The role of competition in structuring biotic communities at fine spatial scales is well known from detailed process-based studies. Our understanding of competition's importance at broader scales is less resolved and mainly based on static species distribution maps. Here, we bridge this gap by examining the joint occupancy dynamics of an invading (barred owl: Strix varia) and a resident species (Northern spotted owl: Strix occidentalis caurina) in a 1000 km2 study area over a 22 - year period. Past studies of these competitors have focused on the dynamics of one species at a time, hindering efforts to parse out the roles of habitat and competition and to forecast the future of the resident species. In addition, while these studies accounted for the imperfect detection of the focal species, no multiseason analysis of these species has accounted for the imperfect detection of the secondary species, potentially biasing inference. We analyze survey data using models that combine the general multistate-multiseason occupancy modeling framework with autologistic modeling - allowing us to account for important aspects of our study system.
\nWe find that local extinction probability increases for each species when the other is present; however, the effect of the invader on the resident is greater. Although the species prefer different habitats, these habitats are highly correlated at the patch scale and the impacts of invader on the resident are greatest in patches that would otherwise be optimal. As a consequence, competition leads to a weaker relationship between habitat and Northern spotted owl occupancy. Colonization and extinction rates of the invader are closely related to neighborhood occupancy, and over the first half of the study the availability of colonists limited the rate of population growth. Competition is likely to exclude the resident species both through its immediate effects on local extinction, and by indirectly lowering colonization rates as Northern spotted owl occupancy declines. Our analysis suggests that dispersal limitation affects both the invasion dynamics and the scale at which the effects of competition are observed. We also provide predictions regarding the potential costs and benefits of managing barred owl populations at different target levels.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/13-0012.1","usgsCitation":"Yackulic, C.B., Reid, J., Nichols, J., Hines, J., Davis, R., and Forsman, E., 2014, The roles of competition and habitat in the dynamics of populations and species distributions: Ecology, v. 95, no. 2, p. 265-279, https://doi.org/10.1890/13-0012.1.","productDescription":"15 p.","startPage":"265","endPage":"279","numberOfPages":"15","ipdsId":"IP-051859","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":278343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278342,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/13-0012.1"}],"country":"United States","state":"Oregon","volume":"95","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5268e1cfe4b0584cbe916841","contributors":{"authors":[{"text":"Yackulic, Charles Brandon","contributorId":63300,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"","middleInitial":"Brandon","affiliations":[],"preferred":false,"id":485074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Janice","contributorId":89391,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":485075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":485071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":485072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Raymond","contributorId":91349,"corporation":false,"usgs":true,"family":"Davis","given":"Raymond","affiliations":[],"preferred":false,"id":485076,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Forsman, Eric","contributorId":28470,"corporation":false,"usgs":true,"family":"Forsman","given":"Eric","affiliations":[],"preferred":false,"id":485073,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70048518,"text":"70048518 - 2014 - Net ecosystem productivity of temperate grasslands in northern China: An upscaling study","interactions":[],"lastModifiedDate":"2013-10-18T14:11:57","indexId":"70048518","displayToPublicDate":"2013-10-18T14:02:35","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Net ecosystem productivity of temperate grasslands in northern China: An upscaling study","docAbstract":"Grassland is one of the widespread biome types globally, and plays an important role in the terrestrial carbon cycle. We examined net ecosystem production (NEP) for the temperate grasslands in northern China from 2000 to 2010. We combined flux observations, satellite data, and climate data to develop a piecewise regression model for NEP, and then used the model to map NEP for grasslands in northern China. Over the growing season, the northern China's grassland had a net carbon uptake of 158 ± 25 g C m−2 during 2000–2010 with the mean regional NEP estimate of 126 Tg C. Our results showed generally higher grassland NEP at high latitudes (northeast) than at low latitudes (central and west) because of different grassland types and environmental conditions. In the northeast, which is dominated by meadow steppes, the growing season NEP generally reached 200–300 g C m−2. In the southwest corner of the region, which is partially occupied by alpine meadow systems, the growing season NEP also reached 200–300 g C m−2. In the central part, which is dominated by typical steppe systems, the growing season NEP generally varied in the range of 100–200 g C m−2. The NEP of the northern China's grasslands was highly variable through years, ranging from 129 (2001) to 217 g C m−2 growing season−1 (2010). The large interannual variations of NEP could be attributed to the sensitivity of temperate grasslands to climate changes and extreme climatic events. The droughts in 2000, 2001, and 2006 reduced the carbon uptake over the growing season by 11%, 29%, and 16% relative to the long-term (2000–2010) mean. Over the study period (2000–2010), precipitation was significantly correlated with NEP for the growing season (R2 = 0.35, p-value < 0.1), indicating that water availability is an important stressor for the productivity of the temperate grasslands in semi-arid and arid regions in northern China. We conclude that northern temperate grasslands have the potential to sequester carbon, but the capacity of carbon sequestration depends on grassland types and environmental conditions. Extreme climate events like drought can significantly reduce the net carbon uptake of grasslands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agricultural and Forest Meteorology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2013.09.004","usgsCitation":"Zhang, L., Guo, H., Jia, G., Wylie, B., Gilmanov, T., Howard, D., Ji, L., Xiao, J., Li, J., Yuan, W., Zhao, T., Chen, S., Zhou, G., and Kato, T., 2014, Net ecosystem productivity of temperate grasslands in northern China: An upscaling study: Agricultural and Forest Meteorology, v. 184, p. 71-81, https://doi.org/10.1016/j.agrformet.2013.09.004.","productDescription":"11 p.","startPage":"71","endPage":"81","ipdsId":"IP-051428","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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as controls on surface water flow on a low-gradient floodplain","interactions":[],"lastModifiedDate":"2014-02-03T11:16:52","indexId":"70056316","displayToPublicDate":"2013-10-01T12:55:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Relative significance of microtopography and vegetation as controls on surface water flow on a low-gradient floodplain","docAbstract":"Surface water flow controls water velocities, water depths, and residence times, and influences sediment and nutrient transport and other ecological processes in shallow aquatic systems. Flow through wetlands is substantially influenced by drag on vegetation stems but is also affected by microtopography. Our goal was to use microtopography data directly in a widely used wetland model while retaining the advantages of the model’s one-dimensional structure. The base simulation with no explicit treatment of microtopography only performed well for a period of high water when vegetation dominated flow resistance. Extended simulations using microtopography can improve the fit to low-water conditions substantially. The best fit simulation had a flow conductance parameter that decreased in value by 70 % during dry season such that mcrotopographic features blocked 40 % of the cross sectional width for flow. Modeled surface water became ponded and flow ceased when 85 % of the cross sectional width became blocked by microtopographic features. We conclude that vegetation drag dominates wetland flow resistance at higher water levels and microtopography dominates at low water levels with the threshold delineated by the top of microtopographic features. Our results support the practicality of predicting flow on floodplains using relatively easily measured physical and biological variables.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Wetlands","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s13157-013-0489-7","usgsCitation":"Choi, J., and Harvey, J.W., 2014, Relative significance of microtopography and vegetation as controls on surface water flow on a low-gradient floodplain: Wetlands, v. 34, no. 1, p. 101-115, https://doi.org/10.1007/s13157-013-0489-7.","productDescription":"15 p.","startPage":"101","endPage":"115","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-051999","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":279178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279165,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s13157-013-0489-7"}],"country":"United States","state":"Florida","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.75,25.5 ], [ -80.75,26.5 ], [ -80.25,26.5 ], [ -80.25,25.5 ], [ -80.75,25.5 ] ] ] } } ] }","volume":"34","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-10-25","publicationStatus":"PW","scienceBaseUri":"528c96b9e4b0c629af44ddfb","contributors":{"authors":[{"text":"Choi, Jungyill","contributorId":70792,"corporation":false,"usgs":true,"family":"Choi","given":"Jungyill","email":"","affiliations":[],"preferred":false,"id":486522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":486521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70073700,"text":"70073700 - 2014 - SemantEco: a semantically powered modular architecture for integrating distributed environmental and ecological data","interactions":[],"lastModifiedDate":"2018-08-10T16:53:02","indexId":"70073700","displayToPublicDate":"2013-09-27T16:10:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1714,"text":"Future Generation Computer Systems","active":true,"publicationSubtype":{"id":10}},"title":"SemantEco: a semantically powered modular architecture for integrating distributed environmental and ecological data","docAbstract":"We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of linked open data. In previous work, we designed and implemented a semantically enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. In this work, we discuss SemantEco’s new architecture that supports modular extensions and makes it easier to support additional domains. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. We conclude with a discussion of how, through the application of semantic technologies, modular designs will make it easier for resource managers to bring in new sources of data to support more complex use cases.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Future Generation Computer Systems","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.future.2013.09.017","usgsCitation":"Patton, E.W., Seyed, P., Wang, P., Fu, L., Dein, F.J., Bristol, R., and McGuinness, D.L., 2014, SemantEco: a semantically powered modular architecture for integrating distributed environmental and ecological data: Future Generation Computer Systems, v. 36, p. 430-440, https://doi.org/10.1016/j.future.2013.09.017.","productDescription":"11 p.","startPage":"430","endPage":"440","numberOfPages":"11","ipdsId":"IP-050938","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":281355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281354,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.future.2013.09.017"}],"volume":"36","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"537717d7e4b02eab8669ef0e","contributors":{"authors":[{"text":"Patton, Evan W.","contributorId":51649,"corporation":false,"usgs":true,"family":"Patton","given":"Evan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":489053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seyed, Patrice","contributorId":7618,"corporation":false,"usgs":true,"family":"Seyed","given":"Patrice","email":"","affiliations":[],"preferred":false,"id":489052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Ping","contributorId":78646,"corporation":false,"usgs":false,"family":"Wang","given":"Ping","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":489055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fu, Linyun","contributorId":62928,"corporation":false,"usgs":true,"family":"Fu","given":"Linyun","email":"","affiliations":[],"preferred":false,"id":489054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dein, F. Joshua fjdein@usgs.gov","contributorId":2772,"corporation":false,"usgs":true,"family":"Dein","given":"F.","email":"fjdein@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":489051,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bristol, R. Sky 0000-0003-1682-4031","orcid":"https://orcid.org/0000-0003-1682-4031","contributorId":88196,"corporation":false,"usgs":true,"family":"Bristol","given":"R. Sky","affiliations":[],"preferred":false,"id":489056,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGuinness, Deborah L.","contributorId":98216,"corporation":false,"usgs":true,"family":"McGuinness","given":"Deborah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":489057,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70048425,"text":"70048425 - 2014 - Large wood budget and transport dynamics on a large river using radio telemetry","interactions":[],"lastModifiedDate":"2014-03-28T09:31:58","indexId":"70048425","displayToPublicDate":"2013-09-26T08:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Large wood budget and transport dynamics on a large river using radio telemetry","docAbstract":"Despite the abundance of large wood (LW) river studies there is still a lack of understanding of LW transport dynamics on large low gradient rivers. This study used 290 radio frequency identification tagged (RFID) LW and 54 metal (aluminum) tagged LW, to quantify the percent of in-channel LW that moves per year and what variables play a role in LW transport dynamics. Aluminum tags were installed and monitored on LW in-transit during the rising limb of a flood, the mean distance traveled by those pieces during the week was 13.3 river kilometers (km) with a maximum distance of 72 km. RFID tagged LW moved a mean of 11.9 km/yr with a maximum observed at 101.1 km/yr. Approximately 41% of LW low on the bank moves per year. The high rate of transport and distance traveled is likely due to the lack of interaction between LW floating in the channel and the channel boundaries, caused primarily by the width of the channel relative to length of the LW. Approximately 80% of the RFID tags moved past a fixed reader during the highest 20% of river stage per year. LW transport and logjam dynamics are complicated at high flows as pieces form temporary jams that continually expand and contract. Unlike most other studies, key members that create a logjam were defined more by stability than jam size or channel/hydrologic conditions. Finally, using an existing geomorphic database for the river, and data from this study, we were able to develop a comprehensive LW budget showing that 5% of the in-channel LW population turns over each year (input from mass wasting and fluvial erosion equals burial, decomposition, and export out of system) and another 16% of the population moving within the system.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earth Surface Processes and Landforms","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/esp.3463","usgsCitation":"Schenk, E.R., Moulin, B., Hupp, C.R., and Richte, J.M., 2014, Large wood budget and transport dynamics on a large river using radio telemetry: Earth Surface Processes and Landforms, v. 39, no. 4, p. 487-498, https://doi.org/10.1002/esp.3463.","productDescription":"12 p.","startPage":"487","endPage":"498","numberOfPages":"12","ipdsId":"IP-049201","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":278174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278172,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/esp.3463"}],"country":"United States","state":"North Carolina;Virginia","otherGeospatial":"Lower Roanoke River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -77.6722,35.96 ], [ -77.6722,36.6291 ], [ -76.6828,36.6291 ], [ -76.6828,35.96 ], [ -77.6722,35.96 ] ] ] } } ] }","volume":"39","issue":"4","noUsgsAuthors":false,"publicationDate":"2013-08-31","publicationStatus":"PW","scienceBaseUri":"5246e919e4b035b7f35addd6","contributors":{"authors":[{"text":"Schenk, Edward R. 0000-0001-6886-5754 eschenk@usgs.gov","orcid":"https://orcid.org/0000-0001-6886-5754","contributorId":2183,"corporation":false,"usgs":true,"family":"Schenk","given":"Edward","email":"eschenk@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":484618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moulin, Bertrand","contributorId":80160,"corporation":false,"usgs":true,"family":"Moulin","given":"Bertrand","email":"","affiliations":[],"preferred":false,"id":484621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":484619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richte, Jean M.","contributorId":25856,"corporation":false,"usgs":true,"family":"Richte","given":"Jean","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":484620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70112697,"text":"tm5.2.2.B - 2014 - Chapter A5. Section 2.2B. Syringe-Filter Procedure for Processing Samples for Analysis of Organic Compounds by DAI LC-MS/MS","interactions":[],"lastModifiedDate":"2021-05-27T14:01:14.644066","indexId":"tm5.2.2.B","displayToPublicDate":"2013-08-18T09:01:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5.2.2.B","title":"Chapter A5. Section 2.2B. Syringe-Filter Procedure for Processing Samples for Analysis of Organic Compounds by DAI LC-MS/MS","docAbstract":"This section of chapter 5 of the National Field Manual for the Collection of Water-Quality Data (NFM) describes the field procedures for collecting small-volume samples using a syringe-tip filtration method. The samples are sent to the U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) for analysis of organic compounds by direct aqueous injection high-performance liquid chromatography/tandem mass spectrometry (DAI LC-MS/MS).\n\nThe DAI LC-MS/MS method was developed specifically for NWQL analytical schedules 2437 (pesticides) and 2440 (pharmaceuticals) and should not be considered transferrable or applicable to other types of samples to be analyzed using methods other than those that use DAI LC-MS/MS or other tandem mass\nspectrometry methods.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"National Field Manual for the Collection of Water-Quality Data. U.S. Geological Survey Techniques of Water-Resources Investigations, Book 9","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/tm5.2.2.B","usgsCitation":"Sandstrom, M.W., and Wilde, F.D., 2014, Chapter A5. Section 2.2B. Syringe-Filter Procedure for Processing Samples for Analysis of Organic Compounds by DAI LC-MS/MS (Version 3.1): U.S. Geological Survey Techniques and Methods 5.2.2.B, 10 p., https://doi.org/10.3133/tm5.2.2.B.","productDescription":"10 p.","onlineOnly":"Y","ipdsId":"IP-057027","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":292359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":292345,"type":{"id":15,"text":"Index Page"},"url":"https://water.usgs.gov/owq/FieldManual/chapter5/pdf/5.2.2.B.pdf"}],"edition":"Version 3.1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53f25fdae4b03334187188fc","contributors":{"authors":[{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":494841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilde, Franceska D. fwilde@usgs.gov","contributorId":92240,"corporation":false,"usgs":true,"family":"Wilde","given":"Franceska","email":"fwilde@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":494842,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70047499,"text":"70047499 - 2014 - An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming","interactions":[],"lastModifiedDate":"2013-08-08T09:45:17","indexId":"70047499","displayToPublicDate":"2013-08-08T09:40:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming","docAbstract":"Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social-value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Indicators","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2013.07.008","usgsCitation":"Sherrouse, B.C., Semmens, D.J., and Clement, J.M., 2014, An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming: Ecological Indicators, v. 36, p. 68-79, https://doi.org/10.1016/j.ecolind.2013.07.008.","productDescription":"12 p.","startPage":"68","endPage":"79","ipdsId":"IP-039048","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":276196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":276195,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolind.2013.07.008"}],"country":"United States","state":"Colorado;Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.0569,36.99 ], [ -111.0569,45.0059 ], [ -102.04,45.0059 ], [ -102.04,36.99 ], [ -111.0569,36.99 ] ] ] } } ] }","volume":"36","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5204afcfe4b0403aa626299e","contributors":{"authors":[{"text":"Sherrouse, Benson C.","contributorId":37831,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":482196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":482195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clement, Jessica M.","contributorId":86105,"corporation":false,"usgs":true,"family":"Clement","given":"Jessica","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":482197,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70046213,"text":"70046213 - 2014 - Mercury cycling in agricultural and managed wetlands of California: experimental evidence of vegetation-driven changes in sediment biogeochemistry and methylmercury production","interactions":[],"lastModifiedDate":"2018-09-18T16:23:32","indexId":"70046213","displayToPublicDate":"2013-07-29T15:01:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mercury cycling in agricultural and managed wetlands of California: experimental evidence of vegetation-driven changes in sediment biogeochemistry and methylmercury production","docAbstract":"The role of live vegetation in sediment methylmercury (MeHg) production and associated biogeochemistry was examined in three types of agricultural wetlands (domesticated or white rice, wild rice, and fallow fields) and adjacent managed natural wetlands (cattail- and bulrush or tule-dominated) in the Yolo Bypass region of California's Central Valley, USA. During the active growing season for each wetland, a vegetated:de-vegetated paired plot experiment demonstrated that the presence of live plants enhanced microbial rates of mercury methylation by 20 to 669% (median = 280%) compared to de-vegetated plots. Labile carbon exudation by roots appeared to be the primary mechanism by which microbial methylation was enhanced in the presence of vegetation. Pore-water acetate (pw[Ac]) decreased significantly with de-vegetation (63 to 99%) among all wetland types, and within cropped fields, pw[Ac] was correlated with both root density (r = 0.92) and microbial Hg(II) methylation (kmeth. r = 0.65). Sediment biogeochemical responses to de-vegetation were inconsistent between treatments for “reactive Hg” (Hg(II)R), as were reduced sulfur and sulfate reduction rates. Sediment MeHg concentrations in vegetated plots were double those of de-vegetated plots (median = 205%), due in part to enhanced microbial MeHg production in the rhizosphere, and in part to rhizoconcentration via transpiration-driven pore-water transport. Pore-water concentrations of chloride, a conservative tracer, were elevated (median = 22%) in vegetated plots, suggesting that the higher concentrations of other constituents around roots may also be a function of rhizoconcentration rather than microbial activity alone. Elevated pools of amorphous iron (Fe) in vegetated plots indicate that downward redistribution of oxic surface waters through transpiration acts as a stimulant to Fe(III)-reduction through oxidation of Fe(II)pools. These data suggest that vegetation significantly affected rhizosphere biogeochemistry through organic exudation and transpiration-driven concentration of pore-water constituents and oxidation of reduced compounds. While the relative role of vegetation varied among wetland types, macrophyte activity enhanced MeHg production.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2013.05.028","usgsCitation":"Windham-Myers, L., Marvin-DiPasquale, M., Stricker, C.A., Agee, J.L., Kieu, L.H., and Kakouros, E., 2014, Mercury cycling in agricultural and managed wetlands of California: experimental evidence of vegetation-driven changes in sediment biogeochemistry and methylmercury production: Science of the Total Environment, v. 484, p. 300-307, https://doi.org/10.1016/j.scitotenv.2013.05.028.","productDescription":"8 p.","startPage":"300","endPage":"307","numberOfPages":"8","ipdsId":"IP-045774","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":275522,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.scitotenv.2013.05.028"},{"id":275523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Yolo County","otherGeospatial":"Yolo Bypass Wildlife Area","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.663971,38.417283 ], [ -121.663971,38.556489 ], [ -121.586037,38.556489 ], [ -121.586037,38.417283 ], [ -121.663971,38.417283 ] ] ] } } ] }","volume":"484","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f780d6e4b02e26443a9331","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":479180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marvin-DiPasquale, Mark","contributorId":57423,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","affiliations":[],"preferred":false,"id":479184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":479179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agee, Jennifer L. 0000-0002-5964-5079 jlagee@usgs.gov","orcid":"https://orcid.org/0000-0002-5964-5079","contributorId":2586,"corporation":false,"usgs":true,"family":"Agee","given":"Jennifer","email":"jlagee@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":479181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kieu, Le H. lkieu@usgs.gov","contributorId":25115,"corporation":false,"usgs":true,"family":"Kieu","given":"Le","email":"lkieu@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":false,"id":479183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kakouros, Evangelos 0000-0002-4778-4039 kakouros@usgs.gov","orcid":"https://orcid.org/0000-0002-4778-4039","contributorId":2587,"corporation":false,"usgs":true,"family":"Kakouros","given":"Evangelos","email":"kakouros@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":479182,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70045551,"text":"70045551 - 2014 - Long-distance transport of Hg, Sb, and As from a mined area, conversion of Hg to methyl-Hg, and uptake of Hg by fish on the Tiber River basin, west-central Italy","interactions":[],"lastModifiedDate":"2014-01-06T09:53:59","indexId":"70045551","displayToPublicDate":"2013-06-24T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1538,"text":"Environmental Geochemistry and Health","active":true,"publicationSubtype":{"id":10}},"title":"Long-distance transport of Hg, Sb, and As from a mined area, conversion of Hg to methyl-Hg, and uptake of Hg by fish on the Tiber River basin, west-central Italy","docAbstract":"Stream sediment, stream water, and fish were collected from a broad region to evaluate downstream transport and dispersion of mercury (Hg) from inactive mines in the Monte Amiata Hg District (MAMD), Tuscany, Italy. Stream sediment samples ranged in Hg concentration from 20 to 1,900 ng/g, and only 5 of the 17 collected samples exceeded the probable effect concentration for Hg of 1,060 ng/g, above which harmful effects are likely to be observed in sediment-dwelling organisms. Concentrations of methyl-Hg in Tiber River sediment varied from 0.12 to 0.52 ng/g, and although there is no established guideline for sediment methyl-Hg, these concentrations exceeded methyl-Hg in a regional baseline site (<0.02 ng/g). Concentrations of Hg in stream water varied from 1.2 to 320 ng/L, all of which were below the 1,000 ng/L Italian drinking water Hg guideline and the 770 ng/L U.S. Environmental Protection Agency (USEPA) guideline recommended to protect against chronic effects to aquatic wildlife. Methyl-Hg concentrations in stream water varied from <0.02 to 0.53 ng/L and were generally elevated compared to the baseline site (<0.02 ng/L). All stream water samples contained concentrations of As (<1.0–6.2 μg/L) and Sb (<0.20–0.37 μg/L) below international drinking water guidelines to protect human health (10 μg/L for As and 20 μg/L for Sb) and for protection against chronic effects to aquatic wildlife (150 μg/L for As and 5.6 μg/L for Sb). Concentrations of Hg in freshwater fish muscle ranged from 0.052–0.56 μg/g (wet weight), mean of 0.17 μg/g, but only 17 % (9 of 54) exceeded the 0.30 μg/g (wet weight) USEPA fish muscle guideline recommended to protect human health. Concentrations of Hg in freshwater fish in this region generally decreased with increasing distance from the MAMD, where fish with the highest Hg concentrations were collected more proximal to the MAMD, whereas all fish collected most distal from Hg mines contained Hg below the 0.30 μg/g fish muscle guideline. Data in this study indicate some conversion of inorganic Hg to methyl-Hg and uptake of Hg in fish on the Paglia River, but less methylation of Hg and Hg uptake by freshwater fish in the larger Tiber River.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Geochemistry and Health","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10653-013-9525-z","usgsCitation":"Gray, J.E., Rimondi, V., Costagliola, P., Vaselli, O., and Lattanzi, P., 2014, Long-distance transport of Hg, Sb, and As from a mined area, conversion of Hg to methyl-Hg, and uptake of Hg by fish on the Tiber River basin, west-central Italy: Environmental Geochemistry and Health, v. 36, no. 1, p. 145-157, https://doi.org/10.1007/s10653-013-9525-z.","productDescription":"13 p.","startPage":"145","endPage":"157","numberOfPages":"13","ipdsId":"IP-045177","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":274096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274095,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10653-013-9525-z"}],"country":"Italy","otherGeospatial":"Tiber River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 6.63,35.49 ], [ 6.63,47.09 ], [ 18.52,47.09 ], [ 18.52,35.49 ], [ 6.63,35.49 ] ] ] } } ] }","volume":"36","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-05-12","publicationStatus":"PW","scienceBaseUri":"51c95c5ae4b0a50a6e8f57b4","contributors":{"authors":[{"text":"Gray, John E. jgray@usgs.gov","contributorId":1275,"corporation":false,"usgs":true,"family":"Gray","given":"John","email":"jgray@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":477830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rimondi, Valentina","contributorId":27772,"corporation":false,"usgs":true,"family":"Rimondi","given":"Valentina","email":"","affiliations":[],"preferred":false,"id":477831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costagliola, Pilario","contributorId":106404,"corporation":false,"usgs":true,"family":"Costagliola","given":"Pilario","email":"","affiliations":[],"preferred":false,"id":477834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vaselli, Orlando","contributorId":97804,"corporation":false,"usgs":true,"family":"Vaselli","given":"Orlando","email":"","affiliations":[],"preferred":false,"id":477833,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lattanzi, Pierfranco","contributorId":87845,"corporation":false,"usgs":true,"family":"Lattanzi","given":"Pierfranco","affiliations":[],"preferred":false,"id":477832,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70101266,"text":"70101266 - 2014 - Status of rainbow smelt in the U.S. waters of Lake Ontario, 2013","interactions":[],"lastModifiedDate":"2020-03-05T12:22:08","indexId":"70101266","displayToPublicDate":"2013-05-28T10:29:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5114,"text":"NYSDEC Lake Ontario Annual Report ","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"2013","chapter":"12","title":"Status of rainbow smelt in the U.S. waters of Lake Ontario, 2013","docAbstract":"Rainbow Smelt Osmerus mordax are the second most abundant pelagic prey fish in Lake Ontario after Alewife Alosa psuedoharengus. The 2013, USGS/NYSDEC bottom trawl assessment indicated the abundance of Lake Ontario age-1 and older Rainbow Smelt decreased by 69% relative to 2012. Length frequency-based age analysis indicated that age-1 Rainbow Smelt constituted approximately 50% of the population, which is similar to recent trends where the proportion of age-1 has ranged from 95% to 42% of the population. While they constituted approximately half of the catch, the overall abundance index for age 1 was one of the lowest observed in the time series, potentially a result of cannibalism from the previous year class. Combined data from all bottom trawl assessments along the southern shore and eastern basin indicate the proportion of the fish community that is Rainbow Smelt has declined over the past 30 years. In 2013 the proportion of the pelagic fish catch (only pelagic species) that was Rainbow Smelt was the second lowest in the time series at 3.1%. Community diversity indices, based on bottom trawl catches, indicate that Lake Ontario fish community diversity, as assessed by bottom trawls, has sharply declined over the past 36 years and in 2013 the index was the lowest value in the time series. Much of this community diversity decline is driven by changes in the pelagic fish community and dominance of Alewife.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2013 Annual report: Bureau of Fisheries, Lake Ontario unit and St. Lawrence River unit, to the Great Lakes Fishery Commission’s Lake Ontario Committee","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"conferenceTitle":"Lake Ontario Committee Meeting","conferenceDate":"March 26-27, 2014","conferenceLocation":"Windsor, ON","language":"English","publisher":"New York State Department of Environmental Conservation","publisherLocation":"Albany, NY","usgsCitation":"Weidel, B., and Connerton, M., 2014, Status of rainbow smelt in the U.S. waters of Lake Ontario, 2013: NYSDEC Lake Ontario Annual Report 2013, 5 p.","productDescription":"5 p.","startPage":"12-11","endPage":"12- 15","ipdsId":"IP-055072","costCenters":[{"id":324,"text":"Great Lakes Science 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During 2005-12, discharge from the Pine Knot Mine Tunnel, the largest AMD source in the upper Schuylkill River Basin, had near-neutral pH and elevated concentrations of iron, manganese, and sulfate. Discharge from the tunnel responded rapidly to recharge but exhibited a prolonged recession compared to nearby streams, consistent with rapid infiltration and slow release of groundwater from the mine. Downstream of the AMD, dissolved iron was attenuated by oxidation and precipitation while dissolved CO2 degassed and pH increased. During high-flow conditions, the AMD and downstream waters exhibited decreased pH, iron, and sulfate with increased acidity that were modeled by mixing net-alkaline AMD with recharge or runoff having low ionic strength and low pH. Attenuation of dissolved iron within the river was least effective during high-flow conditions because of decreased transport time coupled with inhibitory effects of low pH on oxidation kinetics.
\nA numerical model of groundwater flow was calibrated using groundwater levels in the Pine Knot Mine and discharge data for the Pine Knot Mine Tunnel and the West Branch Schuylkill River during a snowmelt event in January 2012. Although the calibrated model indicated substantial recharge to the mine complex took place away from streams, simulation of rapid changes in mine pool level and tunnel discharge during a high flow event in May 2012 required a source of direct recharge to the Pine Knot Mine. Such recharge produced small changes in mine pool level and rapid changes in tunnel flow rate because of extensive unsaturated storage capacity and high transmissivity within the mine complex. Thus, elimination of stream leakage could have a small effect on the annual discharge from the tunnel, but a large effect on peak discharge and associated water quality in streams.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.9885","usgsCitation":"Cravotta, C.A., Goode, D., Bartles, M.D., Risser, D.W., and Galeone, D.G., 2014, Surface-water and groundwater interactions in an extensively mined watershed, upper Schuylkill River, Pennsylvania, USA: Hydrological Processes, v. 28, no. 10, p. 3574-3601, https://doi.org/10.1002/hyp.9885.","productDescription":"28 p.","startPage":"3574","endPage":"3601","numberOfPages":"28","ipdsId":"IP-042703","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":272349,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Schuylkill River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.52,39.72 ], [ -80.52,42.27 ], [ -74.69,42.27 ], [ -74.69,39.72 ], [ -80.52,39.72 ] ] ] } } ] }","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2013-06-21","publicationStatus":"PW","scienceBaseUri":"51974368e4b09a9cb58d5ee2","contributors":{"authors":[{"text":"Cravotta, Charles A. III, 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":2193,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles","suffix":"III,","email":"cravotta@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":478663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goode, Daniel J. 0000-0002-8527-2456 djgoode@usgs.gov","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":2433,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel J.","email":"djgoode@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":478665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartles, Michael D.","contributorId":34405,"corporation":false,"usgs":true,"family":"Bartles","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":478666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Risser, Dennis W. 0000-0001-9597-5406 dwrisser@usgs.gov","orcid":"https://orcid.org/0000-0001-9597-5406","contributorId":898,"corporation":false,"usgs":true,"family":"Risser","given":"Dennis","email":"dwrisser@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galeone, Daniel G. 0000-0002-8007-9278 dgaleone@usgs.gov","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":2301,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel","email":"dgaleone@usgs.gov","middleInitial":"G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478664,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70140924,"text":"70140924 - 2014 - Refocusing Mussel Watch on contaminants of emerging concern (CECs): the California pilot study (2009-10)","interactions":[],"lastModifiedDate":"2018-09-18T16:10:59","indexId":"70140924","displayToPublicDate":"2013-04-30T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Refocusing Mussel Watch on contaminants of emerging concern (CECs): the California pilot study (2009-10)","docAbstract":"To expand the utility of the Mussel Watch Program, local, regional and state agencies in California partnered with NOAA to design a pilot study that targeted contaminants of emerging concern (CECs). Native mussels (Mytilus spp.) from 68 stations, stratified by land use and discharge scenario, were collected in 2009–10 and analyzed for 167 individual pharmaceuticals, industrial and commercial chemicals and current use pesticides. Passive sampling devices (PSDs) and caged Mytilus were co-deployed to expand the list of CECs, and to assess the ability of PSDs to mimic bioaccumulation by Mytilus. A performance-based quality assurance/quality control (QA/QC) approach was developed to ensure a high degree of data quality, consistency and comparability. Data management and analysis were streamlined and standardized using automated software tools. This pioneering study will help shape future monitoring efforts in California’s coastal ecosystems, while serving as a model for monitoring CECs within the region and across the nation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2013.04.027","usgsCitation":"Maruya, K.A., Dodder, N.G., Schaffner, R.A., Weisberg, S., Gregorio, D., Klosterhaus, S., Alvarez, D.A., Furlong, E.T., Kimbrough, K.L., Lauenstein, G.G., and Christensen, J., 2014, Refocusing Mussel Watch on contaminants of emerging concern (CECs): the California pilot study (2009-10): Marine Pollution Bulletin, v. 81, no. 2, p. 334-339, https://doi.org/10.1016/j.marpolbul.2013.04.027.","productDescription":"6 p.","startPage":"334","endPage":"339","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059987","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":297916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Knowledge about mechanistic linkages between the geophysical and biological environments is essential for understanding how global warming may reshape contemporary ecosystems and ecosystem services. Numerous global data sources spanning several decades are available that document key geophysical metrics such as temperature and precipitation, and metrics of primary biological production such as vegetation phenology and ocean phytoplankton. This paper provides an internet directory to portals for visualizing or servers for downloading many of the more commonly used global datasets, as well as a description of how to write simple computer code to efficiently retrieve these data. The data are broadly useful for quantifying relationships between climate, habitat availability, and lower-trophic-level habitat quality - especially in Arctic regions where strong seasonality is accompanied by intrinsically high year-to-year variability. If defensible linkages between the geophysical (climate) and the biological environment can be established, general circulation model (GCM) projections of future climate conditions can be used to infer future biological responses. Robustness of this approach is, however, complicated by the number of direct, indirect, or interacting linkages involved. For example, response of a predator species to climate change will be influenced by the responses of its prey and competitors, and so forth throughout a trophic web. The complexities of ecological systems warrant sensible and parsimonious approaches for assessing and establishing the role of natural climate variability in order to substantiate inferences about the potential effects of global warming.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Gyrfalcons and Ptarmigan in a Changing World, Conference Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Gyrfalcons and Ptarmigan in a Changing World","conferenceDate":"1-3 February 2011","conferenceLocation":"Boise, ID","language":"English","publisher":"The Peregrine Fund book \"Gyrfalcons and Ptarmigan in a Changing World\"","doi":"10.4080/gpcw.2011.0110","usgsCitation":"Douglas, D.C., 2014, Sources of global climate data and visualization portals, in Gyrfalcons and Ptarmigan in a Changing World, Conference Proceedings, v. 1, Boise, ID, 1-3 February 2011, p. 101-116, https://doi.org/10.4080/gpcw.2011.0110.","productDescription":"16 p.","startPage":"101","endPage":"116","ipdsId":"IP-034041","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":488669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4080/gpcw.2011.0110","text":"Publisher Index Page"},{"id":342441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b5e4b0764e6c63eadf","contributors":{"authors":[{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":537259,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70103363,"text":"70103363 - 2014 - LANDFIRE 2001 and 2008 refresh: geographic area report: Alaska","interactions":[],"lastModifiedDate":"2017-04-13T10:15:57","indexId":"70103363","displayToPublicDate":"2011-12-01T16:47:38","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"LANDFIRE 2001 and 2008 refresh: geographic area report: Alaska","docAbstract":"The LANDFIRE National Project (LF_1.0.0) was successfully completed in 2009. The goal of LANDFIRE\nNational was to generate consistent 2001 vintage 30 meter spatial data sets for all 50 States for fire and\nother natural resource applications. This report highlights results from the continuation of LANDFIRE as\na program to update the spatial data layers through 2008. The focus of this phase of the program was\nto improve the data products and account for vegetation change across the landscape caused by\nwildland fire, fuel and vegetation treatments, and management. In addition, changes caused by insects\nand disease, storms, invasive plants, and other natural or anthropogenic events were incorporated\nwhen data were available. This report describes the LANDFIRE 2001/2008 Refresh effort to update\nexisting map layers to reflect more current conditions, focusing primarily on vegetation changes. The\neffort incorporated user feedback and new data, producing two comprehensive Refresh data product\nsets:
\n1. LANDFIRE 2001 Refresh (LF_1.0.5) enhanced LANDFIRE map layers by incorporating\nuser feedback and additional data to provide a foundation to update data to 2008. It\nwas also designed to provide users with a data set to help facilitate comparisons\nbetween 2001 and 2008 (i.e. Refresh LF_1.1.0) data sets.
\n2. LANDFIRE 2008 Refresh (LF_1.1.0) updated map layers to reflect vegetation changes\nand disturbances that occurred between 1999 and 2008.
\nIn this report, we (1) address the background and provide details pertaining to why there are two\nRefresh data sets, (2) explain the requirements, planning, and procedures behind the completion and\ndelivery of the updated products for each of the data product sets, (3) show and describe results, and\n(4) provide case studies illustrating the performance of LANDFIRE National, LANDFIRE 2001 Refresh and\nLANDFIRE 2008 Refresh (LF_1.1.0) data products on some example wildland fires.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70103363","usgsCitation":"Connot, J.A., 2014, LANDFIRE 2001 and 2008 refresh: geographic area report: Alaska, ii, 68 p., https://doi.org/10.3133/70103363.","productDescription":"ii, 68 p.","numberOfPages":"71","ipdsId":"IP-055129","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":289498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":339665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://www.landfire.gov/documents/AK_GA.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbc173e4b084059e8bfed1","contributors":{"authors":[{"text":"Connot, Joel A. 0000-0002-2556-3374 jconnot@usgs.gov","orcid":"https://orcid.org/0000-0002-2556-3374","contributorId":4436,"corporation":false,"usgs":true,"family":"Connot","given":"Joel","email":"jconnot@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":493260,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70005390,"text":"tm11B03 - 2014 - Standard for the U.S. Geological Survey Historical Topographic Map Collection","interactions":[],"lastModifiedDate":"2014-07-31T14:26:55","indexId":"tm11B03","displayToPublicDate":"2011-09-10T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-B3","title":"Standard for the U.S. Geological Survey Historical Topographic Map Collection","docAbstract":"This document defines the digital map product of the U.S. Geological Survey (USGS) Historical Topographic Map Collection (HTMC). The HTMC is a digital archive of about 190,000 printed topographic quadrangle maps published by the USGS from the inception of the topographic mapping program in 1884 until the last paper topographic map using lithographic printing technology was published in 2006. The HTMC provides a comprehensive digital repository of all scales and all editions of USGS printed topographic maps that is easily discovered, browsed, and downloaded by the public at no cost. Each printed topographic map is scanned “as is” and captures the content and condition of each map. The HTMC provides ready access to maps that are no longer available for distribution in print. A new generation of topographic maps called “US Topo” was defined in 2009. US Topo maps, though modeled on the legacy 7.5-minute topographic maps, conform to different standards. For more information on the HTMC, see the project Web site at: http://nationalmap.gov/historical/.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B03","collaboration":"National Geospatial Program. This report is Chapter 3 of Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data.","usgsCitation":"Allord, G.J., Fishburn, K.A., and Walter, J.L., 2014, Standard for the U.S. Geological Survey Historical Topographic Map Collection (Version 1, 2011; Version 2, July 2014): U.S. Geological Survey Techniques and Methods 11-B3, v, 11 p., https://doi.org/10.3133/tm11B03.","productDescription":"v, 11 p.","numberOfPages":"20","onlineOnly":"Y","costCenters":[{"id":425,"text":"National Geospatial Technical Operations Center","active":false,"usgs":true}],"links":[{"id":291495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm11B03.jpg"},{"id":291493,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/11b03/"},{"id":291494,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11b03/pdf/tm11b3.pdf"}],"edition":"Version 1, 2011; Version 2, July 2014","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b96b5e4b08c986b31b684","contributors":{"authors":[{"text":"Allord, Gregory J. gjallord@usgs.gov","contributorId":2714,"corporation":false,"usgs":true,"family":"Allord","given":"Gregory","email":"gjallord@usgs.gov","middleInitial":"J.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":352408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fishburn, Kristin A. 0000-0002-7825-556X kafishburn@usgs.gov","orcid":"https://orcid.org/0000-0002-7825-556X","contributorId":4654,"corporation":false,"usgs":true,"family":"Fishburn","given":"Kristin","email":"kafishburn@usgs.gov","middleInitial":"A.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":352409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walter, Jennifer L. 0000-0001-8183-5015 jlwalter@usgs.gov","orcid":"https://orcid.org/0000-0001-8183-5015","contributorId":5217,"corporation":false,"usgs":true,"family":"Walter","given":"Jennifer","email":"jlwalter@usgs.gov","middleInitial":"L.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":352410,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70134661,"text":"70134661 - 2014 - Vibrational, X-ray absorption, and Mössbauer spectra of sulfate minerals from the weathered massive sulfide deposit at Iron Mountain, California","interactions":[],"lastModifiedDate":"2018-03-05T17:08:46","indexId":"70134661","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Vibrational, X-ray absorption, and Mössbauer spectra of sulfate minerals from the weathered massive sulfide deposit at Iron Mountain, California","docAbstract":"The Iron Mountain Mine Superfund site in California is a prime example of an acid mine drainage (AMD) system with well developed assemblages of sulfate minerals typical for such settings. Here we present and discuss the vibrational (infrared), X-ray absorption, and Mössbauer spectra of a number of these phases, augmented by spectra of a few synthetic sulfates related to the AMD phases. The minerals and related phases studied in this work are (in order of increasing Fe2O3/FeO): szomolnokite, rozenite, siderotil, halotrichite, römerite, voltaite, copiapite, monoclinic Fe2(SO4)3, Fe2(SO4)3·5H2O, kornelite, coquimbite, Fe(SO4)(OH), jarosite and rhomboclase. Fourier transform infrared spectra in the region 750–4000 cm−1 are presented for all studied phases. Position of the FTIR bands is discussed in terms of the vibrations of sulfate ions, hydroxyl groups, and water molecules. Sulfur K-edge X-ray absorption near-edge structure (XANES) spectra were collected for selected samples. The feature of greatest interest is a series of weak pre-edge peaks whose position is determined by the number of bridging oxygen atoms between Fe3+ octahedra and sulfate tetrahedra. Mössbauer spectra of selected samples were obtained at room temperature and 80 K for ferric minerals jarosite and rhomboclase and mixed ferric–ferrous minerals römerite, voltaite, and copiapite. Values of Fe2+/[Fe2+ + Fe3+] determined by Mössbauer spectroscopy agree well with those determined by wet chemical analysis. The data presented here can be used as standards in spectroscopic work where spectra of well-characterized compounds are required to identify complex mixtures of minerals and related phases.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2011.03.008","usgsCitation":"Majzlan, J., Alpers, C.N., Bender Koch, C., McCleskey, R.B., Myneni, S.B., and Neil, J.M., 2014, Vibrational, X-ray absorption, and Mössbauer spectra of sulfate minerals from the weathered massive sulfide deposit at Iron Mountain, California: Chemical Geology, v. 284, no. 3-4, p. 296-305, https://doi.org/10.1016/j.chemgeo.2011.03.008.","productDescription":"10 p.","startPage":"296","endPage":"305","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-012404","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":296421,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Iron Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.4443359375,\n 34.161818161230386\n ],\n [\n -115.11474609375001,\n 34.1890858311724\n ],\n [\n -115.0872802734375,\n 33.99347299511967\n ],\n [\n -115.40588378906249,\n 33.96158628979907\n ],\n [\n -115.4443359375,\n 34.161818161230386\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"284","issue":"3-4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5480342fe4b0ac64d148dcfd","contributors":{"authors":[{"text":"Majzlan, Juraj","contributorId":127677,"corporation":false,"usgs":false,"family":"Majzlan","given":"Juraj","email":"","affiliations":[{"id":7107,"text":"Univ. of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":526279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":526276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bender Koch, Christian","contributorId":127676,"corporation":false,"usgs":false,"family":"Bender Koch","given":"Christian","email":"","affiliations":[{"id":7106,"text":"Royal Vet. and Ag. Univ, Denmark","active":true,"usgs":false}],"preferred":false,"id":526278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052 rbmccles@usgs.gov","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":147399,"corporation":false,"usgs":true,"family":"McCleskey","given":"R.","email":"rbmccles@usgs.gov","middleInitial":"Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":526277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myneni, Satish B.C.","contributorId":127678,"corporation":false,"usgs":false,"family":"Myneni","given":"Satish","email":"","middleInitial":"B.C.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":526280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neil, John M.","contributorId":13957,"corporation":false,"usgs":false,"family":"Neil","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":526281,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":4908,"text":"twri09A2 - 2014 - Chapter A2. Selection of equipment for water sampling","interactions":[],"lastModifiedDate":"2021-05-27T14:03:25.956426","indexId":"twri09A2","displayToPublicDate":"1999-06-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":336,"text":"Techniques of Water-Resources Investigations","code":"TWRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"09-A2","displayTitle":"Chapter A2. Selection of Equipment for Water Sampling","title":"Chapter A2. Selection of equipment for water sampling","docAbstract":"The National Field Manual for the Collection of Water-Quality Data (National Field Manual) describes protocols and provides guidelines for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation's surface-water and ground-water resources. This chapter of the manual addresses the selection of equipment commonly used by USGS personnel to collect and process water-quality samples. Each chapter of the National Field Manual is published separately and revised periodically. Newly published and revised chapters will be announced on the USGS Home Page on the World Wide Web under 'New Publications of the U.S. Geological Survey.'
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"National Field Manual for the Collection of Water-Quality Data. U.S. Geological Survey Techniques of Water-Resources Investigations, Book 9","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/twri09A2","usgsCitation":"Wilde, F.D., Sandstrom, M.W., and Skrobialowski, S.C., 2014, Chapter A2. Selection of equipment for water sampling (Version 3.1, Revised April 2014 (previous version 2.0, revised March 2003, original version published August 1998)): U.S. Geological Survey Techniques of Water-Resources Investigations 09-A2, vii, 78 p., https://doi.org/10.3133/twri09A2.","productDescription":"vii, 78 p.","numberOfPages":"86","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":363695,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-AO","linkHelpText":"- General Introduction for the “National Field Manual for the Collection of Water-Quality Data”"},{"id":651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/twri/twri9a2/Chapter2_V3-1.pdf","text":"Report","size":"5.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TWRI 09A2","linkHelpText":"- Version 3.1"},{"id":362083,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/twri/twri9a2/Ch2.pdf","text":"Report - August 1998","size":"785 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Original"},{"id":362084,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/twri/twri9a2/Chapter2_V2.pdf","text":"Report - March 2003","linkHelpText":"- Version 2.0"},{"id":139885,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/twri/twri9a2/coverthb.jpg"}],"edition":"Version 3.1, Revised April 2014 (previous version 2.0, revised March 2003, original version published August 1998)","contact":"Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Email: nfm@usgs.gov
","tableOfContents":"Fecal indicator bacteria are used to assess the microbiological quality of water because, although not typically disease causing, they are correlated with the presence of several waterborne disease-causing organisms. The concentration of indicator bacteria is a measure of water safety for body-contact recreation or for consumption. This report provides information on the equipment, sampling protocols, and identification, enumeration, and calculation procedures that are in standard use by U.S. Geological Survey (USGS) personnel for the collection of data on fecal indicator bacteria. Each chapter of the National Field Manual is published separately and revised periodically. Newly published and revised chapters will be announced on the USGS Home Page on the World Wide Web under 'New Publications of the U.S. Geological Survey.'
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"National Field Manual for the Collection of Water-Quality Data. U.S. Geological Survey Techniques of Water-Resources Investigations, Book 9","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/twri09A7.1","usgsCitation":"Myers, D.N., Stoeckel, D.M., Bushon, R.N., Francy, D.S., and Brady A.M.G., 2014, Fecal indicator bacteria (ver. 2.1, revised May 2014): U.S. Geological Survey Techniques of Water-Resources Investigations 09–A7.1, 73 p., https://doi.org/10.3133/twri09A7.1.","productDescription":"Documents: 73 p., 73 p., 64 p.; Related Work","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":363225,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"- General Introduction for the “National Field Manual for the Collection of Water-Quality Data”"},{"id":363187,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/twri/twri9a7/twri9a7_7.1_ver2.0_5-12-14.pdf","text":"Report February 2007 (Section 7.1)","linkHelpText":"- Version 2.0"},{"id":363188,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/twri/twri9a7/twri9a7_7.1_ver1.2.pdf","text":"Report November 2004 (Section 7.1)","linkHelpText":"- Version 1.2"},{"id":165252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":363183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/twri/twri9a7/twri9a7_7.1_ver2.1.pdf","text":"Report Chapter 7.1 - Version 2.1, May 2014","size":"622 KB","linkFileType":{"id":1,"text":"pdf"},"description":"TWRI 9A7.1","linkHelpText":"- Fecal Indicator Bacteria"}],"edition":"Version 1.2, Revised Nov 2004","contact":"Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Email: nfm@usgs.gov
","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e3e4b07f02db5e58ec","contributors":{"authors":[{"text":"Myers, Donna N. 0000-0001-6359-2865 dnmyers@usgs.gov","orcid":"https://orcid.org/0000-0001-6359-2865","contributorId":512,"corporation":false,"usgs":true,"family":"Myers","given":"Donna","email":"dnmyers@usgs.gov","middleInitial":"N.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":219590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sylvester, Marc A.","contributorId":90706,"corporation":false,"usgs":true,"family":"Sylvester","given":"Marc","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":219591,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70174161,"text":"70174161 - 2013 - A manual for remote sensing of Maine lake clarity","interactions":[],"lastModifiedDate":"2021-04-02T16:51:06.997095","indexId":"70174161","displayToPublicDate":"2022-03-26T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8024,"text":"Technical Bulletin of the Maine Agricultural & Forest Experiment Station","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"207","title":"A manual for remote sensing of Maine lake clarity","docAbstract":"The purpose of this manual is to support use of satellite-based remote sensing for statewide lake water-quality monitoring in Maine. The authors describe step-by-step methods that combine Landsat and MODIS satellite data with field-collected Secchi disk data for statewide assessment of lake water clarity. Landsat can be simultaneously used to assess more than Maine 1,000 lakes ≥ 8 ha, whereas MODIS can be used to assess a maximum of 364 lakes ≥ 100 ha (250-m image resolution) or 83 lakes ≥ 400 ha (500-m image resolution). Although the methods were specifically developed for Maine, other states or non-Maine agencies may find these methods as useful starting points in developing their own protocols for regional remote lake monitoring.
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McCullough","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":641009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":641008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sader, Steven A.","contributorId":171436,"corporation":false,"usgs":false,"family":"Sader","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":641010,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70045222,"text":"ofr20121205 - 2013 - Coal fields of the conterminous United States—National Coal Resource Assessment updated version","interactions":[],"lastModifiedDate":"2021-12-16T17:29:20.544054","indexId":"ofr20121205","displayToPublicDate":"2021-12-16T12:32:00","publicationYear":"2013","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":"2012-1205","displayTitle":"Coal fields of the conterminous United States—National Coal Resource Assessment updated version","title":"Coal fields of the conterminous United States—National Coal Resource Assessment updated version","docAbstract":"This map sheet with accompanying Geographic Information System (GIS) project is an update of the existing U.S. Geological Survey (USGS) Conterminous U.S. Coal Fields map. This update was compiled using data primarily from the USGS National Coal Resource Assessment (NCRA) and information from other published maps. The five regions examined by NCRA (Eastern, Gulf Coast, Interior, Rocky Mountain, and Northern Great Plains) constituted 93 percent of U.S. coal production at the time of the assessments. The map sheet shows aerial extent, rank, province, name (region and field), and age information, which are also attributes of the GIS project. Due to changing technological and economic constraints for coal usage, along with the potential for geologic carbon dioxide sequestration, this map sheet and the GIS component of this report do not differentiate between potentially minable coal and uneconomic coal. Additional figures on the map sheet show coal formations, current production by State, coal rank definitions, and charts showing historical trends of coal production.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121205","usgsCitation":"East, J.A., 2013, Coal fields of the conterminous United States—National Coal Resource Assessment updated version: U.S. Geological Survey Open-File Report 2012-1205, Map: 1 Sheet: 60 x 39 inches; Downloads Directory, https://doi.org/10.3133/ofr20121205.","productDescription":"Map: 1 Sheet: 60 x 39 inches; Downloads Directory","costCenters":[],"links":[{"id":270522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121205.png"},{"id":391168,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2012/1205/Coal_Fields_Simplified.pdf","text":"Simplified presentation format map","size":"26.4 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Coal Fields of the Conterminous United States—National Coal Resource Assessment Updated Version in Simplified Presentation Format (2021)"},{"id":270520,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1205/pdf/Coal_Fields_Map.pdf","text":"Report","size":"44.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012-1205"},{"id":270519,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1205/index.html"},{"id":270521,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1205/Downloads"},{"id":391169,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2012/1205/simplified-presentation-format.docx","text":"Simplified presentation format explanation","size":"12.5 KB"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -130.0,22.0 ], [ -130.0,53.0 ], [ -62.0,53.0 ], [ -62.0,22.0 ], [ -130.0,22.0 ] ] ] } } ] }","publicComments":"Scale: 1:5,000,000","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515befd8e4b075500ee5c9fa","contributors":{"authors":[{"text":"East, Joseph A. 0000-0003-4226-9174 jeast@usgs.gov","orcid":"https://orcid.org/0000-0003-4226-9174","contributorId":2747,"corporation":false,"usgs":true,"family":"East","given":"Joseph","email":"jeast@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":477063,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70043821,"text":"sir20125282 - 2013 - Hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in eastern Broome and southeastern Chenango Counties, New York","interactions":[],"lastModifiedDate":"2021-11-17T01:53:35.677134","indexId":"sir20125282","displayToPublicDate":"2021-11-16T08:55:00","publicationYear":"2013","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":"2012-5282","displayTitle":"Hydrogeology of the Susquehanna River Valley-Fill Aquifer System and Adjacent Areas in Eastern Broome and Southeastern Chenango Counties, New York","title":"Hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in eastern Broome and southeastern Chenango Counties, New York","docAbstract":"The hydrogeology of the valley-fill aquifer system along a 32-mile reach of the Susquehanna River valley and adjacent areas was evaluated in eastern Broome and southeastern Chenango Counties, New York. The surficial geology, inferred ice-marginal positions, and distribution of stratified-drift aquifers were mapped from existing data. Ice-marginal positions, which represent pauses in the retreat of glacial ice from the region, favored the accumulation of coarse-grained deposits whereas more steady or rapid ice retreat between these positions favored deposition of fine-grained lacustrine deposits with limited coarse-grained deposits at depth. Unconfined aquifers with thick saturated coarse-grained deposits are the most favorable settings for water-resource development, and three several-mile-long sections of valley were identified (mostly in Broome County) as potentially favorable: (1) the southernmost valley section, which extends from the New York–Pennsylvania border to about 1 mile north of South Windsor, (2) the valley section that rounds the west side of the umlaufberg (an isolated bedrock hill within a valley) north of Windsor, and (3) the east–west valley section at the Broome County–Chenango County border from Nineveh to East of Bettsburg (including the lower reach of the Cornell Brook valley). Fine-grained lacustrine deposits form extensive confining units between the unconfined areas, and the water-resource potential of confined aquifers is largely untested. Recharge, or replenishment, of these aquifers is dependent not only on infiltration of precipitation directly on unconfined aquifers, but perhaps more so from precipitation that falls in adjacent upland areas. Surface runoff and shallow groundwater from the valley walls flow downslope and recharge valley aquifers. Tributary streams that drain upland areas lose flow as they enter main valleys on permeable alluvial fans. This infiltrating water also recharges valley aquifers. Current (2012) use of water resources in the area is primarily through domestic wells, most of which are completed in fractured bedrock in upland areas. A few villages in the Susquehanna River valley have supply wells that draw water from beneath alluvial fans and near the Susquehanna River, which is a large potential source of water from induced infiltration.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125282","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Heisig, P.M., 2013, Hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in eastern Broome and southeastern Chenango Counties, New York: U.S. Geological Survey Scientific Investigations Report 2012–5282, 21 p., at https://pubs.usgs.gov/sir/2012/5282.","productDescription":"vii, 21 p.; 1 Appendix; Map: 1 Sheet: 30.50 x 38.00 inches","startPage":"i","endPage":"21","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":267842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5282.gif"},{"id":267840,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2012/5282/appendix1/Appendix%201.xlsx"},{"id":267839,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5282/"},{"id":267841,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2012/5282/plate.html"}],"scale":"24000","country":"United States","state":"New York","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -79.76,40.48 ], [ -79.76,45.02 ], [ -71.85,45.02 ], [ -71.85,40.48 ], [ -79.76,40.48 ] ] ] } } ] }","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
The Shoreline Management Tool is a geographic information system (GIS) based program developed to assist water- and land-resource managers in assessing the benefits and effects of changes in surface-water stage on water depth, inundated area, and water volume. Additionally, the Shoreline Management Tool can be used to identify aquatic or terrestrial habitat areas where conditions may be suitable for specific plants or animals as defined by user-specified criteria including water depth, land-surface slope, and land-surface aspect. The tool can also be used to delineate areas for use in determining a variety of hydrologic budget components such as surface-water storage, precipitation, runoff, or evapotranspiration.
The Shoreline Management Tool consists of two parts, a graphical user interface for use with Esri™ ArcMap™ GIS software to interact with the user to define scenarios and map results, and a spreadsheet in Microsoft® Excel® developed to display tables and graphs of the results. The graphical user interface allows the user to define a scenario consisting of an inundation level (stage), land areas (parcels), and habitats (areas meeting user-specified conditions) based on water depth, slope, and aspect criteria. The tool uses data consisting of land-surface elevation, tables of stage/volume and stage/area, and delineated parcel boundaries to produce maps (data layers) of inundated areas and areas that meet the habitat criteria. The tool can be run in a Single-Time Scenario mode or in a Time-Series Scenario mode, which uses an input file of dates and associated stages. The spreadsheet part of the tool uses a macro to process the results from the graphical user interface to create tables and graphs of inundated water volume, inundated area, dry area, and mean water depth for each land parcel based on the user-specified stage. The macro also creates tables and graphs of the area, perimeter, and number of polygons comprising the user-specified habitat areas within each parcel.
The Shoreline Management Tool is highly transferable, using easily generated or readily available data. The capabilities of the tool are demonstrated using data from the lower Wood River Valley adjacent to Upper Klamath and Agency Lakes in southern Oregon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121247","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Snyder, D.T., Haluska, T.L., and Respini-Irwin, D., 2013, The Shoreline Management Tool—An ArcMap tool for analyzing water depth, inundated area, volume, and selected habitats, with an example for the lower Wood River Valley, Oregon: U.S. Geological Survey Open-File Report 2012–1247, 86 p. (Also available at https://pubs.usgs.gov/of/2012/1247/.)","productDescription":"Report: viii, 86 p.; 2 Videos: 3 minutes; Companion File","numberOfPages":"98","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":270535,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121247.jpg","text":"Report"},{"id":371153,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1247/videos.zip","text":"Videos","size":"45.4 MB","linkFileType":{"id":6,"text":"zip"}},{"id":270531,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1247/ofr20121247.pdf","text":"Report","size":"9.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012-1247"},{"id":371154,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1247/faq.pdf","text":"Shoreline Management Tool—Frequently Asked Questions","size":"105 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":371166,"rank":5,"type":{"id":21,"text":"Referenced Work"},"url":"https://water.usgs.gov/GIS/dsdl/ShorelineManagementTool_OFR2012-1247_v20130410.zip","text":"Generic version","size":"39 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Intended for use in any area. Required input data must be prepared by the users as described in the report. Example Python scripts are provided to assist with the data preparation. Includes all ancillary files except those specific to the lower Wood River Valley example."},{"id":371167,"rank":6,"type":{"id":21,"text":"Referenced Work"},"url":"https://water.usgs.gov/GIS/dsdl/ShorelineManagementTool_NAVD88_OFR2012-1247_v20130410.zip","text":"Example version for the Lower Wood River Valley, Oregon - NAVD88","size":"1.9 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Contains all required data for use in the lower Wood River Valley of southern Oregon. Ready to run. Utilizes the North American Vertical Datum of 1988 (NAVD88) for elevation reference. Useful for training purposes or examination of input datasets and output results. Includes all ancillary files."},{"id":371168,"rank":7,"type":{"id":21,"text":"Referenced Work"},"url":"https://water.usgs.gov/GIS/dsdl/ShorelineManagementTool_NGVD29_OFR2012-1247_v20130410.zip","text":"Example version for the Lower Wood River Valley, Oregon - NGVD29/UKLVD","size":"2.0 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Contains all required data for use in the lower Wood River Valley of southern Oregon. Ready to run. Utilizes the National Geodetic Vertical Datum of 1929 (NGVD29) for elevation reference. Also contains data for use with the Upper Klamath Lake Vertical Datum (UKLVD). Useful for training purposes or examination of input datasets and output results. Includes all ancillary files."}],"country":"United States","state":"Oregon","otherGeospatial":"Wood River Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.61,42.0 ], [ -124.61,46.29 ], [ -116.46,46.29 ], [ -116.46,42.0 ], [ -124.61,42.0 ] ] ] } } ] }","contact":"Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
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A one-dimensional diffusion analogy model for estimating tide heights in coastal marshes was developed and calibrated by using data from previous tidal-marsh studies. The method is simpler to use than other one- and two-dimensional hydrodynamic models because it does not require marsh depth and tidal prism information; however, the one-dimensional diffusion analogy model cannot be used to estimate tide heights, flow velocities, and tide arrival times for tide conditions other than the highest tide for which it is calibrated. Limited validation of the method indicates that it has an accuracy within 0.3 feet. The method can be applied with limited calibration information that is based entirely on remote sensing or geographic information system data layers. The method can be used to estimate high-tide heights in tidal wetlands drained by tide gates where tide levels cannot be observed directly by opening the gates without risk of flooding properties and structures. A geographic information system application of the method is demonstrated for Sybil Creek marsh in Branford, Connecticut. The tidal flux into this marsh is controlled by two tide gates that prevent full tidal inundation of the marsh. The method application shows reasonable tide heights for the gates-closed condition (the normal condition) and the one-gate-open condition on the basis of comparison with observed heights. The condition with all tide gates open (two gates) was simulated with the model; results indicate where several structures would be flooded if the gates were removed as part of restoration efforts or if the tide gates were to fail.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135076","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Bjerklie, D.M., O’Brien, Kevin, and Rozsa, Ron, 2013, A one-dimensional diffusion analogy model for estimation of tide heights in selected tidal marshes in Connecticut (ver. 1.1, December 2019): U.S. Geological Survey Scientific Investigations Report 2013–5076, 17 p., https://doi.org/10.3133/sir20135076.","productDescription":"iv, 17 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":273622,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5076/index.html"},{"id":370389,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2013/5076/versionhist.txt","size":"485 B","linkFileType":{"id":2,"text":"txt"}},{"id":273623,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5076/sir20135076.pdf","text":"Report","size":"3.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2013-5076"},{"id":273624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2013/5076/coverthb2.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Leetes Island, Pine Creek, Sybil Creek, Wilson Cove Marshes","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.5,41 ], [ -73.5,41.6 ], [ -72.3,41.6 ], [ -72.3,41 ], [ -73.5,41 ] ] ] } } ] }","edition":"Version 1.1: December 30, 2019; Version 1.0 June 11, 2013","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532