Historically, adult summer steelhead Oncorhynchus mykiss returning to hatcheries on the lower Cowlitz River were sometimes transported and released in the river (recycled) to provide additional angling opportunity for the popular sport fishery in the basin. However, this practice has not been used in recent years because of concerns associated with interactions between hatchery fish and wild fish. Fishery managers were interested in resuming recycling but lacked information regarding effects of this practice on wild steelhead so we conducted a study during 2012–2013 to: (1) enumerate recycled steelhead that returned to the hatchery or were removed by anglers; and (2) determine if steelhead that were not removed from the river remained in the system where they could interact with wild fish.
During June–August 2012, a total of 549 summer steelhead were captured at the Cowlitz Salmon Hatchery, tagged, and released downstream near the Interstate 5 Bridge. All recycled steelhead were tagged with a white Floy® tag and opercle-punched; 109 (20 percent) of these fish also were radio-tagged. All adult steelhead that return to the hatchery were handled by hatchery staff so recycled steelhead that returned to the hatchery were enumerated daily. A creel survey and voluntary angler reports were used to determine the number of recycled steelhead that were caught by anglers. We established three fixed telemetry monitoring sites on the mainstem Cowlitz River and eight additional sites were deployed on tributaries to the lower Cowlitz River where wild winter steelhead are known to spawn. We also conducted mobile tracking from a boat during October 2012, November 2012, and January 2013 to locate radio-tagged fish.
A total of 10,722 summer steelhead were captured at the Cowlitz Salmon Hatchery in 2012, which was the largest return since 2008. River flows during much of the study period were similar to 2008–2011 average flows, however, high-flow periods in July and November 2012 were nearly twice as high as the 2008–2011 average flows. We determined that 50 percent (273 fish) of the recycled steelhead returned to the hatchery and 18 percent (102 fish) of the recycled steelhead were caught by anglers. Most (243 fish; 89 percent) of the recycled steelhead that returned to the hatchery were recollected during July–August. The average elapsed time from release to recapture at the hatchery was 9 days (d) and 72 percent (182 fish) of the fish returned to the hatchery within 14 d of release. These trends were similar for recycled steelhead that were caught by anglers. Most fish were caught during July–August and the median time from release to capture was 10 d. We determined that 65 percent (70 fish) of the angler-caught fish returned to the hatchery within 14 d of release. River flows appeared to affect both hatchery returns and angler catch. The daily number of recycled steelhead that were recollected at the hatchery were low during periods when river flows were decreasing and high during periods when river flows were increasing. Conversely, daily angler catch of recycled steelhead generally was low when flows were increasing and high when flows were decreasing.
We determined that 32 percent of the recycled steelhead (174 fish) were not removed from the lower Cowlitz River, based on observations from hatchery returns and angler reports, but results from the radio-tagged fish were insightful for understanding what may have happened to these fish. By comparison, we determined that 24 percent of the radio-tagged fish were not known to have been removed from the river. We determined that 12 percent of these fish were actively moving in the lower Cowlitz River during October 2012–January 2013. None of the radio-tagged fish were detected in tributaries during the study period except for a single fish that spent approximately 7 d in the Toutle River during early September. During October 2012–January 2013, 10 percent of the radio-tags from recycled steelhead were detected near popular fishing areas, and 2 percent of the radio-tagged steelhead were never detected during the study period. We suspect that a large proportion of these fish may have been harvested and not reported, or died.
Detection patterns of radio-tagged steelhead showed that most fish (82 percent) moved upstream from the release site and were detected at the Trout Hatchery and the Barrier Dam sites. The median time from release to detection at these sites was 3.7 d and many of these fish made multiple trips between the two sites. Nearly one-third (29 percent) of the recycled steelhead that were detected at the Trout Hatchery and the Barrier Dam made at least two trips between the sites and some fish made as many as six trips. Radio-tagged fish that remained in the lower Cowlitz River during the spawning period (December 2012–January 2013) were observed in the river reach between the mouth of Ostrander Creek (river mile 10) and the Trout Hatchery (river mile 44).
During this study, we collected data on opercle punch regrowth rates to understand the temporal effectiveness of this marking technique. We took opercle measurements for a total of 190 fish during the study. Fresh opercle punches were measured for 63 fish at the time of marking, and the remaining 127 fish were measured when fish returned to the hatchery. We determined that opercle punches remained open for about 30 d. The holes appeared to regrow slowly in the first 20 d after marking, but regrowth accelerated during the 20–30 d post-marking period. After 30 d, all opercle punches that we observed had completely closed due to tissue regrowth.
Our study showed that a large proportion (68 percent) of the recycled steelhead were removed from the lower Cowlitz River. These fish primarily entered the hatchery or were caught by anglers within 14 d of release, which suggests that they present minimal risk to wild fish in the system. However, the remaining fish (32 percent) could not be accounted for, which may complicate fisheries management decisions associated with recycling summer steelhead. Findings from the radiotelemetry study suggest that unreported harvest or mortality could explain a large proportion of those fish that were not reported as having been removed from the river. Furthermore, intensive monitoring of the key spawning tributaries failed to detect a single fish during the spawning period. These findings were supported by observations from weir traps operated by the Washington Department of Fish and Wildlife. Our findings indicate that additional research may be warranted to further examine the effects of recycling hatchery summer steelhead in the lower Cowlitz River.
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Although the theoretical analysis indicated the possibility of hysteresis at this site, the hydrodynamic conditions required to generate hysteresis are not present at this site based on historical data. 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As part of this effort, 60 stream-water samples and 43 corresponding stream-sediment samples were collected in 2010 and 2011 from locations in Colorado and New Mexico. Sample sites were selected from small to midsize watersheds composed of a high percentage of one rock type or geologic unit. Stream-water and stream-sediment samples were collected, processed, preserved, and analyzed in a consistent manner. This report releases geochemical data for this phase of the study.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131064","usgsCitation":"Hageman, P.L., Todd, A., Smith, K.S., DeWitt, E., and Zeigler, M.P., 2013, Geochemical results from stream-water and stream-sediment samples collected in Colorado and New Mexico: U.S. Geological Survey Open-File Report 2013-1064, Report: iii, 11 p.; 6 Appendices, https://doi.org/10.3133/ofr20131064.","productDescription":"Report: iii, 11 p.; 6 Appendices","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":272316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131064.gif"},{"id":272310,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%201_Bulk%20chemistry%20for%20stream%20sediments.xlsx"},{"id":272308,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1064/"},{"id":272311,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%202_Stream%20water%20(FA).xlsx"},{"id":272312,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%203_Stream%20water%20(RA).xlsx"},{"id":272309,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1064/OF13-1064.pdf"},{"id":272313,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%204_QAQC%20Stream%20sediments.xlsx"},{"id":272314,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%205_QAQC%20Stream%20water%20(FA).xlsx"},{"id":272315,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%206_QAQC%20Stream%20water%20(RA).xlsx"}],"country":"United States","state":"Colorado;New Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.06,32.81 ], [ -109.06,41.0 ], [ -102.79,41.0 ], [ -102.79,32.81 ], [ -109.06,32.81 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51955815e4b0a933d82c4c89","contributors":{"authors":[{"text":"Hageman, Philip L. 0000-0002-3440-2150 phageman@usgs.gov","orcid":"https://orcid.org/0000-0002-3440-2150","contributorId":811,"corporation":false,"usgs":true,"family":"Hageman","given":"Philip","email":"phageman@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Todd, Andrew S.","contributorId":33162,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew S.","affiliations":[],"preferred":false,"id":478639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeWitt, Ed","contributorId":65081,"corporation":false,"usgs":true,"family":"DeWitt","given":"Ed","affiliations":[],"preferred":false,"id":478640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":478641,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045982,"text":"gip149 - 2013 - Seventy-five years of science—The U.S. Geological Survey’s Western Fisheries Research Center","interactions":[],"lastModifiedDate":"2013-05-16T11:52:13","indexId":"gip149","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"149","title":"Seventy-five years of science—The U.S. Geological Survey’s Western Fisheries Research Center","docAbstract":"As of January 2010, 75 years have elapsed since Dr. Frederic Fish initiated the pioneering research program that would evolve into today’s Western Fisheries Research Center (WFRC). Fish began his research working alone in the basement of the recently opened Fisheries Biological Laboratory on Lake Union in Seattle, Washington. WFRC’s research began under the aegis of the U.S. Fish and Wildlife Service and ends its first 75 years as part of the U.S. Geological Survey with a staff of more than 150 biologists and support personnel and a heritage of fundamental research that has made important contributions to our understanding of the biology and ecology of the economically important fish and fish populations of the Nation. Although the current staff may rarely stop to think about it, WFRC’s antecedents extend many years into the past and are intimately involved with the history of fisheries conservation in the Western United States. Thus, WFRC Director Lyman Thorsteinson asked me to write the story of this laboratory “while there are still a few of you around who were here for some of the earlier years” to document the rich history and culture of WFRC by recognizing its many famous scientists and their achievements. This historyalso would help document WFRC’s research ‘footprint’ in the Western United States and its strategic directions. Center Director Thorsteinson concluded that WFRC’s heritage told by an emeritus scientist also would add a texture of legitimacy based on personal knowledge that will all-to-soon be lost to the WFRC and to the USGS. The WFRC story is important for the future as well as for historical reasons. It describes how we got to the place we are today by documenting the origin, original mission, and our evolving role in response to the constantly changing technical information requirements of new environmental legislation and organizational decision-making. The WFRC research program owes its existence to the policy requirements of Federal conservation legislation originating with the construction of Grand Coulee Dam in 1933. The research program was shaped by laws enacted in subsequent years such as the Federal Water Pollution Control Act (1972), National Environmental Policy Act (1973), Endangered Species Act (1974), and Northwest Power Planning Act (1980), to name only a few. The WFRC has not been constrained by direct management or regulatory responsibility for a particular fishery (such as providing sustainable catch limits data to a resource management structure). Thus, WFRC has been able to concentrate on scientific pursuits and information needs required by contemporary environmental legislation. Over the years, we have pioneered in several important areas of fisheries research including the diagnoses and control of diseases in economically important fish, effects of environmental alterations on the physiological quality and survival of Pacific salmon released from federal mitigation hatcheries, applications in biotelemetry, and the bioenergetics of predator-prey interactions in the Columbia River. The WFRC of today is a widely distributed organization in the Western United States. Knowledge of the historical connections and accomplishments of our predecessors is important beyond the sense of pride and unity it instills in the WFRC family of today. For example, a discerning reader will note the evolution of WFRC’s research from a single disciplinary focus (early era—hatchery disease problems), to multiple disciplines (middle to late era—species, populations, habitats; threatened and endangered species), to the present era (multidisciplinary and with increasing process focus). For the benefit of the current WFRC staff, more emphasis has been placed on the early years rather than on the present day because people are quite naturally more familiar with the recent past than with the research done during the first decades of WFRC’s existence. By every rational measure, the WFRC has evolved into a fisheries research organization well positioned to provide the biological information needed to support the continued conservation and management of our Nation’s living aquatic natural resources. The high standard of excellence that connects WFRC’s past to our present research program provides a firm foundation on which to base the work yet to be done. In another 75 years, WFRC will undoubtedly be a very different place than it is today, but its evolution will be forever rooted in the story of the research and of the people related here. More about the diverse fisheries research projects WFRC scientists are conducting today is available at WFRC’s website: http://wfrc.usgs.gov/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip149","usgsCitation":"Wedemeyer, G.A., 2013, Seventy-five years of science—The U.S. Geological Survey’s Western Fisheries Research Center: U.S. Geological Survey General Information Product 149, vi, 46 p., https://doi.org/10.3133/gip149.","productDescription":"vi, 46 p.","numberOfPages":"54","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/gip149.jpg"},{"id":272320,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/gip/149/"},{"id":272321,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/149/pdf/gip149.pdf"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Western Fisheries Research Center","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.4,47.5 ], [ -122.4,47.7 ], [ -122.2,47.7 ], [ -122.2,47.5 ], [ -122.4,47.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51955816e4b0a933d82c4c8d","contributors":{"authors":[{"text":"Wedemeyer, Gary A.","contributorId":30668,"corporation":false,"usgs":true,"family":"Wedemeyer","given":"Gary","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478644,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045984,"text":"sir20135066 - 2013 - Estimating irrigation water use in the humid eastern United States","interactions":[],"lastModifiedDate":"2013-05-16T13:41:25","indexId":"sir20135066","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5066","title":"Estimating irrigation water use in the humid eastern United States","docAbstract":"Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135066","collaboration":"Prepared in cooperation with the WaterSMART Initiative","usgsCitation":"Levin, S.B., and Zarriello, P.J., 2013, Estimating irrigation water use in the humid eastern United States: U.S. Geological Survey Scientific Investigations Report 2013-5066, viii, 34 p., https://doi.org/10.3133/sir20135066.","productDescription":"viii, 34 p.","numberOfPages":"44","onlineOnly":"N","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":272329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135066.gif"},{"id":272328,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5066/pdf/sir2013-5066_report_508.pdf"},{"id":272327,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5066/"}],"country":"United States","otherGeospatial":"Eastern United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -85,30 ], [ -85,33.08 ], [ -81,33.08 ], [ -81,30 ], [ -85,30 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51955815e4b0a933d82c4c81","contributors":{"authors":[{"text":"Levin, Sara B. 0000-0002-2448-3129 slevin@usgs.gov","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":1870,"corporation":false,"usgs":true,"family":"Levin","given":"Sara","email":"slevin@usgs.gov","middleInitial":"B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarriello, Phillip J. 0000-0001-9598-9904 pzarriel@usgs.gov","orcid":"https://orcid.org/0000-0001-9598-9904","contributorId":1868,"corporation":false,"usgs":true,"family":"Zarriello","given":"Phillip","email":"pzarriel@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478645,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045985,"text":"ds765 - 2013 - Coastal topography–Northeast Atlantic coast, post-hurricane Sandy, 2012","interactions":[],"lastModifiedDate":"2023-04-04T15:18:04.947613","indexId":"ds765","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","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":"765","title":"Coastal topography–Northeast Atlantic coast, post-hurricane Sandy, 2012","docAbstract":"This Data Series contains lidar-derived bare-earth (BE) topography, dune elevations, and mean-high-water shoreline position datasets for most sandy beaches for Fire Island, New York, and from Cape Henlopen, Delaware to Cape Lookout, North Carolina. The data were acquired post-Hurricane Sandy, which made landfall as an extratropical cyclone on October 29, 2012.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds765","usgsCitation":"Stockdon, H.F., Doran, K., Sopkin, K.L., Smith, K., and Fredericks, X., 2013, Coastal topography–Northeast Atlantic coast, post-hurricane Sandy, 2012: U.S. Geological Survey Data Series 765, HTML Document, https://doi.org/10.3133/ds765.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":272332,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds765.png"},{"id":272331,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/765/pubs765/index.html"},{"id":272330,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/765/"}],"country":"United States","state":"Delaware, Maryland, New York, North Carolina, Virginia","otherGeospatial":"northeast Atlantic coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.5,\n 41\n ],\n [\n -77,\n 41\n ],\n [\n -77,\n 34.5\n ],\n [\n -72.5,\n 34.5\n ],\n [\n -72.5,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5195580de4b0a933d82c4c79","contributors":{"authors":[{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":478647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doran, Kara S. 0000-0001-8050-5727 kdoran@usgs.gov","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":2496,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","email":"kdoran@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":478648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sopkin, Kristin L. ksopkin@usgs.gov","contributorId":4437,"corporation":false,"usgs":true,"family":"Sopkin","given":"Kristin","email":"ksopkin@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":478649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kathryn E. L.","contributorId":20860,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn E. L.","affiliations":[],"preferred":false,"id":478650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fredericks, Xan","contributorId":35704,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","affiliations":[],"preferred":false,"id":478651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045964,"text":"ofr20121256 - 2013 - Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2012: Quality-assurance data and comparison to water-quality standards","interactions":[],"lastModifiedDate":"2015-10-27T18:57:02","indexId":"ofr20121256","displayToPublicDate":"2013-05-15T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1256","title":"Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2012: Quality-assurance data and comparison to water-quality standards","docAbstract":"Air is entrained in water as it is flows through the spillways of dams, which causes an increase in the concentration of total dissolved gas in the water downstream from the dams. The elevated concentrations of total dissolved gas can adversely affect fish and other freshwater aquatic life. An analysis of total-dissolved-gas and water-temperature data collected at eight monitoring stations on the lower Columbia River in Oregon and Washington in 2012 indicated the following:
\nThe synoptic mass balance approach is often used to evaluate constituent mass loading in streams affected by mine drainage. Spatial profiles of constituent mass load are used to identify sources of contamination and prioritize sites for remedial action. This paper presents a field scale study in which replicate synoptic sampling campaigns are used to quantify the aggregate uncertainty in constituent load that arises from (1) laboratory analyses of constituent and tracer concentrations, (2) field sampling error, and (3) temporal variation in concentration from diel constituent cycles and/or source variation. Consideration of these factors represents an advance in the application of the synoptic mass balance approach by placing error bars on estimates of constituent load and by allowing all sources of uncertainty to be quantified in aggregate; previous applications of the approach have provided only point estimates of constituent load and considered only a subset of the possible errors. Given estimates of aggregate uncertainty, site specific data and expert judgement may be used to qualitatively assess the contributions of individual factors to uncertainty. This assessment can be used to guide the collection of additional data to reduce uncertainty. Further, error bars provided by the replicate approach can aid the investigator in the interpretation of spatial loading profiles and the subsequent identification of constituent source areas within the watershed.
The replicate sampling approach is applied to Peru Creek, a stream receiving acidic, metal-rich effluent from the Pennsylvania Mine. Other sources of acidity and metals within the study reach include a wetland area adjacent to the mine and tributary inflow from Cinnamon Gulch. Analysis of data collected under low-flow conditions indicates that concentrations of Al, Cd, Cu, Fe, Mn, Pb, and Zn in Peru Creek exceed aquatic life standards. Constituent loading within the study reach is dominated by effluent from the Pennsylvania Mine, with over 50% of the Cd, Cu, Fe, Mn, and Zn loads attributable to a collapsed adit near the top of the study reach. These estimates of mass load may underestimate the effect of the Pennsylvania Mine as leakage from underground mine workings may contribute to metal loads that are currently attributed to the wetland area. This potential leakage confounds the evaluation of remedial options and additional research is needed to determine the magnitude and location of the leakage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2013.02.031","usgsCitation":"Runkel, R.L., Walton-Day, K., Kimball, B.A., Verplanck, P.L., and Nimick, D.A., 2013, Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado: Journal of Hydrology, v. 489, p. 26-41, https://doi.org/10.1016/j.jhydrol.2013.02.031.","productDescription":"16 p.","startPage":"26","endPage":"41","ipdsId":"IP-044174","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":272199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Peru Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.8287239074707,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.59451160220633\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"489","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5804e4b0b290850f7d13","contributors":{"authors":[{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":68339,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","affiliations":[],"preferred":false,"id":476579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kimball, Briant A. bkimball@usgs.gov","contributorId":533,"corporation":false,"usgs":true,"family":"Kimball","given":"Briant","email":"bkimball@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":476578,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":476575,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045677,"text":"70045677 - 2013 - Evaluation of a new model of aeolian transport in the presence of vegetation","interactions":[],"lastModifiedDate":"2013-05-14T11:05:21","indexId":"70045677","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of a new model of aeolian transport in the presence of vegetation","docAbstract":"Aeolian transport is an important characteristic of many arid and semiarid regions worldwide that affects dust emission and ecosystem processes. The purpose of this paper is to evaluate a recent model of aeolian transport in the presence of vegetation. This approach differs from previous models by accounting for how vegetation affects the distribution of shear velocity on the surface rather than merely calculating the average effect of vegetation on surface shear velocity or simply using empirical relationships. Vegetation, soil, and meteorological data at 65 field sites with measurements of horizontal aeolian flux were collected from the Western United States. Measured fluxes were tested against modeled values to evaluate model performance, to obtain a set of optimum model parameters, and to estimate the uncertainty in these parameters. The same field data were used to model horizontal aeolian flux using three other schemes. Our results show that the model can predict horizontal aeolian flux with an approximate relative error of 2.1 and that further empirical corrections can reduce the approximate relative error to 1.0. The level of error is within what would be expected given uncertainties in threshold shear velocity and wind speed at our sites. The model outperforms the alternative schemes both in terms of approximate relative error and the number of sites at which threshold shear velocity was exceeded. These results lend support to an understanding of the physics of aeolian transport in which (1) vegetation's impact on transport is dependent upon the distribution of vegetation rather than merely its average lateral cover and (2) vegetation impacts surface shear stress locally by depressing it in the immediate lee of plants rather than by changing the bulk surface's threshold shear velocity. Our results also suggest that threshold shear velocity is exceeded more than might be estimated by single measurements of threshold shear stress and roughness length commonly associated with vegetated surfaces, highlighting the variation of threshold shear velocity with space and time in real landscapes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research F: Earth Surface","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGU","doi":"10.1002/jgrf.20040","usgsCitation":"Li, J., Okin, G.S., Herrick, J.E., Belnap, J., Miller, M.E., Vest, K., and Draut, A.E., 2013, Evaluation of a new model of aeolian transport in the presence of vegetation: Journal of Geophysical Research F: Earth Surface, v. 118, no. 1, p. 288-306, https://doi.org/10.1002/jgrf.20040.","productDescription":"9 p.","startPage":"288","endPage":"306","ipdsId":"IP-025701","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jgrf.20040","text":"Publisher Index Page"},{"id":272217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272215,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jgrf.20040"}],"volume":"118","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-03-26","publicationStatus":"PW","scienceBaseUri":"53cd584ee4b0b290850f802d","chorus":{"doi":"10.1002/jgrf.20040","url":"http://dx.doi.org/10.1002/jgrf.20040","publisher":"Wiley-Blackwell","authors":"Li Junran, Okin Gregory S., Herrick Jeffrey E., Belnap Jayne, Miller Mark E., Vest Kimberly, Draut Amy E.","journalName":"Journal of Geophysical Research: Earth Surface","publicationDate":"3/2013","auditedOn":"3/7/2016"},"contributors":{"authors":[{"text":"Li, Junran","contributorId":23418,"corporation":false,"usgs":true,"family":"Li","given":"Junran","affiliations":[],"preferred":false,"id":478037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okin, Gregory S.","contributorId":50025,"corporation":false,"usgs":true,"family":"Okin","given":"Gregory","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":478039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrick, Jeffrey E.","contributorId":26054,"corporation":false,"usgs":false,"family":"Herrick","given":"Jeffrey","email":"","middleInitial":"E.","affiliations":[{"id":12627,"text":"USDA-ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003-8003, USA","active":true,"usgs":false}],"preferred":false,"id":478038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":478036,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":478041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vest, Kimberly","contributorId":83818,"corporation":false,"usgs":true,"family":"Vest","given":"Kimberly","email":"","affiliations":[],"preferred":false,"id":478040,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Draut, Amy E.","contributorId":92215,"corporation":false,"usgs":true,"family":"Draut","given":"Amy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":478042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70043750,"text":"70043750 - 2013 - Evapotranspiration and water balance of an anthropogenic coastal desert wetland: responses to fire, inflows and salinities","interactions":[],"lastModifiedDate":"2013-10-23T10:05:21","indexId":"70043750","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Evapotranspiration and water balance of an anthropogenic coastal desert wetland: responses to fire, inflows and salinities","docAbstract":"Evapotranspiration (ET) and other water balance components were estimated for Cienega de Santa Clara, an anthropogenic brackish wetland in the delta of the Colorado River in Mexico. The marsh is in the Biosphere Reserve of the Upper Gulf of California and Delta of the Colorado River, and supports a high abundance and diversity of wildlife. Over 95% of its water supply originates as agricultural drain water from the USA, sent for disposal in Mexico. This study was conducted from 2009 to 2011, before, during and after a trial run of the Yuma Desalting Plant in the USA, which will divert water from the wetland and replace it with brine from the desalting operation. The goal was to estimate the main components in the water budget to be used in creating management scenarios for this marsh. We used a remote sensing algorithm to estimate ET from meteorological data and Enhanced Vegetation Index values from the Moderate Resolution Imaging Spectrometer (MODIS) sensors on the Terra satellite. ET estimates from the MODIS method were then compared to results from a mass balance of water and salt inflows and outflows over the study period. By both methods, mean annual ET estimates ranged from 2.6 to 3.0 mm d−1, or 50 to 60% of reference ET (ETo). Water entered at a mean salinity of 2.6 g L−1 TDS and mean salinity in the wetland was 3.73 g L−1 TDS over the 33 month study period. Over an annual cycle, 54% of inflows supported ET while the rest exited the marsh as outflows; however, in winter when ET was low, up to 90% of the inflows exited the marsh. An analysis of ET estimates over the years 2000–2011 showed that annual ET was proportional to the volume of inflows, but was also markedly stimulated by fires. Spring fires in 2006 and 2011 burned off accumulated thatch, resulting in vigorous growth of new leaves and a 30% increase in peak summer ET compared to non-fire years. Following fires, peak summer ET estimates were equal to ETo, while in non-fire years peak ET was equal to only one-half to two-thirds of ETo. Over annual cycles, estimated ET was always lower than ETo, because T. domingensis is dormant in winter and shades the water surface, reducing direct evaporation. Thus, ET of a Typha marsh is likely to be less than an open water surface under most conditions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2012.06.043","usgsCitation":"Glenn, E.P., Mexicano, L., Garcia-Hernandez, J., Nagler, P.L., Gomez-Sapiens, M.M., Tang, D., Lomeli, M.A., Ramírez-Hernández, J., and Zamora-Arroyo, F., 2013, Evapotranspiration and water balance of an anthropogenic coastal desert wetland: responses to fire, inflows and salinities: Ecological Engineering, v. 59, p. 176-184, https://doi.org/10.1016/j.ecoleng.2012.06.043.","productDescription":"9 p.","startPage":"176","endPage":"184","ipdsId":"IP-038206","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":272224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272223,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecoleng.2012.06.043"}],"volume":"59","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5268efe3e4b0584cbe916856","contributors":{"authors":[{"text":"Glenn, Edward P.","contributorId":19289,"corporation":false,"usgs":true,"family":"Glenn","given":"Edward","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":474201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mexicano, Lourdes","contributorId":91773,"corporation":false,"usgs":true,"family":"Mexicano","given":"Lourdes","email":"","affiliations":[],"preferred":false,"id":474207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia-Hernandez, Jaqueline","contributorId":37627,"corporation":false,"usgs":true,"family":"Garcia-Hernandez","given":"Jaqueline","email":"","affiliations":[],"preferred":false,"id":474203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":474199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Sapiens, Martha M.","contributorId":58172,"corporation":false,"usgs":true,"family":"Gomez-Sapiens","given":"Martha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":474204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tang, Dawei","contributorId":17515,"corporation":false,"usgs":true,"family":"Tang","given":"Dawei","email":"","affiliations":[],"preferred":false,"id":474200,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lomeli, Marcelo A.","contributorId":60523,"corporation":false,"usgs":true,"family":"Lomeli","given":"Marcelo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":474205,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ramírez-Hernández, Jorge","contributorId":24264,"corporation":false,"usgs":true,"family":"Ramírez-Hernández","given":"Jorge","affiliations":[],"preferred":false,"id":474202,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zamora-Arroyo, Francisco","contributorId":75834,"corporation":false,"usgs":true,"family":"Zamora-Arroyo","given":"Francisco","email":"","affiliations":[],"preferred":false,"id":474206,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70045952,"text":"fs20133018 - 2013 - Integrated synoptic surveys using an autonomous underwater vehicle and manned boats","interactions":[],"lastModifiedDate":"2016-08-10T10:37:22","indexId":"fs20133018","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","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":"2013-3018","title":"Integrated synoptic surveys using an autonomous underwater vehicle and manned boats","docAbstract":"Traditional surface-water surveys are being combined with autonomous technology to produce integrated surveys of bathymetry, water quality, and velocity in inland lakes and reservoirs. This new technology provides valuable, high-resolution, integrated data that allow a systems-based approach to understanding common environmental problems. This fact sheet presents several example applications of integrated surveys within inland lakes and coastal Lake Michigan and Lake Erie.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133018","usgsCitation":"Jackson, P., 2013, Integrated synoptic surveys using an autonomous underwater vehicle and manned boats: U.S. Geological Survey Fact Sheet 2013-3018, 4 p., https://doi.org/10.3133/fs20133018.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":272259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133018.gif"},{"id":272258,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3018/pdf/FS2013-3018.pdf","text":"Report","size":"9.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":272257,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3018/"}],"country":"United States","otherGeospatial":"Lake Erie, Lake Michigan","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd62b1e4b0b290850fe5ab","contributors":{"authors":[{"text":"Jackson, P. Ryan","contributorId":68571,"corporation":false,"usgs":true,"family":"Jackson","given":"P. Ryan","affiliations":[],"preferred":false,"id":478595,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045516,"text":"70045516 - 2013 - Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load","interactions":[],"lastModifiedDate":"2013-05-14T16:15:51","indexId":"70045516","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load","docAbstract":"1. The ability to store energy endogenously is an important ecological mechanism that allows animals to buffer predictable and unpredictable variation in allostatic load. The secretion of glucocorticoids, which reflects changes in allostatic load, is suggested to play a major role in the adjustment of endogenous stores to these varying conditions.\n2. Although crucially important, the relationship between allostatic load and energy stores remains largely unexplored. Two contrasting hypotheses describe how stores may be adjusted: animals may use low allostatic loads to increase stores to a maximum possible (‘fat and fit’), or they can attain a lean physique due to fitness advantages of a low body mass (‘lean and fit’).\n3. We compiled observational and experimental data available for a long-lived seabird to examine the relationship between glucocorticoids and stored energy at two life history stages (incubation and chick-rearing). Data were collected across multiple years and colonies in the North Pacific, thereby reflecting the wide range of environmental conditions birds' encounter in the marine environment. During experimental manipulations, allostatic load was minimized by supplementing food to free-living birds.\n4. We found that the relationship between allostatic load and energy stores was clearly curvilinear at both life history stages. Observational data suggested that energy stores remained relatively stable under low allostatic load and decreased under high loads. Experimental data showed that birds did not maximize energy stores under favourable conditions but maintained energy stores below a physiologically attainable level.\n5. Energy stores remained consistently lower during chick-rearing compared to incubation across the wide range of variations in allostatic load suggesting that stage-specific trade-offs limit the accumulation of energy during favourable environmental conditions. Secretion of glucocorticoids did not appear to mediate this shift in energy stores between the life history stages.\n6. Overall, results of this study support the ‘lean and fit’ hypothesis. We conclude that increased energy stores may not necessarily reflect better environmental conditions experienced by individuals or predict their higher fitness. A major advantage of adopting a lean physique when environmental conditions allow may be the avoidance of additional energetic costs for moving a heavy body. In breeding seabirds, this advantage may be more important during chick-rearing. In the focal species, the secretion of glucocorticoids might be involved in regulation of energy stores within a life history stage but does not appear to mediate an adaptive shift in energy stores between the incubating and chick-rearing stages of reproduction.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Functional Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/j.1365-2435.2012.02058.x","usgsCitation":"Schultner, J., Kitaysky, A.S., Welcker, J., and Hatch, S., 2013, Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load: Functional Ecology, v. 27, no. 1, p. 45-55, https://doi.org/10.1111/j.1365-2435.2012.02058.x.","productDescription":"11 p.","startPage":"45","endPage":"55","ipdsId":"IP-042338","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":499925,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.wur.nl/en/publications/fat-or-lean-adjustment-of-endogenous-energy-stores-to-predictable","text":"External Repository"},{"id":272276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272275,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1365-2435.2012.02058.x"}],"volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-10-08","publicationStatus":"PW","scienceBaseUri":"53cd590de4b0b290850f87c3","contributors":{"authors":[{"text":"Schultner, Jannik","contributorId":77028,"corporation":false,"usgs":true,"family":"Schultner","given":"Jannik","email":"","affiliations":[],"preferred":false,"id":477704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitaysky, Alexander S.","contributorId":13884,"corporation":false,"usgs":true,"family":"Kitaysky","given":"Alexander","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":477701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welcker, Jorg","contributorId":25441,"corporation":false,"usgs":true,"family":"Welcker","given":"Jorg","email":"","affiliations":[],"preferred":false,"id":477703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatch, Scott","contributorId":16268,"corporation":false,"usgs":true,"family":"Hatch","given":"Scott","affiliations":[],"preferred":false,"id":477702,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045947,"text":"70045947 - 2013 - Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds","interactions":[],"lastModifiedDate":"2013-05-14T15:09:44","indexId":"70045947","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2362,"text":"Journal of Irrigation and Drainage Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds","docAbstract":"The rational method for peak discharge (Qp) estimation was introduced in the 1880s. The runoff coefficient (C) is a key parameter for the rational method that has an implicit meaning of rate proportionality, and the C has been declared a function of the annual return period by various researchers. Rate-based runoff coefficients as a function of the return period, C(T), were determined for 36 undeveloped watersheds in Texas using peak discharge frequency from previously published regional regression equations and rainfall intensity frequency for return periods T of 2, 5, 10, 25, 50, and 100 years. The C(T) values and return period adjustments C(T)/C(T=10 year) determined in this study are most applicable to undeveloped watersheds. The return period adjustments determined for the Texas watersheds in this study and those extracted from prior studies of non-Texas data exceed values from well-known literature such as design manuals and textbooks. Most importantly, the return period adjustments exceed values currently recognized in Texas Department of Transportation design guidance when T>10 years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Irrigation and Drainage Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ASCE","doi":"10.1061/(ASCE)IR.1943-4774.0000571","usgsCitation":"Dhakal, N., Fang, X., Asquith, W.H., Cleveland, T., and Thompson, D.B., 2013, Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds: Journal of Irrigation and Drainage Engineering, v. 139, no. 6, p. 476-482, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000571.","productDescription":"7 p.","startPage":"476","endPage":"482","ipdsId":"IP-042345","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":272266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272265,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)IR.1943-4774.0000571"}],"country":"United States","state":"Texas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.6,25.8 ], [ -106.6,36.5 ], [ -93.5,36.5 ], [ -93.5,25.8 ], [ -106.6,25.8 ] ] ] } } ] }","volume":"139","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd70e0e4b0b29085107537","contributors":{"authors":[{"text":"Dhakal, Nirajan","contributorId":93796,"corporation":false,"usgs":true,"family":"Dhakal","given":"Nirajan","email":"","affiliations":[],"preferred":false,"id":478594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fang, Xing","contributorId":27134,"corporation":false,"usgs":true,"family":"Fang","given":"Xing","email":"","affiliations":[],"preferred":false,"id":478591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Theodore G.","contributorId":88029,"corporation":false,"usgs":true,"family":"Cleveland","given":"Theodore G.","affiliations":[],"preferred":false,"id":478593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David B.","contributorId":79954,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":478592,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045078,"text":"70045078 - 2013 - Estimating economic losses from earthquakes using an empirical approach","interactions":[],"lastModifiedDate":"2013-05-12T21:46:04","indexId":"70045078","displayToPublicDate":"2013-05-12T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Estimating economic losses from earthquakes using an empirical approach","docAbstract":"We extended the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) empirical fatality estimation methodology proposed by Jaiswal et al. (2009) to rapidly estimate economic losses after significant earthquakes worldwide. The requisite model inputs are shaking intensity estimates made by the ShakeMap system, the spatial distribution of population available from the LandScan database, modern and historic country or sub-country population and Gross Domestic Product (GDP) data, and economic loss data from Munich Re's historical earthquakes catalog. We developed a strategy to approximately scale GDP-based economic exposure for historical and recent earthquakes in order to estimate economic losses. The process consists of using a country-specific multiplicative factor to accommodate the disparity between economic exposure and the annual per capita GDP, and it has proven successful in hindcast-ing past losses. Although loss, population, shaking estimates, and economic data used in the calibration process are uncertain, approximate ranges of losses can be estimated for the primary purpose of gauging the overall scope of the disaster and coordinating response. The proposed methodology is both indirect and approximate and is thus best suited as a rapid loss estimation model for applications like the PAGER system.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earthquake Spectra","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"EERI","doi":"10.1193/1.4000104","usgsCitation":"Jaiswal, K., and Wald, D.J., 2013, Estimating economic losses from earthquakes using an empirical approach: Earthquake Spectra, v. 29, no. 1, p. 309-324, https://doi.org/10.1193/1.4000104.","productDescription":"16 p.","startPage":"309","endPage":"324","ipdsId":"IP-037500","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":272191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272190,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1193/1.4000104"}],"volume":"29","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-02-01","publicationStatus":"PW","scienceBaseUri":"5190abcee4b05ebc8f7cc329","contributors":{"authors":[{"text":"Jaiswal, Kishor kjaiswal@usgs.gov","contributorId":861,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":476745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":476744,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}