In this study, we aim to understand the variability in eruption volume estimates derived from field studies of pyroclastic deposits. We distributed paper maps of the 1959 Kīlauea Iki tephra to 101 volcanologists worldwide, who produced hand-drawn isopachs. Across the returned maps, uncertainty in isopach areas is 7 % across the well-sampled deposit but increases to over 30 % for isopachs that are governed by the largest and smallest thickness measurements. We fit the exponential, power-law, and Weibull functions through the isopach thickness versus area1/2 values and find volume estimate variations up to a factor of 4.9 for a single map. Across all maps and methodologies, we find an average standard deviation for a total volume of s = 29 %. The volume uncertainties are largest for the most proximal (s = 62 %) and distal field (s = 53 %) and small for the densely sampled intermediate deposit (s = 8 %). For the Kīlauea Iki 1959 eruption, we find that the deposit beyond the 5-cm isopach contains only 2 % of the total erupted volume, whereas the near-source deposit contains 48 % and the intermediate deposit 50 % of the total volume. Thus, the relative uncertainty within each zone impacts the total volume estimates differently. The observed uncertainties for the different deposit regions in this study illustrate a fundamental problem of estimating eruption volumes: while some methodologies may provide better fits to the isopach data or rely on fewer free parameters, the main issue remains the predictive capabilities of the empirical functions for the regions where measurements are missing.
","language":"English","publisher":"International Association of Volcanology and Chemistry of the Earth's Interior","doi":"10.1007/s00445-014-0839-1","usgsCitation":"Klawonn, M., Houghton, B.F., Swanson, D., Fagents, S.A., Wessel, P., and Wolfe, C.J., 2014, From field data to volumes: Constraining uncertainties in pyroclastic eruption parameters: Bulletin of Volcanology, v. 76, no. 839, Article 839; 16 p., https://doi.org/10.1007/s00445-014-0839-1.","productDescription":"Article 839; 16 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075487","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":320876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"839","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2014-06-21","publicationStatus":"PW","scienceBaseUri":"5729cbb3e4b0b13d3919a346","contributors":{"authors":[{"text":"Klawonn, Malin","contributorId":169095,"corporation":false,"usgs":false,"family":"Klawonn","given":"Malin","email":"","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":628439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false},{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":628440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, Don 0000-0002-1680-3591 donswan@usgs.gov","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":168817,"corporation":false,"usgs":true,"family":"Swanson","given":"Don","email":"donswan@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":628438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fagents, Sarah A.","contributorId":66152,"corporation":false,"usgs":true,"family":"Fagents","given":"Sarah","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":628441,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wessel, Paul","contributorId":169097,"corporation":false,"usgs":false,"family":"Wessel","given":"Paul","email":"","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":628442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolfe, Cecily J.","contributorId":29294,"corporation":false,"usgs":true,"family":"Wolfe","given":"Cecily","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":628443,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70114068,"text":"70114068 - 2014 - Spatially explicit habitat models for 28 fishes from the Upper Mississippi River System (AHAG 2.0)","interactions":[],"lastModifiedDate":"2014-07-21T13:03:13","indexId":"70114068","displayToPublicDate":"2014-06-20T12:44:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":44,"text":"Long Term Resource Monitoring Program Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2014-T002","title":"Spatially explicit habitat models for 28 fishes from the Upper Mississippi River System (AHAG 2.0)","docAbstract":"Environmental management actions in the Upper Mississippi River System (UMRS) typically require pre-project assessments of predicted benefits under a range of project scenarios. The U.S. Army Corps of Engineers (USACE) now requires certified and peer-reviewed models to conduct these assessments. Previously, habitat benefits were estimated for fish communities in the UMRS using the Aquatic Habitat Appraisal Guide (AHAG v.1.0; AHAG from hereon). This spreadsheet-based model used a habitat suitability index (HSI) approach that drew heavily upon Habitat Evaluation Procedures (HEP; U.S. Fish and Wildlife Service, 1980) by the U.S. Fish and Wildlife Service (USFWS). The HSI approach requires developing species response curves for different environmental variables that seek to broadly represent habitat. The AHAG model uses species-specific response curves assembled from literature values, data from other ecosystems, or best professional judgment.
\nA recent scientific review of the AHAG indicated that the model’s effectiveness is reduced by its dated approach to large river ecosystems, uncertainty regarding its data inputs and rationale for habitat-species response relationships, and lack of field validation (Abt Associates Inc., 2011). The reviewers made two major recommendations: (1) incorporate empirical data from the UMRS into defining the empirical response curves, and (2) conduct post-project biological evaluations to test pre-project benefits estimated by AHAG.
\nOur objective was to address the first recommendation and generate updated response curves for AHAG using data from the Upper Mississippi River Restoration-Environmental Management Program (UMRR-EMP) Long Term Resource Monitoring Program (LTRMP) element. Fish community data have been collected by LTRMP (Gutreuter and others, 1995; Ratcliff and others, in press) for 20 years from 6 study reaches representing 1,930 kilometers of river and >140 species of fish. We modeled a subset of these data (28 different species; occurrences at sampling sites as observed in day electrofishing samples) using multiple logistic regression with presence/absence responses. Each species’ probability of occurrence, at each sample site, was modeled as a function of 17 environmental variables observed at each sample site by LTRMP standardized protocols. The modeling methods used (1) a forward-selection process to identify the most important predictors and their relative contributions to predictions; (2) partial methods on the predictor set to control variance inflation; and (3) diagnostics for LTRMP design elements that may influence model fits.
\nModels were fit for 28 species, representing 3 habitat guilds (Lentic, Lotic, and Generalist). We intended to develop “systemic models” using data from all six LTRMP study reaches simultaneously; however, this proved impossible. Thus, we “regionalized” the models, creating two models for each species: “Upper Reach” models, using data from Pools 4, 8, and 13; and “Lower Reach” models, using data from Pool 26, the Open River Reach of the Mississippi River, and the La Grange reach of the Illinois River. A total of 56 models were attempted. For any given site-scale prediction, each model used data from the three LTRMP study reaches comprising the regional model to make predictions. For example, a site-scale prediction in Pool 8 was made using data from Pools 4, 8, and 13. This is the fundamental nature and trade-off of regionalizing these models for broad management application.
\nModel fits were deemed “certifiably good” using the Hosmer and Lemeshow Goodness-of-Fit statistic (Hosmer and Lemeshow, 2000). This test post-partitions model predictions into 10 groups and conducts inferential tests on correspondences between observed and expected probability of occurrence across all partitions, under Chi-square distributional assumptions. This permits an inferential test of how well the models fit and a tool for reporting when they did not (and perhaps why). Our goal was to develop regionalized models, and to assess and describe circumstances when a good fit was not possible.
\nSeven fish species composed the Lentic guild. Good fits were achieved for six Upper Reach models. In the Lower Reach, no model produced good fits for the Lentic guild. This was due to (1) lentic species being much less prominent in the Lower Reach study areas, and (2) those that do express greater prominence principally do so only in the La Grange reach of the Illinois River. Thus, developing Lower Reach models for Lentic species will require parsing La Grange from the other two Lower Reach study areas and fitting separate models. We did not do that as part of this study, but it could be done at a later time.
\nNine species comprised the Lotic guild. Good fits were achieved for seven Upper Reach models and six Lower Reach models. Four species had good fits for both regions (flathead catfish, blue sucker, sauger, and shorthead redhorse). Three species showed zoogeographic zonation, with a good model fit in one of the regions, but not in the region in which they were absent or rarely occurred (blue catfish, rock bass, and skipjack herring).
\nTwelve species comprised the Generalist guild. Good fits were achieved for five Upper Reach models and eight Lower Reach models. Six species had good fits for both regions (brook silverside, emerald shiner, freshwater drum, logperch, longnose gar, and white bass). Two species showed zoogeographic zonation, with a good model fit in one of the regions, but not in the region in which they were absent or rarely occurred (red shiner and blackstripe topminnow).
\nPoorly fit models were almost always due to the diagnostic variable “field station,” a surrogate for river mile. In these circumstances, the residuals for “field station” were non-randomly distributed and often strongly ordered. This indicates either fitting “pool scale” models for these species and regions, or explicitly model covariances between “field station” and the other predictors within the existing modeling framework. Further efforts on these models should seek to resolve these issues using one of these two approaches.
\nIn total, nine species, representing two of the three guilds (Lotic and Generalist), produced well-fit models for both regions. These nine species should comprise the basis for AHAG 2.0. Additional work, likely requiring downscaling of the regional models to pool-scale models, will be needed to incorporate additional species. Alternately, a regionalized AHAG could be comprised of those species, per region, that achieved well-fit models. The number of species and the composition of the regional species pools will differ among regions as a consequence. Each of these alternatives has both pros and cons, and managers are encouraged to consider them fully before further advancing this approach to modeling multi-species habitat suitability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","collaboration":"A product of the Long Term Resource Monitoring Program, an element of the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration-Environmental Management Program","usgsCitation":"Ickes, B.S., Sauer, J., Richards, N., Bowler, M., and Schlifer, B., 2014, Spatially explicit habitat models for 28 fishes from the Upper Mississippi River System (AHAG 2.0) (First posted online June 20, 2014; Revised and reposted July 21, 2014, version 1.1): Long Term Resource Monitoring Program Technical Report 2014-T002, vi, 89 p.","productDescription":"vi, 89 p.","numberOfPages":"100","onlineOnly":"N","ipdsId":"IP-050554","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":290578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70114068.jpg"},{"id":289011,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/mis/ltrmp2014-t002/"},{"id":290577,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/mis/ltrmp2014-t002/pdf/ltrmp2014-t002.pdf"}],"country":"United States","otherGeospatial":"Upper Mississippi River System","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.24,36.0 ], [ -97.24,49.38 ], [ -86.76,49.38 ], [ -86.76,36.0 ], [ -97.24,36.0 ] ] ] } } ] }","edition":"First posted online June 20, 2014; Revised and reposted July 21, 2014, version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7399e4b0b290851090ab","contributors":{"authors":[{"text":"Ickes, Brian S.","contributorId":6812,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":495248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, J.S.","contributorId":106455,"corporation":false,"usgs":true,"family":"Sauer","given":"J.S.","email":"","affiliations":[],"preferred":false,"id":495252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richards, N.","contributorId":83844,"corporation":false,"usgs":true,"family":"Richards","given":"N.","email":"","affiliations":[],"preferred":false,"id":495249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowler, M.","contributorId":92177,"corporation":false,"usgs":true,"family":"Bowler","given":"M.","email":"","affiliations":[],"preferred":false,"id":495250,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schlifer, B.","contributorId":103588,"corporation":false,"usgs":true,"family":"Schlifer","given":"B.","email":"","affiliations":[],"preferred":false,"id":495251,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70112750,"text":"ofr20141122 - 2014 - Evaluation of the behavior and movement of adult summer steelhead in the lower Cowlitz River, Washington, following collection and release, 2013-2014","interactions":[],"lastModifiedDate":"2014-06-20T12:01:36","indexId":"ofr20141122","displayToPublicDate":"2014-06-20T11:51:00","publicationYear":"2014","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":"2014-1122","title":"Evaluation of the behavior and movement of adult summer steelhead in the lower Cowlitz River, Washington, following collection and release, 2013-2014","docAbstract":"Summer steelhead (Oncorhynchus mykiss) produced by a hatchery on the lower Cowlitz River, Washington, support a popular sport fishery during June–September each year. Many of these fish return to the Cowlitz Salmon Hatchery and are held until they are spawned in December. In the past, fishery managers have released some of the steelhead that return to the hatchery at downstream release sites (hereafter referred to as “recycled steelhead”) to increase angling opportunity. The recycling of summer steelhead is a potential use of hatchery fish that can benefit anglers in the lower Cowlitz River, provided these fish are harvested or return to the hatchery. However, recycled steelhead that are not removed from the river could compete against or spawn with wild winter steelhead, which would be a negative consequence of recycling. The Washington Department of Fish and Wildlife (WDFW) conducted an evaluation during 1998 and recycled 632 summer steelhead. They determined that 55 percent of the recycled steelhead returned to the hatchery and 15 percent of the fish were harvested by anglers. The remaining 30 percent of recycled fish were not known to have been removed from the river. Recycling has not occurred in recent years because definitive studies have not been conducted to determine the fate of the fish that remain in the lower Cowlitz River after being recycled.
\nThe U.S. Geological Survey and WDFW conducted a 2-year study during 2012–2014 to quantify recycled steelhead that (1) returned to the hatchery, (2) were captured by anglers, or (3) remained in the river. All recycled steelhead were marked with a Floy® tag and opercle punch, and 20 percent of the recycled fish were radio-tagged to determine post-release behavior and movement patterns, and to describe locations of tagged fish that remained in the river during the spawning period. During 2012–2013, we recycled 549 steelhead and determined that 50 percent of the fish returned to the hatchery, 18 percent of the fish were harvested by anglers, and 32 percent of the fish were not known to have been removed from the river. During October–December 2012, only 9 percent of the radio-tagged steelhead remained in the lower Cowlitz River and none of these fish entered tributaries monitored by fixed-telemetry sites.
\nThe second year of the evaluation was conducted during 2013–2014. A total of 502 steelhead were recycled during June–August and releases were conducted weekly with group sizes that ranged from 30 to 76 fish. Results from 2013–2014 were similar to results from 2012–2013. Fifty percent (251 fish) of the recycled steelhead returned to the hatchery, 20 percent (100 fish) were harvested by anglers, and 30 percent (151 fish) were unaccounted for. The median elapsed time from release to hatchery return was 13 days, and the median elapsed time from release to capture by an angler was 11 days. The percentage of unaccounted-for steelhead in the general population was moderately high (30 percent), but detection records of radio-tagged fish suggest that few recycled steelhead were present in the lower Cowlitz River during the spawning period.
\nA total of 109 steelhead were radio-tagged during 2013–2014, and most of these fish (88 percent) moved upstream following release and entered the Trout Hatchery–Salmon Hatchery reach (river miles 44–51). The median elapsed time from release to reach entry was 4.6 days (range of 0.5–65.5 days). After fish entered this reach, they spent a considerable amount of time near the Cowlitz Trout Hatchery (median residence time of 16.7 hours) or Cowlitz Salmon Hatchery (median residence time of 146.3 hours), or they moved back and forth between these two sites. Thirty radio-tagged steelhead made at least two trips between the sites and some fish made as many as seven trips. Detection records showed that 61 percent (66 fish) of the radio-tagged fish returned to the hatchery reach and 21 percent (23 fish) of the fish were captured by anglers. The remaining 18 percent (20 fish) of the radio-tagged fish had various fates. One fish (less than 1 percent) left the Cowlitz River and nine fish (8 percent) died, were harvested, or spit their transmitter near boat launches in the river. The remaining 10 fish (9 percent) had the potential to interact with winter steelhead. Four tagged steelhead (4 percent) entered lower Cowlitz River tributaries (two fish in the Toutle River; two fish in Salmon Creek) during October and November, and five tagged fish (5 percent) were last detected in the lower Cowlitz River in October. One fish (less than 1 percent) was never detected after being released.
\nWe measured the diameter of opercle punches in recycled steelhead to determine the temporal effectiveness of these marks. A total of 116 opercle punches were measured—36 were measured at the time of tagging and 80 were measured when fish returned to the hatchery. Opercle punches remained open for less than 1 month. None of the fish that returned to the hatchery more than 30 days after release had opercle punches that were open. All recycled steelhead were marked with a Floy® tag and opercle punch. However, if a steelhead lost its Floy® tag and was captured by an angler, or returned to the hatchery more than 30 days after being recycled, it likely would not have been accurately identified as having been recycled because of regrowth of the opercle punch.
\nDuring 2013–2014, at least 70 percent of the recycled steelhead were removed from the lower Cowlitz River by anglers, returned to the hatchery, or left the river. Radiotelemetry data indicated that a maximum of 9 percent of the radio-tagged fish remained in the lower Cowlitz River during the spawning period and only 4 percent of the radio-tagged fish entered tributaries where wild steelhead are known to spawn. These results are consistent with findings from previous studies. Overall, results from these studies suggest that about one-third of the recycled steelhead were not known to have been removed from the river. However, the radiotelemetry data indicated that only about 10 percent of the recycled steelhead were present in the lower Cowlitz River during late autumn and early winter, and few of those fish (0 in 2012–2013 and 4 in 2013–2014) entered tributaries where winter steelhead spawn. These results have management implications in the lower Cowlitz River where the risks and rewards of steelhead recycling will be weighed to determine the future of the recycling program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141122","collaboration":"Prepared in cooperation with the Washington Department of Fish and Wildlife","usgsCitation":"Kock, T.J., Liedtke, T.L., Ekstrom, B.K., Gleizes, C., and Dammers, W., 2014, Evaluation of the behavior and movement of adult summer steelhead in the lower Cowlitz River, Washington, following collection and release, 2013-2014: U.S. Geological Survey Open-File Report 2014-1122, iv, 20 p., https://doi.org/10.3133/ofr20141122.","productDescription":"iv, 20 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-056741","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":288976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141122.jpg"},{"id":288974,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1122/"},{"id":288975,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1122/pdf/ofr2014-1122.pdf"}],"country":"United States","state":"Washinton","otherGeospatial":"Lower Cowlitz River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.0997,46.0492 ], [ -123.0997,46.6486 ], [ -122.3416,46.6486 ], [ -122.3416,46.0492 ], [ -123.0997,46.0492 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae76ade4b0abf75cf2bfe3","contributors":{"authors":[{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ekstrom, Brian K. 0000-0002-1162-1780 bekstrom@usgs.gov","orcid":"https://orcid.org/0000-0002-1162-1780","contributorId":3704,"corporation":false,"usgs":true,"family":"Ekstrom","given":"Brian","email":"bekstrom@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gleizes, Chris","contributorId":37233,"corporation":false,"usgs":true,"family":"Gleizes","given":"Chris","email":"","affiliations":[],"preferred":false,"id":494863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dammers, Wolf","contributorId":79385,"corporation":false,"usgs":true,"family":"Dammers","given":"Wolf","email":"","affiliations":[],"preferred":false,"id":494864,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70094483,"text":"sir20135238 - 2014 - A comprehensive population dataset for Afghanistan constructed using GIS-based dasymetric mapping methods","interactions":[],"lastModifiedDate":"2014-06-20T10:38:41","indexId":"sir20135238","displayToPublicDate":"2014-06-20T10:29:00","publicationYear":"2014","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":"2013-5238","title":"A comprehensive population dataset for Afghanistan constructed using GIS-based dasymetric mapping methods","docAbstract":"This report summarizes the application of dasymetric methods for mapping the distribution of population throughout Afghanistan. Because Afghanistan's population has constantly changed through decades of war and conflict, existing vector and raster GIS datasets (such as point settlement densities and intensities of lights at night) do not adequately reflect the changes. The purposes of this report are (1) to provide historic population data at the provincial and district levels that can be used to chart population growth and migration trends within the country and (2) to provide baseline information that can be used for other types of spatial analyses of Afghanistan, such as resource and hazard assessments; infrastructure and capacity rebuilding; and assisting with international, regional, and local planning.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135238","collaboration":"Prepared in cooperation with the Task Force for Business and Stability Operations, Department of Defense","usgsCitation":"Thompson, A.L., and Hubbard, B.E., 2014, A comprehensive population dataset for Afghanistan constructed using GIS-based dasymetric mapping methods: U.S. Geological Survey Scientific Investigations Report 2013-5238, Report: iv, 20 p.; Downloads Directory, https://doi.org/10.3133/sir20135238.","productDescription":"Report: iv, 20 p.; Downloads Directory","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-043149","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":288962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135238.jpg"},{"id":288960,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5238/downloads"},{"id":288958,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5238/"},{"id":288959,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5238/pdf/sir2013-5238.pdf"}],"projection":"Transverse Mercator projection","datum":"World Geodetic System 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7610e4b0abf75cf2be6b","contributors":{"authors":[{"text":"Thompson, Allyson L.","contributorId":90575,"corporation":false,"usgs":true,"family":"Thompson","given":"Allyson","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":490613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":490612,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70102278,"text":"sir20145066 - 2014 - Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models","interactions":[],"lastModifiedDate":"2014-06-20T08:26:05","indexId":"sir20145066","displayToPublicDate":"2014-06-20T08:12:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5066","title":"Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models","docAbstract":"Water quality, habitat, and fish in Minnesota lakes will potentially be facing substantial levels of stress in the coming decades primarily because of two stressors: (1) land-use change (urban and agricultural) and (2) climate change. Several regional and statewide lake modeling studies have identified the potential linkages between land-use and climate change on reductions in the volume of suitable lake habitat for coldwater fish populations. In recent years, water-resource scientists have been making the case for focused assessments and monitoring of sentinel systems to address how these stress agents change lakes over the long term. Currently in Minnesota, a large-scale effort called “Sustaining Lakes in a Changing Environment” is underway that includes a focus on monitoring basic watershed, water quality, habitat, and fish indicators of 24 Minnesota sentinel lakes across a gradient of ecoregions, depths, and nutrient levels. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, developed predictive water quality models to assess water quality and habitat dynamics of three select deepwater lakes in Minnesota. The three lakes (Lake Carlos in Douglas County, Elk Lake in Clearwater County, and Trout Lake in Cook County) were assessed under recent (2010–11) meteorological conditions. The three selected lakes contain deep, coldwater habitats that remain viable during the summer months for coldwater fish species.
\nHydrodynamics and water-quality characteristics for each of the three lakes were simulated using the CE-QUAL-W2 model, which is a carbon-based, laterally averaged, two-dimensional water-quality model. The CE-QUAL-W2 models address the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of temperature and oxygen in lakes.
\nThe CE-QUAL-W2 models for all three lakes successfully predicted water temperature, on the basis of the two metrics of absolute mean error and root mean square error, using measured inputs of water temperature and nutrients. One of the main calibration tools for CE-QUAL-W2 model development was the vertical profile temperature data, available for all three lakes. For all three lakes, the absolute mean error and root mean square error were less than 1.0 degree Celsius and 1.2 degrees Celsius, respectively, for the different depth ranges used for vertical profile comparisons. In Lake Carlos, simulated water temperatures compared better to measured water temperatures in the epilimnion than in the hypolimnion. The reverse was true for the other two lakes, Elk Lake and Trout Lake, where the simulated results were slightly better for the hypolimnion than the epilimnion. The model also was used to approximate the location of the thermocline throughout the simulation periods, approximately April to November, in all three lake models. Deviations between the simulated and measured water temperatures in the vertical lake profile commonly were because of an offset in the timing of thermocline shifts rather than the simulated results missing thermocline shifts altogether.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145066","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources","usgsCitation":"Smith, E.A., Kiesling, R.L., Galloway, J.M., and Ziegeweid, J.R., 2014, Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models: U.S. Geological Survey Scientific Investigations Report 2014-5066, xi, 73 p., https://doi.org/10.3133/sir20145066.","productDescription":"xi, 73 p.","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-016416","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":288945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145066.jpg"},{"id":288939,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5066/"},{"id":288944,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5066/pdf/sir2014-5066.pdf"}],"projection":"Universal Transverse Mercator Zone 15 North","datum":"North American Datum of 1983","country":"United States","state":"Minnesota","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.426,43.3158 ], [ -97.426,49.4915 ], [ -89.2941,49.4915 ], [ -89.2941,43.3158 ], [ -97.426,43.3158 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae78a3e4b0abf75cf2dc0e","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492873,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70113339,"text":"70113339 - 2014 - Concentrations of polycyclic aromatic hydrocarbons (PAHs) and azaarenes in runoff from coal-tar- and asphalt-sealcoated pavement","interactions":[],"lastModifiedDate":"2014-06-19T15:48:19","indexId":"70113339","displayToPublicDate":"2014-06-19T15:46:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Concentrations of polycyclic aromatic hydrocarbons (PAHs) and azaarenes in runoff from coal-tar- and asphalt-sealcoated pavement","docAbstract":"Coal-tar-based sealcoat, used extensively on parking lots and driveways in North America, is a potent source of PAHs. We investigated how concentrations and assemblages of PAHs and azaarenes in runoff from pavement newly sealed with coal-tar-based (CT) or asphalt-based (AS) sealcoat changed over time. Samples of simulated runoff were collected from pavement 5 h to 111 d following application of AS or CT sealcoat. Concentrations of the sum of 16 PAHs (median concentrations of 328 and 35 μg/L for CT and AS runoff, respectively) in runoff varied relatively little, but rapid decreases in concentrations of azaarenes and low molecular weight PAHs were offset by increases in high molecular weight PAHs. The results demonstrate that runoff from CT-sealcoated pavement, in particular, continues to contain elevated concentrations of PAHs long after a 24-h curing time, with implications for the fate, transport, and ecotoxicological effects of contaminants in runoff from CT-sealcoated pavement.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Pollution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2014.01.008","usgsCitation":"Mahler, B., Van Metre, P., and Foreman, W., 2014, Concentrations of polycyclic aromatic hydrocarbons (PAHs) and azaarenes in runoff from coal-tar- and asphalt-sealcoated pavement: Environmental Pollution, v. 188, p. 81-87, https://doi.org/10.1016/j.envpol.2014.01.008.","productDescription":"7 p.","startPage":"81","endPage":"87","numberOfPages":"7","ipdsId":"IP-053111","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":288936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288930,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.envpol.2014.01.008"}],"volume":"188","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae765ee4b0abf75cf2bf49","contributors":{"authors":[{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":495061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Metre, Peter C.","contributorId":34104,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter C.","affiliations":[],"preferred":false,"id":495063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foreman, William T. wforeman@usgs.gov","contributorId":1473,"corporation":false,"usgs":true,"family":"Foreman","given":"William T.","email":"wforeman@usgs.gov","affiliations":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":false,"id":495062,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70098933,"text":"ofr20141061 - 2014 - Particle-tracking investigation of the retention of sucker larvae emerging from spawning grounds in Upper Klamath Lake, Oregon","interactions":[],"lastModifiedDate":"2014-06-19T13:11:03","indexId":"ofr20141061","displayToPublicDate":"2014-06-19T12:56:00","publicationYear":"2014","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":"2014-1061","title":"Particle-tracking investigation of the retention of sucker larvae emerging from spawning grounds in Upper Klamath Lake, Oregon","docAbstract":"This study had two objectives: (1) to use the results of an individual-based particle-tracking model of larval sucker dispersal through the Williamson River delta and Upper Klamath Lake, Oregon, to interpret field data collected throughout Upper Klamath and Agency Lakes, and (2) to use the model to investigate the retention of sucker larvae in the system as a function of Williamson River flow, wind, and lake elevation. This is a follow-up study to work reported in Wood and others (2014) in which the hydrodynamic model of Upper Klamath Lake was combined with an individual-based, particle-tracking model of larval fish entering the lake from spawning areas in the Williamson River. In the previous study, the performance of the model was evaluated through comparison with field data comprising larval sucker distribution collected in 2009 by The Nature Conservancy, Oregon State University (OSU), and the U.S. Geological Survey, primarily from the (at that time) recently reconnected Williamson River Delta and along the eastern shoreline of Upper Klamath Lake, surrounding the old river mouth. The previous study demonstrated that the validation of the model with field data was moderately successful and that the model was useful for describing the broad patterns of larval dispersal from the river, at least in the areas surrounding the river channel immediately downstream of the spawning areas and along the shoreline where larvae enter the lake.
\nIn this study, field data collected by OSU throughout the main body of Upper Klamath Lake, and not just around the Williamson River Delta, were compared to model simulation results. Because the field data were collected throughout the lake, it was necessary to include in the simulations larvae spawned at eastern shoreline springs that were not included in the earlier studies. A complicating factor was that the OSU collected data throughout the main body of the lake in 2011 and 2012, after the end of several years of larval drift collection in the Williamson River by the U.S. Geological Survey. Those larval drift data provided necessary boundary-condition information for the earlier studies, but there were no measured boundary conditions for larval input into model simulations during the years of this study (2011−12). Therefore, we developed a method to estimate a time series of larval drift in the Williamson River, and of the emergence of larvae from the gravel at the eastern shoreline springs, that captured the approximate timing of the larval pulse of the Lost River sucker (Deltistes luxatus) and shortnose sucker (Chasmistes brevirostris) and the relative magnitude of the pulses by species and spawning location. The method is not able to predict larval drift on any given day, but it can reasonably predict the approximate temporal progression of the larval drift through the season, based on counts of adult suckers returning to spawn. The accuracy in the timing of the larval pulses is not better than about plus or minus 5 days.
\nModel results and field data were consistent in the basic progression of both catch per unit effort (CPUE) and larval length through time. The model simulation results also duplicated some of the characteristics of the spatial patterns of density in the field data, notably the tendency for high larval densities closer to the eastern and western shorelines. However, the model simulations could not explain high densities in the northern part of the lake or far into Ball Bay, locations that are far from the source of larvae in the Williamson River or eastern shoreline springs (as measured along the predominant transport pathways simulated in the model). This suggests the possibility of unaccounted-for spawning areas in the northern part of the lake and also that the period during which larvae are transported passively by the currents is shorter than the 46 days simulated in the model. Similarly, the progression of larval lengths in the field data is not a simple progression from smaller to larger fish away from sources in the river and springs, as simulated by the particle-tracking model; the smallest fish were caught at different times near the Williamson River, in the northwestern part of the lake, and in the southernmost part of the lake. This again suggests that fish may be spawning at places other than the river and eastern springs, that our understanding of larval transport is incomplete, or both.
\nThe model was used to run 96 numerical “experiments” in which lake elevation, river discharge, and wind forcing were varied systematically in order to investigate the sensitivity of particle retention to each variable, and with particular emphasis on the idea of managing lake elevation to control emigration. The estimates of particle retention cannot be equated directly to retention of fish larvae, primarily because there was no mortality included in the simulations, but the relative comparison of retention and emigration around the matrix of experimental conditions provided several “big picture” results:
\n - Variables that cannot be controlled—winds and discharge—had the largest effect on retention. For example, at the lowest river discharge (20 cubic meters per second), simulated retention was high regardless of wind or lake elevation, whereas at the highest river discharge (100 cubic meters per second), retention was low regardless of wind or lake elevation.
\n - When river discharge and wind were held constant, a higher elevation delayed the onset of the most rapid exit of particles by 1 (from the springs) to 4 (from the river) days, but did not determine overall retention. Only under the combination of conditions consisting of low discharge (50 cubic meters per second or less) and strong wind reversals for several days was there a consistent effect of lake elevation on overall retention several weeks into the simulation, and, under those conditions, retention was at the high end of the possible range regardless of lake elevation.
\n - Under most combinations of conditions tested, after particles had been in the system for several days, the complex interaction between wind, elevation, and river discharge resulted in particle pathways, and therefore retention, being highly variable and unpredictable, at which point controlling lake elevation could not produce a predictable result. Therefore, on the basis of the model predictions, managing lake elevation probably is not a way to reliably provide any particular level of retention.
Recently, effects of lakes and reservoirs on river nutrient export have been incorporated into landscape biogeochemical models. Because annual export varies with precipitation, there is a need to examine the biogeochemical role of lakes and reservoirs over time frames that incorporate interannual variability in precipitation. We examined long-term (~20 years) time series of river export (annual mass yield, Y, and flow-weighted mean annual concentration, C) for total nitrogen (TN), total phosphorus (TP), and total suspended sediment (TSS) from 54 catchments in Wisconsin, USA. Catchments were classified as small agricultural, large agricultural, and forested by use of a cluster analysis, and these varied in lentic coverage (percentage of catchment lake or reservoir water that was connected to river network). Mean annual export and interannual variability (CV) of export (for both Y and C) were higher in agricultural catchments relative to forested catchments for TP, TN, and TSS. In both agricultural and forested settings, mean and maximum annual TN yields were lower in the presence of lakes and reservoirs, suggesting lentic denitrification or N burial. There was also evidence of long-term lentic TP and TSS retention, especially when viewed in terms of maximum annual yield, suggesting sedimentation during high loading years. Lentic catchments had lower interannual variability in export. For TP and TSS, interannual variability in mass yield was often >50% higher than interannual variability in water yield, whereas TN variability more closely followed water (discharge) variability. Our results indicate that long-term mass export through rivers depends on interacting terrestrial, aquatic, and meteorological factors in which the presence of lakes and reservoirs can reduce the magnitude of export, stabilize interannual variability in export, as well as introduce export time lags.
","language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1002/hyp.10083","usgsCitation":"Powers, S.M., Robertson, D.M., and Stanley, E.H., 2014, Effects of lakes and reservoirs on annual river nitrogen, phosphorus, and sediment export in agricultural and forested landscapes: Hydrological Processes, v. 28, no. 24, p. 5919-5937, https://doi.org/10.1002/hyp.10083.","productDescription":"19 p.","startPage":"5919","endPage":"5937","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050925","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":288915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288913,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/hyp.10083"}],"country":"United States","state":"Wisconsin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.89,42.49 ], [ -92.89,47.08 ], [ -86.76,47.08 ], [ -86.76,42.49 ], [ -92.89,42.49 ] ] ] } } ] }","volume":"28","issue":"24","noUsgsAuthors":false,"publicationDate":"2013-11-05","publicationStatus":"PW","scienceBaseUri":"53ae7698e4b0abf75cf2bfbe","contributors":{"authors":[{"text":"Powers, Stephen M.","contributorId":35238,"corporation":false,"usgs":false,"family":"Powers","given":"Stephen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":495046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":495045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":495047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70125291,"text":"70125291 - 2014 - Testing for multiple invasion routes and source populations for the invasive brown treesnake (Boiga irregularis) on Guam: implications for pest management","interactions":[],"lastModifiedDate":"2014-09-16T11:50:47","indexId":"70125291","displayToPublicDate":"2014-06-19T11:49:46","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Testing for multiple invasion routes and source populations for the invasive brown treesnake (Boiga irregularis) on Guam: implications for pest management","docAbstract":"The brown treesnake (Boiga irregularis) population on the Pacific island of Guam has reached iconic status as one of the most destructive invasive species of modern times, yet no published works have used genetic data to identify a source population. We used DNA sequence data from multiple genetic markers and coalescent-based phylogenetic methods to place the Guam population within the broader phylogeographic context of B. irregularis across its native range and tested whether patterns of genetic variation on the island are consistent with one or multiple introductions from different source populations. We also modeled a series of demographic scenarios that differed in the effective size and duration of a population bottleneck immediately following the invasion on Guam, and measured the fit of these simulations to the observed data using approximate Bayesian computation. Our results exclude the possibility of serial introductions from different source populations, and instead verify a single origin from the Admiralty Archipelago off the north coast of Papua New Guinea. This finding is consistent with the hypothesis thatB. irregularis was accidentally transported to Guam during military relocation efforts at the end of World War II. Demographic model comparisons suggest that multiple snakes were transported to Guam from the source locality, but that fewer than 10 individuals could be responsible for establishing the population. Our results also provide evidence that low genetic diversity stemming from the founder event has not been a hindrance to the ecological success of B. irregularis on Guam, and at the same time offers a unique ‘genetic opening’ to manage snake density using classical biological approaches.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biological Invasions","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10530-014-0733-y","usgsCitation":"Richmond, J.Q., Wood, D.A., Stanford, J.W., and Fisher, R.N., 2014, Testing for multiple invasion routes and source populations for the invasive brown treesnake (Boiga irregularis) on Guam: implications for pest management: Biological Invasions, https://doi.org/10.1007/s10530-014-0733-y.","ipdsId":"IP-056130","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":293944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293873,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10530-014-0733-y"}],"noUsgsAuthors":false,"publicationDate":"2014-06-19","publicationStatus":"PW","scienceBaseUri":"54195157e4b091c7ffc8e870","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanford, James W.","contributorId":65775,"corporation":false,"usgs":true,"family":"Stanford","given":"James","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":501152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501149,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70113254,"text":"70113254 - 2014 - Gully annealing by aeolian sediment: field and remote-sensing investigation of aeolian-hillslope-fluvial interactions, Colorado River corridor, Arizona, USA","interactions":[],"lastModifiedDate":"2014-06-19T11:32:32","indexId":"70113254","displayToPublicDate":"2014-06-19T11:25:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Gully annealing by aeolian sediment: field and remote-sensing investigation of aeolian-hillslope-fluvial interactions, Colorado River corridor, Arizona, USA","docAbstract":"Processes contributing to development of ephemeral gully channels are of great importance to landscapes worldwide, and particularly in dryland regions where soil loss and land degradation from gully erosion pose long-term land-management problems. Whereas gully formation has been relatively well studied, much less is known of the processes that anneal gullies and impede their growth. This study of gully annealing by aeolian sediment, spanning 95 km along the Colorado River corridor in Glen, Marble, and Grand Canyon, Arizona, USA, employed field and remote sensing observations, including digital topographic modelling. Results indicate that aeolian sediment activity can be locally effective at counteracting gully erosion. Gullies are less prevalent in areas where surficial sediment undergoes active aeolian transport, and have a greater tendency to terminate in active aeolian sand. Although not common, examples exist in the record of historical imagery of gullies that underwent infilling by aeolian sediment in past decades and evidently were effectively annealed. We thus provide new evidence for a potentially important interaction of aeolian–hillslope–fluvial processes, which could affect dryland regions substantially in ways not widely recognized. Moreover, because the biologic soil crust plays an important role in determining aeolian sand activity, and so in turn the extent of gully development, this study highlights a critical role of geomorphic–ecologic interactions in determining arid-landscape evolution.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geomorphology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2014.05.028","usgsCitation":"Sankey, J.B., and Draut, A.E., 2014, Gully annealing by aeolian sediment: field and remote-sensing investigation of aeolian-hillslope-fluvial interactions, Colorado River corridor, Arizona, USA: Geomorphology, v. 220, p. 68-80, https://doi.org/10.1016/j.geomorph.2014.05.028.","productDescription":"13 p.","startPage":"68","endPage":"80","numberOfPages":"13","ipdsId":"IP-052875","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":288905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288903,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geomorph.2014.05.028"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River;Glen Canyon;Grand Canyon;Marble Canyon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.43,30.69 ], [ -118.43,44.01 ], [ -104.06,44.01 ], [ -104.06,30.69 ], [ -118.43,30.69 ] ] ] } } ] }","volume":"220","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7732e4b0abf75cf2c0a1","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":495025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Draut, Amy E.","contributorId":92215,"corporation":false,"usgs":true,"family":"Draut","given":"Amy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":495026,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70111838,"text":"fs20143053 - 2014 - The 3D Elevation Program: summary for Oklahoma","interactions":[],"lastModifiedDate":"2016-08-17T15:39:45","indexId":"fs20143053","displayToPublicDate":"2014-06-19T11:18:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3053","title":"The 3D Elevation Program: summary for Oklahoma","docAbstract":"Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Oklahoma, elevation data are critical for flood risk management, infrastructure and construction management, agriculture and precision farming, natural resources conservation, wildlife and habitat management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of local, State, and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment (NEEA; Dewberry, 2011) evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":494479,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70104185,"text":"sir20145078 - 2014 - Estimation of flood discharges at selected annual exceedance probabilities for unregulated, rural streams in Vermont, with a section on Vermont regional skew regression","interactions":[],"lastModifiedDate":"2014-06-19T11:15:42","indexId":"sir20145078","displayToPublicDate":"2014-06-19T11:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5078","title":"Estimation of flood discharges at selected annual exceedance probabilities for unregulated, rural streams in Vermont, with a section on Vermont regional skew regression","docAbstract":"This report provides estimates of flood discharges at selected annual exceedance probabilities (AEPs) for streamgages in and adjacent to Vermont and equations for estimating flood discharges at AEPs of 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent (recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-years, respectively) for ungaged, unregulated, rural streams in Vermont. The equations were developed using generalized least-squares regression. Flood-frequency and drainage-basin characteristics from 145 streamgages were used in developing the equations. The drainage-basin characteristics used as explanatory variables in the regression equations include drainage area, percentage of wetland area, and the basin-wide mean of the average annual precipitation. The average standard errors of prediction for estimating the flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 36.0, 38.7, 42.4, 44.9, 47.3, 50.7, and 55.1 percent, respectively.
\nFlood discharges at selected AEPs for streamgages were computed by using the Expected Moments Algorithm. To improve estimates of the flood discharges for given exceedance probabilities at streamgages in Vermont, a new generalized skew coefficient was developed. The new generalized skew for the region is a constant, 0.44. The mean square error of the generalized skew coefficient is 0.078. This report describes a technique for using results from the regression equations to adjust an AEP discharge computed from a streamgage record. This report also describes a technique for using a drainage-area adjustment to estimate flood discharge at a selected AEP for an ungaged site upstream or downstream from a streamgage.
\nThe final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is a World Wide Web application providing automated regression-equation solutions for user-selected sites on streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145078","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Olson, S.A., and with a section by Veilleux, A.G., 2014, Estimation of flood discharges at selected annual exceedance probabilities for unregulated, rural streams in Vermont, with a section on Vermont regional skew regression: U.S. Geological Survey Scientific Investigations Report 2014-5078, Report: vi, 27 p.; Appendixes 1-8, 7, 9, https://doi.org/10.3133/sir20145078.","productDescription":"Report: vi, 27 p.; Appendixes 1-8, 7, 9","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-052679","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":288898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145078.jpg"},{"id":288893,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5078/"},{"id":288894,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5078/pdf/sir2014-5078.pdf"},{"id":288895,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5078/appendix/sir2014-5078_olson_apend01-08.xlsx"},{"id":288897,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5078/appendix/sir2014-5078_olson_apend09.xlsx"},{"id":288896,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5078/appendix/sir2014-5078_olson_apend07.pdf"}],"country":"United States","state":"Vermont","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -74.7537,42.1919 ], [ -74.7537,45.5564 ], [ -70.5267,45.5564 ], [ -70.5267,42.1919 ], [ -74.7537,42.1919 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae76aae4b0abf75cf2bfd6","contributors":{"authors":[{"text":"Olson, Scott A. 0000-0002-1064-2125 solson@usgs.gov","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":2059,"corporation":false,"usgs":true,"family":"Olson","given":"Scott","email":"solson@usgs.gov","middleInitial":"A.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"with a section by Veilleux, Andrea G.","contributorId":74302,"corporation":false,"usgs":true,"family":"with a section by Veilleux","given":"Andrea","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":493626,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70113245,"text":"70113245 - 2014 - Effects of recruitment, growth, and exploitation on walleye population size structure in northern Wisconsin lakes","interactions":[],"lastModifiedDate":"2014-06-19T10:57:57","indexId":"70113245","displayToPublicDate":"2014-06-19T10:52:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of recruitment, growth, and exploitation on walleye population size structure in northern Wisconsin lakes","docAbstract":"We evaluated the dynamics of walleye Sander vitreus population size structure, as indexed by the proportional size distribution (PSD) of quality-length fish, in Escanaba Lake during 1967–2003 and in 204 other lakes in northern Wisconsin during 1990–2011. We estimated PSD from angler-caught walleyes in Escanaba Lake and from spring electrofishing in 204 other lakes, and then related PSD to annual estimates of recruitment to age-3, length at age 3, and annual angling exploitation rate. In Escanaba Lake during 1967–2003, annual estimates of PSD were highly dynamic, growth (positively) explained 35% of PSD variation, recruitment explained only 3% of PSD variation, and exploitation explained only 7% of PSD variation. In 204 other northern Wisconsin lakes during 1990–2011, PSD varied widely among lakes, recruitment (negatively) explained 29% of PSD variation, growth (positively) explained 21% of PSD variation, and exploitation explained only 4% of PSD variation. We conclude that population size structure was most strongly driven by recruitment and growth, rather than exploitation, in northern Wisconsin walleye populations. Studies of other species over wide spatial and temporal ranges of recruitment, growth, and mortality are needed to determine which dynamic rate most strongly influences population size structure of other species. Our findings indicate a need to be cautious about assuming exploitation is a strong driver of walleye population size structure.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Fish and Wildlife Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/092013-JFWM-065","usgsCitation":"Hansen, M.J., and Nate, N.A., 2014, Effects of recruitment, growth, and exploitation on walleye population size structure in northern Wisconsin lakes: Journal of Fish and Wildlife Management, v. 5, no. 1, p. 99-108, https://doi.org/10.3996/092013-JFWM-065.","productDescription":"10 p.","startPage":"99","endPage":"108","numberOfPages":"10","ipdsId":"IP-055653","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":472931,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/092013-jfwm-065","text":"Publisher Index Page"},{"id":288891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288888,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3996/092013-JFWM-065"}],"country":"United States","state":"Wisconsin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.89,42.49 ], [ -92.89,47.08 ], [ -86.76,47.08 ], [ -86.76,42.49 ], [ -92.89,42.49 ] ] ] } } ] }","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-03-01","publicationStatus":"PW","scienceBaseUri":"53ae769ae4b0abf75cf2bfc1","contributors":{"authors":[{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":495016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":495017,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187705,"text":"70187705 - 2014 - A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010","interactions":[],"lastModifiedDate":"2017-05-15T14:44:11","indexId":"70187705","displayToPublicDate":"2014-06-19T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010","docAbstract":"Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area estimates revealed that total mangrove area decreased by approximately half (51.8%) from 1918 to 2010. This study provides the most current and reliable data regarding the Philippines mangrove area and spatial distribution and delineates where and when mangrove change has occurred in recent decades. The results from this study are useful for developing conservation strategies, biodiversity loss mitigation efforts, and future monitoring and analysis.
","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-13-00057.1","usgsCitation":"Long, J., Napton, D., Giri, C., and Graesser, J., 2014, A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010: Journal of Coastal Research, v. 30, no. 2, p. 260-271, https://doi.org/10.2112/JCOASTRES-D-13-00057.1.","productDescription":"12 p.","startPage":"260","endPage":"271","ipdsId":"IP-046144","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Phillippines","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[126.37681,8.41471],[126.47851,7.75035],[126.53742,7.18938],[126.19677,6.27429],[125.83142,7.29372],[125.36385,6.78649],[125.68316,6.04966],[125.39651,5.581],[124.21979,6.16136],[123.93872,6.88514],[124.24366,7.36061],[123.61021,7.83353],[123.29607,7.41888],[122.82551,7.45737],[122.0855,6.89942],[121.91993,7.19212],[122.31236,8.03496],[122.9424,8.31624],[123.48769,8.69301],[123.84115,8.24032],[124.60147,8.51416],[124.76461,8.96041],[125.47139,8.987],[125.41212,9.76033],[126.22271,9.28607],[126.30664,8.78249],[126.37681,8.41471]]],[[[123.98244,10.27878],[123.62318,9.95009],[123.30992,9.31827],[122.99588,9.02219],[122.38005,9.71336],[122.58609,9.98104],[122.83708,10.26116],[122.94741,10.88187],[123.49885,10.94062],[123.33777,10.26738],[124.07794,11.23273],[123.98244,10.27878]]],[[[118.50458,9.31638],[117.17427,8.3675],[117.66448,9.06689],[118.38691,9.6845],[118.98734,10.37629],[119.5115,11.36967],[119.68968,10.55429],[119.02946,10.00365],[118.50458,9.31638]]],[[[121.88355,11.89176],[122.48382,11.58219],[123.12022,11.58366],[123.10084,11.16593],[122.63771,10.74131],[122.00261,10.44102],[121.96737,10.90569],[122.03837,11.41584],[121.88355,11.89176]]],[[[125.50255,12.16269],[125.78346,11.04612],[125.01188,11.31145],[125.03276,10.97582],[125.27745,10.35872],[124.80182,10.13468],[124.76017,10.838],[124.4591,10.88993],[124.30252,11.49537],[124.89101,11.41558],[124.87799,11.79419],[124.26676,12.55776],[125.22712,12.53572],[125.50255,12.16269]]],[[[121.52739,13.06959],[121.26219,12.20556],[120.8339,12.7045],[120.32344,13.46641],[121.18013,13.4297],[121.52739,13.06959]]],[[[121.32131,18.50406],[121.9376,18.21855],[122.24601,18.47895],[122.33696,18.22488],[122.17428,17.81028],[122.51565,17.0935],[122.25231,16.26244],[121.66279,15.93102],[121.50507,15.12481],[121.72883,14.32838],[122.25893,14.2182],[122.70128,14.33654],[123.9503,13.78213],[123.85511,13.23777],[124.18129,12.99753],[124.07742,12.53668],[123.29804,13.02753],[122.92865,13.55292],[122.67136,13.18584],[122.03465,13.78448],[121.12638,13.63669],[120.62864,13.85766],[120.67938,14.27102],[120.99182,14.52539],[120.69334,14.75667],[120.56415,14.39628],[120.07043,14.97087],[119.92093,15.40635],[119.88377,16.3637],[120.28649,16.03463],[120.39005,17.59908],[120.71587,18.50523],[121.32131,18.50406]]]]},\"properties\":{\"name\":\"Philippines\"}}]}","volume":"30","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe38e4b0a7fdb43c8bfb","contributors":{"authors":[{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Napton, Darrell","contributorId":176288,"corporation":false,"usgs":false,"family":"Napton","given":"Darrell","affiliations":[],"preferred":false,"id":695186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graesser, Jordan","contributorId":192030,"corporation":false,"usgs":false,"family":"Graesser","given":"Jordan","email":"","affiliations":[],"preferred":false,"id":695189,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189943,"text":"70189943 - 2014 - Modeling low-temperature geochemical processes:","interactions":[],"lastModifiedDate":"2022-12-09T16:44:03.209979","indexId":"70189943","displayToPublicDate":"2014-06-19T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7.2","title":"Modeling low-temperature geochemical processes:","docAbstract":"This chapter provides an overview of geochemical modeling that applies to water–rock interactions under ambient conditions of temperature and pressure. Topics include modeling definitions, historical background, issues of activity coefficients, popular codes and databases, examples of modeling common types of water–rock interactions, and issues of model reliability. Examples include speciation, microbial redox kinetics and ferrous iron oxidation, calcite dissolution, pyrite oxidation, combined pyrite and calcite dissolution, dedolomitization, seawater–carbonate groundwater mixing, reactive-transport modeling in streams, modeling catchments, and evaporation of seawater. The chapter emphasizes limitations to geochemical modeling: that a proper understanding and ability to communicate model results well are as important as completing a set of useful modeling computations and that greater sophistication in model and code development is not necessarily an advancement. If the goal is to understand how a particular geochemical system behaves, it is better to collect more field data than rely on computer codes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences: Treatise on geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/B978-0-08-095975-7.00502-7","usgsCitation":"Nordstrom, D.K., and Campbell, K.M., 2014, Modeling low-temperature geochemical processes:, chap. 7.2 of Reference module in earth systems and environmental sciences: Treatise on geochemistry, v. 7, p. 27-68, https://doi.org/10.1016/B978-0-08-095975-7.00502-7.","productDescription":"42 p.","startPage":"27","endPage":"68","ipdsId":"IP-038052","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":345118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","edition":"2nd Edition","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599fe5bce4b038630d022110","contributors":{"authors":[{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":706839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706840,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70112913,"text":"sir20145073 - 2014 - Streamflow, water quality, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007-12","interactions":[],"lastModifiedDate":"2014-06-18T15:06:54","indexId":"sir20145073","displayToPublicDate":"2014-06-18T15:01:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5073","title":"Streamflow, water quality, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007-12","docAbstract":"Efforts to mitigate the effects of urbanization on streams rely on best management practices (BMPs) that are implemented with the intent of reducing and retaining stormwater runoff. A cooperative monitoring effort between the U.S. Geological Survey and Fairfax County, Virginia, was initiated in 2007 to assess the condition of county streams and document watershed-scale responses to the implementation of BMPs. Assessment of the data collected during the first 5 years of this monitoring program focused on characterizing the hydrologic and ecological condition of 14 monitored streams.
\nHydrologic, chemical, and macroinvertebrate community conditions in the streams monitored were found to be consistent, overall, with conditions commonly observed in urban streams. Hydrologically, the monitored streams were found to be flashy, with flashiness positively related to road cover in the watershed. Typical pH values of streams throughout the network centered around neutrality (pH = 7) with strong daily fluctuations apparent in the continuous data. Patterns in specific conductance were largely representative of anthropogenic disturbances—watersheds having the greatest percentage of open space and estate residential land-use had the lowest typical specific conductance values, and specific conductance variability was less than what is observed in watersheds that are more intensively developed. In watersheds having greater road coverage, and more development in general, increases in specific conductance over several orders of magnitude were observed during winter months as a result of the application of de-icing salts on impervious surfaces. Dissolved oxygen conditions were typically within the range required to support healthy biological communities, although occasional departures during summer months at some sites fell below the impairment threshold for streams in Virginia.
\nNitrogen (N) and phosphorus (P), concentration patterns were largely consistent across the network, with few exceptions. Nitrogen concentrations in monthly samples were generally low and dominated by nitrate. Exceptions to the generally low N concentrations occurred at Captain Hickory Run, which had a median total N concentration of approximately 4.9 milligrams per liter (mg/L), compared to the network-wide median of approximately 1.7 mg/L, and at Popes Head Creek Tributary, where total N concentrations spiked to 6–8 mg/L during low-flow periods in August or September of each year. Phosphorus concentrations in monthly samples were generally low and dominated by the dissolved fraction. Two monitoring stations in the network, Flatlick Branch and Frog Branch, are notable for having median total P concentrations that were, on average, approximately three times greater than the median total P concentration of 0.02 mg/L observed at the other 12 stations in the network.
\nSuspended-sediment and nutrient loads and yields were similar to those of urbanized watersheds in other studies, although the yields from these urbanized basins were greater than, or within, the upper quartile of yields observed throughout the Chesapeake Bay watershed. Annual suspended-sediment loads ranged from 289–10,275 tons, with a median of 1,311 tons, and corresponding yields ranged from 107–2,827 tons per square mile (ton/mi2), with a median of 277 ton/mi2. Annual total N loads ranged from 8,014–36,413 pounds, with a median of 21,314 pounds, and corresponding yields ranged from 3,361–8,360 pounds per square mile (lb/mi2), with a median of 6,200 lb/mi2. Annual total P loads ranged from 380–6,558 pounds, with a median of 1,874 pounds, and corresponding yields ranged from 140–1,562 lb/mi2, with a median of 543 lb/mi2.
\nBenthic macroinvertebrate community metrics indicated that streams throughout Fairfax County are generally of poor health. One station, Castle Creek, was an exception with results indicating relatively high quality aquatic health.
\nSix additional monitoring stations were added to the network in 2012 to improve spatial coverage throughout Fairfax County. Monitoring activities are expected to continue at all 20 stations for the foreseeable future as BMP implementation is conducted.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145073","issn":"2328-0328","isbn":"978-1-4113-3788-6","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Jastram, J.D., 2014, Streamflow, water quality, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007-12: U.S. Geological Survey Scientific Investigations Report 2014-5073, x, 68 p., https://doi.org/10.3133/sir20145073.","productDescription":"x, 68 p.","numberOfPages":"82","onlineOnly":"N","temporalStart":"2007-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-051336","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":288839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145073.jpg"},{"id":288837,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5073/"},{"id":288838,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5073/pdf/sir2014-5073.pdf"}],"scale":"2000000","country":"United States","state":"Virginia","county":"Fairfax County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -77.5,38.666667 ], [ -77.5,39.0 ], [ -77.0,39.0 ], [ -77.0,38.666667 ], [ -77.5,38.666667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7843e4b0abf75cf2cf70","contributors":{"authors":[{"text":"Jastram, John D. 0000-0002-9416-3358 jdjastra@usgs.gov","orcid":"https://orcid.org/0000-0002-9416-3358","contributorId":3531,"corporation":false,"usgs":true,"family":"Jastram","given":"John","email":"jdjastra@usgs.gov","middleInitial":"D.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494913,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048478,"text":"70048478 - 2014 - Population dynamics of bowfin in a south Georgia reservoir: latitudinal comparisons of population structure, growth, and mortality","interactions":[],"lastModifiedDate":"2015-11-13T13:40:30","indexId":"70048478","displayToPublicDate":"2014-06-18T14:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics of bowfin in a south Georgia reservoir: latitudinal comparisons of population structure, growth, and mortality","docAbstract":"The objectives of this study were to evaluate the population dynamics of bowfin (Amia calva) in Lake Lindsay Grace, Georgia, and to compare those dynamics to other bowfin populations. Relative abundance of bowfin sampled in 2010 in Lake Lindsay Grace was low and variable (mean±SD; 2.7±4.7 fish per hour of electrofishing). Total length (TL) of bowfin collected in Lake Lindsay Grace varied from 233–683 mm. Age of bowfin in Lake Lindsay Grace varied from 0–5 yr. Total annual mortality (A) was estimated at 68%. Both sexes appeared to be fully mature by age 2 with gonadosomatic index values above 8 for females and close to 1 for males. The majority of females were older, longer, and heavier than males. Bowfin in Lake Lindsay Grace had fast growth up to age 4 and higher total annual mortality than the other populations examined in this study. A chi-square test indicated that size structure of bowfin from Lake Lindsay Grace was different than those of a Louisiana population and two bowfin populations from the upper Mississippi River. To further assess bowfin size structure, we proposed standard length (i.e., TL) categories: stock (200 mm, 8 inches), quality (350 mm, 14 inches), preferred (460 mm, 18 inches), memorable (560 mm, 22, inches), and trophy (710 mm, 28 inches). Because our knowledge of bowfin ecology is limited, additional understanding of bowfin population dynamics provides important insight that can be used in management of bowfin across their distribution.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Proceedings of the Southeastern Association of Fish and Wildlife Agencies","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","publisherLocation":"Maggie Valley, NC","usgsCitation":"Porter, N.J., Bonvechio, T., McCormick, J.L., and Quist, M., 2014, Population dynamics of bowfin in a south Georgia reservoir: latitudinal comparisons of population structure, growth, and mortality: Journal of the Southeastern Association of Fish and Wildlife Agencies, p. 103-109.","productDescription":"7 p.","startPage":"103","endPage":"109","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045974","costCenters":[{"id":342,"text":"Idaho Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":311311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311310,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.bowfinanglers.com/JSEAFWA_2014_Porter_bowfin_Georgia.pdf"}],"country":"United States","state":"Georgia","otherGeospatial":"Lake Lindsay Grace","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.05611228942871,\n 31.59250052022682\n ],\n [\n -82.05319404602051,\n 31.589283616099355\n ],\n [\n -82.05121994018555,\n 31.589722291384547\n ],\n [\n -82.05001831054688,\n 31.58877182565619\n ],\n [\n -82.05053329467773,\n 31.58745578017951\n ],\n [\n -82.05053329467773,\n 31.586066600899585\n ],\n [\n -82.04881668090819,\n 31.584969865786398\n ],\n [\n -82.04941749572754,\n 31.5835806494466\n ],\n [\n -82.04727172851562,\n 31.58160646428677\n ],\n [\n -82.04649925231932,\n 31.579632237308374\n ],\n [\n -82.04469680786133,\n 31.578754789672878\n ],\n [\n -82.04495429992676,\n 31.57707299195037\n ],\n [\n -82.04229354858398,\n 31.574148054073284\n ],\n [\n -82.04083442687988,\n 31.573563055485224\n ],\n [\n -82.04100608825684,\n 31.572100542955447\n ],\n [\n -82.03903198242188,\n 31.571076770534155\n ],\n [\n -82.03688621520996,\n 31.56829790301159\n ],\n [\n -82.03585624694824,\n 31.568882934635436\n ],\n [\n -82.03413963317871,\n 31.567420348693773\n ],\n [\n -82.03336715698241,\n 31.56712782875264\n ],\n [\n -82.03250885009766,\n 31.5668353078939\n ],\n [\n -82.03225135803223,\n 31.56603087080106\n ],\n [\n -82.04143524169922,\n 31.562813053039058\n ],\n [\n -82.04212188720703,\n 31.56427571123956\n ],\n [\n -82.04143524169922,\n 31.565592084007477\n ],\n [\n -82.04049110412598,\n 31.56632339418315\n ],\n [\n -82.04100608825684,\n 31.568005385823263\n ],\n [\n -82.04375267028809,\n 31.571808037696464\n ],\n [\n -82.04572677612305,\n 31.57275867643295\n ],\n [\n -82.04538345336914,\n 31.574367427597423\n ],\n [\n -82.04838752746582,\n 31.57758484664331\n ],\n [\n -82.05079078674315,\n 31.578827910624675\n ],\n [\n -82.04813003540038,\n 31.57860854759723\n ],\n [\n -82.04916000366211,\n 31.579851597926588\n ],\n [\n -82.05010414123534,\n 31.579851597926588\n ],\n [\n -82.05061912536621,\n 31.58211829408449\n ],\n [\n -82.05345153808594,\n 31.587528894304743\n ],\n [\n -82.0557689666748,\n 31.588991164761765\n ],\n [\n -82.05636978149413,\n 31.590526524043113\n ],\n [\n -82.05705642700195,\n 31.592134968533564\n ],\n [\n -82.05611228942871,\n 31.59250052022682\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564717d1e4b0e2669b313123","contributors":{"authors":[{"text":"Porter, Nicholas J.","contributorId":149848,"corporation":false,"usgs":false,"family":"Porter","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":579801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonvechio, Timothy F.","contributorId":149849,"corporation":false,"usgs":false,"family":"Bonvechio","given":"Timothy F.","affiliations":[],"preferred":false,"id":579802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Joshua L.","contributorId":105193,"corporation":false,"usgs":true,"family":"McCormick","given":"Joshua","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":579803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael","contributorId":119346,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","affiliations":[],"preferred":false,"id":518209,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70095721,"text":"ds829 - 2014 - Archive of digital chirp subbottom profile data collected during USGS Cruise 13GFP01, Brownlee Dam and Hells Canyon Reservoir, Idaho and Oregon, 2013","interactions":[],"lastModifiedDate":"2014-06-18T14:43:21","indexId":"ds829","displayToPublicDate":"2014-06-18T14:37:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"829","title":"Archive of digital chirp subbottom profile data collected during USGS Cruise 13GFP01, Brownlee Dam and Hells Canyon Reservoir, Idaho and Oregon, 2013","docAbstract":"From March 16 - 31, 2013, the U.S. Geological Survey in cooperation with the Idaho Power Company conducted a geophysical survey to investigate sediment deposits and long-term sediment transport within the Snake River from Brownlee Dam to Hells Canyon Reservoir, along the Idaho and Oregon border; this effort will help the USGS to better understand geologic processes. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Gained (showing a relative increase in signal amplitude) digital images of the seismic profiles are also provided. Refer to the Acronyms page for expansions of acronyms and abbreviations used in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds829","issn":"2327-638X","collaboration":"Idaho Power Company, Boise, ID","usgsCitation":"Forde, A.S., Dadisman, S.V., Flocks, J.G., Fosness, R.L., Welcker, C., and Kelso, K.W., 2014, Archive of digital chirp subbottom profile data collected during USGS Cruise 13GFP01, Brownlee Dam and Hells Canyon Reservoir, Idaho and Oregon, 2013: U.S. Geological Survey Data Series 829, Report: HTML document; Downloads directory, https://doi.org/10.3133/ds829.","productDescription":"Report: HTML document; Downloads directory","onlineOnly":"N","temporalStart":"2013-03-16","temporalEnd":"2013-03-31","ipdsId":"IP-053170","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":288834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds829.jpg"},{"id":288833,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/0829/downloads"},{"id":288831,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0829/"},{"id":288832,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0829/title.html"}],"country":"United States","state":"Idaho;Oregon","otherGeospatial":"Brownlee Dam;Hells Canyon Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.0,43.5 ], [ -119.0,45.0 ], [ -117.5,45.0 ], [ -117.5,43.5 ], [ -119.0,43.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7630e4b0abf75cf2bebf","contributors":{"authors":[{"text":"Forde, Arnell S. 0000-0002-5581-2255 aforde@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-2255","contributorId":376,"corporation":false,"usgs":true,"family":"Forde","given":"Arnell","email":"aforde@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dadisman, Shawn V. sdadisman@usgs.gov","contributorId":2207,"corporation":false,"usgs":true,"family":"Dadisman","given":"Shawn","email":"sdadisman@usgs.gov","middleInitial":"V.","affiliations":[],"preferred":true,"id":491384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491383,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fosness, Ryan L. 0000-0003-4089-2704 rfosness@usgs.gov","orcid":"https://orcid.org/0000-0003-4089-2704","contributorId":2703,"corporation":false,"usgs":true,"family":"Fosness","given":"Ryan","email":"rfosness@usgs.gov","middleInitial":"L.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491385,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welcker, Chris","contributorId":63314,"corporation":false,"usgs":true,"family":"Welcker","given":"Chris","email":"","affiliations":[],"preferred":false,"id":491387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491386,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70112928,"text":"70112928 - 2014 - Performance of a surface bypass structure to enhance juvenile steelhead passage and survival at Lower Granite Dam, Washington","interactions":[],"lastModifiedDate":"2016-04-26T09:36:35","indexId":"70112928","displayToPublicDate":"2014-06-18T13:57:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Performance of a surface bypass structure to enhance juvenile steelhead passage and survival at Lower Granite Dam, Washington","docAbstract":"An integral part of efforts to recover stocks of Pacific salmon Oncorhynchus spp. and steelhead O. mykiss in Pacific Northwest rivers is to increase passage efficacy and survival of juveniles past hydroelectric dams. As part of this effort, we evaluated the efficacy of a prototype surface bypass structure, the removable spillway weir (RSW), installed in a spillbay at Lower Granite Dam, Washington, on the Snake River during 2002, 2003, 2005, and 2006. Radio-tagged juvenile steelhead were released upstream from the dam and their route of passage through the turbines, juvenile bypass, spillway, or RSW was recorded. The RSW was operated in an on-or-off condition and passed 3–13% of the total discharge at the dam when it was on. Poisson rate models were fit to the passage counts of hatchery- and natural-origin juvenile steelhead to predict the probability of fish passing the dam. Main-effect predictor variables were RSW operation, diel period, day of the year, proportion of flow passed by the spillway, and total discharge at the dam. The combined fish passage through the RSW and spillway was 55–85% during the day and 37–61% during the night. The proportion of steelhead passing through nonturbine routes was <88% when the RSW was off during the day and increased to >95% when the RSW was on during the day. The ratio of the proportion of steelhead passed to the proportion of water passing the RSW was from 6.3:1 to 10.0:1 during the day and from 2.7:1 to 5.2:1 during the night. Steelhead passing through the RSW exited the tailrace about 15 min faster than fish passing through the spillway. Mark–recapture single-release survival estimates for steelhead passing the RSW ranged from 0.95 to 1.00. The RSW appeared to be an effective bypass structure compared with other routes of fish passage at the dam.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2014.901256","usgsCitation":"Adams, N.S., Plumb, J.M., Perry, R.W., and Rondorf, D.W., 2014, Performance of a surface bypass structure to enhance juvenile steelhead passage and survival at Lower Granite Dam, Washington: North American Journal of Fisheries Management, v. 34, no. 3, p. 576-594, https://doi.org/10.1080/02755947.2014.901256.","productDescription":"19 p.","startPage":"576","endPage":"594","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-046409","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":288826,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Lower Granite Dam, Snake River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.56,45.15 ], [ -119.56,47.04 ], [ -114.51,47.04 ], [ -114.51,45.15 ], [ -119.56,45.15 ] ] ] } } ] }","volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-05-22","publicationStatus":"PW","scienceBaseUri":"53ae77a4e4b0abf75cf2c196","contributors":{"authors":[{"text":"Adams, Noah S. 0000-0002-8354-0293 nadams@usgs.gov","orcid":"https://orcid.org/0000-0002-8354-0293","contributorId":3521,"corporation":false,"usgs":true,"family":"Adams","given":"Noah","email":"nadams@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rondorf, Dennis W. drondorf@usgs.gov","contributorId":2970,"corporation":false,"usgs":true,"family":"Rondorf","given":"Dennis","email":"drondorf@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":494947,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70112920,"text":"70112920 - 2014 - The response of stream periphyton to Pacific salmon: using a model to understand the role of environmental context","interactions":[],"lastModifiedDate":"2014-06-18T13:43:10","indexId":"70112920","displayToPublicDate":"2014-06-18T13:39:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"The response of stream periphyton to Pacific salmon: using a model to understand the role of environmental context","docAbstract":"1. In stream ecosystems, Pacific salmon deliver subsidies of marine-derived nutrients and disturb the stream bed during spawning. The net effect of this nutrient subsidy and physical disturbance on biological communities can be hard to predict and is likely to be mediated by environmental conditions. For periphyton, empirical studies have revealed that the magnitude and direction of the response to salmon varies from one location to the next. Salmon appear to increase periphyton biomass and/or production in some contexts (a positive response), but decrease them in others (a negative response).
\n2. To reconcile these seemingly conflicting results, we constructed a system dynamics model that links periphyton biomass and production to salmon spawning. We used this model to explore how environmental conditions influence the periphyton response to salmon.
\n3. Our simulations suggest that the periphyton response to salmon is strongly mediated by both background nutrient concentrations and the proportion of the stream bed suitable for spawning. Positive periphyton responses occurred when both background nutrient concentrations were low (nutrient limiting conditions) and when little of the stream bed was suitable for spawning (because the substratum is too coarse). In contrast, negative responses occurred when nutrient concentrations were higher or a larger proportion of the bed was suitable for spawning.
\n4. Although periphyton biomass generally remained above or below background conditions for several months following spawning, periphyton production returned quickly to background values shortly afterwards. As a result, based upon our simulations, salmon did not greatly increase or decrease overall annual periphyton production. This suggests that any increase in production by fish or invertebrates in response to returning salmon is more likely to occur via direct consumption of salmon carcasses and/or eggs, rather than the indirect effects of greater periphyton production.
\n5. Overall, our simulations suggest that environmental factors need to be taken into account when considering the effects of spawning salmon on aquatic ecosystems. Our model offers researchers a framework for testing periphyton response to salmon across a range of conditions, which can be used to generate hypotheses, plan field experiments and guide data collection.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/fwb.12356","usgsCitation":"Bellmore, J.R., Fremier, A., Mejia, F., and Newsom, M., 2014, The response of stream periphyton to Pacific salmon: using a model to understand the role of environmental context: Freshwater Biology, v. 59, no. 7, p. 1437-1451, https://doi.org/10.1111/fwb.12356.","productDescription":"15 p.","startPage":"1437","endPage":"1451","numberOfPages":"15","ipdsId":"IP-051251","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":288822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288801,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/fwb.12356"}],"volume":"59","issue":"7","noUsgsAuthors":false,"publicationDate":"2014-03-17","publicationStatus":"PW","scienceBaseUri":"53ae7870e4b0abf75cf2d4e3","contributors":{"authors":[{"text":"Bellmore, J. Ryan","contributorId":104790,"corporation":false,"usgs":true,"family":"Bellmore","given":"J.","email":"","middleInitial":"Ryan","affiliations":[],"preferred":false,"id":494932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fremier, Alexander K.","contributorId":104403,"corporation":false,"usgs":true,"family":"Fremier","given":"Alexander K.","affiliations":[],"preferred":false,"id":494931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mejia, Francine","contributorId":106804,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","affiliations":[],"preferred":false,"id":494933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newsom, Michael","contributorId":16753,"corporation":false,"usgs":true,"family":"Newsom","given":"Michael","affiliations":[],"preferred":false,"id":494930,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70112906,"text":"70112906 - 2014 - Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data","interactions":[],"lastModifiedDate":"2014-06-18T13:37:34","indexId":"70112906","displayToPublicDate":"2014-06-18T13:28:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data","docAbstract":"Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"MDPI","doi":"10.3390/rs6065696","usgsCitation":"Liang, L., Chen, Y., Hawbaker, T., Zhu, Z., and Gong, P., 2014, Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data: Remote Sensing, v. 6, no. 6, p. 5696-5716, https://doi.org/10.3390/rs6065696.","productDescription":"21 p.","startPage":"5696","endPage":"5716","numberOfPages":"21","ipdsId":"IP-053363","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":472932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6065696","text":"Publisher Index Page"},{"id":288821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288757,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs6065696"}],"country":"United States","state":"Colorado","county":"Grand County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.666667,39.666667 ], [ -106.666667,40.333333 ], [ -105.666667,40.333333 ], [ -105.666667,39.666667 ], [ -106.666667,39.666667 ] ] ] } } ] }","volume":"6","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-06-18","publicationStatus":"PW","scienceBaseUri":"53ae7773e4b0abf75cf2c133","contributors":{"authors":[{"text":"Liang, Lu","contributorId":72714,"corporation":false,"usgs":true,"family":"Liang","given":"Lu","affiliations":[],"preferred":false,"id":494906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Yanlei","contributorId":18276,"corporation":false,"usgs":true,"family":"Chen","given":"Yanlei","email":"","affiliations":[],"preferred":false,"id":494904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":494903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhi-Liang","contributorId":70726,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhi-Liang","affiliations":[],"preferred":false,"id":494905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gong, Peng","contributorId":102393,"corporation":false,"usgs":true,"family":"Gong","given":"Peng","affiliations":[],"preferred":false,"id":494907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70112760,"text":"70112760 - 2014 - Methylmercury production in sediment from agricultural and non-agricultural wetlands in the Yolo Bypass, California, USA","interactions":[],"lastModifiedDate":"2018-09-25T09:25:04","indexId":"70112760","displayToPublicDate":"2014-06-18T12:59:00","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":"Methylmercury production in sediment from agricultural and non-agricultural wetlands in the Yolo Bypass, California, USA","docAbstract":"As part of a larger study of mercury (Hg) biogeochemistry and bioaccumulation in agricultural (rice growing) and non-agricultural wetlands in California's Central Valley, USA, seasonal and spatial controls on methylmercury (MeHg) production were examined in surface sediment. Three types of shallowly-flooded agricultural wetlands (white rice, wild rice, and fallow fields) and two types of managed (non-agricultural) wetlands (permanently and seasonally flooded) were sampled monthly-to-seasonally. Dynamic seasonal changes in readily reducible ‘reactive’ mercury (Hg(II)R), Hg(II)-methylation rate constants (kmeth), and concentrations of electron acceptors (sulfate and ferric iron) and donors (acetate), were all observed in response to field management hydrology, whereas seasonal changes in these parameters were more muted in non-agricultural managed wetlands. Agricultural wetlands exhibited higher sediment MeHg concentrations than did non-agricultural wetlands, particularly during the fall through late-winter (post-harvest) period. Both sulfate- and iron-reducing bacteria have been implicated in MeHg production, and both were demonstrably active in all wetlands studied. Stoichiometric calculations suggest that iron-reducing bacteria dominated carbon flow in agricultural wetlands during the growing season. Sulfate-reducing bacteria were not stimulated by the addition of sulfate-based fertilizer to agricultural wetlands during the growing season, suggesting that labile organic matter, rather than sulfate, limited their activity in these wetlands. Along the continuum of sediment geochemical conditions observed, values of kmeth increased approximately 10,000-fold, whereas Hg(II)R decreased 100-fold. This suggests that, with respect to the often opposing trends of Hg(II)-methylating microbial activity and Hg(II) availability for methylation, microbial activity dominated the Hg(II)-methylation process, and that along this biogeochemical continuum, conditions that favored microbial sulfate reduction resulted in the highest calculated MeHg production potential rates. Rice straw management options aimed at limiting labile carbon supplies to surface sediment during the post-harvest fall–winter period may be effective in limiting MeHg production within agricultural wetlands.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2013.09.098","usgsCitation":"Marvin-DiPasquale, M., Windham-Myers, L., Agee, J.L., Kakouros, E., Kieu, L.H., Fleck, J., Alpers, C.N., and Stricker, C.A., 2014, Methylmercury production in sediment from agricultural and non-agricultural wetlands in the Yolo Bypass, California, USA: Science of the Total Environment, v. 484, p. 288-299, https://doi.org/10.1016/j.scitotenv.2013.09.098.","productDescription":"12 p.","startPage":"288","endPage":"299","numberOfPages":"12","ipdsId":"IP-046333","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"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":288819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288818,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.scitotenv.2013.09.098"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.8128,38.2329 ], [ -121.8128,38.5804 ], [ -121.5097,38.5804 ], [ -121.5097,38.2329 ], [ -121.8128,38.2329 ] ] ] } } ] }","volume":"484","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7779e4b0abf75cf2c13f","contributors":{"authors":[{"text":"Marvin-DiPasquale, Mark","contributorId":57423,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","affiliations":[],"preferred":false,"id":494872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":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":494869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":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":494870,"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":494871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fleck, Jacob A. 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":1498,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob A.","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":494865,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":494866,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}