLR Basins. Future nutrient and suspended-sediment TMDLs are planned once nutrient criteria and suspended-sediment protocols have been developed for Kentucky. In this study, different approaches were used to identify potential sources of fecal-indicator bacteria (FIB), nitrate, and suspended sediment; to inform the TMDL process; and to aid in the implementation of effective watershed-management activities. The main focus of source identification was in the SFLR Basin.
To begin understanding the potential sources of fecal contamination, samples were collected at 19 sites for densities of FIB (E. coli) in water and fluvial sediment and at 11 sites for Bacteroidales genetic markers (General AllBac, human HF183, ruminant BoBac, canid BacCan, and waterfowl GFD) during the recreational season (May through October) in 2013 and 2014. Results indicated 34 percent of all E. coli water samples (n=227 samples) did not meet the U.S. Environmental Protection Agency 2012 recommended national criteria for primary recreational waters. No criterion currently exists for E. coli in fluvial sediment. By use of the Spearman’s rank correlation test, densities of FIB in fluvial sediments were observed to have a statistically significant positive correlation with drainage area. As drainage area increased, so did the densities of FIB in the fluvial sediments. There was no statistically significant correlation between drainage area and FIB in water. The human-associated marker (HF183) was found above the detection limit in 26 percent of the samples (n=120 samples); a higher proportion of positive samples was in the NFLR Basin. The ruminant-associated marker (BoBac) was above the detection limit in 65 percent of samples; a higher proportion of positive samples was in the headwaters of the SFLR Basin.
Nutrient yields differed between the SFLR and NFLR Basins. Comparatively, the SFLR Basin produced the largest estimated mean yields of total nitrogen (16,000 pounds per year per square mile (lb/yr/mi2) and nitrite plus nitrate nitrogen (12,500 lb/yr/mi2), and the NFLR Basin produced the largest estimated mean yields of ammonia plus organic nitrogen (4,700 lb/yr/mi2), total phosphorus (1,100 lb/yr/mi2), and orthophosphorus (590 lb/yr/mi2).
Nitrate sources in surface water were assessed in both basins using dual-nitrate isotope (nitrogen and oxygen) ratios. Data from the different land uses in the SFLR Basin showed differences in nitrate concentrations and overlapping, but moderately distinct, isotopic signatures. Predominantly forested sites consistently had low nitrate concentrations (median = 0.233 milligrams per liter) with minimal variability, and agricultural sites had the highest nitrate concentrations (median = 7.55 milligrams per liter) with the greatest variability. The median nitrate concentration for sites with mixed land use was 2.66 milligrams per liter. Dual-isotope data for forested sites plotted within ranges characteristic of soil-derived nitrate with possible but minimal influence from recycled atmospheric nitrate. Ranges of dual-isotope data for sites with agricultural and mixed land uses were characteristic of possible mixtures of chemical fertilizer, soil-derived nitrate, and manure and septic wastes. In the NFLR Basin, a positive linear relation was observed between nitrate concentrations and nitrogen isotope ratios (δ15NNO3) (R2=0.56; p-value <0.001) that potentially suggests the NFLR Basin has a higher proportion of δ15NNO3-enriched sources, such as manure and sewage. However, mixing of other nitrate-derived sources cannot be excluded, because many values of δ15NNO3 and concentrations of nitrate showed minimal variation and plotted within dual-nitrate isotope ranges characteristic of fertilizer and soil-derived nitrate sources.
A sediment-fingerprinting approach was used to quantify the relative contribution of four upland sources in the SFLR Basin (agricultural, pasture, riparian/forest, and streambank) to understand how land management affects suspended-sediment concentration. Carbon isotope ratios (δ13C), together with calcium and carbon concentrations, were the best indicators of sediment source; the uncertainty was less than 11 percent. Fine-sediment samples collected at the SFLR Basin outlet indicated streambanks as the largest source of the fine sediment to the stream followed by cropland and riparian/forest-source areas, respectively; pasture was a minor contributing source. Streambanks and cropland were essentially equal contributors of fine sediment at the NFLR Basin outlet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175086","collaboration":"Prepared in cooperation with the Little River Water-Quality Consortium ","usgsCitation":"Crain, A.S., Cherry, M.A., Williamson, T.N., and Bunch, A.R., 2017, Multiple-source tracking—Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14: U.S. Geological Survey Scientific Investigations Report 2017–5086, 60 p., https://doi.org/10.3133/sir20175086.","productDescription":"Report: xi, 60 p; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070254","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":345723,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2TPW","text":"USGS data release","description":"USGS data release"},{"id":345721,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5086/coverthb.jpg"},{"id":345722,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5086/sir20175086.pdf","text":"Report","size":"6.68 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Kentucky","otherGeospatial":"Upper Little River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.35023498535156,\n 36.96086580957587\n ],\n [\n -87.396240234375,\n 36.96799807635307\n ],\n [\n -87.44361877441406,\n 36.9800665440453\n ],\n [\n -87.46147155761719,\n 36.98390610968992\n ],\n [\n -87.48275756835938,\n 36.97842095659727\n ],\n [\n -87.48619079589842,\n 36.97128966642495\n ],\n [\n -87.48893737792969,\n 36.96306042436515\n ],\n [\n -87.50198364257811,\n 36.953732874654285\n ],\n [\n -87.51983642578125,\n 36.958671131530316\n ],\n [\n -87.5390625,\n 36.96031714599471\n ],\n [\n -87.54798889160156,\n 36.94989178681327\n ],\n [\n -87.53768920898438,\n 36.93507434788673\n ],\n [\n -87.52326965332031,\n 36.91421529335739\n ],\n [\n -87.50404357910156,\n 36.87797284779547\n ],\n [\n -87.53082275390625,\n 36.85874638670163\n ],\n [\n -87.56240844726562,\n 36.81862991458789\n ],\n [\n -87.58163452148438,\n 36.773542488140684\n ],\n [\n -87.68531799316406,\n 36.78839127856239\n ],\n [\n -87.76496887207031,\n 36.835118682834256\n ],\n [\n -87.80479431152344,\n 36.8549005138863\n ],\n [\n -87.7862548828125,\n 36.872480066931246\n ],\n [\n -87.78350830078125,\n 36.88126832670563\n ],\n [\n -87.80136108398438,\n 36.88511287236025\n ],\n [\n -87.84049987792969,\n 36.89005557519409\n ],\n [\n -87.86727905273438,\n 36.88126832670563\n ],\n [\n -87.89199829101561,\n 36.89444882017788\n ],\n [\n -87.91328430175781,\n 36.91256828285194\n ],\n [\n -87.93594360351562,\n 36.92135192790115\n ],\n [\n -87.96272277832031,\n 36.887309668681155\n ],\n [\n -87.97920227050781,\n 36.8614933202301\n ],\n [\n -87.96821594238281,\n 36.84061414925051\n ],\n [\n -87.95173645019531,\n 36.829622821570254\n ],\n [\n -87.87071228027344,\n 36.8021375933119\n ],\n [\n -87.83226013183594,\n 36.793340236044536\n ],\n [\n -87.80616760253905,\n 36.77409249464195\n ],\n [\n -87.79312133789062,\n 36.75153899262323\n ],\n [\n -87.7587890625,\n 36.686041276581925\n ],\n [\n -87.72239685058594,\n 36.679433365517774\n ],\n [\n -87.66952514648438,\n 36.677230602346214\n ],\n [\n -87.61940002441405,\n 36.6843893520368\n ],\n [\n -87.57545471191406,\n 36.70200805115167\n ],\n [\n -87.51846313476562,\n 36.71962271257377\n ],\n [\n -87.47383117675781,\n 36.72457610850295\n ],\n [\n -87.40310668945312,\n 36.73393165140215\n ],\n [\n -87.37564086914062,\n 36.72952918496946\n ],\n [\n -87.37770080566405,\n 36.74988847598359\n ],\n [\n -87.38388061523438,\n 36.76474184748192\n ],\n [\n -87.37632751464844,\n 36.78124222006408\n ],\n [\n -87.35710144042969,\n 36.804886560237236\n ],\n [\n -87.34542846679688,\n 36.8087349481474\n ],\n [\n -87.33100891113281,\n 36.82632511529416\n ],\n [\n -87.3358154296875,\n 36.83127162140714\n ],\n [\n -87.32414245605469,\n 36.84336173438382\n ],\n [\n -87.308349609375,\n 36.85380165753812\n ],\n [\n -87.29736328125,\n 36.86698689106877\n ],\n [\n -87.26715087890625,\n 36.88950640179281\n ],\n [\n -87.2589111328125,\n 36.910372213522535\n ],\n [\n -87.29598999023438,\n 36.9400138143685\n ],\n [\n -87.35023498535156,\n 36.96086580957587\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
The Nacatoch Sand in northeastern and southwestern Arkansas and the Tokio Formation in southwestern Arkansas are sources of groundwater for agricultural, domestic, industrial, and public use. Water-level altitudes measured in 51 wells completed in the Nacatoch Sand and 42 wells completed in the Tokio Formation during 2014 and 2015 were used to create potentiometric-surface maps of the two areas. Aquifers in the Nacatoch Sand and Tokio Formation are hereafter referred to as the Nacatoch aquifer and the Tokio aquifer, respectively.
Potentiometric surfaces show that groundwater in the Nacatoch aquifer flows southeast toward the Mississippi River in northeastern Arkansas. Groundwater flow direction is towards the south and southeast in Hempstead, Little River, and Nevada Counties in southwestern Arkansas. An apparent cone of depression exists in southern Clark County and likely alters groundwater flow from a regional direction toward the depression.
In southwestern Arkansas, potentiometric surfaces indicate that groundwater flow in the Tokio aquifer is towards the city of Hope. Northwest of Hope, an apparent cone of depression exists. In southwestern Pike, northwestern Nevada, and northeastern Hempstead Counties, an area of artesian flow (water levels are at or above land surface) exists.
Water-level changes in wells were identified using two methods: (1) linear regression analysis of hydrographs from select wells with a minimum of 20 years of water-level data, and (2) a direct comparison between water-level measurements from 2008 and 2014–15 at each well. Of the six hydrographs analyzed in the Nacatoch aquifer, four indicated a decline in water levels. Compared to 2008 measurements, the largest rise in water levels was 35.14 feet (ft) in a well in Clark County, whereas the largest decline was 14.76 ft in a well in Nevada County, both located in southwestern Arkansas.
Of the four hydrographs analyzed in the Tokio aquifer, one indicated a decline in water levels, while the others remained relatively unchanged. Compared to 2008 measurements, the largest rise in water levels was 21.34 ft in Hempstead County, and the largest water-level decline was 39.37 ft in Clark County. Although changes in water levels since 2008 are spatially varied; long-term trends indicate an overall decline in water levels in both aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175090","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey","usgsCitation":"Rodgers, K.D., 2017, Water-level trends and potentiometric surfaces in the Nacatoch aquifer in northeastern and southwestern Arkansas and in the Tokio aquifer in southwestern Arkansas, 2014–15: U.S. Geological Survey Scientific Investigations Report 2017–5090, 30 p., https://doi.org/10.3133/sir20175090.","productDescription":"Report: iv, 30 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069974","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":345860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5090/sir20175090.pdf","text":"Report","size":"2.54 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
A Record of Change - Science and Elder Observations on the Navajo Nation is a 25-minute documentary about combining observations from Navajo elders with conventional science to determine how tribal lands and culture are affected by climate change. On the Navajo Nation, there is a shortage of historical climate data, making it difficult to assess changing environmental conditions.
This video reveals how a team of scientists, anthropologists, and translators combined the rich local knowledge of Navajo elders with recent scientific investigation to effectively document environmental change. Increasing aridity and declining snowfall in this poorly monitored region of the Southwest are accompanied by declining river flow and migrating sand dunes. The observations of Navajo elders verify and supplement this record of change by informing how shifting weather patterns are reflected in Navajo cultural practices and living conditions.
Director,
Geology, Minerals, Energy, & Geophysics Science Center—Flagstaff
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001-1600