The Clarence Cannon National Wildlife Refuge (CCNWR) in the Mississippi River flood plain of eastern Missouri provides high quality emergent marsh and moist-soil habitat benefitting both nesting marsh birds and migrating waterfowl. Staff of CCNWR manipulate water levels and vegetation in the 17 units of the CCNWR to provide conditions favorable to these two important guilds. Although both guilds include focal species at multiple planning levels and complement objectives to provide a diversity of wetland community types and water regimes, additional decision support is needed for choosing how much emergent marsh and moist-soil habitat should be provided through annual management actions.
To develop decision guidance for balanced delivery of high-energy waterfowl habitat and breeding marsh bird habitat, two measureable management objectives were identified: nonbreeding Anas Linnaeus (dabbling duck) use-days and Rallus elegans (king rail) occupancy of managed units. Three different composite management actions were identified to achieve these objectives. Each composite management action is a unique combination of growing season water regime and soil disturbance. The three composite management actions are intense moist-soil management (moist-soil), intermediate moist-soil (intermediate), and perennial management, which idles soils disturbance (perennial). The two management objectives and three management options were used in a multi-criteria decision analysis to indicate resource allocations and inform annual decision making. Outcomes of the composite management actions were predicted in two ways and multi-criteria decision analysis was used with each set of predictions. First, outcomes were predicted using expert-elicitation techniques and a panel of subject matter experts. Second, empirical data from the Integrated Waterbird Management and Monitoring Initiative collected between 2010 and 2013 were used; where data were lacking, expert judgment was used. Also, a Bayesian decision model was developed that can be updated with monitoring data in an adaptive management framework.
Optimal resource allocations were identified in the form of portfolios of composite management actions for the 17 units in the framework. A constrained optimization (linear programming) was used to maximize an objective function that was based on the sum of dabbling duck and king rail utility. The constraints, which included management costs and a minimum energetic carrying capacity (total moist-soil acres), were applied to balance habitat delivery for dabbling ducks and king rails. Also, the framework was constrained in some cases to apply certain management actions of interest to certain management units; these constraints allowed for a variety of hypothetical Habitat Management Plans, including one based on output from a hydrogeomorphic study of the refuge. The decision analysis thus created numerous refuge-wide scenarios, each representing a unique mix of options (one for each of 17 units) and associated benefits (i.e., outcomes with respect to two management objectives).
Prepared in collaboration with the U.S. Fish and Wildlife Service, the decision framework presented here is designed as a decision-aiding tool for CCNWR managers who ultimately make difficult decisions each year with multiple objectives, multiple management units, and the complexity of natural systems. The framework also provides a way to document hypotheses about how the managed system functions. Furthermore, the framework identifies specific monitoring needs and illustrates precisely how monitoring data will be used for decision-aiding and adaptive management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171051","usgsCitation":"Loges, B.W., Lyons, J.E., and Tavernia, B.G., 2017, Balancing habitat delivery for breeding marsh birds and nonbreeding waterfowl: An integrated waterbird management and monitoring approach at Clarence Cannon National Wildlife Refuge, Missouri: U.S. Geological Survey Open-File ReportDirector, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road, Ste 4039
Laurel, MD 20708
In this study, the scenarios ultimately take the form of gridded, daily (maximum and minimum) temperatures and precipitation totals spanning the entire Truckee-Carson River System, from which meteorological inputs to various hydrologic, water-balance and watermanagement models can be extracted by other parts of the Water for the Seasons project and by other studies and stakeholders.
Climate scenarios are constructed using: 1) survey data from interviews with 66 Truckee-Carson River System water-management and water-interest organizations to identify plausible drought and high-flow events that could stress the system irreparably; 2) input from the Stakeholder Affiliate Group and other modelers on the Water for the Seasons team to gain additional key stakeholder input with regard to organizational survey results and to identify the most pressing water-management issues being faced in the system; and 3) historical climate datasets used to simulate possible future conditions.
","language":"English","publisher":"University of Nevada Cooperative Extension Special Publication","usgsCitation":"Dettinger, M.D., Sterle, K., Simpson, K., Singletary, L., Fitzgerald, K., and McCarthy, M., 2017, Climate scenarios for the Truckee-Carson River system, 12 p.","productDescription":"12 p.","ipdsId":"IP-076327","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":345050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":345049,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.unce.unr.edu/publications/files/nr/2017/sp1705.pdf"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Carson River, Truckee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.75622558593749,\n 38.45789034424927\n ],\n [\n -119.17694091796875,\n 38.45789034424927\n ],\n [\n -119.17694091796875,\n 39.776880380637024\n ],\n [\n -120.75622558593749,\n 39.776880380637024\n ],\n [\n -120.75622558593749,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599e9446e4b04935557fe9ad","contributors":{"authors":[{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":707875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sterle, Kelley","contributorId":195683,"corporation":false,"usgs":false,"family":"Sterle","given":"Kelley","email":"","affiliations":[],"preferred":false,"id":707876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simpson, Karen","contributorId":195684,"corporation":false,"usgs":false,"family":"Simpson","given":"Karen","email":"","affiliations":[],"preferred":false,"id":707877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singletary, Loretta","contributorId":195685,"corporation":false,"usgs":false,"family":"Singletary","given":"Loretta","email":"","affiliations":[],"preferred":false,"id":707878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzgerald, Kelsey","contributorId":195686,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Kelsey","email":"","affiliations":[],"preferred":false,"id":707880,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCarthy, Maureen","contributorId":149897,"corporation":false,"usgs":false,"family":"McCarthy","given":"Maureen","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":707879,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190266,"text":"sir20175073 - 2017 - Flood-inundation maps for the Wabash River at Memorial Bridge at Vincennes, Indiana","interactions":[],"lastModifiedDate":"2017-08-24T08:39:10","indexId":"sir20175073","displayToPublicDate":"2017-08-23T00:00:00","publicationYear":"2017","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":"2017-5073","title":"Flood-inundation maps for the Wabash River at Memorial Bridge at Vincennes, Indiana","docAbstract":"Digital flood-inundation maps for a 10.2-mile reach of the Wabash River from Sevenmile Island to 3.7 mile downstream of Memorial Bridge (officially known as Lincoln Memorial Bridge) at Vincennes, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 03343010, Wabash River at Memorial Bridge at Vincennes, Ind. Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
For this study, flood profiles were computed for the Wabash River reach by means of a one-dimensional stepbackwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03343010, Wabash River at Memorial Bridge at Vincennes, Ind., and preliminary high-water marks from a high-water event on April 27, 2013. The calibrated hydraulic model was then used to determine 19 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 10 feet (ft) or near bankfull to 28 ft, the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar] data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) in order to delineate the area flooded at each water level.
The availability of these maps—along with Internet information regarding current stage from the USGS streamgage 03343010, and forecast stream stages from the NWS AHPS—provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175073","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., and Menke, C.D., 2017, Flood-inundation maps for the Wabash River at Memorial Bridge at Vincennes, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5073, 10 p., https://doi.org/10.3133/sir20175073.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081973","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":345020,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZG6QGC","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Wabash River at Memorial Bridge, Vincennes, Indiana, Flood-Inundation Geospatial Data Sets and Metadata"},{"id":345018,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5073/coverthb.jpg"},{"id":345019,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5073/sir20175073.pdf","text":"Report","size":"1.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5073"}],"country":"United States","state":"Indiana","city":"Vincennes","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.74,\n 38.46434231629165\n ],\n [\n -87.19161987304688,\n 38.46434231629165\n ],\n [\n -87.19161987304688,\n 38.89744587262311\n ],\n [\n -87.74,\n 38.89744587262311\n ],\n [\n -87.74,\n 38.46434231629165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278–1996
The economic growth of an industrialized nation such as the United States requires raw materials for construction (buildings, bridges, highways, and so forth), defense, and processing and manufacture of goods and services. Since the beginning of the 20th century, the types and quantities of raw materials used have increased and changed significantly. This fact sheet quantifies the amounts of raw materials (other than food and fuel) that have been used in the U.S. economy annually for a period of 115 years, from 1900 through 2014. It provides a broad overview of the quantity (weight) of nonfood and nonfuel materials used in the economy and illustrates the use and significance of raw nonfuel minerals in particular as building blocks of society.
These data have been compiled to help the public and policymakers understand the changing annual flow of raw materials put into use in the United States. Such information can be helpful in assessing the potential effects of materials use on the environment, assessing materials’ intensity of use, and examining the role that these materials play in the economy. The data presented indicate the substitution and shift in materials usage from renewable to nonrenewable materials during the 20th century. The disaggregated quantities by commodity (not shown in this fact sheet) may be tested against supply adequacy and end of life issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173062","usgsCitation":"Matos, G.R., 2017, Use of raw materials in the United States from 1900 through 2014: U.S. Geological Survey Fact Sheet 2017–3062, 6 p., https://doi.org/10.3133/fs20173062. [Supersedes Fact Sheet 2012–3140.]","productDescription":"Report: 6 p.; 1 Table","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-083233","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":344924,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/fs/2017/3062/fs20173062_table1.xlsx","text":"Table 1","size":"128 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- U.S. raw materials put into use annually from 1900 through 2014, by category. 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States\"}}]}","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
A global inventory of data from gas hydrate drilling expeditions is used to develop relationships between the base of structure I gas hydrate stability, top of gas hydrate occurrence, sulfate-methane transition depth, pressure (water depth), and geothermal gradients. The motivation of this study is to provide first-order estimates of the top of gas hydrate occurrence and associated thickness of the gas hydrate occurrence zone for climate-change scenarios, global carbon budget analyses, or gas hydrate resource assessments. Results from publicly available drilling campaigns (21 expeditions and 52 drill sites) off Cascadia, Blake Ridge, India, Korea, South China Sea, Japan, Chile, Peru, Costa Rica, Gulf of Mexico, and Borneo reveal a first-order linear relationship between the depth to the top and base of gas hydrate occurrence. The reason for these nearly linear relationships is believed to be the strong pressure and temperature dependence of methane solubility in the absence of large difference in thermal gradients between the various sites assessed. In addition, a statistically robust relationship was defined between the thickness of the gas hydrate occurrence zone and the base of gas hydrate stability (in meters below seafloor). The relationship developed is able to predict the depth of the top of gas hydrate occurrence zone using observed depths of the base of gas hydrate stability within less than 50 m at most locations examined in this study. No clear correlation of the depth to the top and base of gas hydrate occurrences with geothermal gradient and sulfate-methane transition depth was identified.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GC006805","usgsCitation":"Riedel, M., and Collett, T.S., 2017, Observed correlation between the depth to base and top of gas hydrate occurrence from review of global drilling data: Geochemistry, Geophysics, Geosystems, v. 18, no. 7, p. 2543-2561, https://doi.org/10.1002/2017GC006805.","productDescription":"19 p.","startPage":"2543","endPage":"2561","ipdsId":"IP-082912","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gc006805","text":"Publisher Index Page"},{"id":344997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-13","publicationStatus":"PW","scienceBaseUri":"599bf122e4b0b589267ed33b","contributors":{"authors":[{"text":"Riedel, Michael","contributorId":7518,"corporation":false,"usgs":true,"family":"Riedel","given":"Michael","email":"","affiliations":[],"preferred":false,"id":707874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":707873,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190227,"text":"70190227 - 2017 - Combined analysis of roadside and off-road breeding bird survey data to assess population change in Alaska","interactions":[],"lastModifiedDate":"2017-08-20T09:27:33","indexId":"70190227","displayToPublicDate":"2017-08-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Combined analysis of roadside and off-road breeding bird survey data to assess population change in Alaska","docAbstract":"Management interest in North American birds has increasingly focused on species that breed in Alaska, USA, and Canada, where habitats are changing rapidly in response to climatic and anthropogenic factors. We used a series of hierarchical models to estimate rates of population change in 2 forested Bird Conservation Regions (BCRs) in Alaska based on data from the roadside North American Breeding Bird Survey (BBS) and the Alaska Landbird Monitoring Survey, which samples off-road areas on public resource lands. We estimated long-term (1993–2015) population trends for 84 bird species from the BBS and short-term (2003–2015) trends for 31 species from both surveys. Among the 84 species with long-term estimates, 11 had positive trends and 17 had negative trends in 1 or both BCRs; negative trends were primarily found among aerial insectivores and wetland-associated species, confirming range-wide negative continental trends for many of these birds. Three species with negative trends in the contiguous United States and southern Canada had positive trends in Alaska, suggesting different population dynamics at the northern edges of their ranges. Regional population trends within Alaska differed for several species, particularly those represented by different subspecies in the 2 BCRs, which are separated by rugged, glaciated mountain ranges. Analysis of the roadside and off-road data in a joint hierarchical model with shared parameters resulted in improved precision of trend estimates and suggested a roadside-related difference in underlying population trends for several species, particularly within the Northwestern Interior Forest BCR. The combined analysis highlights the importance of considering population structure, physiographic barriers, and spatial heterogeneity in habitat change when assessing patterns of population change across a landscape as broad as Alaska. Combined analysis of roadside and off-road survey data in a hierarchical framework may be particularly useful for evaluating patterns of population change in relatively undeveloped regions with sparse roadside BBS coverage.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-67.1","usgsCitation":"Handel, C.M., and Sauer, J.R., 2017, Combined analysis of roadside and off-road breeding bird survey data to assess population change in Alaska: Condor, v. 119, no. 3, p. 557-575, https://doi.org/10.1650/CONDOR-17-67.1.","productDescription":"19 p.","startPage":"557","endPage":"575","ipdsId":"IP-085966","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461428,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-67.1","text":"Publisher Index Page"},{"id":438244,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SCO7AN","text":"USGS data release","linkHelpText":"Alaska Landbird Monitoring Survey Dataset"},{"id":344972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599a9fb1e4b0b589267d58b3","contributors":{"authors":[{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":708029,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":708030,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190187,"text":"70190187 - 2017 - The role of the North American Breeding Bird Survey in conservation","interactions":[],"lastModifiedDate":"2017-08-20T10:47:06","indexId":"70190187","displayToPublicDate":"2017-08-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"The role of the North American Breeding Bird Survey in conservation","docAbstract":"The North American Breeding Bird Survey (BBS) was established in 1966 in response to a lack of quantitative data on changes in the populations of many bird species at a continental scale, especially songbirds. The BBS now provides the most reliable regional and continental trends and annual indices of abundance available for >500 bird species. This paper reviews some of the ways in which BBS data have contributed to bird conservation in North America over the past 50 yr, and highlights future program enhancement opportunities. BBS data have contributed to the listing of species under the Canadian Species at Risk Act and, in a few cases, have informed species assessments under the U.S. Endangered Species Act. By raising awareness of population changes, the BBS has helped to motivate bird conservation efforts through the creation of Partners in Flight. BBS data have been used to determine priority species and locations for conservation action at regional and national scales through Bird Conservation Region strategies and Joint Ventures. Data from the BBS have provided the quantitative foundation for North American State of the Birds reports, and have informed the public with regard to environmental health through multiple indicators, such as the Canadian Environmental Sustainability Indicators and the U.S. Environmental Protection Agency's Report on the Environment. BBS data have been analyzed with other data (e.g., environmental, land cover, and demographic) to evaluate potential drivers of population change, which have then informed conservation actions. In a few cases, BBS data have contributed to the evaluation of management actions, including informing the management of Mourning Doves (Zenaida macroura), Wood Ducks (Aix sponsa), and Golden Eagles (Aquila chrysaetos). Improving geographic coverage in northern Canada and in Mexico, improving the analytical approaches required to integrate data from other sources and to address variation in detectability, and completing the database, by adding historical bird data at each point count location and pinpointing the current point count locations would further enhance the survey's value.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-17-62.1","usgsCitation":"Hudson, M.R., Francis, C.M., Campbell, K., Downes, C.M., Smith, A.C., and Pardieck, K.L., 2017, The role of the North American Breeding Bird Survey in conservation: Condor, v. 119, no. 3, p. 526-545, https://doi.org/10.1650/CONDOR-17-62.1.","productDescription":"20 p.","startPage":"526","endPage":"545","ipdsId":"IP-085807","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-62.1","text":"Publisher Index Page"},{"id":344979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599a9fb6e4b0b589267d58b7","contributors":{"authors":[{"text":"Hudson, Marie-Anne R.","contributorId":195235,"corporation":false,"usgs":false,"family":"Hudson","given":"Marie-Anne","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Francis, Charles M.","contributorId":195680,"corporation":false,"usgs":false,"family":"Francis","given":"Charles","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Kate J.","contributorId":191414,"corporation":false,"usgs":false,"family":"Campbell","given":"Kate J.","affiliations":[],"preferred":false,"id":707870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downes, Constance M.","contributorId":195681,"corporation":false,"usgs":false,"family":"Downes","given":"Constance","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707871,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Adam C.","contributorId":195234,"corporation":false,"usgs":false,"family":"Smith","given":"Adam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707872,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707867,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187860,"text":"ds1052 - 2017 - The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States","interactions":[],"lastModifiedDate":"2017-11-27T12:29:07","indexId":"ds1052","displayToPublicDate":"2017-08-18T15:00:00","publicationYear":"2017","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":"1052","title":"The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States","docAbstract":"The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (https://doi. org/10.5066/F7WH2N65) represents a seamless, spatial database of 48 State geologic maps that range from 1:50,000 to 1:1,000,000 scale. A national digital geologic map database is essential in interpreting other datasets that support numerous types of national-scale studies and assessments, such as those that provide geochemistry, remote sensing, or geophysical data. The SGMC is a compilation of the individual U.S. Geological Survey releases of the Preliminary Integrated Geologic Map Databases for the United States. The SGMC geodatabase also contains updated data for seven States and seven entirely new State geologic maps that have been added since the preliminary databases were published. Numerous errors have been corrected and enhancements added to the preliminary datasets using thorough quality assurance/quality control procedures. The SGMC is not a truly integrated geologic map database because geologic units have not been reconciled across State boundaries. However, the geologic data contained in each State geologic map have been standardized to allow spatial analyses of lithology, age, and stratigraphy at a national scale.
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\"}}]}\n","edition":"Version 1.0: Originally posted on June 30, 2017; Version 1.1: August 2017","contact":"Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Human perceptions of the landscape can influence land-use and land-management decisions. Recognizing the diversity of landscape perceptions across space and time is essential to understanding land change processes and emergent landscape patterns. We summarize the role of landscape perceptions in the land change process, demonstrate advances in quantifying and mapping landscape perceptions, and describe how these spatially explicit techniques have and may benefit land change research.
Mapping landscape perceptions is becoming increasingly common, particularly in research focused on quantifying ecosystem services provision. Spatial representations of landscape perceptions, often measured in terms of landscape values and functions, provide an avenue for matching social and environmental data in land change studies. Integrating these data can provide new insights into land change processes, contribute to landscape planning strategies, and guide the design and implementation of land change models.
Challenges remain in creating spatial representations of human perceptions. Maps must be accompanied by descriptions of whose perceptions are being represented and the validity and uncertainty of those representations across space. With these considerations, rapid advancements in mapping landscape perceptions hold great promise for improving representation of human dimensions in landscape ecology and land change research.
An accurate inventory of irrigated crop acreage is not available at the level of resolution needed to better estimate agricultural water use or to project future water demands in many Florida counties. A detailed digital map and summary of irrigated acreage was developed for Polk County, Florida, during the 2016 growing season. This cooperative project between the U.S. Geological Survey and the Office of Agricultural Water Policy of the Florida Department of Agriculture and Consumer Services is part of an effort to improve estimates of water use and projections of future demands across all counties in the State. The irrigated areas were delineated by using land-use data provided by the Florida Department of Agriculture and Consumer Services, along with information obtained from the South and Southwest Florida Water Management Districts consumptive water-use permits. Delineations were field verified between April and December 2016. Attribute data such as crop type, primary water source, and type of irrigation system were assigned to the irrigated areas.
The results of this inventory and field verification indicate that during the 2016 growing seasons (spring, summer, fall, and winter), an estimated 88,652 acres were irrigated within Polk County. Of the total field-verified crops, 83,995 acres were in citrus; 2,893 acres were in other non-citrus fruit crops (blueberries, grapes, peaches, and strawberries); 621 acres were in row crops (primarily beans and watermelons); 1,117 acres were in nursery (container and tree farms) and sod production; and 26 acres were in field crops including hay and pasture. Of the total inventoried irrigated acreage within Polk County, 98 percent (86,566 acres) was in the Southwest Florida Water Management District, and the remaining 2 percent (2,086 acres) was in the South Florida Water Management District.
About 85,788 acres (96.8 percent of the acreage inventoried) were irrigated by a microirrigation system, including drip, bubblers, and spray emitters. The remaining 3.2 percent of the irrigated acreage was irrigated by a sprinkler system (2,360 acres) or subsurface flood systems (504 acres). Groundwater was the primary source of water used on irrigated acreage (88 percent, or 78,050 acres); the remaining 10,602 acres (12 percent) used groundwater combined with surface water as the irrigation source.
The irrigated acreage estimated by the U.S. Geological Survey (USGS) for this 2016 inventory (88,652 acres) is about 11 percent higher than the 79,869 acres estimated by the U.S. Department of Agriculture (USDA) for 2012. Citrus and pasture in Polk County show the biggest difference in irrigated acreage between the USGS and USDA totals. Irrigated citrus acreage inventoried in 2016 by the USGS totaled 83,996 acres, whereas the USDA reported 78,305 acres of citrus in 2012. The USGS identified 6 acres of irrigated pasture and 20 acres of hay, whereas the USDA reported 6,631 acres of irrigated pasture and 1,349 acres of hay for 2012. In general, differences between the 2016 USGS field-verified acreage totals and acreage published by the USDA for 2012 could be due to (1) irrigated acreage for some specific crops increased or decreased substantially during the 4-year interval between 2012 and 2016 because of production or economic changes, (2) the assumption that if an irrigation system was present, it was used in 2016, when in fact some landowners may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreage published by the USDA for selected crops may be underestimated as a result of how information is obtained and formulated by the agency during census compilations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171082","collaboration":"Prepared in cooperation with the Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy","usgsCitation":"Marella, R.L., Berry, D.R., and Dixon, J.F., 2017, Agricultural irrigated land-use inventory for Polk County, Florida, 2016: U.S. Geological Survey Open-File Report 2017–1082, 14 p., https://doi.org/10.3133/ofr20171082.","productDescription":"14 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080915","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":344885,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1082/coverthb.jpg"},{"id":344888,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76W98BN","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"GIS data and tables associated with irrigated agricultural land use survey in Polk County, Florida, 2016"},{"id":344886,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1082/ofr20171082.pdf","text":"Report","size":"823 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1082"},{"id":344887,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1082/ofr20171082_Appendix01.pdf","text":"Appendix 1","size":"1.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1082 Appendix 1"}],"country":"United States","state":"Florida","county":"Polk","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.6578,28.3471],[-81.6576,28.2593],[-81.5574,28.2598],[-81.5245,28.2011],[-81.5247,28.1431],[-81.4556,28.1429],[-81.4558,28.0854],[-81.3749,28.0853],[-81.3465,28.085],[-81.3482,28.08],[-81.3468,28.0754],[-81.347,28.0694],[-81.3486,28.0676],[-81.3538,28.0668],[-81.3553,28.0668],[-81.3604,28.0688],[-81.3646,28.068],[-81.3669,28.0607],[-81.3639,28.0574],[-81.365,28.0546],[-81.3642,28.0463],[-81.3617,28.044],[-81.3623,28.0426],[-81.3629,28.0389],[-81.3666,28.0363],[-81.375,28.0296],[-81.3761,28.0287],[-81.3752,28.0259],[-81.381,28.0201],[-81.38,28.0177],[-81.3765,28.0158],[-81.3797,28.0118],[-81.3828,28.0123],[-81.3867,28.0175],[-81.3923,28.0194],[-81.3947,28.0282],[-81.407,28.029],[-81.4135,28.0361],[-81.417,28.038],[-81.4241,28.0405],[-81.4277,28.0414],[-81.4318,28.0425],[-81.4348,28.0476],[-81.4351,28.0527],[-81.4381,28.0569],[-81.4437,28.0593],[-81.4463,28.0589],[-81.4535,28.0573],[-81.4561,28.0564],[-81.4594,28.0514],[-81.4604,28.0496],[-81.4592,28.0399],[-81.4486,28.0318],[-81.4455,28.0331],[-81.4388,28.033],[-81.4335,28.0218],[-81.4299,28.0213],[-81.4279,28.0185],[-81.4274,28.0175],[-81.4212,28.0031],[-81.4182,27.9998],[-81.4057,28.0027],[-81.3948,28.0057],[-81.3877,28.0037],[-81.3823,27.9953],[-81.3774,27.9873],[-81.3755,27.9799],[-81.369,27.976],[-81.3619,27.9713],[-81.3517,27.9683],[-81.3483,27.9627],[-81.3495,27.9553],[-81.3435,27.9529],[-81.3374,27.95],[-81.3365,27.9444],[-81.3387,27.9403],[-81.3428,27.9418],[-81.3459,27.94],[-81.3435,27.9358],[-81.341,27.9321],[-81.3369,27.9324],[-81.3302,27.9318],[-81.3206,27.9279],[-81.314,27.9231],[-81.3117,27.9143],[-81.3141,27.9056],[-81.3123,27.8973],[-81.3069,27.8893],[-81.3046,27.8805],[-81.3037,27.8745],[-81.3024,27.868],[-81.3015,27.8634],[-81.2919,27.859],[-81.2827,27.8579],[-81.2818,27.8537],[-81.2701,27.8493],[-81.2589,27.8471],[-81.2496,27.8478],[-81.2414,27.8471],[-81.2313,27.8423],[-81.2182,27.8332],[-81.2104,27.8224],[-81.2065,27.8158],[-81.2012,27.8046],[-81.1978,27.7967],[-81.1934,27.7902],[-81.1875,27.7831],[-81.1857,27.7761],[-81.1806,27.7737],[-81.1783,27.7677],[-81.1728,27.7629],[-81.1734,27.7592],[-81.177,27.7575],[-81.1767,27.7515],[-81.1718,27.7458],[-81.1678,27.7411],[-81.1644,27.7369],[-81.1656,27.7314],[-81.1673,27.7268],[-81.1623,27.723],[-81.1542,27.7187],[-81.1487,27.7134],[-81.1475,27.7042],[-81.1483,27.6945],[-81.1457,27.6816],[-81.1435,27.6714],[-81.1365,27.6643],[-81.131,27.6609],[-81.1329,27.6517],[-81.1424,27.6432],[-81.1701,27.6431],[-81.1952,27.6442],[-81.2233,27.6449],[-81.3673,27.6463],[-81.4776,27.6467],[-81.4827,27.6464],[-81.5027,27.6464],[-81.5637,27.6464],[-81.617,27.6463],[-81.6247,27.646],[-81.6334,27.6462],[-81.6493,27.6465],[-81.6626,27.6464],[-81.6873,27.646],[-81.6965,27.6466],[-81.7073,27.646],[-81.7283,27.6459],[-81.7416,27.6462],[-81.7498,27.6464],[-81.8749,27.6458],[-81.8841,27.6464],[-82.0543,27.6465],[-82.0545,27.7266],[-82.0564,27.7542],[-82.0546,27.8781],[-82.0566,27.9273],[-82.0562,28.1716],[-82.1062,28.1716],[-82.1063,28.259],[-82.0562,28.259],[-82.0565,28.3119],[-82.045,28.3186],[-82.0326,28.3211],[-82.0232,28.3242],[-82.0093,28.323],[-81.9985,28.3191],[-81.9915,28.3102],[-81.9864,28.3055],[-81.9792,28.3063],[-81.976,28.3086],[-81.9678,28.3079],[-81.958,28.3082],[-81.9581,28.345],[-81.8578,28.3463],[-81.8579,28.3619],[-81.7907,28.3619],[-81.7911,28.3463],[-81.6578,28.3471]]]},\"properties\":{\"name\":\"Polk\",\"state\":\"FL\"}}]}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
12703 Research Parkway
Orlando, Florida 32826
Historical data for six selected U.S. Geological Survey streamgages along Soldier Creek in northeast Kansas were used in an assessment of streamflow characteristics and trends. This information is required by the Prairie Band Potawatomi Nation for the effective management of tribal water resources, including drought contingency planning. Streamflow data for the period of record at each streamgage were used to assess annual mean streamflow, annual mean base flow, mean monthly flow, annual peak flow, and annual minimum flow.
Annual mean streamflows along Soldier Creek were characterized by substantial year-to-year variability with no pronounced long-term trends. On average, annual mean base flow accounted for about 20 percent of annual mean streamflow. Mean monthly flows followed a general seasonal pattern that included peak values in spring and low values in winter. Annual peak flows, which were characterized by considerable year-to-year variability, were most likely to occur in May and June and least likely to occur during November through February. With the exception of a weak yet statistically significant increasing trend at the Soldier Creek near Topeka, Kansas, streamgage, there were no pronounced long-term trends in annual peak flows. Annual 1-day, 30-day, and 90-day mean minimum flows were characterized by considerable year-to-year variability with no pronounced long-term trend. During an extreme drought, as was the case in the mid-1950s, there may be zero flow in Soldier Creek continuously for a period of one to several months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175061","collaboration":"Prepared in cooperation with the Prairie Band Potawatomi Nation","usgsCitation":"Juracek, K.E., 2017, Streamflow characteristics and trends along Soldier Creek, northeast Kansas: U.S. Geological Survey Scientific Investigations Report 2017–5061, 30 p., https://doi.org/10.3133/sir20175061.","productDescription":"v, 30 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-084908","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":344845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5061/coverthb.jpg"},{"id":344846,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5061/sir20175061.pdf","text":"Report","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5061"}],"country":"United States","state":"Kansas","otherGeospatial":"Soldier Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.2896728515625,\n 38.94018471320357\n ],\n [\n -95.2734375,\n 38.94018471320357\n ],\n [\n -95.2734375,\n 39.8928799002948\n ],\n [\n -96.2896728515625,\n 39.8928799002948\n ],\n [\n -96.2896728515625,\n 38.94018471320357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
We determine Modified Mercalli (Seismic) Intensities (MMI) for nine onshore earthquakes of magnitude 4.5 and larger that occurred in central and western Washington between 1989 and 1999, on the basis of effects reported in postal questionnaires, the press, and professional collaborators. The earthquakes studied include four earthquakes of M5 and larger: the M5.0 Deming earthquake of April 13, 1990, the M5.0 Point Robinson earthquake of January 29, 1995, the M5.4 Duvall earthquake of May 3, 1996, and the M5.8 Satsop earthquake of July 3, 1999. The MMI are assigned using data and procedures that evolved at the U.S. Geological Survey (USGS) and its Department of Commerce predecessors and that were used to assign MMI to felt earthquakes occurring in the United States between 1931 and 1986. We refer to the MMI assigned in this report as traditional MMI, because they are based on responses to postal questionnaires and on newspaper reports, and to distinguish them from MMI calculated from data contributed by the public by way of the internet. Maximum traditional MMI documented for the M5 and larger earthquakes are VII for the 1990 Deming earthquake, V for the 1995 Point Robinson earthquake, VI for the 1996 Duvall earthquake, and VII for the 1999 Satsop earthquake; the five other earthquakes were variously assigned maximum intensities of IV, V, or VI. Starting in 1995, the Pacific Northwest Seismic Network (PNSN) published MMI maps for four of the studied earthquakes, based on macroseismic observations submitted by the public by way of the internet. With the availability now of the traditional USGS MMI interpreted for all the sites from which USGS postal questionnaires were returned, the four Washington earthquakes join a rather small group of earthquakes for which both traditional USGS MMI and some type of internet-based MMI have been assigned. The values and distributions of the traditional MMI are broadly similar to the internet-based PNSN intensities; we discuss some differences in detail that reflect differences in data-sampling procedure, differences in the procedure used to assign intensity numbers from macroseismic observations, and differences in how intensities are mapped.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171104","usgsCitation":"Brocher, T.M., Dewey, J.W., and Cassidy, J.F., 2017, Modified Mercalli Intensities for nine earthquakes in central and western Washington between 1989 and 1999: U.S. Geological Survey Open-File Report 2017–1104, 82 p., https://doi.org/10.3133/ofr20171104.","productDescription":"v, 82 p.","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-080341","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":344861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1104/ofr2017.1104.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1104"},{"id":344860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1104/coverthb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.925048828125,\n 44.12702800650004\n ],\n [\n -116.90551757812499,\n 44.12702800650004\n ],\n [\n -116.90551757812499,\n 49.78835749241399\n ],\n [\n -126.925048828125,\n 49.78835749241399\n ],\n [\n -126.925048828125,\n 44.12702800650004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"USGS Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025
In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment at the Hillview Reservoir in southern Westchester County, New York. Monthly site inspections at the reservoir indicated an approximately 90-square-foot depression in the land surface directly upslope from a seep that has episodically flowed since 2007. In July 2008, the U.S. Geological Survey surveyed the topography of land surface in this depression area by collecting high-accuracy (resolution less than 1 inch) measurements. A point of origin was established for the topographic survey by using differentially corrected positional data collected by a global navigation satellite system. Eleven points were surveyed along the edge of the depression area and at arbitrary locations within the depression area by using robotic land-surveying techniques. The points were surveyed again in March 2012 to evaluate temporal changes in land-surface altitude. Survey measurements of the depression area indicated that the land-surface altitude at 8 of the 11 points decreased beyond the accepted measurement uncertainty during the 44 months from July 2008 to March 2012. Two additional control points were established at stable locations along Hillview Avenue, which runs parallel to the embankment. These points were measured during the July 2008 survey and measured again during the March 2012 survey to evaluate the relative accuracy of the altitude measurements. The relative horizontal and vertical (altitude) accuracies of the 11 topographic measurements collected in March 2012 were ±0.098 and ±0.060 feet (ft), respectively. Changes in topography at 8 of the 11 points ranged from 0.09 to 0.63 ft and topography remained constant, or within the measurement uncertainty, for 3 of the 11 points.
Two cross sections were constructed through the depression area by using land-surface altitude data that were interpolated from positional data collected during the two topographic surveys. Cross section A–A′ was approximately 8.5 ft long and consisted of three surveyed points that trended north to south across the depression. Land-surface altitude change decreased along the entire north-south trending cross section during the 44 months, and ranged from 0.2 to more than 0.6 ft. In general, greater land-surface altitude change was measured north of the midpoint as compared to south of the midpoint of the cross section. Cross section B–B′ was 18 ft long and consisted of six surveyed points that trended east to west across the depression. Land-surface altitude change generally decreased or remained constant along the east-west trending cross section during the 44 months and ranged from 0.0 to 0.3 ft. Volume change of the depression area was calculated by using a three-dimensional geographic information system utility that subtracts interpolated surfaces. The results indicated a net volume loss of approximately 38 ±5 cubic feet of material from the depression area during the 44 months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171028","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., and Chu, Anthony, 2017, Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Open-File Report 2017–1028, 15 p., https://doi.org/10.3133/ofr20171028.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077425","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":344641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1028/ofr20171028.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1028"},{"id":344640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1028/coverthb.jpg"}],"country":"United States","state":"New York","county":"Westchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.88648986816406,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.894180824484465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
Potential contamination bias was estimated for 8 nutrient analytes and 40 pesticides in stream water collected by the U.S. Geological Survey at 147 stream sites from across the United States, and representing a variety of hydrologic conditions and site types, for water years 2002–12. This study updates previous U.S. Geological Survey evaluations of potential contamination bias for nutrients and pesticides. Contamination is potentially introduced to water samples by exposure to airborne gases and particulates, from inadequate cleaning of sampling or analytic equipment, and from inadvertent sources during sample collection, field processing, shipment, and laboratory analysis. Potential contamination bias, based on frequency and magnitude of detections in field blanks, is used to determine whether or under what conditions environmental data might need to be qualified for the interpretation of results in the context of comparisons with background levels, drinking-water standards, aquatic-life criteria or benchmarks, or human-health benchmarks. Environmental samples for which contamination bias as determined in this report applies are those from historical U.S. Geological Survey water-quality networks or programs that were collected during the same time frame and according to the same protocols and that were analyzed in the same laboratory as field blanks described in this report.
Results from field blanks for ammonia, nitrite, nitrite plus nitrate, orthophosphate, and total phosphorus were partitioned by analytical method; results from the most commonly used analytical method for total phosphorus were further partitioned by date. Depending on the analytical method, 3.8, 9.2, or 26.9 percent of environmental samples, the last of these percentages pertaining to all results from 2007 through 2012, were potentially affected by ammonia contamination. Nitrite contamination potentially affected up to 2.6 percent of environmental samples collected between 2002 and 2006 and affected about 3.3 percent of samples collected between 2007 and 2012. The percentages of environmental samples collected between 2002 and 2011 that were potentially affected by nitrite plus nitrate contamination were 7.3 for samples analyzed with the low-level method and 0.4 for samples analyzed with the standard-level method. These percentages increased to 14.8 and 2.2 for samples collected in 2012 and analyzed using replacement low- and standard-level methods, respectively. The maximum potentially affected concentrations for nitrite and for nitrite plus nitrate were much less than their respective maximum contamination levels for drinking-water standards. Although contamination from particulate nitrogen can potentially affect up to 21.2 percent and that from total Kjeldahl nitrogen can affect up to 16.5 percent of environmental samples, there are no critical or background levels for these substances.
For total nitrogen, orthophosphate, and total phosphorus, contamination in a small percentage of environmental samples might be consequential for comparisons relative to impairment risks or background levels. At the low ends of the respective ranges of impairment risk for these nutrients, contamination in up to 5 percent of stream samples could account for at least 23 percent of measured concentrations of total nitrogen, for at least 40 or 90 percent of concentrations of orthophosphate, depending on the analytical method, and for 31 to 76 percent of concentrations of total phosphorus, depending on the time period.
Twenty-six pesticides had no detections in field blanks. Atrazine with 12 and metolachlor with 11 had the highest number of detections, mostly occurring in spring or early summer. At a 99-percent level of confidence, contamination was estimated to be no greater than the detection limit in at least 98 percent of all samples for 38 of 40 pesticides. For metolachlor and atrazine, potential contamination was no greater than 0.0053 and 0.0093 micrograms per liter in 98 percent of samples. For 11 of 14 pesticides with at least one detection, the maximum potentially affected concentration of the environmental sample was less than their respective human-health or aquatic-life benchmarks. Small percentages of environmental samples had concentrations high enough that atrazine contamination potentially could account for the entire aquatic-life benchmark for acute effects on nonvascular plants, that dieldrin contamination could account for up to 100 percent of the cancer health-based screening level, or that chlorpyrifos contamination could account for 13 or 12 percent of the concentrations in the aquatic-life benchmarks for chronic effects on invertebrates or the criterion continuous concentration for chronic effects on aquatic life.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165129","usgsCitation":"Medalie, Laura, and Martin, J.D., 2017, Nutrient and pesticide contamination bias estimated from field blanks collected at surface-water sites in U.S. Geological Survey water-quality networks, 2002–12: U.S. Geological Survey Scientific Investigations Report 2016–5129, 40 p., https://doi.org/10.3133/sir20165129.","productDescription":"Report: vi, 40 p.; Appendixes 1-2","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070500","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":344595,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5129/attachments/sir20165129_appendix2_metadata.txt","text":"Appendix 2","size":"1.43 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Metadata, pesticide field-blank data from surface-water 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U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Genetic differentiation among Spotted Owl (Strix occidentalis) subspecies has been established in prior studies. These investigations also provided evidence for introgression and hybridization among taxa but were limited by a lack of samples from geographic regions where subspecies came into close contact. We analyzed new sets of samples from Northern Spotted Owls (NSO: S. o. caurina) and California Spotted Owls (CSO: S. o. occidentalis) in northern California using mitochondrial DNA sequences (mtDNA) and 10 nuclear microsatellite loci to obtain a clearer depiction of genetic differentiation and hybridization in the region. Our analyses revealed that a NSO population close to the northern edge of the CSO range in northern California (the NSO Contact Zone population) is highly differentiated relative to other NSO populations throughout the remainder of their range. Phylogenetic analyses identified a unique lineage of mtDNA in the NSO Contact Zone, and Bayesian clustering analyses of the microsatellite data identified the Contact Zone as a third distinct population that is differentiated from CSO and NSO found in the remainder of the subspecies' range. Hybridization between NSO and CSO was readily detected in the NSO Contact Zone, with over 50% of individuals showing evidence of hybrid ancestry. Hybridization was also identified among 14% of CSO samples, which were dispersed across the subspecies' range in the Sierra Nevada Mountains. The asymmetry of hybridization suggested that the hybrid zone may be dynamic and moving. Although evidence of hybridization existed, we identified no F1 generation hybrid individuals. We instead found evidence for F2 or backcrossed individuals among our samples. The absence of F1 hybrids may indicate that (1) our 10 microsatellites were unable to distinguish hybrid types, (2) primary interactions between subspecies are occurring elsewhere on the landscape, or (3) dispersal between the subspecies' ranges is reduced relative to historical levels, potentially as a consequence of recent regional fires.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3260","usgsCitation":"Miller, M.P., Mullins, T.D., Forsman, E.D., and Haig, S.M., 2017, Genetic differentiation and inferred dynamics of a hybrid zone between Northern Spotted Owls (Strix occidentalis caurina) and California Spotted Owls (S. o. occidentalis) in northern California: Ecology and Evolution, v. 7, no. 17, p. 6871-6883, https://doi.org/10.1002/ece3.3260.","productDescription":"13 p.","startPage":"6871","endPage":"6883","ipdsId":"IP-085456","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3260","text":"Publisher Index Page"},{"id":344850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.76074218749999,\n 37.49229399862877\n ],\n [\n -119.02587890624999,\n 37.49229399862877\n ],\n [\n -119.02587890624999,\n 42.58544425738491\n ],\n [\n -124.76074218749999,\n 42.58544425738491\n ],\n [\n -124.76074218749999,\n 37.49229399862877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"17","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-27","publicationStatus":"PW","scienceBaseUri":"59b76ec2e4b08b1644ddfac6","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":707746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Thomas D. 0000-0001-8948-9604 tom_mullins@usgs.gov","orcid":"https://orcid.org/0000-0001-8948-9604","contributorId":149824,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas","email":"tom_mullins@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":707747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":707748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":707749,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188696,"text":"sir20175063 - 2017 - Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015","interactions":[],"lastModifiedDate":"2021-03-10T18:54:30.655784","indexId":"sir20175063","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","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":"2017-5063","title":"Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015","docAbstract":"A study was done by the U.S. Geological Survey in cooperation with the Kansas Department of Transportation and the Federal Emergency Management Agency to develop regression models to estimate peak streamflows of annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent at ungaged locations in Kansas. Peak streamflow frequency statistics from selected streamgages were related to contributing drainage area and average precipitation using generalized least-squares regression analysis. The peak streamflow statistics were derived from 151 streamgages with at least 25 years of streamflow data through 2015. The developed equations can be used to predict peak streamflow magnitude and frequency within two hydrologic regions that were defined based on the effects of irrigation. The equations developed in this report are applicable to streams in Kansas that are not substantially affected by regulation, surface-water diversions, or urbanization. The equations are intended for use for streams with contributing drainage areas ranging from 0.17 to 14,901 square miles in the nonirrigation effects region and, 1.02 to 3,555 square miles in the irrigation-affected region, corresponding to the range of drainage areas of the streamgages used in the development of the regional equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175063","collaboration":"Prepared in cooperation with the Kansas Department of Transportation and Federal Emergency Management Agency","usgsCitation":"Painter, C.C., Heimann, D.C., and Lanning-Rush, J.L., 2017, Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015 (ver. 1.1, September 2017): U.S. Geological Survey Scientific Investigations Report 2017–5063, 20 p., https://doi.org/10.3133/sir20175063.","productDescription":"Report: vi, 20 p.; 4 Tables","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087048","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":345864,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5063/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017–5063 Version History"},{"id":344871,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table5.xlsx","text":"Table 5","size":"47 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 5"},{"id":344870,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table4.xlsx","text":"Table 4","size":"23 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 4"},{"id":344868,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table2.xlsx","text":"Table 2","size":"42 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 2"},{"id":344869,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table3.xlsx","text":"Table 3","size":"60 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 3"},{"id":344698,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5063/coverthb2.jpg"},{"id":344699,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063.pdf","text":"Report","size":"1.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5063"}],"country":"United States","state":"Colorado, Kansas, Missouri, Nebraska, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.48046875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 36.38591277287651\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 14, 2017; Version 1.1: September 18, 2017","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
The Albuquerque Bernalillo County Water Utility Authority (ABCWUA) drinking water supply was almost exclusively sourced from groundwater from within the Albuquerque Basin before 2008. In 2008, the San Juan-Chama Drinking Water Project (SJCDWP) provided surface-water resources to augment the groundwater supply, allowing for a reduction in groundwater pumping in the Albuquerque Basin. In 2013, the U.S. Geological Survey, in cooperation with the ABCWUA, began a study to measure and compare aquifer-system and land-surface elevation change before and after the SJCDWP in 2008. Three methods of data collection with different temporal and spatial resolutions were used for this study: (1) aquifer-system compaction data collected continuously at a single extensometer from 1994 to 2013; (2) land-surface elevation change from Global Positioning System (GPS) surveys of a network of monuments collected in 1994–95, 2005, and 2014; and (3) spatially distributed Interferometric Synthetic Aperture Radar (InSAR) satellite data from 1993 to 2010. Collection of extensometer data allows for direct and continuous measurement of aquifer-system compaction at the extensometer location. The GPS surveys of a network of monuments allow for periodic measurements of land-surface elevation change at monument locations. Interferograms are limited in time by lifespan of the satellite, orbital pattern, and data quality but allow for measurement of gridded land-surface elevation change over the study area. Each of these methods was employed to provide a better understanding of aquifer-system compaction and land-surface elevation change for the Albuquerque Basin.
Results do not show large magnitudes of subsidence in the Albuquerque Basin. High temporal-resolution but low spatial-resolution data measurements of aquifer-system compaction at the Albuquerque extensometer show elastic aquifer-system response to recovering groundwater levels. Results from the GPS survey of the network of monuments show inconsistent land-surface elevation changes over the Albuquerque Basin, likely because of the lack of significant change and the complexity of subsurface stratigraphy in addition to the spatial and temporal heterogeneity of groundwater withdrawals over the study period. Results from the InSAR analysis show areas of land-surface elevation increase after 2008, which could be attributed to elastic recovery of the aquifer system. The spatial extent to which elastic recovery of the aquifer system has resulted in recovery of land-surface elevation is limited to the in-situ measurements at the extensometer. Examination of spatially distributed InSAR data relative to limited spatial extent of the complex heterogeneity subsurface stratigraphy may explain some of the heterogeneity of land-surface elevation changes over this study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175057","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Driscoll, J.M., and Brandt, J.T., 2017, Land subsidence and recovery in the Albuquerque Basin, New Mexico, 1993–2014: U.S. Geological Survey Scientific Investigations Report 2017–5057, 31 p., https://doi.org/10.3133/sir20175057.","productDescription":"Report: v, 31 p.; Figures: 10A, 10B, 10C","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071011","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":344697,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10C.pdf","text":"Figure 10C","size":"802 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10C","linkHelpText":"C. InSAR measured elevation change along geology profile CC-CC’"},{"id":344694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057.pdf","text":"Report","size":"9.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057"},{"id":344695,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10A.pdf","text":"Figure 10A","size":"594 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10A","linkHelpText":"A. InSAR measured elevation change along geology profile AA-AA’"},{"id":344696,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10B.pdf","text":"Figure 10B","size":"425 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10B","linkHelpText":"B. InSAR measured elevation change along geology profile BB-BB’"},{"id":344693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5057/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 34.85\n ],\n [\n -106.375,\n 34.85\n ],\n [\n -106.375,\n 35.4\n ],\n [\n -107,\n 35.4\n ],\n [\n -107,\n 34.85\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
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This study examined the effects of natural and anthropogenic changes in confining margin width by applying remote sensing techniques – fusing LiDAR topography with image-derived bathymetry – over a large spatial extent: 58 km of the Snake River, Wyoming, USA. Fused digital elevation models from 2007 and 2012 were differenced to quantify changes in the volume of stored sediment, develop morphological sediment budgets, and infer spatial gradients in bed material transport. Our study spanned two similar reaches that were subject to different controls on confining margin width: natural terraces versus artificial levees. Channel planform in reaches with similar slope and confining margin width differed depending on whether the margins were natural or anthropogenic. The effects of tributaries also differed between the two reaches. Generally, the natural reach featured greater confining margin widths and was depositional, whereas artificial lateral constriction in the leveed reach produced a sediment budget that was closer to balanced. Although our remote sensing methods provided topographic data over a large area, net volumetric changes were not statistically significant due to the uncertainty associated with bed elevation estimates. We therefore focused on along-channel spatial differences in bed material transport rather than absolute volumes of sediment. To complement indirect estimates of sediment transport derived by morphological sediment budgeting, we collected field data on bed mobility through a tracer study. Surface and subsurface grain size measurements were combined with bed mobility observations to calculate armoring and dimensionless sediment transport ratios, which indicated that sediment supply exceeded transport capacity in the natural reach and vice versa in the leveed reach. We hypothesize that constriction by levees induced an initial phase of incision and bed armoring. Because levees prevented bank erosion, the channel excavated sediment by migrating rapidly across the restricted braidplain and eroding bars and islands.
","language":"English","publisher":"British Society for Geomorphology","doi":"10.1002/esp.4157","usgsCitation":"Leonard, C., Legleiter, C.J., and Overstreet, B., 2017, Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA: Earth Surface Processes and Landforms, v. 42, no. 13, p. 2119-2138, https://doi.org/10.1002/esp.4157.","productDescription":"20 p.","startPage":"2119","endPage":"2138","ipdsId":"IP-075980","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58906555175781,\n 43.86720808597874\n ],\n [\n -110.61309814453125,\n 43.843936871965695\n ],\n [\n -110.61241149902344,\n 43.819665724206956\n ],\n [\n -110.65155029296875,\n 43.79042818348387\n ],\n [\n -110.70236206054688,\n 43.7492731811147\n ],\n [\n -110.72776794433592,\n 43.708586214366036\n ],\n [\n -110.73532104492186,\n 43.68277040294095\n ],\n [\n -110.73188781738281,\n 43.66042082657193\n ],\n [\n -110.71266174316406,\n 43.64054754952543\n ],\n [\n -110.68450927734375,\n 43.64452273099928\n ],\n [\n -110.6494903564453,\n 43.69419030566581\n ],\n [\n -110.60279846191405,\n 43.73488704685434\n ],\n [\n -110.54237365722656,\n 43.766135280960974\n ],\n [\n -110.49568176269531,\n 43.845917754377275\n ],\n [\n -110.50666809082031,\n 43.85879188670806\n ],\n [\n -110.5279541015625,\n 43.866713048323184\n ],\n [\n -110.58906555175781,\n 43.86720808597874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"13","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-31","publicationStatus":"PW","scienceBaseUri":"59901396e4b09fa1cb17891f","contributors":{"authors":[{"text":"Leonard, Christina","contributorId":195596,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","email":"","affiliations":[],"preferred":true,"id":707576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":707575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon T.","contributorId":195597,"corporation":false,"usgs":false,"family":"Overstreet","given":"Brandon T.","affiliations":[],"preferred":false,"id":707577,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190109,"text":"70190109 - 2017 - Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-08-15T13:16:00","indexId":"70190109","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","docAbstract":"The Colorado River is an important source of water in the western United States, supplying the needs of more than 38 million people in the United States and Mexico. Groundwater discharge to streams has been shown to be a critical component of streamflow in the Upper Colorado River Basin (UCRB), particularly during low-flow periods. Understanding impacts on groundwater in the basin from projected climate change will assist water managers in the region in planning for potential changes in the river and groundwater system. A previous study on changes in basin-wide groundwater recharge in the UCRB under projected climate change found substantial increases in temperature, moderate increases in precipitation, and mostly periods of stable or slight increases in simulated groundwater recharge through 2099. This study quantifies projected spatial and seasonal changes in groundwater recharge within the UCRB from recent historical (1950 to 2015) through future (2016 to 2099) time periods, using a distributed-parameter groundwater recharge model with downscaled climate data from 97 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections. Simulation results indicate that projected increases in basin-wide recharge of up to 15% are not distributed uniformly within the basin or throughout the year. Northernmost subregions within the UCRB are projected an increase in groundwater recharge, while recharge in other mainly southern subregions will decline. Seasonal changes in recharge also are projected within the UCRB, with decreases of 50% or more in summer months and increases of 50% or more in winter months for all subregions, and increases of 10% or more in spring months for many subregions.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12507","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2017, Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin: Groundwater, v. 55, no. 4, p. 506-518, https://doi.org/10.1111/gwat.12507.","productDescription":"13 p.","startPage":"506","endPage":"518","ipdsId":"IP-078645","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":344783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.64404296874999,\n 42.147114459220994\n ],\n [\n -108.74267578124999,\n 42.342305278572816\n ],\n [\n -110.28076171874999,\n 41.983994270935625\n ],\n [\n -111.57714843749999,\n 40.74725696280421\n ],\n [\n -112.85156249999999,\n 38.324420427006515\n ],\n [\n -114.52148437499999,\n 37.84015683604134\n ],\n [\n -115.04882812499999,\n 37.54457732085582\n ],\n [\n -115.04882812499999,\n 36.61552763134925\n ],\n [\n -114.19189453124999,\n 34.50655662164561\n ],\n [\n -114.60937499999999,\n 33.797408767572485\n ],\n [\n -114.78515624999999,\n 32.861132322810946\n ],\n [\n -114.96093749999997,\n 32.15701248607008\n ],\n [\n -113.90624999999999,\n 31.74685416292141\n ],\n [\n -113.29101562499999,\n 31.034108344903483\n ],\n [\n -112.41210937499999,\n 30.164126343161097\n ],\n [\n -110.87402343749999,\n 30.543338954230222\n ],\n [\n -109.24804687499997,\n 31.259769987394286\n ],\n [\n -107.13867187499999,\n 32.97180377635759\n ],\n [\n -106.17187499999999,\n 36.43896124085945\n ],\n [\n -105.95214843749999,\n 39.740986355883564\n ],\n [\n -106.39160156249999,\n 41.52502957323801\n ],\n [\n -107.64404296874999,\n 42.147114459220994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-16","publicationStatus":"PW","scienceBaseUri":"59901397e4b09fa1cb178921","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":707517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":707518,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190149,"text":"70190149 - 2017 - The transtensional offshore portion of the northern San Andreas fault: Fault zone geometry, late Pleistocene to Holocene sediment deposition, shallow deformation patterns, and asymmetric basin growth","interactions":[],"lastModifiedDate":"2017-09-25T13:47:44","indexId":"70190149","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"The transtensional offshore portion of the northern San Andreas fault: Fault zone geometry, late Pleistocene to Holocene sediment deposition, shallow deformation patterns, and asymmetric basin growth","docAbstract":"We mapped an ~120 km offshore portion of the northern San Andreas fault (SAF) between Point Arena and Point Delgada using closely spaced seismic reflection profiles (1605 km), high-resolution multibeam bathymetry (~1600 km2), and marine magnetic data. This new data set documents SAF location and continuity, associated tectonic geomorphology, shallow stratigraphy, and deformation. Variable deformation patterns in the generally narrow (∼1 km wide) fault zone are largely associated with fault trend and with transtensional and transpressional fault bends.
We divide this unique transtensional portion of the offshore SAF into six sections along and adjacent to the SAF based on fault trend, deformation styles, seismic stratigraphy, and seafloor bathymetry. In the southern region of the study area, the SAF includes a 10-km-long zone characterized by two active parallel fault strands. Slip transfer and long-term straightening of the fault trace in this zone are likely leading to transfer of a slice of the Pacific plate to the North American plate. The SAF in the northern region of the survey area passes through two sharp fault bends (∼9°, right stepping, and ∼8°, left stepping), resulting in both an asymmetric lazy Z–shape sedimentary basin (Noyo basin) and an uplifted rocky shoal (Tolo Bank). Seismic stratigraphic sequences and unconformities within the Noyo basin correlate with the previous 4 major Quaternary sea-level lowstands and record basin tilting of ∼0.6°/100 k.y. Migration of the basin depocenter indicates a lateral slip rate on the SAF of 10–19 mm/yr for the past 350 k.y.
Data collected west of the SAF on the south flank of Cape Mendocino are inconsistent with the presence of an offshore fault strand that connects the SAF with the Mendocino Triple Junction. Instead, we suggest that the SAF previously mapped onshore at Point Delgada continues onshore northward and transitions to the King Range thrust.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01367.1","usgsCitation":"Beeson, J.W., Johnson, S.Y., and Goldfinger, C., 2017, The transtensional offshore portion of the northern San Andreas fault: Fault zone geometry, late Pleistocene to Holocene sediment deposition, shallow deformation patterns, and asymmetric basin growth: Geosphere, v. 13, no. 4, p. 1173-1206, https://doi.org/10.1130/GES01367.1.","productDescription":"34 p.","startPage":"1173","endPage":"1206","ipdsId":"IP-076193","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469615,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01367.1","text":"Publisher Index Page"},{"id":344765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-09","publicationStatus":"PW","scienceBaseUri":"598e9035e4b09fa1cb160964","contributors":{"authors":[{"text":"Beeson, Jeffrey W. 0000-0002-7396-237X","orcid":"https://orcid.org/0000-0002-7396-237X","contributorId":194964,"corporation":false,"usgs":false,"family":"Beeson","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Samuel Y. 0000-0001-7972-9977 sjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":2607,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel","email":"sjohnson@usgs.gov","middleInitial":"Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":707702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldfinger, Chris","contributorId":195634,"corporation":false,"usgs":false,"family":"Goldfinger","given":"Chris","email":"","affiliations":[],"preferred":false,"id":707704,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190134,"text":"70190134 - 2017 - Using optimal transport theory to estimate transition probabilities in metapopulation dynamics","interactions":[],"lastModifiedDate":"2017-08-11T18:29:59","indexId":"70190134","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Using optimal transport theory to estimate transition probabilities in metapopulation dynamics","docAbstract":"This work considers the estimation of transition probabilities associated with populations moving among multiple spatial locations based on numbers of individuals at each location at two points in time. The problem is generally underdetermined as there exists an extremely large number of ways in which individuals can move from one set of locations to another. A unique solution therefore requires a constraint. The theory of optimal transport provides such a constraint in the form of a cost function, to be minimized in expectation over the space of possible transition matrices. We demonstrate the optimal transport approach on marked bird data and compare to the probabilities obtained via maximum likelihood estimation based on marked individuals. It is shown that by choosing the squared Euclidean distance as the cost, the estimated transition probabilities compare favorably to those obtained via maximum likelihood with marked individuals. Other implications of this cost are discussed, including the ability to accurately interpolate the population's spatial distribution at unobserved points in time and the more general relationship between the cost and minimum transport energy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.06.003","usgsCitation":"Nichols, J.M., Spendelow, J.A., and Nichols, J.D., 2017, Using optimal transport theory to estimate transition probabilities in metapopulation dynamics: Ecological Modelling, v. 359, p. 311-319, https://doi.org/10.1016/j.ecolmodel.2017.06.003.","productDescription":"9 p.","startPage":"311","endPage":"319","ipdsId":"IP-085663","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2017.06.003","text":"Publisher Index Page"},{"id":344773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"359","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"598e903be4b09fa1cb16096e","contributors":{"authors":[{"text":"Nichols, Jonathan M.","contributorId":195603,"corporation":false,"usgs":false,"family":"Nichols","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spendelow, Jeffrey A. 0000-0001-8167-0898 jspendelow@usgs.gov","orcid":"https://orcid.org/0000-0001-8167-0898","contributorId":4355,"corporation":false,"usgs":true,"family":"Spendelow","given":"Jeffrey","email":"jspendelow@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":707617,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190148,"text":"70190148 - 2017 - Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015","interactions":[],"lastModifiedDate":"2017-08-11T17:47:51","indexId":"70190148","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015","docAbstract":"The U.S. Geological Survey (USGS) maintains a place-based research program in San Francisco Bay (USA) that began in 1969 and continues, providing one of the longest records of water-quality measurements in a North American estuary. Constituents include salinity, temperature, light extinction coefficient, and concentrations of chlorophyll-a, dissolved oxygen, suspended particulate matter, nitrate, nitrite, ammonium, silicate, and phosphate. We describe the sampling program, analytical methods, structure of the data record, and how to access all measurements made from 1969 through 2015. We provide a summary of how these data have been used by USGS and other researchers to deepen understanding of how estuaries are structured and function differently from the river and ocean ecosystems they bridge.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2017.98","usgsCitation":"Schraga, T., and Cloern, J.E., 2017, Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015: Scientific Data, v. 4, Article 170098: 14 p., https://doi.org/10.1038/sdata.2017.98.","productDescription":"Article 170098: 14 p.","ipdsId":"IP-086767","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469616,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2017.98","text":"Publisher Index Page"},{"id":438248,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7D21WGF","text":"USGS data release","linkHelpText":"USGS Measurements of Water Quality in San Francisco Bay (CA), 2016-2021 (ver. 4.0, March 2023)"},{"id":344767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.15673828124999,\n 37.06394430056685\n ],\n [\n -121.37695312499999,\n 37.06394430056685\n ],\n [\n -121.37695312499999,\n 39.036252959636606\n ],\n [\n -123.15673828124999,\n 39.036252959636606\n ],\n [\n -123.15673828124999,\n 37.06394430056685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-08","publicationStatus":"PW","scienceBaseUri":"598e9038e4b09fa1cb160966","contributors":{"authors":[{"text":"Schraga, Tara 0000-0002-2108-5846 tschraga@usgs.gov","orcid":"https://orcid.org/0000-0002-2108-5846","contributorId":1118,"corporation":false,"usgs":true,"family":"Schraga","given":"Tara","email":"tschraga@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":707701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":707700,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187180,"text":"ds1031 - 2017 - Archive of bathymetry data collected in South Florida from 1995 to 2015","interactions":[],"lastModifiedDate":"2017-08-10T17:27:37","indexId":"ds1031","displayToPublicDate":"2017-08-10T15:15:00","publicationYear":"2017","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":"1031","title":"Archive of bathymetry data collected in South Florida from 1995 to 2015","docAbstract":"Land development and alterations of the ecosystem in south Florida over the past 100 years have decreased freshwater and increased nutrient flows into many of Florida's estuaries, bays, and coastal regions. As a result, there has been a decrease in the water quality in many of these critical habitats, often prompting seagrass die-offs and reduced fish and aquatic life populations. Restoration of water quality in many of these habitats will depend partly upon using numerical-circulation and sediment-transport models to establish water-quality targets and to assess progress toward reaching restoration targets. Application of these models is often complicated because of complex sea floor topography and tidal flow regimes. Consequently, accurate and modern sea-floor or bathymetry maps are critical for numerical modeling research. Modern bathymetry data sets will also permit a comparison to historical data in order to help assess sea-floor changes within these critical habitats. New and detailed data sets also support marine biology studies to help understand migratory and feeding habitats of marine life.
This data series is a compilation of 13 mapping projects conducted in south Florida between 1995 and 2015 and archives more than 45 million bathymetric soundings. Data were collected primarily with a single beam sound navigation and ranging (sonar) system called SANDS developed by the U.S. Geological Survey (USGS) in 1993. Bathymetry data for the Estero Bay project were supplemented with the National Aeronautics and Space Administration's (NASA) Experimental Advanced Airborne Research Lidar (EAARL) system. Data from eight rivers in southwest Florida were collected with an interferometric swath bathymetry system. The projects represented in this data series were funded by the USGS Coastal and Marine Geology Program (CMGP), the USGS South Florida Ecosystem Restoration Project- formally named Placed Based Studies, and other non-Federal agencies. The purpose of the data collection for all these projects was to support one or more of the following scientific aspects: numerical model applications, sea floor change analysis, or marine habitat investigations.
This report serves as an archive of processed bathymetry sounding data, digital bathymetric contours, digital bathymetric maps, sea floor surface grids, and formal Federal Geographic Data Committee (FGDC) metadata. Refer to the Abbreviations page for explanations of acronyms and abbreviations used in this report. Since 2006, the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier or Field Activity Number (FAN) for each field data collection. Projects described in this report conducted prior to 2006 do not have a FAN.
Data from the 13 projects presented in this report provided critical hydrographic information to support multiple science projects in south Florida. The projects and the types of sounding data collected are:
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse (Centrocercus urophasianus) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial for other species occupying landscapes with heterogeneous disturbance and climatic regimes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171089","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Coates, P.S., Prochazka, B.G., Ricca, M.A., Wann, G.T., Aldridge, C.L., Hanser, S.E., Doherty, K.E., O’Donnell, M.S., Edmunds, D.R., and, Espinosa, S.P., 2017, Hierarchical population monitoring of greater sage-grouse (Centrocercus urophasianus) in Nevada and California—Identifying populations for management at the appropriate spatial scale: U.S. Geological Survey Open-File Report 2017-1089, 49 p., https://doi.org/10.3133/ofr20171089.","productDescription":"viii, 49 p.","onlineOnly":"Y","ipdsId":"IP-087898","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":344634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1089/ofr20171089.pdf","text":"Report","size":"15.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1089"},{"id":344633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1089/coverthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.9,\n 34\n ],\n [\n -113,\n 34\n ],\n [\n -113,\n 42.25\n ],\n [\n -121.9,\n 42.25\n ],\n [\n -121.9,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819