We studied 18 sampling stations along a transect to investigate the similarity between live (rose Bengal stained) foraminiferal populations and dead assemblages, their small-scale spatial variations and the distribution of infaunal foraminifera in a salt marsh (Toms Creek marsh) at the upper end of the South Slough arm of the Coos Bay estuary, Oregon, USA. We aimed to test to what extent taphonomic processes, small-scale variability and infaunal distribution influence the accuracy of sea-level reconstructions based on intertidal foraminifera. Cluster analyses have shown that dead assemblages occur in distinct zones with respect to elevation, a prerequisite for using foraminifera as sea-level indicators. Our nonparametric multivariate analysis of variance showed that small-scale spatial variability has only a small influence on live (rose Bengal stained) populations and dead assemblages. The dissimilarity was higher, however, between live (rose Bengal stained) populations in the middle marsh. We observed early diagenetic dissolution of calcareous tests in the dead assemblages. If comparable post-depositional processes and similar minor spatial variability also characterize fossil assemblages, then dead assemblage are the best modern analogs for paleoenvironmental reconstructions. The Toms Creek tidal flat and low marsh vascular plant zones are dominated by Miliammina fusca, the middle marsh is dominated by Balticammina pseudomacrescens and Trochammina inflata, and the high marsh and upland–marsh transition zone are dominated by Trochamminita irregularis. Analysis of infaunal foraminifera showed that most living specimens are found in the surface sediments and the majority of live (rose Bengal stained) infaunal specimens are restricted to the upper 10 cm, but living individuals are found to depths of 50 cm. The dominant infaunal specimens are similar to those in the corresponding surface samples and no species have been found living solely infaunally. The total numbers of infaunal foraminifera are small compared to the total numbers of dead specimens in the surface samples. This suggests that surface samples adequately represent the modern intertidal environment in Toms Creek.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marmicro.2015.04.004","usgsCitation":"Milker, Y., Horton, B.P., Nelson, A.R., Engelhart, S.E., and Witter, R., 2015, Variability of intertidal foraminferal assemblages in a salt marsh, Oregon, USA: Marine Micropaleontology, v. 118, p. 1-16, https://doi.org/10.1016/j.marmicro.2015.04.004.","productDescription":"16 p.","startPage":"1","endPage":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063845","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":472086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://durham-repository.worktribe.com/output/1285480","text":"Publisher Index Page"},{"id":300465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Toms Creek marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.32575225830078,\n 43.287577553946846\n ],\n [\n -124.32575225830078,\n 43.29320031385282\n ],\n [\n -124.3157958984375,\n 43.29320031385282\n ],\n [\n -124.3157958984375,\n 43.287577553946846\n ],\n [\n -124.32575225830078,\n 43.287577553946846\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"555aff21e4b0a92fa7eac5d2","chorus":{"doi":"10.1016/j.marmicro.2015.04.004","url":"http://dx.doi.org/10.1016/j.marmicro.2015.04.004","publisher":"Elsevier BV","authors":"Milker Yvonne, Horton Benjamin P., Nelson Alan R., Engelhart Simon E., Witter Robert C.","journalName":"Marine Micropaleontology","publicationDate":"6/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Milker, Yvonne","contributorId":121484,"corporation":false,"usgs":true,"family":"Milker","given":"Yvonne","affiliations":[],"preferred":false,"id":547040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, Benjamin P.","contributorId":63641,"corporation":false,"usgs":true,"family":"Horton","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":547041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Alan R. 0000-0001-7117-7098 anelson@usgs.gov","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":812,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","email":"anelson@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":547042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engelhart, Simon E.","contributorId":60104,"corporation":false,"usgs":false,"family":"Engelhart","given":"Simon","email":"","middleInitial":"E.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":547043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":547044,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70146518,"text":"fs20143123 - 2015 - Groundwater quality in the Cascade Range and Modoc Plateau, California","interactions":[],"lastModifiedDate":"2015-05-19T08:46:36","indexId":"fs20143123","displayToPublicDate":"2015-05-18T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3123","title":"Groundwater quality in the Cascade Range and Modoc Plateau, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. The Cascade Range and Modoc Plateau area constitutes one of the study units being evaluated.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143123","collaboration":"U.S. Geological Survey and the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Shelton, J.L., 2015, Groundwater quality in the Cascade Range and Modoc Plateau, California: U.S. Geological Survey Fact Sheet 2014-3123, 4 p., https://doi.org/10.3133/fs20143123.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033358","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":300445,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3123/"},{"id":300458,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3123/pdf/fs2014-3123.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":300459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143123.JPG"}],"country":"United States","state":"California","otherGeospatial":"Cascade Range, Modoc Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.58544921875,\n 42.00032514831621\n ],\n [\n -122.54150390625,\n 41.85319643776675\n ],\n [\n -122.67333984374999,\n 41.672911819602085\n ],\n [\n -122.2119140625,\n 41.22824901518532\n ],\n [\n -122.40966796874999,\n 41.0130657870063\n ],\n [\n -122.29980468749999,\n 40.76390128094589\n ],\n [\n -122.36572265625,\n 40.54720023441049\n ],\n [\n -122.2119140625,\n 40.26276066437183\n ],\n [\n -121.75048828124999,\n 39.67337039176558\n ],\n [\n -121.22314453124999,\n 40.04443758460859\n ],\n [\n -120.65185546875,\n 40.111688665595956\n ],\n [\n -120.25634765624999,\n 39.99395569397331\n ],\n [\n -120.0146484375,\n 39.707186656826565\n ],\n [\n -120.03662109374999,\n 41.983994270935625\n ],\n [\n -122.58544921875,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"555aff20e4b0a92fa7eac5c8","contributors":{"authors":[{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shelton, Jennifer L. 0000-0001-8508-0270 jshelton@usgs.gov","orcid":"https://orcid.org/0000-0001-8508-0270","contributorId":1155,"corporation":false,"usgs":true,"family":"Shelton","given":"Jennifer","email":"jshelton@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547015,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70138888,"text":"sir20145238 - 2015 - Status and understanding of groundwater quality in the Cascade Range and Modoc Plateau study unit, 2010: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2015-05-18T09:11:07","indexId":"sir20145238","displayToPublicDate":"2015-05-18T08:45:00","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":"2014-5238","title":"Status and understanding of groundwater quality in the Cascade Range and Modoc Plateau study unit, 2010: California GAMA Priority Basin Project","docAbstract":"
Groundwater quality in the Cascade Range and Modoc Plateau study unit was investigated as part of the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The study was designed to provide a statistically unbiased assessment of untreated groundwater quality in the primary aquifer system. The depth of the primary aquifer system for the Cascade Range and Modoc Plateau study unit was delineated by the depths of the screened or open intervals of wells in the State of California’s database of public-supply wells. Two types of assessments were made: a status assessment that described the current quality of the groundwater resource, and an understanding assessment that made evaluations of relations between groundwater quality and potential explanatory factors representing characteristics of the primary aquifer system. The assessments characterize the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors.
\nThe status assessment was based on water-quality data collected in 2010 by the U.S. Geological Survey from 90 wells and springs (USGS-grid wells) and on water-quality data compiled from the State of California’s regulatory compliance database for samples collected from 240 public-supply wells between September 2007 and September 2010. To provide context, the water-quality data discussed in this report were compared to California and Federal drinking-water regulatory and non-regulatory benchmarks for treated drinking water. Groundwater quality is defined in terms of relative concentrations (RCs), which are calculated by dividing the concentration of a constituent in groundwater by the concentration of the benchmark for that constituent. The RCs for inorganic constituents (major ions, trace elements, nutrients, and radioactive constituents) were classified as “high” (the RC is greater than 1.0, indicating that the concentration is above the benchmark), “moderate” (the RC is from 1.0 to greater than 0.5), or “low” (the RC is less than or equal to 0.5). For organic constituents (volatile organic compounds and pesticides) and special-interest constituents (perchlorate), the boundary between moderate and low RCs was set at 0.1. All benchmarks used for organic constituents were health-based. For inorganic constituents, health-based and aesthetic-based benchmarks were used. Constituents without benchmarks were not considered in the status assessment.
\nThe primary metric used for quantifying regional-scale groundwater quality was the aquifer-scale proportion—the areal percentages of the primary aquifer system with high, moderate, and low RCs for a given constituent or class of constituents. The study unit was divided into six study areas on the basis of geologic differences (Eastside Sacramento Valley, Honey Lake Valley groundwater basin, Cascade Range and Modoc Plateau Low Use Basins, Quaternary Volcanic Areas, Shasta Valley and Mount Shasta Volcanic Area, and Tertiary Volcanic Areas), and each study area was divided into equal-area grid cells. Aquifer-scale proportions were calculated for individual constituents and constituent classes for each of the six study areas and for the study unit as a whole by using grid-based (one well per cell) and spatially weighted (many wells per cell) statistical methods.
\nThe status assessment showed that inorganic constituents were present at high and moderate RCs in greater proportions of the Cascade Range and Modoc Plateau study unit than were organic constituents. One or more inorganic constituents with health-based benchmarks were present at high RCs in 9.4 percent, and at moderate RCs in 14.7 percent of the primary aquifer system. Arsenic was present at high RCs in approximately 3 percent of the primary aquifer system; boron, molybdenum, uranium, and vanadium each were present at high RCs in approximately 2 percent of the primary aquifer system. One or more inorganic constituents with aesthetic-based benchmarks were present at high RCs in 15.1 percent of the primary aquifer system and at moderate RCs in 4.9 percent. Manganese, iron, and total dissolved solids were present at high RCs in approximately 12 percent, 5 percent, and 2 percent, respectively, of the primary aquifer system.
\nOrganic constituents were not detected at high or moderate RCs in the primary aquifer system, and one or more organic constituents were detected at low RCs in approximately 40 percent of the primary aquifer system.
\nTwo classes of organic constituents were detected in more than 10 percent of the primary aquifer system: trihalomethanes (chloroform only) and herbicides. The special interest constituent perchlorate was not detected at high RCs, but was detected at moderate RCs in approximately 2 percent of the primary aquifer system.
\nThe understanding assessment relied on statistical tests to evaluate relations between concentrations of constituents and values of potential explanatory factors representing geology, land use, well construction, hydrologic conditions, groundwater age, and geochemical conditions.
\nThe majority of the high and moderate RCs of arsenic, boron, molybdenum, uranium, and total dissolved solids were in samples from the Honey Lake Valley groundwater basin study area. Groundwater mixing with hydrothermal fluids present in the study area, evaporative concentration of groundwater in the Honey Lake playa, presence of uranium-bearing sediment derived from the adjacent Sierra Nevada, and release of arsenic and other trace elements from sediments under high pH and low dissolved oxygen conditions all appeared to contribute to these elevated concentrations. Thermal springs are in many parts of the Cascade Range and Modoc Plateau study unit and could account for locally elevated concentrations of arsenic, boron, molybdenum, and total dissolved solids in samples from the other study areas. Vanadium concentrations were greater in oxic samples than in anoxic samples, but were not correlated with pH, contrary to expectations from previous studies.
\nOrganic constituents were not detected at high or moderate RCs, and the occurrence of low organic constituents at low RCs ranged from 27 percent to 73 percent of the primary aquifers system in the six study areas. The Shasta Valley and Mount Shasta Volcanic study area had significantly greater occurrence of low RCs of herbicides compared to all of the other study areas, which could reflect the greater prevalence of modern groundwater in the Shasta Valley and Mount Shasta Volcanic study area and the presence of potential sources of herbicides, including applications to timberlands and roadside rights-of-way. The Eastside Sacramento Valley study area had the greatest occurrence of low concentrations of chloroform, and chloroform occurrence was most strongly associated with the combination of septic-tank density greater than two tanks per square kilometer and urban land use greater than 10 percent within a radius of 500 meters of the well. These conditions were most prevalent in the Eastside Sacramento Valley study area. The detection frequency of low concentrations of perchlorate was consistent with the probability of occurrence expected under natural conditions, except in the Eastside Sacramento Valley study area, where detection frequencies were much higher than expected and could not be explained by known anthropogenic sources of perchlorate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145238","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Shelton, J.L., 2015, Status and understanding of groundwater quality in the Cascade Range and Modoc Plateau study unit, 2010: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2014-5238, xii, 131 p., https://doi.org/10.3133/sir20145238.","productDescription":"xii, 131 p.","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033356","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":300460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145238.jpg"},{"id":300457,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5238/pdf/sir2014-5238.pdf","text":"Report","size":"28.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":300444,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5238/"}],"projection":"Albers Equal Area Projection","datum":"North American Datum of 1983","country":"United States","state":"California","otherGeospatial":"Cascade Range, Modoc Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.99954223632812,\n 41.99522219923445\n ],\n [\n -122.57720947265624,\n 42.00695837037897\n ],\n [\n -122.50236511230467,\n 41.8322234439287\n ],\n [\n -122.66716003417969,\n 41.75543440328294\n ],\n [\n -122.66166687011719,\n 41.679066225164114\n ],\n [\n -122.55935668945312,\n 41.61492897332632\n ],\n [\n -122.60261535644531,\n 41.53839396783225\n ],\n [\n -122.57377624511719,\n 41.484405435611926\n ],\n [\n -122.48382568359374,\n 41.448902743309674\n ],\n [\n -122.45361328124999,\n 41.32732632036622\n ],\n [\n -122.200927734375,\n 41.244772343082076\n ],\n [\n -122.09381103515624,\n 41.20552261955812\n ],\n [\n -121.80816650390625,\n 41.18278832811288\n ],\n [\n -121.805419921875,\n 41.135227480564936\n ],\n [\n -121.92352294921874,\n 41.11660732012894\n ],\n [\n -122.01141357421875,\n 40.94671366508002\n ],\n [\n -121.9757080078125,\n 40.863679665481676\n ],\n [\n -122.11303710937499,\n 40.72228267283148\n ],\n [\n -122.2174072265625,\n 40.697299008636755\n ],\n [\n -122.12677001953124,\n 40.214538129296336\n ],\n [\n -121.68182373046875,\n 39.67125632523974\n ],\n [\n -121.56921386718751,\n 39.69450749856091\n ],\n [\n -121.0748291015625,\n 40.17467622056341\n ],\n [\n -121.01303100585938,\n 40.287906612507406\n ],\n [\n -120.97183227539061,\n 40.29209669470104\n ],\n [\n -120.904541015625,\n 40.27428705136608\n ],\n [\n -120.96908569335938,\n 40.395718433470364\n ],\n [\n -120.948486328125,\n 40.42499671108253\n ],\n [\n -120.68206787109375,\n 40.408267826445226\n ],\n [\n -120.36621093749999,\n 40.14633904771964\n ],\n [\n -120.24261474609374,\n 40.10538669840983\n ],\n [\n -120.00091552734375,\n 39.902362098539705\n ],\n [\n -119.99954223632812,\n 41.99522219923445\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"555aff21e4b0a92fa7eac5d0","contributors":{"authors":[{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shelton, Jennifer L. 0000-0001-8508-0270 jshelton@usgs.gov","orcid":"https://orcid.org/0000-0001-8508-0270","contributorId":1155,"corporation":false,"usgs":true,"family":"Shelton","given":"Jennifer","email":"jshelton@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173594,"text":"70173594 - 2015 - An evaluation of the efficiency of minnow traps for estimating the abundance of minnows in desert spring systems","interactions":[],"lastModifiedDate":"2016-06-09T17:02:18","indexId":"70173594","displayToPublicDate":"2015-05-18T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of the efficiency of minnow traps for estimating the abundance of minnows in desert spring systems","docAbstract":"Desert springs are sensitive aquatic ecosystems that pose unique challenges to natural resource managers and researchers. Among the most important of these is the need to accurately quantify population parameters for resident fish, particularly when the species are of special conservation concern. We evaluated the efficiency of baited minnow traps for estimating the abundance of two at-risk species, Foskett Speckled Dace Rhinichthys osculus ssp. and Borax Lake Chub Gila boraxobius, in desert spring systems in southeastern Oregon. We evaluated alternative sample designs using simulation and found that capture–recapture designs with four capture occasions would maximize the accuracy of estimates and minimize fish handling. We implemented the design and estimated capture and recapture probabilities using the Huggins closed-capture estimator. Trap capture probabilities averaged 23% and 26% for Foskett Speckled Dace and Borax Lake Chub, respectively, but differed substantially among sample locations, through time, and nonlinearly with fish body size. Recapture probabilities for Foskett Speckled Dace were, on average, 1.6 times greater than (first) capture probabilities, suggesting “trap-happy” behavior. Comparison of population estimates from the Huggins model with the commonly used Lincoln–Petersen estimator indicated that the latter underestimated Foskett Speckled Dace and Borax Lake Chub population size by 48% and by 20%, respectively. These biases were due to variability in capture and recapture probabilities. Simulation of fish monitoring that included the range of capture and recapture probabilities observed indicated that variability in capture and recapture probabilities in time negatively affected the ability to detect annual decreases by up to 20% in fish population size. Failure to account for variability in capture and recapture probabilities can lead to poor quality data and study inferences. Therefore, we recommend that fishery researchers and managers employ sample designs and estimators that can account for this variability.
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","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2015","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2015","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, CA","language":"English","publisher":"World Scientific","doi":"10.1142/9789814689977_0221","usgsCitation":"van Ormondt, M., Hapke, C., Roelvink, D., and Nelson, T., 2015, The effects of geomorphic changes during Hurricane Sandy on water levels in Great South Bay, in The proceedings of the coastal sediments 2015, San Diego, CA, May 11-15, 2015, 14 p., https://doi.org/10.1142/9789814689977_0221.","productDescription":"14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062930","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":311667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Great South Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.27880859375,\n 40.686886382151116\n ],\n [\n -73.26026916503906,\n 40.65511782196881\n ],\n [\n -73.2293701171875,\n 40.66136857063696\n ],\n [\n -73.22113037109375,\n 40.64886648762003\n ],\n [\n -73.26026916503906,\n 40.635319920747456\n ],\n [\n -73.25889587402344,\n 40.629587853312174\n ],\n [\n -73.19503784179688,\n 40.64469860601901\n ],\n [\n -73.13323974609374,\n 40.65720146993478\n ],\n [\n -73.004150390625,\n 40.687407052121316\n ],\n [\n -72.95951843261719,\n 40.71031250340588\n ],\n [\n -72.91213989257812,\n 40.73112880602221\n ],\n [\n -72.88742065429688,\n 40.749337730454826\n ],\n [\n -72.8887939453125,\n 40.760260692426186\n ],\n [\n -72.91900634765625,\n 40.758700379161034\n ],\n [\n -72.93960571289062,\n 40.745696344339564\n ],\n [\n -73.00552368164062,\n 40.747777160820704\n ],\n [\n -73.03298950195312,\n 40.734770989672406\n ],\n [\n -73.07899475097655,\n 40.72124187397379\n ],\n [\n -73.11813354492186,\n 40.72332345541449\n ],\n [\n -73.13461303710938,\n 40.7202010588415\n ],\n [\n -73.14216613769531,\n 40.703545803451426\n ],\n [\n -73.15658569335936,\n 40.69521661351717\n ],\n [\n -73.20121765136719,\n 40.70823051511181\n ],\n [\n -73.23280334472656,\n 40.70927151739564\n ],\n [\n -73.23348999023438,\n 40.714476284709335\n ],\n [\n -73.25752258300781,\n 40.697299008636755\n ],\n [\n -73.2733154296875,\n 40.68844837985795\n ],\n [\n -73.27880859375,\n 40.686886382151116\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-15","publicationStatus":"PW","scienceBaseUri":"565446c6e4b071e7ea53d4db","contributors":{"authors":[{"text":"van Ormondt, Maarten","contributorId":50181,"corporation":false,"usgs":true,"family":"van Ormondt","given":"Maarten","affiliations":[],"preferred":false,"id":543546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hapke, Cheryl 0000-0002-2753-4075 chapke@usgs.gov","orcid":"https://orcid.org/0000-0002-2753-4075","contributorId":139949,"corporation":false,"usgs":true,"family":"Hapke","given":"Cheryl","email":"chapke@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":543545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roelvink, Dano","contributorId":139950,"corporation":false,"usgs":false,"family":"Roelvink","given":"Dano","email":"","affiliations":[{"id":13328,"text":"UNESCO-IHE","active":true,"usgs":false}],"preferred":false,"id":543547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Timothy R. 0000-0002-5005-7617 trnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-5005-7617","contributorId":5814,"corporation":false,"usgs":true,"family":"Nelson","given":"Timothy R.","email":"trnelson@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":543548,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70145272,"text":"70145272 - 2015 - Quantifying the geomorphic resiliency of barrier island beaches","interactions":[],"lastModifiedDate":"2022-12-22T15:10:36.651377","indexId":"70145272","displayToPublicDate":"2015-05-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Quantifying the geomorphic resiliency of barrier island beaches","docAbstract":"Hurricane Sandy had an extensive impact on the beaches along the Atlantic coast. To quantify beach recovery, and examine alongshore variations in coastal resiliency, we develop a morphometric within the upper portion of the beach that is based on observed historical storm response at Fire Island, NY. The beach change envelope (BCE) boundaries are elevation contours which capture the portion of the upper beach that experiences erosion during moderate events but is above the influence of tides and lesser events. The data include ten profile sites that were surveyed seventeen times from October 2012 to October 2014. The time series indicate that there is a temporal trend towards widening and increasing elevation of the BCE that may represent a recovery state of the beach. Rates of recovery are generally higher in undeveloped locations, and areas where dunes did not overwash tend to favor more rapid recovery of the upper beach.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2015","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2015","conferenceDate":"May 11-15, 2015","conferenceLocation":"San Diego, California","language":"English","doi":"10.1142/9789814689977_0249","usgsCitation":"Hapke, C.J., Brenner, O.T., and Henderson, R., 2015, Quantifying the geomorphic resiliency of barrier island beaches, in The proceedings of the coastal sediments 2015, San Diego, California, May 11-15, 2015, 11 p., https://doi.org/10.1142/9789814689977_0249.","productDescription":"11 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062698","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":311100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.75833129882812,\n 40.771181859756496\n ],\n [\n -72.8558349609375,\n 40.74205475883487\n ],\n [\n -72.90664672851562,\n 40.733730386116875\n ],\n [\n -72.94921875,\n 40.71603763556807\n ],\n [\n -73.0316162109375,\n 40.6827208759455\n ],\n [\n -73.14010620117188,\n 40.65772237175813\n ],\n [\n -73.23898315429688,\n 40.63688312646408\n ],\n [\n -73.29116821289062,\n 40.6306300839918\n ],\n [\n -73.311767578125,\n 40.62854560636587\n ],\n [\n -73.32275390625,\n 40.62541876792774\n ],\n [\n -73.29803466796875,\n 40.61812224225511\n ],\n [\n -73.20465087890625,\n 40.6306300839918\n ],\n [\n -73.04672241210938,\n 40.6639728763869\n ],\n [\n -72.94784545898438,\n 40.69521661351717\n ],\n [\n -72.84072875976562,\n 40.733730386116875\n ],\n [\n -72.75146484374999,\n 40.76078078870895\n ],\n [\n -72.75833129882812,\n 40.771181859756496\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-15","publicationStatus":"PW","scienceBaseUri":"563ddd43e4b0831b7d6271f5","contributors":{"authors":[{"text":"Hapke, Cheryl J. 0000-0002-2753-4075 chapke@usgs.gov","orcid":"https://orcid.org/0000-0002-2753-4075","contributorId":2981,"corporation":false,"usgs":true,"family":"Hapke","given":"Cheryl","email":"chapke@usgs.gov","middleInitial":"J.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":true,"id":544132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenner, Owen T. 0000-0002-1588-721X obrenner@usgs.gov","orcid":"https://orcid.org/0000-0002-1588-721X","contributorId":4933,"corporation":false,"usgs":true,"family":"Brenner","given":"Owen","email":"obrenner@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Rachel E. 0000-0001-5810-7941 rhehre@usgs.gov","orcid":"https://orcid.org/0000-0001-5810-7941","contributorId":4934,"corporation":false,"usgs":true,"family":"Henderson","given":"Rachel E.","email":"rhehre@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":544134,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70143988,"text":"70143988 - 2015 - Enhancing evaluation of post-storm morphologic response using aerial orthoimagery from Hurricane Sandy","interactions":[],"lastModifiedDate":"2015-11-23T15:53:03","indexId":"70143988","displayToPublicDate":"2015-05-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Enhancing evaluation of post-storm morphologic response using aerial orthoimagery from Hurricane Sandy","docAbstract":"Improved identification of morphological responses to storms is necessary for developing and maintaining predictive models of coastal change. Morphological responses to Hurricane Sandy were measured using lidar and orthophotos taken before and after the storm. Changes to dune features measured from lidar were compared to the occurrence of overwash deposits measured using orthophotos. Thresholds on morphologic change (e.g. overwash volume and dune height change) were defined to optimize agreement between the classification of lidar and orthophoto-derived dune erosion and overwash. A linear regression showed that overwash volume can be calculated from orthophoto-derived overwash extent.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"The Proceedings of the Coastal Sediments 2015","conferenceTitle":"Coastal Sediments 2015","conferenceDate":"11 – 15 May 2015","conferenceLocation":"San Diego, USA","language":"English","publisher":"World Scientific Publication Company","usgsCitation":"Smith, J.R., Long, J.W., Stockdon, H.F., and Birchler, J.J., 2015, Enhancing evaluation of post-storm morphologic response using aerial orthoimagery from Hurricane Sandy, in The Proceedings of the Coastal Sediments 2015, San Diego, USA, 11 – 15 May 2015, 14 p.","productDescription":"14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062659","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":311668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Fire Island and New Jersey shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.75901794433592,\n 40.76806170936614\n ],\n [\n -72.82768249511719,\n 40.7519385984599\n ],\n [\n -72.93205261230469,\n 40.72280306615735\n ],\n [\n -73.0645751953125,\n 40.67334753512089\n ],\n [\n -73.26164245605469,\n 40.63010897068533\n ],\n [\n -73.3124542236328,\n 40.63115119323159\n ],\n [\n -73.32275390625,\n 40.61968585056766\n ],\n [\n -73.26644897460938,\n 40.613952441166596\n ],\n [\n -73.14628601074219,\n 40.639488387988365\n ],\n [\n -73.0096435546875,\n 40.67282675532094\n ],\n [\n -72.91007995605469,\n 40.71083299030839\n ],\n [\n -72.75283813476562,\n 40.762341053140275\n ],\n [\n -72.75901794433592,\n 40.76806170936614\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0313720703125,\n 40.45948689837198\n ],\n [\n -73.97918701171875,\n 40.327701904195926\n ],\n [\n -74.07257080078125,\n 40.04654018618778\n ],\n [\n -74.15496826171874,\n 39.71775084250469\n ],\n [\n -74.31427001953125,\n 39.50827899034114\n ],\n [\n -74.57244873046875,\n 39.3130504637139\n ],\n [\n -74.7015380859375,\n 39.16839998800286\n ],\n [\n -74.86358642578125,\n 38.96368010198575\n ],\n [\n -74.94873046875,\n 38.965815660189016\n ],\n [\n -74.9761962890625,\n 38.92522904714054\n ],\n [\n -74.79766845703125,\n 38.94872939211093\n ],\n [\n -74.6136474609375,\n 39.1833042481843\n ],\n [\n -74.23187255859375,\n 39.52522954427751\n ],\n [\n -74.06707763671875,\n 39.78110197709871\n ],\n [\n -73.98193359375,\n 40.17467622056341\n ],\n [\n -73.93798828125,\n 40.44276659332215\n ],\n [\n -73.97918701171875,\n 40.490826256468054\n ],\n [\n -74.0313720703125,\n 40.50753459933616\n ],\n [\n -74.0313720703125,\n 40.47829226856015\n ],\n [\n -74.0313720703125,\n 40.45948689837198\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565446c2e4b071e7ea53d4bf","contributors":{"authors":[{"text":"Smith, Jacquelyn Rose jrsmith@usgs.gov","contributorId":5761,"corporation":false,"usgs":true,"family":"Smith","given":"Jacquelyn","email":"jrsmith@usgs.gov","middleInitial":"Rose","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":543235,"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":543236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":543237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birchler, Justin J. jbirchler@usgs.gov","contributorId":127854,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":543238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70146562,"text":"70146562 - 2015 - Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling","interactions":[],"lastModifiedDate":"2021-03-16T20:52:57.064468","indexId":"70146562","displayToPublicDate":"2015-05-15T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling","docAbstract":"Future changes in wind-wave climate have broad implications for coastal geomorphology and management. General circulation models (GCM) are now routinely used for assessing climatological parameters, but generally do not provide parameterizations of ocean wind-waves. To fill this information gap, a growing number of studies use GCM outputs to independently downscale wave conditions to global and regional levels. To consolidate these efforts and provide a robust picture of projected changes, we present strategies from the community-derived multi-model ensemble of wave climate projections (COWCLIP) and an overview of regional contributions. Results and strategies from one contributing regional study concerning changes along the eastern North Pacific coast are presented.
We studied the landscape distribution of endemic Hawaiian hoary bats (Lasiurus cinereus semotus) on the north Ko‘olau Mountains of O‘ahu, Hawai‘i, from May 2013 to May 2014, while simultaneously studying their behavior at wind turbines within the broader landscape. This research aimed to assess the risk that wind turbines pose to bats on the island and integrated a variety of methods, including acoustic monitoring, thermal videography, and fatality searches.Our findings indicate that hoary bats were acoustically cryptic and occurred sparsely in the region. Overall site occupancy rate was 55% during the 1-year period of acoustic monitoring at 23 sites, and there was only an 8% chance of acoustically detecting a bat on a given night if it was present. We detected bats less frequently in windward northern parts of the study area and at windy, lower-elevation sites with rough terrain. Bats were detected more frequently in leeward southern parts of the study area and at wind-sheltered, higher-elevation sites with flat ridgetops. Acoustic detections were consistently low from October through February and increased at most sites to peak in April through August. However, meteorological conditions were not found to be associated with the acoustic prevalence of bats on a night-to-night basis.
We observed more than three thousand events involving bats during six months of nightly video surveillance at four wind turbines. Video monitoring revealed several links to weather at the local scale, despite acoustic detections not clearly relating to weather in our broader landscape analysis. Video demonstrated bats occurring near turbines more often on nights with little rain, warmer temperatures, moderate wind speeds, low humidity, and the low but rising barometric pressures indicative of fair weather and improved foraging conditions. Video monitoring also demonstrated that the presence of bats near turbines strongly correlates with insect presence.
We detected bats on video rather infrequently, averaging only one to two passes per hour. Most detections were brief (median = 4.0 sec) and involved single bats (97%), with the amount of time during which bats were observed totaling to only 0.10% of the video analyzed (about 3.8 hours of 3,847 total hours). Bats frequently foraged in the airspace near turbines. These results differ from a recent similar study on the mainland (continental North America) and may indicate that Hawaiian hoary bats spend less time closely approaching wind turbines and show less interest in them than their more-migratory mainland conspecifics. We speculate that the Hawaiian hoary bats we observed were locally resident and frequenting high-quality habitat near familiar structures. In contrast, hoary bats observed at wind facilities on the mainland appear to approach and investigate unfamiliar landscape structures that they mistake for trees as they migrate long distances. Consequently, Hawaiian hoary bats may be less susceptible to fatality at wind turbines on a per-encounter basis than hoary bats in North America. Only one bat carcass was found at the four turbines searched daily for six months. The relatively high probability of finding carcasses provided strong assurance that few carcasses were likely missed—there was less than a 10% chance that total fatality at the four turbines monitored for half a year exceeded three bats.
As part of a plan to preserve bird habitat on Breton Island, the southernmost extent of the Chandeleur Islands and part of the Breton National Wildlife Refuge in Louisiana, the U.S. Fish and Wildlife Service plans to increase island elevation with sand supplied from offshore resources. Proposed sand extraction sites include areas offshore where the seafloor morphology suggests suitable quantities of sediment may be found. Two proposed locations east and south of the island, between 5.5–9 kilometers from the island in 3–6 meters of water, have been identified. Borrow pits are perturbations to shallow-water bathymetry and thus can affect the wave field in a variety of ways, including alterations in sediment transport and new erosional or accretional patterns along the beach. A scenario-based numerical modeling strategy was used to assess the effects of the proposed offshore borrow pits on the nearshore wave field. Effects were assessed over a range of wave conditions and were gaged by changes in significant wave height and wave direction inshore of the borrow sites, as well as by changes in the calculated longshore sediment transport rate. The change in magnitude of the calculated sediment transport rate with the addition of the two borrow pits was an order of magnitude less than the calculated baseline transport rate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151055","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Dalyander, P.S., Mickey, R.C., Long, J.W., and Flocks, James, 2017, Effects of proposed borrow pits on the nearshore wave climate and longshore sediment transport rate along Breton Island, Louisiana (ver. 2.0, August 2017): U.S. Geological Survey Open-File Report 2015–1055, 44 p., https://doi.org/10.3133/ofr20151055.","productDescription":"Report: vi, 44 p.; HTML Document; Downloads Directory; Data Release; Version History","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059343","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":345194,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1055/VersionHist.txt","size":"1.18 KB","linkFileType":{"id":2,"text":"txt"}},{"id":300407,"rank":1,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2015/1055/pdf/ofr20151055.pdf","text":"Report","size":"3.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":300406,"rank":2,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2015/1055/ofr2015-1055_abstract.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Report"},{"id":299991,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1055/index.html","text":"Index page"},{"id":300409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1055/coverthb2.jpg"},{"id":345198,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZC81B1","text":"USGS data release","description":"USGS data release","linkHelpText":"Wave Scenario Results of Proposed Sediment Borrow Pit 3 on the Nearshore Wave Climate of Breton Island, LA"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.23301696777342,\n 29.453350219723674\n ],\n [\n -89.23301696777342,\n 29.51013490234384\n ],\n [\n -89.10873413085938,\n 29.51013490234384\n ],\n [\n -89.10873413085938,\n 29.453350219723674\n ],\n [\n -89.23301696777342,\n 29.453350219723674\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: May 2015; Version 2.0: August 2017","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The effects of future oil and natural gas development in southwestern Wyoming on wildlife populations are topical to conservation of the sagebrush steppe ecosystem. To aid in understanding these potential effects, the U.S. Geological Survey developed an Energy Footprint simulation model that forecasts the amount and pattern of energy development under different assumptions of development rates and well-drilling methods. The simulated disturbance patterns produced by the footprint model are used to assess the potential effects on wildlife habitat and populations. A goal of this modeling effort is to use measures of energy production (number of simulated wells), well-pad and road-surface disturbance, and potential effects on wildlife to identify build-out designs that minimize the physical and ecological footprint of energy development for different levels of energy production and development costs.
","language":"English","publisher":"Wyoming Land Conservation Initiative","publisherLocation":"Rock Springs, WY","usgsCitation":"Garman, S.L., 2015, Forecasting and evaluating patterns of energy development in southwestern Wyoming: WLCI Fact Sheet 7, 2 p.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059845","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":300415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70148045.jpg"},{"id":300413,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/wlci/fs/7/"},{"id":300414,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wlci/fs/7/pdf/wlci7.pdf","text":"Report","size":"3.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.65674591064453,\n 41.04103806674338\n ],\n [\n -107.64232635498047,\n 41.05864414643029\n ],\n [\n -107.62859344482422,\n 41.100569059403675\n ],\n [\n -107.65640258789062,\n 41.13858889084487\n ],\n [\n -107.69622802734375,\n 41.236511201246216\n ],\n [\n -107.7703857421875,\n 41.35104125623227\n ],\n [\n -107.63031005859375,\n 41.62878802577303\n ],\n [\n -107.53898620605467,\n 41.65777973769656\n ],\n [\n -107.47650146484374,\n 41.65906225544112\n ],\n [\n -107.47718811035156,\n 41.74826273259059\n ],\n [\n -107.33779907226562,\n 41.748775021355044\n ],\n [\n -107.34054565429688,\n 41.65854925140818\n ],\n [\n -107.42088317871094,\n 41.52708581365462\n ],\n [\n -107.40165710449219,\n 41.03948436294272\n ],\n [\n -107.65674591064453,\n 41.04103806674338\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5555b932e4b0a92fa7e95124","contributors":{"authors":[{"text":"Garman, Steven L. 0000-0002-9032-9074 slgarman@usgs.gov","orcid":"https://orcid.org/0000-0002-9032-9074","contributorId":3741,"corporation":false,"usgs":true,"family":"Garman","given":"Steven","email":"slgarman@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":546952,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70144699,"text":"sir20155034 - 2015 - Reducing cross-sectional data using a genetic algorithm method and effects on cross-section geometry and steady-flow profiles","interactions":[],"lastModifiedDate":"2015-05-14T10:43:17","indexId":"sir20155034","displayToPublicDate":"2015-05-14T11:30:00","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":"2015-5034","title":"Reducing cross-sectional data using a genetic algorithm method and effects on cross-section geometry and steady-flow profiles","docAbstract":"Reduction of cross-sectional data using a genetic algorithm method, and the effects of data reduction on channel geometry and steady-flow profiles, were analyzed. Two reduction methods─standard and genetic algorithms─were used to reduce cross-sectional data from the Kootenai River in northern Idaho. Cross sections that are representative of meander, straight, braided, and canyon reaches were used to evalutate the reduction methods. Visual and hydraulic analyses were used to assess the methods. The genetic algorithm-reduced cross sections approximated the shape of the original cross sections better than the standard-reduced cross sections. A greater number of cross-sectional data points were needed for reduced cross sections in the straight reach, and even more in the braided reach, because a greater amount of data points are needed to adequately define cross sections that have greater topographic varability. For the genetic algorithm-reduction method, about 40 data points were needed to adequately define the shape of a reduced cross section in the braided reach compared to 10 to 20 data points in the meander and canyon reaches. The standard-reduction method needed about 70 data points for the braided reach and more than 30 points for the meander and canyon reaches. The genetic algorithm can effectively reduce data while staying within the threshold set by the maximum number of points to be included in the reduced dataset.
\nThe effects of reduced cross-sectional data points on steady-flow profiles were also determined. Thirty-five cross sections of the original steady-flow model of the Kootenai River were used. These two methods were tested for all cross sections with each cross section resolution reduced to 10, 20 and 30 data points, that is, six tests were completed for each of the thirty-five cross sections. Generally, differences from the original water-surface elevation were smaller as the number of data points in reduced cross sections increased, but this was not always the case, especially in the braided reach. Differences were smaller for reduced cross sections developed by the genetic algorithm method than the standard algorithm method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155034","usgsCitation":"Berenbrock, C.E., 2015, Reducing cross-sectional data using a genetic algorithm method and effects on cross-section geometry and steady-flow profiles: U.S. Geological Survey Scientific Investigations Report 2015-5034, iv, 16 p., https://doi.org/10.3133/sir20155034.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-040903","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":300405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155034.jpg"},{"id":300403,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5034/"},{"id":300404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5034/pdf/sir2015-5034.pdf","size":"639 KB","linkFileType":{"id":1,"text":"pdf"}}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5555b938e4b0a92fa7e9512e","contributors":{"authors":[{"text":"Berenbrock, Charles E. ceberenb@usgs.gov","contributorId":857,"corporation":false,"usgs":true,"family":"Berenbrock","given":"Charles","email":"ceberenb@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":543786,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70148053,"text":"70148053 - 2015 - Evapotranspiration trends over the eastern United States during the 20th century","interactions":[],"lastModifiedDate":"2019-09-04T14:35:57","indexId":"70148053","displayToPublicDate":"2015-05-14T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evapotranspiration trends over the eastern United States during the 20th century","docAbstract":"Most models evaluated by the Intergovernmental Panel for Climate change estimate projected increases in temperature and precipitation with rising atmospheric CO2 levels. Researchers have suggested that increases in CO2 and associated increases in temperature and precipitation may stimulate vegetation growth and increase evapotranspiration (ET), which acts as a cooling mechanism, and on a global scale, may slow the climate-warming trend. This hypothesis has been modeled under increased CO2 conditions with models of different vegetation-climate dynamics. The significance of this vegetation negative feedback, however, has varied between models. Here we conduct a century-scale observational analysis of the Eastern US water balance to determine historical evapotranspiration trends and whether vegetation greening has affected these trends. We show that precipitation has increased significantly over the twentieth century while runoff has not. We also show that ET has increased and vegetation growth is partially responsible.
","language":"English","publisher":"European Geophysical Society","publisherLocation":"Katlenburg-Lindau, Germany","doi":"10.3390/hydrology2020093","usgsCitation":"Kramer, R.J., Bounoua, L., Zhang, P., Wolfe, R.E., Huntington, T.G., Imhoff, M.L., Thome, K., and Noyce, G.L., 2015, Evapotranspiration trends over the eastern United States during the 20th century: Hydrology and Earth System Sciences, v. 2, no. 2, p. 93-111, https://doi.org/10.3390/hydrology2020093.","productDescription":"19 p.","startPage":"93","endPage":"111","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056810","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":472089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology2020093","text":"Publisher Index Page"},{"id":300464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-14","publicationStatus":"PW","scienceBaseUri":"555b0d43e4b0a92fa7eac61c","contributors":{"authors":[{"text":"Kramer, Ryan J.","contributorId":140788,"corporation":false,"usgs":false,"family":"Kramer","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":546977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bounoua, Lahouari","contributorId":140790,"corporation":false,"usgs":false,"family":"Bounoua","given":"Lahouari","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":546979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Ping","contributorId":140789,"corporation":false,"usgs":false,"family":"Zhang","given":"Ping","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":546978,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolfe, Robert E.","contributorId":56560,"corporation":false,"usgs":true,"family":"Wolfe","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":546980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":546976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imhoff, Marc L.","contributorId":140791,"corporation":false,"usgs":false,"family":"Imhoff","given":"Marc","email":"","middleInitial":"L.","affiliations":[{"id":13566,"text":"Joint Global Change Research Institute, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":546981,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thome, Kurt","contributorId":140792,"corporation":false,"usgs":false,"family":"Thome","given":"Kurt","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":546982,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Noyce, Genevieve L.","contributorId":140793,"corporation":false,"usgs":false,"family":"Noyce","given":"Genevieve","email":"","middleInitial":"L.","affiliations":[{"id":13567,"text":"Goddard Space Flight Center, 100 St. George Street, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":546983,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70147619,"text":"ds936 - 2015 - High-resolution digital elevation model of lower Cowlitz and Toutle Rivers, adjacent to Mount St. Helens, Washington, based on an airborne lidar survey of October 2007","interactions":[],"lastModifiedDate":"2015-05-14T09:38:52","indexId":"ds936","displayToPublicDate":"2015-05-14T09:15:00","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":"936","title":"High-resolution digital elevation model of lower Cowlitz and Toutle Rivers, adjacent to Mount St. Helens, Washington, based on an airborne lidar survey of October 2007","docAbstract":"The lateral blast, debris avalanche, and lahars of the May 18th, 1980, eruption of Mount St. Helens, Washington, dramatically altered the surrounding landscape. Lava domes were extruded during the subsequent eruptive periods of 1980–1986 and 2004–2008. More than three decades after the emplacement of the 1980 debris avalanche, high sediment production persists in the Toutle River basin, which drains the northern and western flanks of the volcano. Because this sediment increases the risk of flooding to downstream communities on the Toutle and lower Cowlitz Rivers, the U.S. Army Corps of Engineers (USACE), under the direction of Congress to maintain an authorized level of flood protection, continues to monitor and mitigate excess sediment in North and South Fork Toutle River basins to help reduce this risk and to prevent sediment from clogging the shipping channel of the Columbia River. From October 22–27, 2007, Watershed Sciences, Inc., under contract to USACE, collected high-precision airborne lidar (light detection and ranging) data that cover 273 square kilometers (105 square miles) of lower Cowlitz and Toutle River tributaries from the Columbia River at Kelso, Washington, to upper North Fork Toutle River (below the volcano's edifice), including lower South Fork Toutle River. These data provide a digital dataset of the ground surface, including beneath forest cover. Such remotely sensed data can be used to develop sediment budgets and models of sediment erosion, transport, and deposition. The U.S. Geological Survey (USGS) used these lidar data to develop digital elevation models (DEMs) of the study area. DEMs are fundamental to monitoring natural hazards and studying volcanic landforms, fluvial and glacial geomorphology, and surface geology. Watershed Sciences, Inc., provided files in the LASer (LAS) format containing laser returns that had been filtered, classified, and georeferenced. The USGS produced a hydro-flattened DEM from ground-classified points at Castle and Coldwater Lakes. Final results averaged about two laser last-return points per square meter. As reported by Watershed Sciences, Inc., vertical accuracy is 10 centimeters (cm) at the 95-percent confidence interval on bare road surfaces; however, over natural terrain, USGS found vertical accuracy to be 10–50 cm. This USGS data series contains the bare-earth lidar data as 1- and 10-meter (m) resolution Esri grid files. Digital-elevation data can be downloaded (1m_DEM.zip and 10m_DEM.zip), as well as a 1-m resolution hillshade image with pyramids (1m_hillshade.zip). These geospatial data files require geographic information system (GIS) software for viewing.
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2015 - The Holocene history of the North American Monsoon: 'known knowns' and 'known unknowns' in understanding its spatial and temporal complexity","interactions":[],"lastModifiedDate":"2015-05-19T07:47:08","indexId":"70148056","displayToPublicDate":"2015-05-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"The Holocene history of the North American Monsoon: 'known knowns' and 'known unknowns' in understanding its spatial and temporal complexity","docAbstract":"Evidence for climatic change across the North American Monsoon (NAM) and adjacent areas is reviewed, drawing on continental and marine records and the application of climate models. Patterns of change at 12,000, 9000, 6000 and 4000 cal yr BP are presented to capture the nature of change from the Younger Dryas (YD) and through the mid-Holocene. At the YD, conditions were cooler overall, wetter in the north and drier in the south, while moving into the Holocene wetter conditions became established in the south and then spread north as the NAM strengthened. Until c. 8000 cal yr BP, the Laurentide Ice Sheet influenced precipitation in the north by pushing the Bermuda High further south. The peak extent of the NAM seems to have occurred around 6000 cal yr BP. 4000 cal yr BP marks the start of important changes across the NAM region, with drying in the north and the establishment of the clear differences between the summer-rain dominated south and central areas and the north, where winter rain is more important. This differentiation between south and north is crucial to understanding many climate responses across the NAM. This increasing variability is coincident with the declining influence of orbital forcing. 4000 cal yr BP also marks the onset of significant anthropogenic activity in many areas. For the last 2000 years, the focus is on higher temporal resolution change, with strong variations across the region. The Medieval Climate Anomaly (MCA) is characterised by centennial scale ‘megadrought’ across the southwest USA, associated with cooler tropical Pacific SSTs and persistent La Niña type conditions. Proxy data from southern Mexico, Central America and the Caribbean reveal generally wetter conditions, whereas records from the highlands of central Mexico and much of the Yucatan are typified by long -term drought. The Little Ice Age (LIA), in the north, was characterised by cooler, wetter winter conditions that have been linked with increased frequency of El Niño's. Proxy records in the central and southern regions reveal generally dry LIA conditions, consistent with cooler SSTs in the Caribbean and Gulf of Mexico. This synthesis demonstrates that in some periods, one major forcing can dominate across the whole area (e.g. insolation in the early-mid Holocene), but at other times there is strong variability in patterns of change due to the differential impact of forcings such as the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) on precipitation seasonality.
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Here we describe habitat selection and intra-territory movements of 53 Piping Plover (Charadrius melodus) broods (320 observations) during the 2007–2008 breeding seasons on mainland- and island-shoreline habitats at Lake Sakakawea, North Dakota, USA. We used remotely sensed habitat characteristics to separately examine habitat selection and movements at two spatiotemporal scales to account for potential confounding effects of nest-site selection on brood-rearing habitat used. The scales used were (1) the entire brood-rearing period within available brood-rearing areas and (2) 2-day observation intervals within age-specific discrete habitat selection choice sets. Analyses at both scales indicated that broods selected areas which were non-vegetated, moderately level, and nearer to the shoreline. Rate of brood movement increased with age up to 5 days, then stabilized; broods that hatched >50 m away from the shoreline moved toward the shoreline. Brood movements were greater when they were in vegetated areas, when the brood-rearing area was of greater topographic complexity, and when broods aged 6–25 days were further away from the shoreline. Using inferences from our results and those of previously published work, we postulate how a potential tradeoff in habitat selection between nesting and brood-rearing can contribute to an ecological trap in a novel habitat. This work, in the context of published works, suggests that plover breeding habitat is a complex of both nesting and brood-rearing habitats and provides a basis for making remotely sensed abundance estimates of suitable breeding habitat for Piping Plovers.
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Understanding its response to rainfall variability requires consideration of multiple timescales. We examine intra-annual to multi-year relationships using model simulations of soil moisture and vegetation growth over 50 years in the Mojave National Preserve in southeastern California (USA). Ecohydrological model parameters are conditioned on field and remote sensing data using an ensemble Kalman filter. Although no specific periodicities were detected in the rainfall record, simulated leaf-area-index exhibits multi-year dynamics that are driven by multi-year (∼3-years) rains, but with up to a 1-year delay in peak response. Within a multi-year period, Larrea tridentata is more sensitive to winter rains than summer. In the most active part of the root zone (above ∼80 cm), >1-year average soil moisture drives vegetation growth, but monthly average soil moisture is controlled by root uptake. Moisture inputs reach the lower part of the root zone (below ∼80 cm) infrequently, but once there they can persist over a year to help sustain plant growth. Parameter estimates highlight efficient plant physiological properties facilitating persistent growth and high soil hydraulic conductivity allowing deep soil moisture stores. We show that soil moisture as an ecological indicator is complicated by bidirectional interactions with vegetation that depend on timescale and depth. Under changing climate, Larrea tridentata will likely be relatively resilient to shorter-term moisture variability but will exhibit higher sensitivity to shifts in seasonal to multi-year moisture inputs.
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Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Using biotic ligand models to predict metal toxicity in mineralized systems","docAbstract":"The biotic ligand model (BLM) is a numerical approach that couples chemical speciation calculations with toxicological information to predict the toxicity of aquatic metals. This approach was proposed as an alternative to expensive toxicological testing, and the U.S. Environmental Protection Agency incorporated the BLM into the 2007 revised aquatic life ambient freshwater quality criteria for Cu. Research BLMs for Ag, Ni, Pb, and Zn are also available, and many other BLMs are under development. Current BLMs are limited to ‘one metal, one organism’ considerations. Although the BLM generally is an improvement over previous approaches to determining water quality criteria, there are several challenges in implementing the BLM, particularly at mined and mineralized sites. These challenges include: (1) historically incomplete datasets for BLM input parameters, especially dissolved organic carbon (DOC), (2) several concerns about DOC, such as DOC fractionation in Fe- and Al-rich systems and differences in DOC quality that result in variations in metal-binding affinities, (3) water-quality parameters and resulting metal-toxicity predictions that are temporally and spatially dependent, (4) additional influences on metal bioavailability, such as multiple metal toxicity, dietary metal toxicity, and competition among organisms or metals, (5) potential importance of metal interactions with solid or gas phases and/or kinetically controlled reactions, and (6) tolerance to metal toxicity observed for aquatic organisms living in areas with elevated metal concentrations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2014.07.005","usgsCitation":"Smith, K.S., Balistrieri, L.S., and Todd, A.S., 2015, Using biotic ligand models to predict metal toxicity in mineralized systems: Applied Geochemistry, v. 57, p. 55-72, https://doi.org/10.1016/j.apgeochem.2014.07.005.","productDescription":"18 p.","startPage":"55","endPage":"72","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057252","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":472092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2014.07.005","text":"Publisher Index Page"},{"id":300370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"555467aae4b0a92fa7e94f1b","contributors":{"authors":[{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":546874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":546875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Todd, Andrew S. atodd@usgs.gov","contributorId":1022,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew","email":"atodd@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":546876,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70145247,"text":"ofr20151057 - 2015 - Field observations of artificial sand and oil agglomerates","interactions":[],"lastModifiedDate":"2015-05-12T11:41:56","indexId":"ofr20151057","displayToPublicDate":"2015-05-12T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1057","title":"Field observations of artificial sand and oil agglomerates","docAbstract":"Oil that comes into the surf zone following spills, such as occurred during the 2010 Deepwater Horizon (DWH) blowout, can mix with local sediment to form heavier-than-water sand and oil agglomerates (SOAs), at times in the form of mats a few centimeters thick and tens of meters long. Smaller agglomerates that form in situ or pieces that break off of larger mats, sometimes referred to as surface residual balls (SRBs), range in size from sand-sized grains to patty-shaped pieces several centimeters (cm) in diameter. These mobile SOAs can cause beach oiling for extended periods following the spill, on the scale of years as in the case of DWH. Limited research, including a prior effort by the U.S. Geological Survey (USGS) investigating SOA mobility, alongshore transport, and seafloor interaction using numerical model output, focused on the physical dynamics of SOAs. To address this data gap, we constructed artificial sand and oil agglomerates (aSOAs) with sand and paraffin wax to mimic the size and density of genuine SOAs. These aSOAs were deployed in the nearshore off the coast of St. Petersburg, Florida, during a field experiment to investigate their movement and seafloor interaction. This report presents the methodology for constructing aSOAs and describes the field experiment. Data acquired during the field campaign, including videos and images of aSOA movement in the nearshore (1.5-meter and 0.5-meter water depth) and in the swash zone, are also presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151057","usgsCitation":"Dalyander, P., Long, J.W., Plant, N.G., McLaughlin, M.R., and Mickey, R., 2015, Field observations of artificial sand and oil agglomerates: U.S. Geological Survey Open-File Report 2015-1057, HTML Document, https://doi.org/10.3133/ofr20151057.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059854","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":300347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151057.jpg"},{"id":300346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1057/ofr2015-1057_title-page.html","linkFileType":{"id":5,"text":"html"}},{"id":299400,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1057/"}],"country":"United States","state":"Florida","county":"Pinellas County","city":"St. Petersberg","otherGeospatial":"Fort De Soto Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.75074005126953,\n 27.604910228553223\n ],\n [\n -82.75074005126953,\n 27.633048834227715\n ],\n [\n -82.72069931030273,\n 27.633048834227715\n ],\n [\n -82.72069931030273,\n 27.604910228553223\n ],\n [\n -82.75074005126953,\n 27.604910228553223\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55531620e4b0a92fa7e94c43","contributors":{"authors":[{"text":"Dalyander, Patricia (Soupy) 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":5318,"corporation":false,"usgs":true,"family":"Dalyander","given":"Patricia (Soupy)","email":"sdalyander@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":544125,"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":544126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McLaughlin, Molly R. 0000-0001-6962-6392 mmclaughlin@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-6392","contributorId":4089,"corporation":false,"usgs":true,"family":"McLaughlin","given":"Molly","email":"mmclaughlin@usgs.gov","middleInitial":"R.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544128,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mickey, Rangley C. rmickey@usgs.gov","contributorId":5741,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley C.","email":"rmickey@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":544129,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70145564,"text":"sir20155048 - 2015 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, June 2014","interactions":[],"lastModifiedDate":"2015-05-12T10:31:47","indexId":"sir20155048","displayToPublicDate":"2015-05-12T11:30:00","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":"2015-5048","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, June 2014","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, in the vicinity of 8 bridges at 7 highway crossings of the Missouri and Mississippi Rivers on the periphery of Missouri from June 3 to 11, 2014. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,525 to 1,640 feet longitudinally, and extending laterally across the active channel from bank to bank during low- to moderate-flow conditions. These bathymetric surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low- to moderate-flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
\nBathymetric data were collected around every pier that was in water, except those at the edge of water or in very shallow water (less than about 6 feet). Scour holes were observed at most piers for which bathymetry could be obtained, except at piers on channel banks, on exposed bedrock outcrops, or surrounded by riprap. Scour holes observed at the surveyed bridges were examined with respect to depth and shape, and the effects of riprap blankets or other rock near the piers. The presence of riprap blankets, depth of fluvial material on top of a riprap blanket, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to non-existent) compared to piers at which no rock or riprap was present. Although exposure of parts of foundational support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in channel-bed material; however, there were several notable exceptions where the bed material thickness between the bottom of the scour hole and bedrock was less than 6 feet. Such substantial exposure of usually buried substructural elements may warrant special observation in future flood events, even when designed to be exposed.
\nPrevious bathymetric surveys had been done at both of the sites on the Missouri River and one of the sites on the Mississippi River examined in this study. Comparisons between bathymetric surfaces from the previous surveys during the 2011 flood and those of this study generally indicate that there was an increase in the elevation of the channel bed at these sites that likely was caused by a substantial decrease in discharge and water-surface elevation compared to the 2011 surveys. However, the scour holes observed at these sites were either the same size or larger in 2014 compared to the 2011 surveys, indicating that the flow condition is not the sole variable in the determination of the size of scour holes, and that local velocity and depth also are critical variables, as indicated by predictive pier scour equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155048","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2015, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, June 2014: U.S. Geological Survey Scientific Investigations Report 2015-5048, ix, 81 p., https://doi.org/10.3133/sir20155048.","productDescription":"ix, 81 p.","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-06-01","temporalEnd":"2014-06-30","ipdsId":"IP-062935","costCenters":[{"id":396,"text":"Missouri Water Science 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Letters","active":true,"publicationSubtype":{"id":10}},"title":"Temperature impacts on the water year 2014 drought in California","docAbstract":"California is experiencing one of the worst droughts on record. Here we use a hydrological model and risk assessment framework to understand the influence of temperature on the water year (WY) 2014 drought in California and examine the probability that this drought would have been less severe if temperatures resembled the historical climatology. Our results indicate that temperature played an important role in exacerbating the WY 2014 drought severity. We found that if WY 2014 temperatures resembled the 1916–2012 climatology, there would have been at least an 86% chance that winter snow water equivalent and spring-summer soil moisture and runoff deficits would have been less severe than the observed conditions. We also report that the temperature forecast skill in California for the important seasons of winter and spring is negligible, beyond a lead-time of one month, which we postulate might hinder skillful drought prediction in California.
","language":"English","publisher":"Wiley","doi":"10.1002/2015GL063666","usgsCitation":"Shukla, S., Safeeq, M., AghaKouchak, A., Guan, K., and Funk, C.C., 2015, Temperature impacts on the water year 2014 drought in California: Geophysical Research Letters, v. 42, no. 11, p. 4384-4393, https://doi.org/10.1002/2015GL063666.","productDescription":"10 p.","startPage":"4384","endPage":"4393","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2013-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-064133","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472096,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/6wk3p5x0","text":"External Repository"},{"id":300324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": 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0000-0003-0529-3925","orcid":"https://orcid.org/0000-0003-0529-3925","contributorId":77814,"corporation":false,"usgs":false,"family":"Safeeq","given":"Mohammad","email":"","affiliations":[{"id":6641,"text":"University of California at Merced","active":true,"usgs":false}],"preferred":false,"id":546722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"AghaKouchak, Amir","contributorId":140736,"corporation":false,"usgs":false,"family":"AghaKouchak","given":"Amir","email":"","affiliations":[{"id":13550,"text":"Civil & Environmental Engineering, University of California Irvine","active":true,"usgs":false}],"preferred":false,"id":546723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guan, Kaiyu","contributorId":140737,"corporation":false,"usgs":false,"family":"Guan","given":"Kaiyu","email":"","affiliations":[{"id":13551,"text":"Kaiyu Guan, Department of Environmental Earth System Science, Stanford University","active":true,"usgs":false}],"preferred":false,"id":546724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Christopher C. 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":721,"corporation":false,"usgs":true,"family":"Funk","given":"Christopher","email":"cfunk@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":546720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70145954,"text":"sir20155053 - 2015 - Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion hydrological modeling","interactions":[],"lastModifiedDate":"2015-05-12T09:30:22","indexId":"sir20155053","displayToPublicDate":"2015-05-12T10:30:00","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":"2015-5053","title":"Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion hydrological modeling","docAbstract":"The Lake Michigan Diversion Accounting (LMDA) system has been developed by the U.S. Army Corps of Engineers, Chicago District (USACE-Chicago) and the State of Illinois as a part of the interstate Great Lakes water regulatory program. The diverted Lake Michigan watershed is a 673-square-mile watershed that is comprised of the Chicago River and Calumet River watersheds. They originally drained into Lake Michigan, but now flow to the Mississippi River watershed via three canals constructed in the Chicago area in the early twentieth century. Approximately 393 square miles of the diverted watershed is ungaged, and the runoff from the ungaged portion of the diverted watershed has been estimated by the USACE-Chicago using the Hydrological Simulation Program-FORTRAN (HSPF) program. The accuracy of simulated runoff depends on the accuracy of the parameter set used in the HSPF program. Nine parameter sets comprised of the North Branch, Little Calumet, Des Plaines, Hickory Creek, CSSC, NIPC, 1999, CTE, and 2008 have been developed at different time periods and used by the USACE-Chicago. In this study, the U.S. Geological Survey and the USACE-Chicago collaboratively analyzed the parameter sets using nine gaged watersheds in or adjacent to the diverted watershed to assess the predictive accuracies of selected parameter sets. Six of the parameter sets, comprising North Branch, Hickory Creek, NIPC, 1999, CTE, and 2008, were applied to the nine gaged watersheds for evaluating their simulation accuracy from water years 1996 to 2011. The nine gaged watersheds were modeled by using the three LMDA land-cover types (grass, forest, and hydraulically connected imperviousness) based on the 2006 National Land Cover Database, and the latest meteorological and precipitation data consistent with the current (2014) LMDA modeling framework.
\nResults indicate that the North Branch and Hickory Creek parameter sets, which belong to the original calibration group, attained an overall “satisfactory” rating on monthly runoff volumes based on the three performance statistics selected, but the annual and over-the-period runoff volumes were generally underestimated. Parameter sets CTE and 2008 attained a similar satisfactory rating on monthly runoff volumes but the annual and over-the-period runoff volumes were overestimated in general. Although the percent bias was improved, the CTE and 2008 parameter sets also had increased residuals in monthly runoff volumes and decreased quality of the model fit to the measured streamflows relative to the North Branch and Hickory Creek parameter sets. The NIPC and 1999 parameter sets, on the other hand, had larger percent bias and residuals in monthly runoff volumes, and underestimated the annual and over-the-period runoff volumes.
\nRecalibration of the HSPF parameters to the updated inputs and land covers was completed on two representative watershed models selected from the nine by using a manual method (HSPEXP) and an automatic method (PEST). The objective of the recalibration was to develop a regional parameter set that improves the accuracy in runoff volume prediction for the nine study watersheds. Knowledge about flow and watershed characteristics plays a vital role for validating the calibration in both manual and automatic methods. The best performing parameter set was determined by the automatic calibration method on a two-watershed model. Applying this newly determined parameter set to the nine watersheds for runoff volume simulation resulted in “very good” ratings in five watersheds, an improvement as compared to “very good” ratings achieved for three watersheds by the North Branch parameter set.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155053","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Soong, D.T., and Over, T.M., 2015, Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion hydrological modeling: U.S. Geological Survey Scientific Investigations Report 2015-5053, vii, 55 p., https://doi.org/10.3133/sir20155053.","productDescription":"vii, 55 p.","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-044193","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":300319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155053.jpg"},{"id":300317,"rank":1,"type":{"id":15,"text":"Index 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genetic effects of a sterilant applied to wild horse mares","docAbstract":"Wild horse populations on western ranges can increase rapidly, resulting in the need for the Bureau of Land Management (BLM) to remove animals in order to protect the habitat that horses share with numerous other species. As an alternative to removals, BLM has sought to develop a long-term, perhaps even permanent, contraceptive to aid in reducing population growth rates. With long-term (perhaps even permanent) efficacy of contraception, however, comes increased concern about the genetic health of populations and about the potential for local extirpation. We used simulation modeling to examine the potential demographic and genetic consequences of applying a mare sterilant to wild horse populations. Using the VORTEX software package, we modeled the potential effects of a sterilant on 70 simulated populations having different initial sizes (7 values), growth rates (5 values), and genetic diversity (2 values). For each population, we varied the treatment rate of mares from 0 to 100 percent in increments of 10 percent. For each combination of these treatment levels, we ran 100 stochastic simulations, and we present the results in the form of tables and graphs showing mean population size after 20 years, mean number of removals after 20 years, mean probability of extirpation after 50 years, and mean heterozygosity after 50 years. By choosing one or two combinations of initial population size, population growth rate, and genetic diversity that best represent a herd of interest, a manager can assess the likely effects of a contraceptive program by examining the output tables and graphs representing the selected conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151045","collaboration":"Bureau of Land Management","usgsCitation":"Roelle, J.E., and Oyler-McCance, S.J., 2015, Potential demographic and genetic effects of a sterilant applied to wild horse mares: U.S. Geological Survey Open-File Report 2015-1045, 153 p., https://doi.org/10.3133/ofr20151045.","productDescription":"153 p.","numberOfPages":"159","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-058694","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":300306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151045.jpg"},{"id":300304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1045/"},{"id":300305,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1045/pdf/ofr2015-1045.pdf","text":"Report","size":"2.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5551c4aae4b0a92fa7e93b92","contributors":{"authors":[{"text":"Roelle, James E. roelleb@usgs.gov","contributorId":2330,"corporation":false,"usgs":true,"family":"Roelle","given":"James","email":"roelleb@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":543780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":543781,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70144853,"text":"ofr20151065 - 2015 - Results from laboratory and field testing of nitrate measuring spectrophotometers","interactions":[],"lastModifiedDate":"2015-05-12T13:25:42","indexId":"ofr20151065","displayToPublicDate":"2015-05-11T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1065","title":"Results from laboratory and field testing of nitrate measuring spectrophotometers","docAbstract":"Five ultraviolet (UV) spectrophotometer nitrate analyzers were evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) during a two-phase evaluation. In Phase I, the TriOS ProPs (10-millimeter (mm) path length), Hach NITRATAX plus sc (5-mm path length), Satlantic Submersible UV Nitrate Analyzer (SUNA, 10-mm path length), and S::CAN Spectro::lyser (5-mm path length) were evaluated in the HIF Water-Quality Servicing Laboratory to determine the validity of the manufacturer's technical specifications for accuracy, limit of linearity (LOL), drift, and range of operating temperature. Accuracy specifications were met in the TriOS, Hach, and SUNA. The stock calibration of the S::CAN required two offset adjustments before the analyzer met the manufacturer's accuracy specification. Instrument drift was observed only in the S::CAN and was the result of leaching from the optical path insert seals. All tested models, except for the Hach, met their specified LOL in the laboratory testing. The Hach's range was found to be approximately 18 milligrams nitrogen per liter (mg-N/L) and not the manufacturer-specified 25 mg-N/L. Measurements by all of the tested analyzers showed signs of hysteresis in the operating temperature tests. Only the SUNA measurements demonstrated excessive noise and instability in temperatures above 20 degrees Celsius (°C). The SUNA analyzer was returned to the manufacturer at the completion of the Phase II field deployment evaluation for repair and recalibration, and the performance of the sensor improved significantly.
\nIn Phase II, the analyzers were deployed in field conditions at three diferent USGS sites. The measured nitrate concentrations were compared to discrete (reference) samples analyzed by the Direct UV method on a Shimadzu UV1800 bench top spectrophotometer, and by the National Environmental Methods Index (NEMI) method I-2548-11 at the USGS National Water Quality Laboratory. The first deployment at USGS site 0249620 on the East Pearl River in Hancock County, Mississippi, tested the ability of the TriOs ProPs (10-mm path length), Hach NITRATAX (5 mm), Satlantic SUNA (10 mm), and the S::CAN Spectro::lyser (5 mm) to accurately measure low-level (less than 2 mg-N/L) nitrate concentrations while observing the effect turbidity and colored dissolved organic matter (CDOM) would have on the analyzers' measurements. The second deployment at USGS site 01389005 Passaic River below Pompton River at Two Bridges, New Jersey, tested the analyzer's accuracy in mid-level (2-8 mg-N/L) nitrate concentrations. This site provided the means to test the analyzers' performance in two distinct matrices—the Passaic and the Pompton Rivers. In this deployment, three instruments tested in Phase I (TriOS, Hach, and SUNA) were deployed with the S::CAN Spectro::lyser (35 mm) already placed by the New Jersey Water Science Center (WSC). The third deployment at USGS site 05579610 Kickapoo Creek at 2100E Road near Bloomington, Illinois, tested the ability of the analyzers to measure high nitrate concentrations (greater than 8 mg-N/L) in turbid waters. For Kickapoo Creek, the HIF provided the TriOS (10 mm) and S::CAN (5 mm) from Phase I, and a SUNA V2 (5 mm) to be deployed adjacent to the Illinois WSC-owned Hach (2 mm). A total of 40 discrete samples were collected from the three deployment sites and analyzed. The nitrate concentration of the samples ranged from 0.3–22.2 mg-N/L. The average absolute difference between the TriOS measurements and discrete samples was 0.46 mg-N/L. For the combined data from the Hach 5-mm and 2-mm analyzers, the average absolute difference between the Hach samples and the discrete samples was 0.13 mg-N/L. For the SUNA and SUNA V2 combined data, the average absolute difference between the SUNA samples and the discrete samples was 0.66 mg-N/L. The average absolute difference between the S::CAN samples and the discrete samples was 0.63 mg-N/L.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151065","usgsCitation":"Snazelle, T., 2015, Results from laboratory and field testing of nitrate measuring spectrophotometers: U.S. Geological Survey Open-File Report 2015-1065, v, 15 p., https://doi.org/10.3133/ofr20151065.","productDescription":"v, 15 p.","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057525","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":300295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151065.jpg"},{"id":300294,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1065/pdf/ofr2015-1065.pdf","text":"Report","size":"2.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":300293,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1065/"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5551c4aae4b0a92fa7e93b94","contributors":{"authors":[{"text":"Snazelle, Teri T. tsnazelle@usgs.gov","contributorId":5663,"corporation":false,"usgs":true,"family":"Snazelle","given":"Teri T.","email":"tsnazelle@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":543804,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70144854,"text":"ofr20151063 - 2015 - Evaluation of Xylem EXO water-quality sondes and sensors","interactions":[],"lastModifiedDate":"2015-05-11T11:41:57","indexId":"ofr20151063","displayToPublicDate":"2015-05-11T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1063","title":"Evaluation of Xylem EXO water-quality sondes and sensors","docAbstract":"Two models of multiparameter sondes manufactured by Xylem, parent company of Yellow Springs Incorporated (YSI)—EXO1 and EXO2—equipped with EXO conductivity/temperature (C/T), pH, dissolved oxygen (DO), and turbidity sensors, were evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility. The sondes and sensors were evaluated in two phases for compliance with the manufacturer’s specifications and the USGS acceptance criteria for continuous water-quality monitors. Phase one tested the accuracy of the water-quality sondes equipped: (a) with a C/T, pH, DO, and turbidity sensor by comparing the EXO sensors’ measured values to those of an equivalently configured YSI 6920 V2-2 sensor, and (b) with multiple sensors of the same parameter type (such as three pH sensors and a C/T sensor) on a single sonde at room temperature and at an extended temperature range. In addition to accuracy, the communication protocols and the manufacturing specifications for range of detection and operating temperature were also tested during this phase. Phase two evaluated the sondes’ performance in a surface-water environment by deploying an EXO1 and an EXO2 equipped with pH, C/T, DO, and turbidity sensors at USGS site 02492620 located at East Pearl River near Bay Saint Louis, Mississippi. The EXO sondes’ temperature deviations from a certified YSI 4600 digital thermometer were within the ±0.2 degree Celsius (°C) USGS criteria, but were greater than the ±0.01 °C manufacturing specification. The conductivity sensors met the ±3 percent USGS criteria for specific conductance greater than 100 microsiemens per centimeter. The sensors met the more stringent ±0.5 percent manufacturing specification only at room temperature in the 250 microsiemens per centimeter (µS/cm) standard. The manufacturing and USGS criteria (±0.2 pH unit) were met in pH standards 4, 9.2, 10, and 12.45, but were not met in pH 1.68 standard. The DO sensors met both the ±0.3 milligram per liter (mg/L) USGS criteria and the ±1 percent manufacturing specification. The ±5 percent USGS criteria for turbidity in waters not exceeding 2,000 formazin nephelometric units (FNU) were met by the five turbidity sensors tested; however, all five sensors failed to meet these requirements at turbidities exceeding 2,000 FNU. The more stringent ±2 percent manufacturing turbidity specification for water with less than 1,000 FNU was met by only one of the five sensors tested. The results from the field deployment indicated acceptable agreement in temperature, specific conductance, pH, and DO between the EXO sondes, the site sonde, and the reference sonde. The EXO1 and EXO2 turbidity measurements differed from the site sonde by approximately 23 and 25 percent, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151063","usgsCitation":"Snazelle, T., 2015, Evaluation of Xylem EXO water-quality sondes and sensors: U.S. Geological Survey Open-File Report 2015-1063, vi, 14 p., https://doi.org/10.3133/ofr20151063.","productDescription":"vi, 14 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057551","costCenters":[{"id":339,"text":"Hydrologic Instrumentation Facility","active":false,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":300291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1063/pdf/ofr2015-1063.pdf","text":"Report","size":"2.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":300290,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1063/"},{"id":300292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151063.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5551c4a8e4b0a92fa7e93b90","contributors":{"authors":[{"text":"Snazelle, Teri T. tsnazelle@usgs.gov","contributorId":5663,"corporation":false,"usgs":true,"family":"Snazelle","given":"Teri T.","email":"tsnazelle@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":543805,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}