When a major earthquake strikes, the resulting devastation can be compounded or even exceeded by the subsequent cascade of triggered seismicity. As the Nepalese recover from the 25 April 2015 shock, knowledge of what comes next is essential. We calculate the redistribution of crustal stresses and implied earthquake probabilities for different periods, from daily to 30 years into the future. An initial forecast was completed before an M 7.3 earthquake struck on 12 May 2015 that enables a preliminary assessment; postforecast seismicity has so far occurred within a zone of fivefold probability gain. Evaluation of the forecast performance, using two months of seismic data, reveals that stress‐based approaches present improved skill in higher‐magnitude triggered seismicity. Our results suggest that considering the total stress field, rather than only the coseismic one, improves the spatial performance of the model based on the estimation of a wide range of potential triggered faults following a mainshock.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220150195","usgsCitation":"Segou, M., and Parsons, T.E., 2016, Prospective earthquake forecasts at the Himalayan Front after the 25 April 2015 M 7.8 Gorkha Mainshock: Seismological Research Letters, v. 87, no. 4, p. 816-825, https://doi.org/10.1785/0220150195.","productDescription":"10 p.","startPage":"816","endPage":"825","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065687","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470810,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://nora.nerc.ac.uk/id/eprint/514329/1/SegouParsons-SRL-2016-NORA-all.pdf","text":"External Repository"},{"id":324624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-08","publicationStatus":"PW","scienceBaseUri":"5774e33fe4b07dd077c5fc6b","contributors":{"authors":[{"text":"Segou, Margaret","contributorId":140800,"corporation":false,"usgs":false,"family":"Segou","given":"Margaret","email":"","affiliations":[{"id":13572,"text":"Geoscience Azur","active":true,"usgs":false}],"preferred":false,"id":623869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":623868,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70160648,"text":"70160648 - 2016 - Ground motions at the outermost limits of seismically triggered landslides","interactions":[],"lastModifiedDate":"2016-07-06T16:41:35","indexId":"70160648","displayToPublicDate":"2016-06-29T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Ground motions at the outermost limits of seismically triggered landslides","docAbstract":"Over the last few decades, we and our colleagues have conducted field investigations in which we mapped the outermost limits of triggered landslides in four earthquakes: 1987 Whittier Narrows, California (M 5.9), 1987 Superstition Hills, California (M 6.5), 1994 Northridge, California (M 6.7), and 2011 Mineral, Virginia (M 5.8). In an additional two earthquakes, 1976 Guatemala (M 7.5) and 1983 Coalinga, California (M 6.5), we determined limits using high‐resolution aerial‐photographic interpretation in conjunction with more limited ground investigation. Limits in these earthquakes were defined by the locations of the very smallest failures (<1 m3) from the most susceptible slopes that can be identified positively as having been triggered by earthquake shaking. Because we and our colleagues conducted all of these investigations, consistent methodology and criteria were used in determining limits. In the six earthquakes examined, we correlated the outermost landslide limits with peak ground accelerations (PGAs) from ShakeMap models of each earthquake. For the four earthquakes studied by field investigation, the minimum PGA values associated with farthest landslide limits ranged from 0.02g to 0.08g. The range for the two earthquakes investigated using aerial‐photographic interpretations was 0.05–0.11g. Although PGA values at landslide limits depend on several factors, including material strength, topographic amplification, and hydrologic conditions, these values provide an empirically useful lower limiting range of PGA needed to trigger the smallest failures on very susceptible slopes. In a well‐recorded earthquake, this PGA range can be used to identify an outer boundary within which we might expect to find landsliding; in earthquakes that are not well recorded, mapping the outermost landslide limits provides a useful clue about ground‐motion levels at the mapped limits.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150141","usgsCitation":"Jibson, R.W., and Harp, E.L., 2016, Ground motions at the outermost limits of seismically triggered landslides: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 708-719, https://doi.org/10.1785/0120150141.","productDescription":"12 p.","startPage":"708","endPage":"719","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071276","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":324608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-09","publicationStatus":"PW","scienceBaseUri":"5774e33be4b07dd077c5fc44","contributors":{"authors":[{"text":"Jibson, Randall W. 0000-0003-3399-0875 jibson@usgs.gov","orcid":"https://orcid.org/0000-0003-3399-0875","contributorId":2985,"corporation":false,"usgs":true,"family":"Jibson","given":"Randall","email":"jibson@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":583460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harp, Edwin L. harp@usgs.gov","contributorId":1290,"corporation":false,"usgs":true,"family":"Harp","given":"Edwin","email":"harp@usgs.gov","middleInitial":"L.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":583461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170935,"text":"70170935 - 2016 - Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model","interactions":[],"lastModifiedDate":"2025-05-14T18:39:18.093972","indexId":"70170935","displayToPublicDate":"2016-06-29T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model","docAbstract":"Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2016.05.016","usgsCitation":"Ellefsen, K.J., and Smith, D., 2016, Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model: Applied Geochemistry, v. 75, p. 200-210, https://doi.org/10.1016/j.apgeochem.2016.05.016.","productDescription":"11 p.","startPage":"200","endPage":"210","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073180","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":324600,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":470811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2016.05.016","text":"Publisher Index Page"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 37\n ],\n [\n -107,\n 41\n ],\n [\n -102,\n 41\n ],\n [\n -102,\n 37\n ],\n [\n -107,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5774e345e4b07dd077c5fcab","contributors":{"authors":[{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":629166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, David 0000-0002-9543-800X","orcid":"https://orcid.org/0000-0002-9543-800X","contributorId":169280,"corporation":false,"usgs":true,"family":"Smith","given":"David","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":629167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177751,"text":"70177751 - 2016 - The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","interactions":[],"lastModifiedDate":"2018-08-08T10:25:00","indexId":"70177751","displayToPublicDate":"2016-06-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","docAbstract":"Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.
","language":"English","publisher":"Pergamon Press","doi":"10.1016/j.marpolbul.2016.02.076","usgsCitation":"Slonecker, E.T., Jones, D.K., and Pellerin, B.A., 2016, The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM): Marine Pollution Bulletin, v. 107, no. 2, p. 518-527, https://doi.org/10.1016/j.marpolbul.2016.02.076.","productDescription":"10 p.","startPage":"518","endPage":"527","ipdsId":"IP-069654","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470815,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2016.02.076","text":"Publisher Index Page"},{"id":438605,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7125QQM","text":"USGS data release","linkHelpText":"CDOM/fDOM and Landsat 8 Comparisons"},{"id":330242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5809d7c4e4b0f497e78fca62","chorus":{"doi":"10.1016/j.marpolbul.2016.02.076","url":"http://dx.doi.org/10.1016/j.marpolbul.2016.02.076","publisher":"Elsevier BV","authors":"Slonecker E. Terrence, Jones Daniel K., Pellerin Brian A.","journalName":"Marine Pollution Bulletin","publicationDate":"6/2016","auditedOn":"3/21/2016","publiclyAccessibleDate":"3/4/2016"},"contributors":{"authors":[{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":true,"id":651634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Daniel K. 0000-0003-0724-8001 dkjones@usgs.gov","orcid":"https://orcid.org/0000-0003-0724-8001","contributorId":4959,"corporation":false,"usgs":true,"family":"Jones","given":"Daniel","email":"dkjones@usgs.gov","middleInitial":"K.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. bpeller@usgs.gov","contributorId":1451,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651653,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175475,"text":"70175475 - 2016 - Increased water deficit decreases Douglas fir growth throughout western US forests","interactions":[],"lastModifiedDate":"2016-08-26T11:04:58","indexId":"70175475","displayToPublicDate":"2016-06-28T18:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Increased water deficit decreases Douglas fir growth throughout western US forests","docAbstract":"Changes in tree growth rates can affect tree mortality and forest feedbacks to the global carbon cycle. As air temperature increases, evaporative demand also increases, increasing effective drought in forest ecosystems. Using a spatially comprehensive network of Douglas-fir (Pseudotsuga menziesii) chronologies from 122 locations that experience distinctly different climate in the western United States, we show that increased temperature decreases growth via vapor pressure deficit (VPD) across all latitudes. Under an ensemble of global circulation models, we project an increase in both the mean VPD associated with the lowest growth extremes and the probability of exceeding these VPD values. As temperature continues to increase in future decades, we can expect deficit-related stress to increase and consequently Douglas-fir growth to decrease throughout its US range.
","language":"English","publisher":"National Academy of Sciences of the United States","doi":"10.1073/pnas.1602384113","usgsCitation":"Restaino, C.M., Peterson, D.L., and Littell, J.S., 2016, Increased water deficit decreases Douglas fir growth throughout western US forests: Proceedings of the National Academy of Sciences of the United States of America, v. 113, no. 34, p. 9557-9562, https://doi.org/10.1073/pnas.1602384113.","productDescription":"6 p.","startPage":"9557","endPage":"9562","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073677","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"links":[{"id":470816,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5003285","text":"External Repository"},{"id":326464,"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 -128.2763671875,\n 51.536085601784755\n ],\n [\n -103.84277343749999,\n 50.401515322782366\n ],\n [\n -101.689453125,\n 38.37611542403604\n ],\n [\n -102.5244140625,\n 33.61461929233378\n ],\n [\n -104.1064453125,\n 31.31610138349565\n ],\n [\n -120.9375,\n 32.80574473290688\n ],\n [\n -125.2880859375,\n 39.16414104768742\n ],\n [\n -128.80371093749997,\n 50.62507306341435\n ],\n [\n -128.2763671875,\n 51.536085601784755\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","issue":"34","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-08","publicationStatus":"PW","scienceBaseUri":"57aef33ee4b0fc09faae0388","contributors":{"authors":[{"text":"Restaino, Christina M","contributorId":173657,"corporation":false,"usgs":false,"family":"Restaino","given":"Christina","email":"","middleInitial":"M","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":645376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, David L.","contributorId":94643,"corporation":false,"usgs":false,"family":"Peterson","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":645377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Littell, Jeremy S. 0000-0002-5302-8280 jlittell@usgs.gov","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":4428,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","email":"jlittell@usgs.gov","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":645375,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173894,"text":"70173894 - 2016 - Seasonal Variability in Vadose zone biodegradation at a crude oil pipeline rupture site","interactions":[],"lastModifiedDate":"2018-08-09T12:03:11","indexId":"70173894","displayToPublicDate":"2016-06-28T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal Variability in Vadose zone biodegradation at a crude oil pipeline rupture site","docAbstract":"Understanding seasonal changes in natural attenuation processes is critical for evaluating source-zone longevity and informing management decisions. The seasonal variations of natural attenuation were investigated through measurements of surficial CO2 effluxes, shallow soil CO2 radiocarbon contents, subsurface gas concentrations, soil temperature, and volumetric water contents during a 2-yr period. Surficial CO2 effluxes varied seasonally, with peak values of total soil respiration (TSR) occurring in the late spring and summer. Efflux and radiocarbon data indicated that the fractional contributions of natural soil respiration (NSR) and contaminant soil respiration (CSR) to TSR varied seasonally. The NSR dominated in the spring and summer, and CSR dominated in the fall and winter. Subsurface gas concentrations also varied seasonally, with peak values of CO2 and CH4 occurring in the fall and winter. Vadose zone temperatures and subsurface CO2 concentrations revealed a correlation between contaminant respiration and temperature. A time lag of 5 to 7 mo between peak subsurface CO2 concentrations and peak surface efflux is consistent with travel-time estimates for subsurface gas migration. Periods of frozen soils coincided with depressed surface CO2 effluxes and elevated CO2 concentrations, pointing to the temporary presence of an ice layer that inhibited gas transport. Quantitative reactive transport simulations demonstrated aspects of the conceptual model developed from field measurements. Overall, results indicated that source-zone natural attenuation (SZNA) rates and gas transport processes varied seasonally and that the average annual SZNA rate estimated from periodic surface efflux measurements is 60% lower than rates determined from measurements during the summer.
","language":"English","publisher":"Soil Science Society of America","publisherLocation":"Fitchburg, WI","doi":"10.2136/vzj2015.09.0125","usgsCitation":"Sihota, N.J., Trost, J.J., Bekins, B., Berg, A.M., Delin, G.N., Mason, B.E., Warren, E., and Mayer, K.U., 2016, Seasonal Variability in Vadose zone biodegradation at a crude oil pipeline rupture site: Vadose Zone Journal, v. 15, no. 5, 14 p., https://doi.org/10.2136/vzj2015.09.0125.","productDescription":"14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057205","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":324558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-13","publicationStatus":"PW","scienceBaseUri":"577391a7e4b07657d1a88bd8","contributors":{"authors":[{"text":"Sihota, Natasha J.","contributorId":46431,"corporation":false,"usgs":true,"family":"Sihota","given":"Natasha","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":638902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trost, Jared J. 0000-0003-0431-2151 jtrost@usgs.gov","orcid":"https://orcid.org/0000-0003-0431-2151","contributorId":3749,"corporation":false,"usgs":true,"family":"Trost","given":"Jared","email":"jtrost@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":638901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekins, Barbara 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":139407,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":638903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berg, Andrew M. 0000-0001-9312-240X aberg@usgs.gov","orcid":"https://orcid.org/0000-0001-9312-240X","contributorId":5642,"corporation":false,"usgs":true,"family":"Berg","given":"Andrew","email":"aberg@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":638904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Delin, Geoffrey N. 0000-0001-7991-6158 delin@usgs.gov","orcid":"https://orcid.org/0000-0001-7991-6158","contributorId":2610,"corporation":false,"usgs":true,"family":"Delin","given":"Geoffrey","email":"delin@usgs.gov","middleInitial":"N.","affiliations":[{"id":5063,"text":"Central Water Science Field Team","active":true,"usgs":true}],"preferred":true,"id":638905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Brent E. bmason@usgs.gov","contributorId":5196,"corporation":false,"usgs":true,"family":"Mason","given":"Brent","email":"bmason@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":638906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Warren, Ean ewarren@usgs.gov","contributorId":1351,"corporation":false,"usgs":true,"family":"Warren","given":"Ean","email":"ewarren@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":638907,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mayer, K. 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In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends.
\nAn assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.03.036","usgsCitation":"Zhu, Z., Fu, Y., Woodcock, C., Olofsson, P., Vogelmann, J., Holden, C., Wang, M., Dai, S., and Yu, Y., 2016, Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014): Remote Sensing of Environment, v. 185, p. 243-257, https://doi.org/10.1016/j.rse.2016.03.036.","productDescription":"15 p.","startPage":"243","endPage":"257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068963","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470818,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.03.036","text":"Publisher Index Page"},{"id":324550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","city":"Guangzhou","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.5,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 26.86328062676624\n ],\n [\n 112.5,\n 26.86328062676624\n ],\n [\n 112.5,\n 21.453068633086783\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"577391a6e4b07657d1a88bd0","contributors":{"authors":[{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":627309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fu, Yingchun","contributorId":172520,"corporation":false,"usgs":false,"family":"Fu","given":"Yingchun","email":"","affiliations":[],"preferred":false,"id":641108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":641109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olofsson, Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":641110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":127752,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":641111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holden, Christopher","contributorId":172521,"corporation":false,"usgs":false,"family":"Holden","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":641112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Min","contributorId":145692,"corporation":false,"usgs":false,"family":"Wang","given":"Min","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":641113,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dai, Shu","contributorId":172522,"corporation":false,"usgs":false,"family":"Dai","given":"Shu","email":"","affiliations":[],"preferred":false,"id":641114,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yu, Yang","contributorId":172524,"corporation":false,"usgs":false,"family":"Yu","given":"Yang","email":"","affiliations":[],"preferred":false,"id":641115,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170411,"text":"70170411 - 2016 - Spatiotemporal patterns of mercury accumulation in lake sediments of western North America","interactions":[],"lastModifiedDate":"2018-08-09T12:04:23","indexId":"70170411","displayToPublicDate":"2016-06-28T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal patterns of mercury accumulation in lake sediments of western North America","docAbstract":"For the Western North America Mercury Synthesis, we compiled mercury records from 165 dated sediment cores from 138 natural lakes across western North America. Lake sediments are accepted as faithful recorders of historical mercury accumulation rates, and regional and sub-regional temporal and spatial trends were analyzed with descriptive and inferential statistics. Mercury accumulation rates in sediments have increased, on average, four times (4×) from 1850 to 2000 and continue to increase by approximately 0.2 μg/m2 per year. Lakes with the greatest increases were influenced by the Flin Flon smelter, followed by lakes directly affected by mining and wastewater discharges. Of lakes not directly affected by point sources, there is a clear separation in mercury accumulation rates between lakes with no/little watershed development and lakes with extensive watershed development for agricultural and/or residential purposes. Lakes in the latter group exhibited a sharp increase in mercury accumulation rates with human settlement, stabilizing after 1950 at five times (5×) 1850 rates. Mercury accumulation rates in lakes with no/little watershed development were controlled primarily by relative watershed size prior to 1850, and since have exhibited modest increases (in absolute terms and compared to that described above) associated with (regional and global) industrialization. A sub-regional analysis highlighted that in the ecoregion Northwestern Forest Mountains, <1% of mercury deposited to watersheds is delivered to lakes. Research is warranted to understand whether mountainous watersheds act as permanent sinks for mercury or if export of “legacy” mercury (deposited in years past) will delay recovery when/if emissions reductions are achieved.
Supraglacial rivers on the Greenland ice sheet (GrIS) transport large volumes of surface meltwater toward the ocean, yet have received relatively little direct research. This study presents field observations of channel width, depth, velocity, and water surface slope for nine supraglacial channels on the southwestern GrIS collected between 23 July and 20 August, 2012. Field sites are located up to 74 km inland and span 494-1485 m elevation, and contain measured discharges larger than any previous in situ study: from 0.006 to 23.12 m3/s in channels 0.20 to 20.62 m wide. All channels were deeply incised with near vertical banks, and hydraulic geometry results indicate that supraglacial channels primarily accommodate greater discharges by increasing velocity. Smaller streams had steeper water surface slopes (0.74-8.83%) than typical in terrestrial settings, yielding correspondingly high velocities (0.40-2.60 m/s) and Froude numbers (0.45-3.11) with supercritical flow observed in 54% of measurements. Derived Manning's n values were larger and more variable than anticipated from channels of uniform substrate, ranging from 0.009 to 0.154 with a mean value of 0.035 +/- 0.027 despite the absence of sediment, debris, or other roughness elements. Ubiquitous micro-depressions in shallow sections of the channel bed may explain some of these roughness values. However, we find that other, unobserved sources of flow resistance likely contributed to these elevated n values: future work should explicitly consider additional sources of flow resistance beyond bed roughness in supraglacial channels. We conclude that hydraulic modelling for these channels must allow for both sub- and supercritical flow, and most importantly must refrain from assuming that all ice-substrate channels exhibit similar hydraulic behavior, especially for Froude numbers and Manning's n. Finally, this study highlights that further theoretical and empirical work on supraglacial channel hydraulics is necessary before broad scale understanding of ice sheet hydrology can be achieved. This article is protected by copyright. All rights reserved.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/esp.3977","usgsCitation":"Gleason, C.J., Smith, L., Chu, V.W., Legleiter, C.J., Pitcher, L.H., Overstreet, B.T., Rennermalm, A.K., Forster, R.R., and Yang, K., 2016, Characterizing supraglacial meltwater channel hydraulics on the Greenland Ice Sheet from in situ observations: Earth Surface Processes and Landforms, v. 41, no. 14, p. 2111-2122, https://doi.org/10.1002/esp.3977.","productDescription":"12 p.","startPage":"2111","endPage":"2122","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075253","costCenters":[{"id":5044,"text":"National Research Program - Central 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H.","contributorId":169006,"corporation":false,"usgs":false,"family":"Pitcher","given":"Lincoln","email":"","middleInitial":"H.","affiliations":[{"id":13022,"text":"Department of Geography, University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":628027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Overstreet, Brandon T. 0000-0001-7845-6671","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":63257,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":628028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rennermalm, Asa K.","contributorId":169007,"corporation":false,"usgs":false,"family":"Rennermalm","given":"Asa","email":"","middleInitial":"K.","affiliations":[{"id":25395,"text":"Department of Geography, Rutgers University, New 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Mars","interactions":[],"lastModifiedDate":"2018-11-08T17:09:01","indexId":"70168946","displayToPublicDate":"2016-06-28T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":"High concentrations of manganese and sulfur in deposits on Murray Ridge, Endeavour Crater, Mars","docAbstract":"Mars Reconnaissance Orbiter HiRISE images and Opportunity rover observations of the ~22 km wide Noachian age Endeavour Crater on Mars show that the rim and surrounding terrains were densely fractured during the impact crater-forming event. Fractures have also propagated upward into the overlying Burns formation sandstones. Opportunity’s observations show that the western crater rim segment, called Murray Ridge, is composed of impact breccias with basaltic compositions, as well as occasional fracture-filling calcium sulfate veins. Cook Haven, a gentle depression on Murray Ridge, and the site where Opportunity spent its sixth winter, exposes highly fractured, recessive outcrops that have relatively high concentrations of S and Cl, consistent with modest aqueous alteration. Opportunity’s rover wheels serendipitously excavated and overturned several small rocks from a Cook Haven fracture zone. Extensive measurement campaigns were conducted on two of them: Pinnacle Island and Stuart Island. These rocks have the highest concentrations of Mn and S measured to date by Opportunity and occur as a relatively bright sulfate-rich coating on basaltic rock, capped by a thin deposit of one or more dark Mn oxide phases intermixed with sulfate minerals. We infer from these unique Pinnacle Island and Stuart Island rock measurements that subsurface precipitation of sulfate-dominated coatings was followed by an interval of partial dissolution and reaction with one or more strong oxidants (e.g., O2) to produce the Mn oxide mineral(s) intermixed with sulfate-rich salt coatings. In contrast to arid regions on Earth, where Mn oxides are widely incorporated into coatings on surface rocks, our results demonstrate that on Mars the most likely place to deposit and preserve Mn oxides was in fracture zones where migrating fluids intersected surface oxidants, forming precipitates shielded from subsequent physical erosion.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/am-2016-5599","usgsCitation":"Arvidson, R.E., Squyres, S.W., Morris, R., Knoll, A.H., Gellert, R., Clark, B., Catalano, J.G., Jolliff, B.L., McLennan, S.M., Herkenhoff, K.E., VanBommel, S., Mittelfehldt, D.W., Grotzinger, J., Guinness, E.A., Johnson, J., Bell, J.F., Farrand, W., Stein, N., Fox, V.K., Golombek, M., Hinkle, M.A., Calvin, W.M., and de Souza, P.A., 2016, High concentrations of manganese and sulfur in deposits on Murray Ridge, Endeavour Crater, Mars: American Mineralogist, v. 101, no. 6, p. 1389-1405, https://doi.org/10.2138/am-2016-5599.","productDescription":"17 p.","startPage":"1389","endPage":"1405","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069984","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":470822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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G.","contributorId":167653,"corporation":false,"usgs":false,"family":"Hinkle","given":"Margaret","email":"","middleInitial":"A. G.","affiliations":[{"id":24730,"text":"Department of Earth and Planetary Sciences, Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":623025,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Calvin, Wendy M.","contributorId":93508,"corporation":false,"usgs":true,"family":"Calvin","given":"Wendy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":623026,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"de Souza, Paulo A. Jr.","contributorId":167654,"corporation":false,"usgs":false,"family":"de Souza","given":"Paulo","suffix":"Jr.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":623027,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70170850,"text":"70170850 - 2016 - Impact of formation water geochemistry and crude oil biodegradation on microbial methanogenesis","interactions":[],"lastModifiedDate":"2016-06-28T11:41:03","indexId":"70170850","displayToPublicDate":"2016-06-28T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2958,"text":"Organic Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Impact of formation water geochemistry and crude oil biodegradation on microbial methanogenesis","docAbstract":"Converting non-producible crude oil to CH4 via methanogenic crude oil biodegradation in oil reservoirs could serve as one way to increase our energy profile. Yet, field data supporting the direct relationship between methanogenesis and crude oil biodegradation are sparse. Indicators of methanogenesis, based on the formation water and gas geochemistry (e.g. alkalinity, δ13C–CO2) were compared with indicators of crude oil biodegradation (e.g. pristane/phytane and n-alkane ratios) from wells in the Wilcox Group of Louisiana to determine if increases in extent of methanogenesis were related to increases in extent of crude oil biodegradation.
\nShallow wells (393–442 m depth) contained highly biodegraded oils associated with low extent of methanogenesis, while the deepest (> 1208 m) wells contained minimally degraded oils and produced fluids suggesting a low extent of methanogenesis. Mid-depth wells (666–857 m) in the central field had the highest indicators of methanogenesis and contained moderately biodegraded oils. Little correlation existed between extents of crude oil biodegradation and methanogenesis across the whole transect (avg.R2 = 0.13). However, when wells with the greatest extent of crude oil biodegradation were eliminated (3 of 6 oilfields), better correlation between extent of methanogenesis and biodegradation (avg. R2 = 0.53) was observed. The results suggest that oil quality and salinity impact methanogenic crude oil biodegradation. Reservoirs indicating moderate extent of crude oil biodegradation and high extent of methanogenesis, such as the central field, would be good candidates for attempting to enhance methanogenic crude oil biodegradation as a result of the observations from the study.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2016.05.008","usgsCitation":"Shelton, J., McIntosh, J.C., Warwick, P.D., and McCray, J.E., 2016, Impact of formation water geochemistry and crude oil biodegradation on microbial methanogenesis: Organic Geochemistry, v. 98, p. 105-117, https://doi.org/10.1016/j.orggeochem.2016.05.008.","productDescription":"13 p.","startPage":"105","endPage":"117","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073458","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":470824,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2016.05.008","text":"Publisher Index Page"},{"id":324501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"577391a5e4b07657d1a88bce","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":628816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McIntosh, Jennifer C. 0000-0001-5055-4202","orcid":"https://orcid.org/0000-0001-5055-4202","contributorId":150557,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":628817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":628818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCray, John E.","contributorId":139258,"corporation":false,"usgs":false,"family":"McCray","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":628819,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170928,"text":"sir20165050 - 2016 - Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois","interactions":[],"lastModifiedDate":"2024-09-18T14:34:15.573847","indexId":"sir20165050","displayToPublicDate":"2016-06-28T00:00:00","publicationYear":"2016","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":"2016-5050","displayTitle":"Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois","title":"Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois","docAbstract":"This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.
The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.
This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant.
The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted annual maximum peak discharge records by streamgage are provided at https://doi.org/10.3133/sir20165050 for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165050","collaboration":"Prepared in cooperation with the Illinois Center for Transportation, the Illinois Department of Transportation, and the Federal Highway Administration","usgsCitation":"Over, T.M., Saito, R.J., Veilleux, A.G., O’Shea, P.S., Sharpe, J.B., Soong, D.T., and Ishii, A.L., 2016, Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois (ver. 3.0, June 2021): U.S. Geological Survey Scientific Investigations Report 2016–5050, 50 p. with appendix, https://doi.org/10.3133/sir20165050.","productDescription":"Report: x, 51 p.; Tables; Companion Files; Version History","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072125","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":386876,"rank":13,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5050/versionHist.txt","text":"Version History","size":"20.7 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016–5050 Version History"},{"id":386859,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_13.csv","text":"Table 13","size":"4.33 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 13","linkHelpText":"— Components of variance of prediction for the selected spatial regression equations in this study in northeastern Illinois"},{"id":386858,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_08.csv","text":"Table 8","size":"2.36 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 8","linkHelpText":"— Quantile regression coefficients from temporal analysis of 117 streamgages in northeastern Illinois and adjacent states, as a function of annual exceedance probability"},{"id":386856,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_04.csv","text":"Table 4","size":"9.96 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 4","linkHelpText":"— Segment information for 181 U.S. Geological Survey streamgages used in this study, northeastern Illinois and adjacent states"},{"id":386855,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_03.csv","text":"Table 3","size":"7.02 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 3","linkHelpText":"— Spatially averaged basin characteristics considered for developing spatial regression equations in this study in northeastern Illinois"},{"id":386854,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_02.csv","text":"Table 2","size":"104 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 2","linkHelpText":"— Estimated peak discharge quantiles for 181 streamgages in northeastern Illinois and adjacent states, at selected exceedance probabilities"},{"id":386853,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_01.csv","text":"Table 1","size":"29.2 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 1","linkHelpText":"— U.S. Geological Survey streamgages used in this study in northeastern Illinois and adjacent states"},{"id":386852,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050.pdf","text":"Report","size":"6.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5050"},{"id":324328,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_Links_To_Files.html","text":"Annual maximum peak discharge and associated urban fraction and precipitation values by streamgage","size":"29 kB","linkFileType":{"id":5,"text":"html"},"description":"SIR 2016–5050 Supplemental Graphs and Tables"},{"id":386861,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_tables.xlsx","text":"Tables 1 through 4, 6, 8 and 13 and Table 1–1","size":"673 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5050 Tables"},{"id":386860,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_appendix_table_1.1.csv","text":"Table 1.1","size":"7.58 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 1.1","linkHelpText":"— Skew statistics at streamgages used in the development of the regional skew model in this study in northeastern Illinois"},{"id":386857,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5050/sir20165050_table_06.csv","text":"Table 6","size":"345 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2016–5050 Table 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]\n}","edition":"Version 1.0: June 2016; Version 2.0: November 2017; Version 3.0: June 2021","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
The relationship (scaling) between scalar moment, M0, and duration, T, potentially provides key constraints on the physics governing fault slip. The prevailing interpretation of M0-T observations proposes different scaling for fast (earthquakes) and slow (mostly aseismic) slip populations and thus fundamentally different driving mechanisms. We show that a single model of slip events within bounded slip zones may explain nearly all fast and slow slip M0-T observations, and both slip populations have a change in scaling, where the slip area growth changes from 2-D when too small to sense the boundaries to 1-D when large enough to be bounded. We present new fast and slow slip M0-T observations that sample the change in scaling in each population, which are consistent with our interpretation. We suggest that a continuous but bimodal distribution of slip modes exists and M0-T observations alone may not imply a fundamental difference between fast and slow slip.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL069967","usgsCitation":"Gomberg, J.S., Wech, A.G., Creager, K., Obara, K., and Agnew, D., 2016, Reconsidering earthquake scaling: Geophysical Research Letters, v. 43, no. 12, p. 6243-6251, https://doi.org/10.1002/2016GL069967.","productDescription":"9 p.","startPage":"6243","endPage":"6251","ipdsId":"IP-065616","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":500016,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/bb761be9ddba4e658205300bdcc9cf2b","text":"External Repository"},{"id":343967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"596f1e26e4b0d1f9f064076a","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wech, Aaron G. 0000-0003-4983-1991 awech@usgs.gov","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":5344,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","email":"awech@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":705269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creager, Kenneth","contributorId":194763,"corporation":false,"usgs":false,"family":"Creager","given":"Kenneth","affiliations":[],"preferred":false,"id":705270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Obara, K.","contributorId":194775,"corporation":false,"usgs":false,"family":"Obara","given":"K.","affiliations":[],"preferred":false,"id":705305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Agnew, Duncan 0000-0002-2360-7783","orcid":"https://orcid.org/0000-0002-2360-7783","contributorId":178605,"corporation":false,"usgs":false,"family":"Agnew","given":"Duncan","email":"","affiliations":[],"preferred":false,"id":705271,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171552,"text":"sir20165080 - 2016 - Groundwater-flow model for the Wood River Valley aquifer system, south-central Idaho","interactions":[],"lastModifiedDate":"2016-08-22T09:04:33","indexId":"sir20165080","displayToPublicDate":"2016-06-27T17:00:00","publicationYear":"2016","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":"2016-5080","title":"Groundwater-flow model for the Wood River Valley aquifer system, south-central Idaho","docAbstract":"A three-dimensional numerical model of groundwater flow was developed for the Wood River Valley (WRV) aquifer system, Idaho, to evaluate groundwater and surface-water availability at the regional scale. This mountain valley is located in Blaine County and has a drainage area of about 2,300 square kilometers (888 square miles). The model described in this report can serve as a tool for water-rights administration and water-resource management and planning. The model was completed with support from the Idaho Department of Water Resources, and is part of an ongoing U.S. Geological Survey effort to characterize the groundwater resources of the WRV. A highly reproducible approach was taken for constructing the WRV groundwater-flow model. The collection of datasets, source code, and processing instructions used to construct and analyze the model was distributed as an R statistical-computing and graphics package.
\nFlow in the WRV aquifer was simulated using the MODFLOW-USG groundwater flow model. The transient flow model simulates groundwater flow between 1995 and 2010. The model uses a 100-meter (328-feet) uniform grid spacing with 54,922 active model cells distributed over three model layers. A confining unit in the south-central part of the Bellevue fan necessitated the use of a multi-layer model. Specified-flow boundaries were used to simulate the groundwater inflows from each of the major tributary basins (also known as tributary basin underflow) and the areal recharge of precipitation and applied irrigation. Head‑dependent flow boundaries were used to simulate the stream-aquifer flow exchange in river reaches and the groundwater discharge at the outlet boundaries of Stanton Crossing and Silver Creek. The model was calibrated by adjusting aquifer hydraulic properties to match simulated and measured water levels and stream-aquifer flow exchange, using the parameter-estimation program PEST. The model reasonably simulated the measured water-table elevation, orientation, and gradients. Stream-aquifer flow exchange along river reaches also was reasonably simulated by the model.
\nInflow into the WRV aquifer system originates from three sources (from largest to smallest):
\nOutflow from the WRV aquifer system originates from five sources (from largest to smallest):
\nTemporal changes in aquifer storage are most affected by areal recharge and groundwater pumping, and also contribute to changes in streamflow gains.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165080","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Fisher, J.C., Bartolino, J.R., Wylie, A.H., Sukow, Jennifer, and McVay, Michael, 2016, Groundwater-flow model of the Wood River Valley aquifer system, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2016–5080, 71 p., https://dx.doi.org/10.3133/sir20165080.","productDescription":"Report: viii, 71 p.; Appendixes A-H; Model Archive; Data Repository","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-039541","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":324425,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixE.pdf","text":"Appendix E","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix E","linkHelpText":"Tributary Basin Underflow into the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324424,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixD.pdf","text":"Appendix D","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix D","linkHelpText":"Uncalibrated Groundwater-Flow Model for the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324426,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixF.pdf","text":"Appendix F","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix F","linkHelpText":"Natural Groundwater Recharge and Discharge in the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324428,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixH.pdf","text":"Appendix H","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix H","linkHelpText":"Calibration of the Wood River Valley Groundwater Flow Model"},{"id":324427,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixG.pdf","text":"Appendix G","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix G","linkHelpText":"Incidental Groundwater Recharge and Pumping Demand in the Wood River Valley Aquifer System, South-Central Idaho"},{"id":324430,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://github.com/USGS-R/wrv","text":"R-package repository"},{"id":324423,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixC.pdf","text":"Appendix C","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix C","linkHelpText":"Creating Datasets for the R-Package ‘wrv’"},{"id":324429,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://dx.doi.org/10.5066/F7C827DT","text":"Model Archive"},{"id":324419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5080/coverthb.jpg"},{"id":324420,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Report PDF"},{"id":324421,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixA.pdf","text":"Appendix A","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix A","linkHelpText":"An Introduction to the R-Package ‘wrv’"},{"id":324422,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5080/sir20165080_appendixB.pdf","text":"Appendix B","size":"525 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5080 Appendix B","linkHelpText":"Manual for Functions and Datasets in the R-Package ‘wrv’"}],"country":"United States","state":"Idaho","otherGeospatial":"Wood River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.47753906249999,\n 43.30119623257966\n ],\n [\n -114.47753906249999,\n 43.82065657651685\n ],\n [\n -114.04083251953124,\n 43.82065657651685\n ],\n [\n -114.04083251953124,\n 43.30119623257966\n ],\n [\n -114.47753906249999,\n 43.30119623257966\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Interactions and feedbacks between abundant surface waters and permafrost fundamentally shape lowland Arctic landscapes. Sublake permafrost is maintained when the maximum ice thickness (MIT) exceeds lake depth and mean annual bed temperatures (MABTs) remain below freezing. However, declining MIT since the 1970s is likely causing talik development below shallow lakes. Here we show high-temperature sensitivity to winter ice growth at the water-sediment interface of shallow lakes based on year-round lake sensor data. Empirical model experiments suggest that shallow (1 m depth) lakes have warmed substantially over the last 30 years (2.4°C), with MABT above freezing 5 of the last 7 years. This is in comparison to slower rates of warming in deeper (3 m) lakes (0.9°C), with already well-developed taliks. Our findings indicate that permafrost below shallow lakes has already begun crossing a critical thawing threshold approximately 70 years prior to predicted terrestrial permafrost thaw in northern Alaska.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL068506","usgsCitation":"Arp, C.D., Jones, B.M., Grosse, G., Bondurant, A.C., Romanovksy, V.E., Hinkel, K.M., and Parsekian, A.D., 2016, Threshold sensitivity of shallow Arctic lakes and sublake permafrost to changing winter climate: Geophysical Research Letters, v. 43, no. 12, p. 6358-6365, https://doi.org/10.1002/2016GL068506.","productDescription":"8 p.","startPage":"6358","endPage":"6365","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073772","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":470830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068506","text":"Publisher Index Page"},{"id":324491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Teshekpuk Lake, Umiat Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150,\n 69\n ],\n [\n -150,\n 72\n ],\n [\n -158,\n 72\n ],\n [\n -158,\n 69\n ],\n [\n -150,\n 69\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-24","publicationStatus":"PW","scienceBaseUri":"57724023e4b07657d1a793b8","chorus":{"doi":"10.1002/2016gl068506","url":"http://dx.doi.org/10.1002/2016gl068506","publisher":"Wiley-Blackwell","authors":"Arp Christopher D., Jones Benjamin M., Grosse Guido, Bondurant Allen C., Romanovsky Vladimir E., Hinkel Kenneth M., Parsekian Andrew D.","journalName":"Geophysical Research Letters","publicationDate":"6/24/2016","publiclyAccessibleDate":"6/24/2016"},"contributors":{"authors":[{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":640934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":640933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":640935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bondurant, Allen C.","contributorId":172493,"corporation":false,"usgs":false,"family":"Bondurant","given":"Allen","email":"","middleInitial":"C.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":640936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romanovksy, Vladimir E.","contributorId":172494,"corporation":false,"usgs":false,"family":"Romanovksy","given":"Vladimir","email":"","middleInitial":"E.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":640937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hinkel, Kenneth M.","contributorId":15405,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":640938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parsekian, Andrew D.","contributorId":23829,"corporation":false,"usgs":false,"family":"Parsekian","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":640939,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171556,"text":"sir20165079 - 2016 - A spatially explicit suspended-sediment load model for western Oregon","interactions":[],"lastModifiedDate":"2016-07-20T09:48:24","indexId":"sir20165079","displayToPublicDate":"2016-06-27T16:00:00","publicationYear":"2016","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":"2016-5079","title":"A spatially explicit suspended-sediment load model for western Oregon","docAbstract":"We calibrated the watershed model SPARROW (Spatially Referenced Regressions on Watershed attributes) to give estimates of suspended-sediment loads for western Oregon and parts of northwestern California. Estimates of suspended-sediment loads were derived from a nonlinear least squares regression that related explanatory variables representing landscape and transport conditions to measured suspended-sediment loads at 68 measurement stations. The model gives estimates of model coefficients and their uncertainty within a spatial framework defined by the National Hydrography Dataset Plus hydrologic network. The resulting model explained 64 percent of the variability in suspended-sediment yield and had a root mean squared error value of 0.737. The predictor variables selected for the final model were (1) generalized lithologic province, (2) mean annual precipitation, and (3) burned area (by recent wildfire). Other landscape characteristics also were considered, but they were not significant predictors of sediment transport, were strongly correlated with another predictor variable, or were not as significant as the predictors selected for the final model.
\nThe northern Oregon coastal drainages had the highest predicted suspended sediment yields (median yield 475 kilograms per hectare per year) and the Klamath River Basin had the lowest (median yield 53 kilograms per hectare per year). Quaternary deposits were, on average, the largest contributor to incremental suspended-sediment yield even though this lithologic province only makes up 17 percent of the modeling domain. Coast Range sedimentary rocks and Coast Range volcanic rocks had high suspended-sediment yields whereas, in addition to the Klamath terrane, the Western Cascade and High Cascade lithologic provinces had low suspended-sediment yields. Precipitation and the area affected by recent wildfire both positively correlated with suspended-sediment load.
\nSuspended-sediment transport rates predicted by this SPARROW model are less than historical (1956–73) and long‑term (thousands of years) geological rates. This difference likely results, in part, from biases in the data underlying the SPARROW model, probably resulting in predicted suspended-sediment estimates that underestimate actual transport rates. However, the differences also likely owe to natural and human-caused variation in suspended-sediment yields as they respond to changes in climate, vegetation, fire frequency, and land use. In particular, decreases in mean annual suspended-sediment yields within the Umpqua River Basin since 1956–73 may owe to less intense forest harvest, passage of the Oregon Forest Practices Act of 1971, and increased emphasis in habitat protection in recent decades. Such sensitivity may have implications for the spatial and temporal distributions of aquatic and riparian habitats.
\nKnowledge of the regionally important patterns and factors in suspended-sediment sources and transport could support broad-scale, water-quality management objectives and priorities. Because of biases and limitations of this model, however, these results are most applicable for general comparisons and for broad areas such as large watersheds. For example, despite having similar area, precipitation, and land-use, the Umpqua River Basin generates 68 percent more suspended sediment than the Rogue River Basin, chiefly because of the large area of Coast Range sedimentary province in the Umpqua River Basin. By contrast, the Rogue River Basin contains a much larger area of Klamath terrane rocks, which produce significantly less suspended load, although recent fire disturbance (in 2002) has apparently elevated suspended sediment yields in the tributary Illinois River watershed. Fine-scaled analysis, however, will require more intensive, locally focused measurements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165079","usgsCitation":"Wise, D.R., and O’Connor, J.E., 2016, A spatially explicit suspended-sediment load model for western Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5079, 25 p., https://dx.doi.org/10.3133/sir20165079.","productDescription":"Report: v, 25 p.; Appendix A; Companion File","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064150","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":324455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5079/coverthb.jpg"},{"id":324458,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079_NHDV2_predict_data.txt","text":"Mean annual suspended loads estimated by the SPARROW model","size":"1 MB","linkFileType":{"id":2,"text":"txt"}},{"id":324456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5079 Report PDF"},{"id":324457,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5079/sir20165079_appendixa.xlsx","text":"Appendix A ","size":"23 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5079 Appendix A","linkHelpText":"Summary of Calibration Data for the Suspended Sediment Sparrow Model Developed for Western Oregon and Northwestern California"}],"contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
The U.S. Geological Survey, in cooperation with the Idaho Transportation Department, updated regional regression equations to estimate peak-flow statistics at ungaged sites on Idaho streams using recent streamflow (flow) data and new statistical techniques. Peak-flow statistics with 80-, 67-, 50-, 43-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (1.25-, 1.50-, 2.00-, 2.33-, 5.00-, 10.0-, 25.0-, 50.0-, 100-, 200-, and 500-year recurrence intervals, respectively) were estimated for 192 streamgages in Idaho and bordering States with at least 10 years of annual peak-flow record through water year 2013. The streamgages were selected from drainage basins with little or no flow diversion or regulation. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and applying two additional statistical methods: (1) the Expected Moments Algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized Multiple Grubbs Beck Test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew was estimated for the Pacific Northwest and used to weight at-station skew at most streamgages. The streamgages were grouped into six regions (numbered 1_2, 3, 4, 5, 6_8, and 7, to maintain consistency in region numbering with a previous study), and the estimated peak-flow statistics were related to basin and climatic characteristics to develop regional regression equations using a generalized least squares procedure. Four out of 24 evaluated basin and climatic characteristics were selected for use in the final regional peak-flow regression equations.
Overall, the standard error of prediction for the regional peak-flow regression equations ranged from 22 to 132 percent. Among all regions, regression model fit was best for region 4 in west-central Idaho (average standard error of prediction=46.4 percent; pseudo-R2>92 percent) and region 5 in central Idaho (average standard error of prediction=30.3 percent; pseudo-R2>95 percent). Regression model fit was poor for region 7 in southern Idaho (average standard error of prediction=103 percent; pseudo-R2<78 percent) compared to other regions because few streamgages in region 7 met the criteria for inclusion in the study, and the region’s semi-arid climate and associated variability in precipitation patterns causes substantial variability in peak flows.
A drainage area ratio-adjustment method, using ratio exponents estimated using generalized least-squares regression, was presented as an alternative to the regional regression equations if peak-flow estimates are desired at an ungaged site that is close to a streamgage selected for inclusion in this study. The alternative drainage area ratio-adjustment method is appropriate for use when the drainage area ratio between the ungaged and gaged sites is between 0.5 and 1.5.
The updated regional peak-flow regression equations had lower total error (standard error of prediction) than all regression equations presented in a 1982 study and in four of six regions presented in 2002 and 2003 studies in Idaho. A more extensive streamgage screening process used in the current study resulted in fewer streamgages used in the current study than in the 1982, 2002, and 2003 studies. Fewer streamgages used and the selection of different explanatory variables were likely causes of increased error in some regions compared to previous studies, but overall, regional peak‑flow regression model fit was generally improved for Idaho. The revised statistical procedures and increased streamgage screening applied in the current study most likely resulted in a more accurate representation of natural peak-flow conditions.
The updated, regional peak-flow regression equations will be integrated in the U.S. Geological Survey StreamStats program to allow users to estimate basin and climatic characteristics and peak-flow statistics at ungaged locations of interest. StreamStats estimates peak-flow statistics with quantifiable certainty only when used at sites with basin and climatic characteristics within the range of input variables used to develop the regional regression equations. Both the regional regression equations and StreamStats should be used to estimate peak-flow statistics only in naturally flowing, relatively unregulated streams without substantial local influences to flow, such as large seeps, springs, or other groundwater-surface water interactions that are not widespread or characteristic of the respective region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165083","collaboration":"Prepared in cooperation with Idaho Transportation Department","usgsCitation":"Wood, M.S., Fosness, R.L., Skinner, K.D., and Veilleux, A.G., 2016, Estimating peak-flow frequency statistics for selected gaged and ungaged sites in naturally flowing streams and rivers in Idaho (ver. 1.1, April 2017): U.S. Geological Survey Scientific Investigations Report 2016–5083, 56 p., https://doi.org/10.3133/sir20165083.","productDescription":"Report: vi, 56 p.; Appendix A","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-046287","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":324444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5083/sir20165083.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5083 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\"}}]}","edition":"Version 1.0: Originally posted June 27, 2016; Version 1.1: April 26, 2017","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
https://id.water.usgs.gov
1.The Leaf Economics Spectrum (LES) describes global covariation in the traits of plant leaves. The LES is thought to arise from biophysical constraints and habitat filtering (ecological selection against unfit trait combinations along environmental gradients). However, the role of habitat filtering in generating the LES has not been tested experimentally.
\n2.If the process of habitat filtering plays a role in generating the LES, the LES could weaken in communities that have yet to be filtered by the current environment, for example after abiotic environmental change. LES traits are commonly used to predict community and ecosystem processes, and if the LES weakens in unfiltered communities, LES-based models may no longer apply.
\n3.In the greenhouse, we experimentally simulated three stages of habitat filtering in response to abiotic change: from unfiltered, to semi-filtered, to completely filtered communities. In each stage, we quantified the strength of the LES and assessed the accuracy of trait-based models of an important ecological process, pathogen infection.
\n4.The strength of the LES increased with the completeness of habitat filtering, as did the accuracy of trait-based models of plant susceptibility to pathogen infection.
\n5.Synthesis. Our results suggest that habitat filtering plays a fundamental role in strengthening the trait correlations of the LES, and that trait-based models may be less accurate when communities have not been filtered by the current environment, for example, following rapid environmental change.
","language":"English","publisher":"Wiley","doi":"10.1111/1365-2745.12632","usgsCitation":"Welsh, M.E., Cronin, J.P., and Mitchell, C., 2016, The role of habitat filtering in the leaf economics spectrum and plant susceptibility to pathogen infection: Journal of Ecology, v. 104, no. 6, p. 1768-1777, https://doi.org/10.1111/1365-2745.12632.","productDescription":"10 p.","startPage":"1768","endPage":"1777","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065238","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470831,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2745.12632","text":"Publisher Index Page"},{"id":324433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","issue":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-02","publicationStatus":"PW","scienceBaseUri":"57724023e4b07657d1a793b5","chorus":{"doi":"10.1111/1365-2745.12632","url":"http://dx.doi.org/10.1111/1365-2745.12632","publisher":"Wiley-Blackwell","authors":"Welsh Miranda E., Cronin James Patrick, Mitchell Charles E.","journalName":"Journal of Ecology","publicationDate":"8/2/2016"},"contributors":{"authors":[{"text":"Welsh, Miranda E","contributorId":172466,"corporation":false,"usgs":false,"family":"Welsh","given":"Miranda","email":"","middleInitial":"E","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":640817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, James P. 0000-0001-6791-5828 jcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-6791-5828","contributorId":5834,"corporation":false,"usgs":true,"family":"Cronin","given":"James","email":"jcronin@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":640816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Charles E.","contributorId":99689,"corporation":false,"usgs":true,"family":"Mitchell","given":"Charles E.","affiliations":[],"preferred":false,"id":640818,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174072,"text":"70174072 - 2016 - The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources","interactions":[],"lastModifiedDate":"2016-06-27T14:56:51","indexId":"70174072","displayToPublicDate":"2016-06-27T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3459,"text":"Special Paper of the Geological Society of America","active":true,"publicationSubtype":{"id":10}},"title":"The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources","docAbstract":"Groundwater is the source of drinking water for ∼1.4 million people in the Coastal Plain Province of Maryland (USA). In addition, groundwater is essential for commercial, industrial, and agricultural uses. Approximately 0.757 × 109 L d–1 (200 million gallons/d) were withdrawn in 2010. As a result of decades of withdrawals from the coastal plain confined aquifers, groundwater levels have declined by as much as 70 m (230 ft) from estimated prepumping levels. Other issues posing challenges to long-term groundwater sustainability include degraded water quality from both man-made and natural sources, reduced stream base flow, land subsidence, and changing recharge patterns (drought) caused by climate change. In Maryland, groundwater supply is managed primarily by the Maryland Department of the Environment, which seeks to balance reasonable use of the resource with long-term sustainability. The chief goal of groundwater management in Maryland is to ensure safe and adequate supplies for all current and future users through the implementation of appropriate usage, planning, and conservation policies. To assist in that effort, the geographic information system (GIS)–based Maryland Coastal Plain Aquifer Information System was developed as a tool to help water managers access and visualize groundwater data for use in the evaluation of groundwater allocation and use permits. The system, contained within an ESRI ArcMap desktop environment, includes both interpreted and basic data for 16 aquifers and 14 confining units. Data map layers include aquifer and confining unit layer surfaces, aquifer extents, borehole information, hydraulic properties, time-series groundwater-level data, well records, and geophysical and lithologic logs. The aquifer and confining unit layer surfaces were generated specifically for the GIS system. The system also contains select groundwater-quality data and map layers that quantify groundwater and surface-water withdrawals. The aquifer information system can serve as a pre- and postprocessing environment for groundwater-flow models for use in water-supply planning, development, and management. The system also can be expanded to include features that evaluate constraints to groundwater development, such as insufficient available drawdown, degraded groundwater quality, insufficient aquifer yields, and well-field interference. Ultimately, the aquifer information system is intended to function as an interactive Web-based utility that provides a broad array of information related to groundwater resources in Maryland’s coastal plain to a wide-ranging audience, including well drillers, consultants, academia, and the general public.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.2520(15)","usgsCitation":"Andreasen, D., Nardi, M.R., Staley, A., Achmad, G., and Grace, J.W., 2016, The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources: Special Paper of the Geological Society of America, v. 520, p. 159-170, https://doi.org/10.1130/2016.2520(15).","productDescription":"12 p.","startPage":"159","endPage":"170","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068540","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":324417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57724023e4b07657d1a793b0","contributors":{"authors":[{"text":"Andreasen, David C.","contributorId":59003,"corporation":false,"usgs":true,"family":"Andreasen","given":"David C.","affiliations":[],"preferred":false,"id":640806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nardi, Mark R. 0000-0002-7310-8050 mrnardi@usgs.gov","orcid":"https://orcid.org/0000-0002-7310-8050","contributorId":1859,"corporation":false,"usgs":true,"family":"Nardi","given":"Mark","email":"mrnardi@usgs.gov","middleInitial":"R.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Andrew W.","contributorId":43319,"corporation":false,"usgs":true,"family":"Staley","given":"Andrew W.","affiliations":[],"preferred":false,"id":640807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Achmad, Grufron","contributorId":172464,"corporation":false,"usgs":false,"family":"Achmad","given":"Grufron","email":"","affiliations":[{"id":25435,"text":"Maryland Geological Survey","active":true,"usgs":false}],"preferred":false,"id":640808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grace, John W.","contributorId":172465,"corporation":false,"usgs":false,"family":"Grace","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":640809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174058,"text":"70174058 - 2016 - Mercury risk to avian piscivores across western United States and Canada","interactions":[],"lastModifiedDate":"2018-08-06T13:09:16","indexId":"70174058","displayToPublicDate":"2016-06-27T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mercury risk to avian piscivores across western United States and Canada","docAbstract":"The widespread distribution of mercury (Hg) threatens wildlife health, particularly piscivorous birds. Western North America is a diverse region that provides critical habitat to many piscivorous bird species, and also has a well-documented history of mercury contamination from legacy mining and atmospheric deposition. The diversity of landscapes in the west limits the distribution of avian piscivore species, complicating broad comparisons across the region. Mercury risk to avian piscivores was evaluated across the western United States and Canada using a suite of avian piscivore species representing a variety of foraging strategies that together occur broadly across the region. Prey fish Hg concentrations were size-adjusted to the preferred size class of the diet for each avian piscivore (Bald Eagle = 36 cm, Osprey = 30 cm, Common and Yellow-billed Loon = 15 cm, Western and Clark's Grebe = 6 cm, and Belted Kingfisher = 5 cm) across each species breeding range. Using a combination of field and lab-based studies on Hg effect in a variety of species, wet weight blood estimates were grouped into five relative risk categories including: background (< 0.5 μg/g), low (0.5–1 μg/g), moderate (1–2 μg/g), high (2–3 μg/g), and extra high (> 3 μg/g). These risk categories were used to estimate potential mercury risk to avian piscivores across the west at a 1 degree-by-1 degree grid cell resolution. Avian piscivores foraging on larger-sized fish generally were at a higher relative risk to Hg. Habitats with a relatively high risk included wetland complexes (e.g., prairie pothole in Saskatchewan), river deltas (e.g., San Francisco Bay, Puget Sound, Columbia River), and arid lands (Great Basin and central Arizona). These results indicate that more intensive avian piscivore sampling is needed across Western North America to generate a more robust assessment of exposure risk.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.197","usgsCitation":"Jackson, A., Evers, D.C., Eagles-Smith, C.A., Ackerman, J., Willacker, J.J., Elliott, J., Lepak, J.M., Vander Pol, S.S., and Bryan, C.E., 2016, Mercury risk to avian piscivores across western United States and Canada: Science of the Total Environment, v. 568, p. 685-696, https://doi.org/10.1016/j.scitotenv.2016.02.197.","productDescription":"12 p.","startPage":"685","endPage":"696","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070590","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":470832,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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Impacts from the spill include observation of corals covered with flocculent material, with bare skeleton, excessive mucous production, sloughing tissue, and subsequent colonization of damaged areas by hydrozoans. Information on growth rates and life spans of deep-sea corals is important for understanding the vulnerability of these ecosystems to both natural and anthropogenic perturbations, as well as the likely duration of any observed adverse impacts. We report radiocarbon ages and radial and linear growth rates based on octocorals (Paramuricea spp. and Chrysogorgia sp.) collected in 2010 and 2011 from areas of the DWH impact. The oldest coral radiocarbon ages were measured on specimens collected 11 km to the SW of the oil spill from the Mississippi Canyon (MC) 344 site: 599 and 55 cal yr BP, suggesting continuous life spans of over 600 years for Paramuricea biscaya, the dominant coral species in the region. Calculated radial growth rates, between 0.34 μm yr−1 and 14.20 μm yr−1, are consistent with previously reported proteinaceous corals from the GoM. Anomalously low radiocarbon (Δ14C) values for soft tissue from some corals indicate that these corals were feeding on particulate organic carbon derived from an admixture of modern surface carbon and a low 14C carbon source. Results from this work indicate fossil carbon could contribute 5–10% to the coral soft tissue Δ14C signal within the area of the spill impact. The influence of a low 14C carbon source (e.g., petro-carbon) on the particulate organic carbon pool was observed at all sites within 30 km of the spill site, with the exception of MC118, which may have been outside of the dominant northeast-southwest zone of impact. The quantitatively assessed extreme longevity and slow growth rates documented here highlight the vulnerability of these long-lived deep sea coral species to disturbance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2014.10.021","usgsCitation":"Prouty, N.G., Fisher, C.R., Demopoulos, A., and Druffel, E.R., 2016, Growth rates and ages of deep-sea corals impacted by the Deepwater Horizon oil spill: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 129, https://doi.org/10.1016/j.dsr2.2014.10.021.","productDescription":"17 p.","startPage":"212","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056047","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470834,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2014.10.021","text":"Publisher Index Page"},{"id":296221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89,\n 30\n ],\n [\n -89,\n 27\n ],\n [\n -92,\n 27\n ],\n [\n -92,\n 30\n ],\n [\n -89,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","edition":"196","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"546db11ee4b0fc7976bf1e33","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":525501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Charles R.","contributorId":127497,"corporation":false,"usgs":false,"family":"Fisher","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":525502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":371,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda W.J.","email":"ademopoulos@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":525503,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Druffel, Ellen R. M.","contributorId":127498,"corporation":false,"usgs":false,"family":"Druffel","given":"Ellen","email":"","middleInitial":"R. 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This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R25 0.79) and precision (0.27 m) were comparable to an established band ratio-based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR018730","usgsCitation":"Legleiter, C.J., 2016, Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT): Water Resources Research, v. 52, no. 5, p. 3722-3741, https://doi.org/10.1002/2016WR018730.","productDescription":"20 p.","startPage":"3722","endPage":"3741","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072989","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr018730","text":"Publisher Index Page"},{"id":324402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-14","publicationStatus":"PW","scienceBaseUri":"5772401fe4b07657d1a7937e","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":640728,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70174064,"text":"70174064 - 2016 - A synthesis of the basal thermal state of the Greenland Ice Sheet","interactions":[],"lastModifiedDate":"2016-08-12T10:17:20","indexId":"70174064","displayToPublicDate":"2016-06-27T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of the basal thermal state of the Greenland Ice Sheet","docAbstract":"The basal thermal state of an ice sheet (frozen or thawed) is an important control upon its evolution, dynamics and response to external forcings. However, this state can only be observed directly within sparse boreholes or inferred conclusively from the presence of subglacial lakes. Here we synthesize spatially extensive inferences of the basal thermal state of the Greenland Ice Sheet to better constrain this state. Existing inferences include outputs from the eight thermomechanical ice-flow models included in the SeaRISE effort. New remote-sensing inferences of the basal thermal state are derived from Holocene radiostratigraphy, modern surface velocity and MODIS imagery. Both thermomechanical modeling and remote inferences generally agree that the Northeast Greenland Ice Stream and large portions of the southwestern ice-drainage systems are thawed at the bed, whereas the bed beneath the central ice divides, particularly their west-facing slopes, is frozen. Elsewhere, there is poor agreement regarding the basal thermal state. Both models and remote inferences rarely represent the borehole-observed basal thermal state accurately near NorthGRIP and DYE-3. This synthesis identifies a large portion of the Greenland Ice Sheet (about one third by area) where additional observations would most improve knowledge of its overall basal thermal state.
","language":"English","publisher":"Americal Geophysical Union","doi":"10.1002/2015JF003803","usgsCitation":"MacGregor, J.A., Fahnestock, M.A., Catania, G.A., Aschwanden, A., Clow, G.D., Colgan, W.T., Gogineni, P.S., Morlighem, M., Nowicki, S.M., Paden, J.D., Price, S., and Seroussi, H., 2016, A synthesis of the basal thermal state of the Greenland Ice Sheet: Journal of Geophysical Research F: Earth Surface, v. 121, no. 7, p. 1328-1350, https://doi.org/10.1002/2015JF003803.","productDescription":"23 p.","startPage":"1328","endPage":"1350","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071124","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jf003803","text":"Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":640767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Colgan, William T.","contributorId":172448,"corporation":false,"usgs":false,"family":"Colgan","given":"William","email":"","middleInitial":"T.","affiliations":[{"id":27047,"text":"Dept of Earth and Space Science, York University, Toronto","active":true,"usgs":false}],"preferred":false,"id":640772,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gogineni, Prasad S.","contributorId":141049,"corporation":false,"usgs":false,"family":"Gogineni","given":"Prasad","email":"","middleInitial":"S.","affiliations":[{"id":13661,"text":"Center for Remote Sensing of Ice Sheets, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":640773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morlighem, Mathieu","contributorId":141050,"corporation":false,"usgs":false,"family":"Morlighem","given":"Mathieu","email":"","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":640774,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nowicki, Sophie M .J.","contributorId":172451,"corporation":false,"usgs":false,"family":"Nowicki","given":"Sophie","email":"","middleInitial":"M .J.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":640775,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Paden, John D","contributorId":141046,"corporation":false,"usgs":false,"family":"Paden","given":"John","email":"","middleInitial":"D","affiliations":[{"id":13661,"text":"Center for Remote Sensing of Ice Sheets, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":640776,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Price, Stephen F.","contributorId":169436,"corporation":false,"usgs":false,"family":"Price","given":"Stephen F.","affiliations":[],"preferred":false,"id":640777,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Seroussi, Helene","contributorId":141052,"corporation":false,"usgs":false,"family":"Seroussi","given":"Helene","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":640778,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70174066,"text":"70174066 - 2016 - Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA","interactions":[],"lastModifiedDate":"2016-06-27T11:09:22","indexId":"70174066","displayToPublicDate":"2016-06-27T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA","docAbstract":"Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.
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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":640788,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174065,"text":"70174065 - 2016 - Gravel-bed river floodplains are the ecological nexus of glaciated mountain landscapes","interactions":[],"lastModifiedDate":"2016-06-27T11:11:47","indexId":"70174065","displayToPublicDate":"2016-06-27T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Gravel-bed river floodplains are the ecological nexus of glaciated mountain landscapes","docAbstract":"Gravel-bed river floodplains in mountain landscapes disproportionately concentrate diverse habitats, nutrient cycling, productivity of biota, and species interactions. Although stream ecologists know that river channel and floodplain habitats used by aquatic organisms are maintained by hydrologic regimes that mobilize gravel-bed sediments, terrestrial ecologists have largely been unaware of the importance of floodplain structures and processes to the life requirements of a wide variety of species. We provide insight into gravel-bed rivers as the ecological nexus of glaciated mountain landscapes. We show why gravel-bed river floodplains are the primary arena where interactions take place among aquatic, avian, and terrestrial species from microbes to grizzly bears and provide essential connectivity as corridors for movement for both aquatic and terrestrial species. Paradoxically, gravel-bed river floodplains are also disproportionately unprotected where human developments are concentrated. Structural modifications to floodplains such as roads, railways, and housing and hydrologicaltering hydroelectric or water storage dams have severe impacts to floodplain habitat diversity and productivity, restrict local and regional connectivity, and reduce the resilience of both aquatic and terrestrial species, including adaptation to climate change. To be effective, conservation efforts in glaciated mountain landscapes intended to benefit the widest variety of organisms need a paradigm shift that has gravel-bed rivers and their floodplains as the central focus and that prioritizes the maintenance or restoration of the intact structure and processes of these critically important systems throughout their length and breadth.
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