In 2010, the U.S. Geological Survey, in cooperation with the San Antonio Water System, began a study to assess the brackish-water movement within the Edwards aquifer (more specifically the potential for brackish-water encroachment into wells near the interface between the freshwater and brackish-water transition zones, referred to in this report as the transition-zone interface) and effects on spring discharge at Comal and San Marcos Springs under drought conditions using a numerical model. The quantitative targets of this study are to predict the effects of higher-than-average groundwater withdrawals from wells and drought-of-record rainfall conditions of 1950–56 on (1) dissolved-solids concentration changes at production wells near the transition-zone interface, (2) total spring discharge at Comal and San Marcos Springs, and (3) the groundwater head (head) at Bexar County index well J-17. The predictions of interest, and the parameters implemented into the model, were evaluated to quantify their uncertainty so the results of the predictions could be presented in terms of a 95-percent credible interval.
\nThe model area covers the San Antonio and Barton Springs segments of the Edwards aquifer; the history-matching effort was focused on the San Antonio segment. A previously developed diffuse-flow model of the Edwards aquifer, which forms the basis for the model in this assessment, is primarily based on a conceptualization in which flow in the aquifer is predominately through a network of numerous small fractures and openings. Primary updates to this model include an extension of the active area downdip, a conversion to an 8-layer SEAWAT variable-density flow and transport model to simulate dissolved-solids concentration effects on water density, history matching to 1999–2009 conditions, and parameter estimation in a highly parameterized context using automated methods in PEST (a model-independent Parameter ESTimation code).
\nIn addition to the best-fit parameter values derived from history matching, the uncertainty of model parameters was also estimated by using linear uncertainty analysis. Comparison of “prior” (before history matching) and “posterior” (after history matching) variances of parameters indicate that the information within the observation dataset used for history matching informs many parameters. The concentration threshold parameters were well-informed by the observation dataset as their posterior distributions were much narrower than their prior distributions. The transition-zone scaling parameters of hydraulic conductivity, effective porosity, and specific storage were all informed by the observation dataset, as evidenced by the difference between the prior and posterior variances. Saline-zone scaling parameters, alternatively, were not informed by the observation dataset for effective porosity and specific storage. Resulting posterior drier-month, wetter-month, and annual recharge multiplier parameter variances are important to understanding how well recharge is estimated and implemented within the model. The shifts of the posterior distributions left and right indicate that there were zones where less or more water was needed in the model. The widths of the distributions were not decreased substantially, indicating that many of the best-fit recharge parameters are not statistically different from the initial values specified in the history-matching effort. Recharge from rainfall is the driving force behind groundwater flow and heads in the aquifer; therefore, an increase in understanding of this process would benefit model development by potentially decreasing the uncertainty of this parameter. The history-matching effort was most helpful in informing the parameters in the model that control discharge at springs, namely, the spring orifice (drain) altitude and drain conductance parameters for each spring.
\nThe uncertainty assessment of the predictive model (a hypothetical recurrence of 1950–56 drought conditions and higher-than-average groundwater withdrawals from wells) provided insights into the potential effects of these conditions on dissolved-solids concentration changes at production wells near the transition-zone interface, discharges at Comal and San Marcos Springs, and heads at Bexar County index well J-17. Results at the 25 production wells near the transition-zone interface indicate that the uncertainty of model input parameters based on expert knowledge yielded an upper bound of the 95-percent credible interval of dissolved-solids concentrations that exceeds the secondary drinking water standards of 1,000 milligrams per liter (mg/L) of the Texas Commission on Environmental Quality (TCEQ) for many wells. However, the history-matching process provided key information to inform prediction-sensitive model parameters and therefore, contributed to a substantial decrease of the upper bound of the 95-percent credible interval to below the secondary drinking water standards. Reductions in dissolved-solids concentration changes were on the order of 400 mg/L to 1,300 mg/L. The reduction in uncertainty in regards to this prediction implies that this prediction of dissolved-solids concentration change can be made with some certainty using this current model and that those parameters that control this prediction are informed by the observation dataset. Even though predictive uncertainty was reduced for this prediction, dissolved-solids concentration changes were still greater than zero, indicating a minimal increase in concentration at these 25 production wells during the 7-year simulation period is likely. However, this minimal concentration increase indicates a small potential for movement of the brackish-water transition zone near these wells during the 7-year simulation period of drought-ofrecord (1950–56) rainfall conditions with higher-than-average groundwater withdrawals by wells.
\nPredictive results of total spring discharge during the 7-year period, as well as head predictions at Bexar County index well J-17, were much different than the dissolved-solids concentration change results at the production wells. These upper bounds are an order of magnitude larger than the actual prediction which implies that (1) the predictions of total spring discharge at Comal and San Marcos Springs and head at Bexar County index well J-17 made with this model are not reliable, and (2) parameters that control these predictions are not informed well by the observation dataset during historymatching, even though the history-matching process yielded parameters to reproduce spring discharges and heads at these locations during the history-matching period. Furthermore, because spring discharges at these two springs and heads at Bexar County index well J-17 represent more of a cumulative effect of upstream conditions over a larger distance (and longer time), many more parameters (with their own uncertainties) are potentially controlling these predictions than the prediction of dissolved-solids concentration change at the prediction wells, and therefore contributing to a large posterior uncertainty.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155081","collaboration":"Prepared in cooperation with the San Antonio Water System","usgsCitation":"Brakefield, L., White, J., Houston, N.A., and Thomas, J.V., 2015, Updated numerical model with uncertainty assessment of 1950-56 drought conditions on brackish-water movement within the Edwards aquifer, San Antonio, Texas: U.S. Geological Survey Scientific Investigations Report 2015-5081, viii, 54 p., https://doi.org/10.3133/sir20155081.","productDescription":"viii, 54 p.","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056599","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":305941,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5081/sir2015-5081.pdf","text":"Report","size":"6.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305942,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5081/coverthb.jpg"}],"country":"United States","state":"Texas","city":"San Antonio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.1181640625,\n 29.477861195816843\n ],\n [\n -98.173828125,\n 30.486550842588485\n ],\n [\n -97.9541015625,\n 30.562260950499414\n ],\n [\n -97.37182617187499,\n 29.44916482692468\n ],\n [\n -100.338134765625,\n 28.36240173523821\n ],\n [\n -101.063232421875,\n 29.430029404571762\n ],\n [\n -101.1181640625,\n 29.477861195816843\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a5b8e0e4b0ebae89b78a9e","contributors":{"authors":[{"text":"Brakefield, Linzy K. lbrake@usgs.gov","contributorId":145899,"corporation":false,"usgs":true,"family":"Brakefield","given":"Linzy K.","email":"lbrake@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. jwhite@usgs.gov","contributorId":3930,"corporation":false,"usgs":true,"family":"White","given":"Jeremy T.","email":"jwhite@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":false,"id":565607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Houston, Natalie A. 0000-0002-6071-4545 nhouston@usgs.gov","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":1682,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","email":"nhouston@usgs.gov","middleInitial":"A.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713 jvthomas@usgs.gov","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":2194,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan","email":"jvthomas@usgs.gov","middleInitial":"V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565609,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159947,"text":"70159947 - 2015 - Richness, diversity, and similarity of arthropod prey consumed by a community of Hawaiian forest birds.","interactions":[],"lastModifiedDate":"2018-01-04T13:05:50","indexId":"70159947","displayToPublicDate":"2015-07-23T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"HCSU-066","title":"Richness, diversity, and similarity of arthropod prey consumed by a community of Hawaiian forest birds.","docAbstract":"We evaluated the diet richness, diversity, and similarity of a community of seven endemic and two introduced passerine birds by analyzing the composition of arthropod prey in fecal samples collected during 1994–1998 at Hakalau Forest National Wildlife Refuge, Hawai‘i Island. Most prey fragments were identified to order, but we also distinguished among morpho-species of Lepidoptera based on the shape of larval (caterpillar) mandibles for higher resolution of this important prey type. Diets were compared among feeding specialists, generalists, and “intermediate” species and among introduced and three endangered Hawaiian honeycreeper (Fringillidae) species. Lepidoptera (moths), especially the larval (caterpillar) stage, comprised the greatest proportion of prey in samples of all bird species except for the introduced Japanese white-eye (Zosterops japonicus; JAWE). Araneae (spiders) was the most abundant order in JAWE samples and the second most abundant order for most other species. The two specialist honeycreepers ranked lowest in the richness and diversity of arthropod orders, but only the ‘akiapōlā‘au (Hemignathus munroi, AKIP) was significantly lower than the three generalist or intermediate honeycreeper species. The diversity of arthropod orders was significantly lower for the three endangered honeycreeper species compared to the two introduced species. No significant differences were observed among the five honeycreepers with respect to the arthropod orders they consumed. The use of arthropod orders taken by endangered honeycreepers and introduced species was significantly different in all paired comparisons except for JAWE and ‘ākepa (Loxops coccineus; AKEP). In terms of richness and diversity of caterpillar morpho-species in the diet, only the specialist, AKEP, was significantly lower than all three generalist and intermediate species. Both AKEP and AKIP consumed a significantly different diet of caterpillar morpho-species compared to at least one honeycreeper generalist or intermediate species. Among the endangered honeycreepers and introduced species, the richness and diversity of caterpillar morpho-species was significantly lower only for AKEP compared to both introduced species. Significant differences were not observed between endangered and introduced species in the distribution of caterpillar morpho-species in the diet. Only three morpho-species were heavily exploited, with one being consumed by all bird species. The heavy exploitation of very few morpho-species by specialists underscored their greater vulnerability to changes in forest food webs and threats to key arthropod prey. When evaluated together with data on overlap in foraging behavior, our results could be useful in evaluating competition between bird species at Hakalau. Nevertheless, invasive parasitoid wasps may impact key caterpillar prey more substantially than do introduced birds, highlighting the need for additional research to understand the ecology of caterpillar species and their interactions with both invertebrate and vertebrate consumers. The severe decline of specialist bird species historically and recently is a reminder of the importance of maintaining food web resilience, potentially through vigorous habitat restoration, to withstand the continuing and perhaps increasing threats from a diverse array of invasive species and climate change.
","language":"English","publisher":"University of Hawaii at Hilo","publisherLocation":"Hilo, HI","usgsCitation":"Banko, P.C., Peck, R.W., Brinck, K., and Leonard, D., 2015, Richness, diversity, and similarity of arthropod prey consumed by a community of Hawaiian forest birds.: Technical Report HCSU-066, Report: iii, 38 p.","productDescription":"Report: iii, 38 p.","startPage":"1","endPage":"38","numberOfPages":"42","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066651","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":326248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a9ad70e4b05e859bdfbadd","contributors":{"authors":[{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":581158,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peck, Robert W.","contributorId":45629,"corporation":false,"usgs":true,"family":"Peck","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":581159,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":581160,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leonard, David L.","contributorId":105191,"corporation":false,"usgs":true,"family":"Leonard","given":"David L.","affiliations":[],"preferred":false,"id":581161,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148719,"text":"sim3336 - 2015 - Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010","interactions":[],"lastModifiedDate":"2015-07-24T09:00:02","indexId":"sim3336","displayToPublicDate":"2015-07-23T07:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3336","title":"Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010","docAbstract":"Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types (that is, fresh, intermediate, brackish, and saline) for modeling habitat capacities and needs of marsh dependent taxa (such as waterfowl and alligator). Detailed information on the extent and distribution of emergent marsh vegetation types throughout the northern Gulf of Mexico coast has been historically unavailable. In response, the U.S. Geological Survey, in collaboration with the Gulf Coast Joint Venture, the University of Louisiana at Lafayette, Ducks Unlimited, Inc., and the Texas A&M University-Kingsville, produced a classification of emergent marsh vegetation types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama.
\nThis study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3336","collaboration":"Prepared in collaboration with the Gulf Coast Joint Venture, the University of Louisiana at Lafayette, Ducks Unlimited, Inc., and Texas A&M University-Kingsville","usgsCitation":"Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000, https://dx.doi.org/10.3133/sim3336.","productDescription":"Map: 52 x 38 inches; ReadMe; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064404","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":305863,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3336/coverthb.jpg"},{"id":305865,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3336/SIM_3336_Spatial_Data.zip","text":"Spatial Data"},{"id":305864,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3336/sim3336.pdf","text":"Map","size":"2.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3336"},{"id":305911,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3336/readME.txt","text":"ReadMe","size":"1.19 kB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","otherGeospatial":"Corpus Christi Bay, Perdido Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.80029296875,\n 26.941659545381516\n ],\n [\n -97.80029296875,\n 31.31610138349565\n ],\n [\n -87.34130859375,\n 31.31610138349565\n ],\n [\n -87.34130859375,\n 26.941659545381516\n ],\n [\n -97.80029296875,\n 26.941659545381516\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, National Wetlands Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
http://www.nwrc.usgs.gov/
Mountain glaciers comprise a small and widely distributed fraction of the world's terrestrial ice, yet their rapid losses presently drive a large percentage of the cryosphere's contribution to sea level rise. Regional mass balance assessments are challenging over large glacier populations due to remote and rugged geography, variable response of individual glaciers to climate change, and episodic calving losses from tidewater glaciers. In Alaska, we use airborne altimetry from 116 glaciers to estimate a regional mass balance of −75 ± 11 Gt yr−1 (1994–2013). Our glacier sample is spatially well distributed, yet pervasive variability in mass balances obscures geospatial and climatic relationships. However, for the first time, these data allow the partitioning of regional mass balance by glacier type. We find that tidewater glaciers are losing mass at substantially slower rates than other glaciers in Alaska and collectively contribute to only 6% of the regional mass loss.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GL064349","usgsCitation":"F, L.C., Burgess, E., Arendt, A., O’Neel, S., Johnson, A.J., and Kienholz, C., 2015, Surface melt dominates Alaska glacier mass balance: Geophysical Research Letters, v. 42, no. 14, p. 5902-5908, https://doi.org/10.1002/2015GL064349.","productDescription":"7 p.","startPage":"5902","endPage":"5908","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065349","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":471930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl064349","text":"Publisher Index Page"},{"id":306759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, Yukon Territory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.86328125,\n 53.04121304075649\n ],\n [\n -154.86328125,\n 62.87518837993309\n ],\n [\n -125.94726562499999,\n 62.87518837993309\n ],\n [\n -125.94726562499999,\n 53.04121304075649\n ],\n [\n -154.86328125,\n 53.04121304075649\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"14","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-23","publicationStatus":"PW","scienceBaseUri":"55cf112ce4b01487cbfc77c3","chorus":{"doi":"10.1002/2015gl064349","url":"http://dx.doi.org/10.1002/2015gl064349","publisher":"Wiley-Blackwell","authors":"Larsen C. F., Burgess E., Arendt A. A., O'Neel S., Johnson A. J., Kienholz C.","journalName":"Geophysical Research Letters","publicationDate":"7/23/2015","auditedOn":"1/29/2017","publiclyAccessibleDate":"7/23/2015"},"contributors":{"authors":[{"text":"F, Larsen Chris","contributorId":146362,"corporation":false,"usgs":false,"family":"F","given":"Larsen","email":"","middleInitial":"Chris","affiliations":[{"id":16682,"text":"Univ AK","active":true,"usgs":false}],"preferred":false,"id":567571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgess, E","contributorId":146537,"corporation":false,"usgs":false,"family":"Burgess","given":"E","email":"","affiliations":[{"id":13662,"text":"Geophysical Institute, University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":568176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arendt, A.A.","contributorId":99379,"corporation":false,"usgs":false,"family":"Arendt","given":"A.A.","email":"","affiliations":[{"id":12920,"text":"Applied Physics Laboratory, University of Washington","active":true,"usgs":false}],"preferred":false,"id":568177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Neel, Shad 0000-0002-9185-0144 soneel@usgs.gov","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":166740,"corporation":false,"usgs":true,"family":"O’Neel","given":"Shad","email":"soneel@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":567570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, A. J.","contributorId":146538,"corporation":false,"usgs":false,"family":"Johnson","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":568178,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kienholz, C.","contributorId":146539,"corporation":false,"usgs":false,"family":"Kienholz","given":"C.","email":"","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":568179,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70154755,"text":"70154755 - 2015 - In situ densimetric measurements as a surrogate for suspended-sediment concentrations in the Rio Puerco, New Mexico","interactions":[],"lastModifiedDate":"2017-05-08T16:02:19","indexId":"70154755","displayToPublicDate":"2015-07-23T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"In situ densimetric measurements as a surrogate for suspended-sediment concentrations in the Rio Puerco, New Mexico","docAbstract":"Surrogate measurements of suspended-sediment concentration (SSC) are increasingly used to provide continuous, high-resolution, and demonstrably accurate data at a reasonable cost. Densimetric data, calculated from the difference between two in situ pressure measurements, exploit variations in real-time streamflow densities to infer SSCs. Unlike other suspendedsediment surrogate technologies based on bulk or digital optics, laser, or hydroacoustics, the accuracy of SSC data estimated using the pressure-difference (also referred to as densimetric) surrogate technology theoretically improves with increasing SCCs. Coupled with streamflow data, continuous suspended-sediment discharges can be calculated using SSC data estimated in real-time using the densimetric technology.
The densimetric technology was evaluated at the Rio Puerco in New Mexico, a stream where SSC values regularly range from 10,000-200,000 milligrams per liter (mg/L) and have exceeded 500,000 mg/L. The constant-flow dual-orifice bubbler measures pressure using two precision pressure-transducer sensors at vertically aligned fixed locations in a water column. Water density is calculated from the temperature-compensated differential pressure and SSCs are inferred from the density data.
A linear regression model comparing density values to field-measured SSC values yielded an R² of 0.74. Although the application of the densimetric surrogate is likely limited to fluvial systems with SSCs larger than about 10,000 mg/L, based on this and previous studies, the densimetric technology fills a void for monitoring streams with high SSCs.
","conferenceTitle":"10th Federal Interagency Sedimentation Conference / 5th Federal Interagency Hydrologic Modeling Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","usgsCitation":"Brown, J.E., Gray, J.R., and Hornewer, N.J., 2015, In situ densimetric measurements as a surrogate for suspended-sediment concentrations in the Rio Puerco, New Mexico, 10th Federal Interagency Sedimentation Conference / 5th Federal Interagency Hydrologic Modeling Conference, Reno, NV, April 19-23, 2015, 12 p.","productDescription":"12 p.","ipdsId":"IP-062739","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":340963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio Puerco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.95965576171875,\n 34.39331222316112\n ],\n [\n -106.81182861328125,\n 34.39331222316112\n ],\n [\n -106.81182861328125,\n 36.11125252076156\n ],\n [\n -108.95965576171875,\n 36.11125252076156\n ],\n [\n -108.95965576171875,\n 34.39331222316112\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591183b5e4b0e541a03c1a60","contributors":{"authors":[{"text":"Brown, Jeb E. 0000-0001-7671-2379 jebbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-7671-2379","contributorId":4357,"corporation":false,"usgs":true,"family":"Brown","given":"Jeb","email":"jebbrown@usgs.gov","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":563972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, John R. 0000-0002-8817-3701 jrgray@usgs.gov","orcid":"https://orcid.org/0000-0002-8817-3701","contributorId":1158,"corporation":false,"usgs":true,"family":"Gray","given":"John","email":"jrgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true}],"preferred":true,"id":563973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hornewer, Nancy J. njhornew@usgs.gov","contributorId":910,"corporation":false,"usgs":true,"family":"Hornewer","given":"Nancy","email":"njhornew@usgs.gov","middleInitial":"J.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":563974,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155186,"text":"70155186 - 2015 - On the lognormality of historical magnetic-storm intensity statistics: Implications for extreme-event probabilities","interactions":[],"lastModifiedDate":"2021-04-19T16:54:53.959231","indexId":"70155186","displayToPublicDate":"2015-07-22T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"On the lognormality of historical magnetic-storm intensity statistics: Implications for extreme-event probabilities","docAbstract":"An examination is made of the hypothesis that the statistics of magnetic storm maximum intensities are the realization of a lognormal stochastic process. Weighted least squares and maximum likelihood methods are used to fit lognormal functions to −Dst storm time maxima for years 1957–2012; bootstrap analysis is used to established confidence limits on forecasts. Both methods provide fits that are reasonably consistent with the data; both methods also provide fits that are superior to those that can be made with a power‐law function. In general, the maximum likelihood method provides forecasts having tighter confidence intervals than those provided by weighted least squares. From extrapolation of maximum likelihood fits: a magnetic storm with intensity exceeding that of the 1859 Carrington event, −Dst ≥ 850 nT, occurs about 1.13 times per century and a wide 95% confidence interval of [0.42, 2.41] times per century; a 100 year magnetic storm is identified as having a −Dst ≥ 880 nT (greater than Carrington) but a wide 95% confidence interval of [490, 1187] nT.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015GL064842","usgsCitation":"Love, J.J., Rigler, E.J., Pulkkinen, A., and Riley, P., 2015, On the lognormality of historical magnetic-storm intensity statistics: Implications for extreme-event probabilities: Geophysical Research Letters, v. 42, no. 16, p. 6544-6553, https://doi.org/10.1002/2015GL064842.","productDescription":"10 p.","startPage":"6544","endPage":"6553","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066930","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl064842","text":"Publisher Index Page"},{"id":306311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"16","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-19","publicationStatus":"PW","scienceBaseUri":"55c090b4e4b033ef521042ad","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":565020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":565021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulkkinen, Antti","contributorId":145703,"corporation":false,"usgs":false,"family":"Pulkkinen","given":"Antti","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":565022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riley, Pete","contributorId":145704,"corporation":false,"usgs":false,"family":"Riley","given":"Pete","email":"","affiliations":[{"id":16202,"text":"Predictive Science Inc.","active":true,"usgs":false}],"preferred":false,"id":565023,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154953,"text":"70154953 - 2015 - Estimating wind-turbine-caused bird and bat fatality when zero carcasses are observed","interactions":[],"lastModifiedDate":"2017-11-22T10:39:56","indexId":"70154953","displayToPublicDate":"2015-07-22T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Estimating wind-turbine-caused bird and bat fatality when zero carcasses are observed","docAbstract":"Many wind-power facilities in the United States have established effective monitoring programs to determine turbine-caused fatality rates of birds and bats, but estimating the number of fatalities of rare species poses special difficulties. The loss of even small numbers of individuals may adversely affect fragile populations, but typically, few (if any) carcasses are observed during monitoring. If monitoring design results in only a small proportion of carcasses detected, then finding zero carcasses may give little assurance that the number of actual fatalities is small. Fatality monitoring at wind-power facilities commonly involves conducting experiments to estimate the probability (g) an individual will be observed, accounting for the possibilities that it falls in an unsearched area, is scavenged prior to detection, or remains undetected even when present. When g < 1, the total carcass count (X) underestimates the total number of fatalities (M). Total counts can be 0 when M is small or when M is large and g ≪1. Distinguishing these two cases is critical when estimating fatality of a rare species. Observing no individuals during searches may erroneously be interpreted as evidence of absence. We present an approach that uses Bayes' theorem to construct a posterior distribution for M, i.e., P(M | X, ĝ), reflecting the observed carcass count and previously estimated g. From this distribution, we calculate two values important to conservation: the probability that M is below a predetermined limit and the upper bound (M*) of the 100(1 − α)% credible interval for M. We investigate the dependence of M* on α, g, and the prior distribution of M, asking what value of g is required to attain a desired M* for a given α. We found that when g < ~0.15, M* was clearly influenced by the mean and variance of ĝ and the choice of prior distribution for M, but the influence of these factors is minimal when g > ~0.45. Further, we develop extensions for temporal replication that can inform prior distributions of M and methods for combining information across several areas or time periods. We apply the method to data collected at a wind-power facility where scheduled searches yielded X = 0 raptor carcasses
Better understanding the influence of precipitation and temperature on plant assemblages is needed to predict the effects of climate change. Many studies have examined the relationship between plant productivity and weather (primarily precipitation), but few have directly assessed the relationship between plant richness or diversity and weather despite their increased use as metrics of ecosystem condition. We focus on the grasslands of central North America, which are characterized by high temporal climatic variability. Over the next 100 years, these grasslands are predicted to experience further increased variability in growing season precipitation, as well as increased temperatures, due to global climate change. We assess 1) the portion of interannual variability of richness and diversity explained by weather, 2) how relationships between these metrics and weather vary among plant assemblages, and 3) which aspects of weather best explain temporal variability. We used an information-theoretic approach to assess relationships between long-term plant richness and diversity patterns and a priori weather covariates using six datasets from four grasslands. Weather explained up to 49% and 63% of interannual variability in total plant species richness and diversity, respectively. However, richness and diversity responses to specific weather variables varied both among sites and among experimental treatments within sites. In general, we found many instances in which temperature was of equal or greater importance as precipitation, as well as evidence of the importance of lagged effects and precipitation or temperature variability. Although precipitation has been shown to be a key driver of productivity in grasslands, our results indicate that increasing temperatures alone, without substantial changes in precipitation patterns, could have measurable effects on Great Plains grassland plant assemblages and biodiversity metrics. Our results also suggest that richness and diversity will respond in unique ways to changing climate and management can affect these responses; additional research and monitoring will be essential for further understanding of these complex relationships.
Read More: http://www.esajournals.org/doi/abs/10.1890/14-1989.1
Most of the land-surface subsidence in the Houston-Galveston region, Texas, has occurred as a direct result of groundwater withdrawals for municipal supply, commercial and industrial use, and irrigation that depressured and dewatered the Chicot and Evangeline aquifers, thereby causing compaction of the aquifer sediments, mostly in the fine-grained silt and clay layers. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting water-level altitudes and water-level changes in the Chicot, Evangeline, and Jasper aquifers and measured cumulative compaction of subsurface sediments in the Chicot and Evangeline aquifers in the Houston-Galveston region. The report contains regional-scale maps depicting approximate 2015 water-level altitudes (represented by measurements made during December 2014–March 2015) for the Chicot, Evangeline, and Jasper aquifers; maps depicting 1-year (2014–15) water-level changes for each aquifer; maps depicting approximate contoured 5-year (2010–15) water-level changes for each aquifer; maps depicting approximate contoured long-term (1990–2015 and 1977–2015) water-level changes for the Chicot and Evangeline aquifers; a map depicting approximate contoured long-term (2000–15) water-level changes for the Jasper aquifer; a map depicting locations of borehole-extensometer sites; and graphs depicting measured cumulative compaction of subsurface sediments at the borehole extensometers during 1973–2014. Three tables listing the water-level data used to construct each water-level map for each aquifer and a table listing the measured cumulative compaction data for each extensometer site and graphs are included.
\nIn 2015, water-level-altitude contours for the Chicot aquifer ranged from 175 feet (ft) below the vertical datum (the National Geodetic Vertical Datum of 1929 or the North American Vertical Datum of 1988; hereinafter, datum) in a localized area in northwestern Harris County to 200 ft above datum in northern and western Montgomery County. Water-level changes for 2014–15 in the Chicot aquifer ranged from a 24-ft decline to a 31-ft rise. Contoured 5-year and long-term water-level changes in the Chicot aquifer ranged from a 40-ft decline to a 40-ft rise (2010–15), from a 100-ft decline to a 100-ft rise (1990–2015), and from a 100-ft decline to a 200-ft rise (1977–2015). In 2015, water-level-altitude contours for the Evangeline aquifer ranged from 250 ft below datum in a localized area extending from south-central Montgomery County into north-central Harris County and in an additional area located in central Harris County to 200 ft above datum in southeastern Grimes and northwestern Montgomery Counties. Water-level changes for 2014–15 in the Evangeline aquifer ranged from a 66-ft decline to a 78-ft rise. Contoured 5-year and long-term water-level changes in the Evangeline aquifer ranged from a 60-ft decline to an 80-ft rise (2010–15), from a 200-ft decline to a 240-ft rise (1990–2015), and from a 320-ft decline to a 240-ft rise (1977–2015). In 2015, water-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum in south-central Montgomery County that extends into north-central Harris County to 250 ft above datum in northwestern Montgomery County. Water-level changes for 2014–15 in the Jasper aquifer ranged from a 17-ft decline to a 35-ft rise. Contoured 5-year and long-term water-level changes in the Jasper aquifer ranged from a 60-ft decline to four small, localized areas of 10-ft rises (2010–15) and from a 220-ft decline to no change (2000–15).
\nCompaction of subsurface sediments (mostly in the fine-grained silt and clay layers) composing the Chicot and Evangeline aquifers was recorded continuously by using analog technology at the 13 borehole extensometers at 11 sites that were either activated or installed between 1973 and 1980. For the period of record beginning in 1973 (or later depending on activation or installation date) and ending in December 2014, measured cumulative compaction at the 13 extensometers ranged from 0.101 ft at the Texas City-Moses Lake extensometer to 3.668 ft at the Addicks extensometer. During 2014, a total of 10 of the 13 extensometers recorded a slight net decrease of land-surface elevation; the extensometers at the Lake Houston and Clear Lake (shallow) sites recorded slight net increases of land-surface elevation, and the extensometer at the Texas City-Moses Lake site recorded no change in elevation. The rate of compaction varies from site to site because of differences in rates of groundwater withdrawal in the areas adjacent to each extensometer site and differences among sites in the ratios of sand, silt, and clay and compressibilities of the subsurface sediments. It is not appropriate, therefore, to extrapolate or infer a rate of compaction for an adjacent area on the basis of the rate of compaction measured at nearby extensometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3337","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Kasmarek, M.C., Ramage, J.K., Houston, N.A., Johnson, M., and Schmidt, T.S., 2015, Water-level altitudes 2015 and water-level changes in the Chicot, Evangeline, and Jasper aquifers and compaction 1973-2014 in the Chicot and Evangeline aquifers, Houston-Galveston region, Texas (Version 1.0: Originally posted July 21, 2015; Version 1.1: October 16, 2015): U.S. Geological Survey Scientific Investigations Map 3337, Report: viii, 23 p.; 16 sheets; 17.0 x 21.99 in or smaller; 4 tables; 1 Appendix; Datasets; README file, https://doi.org/10.3133/sim3337.","productDescription":"Report: viii, 23 p.; 16 sheets; 17.0 x 21.99 in or smaller; 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Version 1.1: October 16, 2015","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55b0b0a5e4b09a3b01b53074","contributors":{"authors":[{"text":"Kasmarek, Mark C. 0000-0003-2808-2506 mckasmar@usgs.gov","orcid":"https://orcid.org/0000-0003-2808-2506","contributorId":1968,"corporation":false,"usgs":true,"family":"Kasmarek","given":"Mark","email":"mckasmar@usgs.gov","middleInitial":"C.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramage, Jason K. 0000-0001-8014-2874 jkramage@usgs.gov","orcid":"https://orcid.org/0000-0001-8014-2874","contributorId":3856,"corporation":false,"usgs":true,"family":"Ramage","given":"Jason","email":"jkramage@usgs.gov","middleInitial":"K.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Houston, Natalie A. 0000-0002-6071-4545 nhouston@usgs.gov","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":1682,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","email":"nhouston@usgs.gov","middleInitial":"A.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565219,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, Tiffany S. tsschmidt@usgs.gov","contributorId":145774,"corporation":false,"usgs":true,"family":"Schmidt","given":"Tiffany","email":"tsschmidt@usgs.gov","middleInitial":"S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565220,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188611,"text":"70188611 - 2015 - Hundreds of earthquakes per day: The 2014 Guthrie, Oklahoma, Earthquake Sequence","interactions":[],"lastModifiedDate":"2017-06-29T12:09:20","indexId":"70188611","displayToPublicDate":"2015-07-22T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Hundreds of earthquakes per day: The 2014 Guthrie, Oklahoma, Earthquake Sequence","docAbstract":"A remarkable increase in seismic activity in Oklahoma since 2009 has been shown\nto correlate closely with enhanced hydrocarbon extraction and associated\nwastewater disposal; 99% of this recent Oklahoma earthquake activity has \noccurred within 15 km of a call II injection well (Ellsworth, 2013). In response\nto this increase in seismic activity, the U.S. Geological Survey (USGS) partnered\nwith the Oklahoma Geological Survey (OGS) to exchange waveform data from\npermanent and temporary seismic stations to improve the cataloging of\nearthquake source parameters for a broad region of north-central Oklahoma. For\na particularly persistent earthquake sequence near Guthrie, Oklahoma, a \nsubspace detection method is applied to data from nearby seismic stations. This\napproach documents the occurrence of hundreds of readily detectable, highly\nsimilar, earthquakes per day, with rates occasionally exceeding 1000 \nearthquakes per day. Time-varying changes in b-value appear episodic,\nsuggesting a correlation with periods of reversible fault weakening and\nassociated failure.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220150019","usgsCitation":"Benz, H.M., McMahon, N.D., Aster, R., McNamara, D.E., and Harris, D.J., 2015, Hundreds of earthquakes per day: The 2014 Guthrie, Oklahoma, Earthquake Sequence: Seismological Research Letters, v. 86, no. 5, p. 1318-1325, https://doi.org/10.1785/0220150019.","productDescription":"8 p. ","startPage":"1318","endPage":"1325","ipdsId":"IP-064752","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoman","city":"Guthrie ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.58537292480469,\n 35.776043045943254\n ],\n [\n -97.27020263671875,\n 35.776043045943254\n ],\n [\n -97.27020263671875,\n 35.94465937365276\n ],\n [\n -97.58537292480469,\n 35.94465937365276\n ],\n [\n -97.58537292480469,\n 35.776043045943254\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-22","publicationStatus":"PW","scienceBaseUri":"59463fa7e4b062508e344091","contributors":{"authors":[{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMahon, Nicole D 0000-0003-0308-3705 nmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0003-0308-3705","contributorId":5811,"corporation":false,"usgs":true,"family":"McMahon","given":"Nicole","email":"nmcmahon@usgs.gov","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aster, R","contributorId":193059,"corporation":false,"usgs":false,"family":"Aster","given":"R","affiliations":[],"preferred":false,"id":698600,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698601,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, David J.","contributorId":139108,"corporation":false,"usgs":false,"family":"Harris","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":698602,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70144993,"text":"sir20155036 - 2015 - Analysis of storm-tide impacts from Hurricane Sandy in New York","interactions":[],"lastModifiedDate":"2015-08-11T15:41:36","indexId":"sir20155036","displayToPublicDate":"2015-07-21T11:00:00","publicationYear":"2015","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":"2015-5036","title":"Analysis of storm-tide impacts from Hurricane Sandy in New York","docAbstract":"The hybrid cyclone-nor’easter known as Hurricane Sandy affected the mid-Atlantic and northeastern United States during October 28-30, 2012, causing extensive coastal flooding. Prior to storm landfall, the U.S. Geological Survey (USGS) deployed a temporary monitoring network from Virginia to Maine to record the storm tide and coastal flooding generated by Hurricane Sandy. This sensor network augmented USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent monitoring sites that also documented storm surge. Continuous data from these networks were supplemented by an extensive post-storm high-water-mark (HWM) flagging and surveying campaign. The sensor deployment and HWM campaign were conducted under a directed mission assignment by the Federal Emergency Management Agency (FEMA). The need for hydrologic interpretation of monitoring data to assist in flood-damage analysis and future flood mitigation prompted the current analysis of Hurricane Sandy by the USGS under this FEMA mission assignment.
\nThe analysis of storm-tide impacts focused on three distinct but related aspects of coastal flooding from Hurricane Sandy, including flooding inland along the tidal reach of the Hudson River. These aspects are (1) comparisons of peak storm-tide elevations to those of historical storms and to annual exceedance probabilities, (2) assessments of storm-surge characteristics, and (3) comparisons of maps of inundation extent that were derived from differing amounts of available storm-tide data. Most peak storm-tide elevations from Hurricane Sandy were greater than about 9.5 feet (ft) above North American Vertical Datum of 1988.
\nPeak storm-tide elevations from Hurricane Sandy were compared with data for the intense nor’easter of December 11–13, 1992, and Hurricane Irene (August 27–28, 2011), which weakened to a tropical storm before arriving in New York. Peak storm-tide elevations from Hurricane Sandy were higher than those from the December 1992 nor’easter at 24 of 27 sites; most differences were greater than about 0.7 ft or 9 percent (above the historical storm tide). Peak storm-tide elevations from Hurricane Sandy were higher than those from Tropical Storm Irene at all sites; most differences were greater than about 2.5 ft or 48 percent. Data from permanent and temporary monitoring sites and HWM sites were compared with corresponding FEMA flood elevations for the 10-, 2-, 1-, and 0.2-percent annual exceedance probabilities in New York. Peak storm-tide elevations from Hurricane Sandy had annual exceedance probabilities less than or equal to 1 percent and (or) greater than 0.2 percent at a plurality of sites—184 of 413. Peak storm-tide elevations greater than or equal to the 0.2-percent flood elevation accounted for 81 of 413 sites. Peak storm-tide elevations less than the 10-percent flood elevation accounted for only 10 of 413 sites.
\nData from selected permanent monitoring sites in the USGS and NOAA networks were used to assess storm-surge magnitude associated with the peak storm tide, and magnitude and timing of the peak storm surge. Most magnitudes of the peak storm surge were greater than about 8.3 ft, and most magnitudes of the storm surge component of the peak storm tide were greater than about 7.8 ft. Timing of peak storm surge arrival with respect to local phase of tide controlled where the most extreme peak storm-tide levels and coastal flooding occurred. This finding has bearing not only for locations impacted by the highest storm tides from Hurricane Sandy, but also for those that had the greatest storm surges yet were spared the worst flooding because of fortuitous timing during this storm.
\nResults of FEMA Hazus Program (HAZUS) flood loss analyses performed for New York counties were compared for extents of storm-tide inundation from Hurricane Sandy mapped (1) pre-storm, (2) on November 11, 2012, and (3) on February 14, 2013. The resulting depictions of estimated total building stock losses document how differing amounts of available USGS data affect the resolution and accuracy of storm-tide inundation extents. Using the most accurate results from the final (February 14, 2013) inundation extent, estimated losses range from $380 million to $5.9 billion for individual New York counties; total estimated aggregate losses are about $23 billion for all New York counties. Quality of the inundation extents used in HAZUS analyses has a substantial effect on final results. These findings can be used to inform future post-storm reconstruction planning and estimation of insurance claims.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155036","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Schubert, C.E., Busciolano, Ronald, Hearn, P.P., Jr., Rahav, A.N., Behrens, Riley, Finkelstein, Jason, Monti, Jack, Jr., and Simonson, A.E., 2015, Analysis of storm-tide impacts from Hurricane Sandy in New York: U.S. Geological Survey Scientific Investigations Report 2015–5036, 75 p., https://dx.doi.org/10.3133/sir20155036.","productDescription":"iv, 75 p.","startPage":"1","endPage":"75","numberOfPages":"79","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052333","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":305838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5036/coverthb.jpg"},{"id":305840,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5036/sir20155036.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5036"},{"id":306207,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5036/sir20155036_printversion.pdf","text":"Report - Print Version","size":"19,468 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5036"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.805908203125,\n 40.371658891506094\n ],\n [\n -74.805908203125,\n 42.827638636242284\n ],\n [\n -71.5869140625,\n 42.827638636242284\n ],\n [\n -71.5869140625,\n 40.371658891506094\n ],\n [\n -74.805908203125,\n 40.371658891506094\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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2045 Route 112, Building 4
Coram, NY 11727
http://ny.water.usgs.gov
A number of researchers have attempted to estimate salmonid smolt survival during outmigration through an estuary. However, it is currently unclear how the design of such studies influences the accuracy and precision of survival estimates. In this simulation study we consider four patterns of smolt survival probability in the estuary, and test the performance of several different sampling strategies for estimating estuarine survival assuming perfect detection. The four survival probability patterns each incorporate a systematic component (constant, linearly increasing, increasing and then decreasing, and two pulses) and a random component to reflect daily fluctuations in survival probability. Generally, spreading sampling effort (tagging) across the season resulted in more accurate estimates of survival. All sampling designs in this simulation tended to under-estimate the variation in the survival estimates because seasonal and daily variation in survival probability are not incorporated in the estimation procedure. This under-estimation results in poorer performance of estimates from larger samples. Thus, tagging more fish may not result in better estimates of survival if important components of variation are not accounted for. The results of our simulation incorporate survival probabilities and run distribution data from previous studies to help illustrate the tradeoffs among sampling strategies in terms of the number of tags needed and distribution of tagging effort. This information will assist researchers in developing improved monitoring programs and encourage discussion regarding issues that should be addressed prior to implementation of any telemetry-based monitoring plan. We believe implementation of an effective estuary survival monitoring program will strengthen the robustness of life cycle models used in recovery plans by providing missing data on where and how much mortality occurs in the riverine and estuarine portions of smolt migration. These data could result in better informed management decisions and assist in guidance for more effective estuarine restoration projects.
","language":"English","publisher":"PLOS one","doi":"10.1371/journal.pone.0132912","usgsCitation":"Romer, J.D., Gitelman, A.I., Clements, S., and Schreck, C.B., 2015, Designing a monitoring program to estimate estuarine survival of anadromous salmon smolts: simulating the effect of sample design on inference: PLoS ONE, 11 p., https://doi.org/10.1371/journal.pone.0132912.","productDescription":"11 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066437","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471935,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0132912","text":"Publisher Index Page"},{"id":323413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-21","publicationStatus":"PW","scienceBaseUri":"575a9330e4b04f417c275131","contributors":{"authors":[{"text":"Romer, Jeremy D.","contributorId":171684,"corporation":false,"usgs":false,"family":"Romer","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":638299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gitelman, Alix I.","contributorId":168402,"corporation":false,"usgs":false,"family":"Gitelman","given":"Alix","email":"","middleInitial":"I.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":638300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clements, Shaun","contributorId":171685,"corporation":false,"usgs":false,"family":"Clements","given":"Shaun","email":"","affiliations":[],"preferred":false,"id":638301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schreck, Carl B. 0000-0001-8347-1139 carl.schreck@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-1139","contributorId":878,"corporation":false,"usgs":true,"family":"Schreck","given":"Carl","email":"carl.schreck@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":637222,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154766,"text":"ds945 - 2015 - Post-Hurricane Ivan coastal oblique aerial photographs collected from Crawfordville, Florida, to Petit Bois Island, Mississippi, September 17, 2004","interactions":[],"lastModifiedDate":"2015-07-20T11:51:18","indexId":"ds945","displayToPublicDate":"2015-07-20T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"945","title":"Post-Hurricane Ivan coastal oblique aerial photographs collected from Crawfordville, Florida, to Petit Bois Island, Mississippi, September 17, 2004","docAbstract":"The U.S. Geological Survey (USGS) conducts baseline and storm response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On September 17, 2004, the USGS conducted an oblique aerial photographic survey from Crawfordville, Florida, to Petit Bois Island, Mississippi aboard a Piper Navajo Chieftain (aircraft) at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect post-Hurricane Ivan data for assessing incremental changes in the beach and nearshore area since the last survey in 2001, and the data can be used in the assessment of future coastal change.
\nThe images provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft and do not indicate the location of any feature in the images. These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. The photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
\nTable 1 provides detailed information about the GPS location, image name, date, and time for each of the 3,381 photographs taken, along with links to each photograph. The photographs are organized into segments, also referred to as contact sheets, and represent approximately 5 minutes of flight time. In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided, which can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds945","usgsCitation":"Morgan, K., Krohn, M.D., Peterson, R., Thompson, P.R., and Subino, J.A., 2015, Post-Hurricane Ivan coastal oblique aerial photographs collected from Crawfordville, Florida, to Petit Bois Island, Mississippi, September 17, 2004: U.S. Geological Survey Data Series 945, HTML Document, https://doi.org/10.3133/ds945.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2004-09-17","temporalEnd":"2004-09-17","ipdsId":"IP-039096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":305835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds945.jpg"},{"id":305833,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0945/"},{"id":305834,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0945/ds945_title.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DS 945"}],"country":"United States","state":"Alabama, Florida, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.219970703125,\n 30.5717205651999\n ],\n [\n -88.70361328125,\n 30.64736425824319\n ],\n [\n -88.24218749999999,\n 30.600093873550072\n ],\n [\n -88.13232421875,\n 31.005862904624205\n ],\n [\n -87.78076171875,\n 30.892797477508154\n ],\n [\n -87.51708984375,\n 30.56226095049944\n ],\n [\n -87.08862304687499,\n 30.798474179567823\n ],\n [\n -86.583251953125,\n 30.6662659463233\n ],\n [\n -86.0009765625,\n 30.704058230919504\n ],\n [\n -85.682373046875,\n 30.496017831341284\n ],\n [\n -85.045166015625,\n 30.35391637229704\n ],\n [\n -84.30908203125,\n 30.4297295750316\n ],\n [\n -83.9794921875,\n 30.20211367909724\n ],\n [\n -83.935546875,\n 29.92637417863576\n ],\n [\n -83.9794921875,\n 29.142566155107065\n ],\n [\n -85.968017578125,\n 29.1233732108192\n ],\n [\n -86.033935546875,\n 29.888280933159265\n ],\n [\n -88.670654296875,\n 29.869228848968312\n ],\n [\n -88.70361328125,\n 30.116621582819377\n ],\n [\n -89.219970703125,\n 30.15462722077597\n ],\n [\n -89.219970703125,\n 30.5717205651999\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09ed86","contributors":{"authors":[{"text":"Morgan, Karen L.M. 0000-0002-2994-5572 kmorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-2994-5572","contributorId":140446,"corporation":false,"usgs":true,"family":"Morgan","given":"Karen L.M.","email":"kmorgan@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":564027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krohn, M. Dennis dkrohn@usgs.gov","contributorId":3378,"corporation":false,"usgs":true,"family":"Krohn","given":"M.","email":"dkrohn@usgs.gov","middleInitial":"Dennis","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":564025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Russell D.","contributorId":107344,"corporation":false,"usgs":true,"family":"Peterson","given":"Russell D.","affiliations":[],"preferred":false,"id":564028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Philip R. prthompson@usgs.gov","contributorId":4483,"corporation":false,"usgs":true,"family":"Thompson","given":"Philip","email":"prthompson@usgs.gov","middleInitial":"R.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":564029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Subino, Janice A.","contributorId":50386,"corporation":false,"usgs":true,"family":"Subino","given":"Janice","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":564030,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70147240,"text":"sir20155064 - 2015 - Flood-Inundation maps for the Hohokus Brook in Waldwick Borough, Ho-Ho-Kus Borough, and the Village of Ridgewood, New Jersey, 2014","interactions":[],"lastModifiedDate":"2015-07-20T10:37:04","indexId":"sir20155064","displayToPublicDate":"2015-07-20T11:15:00","publicationYear":"2015","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":"2015-5064","title":"Flood-Inundation maps for the Hohokus Brook in Waldwick Borough, Ho-Ho-Kus Borough, and the Village of Ridgewood, New Jersey, 2014","docAbstract":"Digital flood-inundation maps for a 6-mile reach of the Hohokus Brook in New Jersey from White's Lake Dam in Waldwick Borough, through Ho-Ho-Kus Borough to Grove Street in the Village of Ridgewood were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection. The flood inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Hohokus Brook at Ho-Ho-Kus, New Jersey (station number 01391000). Stage data at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/nwis/uv?site_no=01391000 or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps2/hydrograph.php?gage=hohn4&wfo=okx.
\nFlood profiles were simulated for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relation at the Hohokus Brook at Ho-Ho-Kus, New Jersey, streamgage (station number 01391000). The hydraulic model was then used to compute 12 water-surface profiles for flood stages at 0.5-foot (ft) intervals referenced to the streamgage datum and ranging from 2.5 ft, the NWS “action stage” or near bankfull, to 8.0 ft, which exceeds the stage that corresponds to the maximum recorded peak flow (7.32 ft) and is the extent of the current stage-discharge relation for the streamgage. The simulated water-surface profiles were then combined with a geographic information system 3-meter (9.84 ft) digital elevation model [derived from light detection and ranging (lidar) data] to delineate the area flooded at each water level.
\nThe availability of these maps along with information on the Internet regarding current stage from the USGS streamgage will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155064","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Watson, K.M., and Niemoczynski, M.J., 2015, Flood-Inundation maps for the Hohokus Brook in Waldwick Borough, Ho-Ho-Kus Borough, and the Village of Ridgewood, New Jersey, 2014: U.S. Geological Survey Scientific Investigations Report 2015–5064, 12 p., https://dx.doi.org/10.3133/sir20155064.","productDescription":"v, 12 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053102","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":305705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5064/coverthb.jpg"},{"id":305706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5064/sir20155064.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5064"},{"id":305707,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5064/downloads/depth_raster/","text":"Depth_Raster","size":"112 MB","description":"XML, ovr, adf, and Other Files"},{"id":305708,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5064/downloads/KML/","text":"KML","size":"116 KB","description":"KMZ"},{"id":305709,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2015/5064/downloads/readme.txt","text":"Readme","size":"9.72 KB","linkFileType":{"id":2,"text":"txt"},"description":"Readme"},{"id":305710,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5064/downloads/water_surface_final/","text":"Water Data","size":"1.43 MB","linkFileType":{"id":4,"text":"shapefile"},"description":"Water Surface"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.09042358398438,\n 40.86627605595889\n ],\n [\n -74.09042358398438,\n 40.914550362677204\n ],\n [\n -74.01592254638672,\n 40.914550362677204\n ],\n [\n -74.01592254638672,\n 40.86627605595889\n ],\n [\n -74.09042358398438,\n 40.86627605595889\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
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3450 Princeton Pike, Suite 110
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http://nj.usgs.gov/
","tableOfContents":"
In August of 2013, the U.S. Geological Survey conducted a geophysical survey offshore of Petit Bois Island, Mississippi. This effort was part of the U.S. Geological Survey Gulf of Mexico Science Coordination partnership with the U.S. Army Corps of Engineers to assist the Mississippi Coastal Improvements Program and the Northern Gulf of Mexico Ecosystem Change and Hazards Susceptibility Project, by mapping the shallow geologic stratigraphic framework of the Mississippi Barrier Island Complex.
\nThis geophysical survey will provide additional data necessary for scientists to define, interpret, and provide baseline bathymetry and seafloor habitat for this area, and to aid scientists in predicting future geomorphological changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data will provide information for barrier island restoration, particularly in Camille Cut, and protection for the historical Fort Massachusetts on Ship Island, Mississippi.
\nThe geophysical data were collected during one cruise (USGS Field Activity Numbers 13CCT04) aboard the Research Vessel Tommy Munro offshore along the gulf side of Petit Bois Island, Gulf Islands National Seashore, Mississippi. Data were acquired with the following equipment: a Systems Engineering and Assessment, Ltd., SWATHplus interferometric sonar (468 kilohertz (kHz)), an EdgeTech 424 (4-24 kHz), an EdgeTech 525i chirp subbottom profiling system, and a Klein 3900 sidescan sonar system.
\nThis report serves as an archive of the processed interferometric swath bathymetry and sidescan sonar data. Geographic information system data products include an interpolated digital elevation model, an acoustic backscatter mosaic, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.
\nNOTE: These data are scientific in nature and are not to be used for navigation. Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds917","usgsCitation":"DeWitt, N.T., Flocks, J.G., Kindinger, J.L., Bernier, J., Kelso, K.W., Wiese, D.S., Finlayson, D.P., and Pfeiffer, W.R., 2015, Archive of Sidescan Sonar and Swath Bathymetry Data Collected During USGS Cruise 13CCT04 Offshore of Petit Bois Island, Gulf Islands National Seashore, Mississippi, August 2013: U.S. Geological Survey Data Series 917, HTML Document, https://doi.org/10.3133/ds917.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058072","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science 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jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":539460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kindinger, Jack L. jkindinger@usgs.gov","contributorId":815,"corporation":false,"usgs":true,"family":"Kindinger","given":"Jack","email":"jkindinger@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":539461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernier, Julie 0000-0002-9918-5353 jbernier@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-5353","contributorId":3549,"corporation":false,"usgs":true,"family":"Bernier","given":"Julie","email":"jbernier@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":539462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":539463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":539464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Finlayson, David P. dfinlayson@usgs.gov","contributorId":1381,"corporation":false,"usgs":true,"family":"Finlayson","given":"David","email":"dfinlayson@usgs.gov","middleInitial":"P.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":539465,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pfeiffer, William R. wpfeiffer@usgs.gov","contributorId":3725,"corporation":false,"usgs":true,"family":"Pfeiffer","given":"William","email":"wpfeiffer@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":539466,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70148717,"text":"sir20155086 - 2015 - Water resources during drought conditions and postfire water quality in the upper Rio Hondo Basin, Lincoln County, New Mexico, 2010-13","interactions":[],"lastModifiedDate":"2015-07-20T08:55:28","indexId":"sir20155086","displayToPublicDate":"2015-07-17T13:15:00","publicationYear":"2015","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":"2015-5086","title":"Water resources during drought conditions and postfire water quality in the upper Rio Hondo Basin, Lincoln County, New Mexico, 2010-13","docAbstract":"Stakeholders and water-resource managers in Lincoln County, New Mexico, have had long-standing concerns over the impact of population growth and groundwater withdrawals. These concerns have been exacerbated in recent years by extreme drought conditions and two major wildfires in the upper Rio Hondo Basin, located in south-central New Mexico. The U.S. Geological Survey (USGS), in cooperation with Lincoln County, initiated a study in 2006 to assess and characterize water resources in the upper Rio Hondo Basin. Data collected during water years 2010–13 are presented and interpreted in this report. All data presented in this report are described in water years unless stated otherwise.
\nAnnual mean streamflow at the Rio Ruidoso at Hollywood, N. Mex., streamflow-gaging station was less than 50 percent of the average streamflow during 2011–13 and was of similar magnitude to annual mean streamflow values measured during the drought of the 1950s. The first zero-streamflow values for the period of record (1954–2013) were recorded at the Rio Ruidoso at Hollywood, N. Mex., streamflow-gaging station on June 27–29, 2013. The lowest annual mean streamflow on record (1969–80; 1988–2013) occurred in 2011 at the Eagle Creek below South Fork near Alto, N. Mex., streamflow-gaging station, with the station recording zero streamflow for approximately 50 percent of the year.
\nDiscrete and continuous groundwater-level measurements indicated basinwide water-level declines during drought conditions in 2011–13. The average water-level change among 37 wells in which discrete groundwater-level measurements were collected was -7.6 ft from 2010 to 2013. The largest water-level declines were observed in the upper reaches of the Rio Bonito and Rio Ruidoso watersheds, and smaller declines were observed in the lower reaches of the watersheds. In general, water-level changes observed during 2010–13 were on the order of decadal-scale changes that previously have been observed in the upper Rio Hondo Basin.
\nStable-isotope data indicate that high-elevation winter precipitation generally contributes more to groundwater recharge than summer rains, except when there are large summer recharge events. This implies that little recharge is occurring at the lower elevations in the upper Rio Hondo Basin because these areas receive a smaller amount of total precipitation, receive a smaller proportion of the annual total falling as winter precipitation, and have higher average temperatures that result in more evaporative losses. Groundwater in the upper Rio Hondo Basin is a mix of younger and older water, and recharge likely is occurring primarily at higher elevations but there may be some areas where localized recharge is occurring at lower elevations.
\nSurface-water- and groundwater-quality results from samples collected in 2012–13 were examined to characterize overall chemistry and were compared to historical waterquality data from streams in the upper Rio Hondo Basin collected during 1926–57. In general, specific conductance showed an increasing trend moving eastward (downstream) through the upper Rio Hondo Basin in surface-water and groundwater samples. Surface-water and groundwater samples appear to have similar overall major-ion chemical characteristics when compared to historical water-quality data. Geology was found to influence the chemical characteristics of surface-water and groundwater samples, with relatively higher concentrations of sulfate occurring in samples collected at lower elevations in the Permian regional aquifer system.
\nSurface-water sample results also were analyzed to determine differences in unfiltered and filtered water-quality samples of streams in burned and unburned watersheds after the occurrence of the Little Bear Fire in June 2012. Samples were collected after postfire monsoon rain events and during periods of stable hydrologic conditions. The first postfire monsoon rain event in July 2012 generally produced the highest measured concentrations of selected fire-related constituents in unfiltered samples collected in the burned watersheds relative to later samples collected in burned watersheds and all samples collected in the unburned watershed. Monsoon rain events have impacted water quality by delivering larger sediment loads and fire-related constituents into streams in the upper Rio Hondo Basin.
\nChanges in climate and increased groundwater and surface-water use are likely to affect the availability of water in the upper Rio Hondo Basin. Increased drought probably will increase the potential for wildfires, which can affect downstream water quality and increase flood potential. Climate-research predicted decreases in winter precipitation may have an adverse effect on the amount of groundwater recharge that occurs in the upper Rio Hondo Basin, given the predominance of winter precipitation recharge as indicated by the stable isotope results. Decreases in surface-water supplies because of persistent drought conditions and reductions in the quality of water because of the effects of wildfire may lead to a larger reliance on groundwater reserves in the upper Rio Hondo Basin. Decreasing water levels because of increasing groundwater withdrawal could reduce base flows in the Rio Bonito and Rio Ruidoso. Well organized and scientifically supported regional water-resources management will be necessary for dealing with the likely scenario of increases in demand coupled with decreases in supply in the upper Rio Hondo Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155086","collaboration":"Prepared in cooperation with Lincoln County, New Mexico","usgsCitation":"Sherson, L.R. and Rice, S.E., 2015, Water resources during drought conditions and postfire water quality in the upper Rio Hondo Basin, Lincoln County, New Mexico, 2010–13: U.S. Geological Survey Scientific Investigations Report 2015–5086, 56 p., https://dx.doi.org/10.3133/sir20155086.","productDescription":"vii, 56 p.","numberOfPages":"67","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-058239","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":305800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5086/coverthb.jpg"},{"id":305801,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5086/sir20155086.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5086"}],"country":"United States","state":"New Mexico","county":"Lincoln County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.20208740234375,\n 33.40163829558248\n ],\n [\n -106.20208740234375,\n 34.31394984163214\n ],\n [\n -104.70794677734374,\n 34.31394984163214\n ],\n [\n -104.70794677734374,\n 33.40163829558248\n ],\n [\n -106.20208740234375,\n 33.40163829558248\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montgomery Blvd NE, Suite 400
Albuquerque, NM 87109
http://nm.water.usgs.gov/
As part of a larger study, the U.S. Geological Survey undertook a study to identify the potential for phosphate deposits in Afghanistan. As part of this study, a geographic information system was constructed containing a database of phosphate occurrences in Afghanistan and adjacent countries, and a database of potential host lithologies compiled from 1:1,000,000 scale maps. Within Afghanistan, a handful of known occurrences and reports indicate the presence of phosphate in Permian, Cretaceous, and Paleogene sediments and in carbonatite. With the exception of the Khanneshin carbonatite, very little is known about these occurrences. In the countries surrounding Afghanistan, economic phosphate is known to occur in Cambrian, Devonian, and Paleogene sediments and in Kiruna-type Fe-apatite deposits. Many of the host units may extend into Afghanistan or equivalent units may be present. Although the possibility of economic phosphate deposits exist for Afghanistan, the need for detailed exploration for phosphate, the remoteness of some locations, and the probability that a deposit would not be exposed at the surface mean that one or more deposits are not likely to be identified in the near future. Even if a phosphate-bearing deposit is identified in Afghanistan, it is not clear if the probable size, thickness, and grade ranges would allow economic development of the hypothesized resource.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151121","usgsCitation":"Orris, G.J., Dunlap, P., Wallis, J., and Wynn, J., 2015, Phosphate occurrence and potential in the region of Afghanistan, including parts of China, Iran, Pakistan, Tajikistan, Turkmenistan, and Uzbekistan: U.S. Geological Survey Open-File Report 2015-1121, vi, 70 p., https://doi.org/10.3133/ofr20151121.","productDescription":"vi, 70 p.","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":305804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151121.gif"},{"id":305803,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2015/1121/downloads/ofr20151121_gis.zip","text":"GIS package","size":"19.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2015-1121 GIS database","linkHelpText":"Contains: geospatial database. Refer to the Readme and Metadata files for more information."},{"id":305796,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1121/"},{"id":305802,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1121/pdf/ofr20151121_report.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1121 Report"}],"country":"China, Iran, Pakistan, Tajikistan, Turkmenistan, Uzbekistan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 43.857421875,\n 24.766784522874453\n ],\n [\n 43.857421875,\n 40.04443758460859\n ],\n [\n 67.236328125,\n 40.04443758460859\n ],\n [\n 67.236328125,\n 24.766784522874453\n ],\n [\n 43.857421875,\n 24.766784522874453\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09ed92","contributors":{"authors":[{"text":"Orris, Greta J. 0000-0002-2340-9955 greta@usgs.gov","orcid":"https://orcid.org/0000-0002-2340-9955","contributorId":3472,"corporation":false,"usgs":true,"family":"Orris","given":"Greta","email":"greta@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":564965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunlap, Pamela pdunlap@usgs.gov","contributorId":5329,"corporation":false,"usgs":true,"family":"Dunlap","given":"Pamela","email":"pdunlap@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":564966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallis, John jwallis@usgs.gov","contributorId":143684,"corporation":false,"usgs":true,"family":"Wallis","given":"John","email":"jwallis@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":564967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wynn, Jeff 0000-0002-8102-3882 jwynn@usgs.gov","orcid":"https://orcid.org/0000-0002-8102-3882","contributorId":2803,"corporation":false,"usgs":true,"family":"Wynn","given":"Jeff","email":"jwynn@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":564968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148547,"text":"sir20155083 - 2015 - Simulation of groundwater flow and chloride transport in the “1,200-foot” sand with scenarios to mitigate saltwater migration in the “2,000-foot” sand in the Baton Rouge area, Louisiana","interactions":[],"lastModifiedDate":"2015-09-17T09:38:10","indexId":"sir20155083","displayToPublicDate":"2015-07-16T14:30:00","publicationYear":"2015","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":"2015-5083","title":"Simulation of groundwater flow and chloride transport in the “1,200-foot” sand with scenarios to mitigate saltwater migration in the “2,000-foot” sand in the Baton Rouge area, Louisiana","docAbstract":"Groundwater withdrawals have caused saltwater to encroach into freshwater-bearing aquifers beneath Baton Rouge, Louisiana. The 10 aquifers beneath the Baton Rouge area, which includes East and West Baton Rouge Parishes, Pointe Coupee Parish, and East and West Feliciana Parishes, provided about 184.3 million gallons per day (Mgal/d) for public supply and industrial use in 2012. Groundwater withdrawals from the “1,200-foot” sand in East Baton Rouge Parish have caused water-level drawdown as large as 177 feet (ft) north of the Baton Rouge Fault and limited saltwater encroachment from south of the fault. The recently developed groundwater model for simulating transport in the “2,000-foot” sand was rediscretized to also enable transport simulation within the “1,200-foot” sand and was updated with groundwater withdrawal data through 2012. The model was recalibrated to water-level observation data through 2012 with the parameter-estimation code PEST and calibrated to observed chloride concentrations at observation wells within the “1,200-foot” sand and “2,000-foot” sand. The model is designed to evaluate strategies to control saltwater migration, including changes in the distribution of groundwater withdrawals and installation of scavenger wells to intercept saltwater before it reaches existing production wells.
\nSeven hypothetical scenarios predict the effects of different groundwater withdrawal options on groundwater levels and the transport of chloride within the “1,200-foot” sand and the “2,000-foot” sand during 2015–2112. The predicted water levels and concentrations for all scenarios are depicted in maps for the years 2047 and 2112. The first scenario is a base case for comparison to the six other scenarios and simulates continuation of 2012 reported groundwater withdrawals through 2112 (100 years). The second scenario that simulates increased withdrawals from industrial wells in the “1,200-foot” sand predicts that water levels will be 12–25 ft lower by 2047 and that there will be a negligible difference in chloride concentrations within the “1,200-foot” sand. The five other scenarios simulate the effects of various withdrawal schemes on water levels and chloride concentrations within the “2,000-foot” sand. Amongst these five other scenarios, three of the scenarios simulate only various withdrawal reductions, whereas the two others also incorporate withdrawals from a scavenger well that is designed to extract salty water from the base of the “2,000-foot” sand. Two alternative pumping rates (2.5 Mgal/d and 1.25 Mgal/d) are simulated in each of the scavenger-well scenarios. For the “2,000-foot” sand scenarios, comparison of the predicted effects of the scenarios is facilitated by graphs of predicted chloride concentrations through time at selected observation wells, plots of salt mass in the aquifer through time, and a summary of the predicted plume area and average concentration. In all scenarios, water levels essentially equilibrate by 2047, after 30 years of simulated constant withdrawal rates. Although predicted water-level recovery within the “2,000-foot” sand is greatest for the scenario with the greatest reduction in groundwater withdrawal from that aquifer, the scavenger-well scenarios are most effective in mitigating the future extent and concentration of the chloride plume. The simulated scavenger-well withdrawal rate has more influence on the plume area and concentration than do differences among the scenarios in industrial and public-supply withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155083","collaboration":"Prepared in cooperation with the Capital Area Groundwater Conservation Commission; the Louisiana Department of Transportation and Development, Public Works and Water Resources Division; and the City of Baton Rouge and Parish of East Baton Rouge","usgsCitation":"Heywood, C.E., Lovelace, J.K., and Griffith, J.M., 2015, Simulation of groundwater flow and chloride transport in the “1,200-foot” sand with scenarios to mitigate saltwater migration in the “2,000-foot” sand in the Baton Rouge area, Louisiana (ver. 1.1, September 2015): U.S. Geological Survey Scientific Investigations Report 2015–5083, 69 p.,\nhttps://dx.doi.org/10.3133/sir20155083.","productDescription":"xi, 69 p.","numberOfPages":"85","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060614","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"links":[{"id":308118,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2015/5083/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":305784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5083/coverthb.jpg"},{"id":305785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5083/sir20155083.pdf","text":"Report","size":"17.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5083 ver1.1"}],"country":"United States","state":"Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.318115234375,\n 30.372875188118016\n ],\n [\n -92.318115234375,\n 31.44741029142872\n ],\n [\n -90.52734374999999,\n 31.44741029142872\n ],\n [\n -90.52734374999999,\n 30.372875188118016\n ],\n [\n -92.318115234375,\n 30.372875188118016\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted July 16, 2015; Version 1.1: September 14, 2015","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
http://la.water.usgs.gov/
This Data Series summarizes results from July 2013 sampling in the western Alaska Range near Mount Estelle, Alaska. The fieldwork combined in situ and camp-based spectral measurements of talus/soil and rock samples. Five rock and 48 soil samples were submitted for quantitative geochemical analysis (for 55 major and trace elements), and the 48 soils samples were also analyzed by x-ray diffraction to establish mineralogy and geochemistry. The results and sample photographs are presented in a geodatabase that accompanies this report. The spectral, mineralogical, and geochemical characterization of these samples and the sites that they represent can be used to validate existing remote-sensing datasets (for example, ASTER) and future hyperspectral studies. Empirical evidence of jarosite (as identified by x-ray diffraction and spectral analysis) corresponding with gold concentrations in excess of 50 parts per billion in soil samples suggests that surficial mapping of jarosite in regional surveys may be useful for targeting areas of prospective gold occurrences in this sampling area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds943","usgsCitation":"Johnson, M.R., Graham, G.E., Hubbard, B.E., and Benzel, W.M., 2015, Geospatial compilation of results from field sample collection in support of mineral resource investigations, Western Alaska Range, Alaska, July 2013: U.S. Geological Survey Data Series 943, 12 p., https://dx.doi.org/10.3133/ds943.","productDescription":"Report: iv, 12 p.; 1 Table","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060029","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":305729,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/0943/downloads/Western_Alaska_Range_samples_July2013.gdb.zip","text":"File Geodatabase","size":"656 MB","description":"DS 943 File Geodatabase"},{"id":305727,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0943/ds0943.pdf","text":"Report","size":"14.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 943"},{"id":305732,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0943/downloads/Metadata","text":"Metadata","description":"DS 943 Metadata"},{"id":305726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0943/coverthb.jpg"},{"id":305730,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/ds/0943/downloads/Western_Alaska_Range_samples_July2013_shp.zip","text":"Shapefiles","size":"647 MB","description":"DS 943 Shapefiles"},{"id":305731,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0943/downloads/RESULTS_data_tables","text":"Comma-delimited tables","linkFileType":{"id":7,"text":"csv"},"description":"DS 943 Comma-delimited tables"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.75,\n 61.25\n ],\n [\n -153.75,\n 62.5\n ],\n [\n -152.25,\n 62.5\n ],\n [\n -152.25,\n 61.25\n ],\n [\n -153.75,\n 61.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
http://crustal.usgs.gov/
During October 2012 through March 2013, the U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (EPA) Region 4, Superfund Section, conducted borehole geophysical logging, surface geophysical surveys, and water-quality profiling in selected wells and areas to characterize or delineate the extent of elevated subsurface electrical conductivity at the EPA Barite Hill/Nevada Goldfields Superfund site near McCormick, South Carolina. Elevated electrical conductivity measured at the site may be related to native rock materials, waste rock disposal areas used in past operations, and (or) groundwater having elevated dissolved solids (primarily metals and major ions) related to waste migration. Five shallow screened wells and four open-borehole bedrock wells were logged by using a suite of borehole tools, and downhole water-quality profiles were recorded in two additional wells. Well depths ranged from about 26 to 300 feet below land surface. Surface geophysical surveys based on frequency-domain electromagnetic and distributed temperature sensing (DTS) techniques were used to identify areas of elevated electrical conductivity (Earth materials and groundwater) and potential high dissolved solids in groundwater and surface water on land and in areas along the northern unnamed tributary at the site.
\nResults from the electromagnetic-induction logging of four selected wells near the Main Pit and one well located about 800 feet southeast of the Main Pit lake indicate that elevated electrical conductivity extends to a depth of about 110 feet below land surface. Groundwater-quality properties recorded in eight selected wells were highly variable, suggesting a broad spectrum of geochemical conditions and contaminant concentrations within the groundwater system. Ranges of field water-quality properties recorded from water-profiling of groundwater in all wells logged were as follows: pH, 3.1 to 9.2; specific conductance, 48 to 5,300 microsiemens per centimeter; dissolved oxygen, 0.2 to 4.4 milligrams per liter; and water temperature, 17.0 to 18.0 degrees Celsius. The highest specific conductance and lowest pH measurements were made in boreholes located between the Main Pit lake and the northern unnamed tributary. Conceptually, these wells may intercept elevated dissolved solids in groundwater leaking from the Main Pit lake along a flow path that discharges into the unnamed tributary to the north. Results from surface geophysical electromagnetic and fiber-optics surveys confirm areas of focused discharge of groundwater near the Main Pit lake along the northern unnamed tributary. The frequency-domain surface electromagnetic surveys also identified an area with higher levels of elevated electrical conductivity located northwest of the former Rainsford Pit area.
\nBedrock properties were characterized from borehole geophysical logs collected from three open-borehole bedrock wells. The mean strike azimuth of the borehole foliation data measured in bedrock well IR-1 was 221° (N. 41° E.), and the mean dip angle was 78° to the northwest. Dominant strike azimuth orientations of primary fractures measured in three boreholes were from 210° to 250° (N. 30° E. to N. 70° E.) with a mean dip of 68° northwest. Transmissivity estimates interpreted from the heat-pulse flowmeter data from bedrock well IR-1 were about 69 feet squared per day, and the radius of influence was estimated at about 640 feet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155084","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Region 4, Superfund Section","usgsCitation":"Chapman, M.J., Huffman, B.A., and McSwain, K.B., 2015, Delineation of areas having elevated electrical conductivity, orientation and characterization of bedrock fractures, and occurrence of groundwater discharge to surface water at the U.S. Environmental Protection Agency Barite Hill/Nevada Goldfields Superfund site near McCormick, South Carolina: U.S. Geological Survey Scientific Investigations Report 2015–5084, 95 p., https://dx.doi.org/10.3133/sir20155084.","productDescription":"ix, 95 p.","numberOfPages":"109","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-049026","costCenters":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"links":[{"id":305692,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5084/coverthb.jpg"},{"id":305693,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5084/sir20155084.pdf","text":"Report","size":"9.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5084"}],"country":"United States","state":"South Carolina","city":"McCormick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.33772277832031,\n 33.868135032968624\n ],\n [\n -82.33772277832031,\n 33.937093739554385\n ],\n [\n -82.22854614257812,\n 33.937093739554385\n ],\n [\n -82.22854614257812,\n 33.868135032968624\n ],\n [\n -82.33772277832031,\n 33.868135032968624\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
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","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2015-07-16","noUsgsAuthors":false,"publicationDate":"2015-07-16","publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09ed98","contributors":{"authors":[{"text":"Chapman, Melinda J. 0000-0003-4021-0320 mjchap@usgs.gov","orcid":"https://orcid.org/0000-0003-4021-0320","contributorId":1597,"corporation":false,"usgs":true,"family":"Chapman","given":"Melinda","email":"mjchap@usgs.gov","middleInitial":"J.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huffman, Brad A. 0000-0003-4025-1325 bahuffma@usgs.gov","orcid":"https://orcid.org/0000-0003-4025-1325","contributorId":1596,"corporation":false,"usgs":true,"family":"Huffman","given":"Brad","email":"bahuffma@usgs.gov","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McSwain, Kristen Bukowski","contributorId":74694,"corporation":false,"usgs":true,"family":"McSwain","given":"Kristen Bukowski","affiliations":[],"preferred":false,"id":548854,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154745,"text":"70154745 - 2015 - Coastal and wetland ecosystems of the Chesapeake Bay watershed: Applying palynology to understand impacts of changing climate, sea level, and land use","interactions":[],"lastModifiedDate":"2017-05-08T16:14:58","indexId":"70154745","displayToPublicDate":"2015-07-16T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Coastal and wetland ecosystems of the Chesapeake Bay watershed: Applying palynology to understand impacts of changing climate, sea level, and land use","docAbstract":"
The mid-Atlantic region and Chesapeake Bay watershed have been influenced by fluctuations in climate and sea level since the Cretaceous, and human alteration of the landscape began ~12,000 years ago, with greatest impacts since colonial times. Efforts to devise sustainable management strategies that maximize ecosystem services are integrating data from a range of scientific disciplines to understand how ecosystems and habitats respond to different climatic and environmental stressors. Palynology has played an important role in improving understanding of the impact of changing climate, sea level, and land use on local and regional vegetation. Additionally, palynological analyses have provided biostratigraphic control for surficial mapping efforts and documented agricultural activities of both Native American populations and European colonists. This field trip focuses on sites where palynological analyses have supported efforts to understand the impacts of changing climate and land use on the Chesapeake Bay ecosystem.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","usgsCitation":"Willard, D.A., Bernhardt, C.E., Hupp, C.R., and Newell, W.L., 2015, Coastal and wetland ecosystems of the Chesapeake Bay watershed: Applying palynology to understand impacts of changing climate, sea level, and land use, v. 40, p. 281-308.","productDescription":"28 p.","startPage":"281","endPage":"308","ipdsId":"IP-066190","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"links":[{"id":340966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.79443359375,\n 36.90597988519294\n ],\n [\n 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Center","active":true,"usgs":true}],"preferred":true,"id":694549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":694550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newell, Wayne L. wnewell@usgs.gov","contributorId":2512,"corporation":false,"usgs":true,"family":"Newell","given":"Wayne","email":"wnewell@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":false,"id":694551,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70148395,"text":"70148395 - 2015 - Long-term monitoring of sandbars on the Colorado River in Grand Canyon using remote sensing","interactions":[],"lastModifiedDate":"2018-04-23T13:08:16","indexId":"70148395","displayToPublicDate":"2015-07-16T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Long-term monitoring of sandbars on the Colorado River in Grand Canyon using remote sensing","docAbstract":"Closure of Glen Canyon Dam in 1963 dramatically changed discharge and sediment supply to the downstream Colorado River in Marble and Grand Canyons. Magnitudes of seasonal flow variation have been suppressed, while daily fluctuations have increased because of hydropower generation. Lake Powell, the upstream reservoir, traps all sediment, leaving the Paria and Little Colorado Rivers as the main suppliers of fine sediment to the system below Glen Canyon Dam. The reduction in sediment supply, along with changes in discharge, have resulted in finesediment deficit (Topping et al., 2000), leading to a decrease in the size and number of alluvial sandbars (Schmidt and Graf, 1990; Schmidt et al., 2004). However, the understanding of these important spatial and temporal changes in sandbars located along the banks of the river have been limited to infrequent measurements mostly made by direct visitation and topographic surveying (Hazel et al., 2010).
Aerial photographs are the only data available from which it is possible to evaluate changes in alluvial deposits at a large number of sites and compare recent conditions with those that existed prior to the initiation of ground-based monitoring in the early 1990s. Previous studies have evaluated the effects of Glen Canyon Dam on sandbars by analysis of comprehensive maps of surficial geology that are based on seven sets of aerial imagery taken between 1935 and 1996 for selected reaches in the first 120 km downstream from Lees Ferry, Arizona (Figure 1). These studies showed that the area of exposed sand in eddy-deposition zones was less in the post-dam period than in the pre-dam period (Leschin and Schmidt, 1995; Schmidt et al., 1999b; Sondossi, 2001, Sondossi and Schmidt, 2001, Schmidt et al., 2004).
In this study, we extend these analyses to encompass a 74-year period by including maps of sand deposits visible in aerial imagery taken in 2002, 2005, and 2009 for the same reaches that were mapped in the earlier studies. Results are analyzed for two post-dam periods, based on the implementation of the first controlled flood in March 1996. The period from 1965 to March 1996 is the pre-controlled flood period and was dominated by flows that fluctuated up to the maximum capacity of the Glen Canyon Dam powerplant. Beginning in 1991, fluctuations were constrained such that maximum daily flows were typically less than 65 percent of powerplant capacity. Thus, the pre-controlled flood period also includes five years of restricted dam operations. This period also included unplanned spills from the reservoir in 1983, 1984, and 1986. We refer to the period from April 1996 to 2009 as the controlled-flood period. This period consisted entirely of restricted dam operations and included three controlled floods conducted as sandbar-building experiments. We show that the areal extent of exposed sand was greater in the images taken in the controlled-flood period than in the pre-controlled flood period. We also show that in the controlled-flood period, the area of exposed sand is negatively correlated with the elapsed time since the most recent controlled flood.
","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Ross, R.P., and Grams, P.E., 2015, Long-term monitoring of sandbars on the Colorado River in Grand Canyon using remote sensing, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, p. 86-96.","productDescription":"11 p.","startPage":"86","endPage":"96","ipdsId":"IP-061877","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353655,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2015/openconf/modules/request.php?module=oc_program&action=summary.php&id=212"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.57302856445312,\n 36.87193076711956\n ],\n [\n -111.60049438476561,\n 36.876325055012316\n ],\n [\n -111.62384033203124,\n 36.855449936136495\n ],\n [\n -111.65267944335938,\n 36.832370801556834\n ],\n [\n -111.6815185546875,\n 36.798288873837045\n ],\n [\n -111.697998046875,\n 36.760891249565624\n ],\n [\n -111.73507690429688,\n 36.731180139463724\n ],\n [\n -111.7584228515625,\n 36.6739263393281\n ],\n [\n -111.796875,\n 36.63316209558658\n ],\n [\n -111.79962158203125,\n 36.59678659300208\n ],\n [\n -111.83670043945312,\n 36.57032090532844\n ],\n [\n -111.87103271484375,\n 36.527294814546245\n ],\n [\n -111.8902587890625,\n 36.50853235745396\n ],\n [\n -111.88201904296875,\n 36.48314061639213\n ],\n [\n -111.88888549804686,\n 36.45000844447082\n ],\n [\n -111.92047119140624,\n 36.42680750026265\n ],\n [\n -111.92459106445312,\n 36.38591277287651\n ],\n [\n -111.91085815429688,\n 36.35052700542763\n ],\n [\n -111.90536499023438,\n 36.30848550829368\n ],\n [\n -111.87789916992188,\n 36.26309950975007\n ],\n [\n -111.84768676757812,\n 36.22211876039103\n ],\n [\n -111.84356689453125,\n 36.18000806322456\n ],\n [\n -111.851806640625,\n 36.13232917178139\n ],\n [\n -111.86965942382812,\n 36.10570509327921\n ],\n [\n -111.90261840820312,\n 36.0857311106239\n ],\n [\n -111.93832397460938,\n 36.06686213257888\n ],\n [\n -111.88339233398438,\n 36.045767917668705\n ],\n [\n -111.80511474609375,\n 36.113471382052175\n ],\n [\n -111.77902221679686,\n 36.16892253622743\n ],\n [\n -111.77215576171874,\n 36.20439070158873\n ],\n [\n -111.79550170898436,\n 36.24648828212654\n ],\n [\n -111.81060791015624,\n 36.295204533693536\n ],\n [\n -111.8353271484375,\n 36.31733823488624\n ],\n [\n -111.8463134765625,\n 36.35605709240176\n ],\n [\n -111.82296752929688,\n 36.39144034515155\n ],\n [\n -111.8353271484375,\n 36.4113363510602\n ],\n [\n -111.82022094726562,\n 36.436751611390264\n ],\n [\n -111.82022094726562,\n 36.46657630040234\n ],\n [\n -111.82708740234374,\n 36.50190922765839\n ],\n [\n -111.78863525390625,\n 36.54384614538856\n ],\n [\n -111.7474365234375,\n 36.58024660149866\n ],\n [\n -111.72821044921875,\n 36.61662990355664\n ],\n [\n -111.7144775390625,\n 36.64528344930188\n ],\n [\n -111.69387817382811,\n 36.679433365517774\n ],\n [\n -111.66915893554688,\n 36.71687068791304\n ],\n [\n -111.63345336914062,\n 36.75979104322286\n ],\n [\n -111.60186767578125,\n 36.796089518731506\n ],\n [\n -111.57989501953125,\n 36.82907321372808\n ],\n [\n -111.5606689453125,\n 36.84226271217676\n ],\n [\n -111.57302856445312,\n 36.87193076711956\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59366dabe4b0f6c2d0d7d634","contributors":{"authors":[{"text":"Ross, Robert P. rross@usgs.gov","contributorId":4734,"corporation":false,"usgs":true,"family":"Ross","given":"Robert","email":"rross@usgs.gov","middleInitial":"P.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547980,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148394,"text":"70148394 - 2015 - Morphodynamic data assimilation used to understand changing coasts","interactions":[],"lastModifiedDate":"2017-06-05T11:23:54","indexId":"70148394","displayToPublicDate":"2015-07-16T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Morphodynamic data assimilation used to understand changing coasts","docAbstract":"Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.
","conferenceTitle":"Coastal Sediments 2015","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, CA","language":"English","publisher":"World Scientific Publishing Company","doi":"10.1142/9789814689977_0244","usgsCitation":"Plant, N.G., and Long, J.W., 2015, Morphodynamic data assimilation used to understand changing coasts, Coastal Sediments 2015, San Diego, CA, May 11-15, 2015, https://doi.org/10.1142/9789814689977_0244.","ipdsId":"IP-063044","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-15","publicationStatus":"PW","scienceBaseUri":"59366dabe4b0f6c2d0d7d636","contributors":{"authors":[{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":547977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547978,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188564,"text":"70188564 - 2015 - Can low-resolution airborne laser scanning data be used to model stream rating curves?","interactions":[],"lastModifiedDate":"2017-06-15T13:23:12","indexId":"70188564","displayToPublicDate":"2015-07-16T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Can low-resolution airborne laser scanning data be used to model stream rating curves?","docAbstract":"This pilot study explores the potential of using low-resolution (0.2 points/m2) airborne laser scanning (ALS)-derived elevation data to model stream rating curves. Rating curves, which allow the functional translation of stream water depth into discharge, making them integral to water resource monitoring efforts, were modeled using a physics-based approach that captures basic geometric measurements to establish flow resistance due to implicit channel roughness. We tested synthetically thinned high-resolution (more than 2 points/m2) ALS data as a proxy for low-resolution data at a point density equivalent to that obtained within most national-scale ALS strategies. Our results show that the errors incurred due to the effect of low-resolution versus high-resolution ALS data were less than those due to flow measurement and empirical rating curve fitting uncertainties. As such, although there likely are scale and technical limitations to consider, it is theoretically possible to generate rating curves in a river network from ALS data of the resolution anticipated within national-scale ALS schemes (at least for rivers with relatively simple geometries). This is promising, since generating rating curves from ALS scans would greatly enhance our ability to monitor streamflow by simplifying the overall effort required.
","language":"English","publisher":"MDPI","doi":"10.3390/w7041324","usgsCitation":"Lyon, S., Nathanson, M., Lam, N., Dahlke, H., Rutzinger, M., Kean, J.W., and Laudon, H., 2015, Can low-resolution airborne laser scanning data be used to model stream rating curves?: Water, v. 7, no. 4, p. 1324-1339, https://doi.org/10.3390/w7041324.","productDescription":"16 p.","startPage":"1324","endPage":"1339","ipdsId":"IP-063479","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w7041324","text":"Publisher Index Page"},{"id":342554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Sweden","otherGeospatial":"Krycklan catchment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 19.566650390625,\n 63.83340220990062\n ],\n [\n 20.6927490234375,\n 63.83340220990062\n ],\n [\n 20.6927490234375,\n 64.36724945936612\n ],\n [\n 19.566650390625,\n 64.36724945936612\n ],\n [\n 19.566650390625,\n 63.83340220990062\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-24","publicationStatus":"PW","scienceBaseUri":"59439c95e4b062508e31a9ce","contributors":{"authors":[{"text":"Lyon, Steve","contributorId":192971,"corporation":false,"usgs":false,"family":"Lyon","given":"Steve","affiliations":[],"preferred":false,"id":698353,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nathanson, Marcus","contributorId":192972,"corporation":false,"usgs":false,"family":"Nathanson","given":"Marcus","email":"","affiliations":[],"preferred":false,"id":698354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lam, Norris","contributorId":192973,"corporation":false,"usgs":false,"family":"Lam","given":"Norris","email":"","affiliations":[],"preferred":false,"id":698355,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahlke, Helen","contributorId":192974,"corporation":false,"usgs":false,"family":"Dahlke","given":"Helen","email":"","affiliations":[],"preferred":false,"id":698356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rutzinger, Martin","contributorId":192975,"corporation":false,"usgs":false,"family":"Rutzinger","given":"Martin","email":"","affiliations":[],"preferred":false,"id":698357,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698358,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Laudon, Hjalmar","contributorId":192976,"corporation":false,"usgs":false,"family":"Laudon","given":"Hjalmar","email":"","affiliations":[],"preferred":false,"id":698359,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70154885,"text":"70154885 - 2015 - Occupancy and abundance of the endangered yellowcheek darter in Arkansas","interactions":[],"lastModifiedDate":"2015-07-15T14:03:42","indexId":"70154885","displayToPublicDate":"2015-07-15T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1337,"text":"Copeia","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy and abundance of the endangered yellowcheek darter in Arkansas","docAbstract":"The Yellowcheek Darter (Etheostoma moorei) is a rare fish endemic to the Little Red River watershed in the Boston Mountains of northern Arkansas. Remaining populations of this species are geographically isolated and declining, and the species was listed in 2011 as federally endangered. Populations have declined, in part, due to intense seasonal stream drying and inundation of lower reaches by a reservoir. We used a kick seine sampling approach to examine distribution and abundance of Yellowcheek Darter populations in the Middle Fork and South Fork Little Red River. We used presence data to estimate occupancy rates and detection probability and examined relationships between Yellowcheek Darter density and environmental variables. The species was found at five Middle Fork and South Fork sites where it had previously been present in 2003–2004. Occupancy rates were >0.6 but with wide 95% CI, and where the darters occurred, densities were typical of other Ozark darters but highly variable. Detection probability and density were positively related to current velocity. Given that stream drying has become more extreme over the past 30 years and anthropogenic threats have increased, regular monitoring and active management may be required to reduce extinction risk of Yellowcheek Darter populations.
","language":"English","publisher":"American Society of Ichthyologists and Herpetologists","doi":"10.1643/CE-14-116","usgsCitation":"Magoulick, D.D., and Lynch, D.T., 2015, Occupancy and abundance of the endangered yellowcheek darter in Arkansas: Copeia, v. 103, no. 2, p. 433-439, https://doi.org/10.1643/CE-14-116.","productDescription":"7 p.","startPage":"433","endPage":"439","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056381","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Middle Fork and South Fork Little Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.7802276611328,\n 35.53166744135354\n ],\n [\n -92.7802276611328,\n 35.708607653285505\n ],\n [\n -92.22679138183592,\n 35.708607653285505\n ],\n [\n -92.22679138183592,\n 35.53166744135354\n ],\n [\n -92.7802276611328,\n 35.53166744135354\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"103","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55a77623e4b0183d66e45e6b","contributors":{"authors":[{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564312,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Dustin T.","contributorId":145645,"corporation":false,"usgs":false,"family":"Lynch","given":"Dustin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":564874,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}