The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-016-1782-2","usgsCitation":"Williamson, T., Nystrom, E.A., and Milly, P., 2016, Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration: Climatic Change, v. 139, no. 2, p. 215-228, https://doi.org/10.1007/s10584-016-1782-2.","productDescription":"14 p.","startPage":"215","endPage":"228","ipdsId":"IP-072262","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":330971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-16","publicationStatus":"PW","scienceBaseUri":"582adb44e4b0c253bdfff09a","chorus":{"doi":"10.1007/s10584-016-1782-2","url":"http://dx.doi.org/10.1007/s10584-016-1782-2","publisher":"Springer Nature","authors":"Williamson Tanja N., Nystrom Elizabeth A., Milly Paul C. D.","journalName":"Climatic Change","publicationDate":"9/16/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/16/2016"},"contributors":{"authors":[{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":148942,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":653642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":653644,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177933,"text":"fs20163081 - 2016 - Beryllium—A critical mineral commodity—Resources, production, and supply chain","interactions":[],"lastModifiedDate":"2018-10-22T09:08:22","indexId":"fs20163081","displayToPublicDate":"2016-11-14T11:45:00","publicationYear":"2016","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":"2016-3081","title":"Beryllium—A critical mineral commodity—Resources, production, and supply chain","docAbstract":"Beryllium is a lightweight metallic element used in a wide variety of specialty and industrial applications. As a function of its unique chemical and physical properties, such as a high stiffness-to-weight ratio, resistance to temperature extremes, and high thermal conductivity, beryllium cannot be easily replaced by substitute materials in applications where combinations of these properties make it the material of choice. Because the number of beryllium producers is limited and the use of substitute materials in specific defense-related applications that are vital to national security is inadequate, several studies have categorized beryllium as a critical and strategic material. This categorization has led to the United States Government recommending that beryllium be stockpiled for use in the event of a national emergency. As of December 31, 2015, the National Defense Stockpile inventory of hot-pressed beryllium metal powder, structured beryllium metal powder, and vacuum-cast beryllium metal totaled 78 metric tons (t).
The U.S. Geological Survey (USGS) Mineral Resources Program supports research on the occurrence, quality, quantity, and availability of mineral resources vital to the economy and national security. The USGS, through its National Minerals Information Center (NMIC), collects, analyzes, and disseminates information on more than 90 nonfuel mineral commodities from more than 180 countries. This fact sheet provides information on the production, consumption, supply chain, geology, and resource availability of beryllium in a global context.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163081","usgsCitation":"Lederer, G.W., Foley, N.K., Jaskula, B.W., and Ayuso, R.A., 2016, Beryllium—A critical mineral commodity—Resources, production, and supply chain: U.S. Geological Survey Fact Sheet 2016–3081, 4 p., https://dx.doi.org/10.3133/fs20163081.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-077851","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":330523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3081/coverthb3.jpg"},{"id":330524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3081/fs20163081.pdf","text":"Report","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3081"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at: http://minerals.usgs.gov/minerals
","tableOfContents":"Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0164979","usgsCitation":"Thieler, E.R., Zeigler, S.L., Winslow, L., Hines, M., Read, J.S., and Walker, J.I., 2016, Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability: PLoS ONE, v. 11, no. 11, e0164979; 22 p., https://doi.org/10.1371/journal.pone.0164979.","productDescription":"e0164979; 22 p.","ipdsId":"IP-077649","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470425,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164979","text":"Publisher Index Page"},{"id":330973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0a5","contributors":{"authors":[{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":169601,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, Megan 0000-0002-9845-4849 mhines@usgs.gov","orcid":"https://orcid.org/0000-0002-9845-4849","contributorId":4783,"corporation":false,"usgs":true,"family":"Hines","given":"Megan","email":"mhines@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":653638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":653639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jordan I. 0000-0003-2226-3373 jiwalker@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-3373","contributorId":4608,"corporation":false,"usgs":true,"family":"Walker","given":"Jordan","email":"jiwalker@usgs.gov","middleInitial":"I.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653640,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176823,"text":"ds1023 - 2016 - Quality of surface water in Missouri, water year 2015","interactions":[],"lastModifiedDate":"2016-11-14T12:56:11","indexId":"ds1023","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","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":"1023","title":"Quality of surface water in Missouri, water year 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2015 (October 1, 2014, through September 30, 2015), data were collected at 74 stations—72 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 71 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.
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There is considerable debate about the role of competition in shaping species distributions over broad spatial extents. This debate has practical implications because predicting changes in species' geographic ranges in response to ongoing environmental change would be simpler if competition could be ignored. While this debate has been the subject of many reviews, recent literature has not addressed the rates of relevant processes. This omission is surprising in that ecologists hypothesized decades ago that regional competitive exclusion is a slow process. The goal of this review is to reassess the debate under the hypothesis that competitive exclusion over broad spatial extents is a slow process.
Available evidence, including simulations presented for the first time here, suggests that competitive exclusion over broad spatial extents occurs slowly over temporal extents of many decades to millennia. Ecologists arguing against an important role for competition frequently study modern patterns and/or range dynamics over periods of decades, while much of the evidence for competition shaping geographic ranges at broad spatial extents comes from paleoecological studies over time scales of centuries or longer. If competition is slow, as evidence suggests, the geographic distributions of some, perhaps many species, would continue to change over time scales of decades to millennia, even if environmental conditions did not continue to change. If the distributions of competing species are at equilibrium it is possible to predict species distributions based on observed species–environment relationships. However, disequilibrium is widespread as a result of competition and many other processes. Studies whose goal is accurate predictions over intermediate time scales (decades to centuries) should focus on factors associated with range expansion (colonization) and loss (local extinction), as opposed to current patterns. In general, understanding of modern range dynamics would be enhanced by considering the rates of relevant processes.
","language":"English","publisher":"Blackwell Publishers ","doi":"10.1111/ecog.02836","usgsCitation":"Yackulic, C.B., 2016, Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling: Ecography, v. 40, no. 2, p. 305-313, https://doi.org/10.1111/ecog.02836.","productDescription":"9 p.","startPage":"305","endPage":"313","ipdsId":"IP-073460","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.02836","text":"Publisher Index Page"},{"id":331661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"58492df2e4b06d80b7b093a2","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":654997,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177049,"text":"fs20163083 - 2016 - Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016","interactions":[],"lastModifiedDate":"2018-02-15T14:59:47","indexId":"fs20163083","displayToPublicDate":"2016-11-10T18:00:00","publicationYear":"2016","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":"2016-3083","title":" Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean continuous resources of 12 billion barrels of oil and 75 trillion cubic feet of gas in the Bazhenov Formation of the West Siberian Basin Province, Russia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163083","usgsCitation":"Klett, T.R., Schenk, C.J., Brownfield, M.E., Leathers-Miller, H.M., Mercier, T.J., Pitman, J.K., and Tennyson, M.E., 2016, Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016: U.S. Geological Survey Fact Sheet 2016–3083, 2 p., https://dx.doi.org/10.3133/fs20163083.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-076584","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":330918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3083/coverthb2.jpg"},{"id":330919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3083/fs20163083.pdf","text":"Report","size":"501 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3083"}],"country":"Russia","otherGeospatial":"Bazhenov Formation of the West Siberian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 69.12597656249999,\n 76.63922560965885\n ],\n [\n 68.73046875,\n 76.27912201826652\n ],\n [\n 67.4560546875,\n 76.04791619059357\n ],\n [\n 60.9521484375,\n 75.06168594936098\n ],\n [\n 60.16113281250001,\n 74.54332982677906\n ],\n [\n 56.953125,\n 73.12494524712693\n ],\n [\n 55.986328125,\n 72.10094360009953\n ],\n [\n 56.2939453125,\n 71.37110941823617\n ],\n [\n 58.75488281249999,\n 70.65633017009853\n ],\n [\n 60.29296874999999,\n 70.09552886456429\n ],\n [\n 63.8525390625,\n 69.71810669906763\n ],\n [\n 66.181640625,\n 69.1312712296365\n ],\n [\n 67.8955078125,\n 68.5443150407769\n ],\n [\n 67.3681640625,\n 67.7261081859507\n ],\n [\n 65.21484375,\n 67.47492238478702\n ],\n [\n 61.787109375,\n 66.60067571342496\n ],\n [\n 59.4140625,\n 65.44000165965534\n ],\n [\n 58.447265625,\n 64.14895190024562\n ],\n [\n 59.0185546875,\n 62.77483725887553\n ],\n [\n 59.45800781249999,\n 61.66902436927201\n ],\n [\n 63.7646484375,\n 58.424729753759124\n ],\n [\n 66.4892578125,\n 56.68037378950137\n ],\n [\n 67.32421875,\n 55.7765730186677\n ],\n [\n 69.521484375,\n 55.50374985927514\n ],\n [\n 71.4111328125,\n 55.229023057406344\n ],\n [\n 71.806640625,\n 54.41892996865827\n ],\n [\n 73.0810546875,\n 54.49556752187406\n ],\n [\n 74.6630859375,\n 54.265224078605684\n ],\n [\n 75.9814453125,\n 54.41892996865827\n ],\n [\n 79.2333984375,\n 54.6992335284814\n ],\n [\n 80.68359375,\n 55.27911529201561\n ],\n [\n 81.73828125,\n 56.63206372054474\n ],\n [\n 82.353515625,\n 58.00809779306888\n ],\n [\n 81.6943359375,\n 59.866883195210214\n ],\n [\n 81.7822265625,\n 62.02152819100765\n ],\n [\n 81.7822265625,\n 63.470144746565424\n ],\n [\n 81.4306640625,\n 65.80277639340238\n ],\n [\n 81.474609375,\n 67.7926408447319\n ],\n [\n 81.6064453125,\n 68.56038368664157\n ],\n [\n 81.73828125,\n 69.02141408835533\n ],\n [\n 79.62890625,\n 69.16255790810501\n ],\n [\n 78.3544921875,\n 69.88501003874241\n ],\n [\n 78.9697265625,\n 70.48089578887483\n ],\n [\n 79.8486328125,\n 70.94535555009823\n ],\n [\n 79.013671875,\n 71.51097803770108\n ],\n [\n 79.189453125,\n 71.78768067220726\n ],\n [\n 78.8818359375,\n 72.01972876525514\n ],\n [\n 78.0908203125,\n 72.36910450725075\n ],\n [\n 78.046875,\n 72.80308575588377\n ],\n [\n 75.4541015625,\n 73.20131708476698\n ],\n [\n 74.4873046875,\n 75.01630602063719\n ],\n [\n 74.5751953125,\n 75.90950400691688\n ],\n [\n 74.0478515625,\n 76.31035754301745\n ],\n [\n 73.037109375,\n 76.56795488916347\n ],\n [\n 70.9716796875,\n 76.68990582156862\n ],\n [\n 69.12597656249999,\n 76.63922560965885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
Coastal wetlands are among the most valuable ecosystems on Earth, where ecosystem services such as flood protection depend nonlinearly on wetland size and are threatened by sea level rise and coastal development. Here we propose a simple model of marsh migration into adjacent uplands and couple it with existing models of seaward edge erosion and vertical soil accretion to explore how ecosystem connectivity influences marsh size and response to sea level rise. We find that marsh loss is nearly inevitable where topographic and anthropogenic barriers limit migration. Where unconstrained by barriers, however, rates of marsh migration are much more sensitive to accelerated sea level rise than rates of edge erosion. This behavior suggests a counterintuitive, natural tendency for marsh expansion with sea level rise and emphasizes the disparity between coastal response to climate change with and without human intervention.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016GL068507","usgsCitation":"Kirwan, M., Walters, D.C., Reay, W.G., and Carr, J., 2016, Sea level driven marsh expansion in a coupled model of marsh erosion and migration: Geophysical Research Letters, v. 43, no. 9, p. 4366-4373, https://doi.org/10.1002/2016GL068507.","productDescription":"8 p.","startPage":"4366","endPage":"4373","ipdsId":"IP-071126","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470428,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068507","text":"Publisher Index Page"},{"id":330911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-03","publicationStatus":"PW","scienceBaseUri":"58259560e4b01fad86db2412","contributors":{"authors":[{"text":"Kirwan, Matthew L. 0000-0002-0658-3038","orcid":"https://orcid.org/0000-0002-0658-3038","contributorId":84060,"corporation":false,"usgs":true,"family":"Kirwan","given":"Matthew L.","affiliations":[],"preferred":false,"id":653426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David C.","contributorId":176766,"corporation":false,"usgs":false,"family":"Walters","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":653427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reay, William G.","contributorId":176767,"corporation":false,"usgs":false,"family":"Reay","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":653428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653429,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178283,"text":"70178283 - 2016 - Climate change impacts on ecosystems and ecosystem services in the United States: Process and prospects for sustained assessment","interactions":[],"lastModifiedDate":"2020-07-28T15:29:24.14632","indexId":"70178283","displayToPublicDate":"2016-11-10T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate change impacts on ecosystems and ecosystem services in the United States: Process and prospects for sustained assessment","docAbstract":"The third United States National Climate Assessment emphasized an evaluation of not just the impacts of climate change on species and ecosystems, but also the impacts of climate change on the benefits that people derive from nature, known as ecosystem services. The ecosystems, biodiversity, and ecosystem services component of the assessment largely drew upon the findings of a transdisciplinary workshop aimed at developing technical input for the assessment, involving participants from diverse sectors. A small author team distilled and synthesized this and hundreds of other technical input to develop the key findings of the assessment. The process of developing and ranking key findings hinged on identifying impacts that had particular, demonstrable effects on the U.S. public via changes in national ecosystem services. Findings showed that ecosystem services are threatened by the impacts of climate change on water supplies, species distributions and phenology, as well as multiple assaults on ecosystem integrity that, when compounded by climate change, reduce the capacity of ecosystems to buffer against extreme events. As ecosystems change, such benefits as water sustainability and protection from storms that are afforded by intact ecosystems are projected to decline across the continent due to climate change. An ongoing, sustained assessment that focuses on the co-production of actionable climate science will allow scientists from a range of disciplines to ascertain the capability of their forecasting models to project environmental and ecological change and link it to ecosystem services; additionally, an iterative process of evaluation, development of management strategies, monitoring, and reevaluation will increase the applicability and usability of the science by the U.S. public.
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maps","interactions":[],"lastModifiedDate":"2017-07-19T10:47:31","indexId":"70189628","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps","docAbstract":"We present probabilistic and deterministic seismic and liquefaction hazard maps for the densely populated St. Louis metropolitan area that account for the expected effects of surficial geology on earthquake ground shaking. Hazard calculations were based on a map grid of 0.005°, or about every 500 m, and are thus higher in resolution than any earlier studies. To estimate ground motions at the surface of the model (e.g., site amplification), we used a new detailed near‐surface shear‐wave velocity model in a 1D equivalent‐linear response analysis. When compared with the 2014 U.S. Geological Survey (USGS) National Seismic Hazard Model, which uses a uniform firm‐rock‐site condition, the new probabilistic seismic‐hazard estimates document much more variability. Hazard levels for upland sites (consisting of bedrock and weathered bedrock overlain by loess‐covered till and drift deposits), show up to twice the ground‐motion values for peak ground acceleration (PGA), and similar ground‐motion values for 1.0 s spectral acceleration (SA). Probabilistic ground‐motion levels for lowland alluvial floodplain sites (generally the 20–40‐m‐thick modern Mississippi and Missouri River floodplain deposits overlying bedrock) exhibit up to twice the ground‐motion levels for PGA, and up to three times the ground‐motion levels for 1.0 s SA. Liquefaction probability curves were developed from available standard penetration test data assuming typical lowland and upland water table levels. A simplified liquefaction hazard map was created from the 5%‐in‐50‐year probabilistic ground‐shaking model. The liquefaction hazard ranges from low (<40% of area expected to liquefy) in the uplands to severe (>60% of area expected to liquefy) in the lowlands. Because many transportation routes, power and gas transmission lines, and population centers exist in or on the highly susceptible lowland alluvium, these areas in the St. Louis region are at significant potential risk from seismically induced liquefaction and associated ground deformation","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160028","usgsCitation":"Cramer, C.H., Bauer, R.A., Chung, J., Rogers, D., Pierce, L., Voigt, V., Mitchell, B., Gaunt, D., Williams, R., Hoffman, D., Hempen, G.L., Steckel, P., Boyd, O.S., Watkins, C.M., Tucker, K., and McCallister, N., 2016, St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps: Seismological Research Letters, v. 88, no. 1, p. 206-223, https://doi.org/10.1785/0220160028.","productDescription":"18 p.","startPage":"206","endPage":"223","ipdsId":"IP-079759","costCenters":[{"id":300,"text":"Geologic Hazards Science 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2012","interactions":[],"lastModifiedDate":"2016-11-09T10:18:09","indexId":"sim3368","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3368","title":"Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012","docAbstract":"During September–November 2012, the U.S. Geological Survey, in cooperation with the Puerto Rico Aqueduct and Sewer Authority, conducted a sedimentation survey of Lago Caonillas to estimate current (2012) reservoir storage capacity and the recent (2000–2012) reservoir sedimentation rate by comparing the 2012 bathymetric survey data with the February 2000 data. The Lago Caonillas storage capacity, which was 42.27 million cubic meters in February 2000, decreased to 39.55 million cubic meters by September–November 2012. The intersurvey (2000–2012) storage capacity loss was about 6 percent, corresponding to a decrease of about 0.5 percent per year; this loss represents a reservoir sedimentation rate of about 226,670 cubic meters per year between 2000 and 2012. On a long-term basis, however, the sedimentation rate has remained nearly constant, decreasing from about 257,500 to 251,720 cubic meters per year during 1948–2000 and 1948–2012, respectively. Most of the sediment accumulation and associated storage capacity loss of Lago Caonillas has occurred within the eastern and Río Caonillas branches of the reservoir. In the vicinity of the Caonillas Dam, minor sediment deposition and scour have occurred. The Lago Caonillas drainage area sediment yield has decreased by about 2 percent since the previous survey, from 1,266 cubic meters per square kilometer per year in 2000 to 1,237 cubic meters per square kilometer per year in 2012. If the long-term sedimentation rate of 251,720 cubic meters per year remains constant, the useful life of Lago Caonillas may end in about 2169.
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4446 Pet Lane, Suite 108
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This map shows the generalized geology of Alaska, which helps us to understand where potential mineral deposits and energy resources might be found, define ecosystems, and ultimately, teach us about the earth history of the State. Rock units are grouped in very broad categories on the basis of age and general rock type. A much more detailed and fully referenced presentation of the geology of Alaska is available in the Geologic Map of Alaska (http://dx.doi.org/10.3133/sim3340). This product represents the simplification of thousands of individual rock units into just 39 broad groups. Even with this generalization, the sheer complexity of Alaskan geology remains evident.
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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
In natural capital accounting, ecosystems are assets that provide ecosystem services to people. Assets can be measured using both physical and monetary units. In the international System of Environmental-Economic Accounting, ecosystem assets are generally valued on the basis of the net present value of the expected flow of ecosystem services. In this paper we argue that several additional conceptualisations of ecosystem assets are needed to understand ecosystems as assets, in support of ecosystem assessments, ecosystem accounting and ecosystem management. In particular, we define ecosystems’ capacity and capability to supply ecosystem services, as well as the potential supply of ecosystem services. Capacity relates to sustainable use levels of multiple ecosystem services, capability involves prioritising the use of one ecosystem service over a basket of services, and potential supply considers the ability of ecosystems to generate services regardless of demand for these services. We ground our definitions in the ecosystem services and accounting literature, and illustrate and compare the concepts of flow, capacity, capability, and potential supply with a range of conceptual and real-world examples drawn from case studies in Europe and North America. Our paper contributes to the development of measurement frameworks for natural capital to support environmental accounting and other assessment frameworks.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0164460","usgsCitation":"Hein, L., Bagstad, K.J., Edens, B., Obst, C., de Jong, R., and Lesschen, J.P., 2016, Defining ecosystem assets for natural capital accounting: PLoS ONE, v. 11, no. 11, e0164460; 25 p., https://doi.org/10.1371/journal.pone.0164460.","productDescription":"e0164460; 25 p.","ipdsId":"IP-075433","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164460","text":"Publisher Index Page"},{"id":331071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582dd8eae4b04d580bd3fa91","contributors":{"authors":[{"text":"Hein, Lars","contributorId":176849,"corporation":false,"usgs":false,"family":"Hein","given":"Lars","email":"","affiliations":[],"preferred":false,"id":653773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":653772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edens, Bram","contributorId":176850,"corporation":false,"usgs":false,"family":"Edens","given":"Bram","email":"","affiliations":[],"preferred":false,"id":653774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Obst, Carl","contributorId":176851,"corporation":false,"usgs":false,"family":"Obst","given":"Carl","email":"","affiliations":[],"preferred":false,"id":653775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Jong, Rixt","contributorId":176852,"corporation":false,"usgs":false,"family":"de Jong","given":"Rixt","email":"","affiliations":[],"preferred":false,"id":653776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lesschen, Jan Peter","contributorId":176853,"corporation":false,"usgs":false,"family":"Lesschen","given":"Jan","email":"","middleInitial":"Peter","affiliations":[],"preferred":false,"id":653777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178093,"text":"ofr20161187 - 2016 - Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","interactions":[],"lastModifiedDate":"2018-03-08T16:08:01","indexId":"ofr20161187","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","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":"2016-1187","title":"Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","docAbstract":"Understanding if and how community exposure to coastal hazards may change over time is crucial information for coastal managers tasked with developing climate adaptation plans. This report summarizes estimates of population and asset exposure to coastal-inundation hazards associated with sea-level-rise and storm scenarios in six coastal communities of the Great Marsh region of Essex County, Massachusetts. This U.S. Geological Survey (USGS) analysis was conducted in collaboration with National Wildlife Federation (NWF) representatives, who are working with local stakeholders to develop local climate adaptation plans for the Towns of Salisbury, Newbury, Rowley, Ipswich, and Essex and the City of Newburyport (hereafter referred to as communities). Community exposure was characterized by integrating various community indicators (land cover and land use, population, economic assets, critical facilities, and infrastructure) with coastal-hazard zones that estimate inundation extents and water depth for three time periods.
Estimates of community exposure are based on the presence of people, businesses, and assets in hazard zones that are calculated from geospatial datasets using geographic-information-system (GIS) tools. Results are based on current distributions of people and assets in hazard zones and do not take into account projections of human population, asset, or land-use changes over time. Results are not loss estimates based on engineering analysis or field surveys for any particular facility and do not take into account aspects of individual and household preparedness before an extreme event, adaptive capacity of a community during an event, or long-term resilience of individuals and communities after an event. Potential losses would match reported inventories only if all residents, business owners, public managers, and elected officials were unaware of what to do if warned of an imminent threat, failed to take protective measures during an extreme event, or failed to implement any long-term strategies to mitigate potential impacts. This analysis is intended to serve as a foundation for additional risk-related studies, plans, and mitigation efforts that are tailored to local needs. After a summary of the geospatial methods used in the analysis, results are organized by community so that local officials can easily use them in their local adaptation planning efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161187","usgsCitation":"Abdollahian, Nina, Jones, J.L., and Wood, N.J., 2016, Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts: U.S. Geological Survey Open-File Report 2016–1187, 87 p., https://dx.doi.org/10.3133/ofr20161187.","productDescription":"ix, 97 p.","onlineOnly":"Y","ipdsId":"IP-076664","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":330895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1187/ofr20161187.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1187"},{"id":330894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1187/coverthb.jpg"}],"country":"United States","state":"Massachusetts","county":"Essex County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.71006774902344,\n 42.68193062780271\n ],\n [\n -70.68603515625,\n 42.661736441708726\n ],\n [\n -70.67985534667969,\n 42.645576368740564\n ],\n [\n -70.68946838378906,\n 42.61374895431491\n ],\n [\n -70.67779541015624,\n 42.58594981115061\n ],\n [\n -70.70938110351562,\n 42.56926437219384\n ],\n [\n -70.74783325195311,\n 42.5626900946744\n ],\n [\n -70.79315185546875,\n 42.55813826591757\n ],\n [\n -70.81443786621094,\n 42.5530802889558\n ],\n [\n -70.84396362304688,\n 42.549033612225145\n ],\n [\n -70.90164184570312,\n 42.558644041066486\n ],\n [\n -70.92567443847656,\n 42.58443313755392\n ],\n [\n -70.94352722167969,\n 42.61273829368574\n ],\n [\n -70.9771728515625,\n 42.65315192577889\n ],\n [\n -70.98609924316406,\n 42.6970719613491\n ],\n [\n -71.00807189941406,\n 42.74196953252084\n ],\n [\n -71.01837158203125,\n 42.77221922375653\n ],\n [\n -71.026611328125,\n 42.818573401893836\n ],\n [\n -71.03347778320312,\n 42.85834982271733\n ],\n [\n -70.99296569824219,\n 42.86740922288482\n ],\n [\n -70.94627380371094,\n 42.877473663905946\n ],\n [\n -70.92498779296875,\n 42.882505268943774\n ],\n [\n -70.88447570800781,\n 42.882505268943774\n ],\n [\n -70.85975646972656,\n 42.87495770751784\n ],\n [\n -70.83915710449219,\n 42.87042872752173\n ],\n [\n -70.81718444824219,\n 42.87042872752173\n ],\n [\n -70.806884765625,\n 42.835191982798925\n ],\n [\n -70.8013916015625,\n 42.79288135293722\n ],\n [\n -70.77392578125,\n 42.73944805837745\n ],\n [\n -70.73204040527342,\n 42.70110902692345\n ],\n [\n -70.71006774902344,\n 42.68193062780271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Western Geographic Science Center
U.S. Geological Survey
345 Middlefield Road, MS 531
Menlo Park, CA 94025
http://geography.wr.usgs.gov/
Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean undiscovered, technically recoverable continuous resources that total 464 million barrels of oil and 4.08 trillion cubic feet of gas in the Lower Mississippian Sunbury Shale and Middle Devonian–Lower Mississippian Chattanooga Shale of the Appalachian Basin Province.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163085","usgsCitation":"Higley, D.K., Rouse, W.A., Enomoto, C.B., Trippi, M.H., Klett, T.R., Mercier, T.J., Brownfield, M.E., Tennyson, M.E., Drake, R.M., II, Finn, T.M., Gianoutsos, N.J., Pearson, O.N., Doolan, Colin, Le, P.A., and Schenk, C.J., Assessment of undiscovered oil and gas resources of the Mississippian Sunbury shale and Devonian–Mississippian Chattanooga shale in the Appalachian Basin Province, 2016: U.S. Geological Survey Fact Sheet 2016–3085, 2 p., https://dx.doi.org/10.3133/fs20163085.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-076996","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438511,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLVVQ2","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Sunbury and Chattanooga Shales Assessment Unit Boundaries and Assessment Input Forms"},{"id":330779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3085/fs20163085.pdf","text":"Report","size":"382 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3085"},{"id":330778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3085/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.705078125,\n 41.44272637767212\n ],\n [\n -83.3642578125,\n 38.634036452919226\n ],\n [\n -86.7919921875,\n 34.994003757575776\n ],\n [\n -87.60498046875,\n 32.36140331527543\n ],\n [\n -87.01171875,\n 32.0639555946604\n ],\n [\n -85.45166015624999,\n 33.94335994657882\n ],\n [\n -84.22119140625,\n 35.38904996691167\n ],\n [\n -81.5185546875,\n 36.59788913307022\n ],\n [\n -79.27734374999999,\n 37.70120736474139\n ],\n [\n -79.12353515625,\n 42.68243539838623\n ],\n [\n -82.705078125,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
http://energy.usgs.gov
Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
","language":"English","publisher":"JoVE","doi":"10.3791/54578","usgsCitation":"West, A.M., Evangelista, P.H., Jarnevich, C.S., Young, N.E., Stohlgren, T.J., Talbert, C., Talbert, M., Morisette, J., and Anderson, R., 2016, Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM): Journal of Visualized Experiments, v. 116, e54578, https://doi.org/10.3791/54578.","productDescription":"e54578","ipdsId":"IP-070978","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5092193","text":"Publisher Index Page"},{"id":330808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5821a0dbe4b02f1a881de95a","contributors":{"authors":[{"text":"West, Amanda M.","contributorId":176705,"corporation":false,"usgs":false,"family":"West","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evangelista, Paul H.","contributorId":14747,"corporation":false,"usgs":true,"family":"Evangelista","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Colin talbertc@usgs.gov","contributorId":4668,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":653205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Talbert, Marian mtalbert@usgs.gov","contributorId":5180,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":653206,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morisette, Jeffrey","contributorId":100739,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","affiliations":[],"preferred":false,"id":653207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Ryan","contributorId":106029,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","affiliations":[],"preferred":false,"id":653208,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70168511,"text":"sir20155142 - 2016 - Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","interactions":[],"lastModifiedDate":"2021-09-27T18:32:45.902579","indexId":"sir20155142","displayToPublicDate":"2016-11-07T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5142","title":"Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","docAbstract":"The Delaware River Basin (DRB) was selected as a Focus Area Study in 2011 by the U.S. Geological Survey (USGS) as part of the USGS National Water Census. The National Water Census is a USGS research program that focuses on national water availability and use and then develops new water accounting tools and assesses water availability at both the regional and national scales. One of the water management needs that the DRB study addressed, and that was identified by stakeholder groups from the DRB, was to improve the integration of state water use and water-supply data and to provide the compiled water use information to basin users. This water use information was also used in the hydrologic modeling and ecological components of the study.
Instream and offstream water use was calculated for 2010 for the DRB based on information received from Delaware, New Jersey, New York, and Pennsylvania. Water withdrawal, interbasin transfers, return flow, and hydroelectric power generation release data were compiled for 11 categories by hydrologic subregion, basin, subbasin, and subwatershed. Data availability varied by state. Site-specific data were used whenever possible to calculate public supply, irrigation (golf courses, nurseries, sod farms, and crops), aquaculture, self-supplied industrial, commercial, mining, thermoelectric, and hydroelectric power withdrawals. Where site-specific data were not available, primarily for crop irrigation, livestock, and domestic use, various techniques were used to estimate water withdrawals.
Total water withdrawals in the Delaware River Basin were calculated to be about 7,130 million gallons per day (Mgal/d) in 2010. Calculations of withdrawals by source indicate that freshwater withdrawals were about 4,130 Mgal/d (58 percent of the total) and the remaining 3,000 Mgal/d (42 percent) were from saline water. Total surface-water withdrawals were calculated to be 6,590 Mgal/d, or 92 percent of the total; about 54 percent (3,590 Mgal/d) of surface water withdrawn was freshwater. Total groundwater withdrawals were calculated to be 545 Mgal/d (8 percent of the total), all of which was freshwater. During 2010, calculated withdrawals by category, in decreasing order, were: thermoelectric power, 4,910 Mgal/d; public supply, 1,490 Mgal/d; self-supplied industrial, 350 Mgal/d; irrigation, 175 Mgal/d; self-supplied domestic, 117 Mgal/d; mining, 41.3 Mgal/d; aquaculture, 19.3 Mgal/d; livestock, 6.72 Mgal/d, and commercial, 5.89 Mgal/d. The amount of instream use for hydroelectric power generation purposes in 2010 was reported to be 273 Mgal/d for the Wallenpaupack Plant and 127 Mgal/d for the Mongaup River system.
Total return flows in the DRB were 2,960 Mgal/d in 2010. Although municipal wastewater-treatment plants accounted for 539 (97 percent) of the return-flow sites, they accounted for about 70 percent of the total return flows in the DRB. There was limited information on return flows from thermoelectric power.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155142","usgsCitation":"Hutson, S.S., Linsey, K.S., Ludlow, R.A., Reyes, Betzaida, and Shourds, J.L., 2016, Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010: U.S. Geological Survey Scientific Investigations Report 2015–5142, 76 p., https://dx.doi.org/10.3133/sir20155142.","productDescription":"Report: vii, 76 p.; 2 Appendixes; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058986","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":438513,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM787C","text":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8 and HUC12) in the Delaware River Basin, 2010"},{"id":330727,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix3.xlsx","text":"Appendix 3","size":"269 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5142 Appendix 3","linkHelpText":"- Delaware River Basin Water Use by Subbasin, 2010"},{"id":330724,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5142/coverthb.jpg"},{"id":330725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142.pdf","text":"Report","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5142"},{"id":330726,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix2.docx","text":"Appendix 2","size":"2.49 MB docx","description":"SIR 2015-5142 Appendix 2","linkHelpText":"- Hydrologic Subbasins, Watersheds, and Subwatersheds in the Delaware River Basin"},{"id":330728,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TM787C","text":"USGS data release","description":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8) and Subwatershed (HUC12) in the Delaware River Basin, 2010"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.652099609375,\n 39.18117526158749\n ],\n [\n -74.50927734375,\n 40.195659093364654\n ],\n [\n -74.586181640625,\n 41.36856413680967\n ],\n [\n -74.300537109375,\n 42.09822241118974\n ],\n [\n -74.619140625,\n 42.53689200787315\n ],\n [\n -75.498046875,\n 42.10637370579324\n ],\n [\n -75.9375,\n 41.12074559016745\n ],\n [\n -76.607666015625,\n 40.36328834091583\n ],\n [\n -75.860595703125,\n 39.715638134796336\n ],\n [\n -75.43212890625,\n 38.685509760012\n ],\n [\n -75.069580078125,\n 38.77121637244273\n ],\n [\n -74.652099609375,\n 39.18117526158749\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator—National Water Census
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Or visit the National Water Census Web site at: http://water.usgs.gov/watercensus
","tableOfContents":"Infrastructure—the physical framework of transportation, energy, communications, water supply, and other systems—and construction management—the overall planning, coordination, and control of a project from beginning to end—are critical to the Nation’s prosperity. The American Society of Civil Engineers has warned that, despite the importance of the Nation’s infrastructure, it is in fair to poor condition and needs sizable and urgent investments to maintain and modernize it, and to ensure that it is sustainable and resilient.
Three-dimensional (3D) light detection and ranging (lidar) elevation data provide valuable productivity, safety, and cost-saving benefits to infrastructure improvement projects and associated construction management. By providing data to users, the 3D Elevation Program (3DEP) of the U.S. Geological Survey reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data.
The size and breadth of improvements for the Nation’s infrastructure and construction management needs call for an efficient, systematic approach to acquiring foundational 3D elevation data. The 3DEP approach to national data coverage will yield large cost savings over individual project-by-project acquisitions and will ensure that data are accessible for other critical applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163093","usgsCitation":"Lukas, Vicki, and Carswell, W.J., Jr., 2016, The 3D Elevation Program and America's infrastructure: U.S. Geological Survey Fact Sheet 2016–3093, 2 p., https://dx.doi.org/10.3133/fs20163093.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077294","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":330670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3093/fs20163093.pdf","text":"Report","size":"425 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3093"},{"id":330669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3093/coverthb.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
511 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
Decision support tools can aid decision making by systematically incorporating information, accounting for uncertainties, and facilitating evaluation between alternatives. Without user buy-in, however, decision support tools can fail to influence decision-making processes. We surveyed fishery researchers, managers, and fishers affiliated with the Lake Whitefish Coregonus clupeaformis fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior to assess opinions of current and future management needs to identify barriers to, and opportunities for, developing a decision support tool based on Lake Whitefish recruitment projections with climate change. Approximately 64% of 39 respondents were satisfied with current management, and nearly 85% agreed that science was well integrated into management programs. Though decision support tools can facilitate science integration into management, respondents suggest that they face significant implementation barriers, including lack of political will to change management and perceived uncertainty in decision support outputs. Recommendations from this survey can inform development of decision support tools for fishery management in the Great Lakes and other regions.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/03632415.2016.1232960","usgsCitation":"Lynch, A.J., Taylor, W., and McCright, A.M., 2016, Stakeholder views of management and decision support tools to integrate climate change into Great Lakes Lake Whitefish management: Fisheries, v. 41, no. 11, p. 644-652, https://doi.org/10.1080/03632415.2016.1232960.","productDescription":"9 p.","startPage":"644","endPage":"652","ipdsId":"IP-067035","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":330860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"5821a0dce4b02f1a881de96d","contributors":{"authors":[{"text":"Lynch, Abigail J. 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":5645,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":653320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, William W.","contributorId":113795,"corporation":false,"usgs":true,"family":"Taylor","given":"William W.","affiliations":[],"preferred":false,"id":653321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCright, Aaron M.","contributorId":176733,"corporation":false,"usgs":false,"family":"McCright","given":"Aaron","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70179159,"text":"70179159 - 2016 - Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux","interactions":[],"lastModifiedDate":"2016-12-20T11:59:51","indexId":"70179159","displayToPublicDate":"2016-11-07T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux","docAbstract":"Methane concentration and isotopic composition was measured in ice-covered and ice-free waters of the Arctic Ocean during eleven surveys spanning the years of 1992-1995 and 2009. During ice-free periods, methane flux from the Beaufort shelf varies from 0.14 to 0.43 mg CH4 m-2 day-1. Maximum fluxes from localized areas of high methane concentration are up to 1.52 mg CH4 m-2 day-1. Seasonal buildup of methane under ice can produce short-term fluxes of methane from the Beaufort shelf that varies from 0.28 to 1.01 to mg CH4 m-2 day-1. Scaled-up estimates of minimum methane flux from the Beaufort Sea and pan-Arctic shelf for both ice-free and ice-covered periods range from 0.02 Tg CH4 yr-1 and 0.30 Tg CH4 yr-1 respectively to maximum fluxes of 0.18 Tg CH4 yr-1 and 2.2 Tg CH4 yr-1 respectively. A methane flux of 0.36 Tg CH4 yr-1from the deep Arctic Ocean was estimated using data from 1993-94. The flux can be as much as 2.35 Tg CH4 yr-1 estimated from maximum methane concentrations and wind speeds of 12 m/s, representing only 0.42% of the annual atmospheric methane budget of ~560 Tg CH4 yr-1. There were no significant changes in methane fluxes during the time period of this study. Microbial methane sources predominate with minor influxes from thermogenic methane offshore Prudhoe Bay and the Mackenzie River delta and may include methane from gas hydrate. Methane oxidation is locally important on the shelf and is a methane sink in the deep Arctic Ocean.","language":"English","publisher":"Wiley","doi":"10.1002/lno.10457","usgsCitation":"Lorenson, T., Greinert, J., and Coffin, R.B., 2016, Dissolved methane in the Beaufort Sea and the Arctic Ocean, 1992-2009; sources and atmospheric flux: Limnology and Oceanography, v. 61, no. 51, p. S300-S323, https://doi.org/10.1002/lno.10457.","productDescription":"24 p.","startPage":"S300","endPage":"S323","ipdsId":"IP-079351","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10457","text":"Publisher Index Page"},{"id":332340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.125,\n 72.39570570653261\n ],\n [\n -146.6015625,\n 70.1403642720717\n ],\n [\n -136.7578125,\n 69.53451763078358\n ],\n [\n -128.32031249999997,\n 70.37785394109224\n ],\n [\n -117.7734375,\n 78.42019327591201\n ],\n [\n -95.2734375,\n 81.20141954209073\n ],\n [\n -76.2890625,\n 83.19489563661588\n ],\n [\n -30.585937499999996,\n 83.82994542398042\n ],\n [\n -23.90625,\n 84.89714695160268\n ],\n [\n -51.67968749999999,\n 85.0511287798066\n ],\n [\n -86.1328125,\n 85.11141578062661\n ],\n [\n -133.2421875,\n 85.02070774312594\n ],\n [\n -172.96875,\n 81.4139332828511\n ],\n [\n -176.1328125,\n 71.52490903732816\n ],\n [\n -161.015625,\n 71.41317683396566\n ],\n [\n -163.125,\n 72.39570570653261\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"61","issue":"51","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"585a51bde4b01224f329b5e5","contributors":{"authors":[{"text":"Lorenson, Thomas D.","contributorId":177573,"corporation":false,"usgs":false,"family":"Lorenson","given":"Thomas D.","affiliations":[],"preferred":false,"id":656252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greinert, Jens","contributorId":101809,"corporation":false,"usgs":true,"family":"Greinert","given":"Jens","email":"","affiliations":[],"preferred":false,"id":656253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coffin, Richard B.","contributorId":177575,"corporation":false,"usgs":false,"family":"Coffin","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":656254,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178096,"text":"ofr20161186 - 2016 - Summary of environmental flow monitoring for the Sustainable Rivers Project on the Middle Fork Willamette and McKenzie Rivers, western Oregon, 2014–15","interactions":[],"lastModifiedDate":"2016-11-08T10:34:43","indexId":"ofr20161186","displayToPublicDate":"2016-11-07T00:00:00","publicationYear":"2016","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":"2016-1186","title":"Summary of environmental flow monitoring for the Sustainable Rivers Project on the Middle Fork Willamette and McKenzie Rivers, western Oregon, 2014–15","docAbstract":"This report presents the results of an ongoing environmental flow monitoring study by The Nature Conservancy (TNC), U.S. Army Corps of Engineers (USACE), and U.S. Geological Survey in support of the Sustainable Rivers Project (SRP) of TNC and USACE. The overarching goal of this study is to evaluate and characterize relations between streamflow, geomorphic processes, and black cottonwood (Populus trichocarpa) recruitment on the Middle Fork Willamette and McKenzie Rivers, western Oregon, that were hypothesized in earlier investigations. The SRP can use this information to plan future monitoring and scientific investigations, and to help mitigate the effects of dam operations on streamflow regimes, geomorphic processes, and biological communities, such as black cottonwood forests, in consultation with regional experts. The four tasks of this study were to:
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Clawpack is a software package designed to solve nonlinear hyperbolic partial differential equations using high-resolution finite volume methods based on Riemann solvers and limiters. The package includes a number of variants aimed at different applications and user communities. Clawpack has been actively developed as an open source project for over 20 years. The latest major release, Clawpack 5, introduces a number of new features and changes to the code base and a new development model based on GitHub and Git submodules. This article provides a summary of the most significant changes, the rationale behind some of these changes, and a description of our current development model. Clawpack: building an open source ecosystem for solving hyperbolic PDEs.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj-cs.68","usgsCitation":"Iverson, R.M., Mandli, K., Ahmadia, A.J., Berger, M., Calhoun, D., George, D.L., Hadjimichael, Y., Ketcheson, D.I., Lemoine, G.L., and LeVeque, R.J., 2016, Clawpack: Building an open source ecosystem for solving hyperbolic PDEs: PeerJ, v. 2, e68, https://doi.org/10.7717/peerj-cs.68.","productDescription":"e68","onlineOnly":"N","ipdsId":"IP-078524","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj-cs.68","text":"Publisher Index Page"},{"id":348284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-08","publicationStatus":"PW","scienceBaseUri":"5a07e9aae4b09af898c8cc3a","contributors":{"authors":[{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":720048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandli, K.T.","contributorId":199786,"corporation":false,"usgs":false,"family":"Mandli","given":"K.T.","affiliations":[],"preferred":false,"id":720049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ahmadia, Aron J.","contributorId":199787,"corporation":false,"usgs":false,"family":"Ahmadia","given":"Aron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berger, M.J.","contributorId":199788,"corporation":false,"usgs":false,"family":"Berger","given":"M.J.","email":"","affiliations":[],"preferred":false,"id":720051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calhoun, Donna","contributorId":199789,"corporation":false,"usgs":false,"family":"Calhoun","given":"Donna","affiliations":[],"preferred":false,"id":720052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":720053,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hadjimichael, Y.","contributorId":199790,"corporation":false,"usgs":false,"family":"Hadjimichael","given":"Y.","affiliations":[],"preferred":false,"id":720054,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ketcheson, David I.","contributorId":199791,"corporation":false,"usgs":false,"family":"Ketcheson","given":"David","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":720055,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lemoine, Grady L.","contributorId":199792,"corporation":false,"usgs":false,"family":"Lemoine","given":"Grady","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":720056,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"LeVeque, Randall J.","contributorId":198359,"corporation":false,"usgs":false,"family":"LeVeque","given":"Randall","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":720057,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70191521,"text":"70191521 - 2016 - Streamflow data","interactions":[],"lastModifiedDate":"2020-08-25T16:55:00.091561","indexId":"70191521","displayToPublicDate":"2016-11-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Streamflow data","docAbstract":"Streamflow data are vital for a variety of water-resources issues, from flood warning to water supply planning. The collection of streamflow data is usually an involved and complicated process. This chapter serves as an overview of the streamflow data collection process. Readers with the need for the detailed information on the streamflow data collection process are referred to the many references noted in this chapter.
Kelp forests provide habitat for diverse and abundant fish assemblages, but the extent to which kelp provides a source of energy to fish and other predators is unclear. To examine the use of kelp-derived energy by fishes we estimated the contribution of kelp- and phytoplankton-derived carbon using carbon (δ13C) and nitrogen (δ15N) isotopes measured in muscle tissue. Benthic-foraging kelp greenling (Hexagrammos decagrammus) and pelagic-foraging black rockfish (Sebastes melanops) were collected at eight sites spanning ∼35 to 60°N from the California Current (upwelling) to Alaska Coastal Current (downwelling) in the northeast Pacific Ocean. Muscle δ13C values were expected to be higher for fish tissue primarily derived from kelp, a benthic macroalgae, and lower for tissue primarily derived from phytoplankton, pelagic microalgae. Muscle δ13C values were higher in benthic-feeding kelp greenling than in pelagic-feeding black rockfish at seven of eight sites, indicating more kelp-derived carbon in greenling as expected. Estimates of kelp carbon contributions ranged from 36 to 89% in kelp greenling and 32 to 65% in black rockfish using carbon isotope mixing models. Isotopic evidence suggests that these two nearshore fishes routinely derive energy from kelp and phytoplankton, across coastal upwelling and downwelling systems. Thus, the foraging mode of nearshore predators has a small influence on their ultimate energy source as energy produced by benthic macroalgae and pelagic microalgae were incorporated in fish tissue regardless of feeding mode and suggest strong and widespread benthic-pelagic coupling. Widespread kelp contributions to benthic- and pelagic-feeding fishes suggests that kelp energy provides a benefit to nearshore fishes and highlights the potential for kelp and fish production to be linked.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.ecss.2016.08.039","usgsCitation":"von Biela, V.R., Newsome, S.D., Bodkin, J.L., Kruse, G.H., and Zimmerman, C.E., 2016, Widespread kelp-derived carbon in pelagic and benthic nearshore fishes: Estuarine, Coastal and Shelf Science, v. 181, p. 364-374, https://doi.org/10.1016/j.ecss.2016.08.039.","productDescription":"11 p. ","startPage":"364","endPage":"374","ipdsId":"IP-062461","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":470434,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2016.08.039","text":"Publisher Index Page"},{"id":336765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"181","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf3","chorus":{"doi":"10.1016/j.ecss.2016.08.039","url":"http://dx.doi.org/10.1016/j.ecss.2016.08.039","publisher":"Elsevier BV","authors":"von Biela Vanessa R., Newsome Seth D., Bodkin James L., Kruse Gordon H., Zimmerman Christian E.","journalName":"Estuarine, Coastal and Shelf Science","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":673747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newsome, Seth D.","contributorId":81640,"corporation":false,"usgs":false,"family":"Newsome","given":"Seth","email":"","middleInitial":"D.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":680456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":680457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kruse, Gordon H.","contributorId":187450,"corporation":false,"usgs":false,"family":"Kruse","given":"Gordon","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":680458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":680459,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178179,"text":"70178179 - 2016 - Impacts of shore expansion and catchment characteristics on lacustrine thermokarst records in permafrost lowlands, Alaska Arctic Coastal Plain","interactions":[],"lastModifiedDate":"2016-11-04T14:55:21","indexId":"70178179","displayToPublicDate":"2016-11-04T15:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5224,"text":"arktos","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of shore expansion and catchment characteristics on lacustrine thermokarst records in permafrost lowlands, Alaska Arctic Coastal Plain","docAbstract":"Arctic lowland landscapes have been modified by thermokarst lake processes throughout the Holocene. Thermokarst lakes form as a result of ice-rich permafrost degradation, and they may expand over time through thermal and mechanical shoreline erosion. We studied proximal and distal sedimentary records from a thermokarst lake located on the Arctic Coastal Plain of northern Alaska to reconstruct the impact of catchment dynamics and morphology on the lacustrine depositional environment and to quantify carbon accumulation in thermokarst lake sediments. Short cores were collected for analysis of pollen, sedimentological, and geochemical proxies. Radiocarbon and 210Pb/137Cs dating, as well as extrapolation of measured historic lake expansion rates, were applied to estimate a minimum lake age of ~1400 calendar years BP. The pollen record is in agreement with the young lake age as it does not include evidence of the “alder high” that occurred in the region ~4000 cal yr BP. The lake most likely initiated from a remnant pond in a drained thermokarst lake basin (DTLB) and deepened rapidly as evidenced by accumulation of laminated sediments. Increasing oxygenation of the water column as shown by higher Fe/Ti and Fe/S ratios in the sediment indicate shifts in ice regime with increasing water depth. More recently, the sediment source changed as the thermokarst lake expanded through lateral permafrost degradation, alternating from redeposited DTLB sediments, to increased amounts of sediment from eroding, older upland deposits, followed by a more balanced combination of both DTLB and upland sources. The characterizing shifts in sediment sources and depositional regimes in expanding thermokarst lakes were, therefore, archived in the thermokarst lake sedimentary record. This study also highlights the potential for Arctic lakes to recycle old carbon from thawing permafrost and thermokarst processes.
","language":"English","publisher":"Springer","doi":"10.1007/s41063-016-0025-0","usgsCitation":"Lenz, J., Jones, B.M., Wetterich, S., Tjallingii, R., Fritz, M., Arp, C.D., Rudaya, N., and Grosse, G., 2016, Impacts of shore expansion and catchment characteristics on lacustrine thermokarst records in permafrost lowlands, Alaska Arctic Coastal Plain: arktos, v. 2, Article 25; 15 p., https://doi.org/10.1007/s41063-016-0025-0.","productDescription":"Article 25; 15 p.","ipdsId":"IP-076197","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":462039,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s41063-016-0025-0","text":"Publisher Index Page"},{"id":330776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.5721435546875,\n 70.43863810197413\n ],\n [\n -154.5721435546875,\n 70.9471488208593\n ],\n [\n -152.8802490234375,\n 70.9471488208593\n ],\n [\n -152.8802490234375,\n 70.43863810197413\n ],\n [\n -154.5721435546875,\n 70.43863810197413\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-03","publicationStatus":"PW","scienceBaseUri":"581d9e29e4b0dee4cc90cbb7","contributors":{"authors":[{"text":"Lenz, Josefine","contributorId":146181,"corporation":false,"usgs":false,"family":"Lenz","given":"Josefine","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":653146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":653145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetterich, Sebastian","contributorId":146186,"corporation":false,"usgs":false,"family":"Wetterich","given":"Sebastian","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":653147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tjallingii, Rik","contributorId":176700,"corporation":false,"usgs":false,"family":"Tjallingii","given":"Rik","email":"","affiliations":[],"preferred":false,"id":653148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fritz, Michael","contributorId":176701,"corporation":false,"usgs":false,"family":"Fritz","given":"Michael","email":"","affiliations":[],"preferred":false,"id":653149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":653150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rudaya, Natalia","contributorId":176702,"corporation":false,"usgs":false,"family":"Rudaya","given":"Natalia","email":"","affiliations":[],"preferred":false,"id":653151,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":653152,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70178180,"text":"70178180 - 2016 - Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints","interactions":[],"lastModifiedDate":"2016-12-29T09:20:29","indexId":"70178180","displayToPublicDate":"2016-11-04T15:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints","docAbstract":"Borehole logging data from legacy wells directly constrain the contemporary distribution of subsea permafrost in the sedimentary section at discrete locations on the U.S. Beaufort Margin and complement recent regional analyses of exploration seismic data to delineate the permafrost's offshore extent. Most usable borehole data were acquired on a ∼500 km stretch of the margin and within 30 km of the contemporary coastline from north of Lake Teshekpuk to nearly the U.S.-Canada border. Relying primarily on deep resistivity logs that should be largely unaffected by drilling fluids and hole conditions, the analysis reveals the persistence of several hundred vertical meters of ice-bonded permafrost in nearshore wells near Prudhoe Bay and Foggy Island Bay, with less permafrost detected to the east and west. Permafrost is inferred beneath many barrier islands and in some nearshore and lagoonal (back-barrier) wells. The analysis of borehole logs confirms the offshore pattern of ice-bearing subsea permafrost distribution determined based on regional seismic analyses and reveals that ice content generally diminishes with distance from the coastline. Lacking better well distribution, it is not possible to determine the absolute seaward extent of ice-bearing permafrost, nor the distribution of permafrost beneath the present-day continental shelf at the end of the Pleistocene. However, the recovery of gas hydrate from an outer shelf well (Belcher) and previous delineation of a log signature possibly indicating gas hydrate in an inner shelf well (Hammerhead 2) imply that permafrost may once have extended across much of the shelf offshore Camden Bay.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GC006582","usgsCitation":"Ruppel, C., Herman, B.M., Brothers, L., and Hart, P.E., 2016, Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints: Geochemistry, Geophysics, Geosystems, v. 17, no. 11, p. 4333-4353, https://doi.org/10.1002/2016GC006582.","productDescription":"21 p.","startPage":"4333","endPage":"4353","ipdsId":"IP-077887","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470435,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gc006582","text":"Publisher Index Page"},{"id":330774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"581d9e2ae4b0dee4cc90cbb9","contributors":{"authors":[{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":145770,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn D.","email":"cruppel@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":653153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herman, Bruce M.","contributorId":176704,"corporation":false,"usgs":false,"family":"Herman","given":"Bruce","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brothers, Laura L. lbrothers@usgs.gov","contributorId":4502,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura L.","email":"lbrothers@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":653155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653157,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}