Groundwater resources information was needed to understand regional aquifer systems and water available to wells and springs for rearing important Lake Michigan fish species at the Kettle Moraine Springs State Fish Hatchery in Sheboygan County, Wisconsin. As a basis for estimating the groundwater resources available, an existing groundwater-flow model was refined, and new groundwater-flow models were developed for the Kettle Moraine Springs State Fish Hatchery area using the U.S. Geological Survey (USGS) finite-difference code MODFLOW. This report describes the origin and construction of these groundwater-flow models and their use in testing conceptual models and simulating the hydrogeologic system.
The study area is in the Eastern Ridges and Lowlands geographical province of Wisconsin, and the hatchery property is situated on the southeastern edge of the Kettle Moraine, a north-south trending topographic high of glacial origin. The bedrock units underlying the study area consist of Cambrian, Ordovician, and Silurian units of carbonate and siliciclastic lithology. In the Sheboygan County area, the sedimentary bedrock sequence reaches a thickness of as much as about 1,600 feet (ft).
Two aquifer systems are present at the Kettle Moraine Springs State Fish Hatchery. A shallow system is made up of Silurian bedrock, consisting chiefly of dolomite, overlain by unconsolidated Quaternary-age glacial deposits. The glacial deposits of this aquifer system are the typical source of water to local springs, including the springs that have historically supplied the hatchery. The shallow aquifer system, therefore, consists of the unconsolidated glacial aquifer and the underlying bedrock Silurian aquifer. Most residential wells in the area draw from the Silurian aquifer. A deeper confined aquifer system is made up of Cambrian- and Ordovician-age bedrock units including sandstone formations. Because of its depth, very few wells are completed in the Cambrian-Ordovician aquifer system (COAS) near the Kettle Moraine Springs State Fish Hatchery.
Three groundwater-flow models were used to estimate the water resources available to the hatchery from bedrock aquifers under selected scenarios of well placement and seasonal water requirements and subject to constraints on the effects of pumping on neighboring wells, local springs, and creeks. Model input data (recharge, water withdrawal, and boundary conditions) for these models were compiled from a number of data and information sources.
The first model, named the “KMS model,” (KMS stands for Kettle Moraine Springs) is an inset model derived from a published USGS regional Lake Michigan Basin model and was constructed to simulate groundwater pumping from the semiconfined Silurian aquifer. The second model, named the “Pumping Test model,” was constructed to evaluate an aquifer pumping test conducted in the COAS as part of this project. The Pumping Test model was also used to simulate the local effects of 20 years of groundwater pumping from this deep bedrock aquifer for future hatchery operations. The third model, named the “LMB modified model,” is a version of the published Lake Michigan Basin (LMB) model that was modified with aquifer parameters refined in an area around the hatchery (approximately a 5-mile radius circle, corresponding to the area stressed by the aquifer pumping test). This LMB modified model was applied to evaluate regional effects of pumping from the confined COAS.
The available Silurian aquifer groundwater resource was estimated using the KMS model with three scenarios—named “AllConstraints,” “Constraints2,” and “Constraints3”—that specified local water-level and flow constraints such as drawdown at nearby household wells, water levels inside pumping well boreholes, and flow in local streams and springs. Each scenario utilized the MODFLOW Groundwater Management Process (GWM) to select three locations from six candidate locations that provided the greatest combined flow while satisfying the constraints. The three constraint scenarios provided estimates of 430 gallons per minute (gal/min), 480 gal/min, and 520 gal/min pumping from three wells—AllConstraints, Constraints2, and Constraints3, respectively. The same three wells were selected for the scenarios that estimated 480 gal/min and 520 gal/min; the scenario that estimated 430 gal/min shared two of these same wells, but the third selected well was different.
The available COAS groundwater resource was estimated by two scenarios with each conducted over a period of 20 years with the Pumping Test model and the LMB modified model. The Pumping Test model was used to simulate local effects of pumping, and the LMB modified model was used to simulate regional effects of pumping. The scenarios simulate a range of total and seasonal pumping rates potentially linked to site activities. Scenario 1 simulates two wells completed in the Cambrian-Ordovician aquifer system, each pumping for 8 months at 300 gal/min, followed by pumping for 4 months at 600 gal/min. The average yearly pumping rate of Scenario 1 is 800 gal/min. Scenario 2 simulates three wells completed in the Cambrian-Ordovician aquifer system pumping for 8 months at 200 gal/min, followed by pumping for 4 months at 500 gal/min. The average yearly pumping rate of Scenario 2 is 900 gal/min. The Pumping Test model simulations confirmed that drawdown in the boreholes of the pumping wells at the selected 2-well or 3-well rates will meet the desired condition that the pumping water level remains at least 100 ft above the highest Cambrian-Ordovician unit open to the well.
The LMB modified model was used to evaluate the regional drawdown of the pumping from the confined COAS under the same 2-well and 3-well scenarios. At the nearest known existing COAS well, Campbellsport production well #4, the simulated drawdown for Scenario 1 after 20 years of cyclical pumping with two pumping wells averaging a total of 800 gal/min is 16.9 ft, whereas the simulated drawdown for Scenario 2 after 20 years of pumping with three pumping wells averaging a total of 900 gal/min is 19.0 ft. The total deep aquifer thickness at the Campbellsport location is on the order of 620 ft, meaning that the simulated drawdown for either scenario is about 3 percent of the confined aquifer thickness.
The models developed as part of this project are archived in the project data release. The archive includes the model input and output files as well as MODFLOW source code and executables. (Haserodt and others, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175074","collaboration":"Prepared in cooperation with the Fisheries Management Program of the Wisconsin Department of Natural Resources","usgsCitation":"Dunning, C.P., Feinstein, D.T., Buchwald, C.A., Hunt, R.J., and Haserodt, M.J., 2017, Estimation of the groundwater resources of the bedrock aquifers at the Kettle Moraine Springs State Fish Hatchery, Sheboygan County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2017–5074, 104 p., https://doi.org/10.3133/sir20175074.","productDescription":"Report: ix, 104 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-079387","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":346498,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5074/sir20175074.pdf","text":"Report","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5074"},{"id":346497,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5074/coverthb.jpg"},{"id":346499,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77S7KW2","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"GWM-2005, MODFLOW-2005, MODFLOW-NWT, and SEAWAT-2000 groundwater flow models of the Bedrock Aquifers at the Kettle Moraine Springs State Fish Hatchery, Sheboygan County, Wisconsin"}],"country":"United States","state":"Wisconsin","county":"Sheboygan County","otherGeospatial":"Kettle Moraine Springs State Fish Hatchery","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.0778,\n 43.5944\n ],\n [\n -88.0889,\n 43.5944\n ],\n [\n -88.0889,\n 43.6167\n ],\n [\n -88.0778,\n 43.6167\n ],\n [\n -88.0778,\n 43.5944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The U.S. Geological Survey (USGS), in cooperation with the DuPage County Stormwater Management Department, maintains a database of hourly meteorological and hydrologic data for use in a near real-time streamflow simulation system. This system is used in the management and operation of reservoirs and other flood-control structures in the West Branch DuPage River watershed in DuPage County, Illinois. The majority of the precipitation data are collected from a tipping-bucket rain-gage network located in and near DuPage County. The other meteorological data (air temperature, dewpoint temperature, wind speed, and solar radiation) are collected at Argonne National Laboratory in Argonne, Ill. Potential evapotranspiration is computed from the meteorological data using the computer program LXPET (Lamoreux Potential Evapotranspiration). The hydrologic data (water-surface elevation [stage] and discharge) are collected at U.S.Geological Survey streamflow-gaging stations in and around DuPage County. These data are stored in a Watershed Data Management (WDM) database.
This report describes a version of the WDM database that is quality-assured and quality-controlled annually to ensure datasets are complete and accurate. This database is named WBDR13.WDM. It contains data from January 1, 2007, through September 30, 2013. Each precipitation dataset may have time periods of inaccurate data. This report describes the methods used to estimate the data for the periods of missing, erroneous, or snowfall-affected data and thereby improve the accuracy of these data. The other meteorological datasets are described in detail in Over and others (2010), and the hydrologic datasets in the database are fully described in the online USGS annual water data reports for Illinois (U.S. Geological Survey, 2016) and, therefore, are described in less detail than the precipitation datasets in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171099","collaboration":"Prepared in cooperation with DuPage County Stormwater Management Department","usgsCitation":"Bera, Maitreyee, 2017, Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013: U.S. Geological Survey Open-File Report 2017–1099, 39 p., https://doi.org/10.3133/ofr20171099.","productDescription":"Report: v, 39 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-078980","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":346557,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1099/ofr20171099.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1099"},{"id":346556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1099/coverthb.jpg"},{"id":346558,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71Z42M0","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Watershed Data Management (WDM) Database (WBDR13.WDM) for West Branch DuPage River Streamflow Simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013"}],"country":"United States","state":"Illinois","county":"DuPage County","otherGeospatial":"West Branch DuPage River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.28338623046875,\n 41.70214000452559\n ],\n [\n -87.99087524414062,\n 41.70214000452559\n ],\n [\n -87.99087524414062,\n 41.99726342796974\n ],\n [\n -88.28338623046875,\n 41.99726342796974\n ],\n [\n -88.28338623046875,\n 41.70214000452559\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801-2347
Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in government-funded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale.
","language":"English","publisher":"Springer","doi":"10.1007/s10531-017-1388-7","usgsCitation":"Schmeller, D.S., Böhm, M., Arvanitidis, C., Barber-Meyer, S., Brummitt, N., Chandler, M., Chatzinikolaou, E., Costello, M.J., Ding, H., Garcia-Moreno, J., Gill, M.J., Haase, P., Jones, M., Juillard, R., Magnusson, W.E., Martin, C.S., McGeoch, M.A., Mihoub, J., Pettorelli, N., Proença, V., Peng, C., Regan, E., Schmiedel, U., Simsika, J.P., Weatherdon, L., Waterman, C., Xu, H., and Belnap, J., 2017, Building capacity in biodiversity monitoring at the global scale: Biodiversity and Conservation, v. 26, no. 12, p. 2765-2790, https://doi.org/10.1007/s10531-017-1388-7.","productDescription":"26 p.","startPage":"2765","endPage":"2790","ipdsId":"IP-058340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469447,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://discovery.ucl.ac.uk/id/eprint/1558305","text":"External Repository"},{"id":346547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-20","publicationStatus":"PW","scienceBaseUri":"59e07f2fe4b05fe04ccfcd09","contributors":{"authors":[{"text":"Schmeller, Dirk S.","contributorId":147645,"corporation":false,"usgs":false,"family":"Schmeller","given":"Dirk","email":"","middleInitial":"S.","affiliations":[{"id":16875,"text":"(1)Dept of Conservation Biology, Helmholtz Centre for Environmental Research – UFZ, Permoserstrasse 15, 04318 Leipzig, Germany;","active":true,"usgs":false}],"preferred":false,"id":712226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Böhm, 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China","active":true,"usgs":false}],"preferred":false,"id":712239,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Regan, Eugenie","contributorId":197019,"corporation":false,"usgs":false,"family":"Regan","given":"Eugenie","email":"","affiliations":[],"preferred":false,"id":712278,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Schmiedel, Ute","contributorId":197007,"corporation":false,"usgs":false,"family":"Schmiedel","given":"Ute","email":"","affiliations":[],"preferred":false,"id":712240,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Simsika, John P.","contributorId":197008,"corporation":false,"usgs":false,"family":"Simsika","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":712241,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Weatherdon, Lauren","contributorId":197020,"corporation":false,"usgs":false,"family":"Weatherdon","given":"Lauren","affiliations":[],"preferred":false,"id":712279,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Waterman, Carly","contributorId":197021,"corporation":false,"usgs":false,"family":"Waterman","given":"Carly","email":"","affiliations":[],"preferred":false,"id":712280,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Xu, Haigen","contributorId":197009,"corporation":false,"usgs":false,"family":"Xu","given":"Haigen","email":"","affiliations":[],"preferred":false,"id":712243,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":712225,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"id":70191442,"text":"70191442 - 2017 - The evolution, approval and implementation of the U.S. Geological Survey Science Data Lifecycle Model","interactions":[],"lastModifiedDate":"2018-08-10T12:29:29","indexId":"70191442","displayToPublicDate":"2017-10-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5123,"text":"Journal of eScience Librarianship","active":true,"publicationSubtype":{"id":10}},"title":"The evolution, approval and implementation of the U.S. Geological Survey Science Data Lifecycle Model","docAbstract":"This paper details how the United States Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies. Starting with an extensive literature review of existing data Lifecycle models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the model and documentation in a formal agency publication .
The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP) for increased public access to federally funded research, and the Office of Management and Budget (OMB) 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data. Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes.
","language":"English","publisher":"University of Massachusetts","doi":"10.7191/jeslib.2017.1117","usgsCitation":"Faundeen, J., and Hutchison, V.B., 2017, The evolution, approval and implementation of the U.S. Geological Survey Science Data Lifecycle Model: Journal of eScience Librarianship, v. 6, no. 2, p. 1-10, https://doi.org/10.7191/jeslib.2017.1117.","productDescription":"e1117; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-076346","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469446,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7191/jeslib.2017.1117","text":"Publisher Index Page"},{"id":346546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-12","publicationStatus":"PW","scienceBaseUri":"59e07f2ee4b05fe04ccfcd02","contributors":{"authors":[{"text":"Faundeen, John 0000-0003-0287-2921 faundeen@usgs.gov","orcid":"https://orcid.org/0000-0003-0287-2921","contributorId":3097,"corporation":false,"usgs":true,"family":"Faundeen","given":"John","email":"faundeen@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":712269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchison, Vivian B. 0000-0001-5301-3698 vhutchison@usgs.gov","orcid":"https://orcid.org/0000-0001-5301-3698","contributorId":173674,"corporation":false,"usgs":true,"family":"Hutchison","given":"Vivian","email":"vhutchison@usgs.gov","middleInitial":"B.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":712270,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191364,"text":"ofr20171090 - 2017 - Description of chronostratigraphic units preserved as channel deposits and geomorphic processes following a basin-scale disturbance by a wildfire in Colorado","interactions":[],"lastModifiedDate":"2017-10-12T10:18:15","indexId":"ofr20171090","displayToPublicDate":"2017-10-11T19:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1090","title":"Description of chronostratigraphic units preserved as channel deposits and geomorphic processes following a basin-scale disturbance by a wildfire in Colorado","docAbstract":"The consequence of a 1996 wildfire disturbance and a subsequent high-intensity summer convective rain storm (about 110 millimeters per hour) was the deposition of a sediment superslug in the Spring Creek basin (26.8 square kilometers) of the Front Range Mountains in Colorado. Spring Creek is a tributary to the South Platte River upstream from Strontia Springs Reservoir, which supplies domestic water for the cities of Denver and Aurora. Changes in a superslug were monitored over the course of 18 years (1996–2014) by repeat surveys at 18 channel cross sections spaced at nearly equal intervals along a 1,500-meter study reach and by a time series of photographs of each cross section. Surveys were not repeated at regular time intervals but after major changes caused by different geomorphic processes. The focus of this long-term study was to understand the evolution and internal alluvial architecture of chronostratigraphic units (defined as the volume of sediment deposited between two successive surveys), and the preservation or storage of these units in the superslug. The data are presented as a series of 18 narratives (one for each cross section) that summarize the changes, illustrate these changes with photographs, and provide a preservation plot showing the amount of each chronostratigraphic unit still remaining in June 2014.
The most significant hydrologic change after the wildfire was an exponential decrease in peak discharge of flash floods caused by summer convective rain storms. In response to these hydrologic changes, all 18 locations went through an aggradation phase, an incision phase, and finally a stabilization phase. However, the architecture of the chronostratigraphic units differs from cross section to cross section, and units are characterized by either a laminar, fragmented, or hybrid alluvial architecture. In response to the decrease in peak-flood discharge and the increase in hillslope and riparian vegetation, Spring Creek abandoned many of the nearly horizontal erosional and depositional surfaces and left a landscape consisting of a series of cut-and-fill terraces as a legacy of this wildfire disturbance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171090","usgsCitation":"Moody, J.A., and Martin, D.A., 2017, Description of chronostratigraphic units preserved as channel deposits and geomorphic processes following a basin-scale disturbance by a wildfire in Colorado: U.S. Geological Survey Open-File Report 2017–1090, 73 p., https://doi.org/10.3133/ofr20171090.","productDescription":"vi, 73 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-081971","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":346458,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1090/coverthb.jpg"},{"id":346459,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1090/ofr20171090.pdf","text":"Report","size":"43.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1090"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.0455322265625,\n 38.89958342598271\n ],\n [\n -104.600830078125,\n 38.89958342598271\n ],\n [\n -104.600830078125,\n 39.50827899034114\n ],\n [\n -106.0455322265625,\n 39.50827899034114\n ],\n [\n -106.0455322265625,\n 38.89958342598271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Branch Chief, Hydrodynamics Branch
Earth System Processes Division
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
During spring 2015, Nebraska Game and Parks Commission (NGPC) biologists noted that pallid sturgeon (Scaphirhynchus albus) were in poor condition during sampling associated with the Pallid Sturgeon Population Assessment Project and NGPC’s annual pallid sturgeon broodstock collection effort. These observations prompted concerns that reduced fish condition could compromise reproductive health and population growth of pallid sturgeon. There was a further concern that compromised condition could possibly be linked to U.S. Army Corps of Engineers management actions and increase jeopardy to the species. An evaluation request was made to the Missouri River Recovery Program and the Effects Analysis Team was chartered to evaluate the issue. Data on all Missouri River pallid sturgeon captures were requested and received from the National Pallid Sturgeon Database. All data were examined for completeness and accuracy; 12,053 records of captures between 200 millimeters fork length (mm FL) and 1,200 mm FL were accepted. We analyzed condition using (1) the condition formula (Kn) from Shuman and others (2011); (2) a second Kn formulation derived from the 12,053 records (hereafter referred to as “Alternative Kn”); and (3) an analysis of covariance (ANCOVA) approach that did not rely on a Kn formulation. The Kn data were analyzed using group (average annual Kn) and individual (percentage in low, normal, and robust conditions) approaches. Using the Shuman Kn formulation, annual mean Kn was fairly static from 2005 to 2011 (although always higher in the upper basin), declined from 2012 to 2015, then remained either static (lower basin) or increasing (upper basin) in 2016. Under the Alternative Kn formulation, the upper basin showed no decline in Kn, whereas the lower basin displayed the same trend as the Shuman Kn formulation. Using both formulations, the individual approach revealed a more complex situation; at the same times and locations that there are fish in poor condition, there are nearby fish in normal or robust condition. The ANCOVA approach revealed that fish condition at size changed between 400 and 600 mm and that some of the apparent trend in low condition was caused by differences in sample size across the size range of the population (that is, greater catch of intermediate-sized fish compared to large fish). We examined basin, year, origin (hatchery compared to wild), segment, and size class for effects on condition and concluded that, since 2012, there has been an increase in the percentage of pallid sturgeon in low condition. There are basin, year, and segment effects; origin and size class do not seem to have an effect. The lower basin, in particular segment 9 (Platte River to Kansas River), had a high percentage of low-condition fish. Within the segment, there were bend-level effects, but the bend effect was not spatially contiguous.
We concluded that existing data confirm concerns about declining fish condition, especially in the segments between Sioux City, Iowa, and Kansas City, Missouri. Although the evidence is strong that fish condition has been in decline from 2011 to 2015, additional analysis of individual fish histories may provide more confidence in this conclusion; such analysis was beyond the scope of this effort but is part of our recommendations. The most recent data in 2016 indicate that decline of condition may have leveled off; however, the length of record is insufficient to determine whether recent declines are within the background range of variation. We recommend that monitoring of fish condition should be increased and enhanced with additional health metrics. We also recommend that, should condition continue to decline, processes are deployed to bring low-condition adult fish into the hatchery to improve nutrition and condition. We could not determine the cause of declining fish condition with available data, but we compiled information on several dominant hypotheses in two main categories: inter- or intraspecific competition for resources and habitat conditions. Data are insufficient to indicate a specific causation or solution, and it is possible that multiple causes apply. We make recommendations for additional research that can be pursued to address uncertainties in trends in fish health as well as potential causes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171121","collaboration":"Prepared in cooperation with the Missouri River Recovery Program","usgsCitation":"Randall, M.T., Colvin, M.E., Steffensen, K.D., Welker, T.L., Pierce, L.L., and Jacobson, R.B., 2017, Assessment of adult pallid sturgeon fish condition, Lower Missouri River—Application of new information to the Missouri River Recovery Program: U.S. Geological Survey Open-File Report 2017–1121, 103 p., https://doi.org/10.3133/ofr20171121. ","productDescription":"vi, 103 p.","numberOfPages":"111","onlineOnly":"N","ipdsId":"IP-082584","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":346492,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1121/coverthb.jpg"},{"id":346493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1121/ofr20171121.pdf","text":"Report","size":"7.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1121"}],"country":"United States","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.34765625,\n 38.496593518947584\n ],\n [\n -90.10986328125,\n 38.496593518947584\n ],\n [\n -90.10986328125,\n 48.23930899024907\n ],\n [\n -106.34765625,\n 48.23930899024907\n ],\n [\n -106.34765625,\n 38.496593518947584\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, Missouri 65201
Streamflow statistics are needed by decision makers for many planning, management, and design activities. The U.S. Geological Survey (USGS) StreamStats Web application provides convenient access to streamflow statistics for many streamgages by accessing the underlying StreamStatsDB database. In 2016, non-interpretive streamflow statistics were compiled for streamgages located throughout the Nation and stored in StreamStatsDB for use with StreamStats and other applications. Two previously published USGS computer programs that were designed to help calculate streamflow statistics were updated to better support StreamStats as part of this effort. These programs are named “GNWISQ” (Get National Water Information System Streamflow (Q) files), updated to version 1.1.1, and “QSTATS” (Streamflow (Q) Statistics), updated to version 1.1.2.
Statistics for 20,438 streamgages that had 1 or more complete years of record during water years 1901 through 2015 were calculated from daily mean streamflow data; 19,415 of these streamgages were within the conterminous United States. About 89 percent of the 20,438 streamgages had 3 or more years of record, and about 65 percent had 10 or more years of record. Drainage areas of the 20,438 streamgages ranged from 0.01 to 1,144,500 square miles. The magnitude of annual average streamflow yields (streamflow per square mile) for these streamgages varied by almost six orders of magnitude, from 0.000029 to 34 cubic feet per second per square mile. About 64 percent of these streamgages did not have any zero-flow days during their available period of record. The 18,122 streamgages with 3 or more years of record were included in the StreamStatsDB compilation so they would be available via the StreamStats interface for user-selected streamgages. All the statistics are available in a USGS ScienceBase data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171108","collaboration":"Prepared in cooperation with the Federal Highway Administration Office of Project Development and Environmental Review","usgsCitation":"Granato G.E., Ries, K.G., III, and Steeves, P.A., 2017, Compilation of streamflow statistics calculated from daily mean streamflow data collected during water years 1901–2015 for selected U.S. Geological Survey streamgages: U.S. Geological Survey Open-File Report 2017–1108, 17 p., https://doi.org/10.3133/ofr20171108.","productDescription":"Report: vi, 17 p.; 4 Figures; Data Release","onlineOnly":"Y","ipdsId":"IP-077435","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":346453,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5CFT","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Streamflow statistics calculated from daily mean streamflow data collected during water years 1901–2015 for selected U.S. Geological Survey streamgages"},{"id":346446,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1108/coverthb.jpg"},{"id":346447,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1108/ofr20171108.pdf","text":"Report","size":"5.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1108"},{"id":346448,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2017/1108/ofr20171108_fig3a_interactive.pdf","text":"Figure 3A","size":"4.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1108 - Figure 3A","linkHelpText":"—Streamgages by record length [layered pdf; view in Adobe Reader or Microsoft Internet Explorer]"},{"id":346449,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2017/1108/ofr20171108_fig3b_interactive.pdf","text":"Figure 3B","size":"4.44 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\"}}]}\n","publicComments":"Groundwater and Streamflow Information Program","contact":"Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best-performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid-Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.
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States\"}}]}","volume":"53","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-28","publicationStatus":"PW","scienceBaseUri":"59dddc09e4b05fe04ccd05c6","contributors":{"authors":[{"text":"Austin, Samuel H. 0000-0001-5626-023X saustin@usgs.gov","orcid":"https://orcid.org/0000-0001-5626-023X","contributorId":153,"corporation":false,"usgs":true,"family":"Austin","given":"Samuel","email":"saustin@usgs.gov","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":712131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelms, David L. 0000-0001-5747-642X dlnelms@usgs.gov","orcid":"https://orcid.org/0000-0001-5747-642X","contributorId":1892,"corporation":false,"usgs":true,"family":"Nelms","given":"David","email":"dlnelms@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712132,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191355,"text":"70191355 - 2017 - Linear and nonlinear effects of temperature and precipitation on ecosystem properties in tidal saline wetlands","interactions":[],"lastModifiedDate":"2017-10-07T08:43:34","indexId":"70191355","displayToPublicDate":"2017-10-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Linear and nonlinear effects of temperature and precipitation on ecosystem properties in tidal saline wetlands","docAbstract":"Climate greatly influences the structure and functioning of tidal saline wetland ecosystems. However, there is a need to better quantify the effects of climatic drivers on ecosystem properties, particularly near climate-sensitive ecological transition zones. Here, we used climate- and literature-derived ecological data from tidal saline wetlands to test hypotheses regarding the influence of climatic drivers (i.e., temperature and precipitation regimes) on the following six ecosystem properties: canopy height, biomass, productivity, decomposition, soil carbon density, and soil carbon accumulation. Our analyses quantify and elucidate linear and nonlinear effects of climatic drivers. We quantified positive linear relationships between temperature and above-ground productivity and strong positive nonlinear (sigmoidal) relationships between (1) temperature and above-ground biomass and canopy height and (2) precipitation and canopy height. Near temperature-controlled mangrove range limits, small changes in temperature are expected to trigger comparatively large changes in biomass and canopy height, as mangrove forests grow, expand, and, in some cases, replace salt marshes. However, within these same transition zones, temperature-induced changes in productivity are expected to be comparatively small. Interestingly, despite the significant above-ground height, biomass, and productivity relationships across the tropical–temperate mangrove–marsh transition zone, the relationships between temperature and soil carbon density or soil carbon accumulation were not significant. Our literature review identifies several ecosystem properties and many regions of the world for which there are insufficient data to fully evaluate the influence of climatic drivers, and the identified data gaps can be used by scientists to guide future research. Our analyses indicate that near precipitation-controlled transition zones, small changes in precipitation are expected to trigger comparatively large changes in canopy height. However, there are scant data to evaluate the influence of precipitation on other ecosystem properties. There is a need for more decomposition data across climatic gradients, and to advance understanding of the influence of changes in precipitation and freshwater availability, additional ecological data are needed from tidal saline wetlands in arid climates. Collectively, our results can help scientists and managers better anticipate the linear and nonlinear ecological consequences of climate change for coastal wetlands.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1956","usgsCitation":"Feher, L.C., Osland, M.J., Griffith, K.T., Grace, J.B., Howard, R.J., Stagg, C.L., Enwright, N.M., Krauss, K.W., Gabler, C., Day, R.H., and Rogers, K., 2017, Linear and nonlinear effects of temperature and precipitation on ecosystem properties in tidal saline wetlands: Ecosphere, v. 8, no. 10, Article e01956; 23 p., https://doi.org/10.1002/ecs2.1956.","productDescription":"Article e01956; 23 p.","ipdsId":"IP-081607","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469451,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1956","text":"Publisher Index Page"},{"id":438192,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S180QJ","text":"USGS data release","linkHelpText":"Linear and nonlinear effects of temperature and precipitation on ecosystem properties in tidal saline wetlands"},{"id":346462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"10","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-02","publicationStatus":"PW","scienceBaseUri":"59defbb4e4b05fe04ccd3d45","contributors":{"authors":[{"text":"Feher, Laura C. 0000-0002-5983-6190 lhundy@usgs.gov","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":176788,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","email":"lhundy@usgs.gov","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research 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Center","active":true,"usgs":true}],"preferred":true,"id":712069,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rogers, Kerrylee","contributorId":64151,"corporation":false,"usgs":false,"family":"Rogers","given":"Kerrylee","email":"","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":712070,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70191359,"text":"70191359 - 2017 - Smartphone technologies and Bayesian networks to assess shorebird habitat selection","interactions":[],"lastModifiedDate":"2018-01-05T14:28:23","indexId":"70191359","displayToPublicDate":"2017-10-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Smartphone technologies and Bayesian networks to assess shorebird habitat selection","docAbstract":"Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers (Charadrius melodus) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications.
We estimate the location and population served by domestic wells in the contiguous United States in two ways: (1) the “Block Group Method” or BGM, uses data from the 1990 census, and (2) the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally-consistent domestic-well population datasets.
While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map has a smaller amount of spatial bias (Type 1 and Type 2 errors nearly equal vs biased in Type 1), total error (10.9% vs 23.7%), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a calibration map in California. However, the BGM map is more inclusive of all potential locations for domestic wells. Independent domestic well datasets from the USGS, and the States of MN, NV, and TX show that the BGM captures about 5 to 10% more wells than the REM.
One key difference between the BGM and the REM is the mapping of low density areas. The REM reduces areas mapped as low density by 57%, concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used.
Groundwater-quality data were collected from 559 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program from January through December 2014. The data were collected from four types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; and enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI] and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1063","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063.","productDescription":"Report: viii, 83 p.; Data Release","numberOfPages":"96","onlineOnly":"Y","ipdsId":"IP-079602","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":346378,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7W0942N","text":"USGS 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\"}}]}\n","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801-2347
https://il.water.usgs.gov/
The duration of winter ice cover on lakes varies substantially with climate variability, and has decreased over the last several decades in many temperate lakes. However, little is known of how changes in seasonal ice cover may affect biogeochemical processes under ice. We examined winter nitrogen (N) dynamics under ice using a 30+ yr dataset from five oligotrophic/mesotrophic north temperate lakes to determine how changes in inorganic N species varied with ice duration. Nitrate accumulated during winter and was strongly related to the number of days since ice-on. Exogenous inputs accounted for less than 3% of nitrate accumulation in four of the five lakes, suggesting a paramount role of nitrification in regulating N transformation and the timing of chemical conditions under ice. Winter nitrate accumulation rates ranged from 0.15 μg N L−1 d−1 to 2.7 μg N L−1 d−1 (0.011–0.19 μM d−1), and the mean for intermediate depths was 0.94 μg N L−1 d−1(0.067 μM d−1). Given that winters with shorter ice duration (< 120 d) have become more frequent in these lakes since the late 1990s, peak winter nitrate concentrations and cumulative nitrate production under ice may be declining. As ice extent and duration change, the physical and chemical conditions supporting life will shift. This research suggests we may expect changes in the form and amount of inorganic N, and altered dissolved nitrogen : phosphorus ratios, in lakes during winters with shorter ice duration.
","language":"English","publisher":"ASLO","doi":"10.1002/lol2.10048","usgsCitation":"Powers, S.M., Labou, S.G., Baulch, H.M., Hunt, R.J., Lottig, N.R., Hampton, S.E., and Stanley, E.H., 2017, Ice duration drives winter nitrate accumulation in north temperate lakes: Limnology and Oceanography Letters, v. 2, no. 5, p. 177-186, https://doi.org/10.1002/lol2.10048.","productDescription":"12 p.","startPage":"177","endPage":"186","ipdsId":"IP-082184","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":469455,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10048","text":"Publisher Index Page"},{"id":346432,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-13","publicationStatus":"PW","scienceBaseUri":"59d7449ce4b05fe04cc7e2f8","contributors":{"authors":[{"text":"Powers, Steven M","contributorId":196949,"corporation":false,"usgs":false,"family":"Powers","given":"Steven","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":712024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Labou, Stephanie G.","contributorId":194571,"corporation":false,"usgs":false,"family":"Labou","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":712025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baulch, Helen M.","contributorId":194573,"corporation":false,"usgs":false,"family":"Baulch","given":"Helen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":712026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lottig, Noah R.","contributorId":172031,"corporation":false,"usgs":false,"family":"Lottig","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":712027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hampton, Stephanie E.","contributorId":178718,"corporation":false,"usgs":false,"family":"Hampton","given":"Stephanie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":712028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":712029,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191350,"text":"70191350 - 2017 - 2016 National Park visitor spending effects: Economic contributions to local communities, states, and the Nation","interactions":[],"lastModifiedDate":"2018-06-11T15:11:59","indexId":"70191350","displayToPublicDate":"2017-10-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/EQD/NRR—2017/1421","title":"2016 National Park visitor spending effects: Economic contributions to local communities, states, and the Nation","docAbstract":"The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports a considerable amount of economic activity within park gateway communities. This economic effects analysis measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income.
In 2016, the National Park System received an estimated 330,971,689 recreation visits. Visitors to National Parks spent an estimated \\$18.4 billion in local gateway regions (defined as communities within 60 miles of a park). The contribution of this spending to the national economy was 318 thousand jobs, \\$12.0 billion in labor income, \\$19.9 billion in value added, and \\$34.9 billion in economic output. The lodging sector saw the highest direct contributions with \\$5.7 billion in economic output directly contributed to local gateway economies nationally. The sector with the next greatest direct contributions was the restaurants and bars sector, with \\$3.7 billion in economic output directly contributed to local gateway economies nationally.Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. This interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.
","language":"English","publisher":"National Park Service","usgsCitation":"Cullinane Thomas, C., and Koontz, L., 2017, 2016 National Park visitor spending effects: Economic contributions to local communities, states, and the Nation: Natural Resource Report NPS/NRSS/EQD/NRR—2017/1421, v, 38 p.","productDescription":"v, 38 p.","numberOfPages":"48","ipdsId":"IP-084996","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346422,"type":{"id":15,"text":"Index Page"},"url":"https://www.nps.gov/nature/customcf/NPS_Data_Visualization/docs/2016_VSE.pdf"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d7449be4b05fe04cc7e2f3","contributors":{"authors":[{"text":"Cullinane Thomas, Catherine 0000-0001-8168-1271 ccullinanethomas@usgs.gov","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":141097,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine","email":"ccullinanethomas@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":712038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":712039,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190780,"text":"ofr20171114 - 2017 - User’s Manual for the National Water Information System of the U.S. Geological Survey: Aggregate Water-Use Data System, Version 3.2","interactions":[],"lastModifiedDate":"2017-10-10T11:40:29","indexId":"ofr20171114","displayToPublicDate":"2017-10-04T03:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1114","title":"User’s Manual for the National Water Information System of the U.S. Geological Survey: Aggregate Water-Use Data System, Version 3.2","docAbstract":"The Aggregate Water-Use Data System (AWUDS) is the database management system used to enter, store, and analyze state aggregate water-use data. It is part of the U.S. Geological Survey National Water Information System. AWUDS has a graphical user interface that facilitates data entry, revision, review, and approval. This document provides information on the basic functions of AWUDS and the steps for carrying out common tasks that are a part of compiling an aggregated dataset. Also included are explanations of terminology and descriptions of user-interface structure, procedures for using the AWUDS operations, and dataset-naming conventions. Information on water-use category definitions, data-collection methods, and data sources are found in the report “Guidelines for preparation of State water-use estimates,” available at https://pubs.er.usgs.gov/publication/ofr20171029.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171114","usgsCitation":"Nawyn, J.P., Sargent, B.P., Hoopes, B.C., Augenstein, T.W., Rowland, K.M., and Barber, N.L., 2017, User’s Manual for the National Water Information System of the U.S. Geological Survey: Aggregate Water-Use Data System, Version 3.2: U.S. Geological Survey Open-File Report 2017–1114, 29 p., https://doi.org/10.3133/ofr20171114.","productDescription":"iv, 29 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-088071","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":346324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1114/coverthb.jpg"},{"id":346325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1114/ofr20171114.pdf","text":"Report","size":".99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1114"}],"contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
or visit our website
https://nj.usgs.gov
Many large rivers around the world no longer flow to their deltas, due to ever greater water withdrawals and diversions for human needs. However, the importance of riparian ecosystems is drawing increasing recognition, leading to the allocation of environmental flows to restore river processes. Accurate estimates of riparian plant evapotranspiration (ET) are needed to understand how the riverine system responds to these rare events and achieve the goals of environmental flows. In 2014, historic environmental flows were released into the Lower Colorado River at Morelos Dam (Mexico); this once perennial but now dry reach is the final stretch to the mighty Colorado River Delta. One of the primary goals was to supply native vegetation restoration sites along the reach with water to help seedlings establish and boost groundwater levels to foster the planted saplings. Patterns in ET before, during, and after the flows are useful for evaluating whether this goal was met and understanding the role that ET plays in this now ephemeral river system. Here, diurnal fluctuations in groundwater levels and MODIS data were used to compare estimates of ET specifically at three native vegetation restoration sites during 2014 planned flow events, while MODIS data was used to evaluate long-term (2002 – 2016) ET responses to restoration efforts at these sites. Overall, ET was generally 0 - 10 mm d-1 across sites and although daily ET values from groundwater data were highly variable, weekly averaged estimates were highly correlated with MODIS-derived estimates at most sites. The influence of the 2014 flow events was not immediately apparent in the results, although the process of clearing vegetation and planting native vegetation at the restoration sites was clearly visible in the results.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11359","usgsCitation":"Shanafield, M., Jurado, H.G., Burgueno, J.E., Hernandez, J.R., Jarchow, C., and Nagler, P.L., 2017, Short-term and long-term evapotranspiration rates at ecological restoration sites along a large river receiving rare flow events: Hydrological Processes, v. 31, no. 24, p. 4328-4337, https://doi.org/10.1002/hyp.11359.","productDescription":"10 p.","startPage":"4328","endPage":"4337","ipdsId":"IP-068603","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11359","text":"Publisher Index Page"},{"id":346385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.08453369140625,\n 32.217448573031014\n ],\n [\n -114.63821411132812,\n 32.217448573031014\n ],\n [\n -114.63821411132812,\n 32.751477587458865\n ],\n [\n -115.08453369140625,\n 32.751477587458865\n ],\n [\n -115.08453369140625,\n 32.217448573031014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-17","publicationStatus":"PW","scienceBaseUri":"59d5f342e4b05fe04cc652b8","contributors":{"authors":[{"text":"Shanafield, Margaret","contributorId":196916,"corporation":false,"usgs":false,"family":"Shanafield","given":"Margaret","email":"","affiliations":[],"preferred":false,"id":711930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurado, Hugo Gutierrez","contributorId":196917,"corporation":false,"usgs":false,"family":"Jurado","given":"Hugo","email":"","middleInitial":"Gutierrez","affiliations":[],"preferred":false,"id":711931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgueno, Jesus Eliana Rodriguez","contributorId":196918,"corporation":false,"usgs":false,"family":"Burgueno","given":"Jesus","email":"","middleInitial":"Eliana Rodriguez","affiliations":[],"preferred":false,"id":711932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hernandez, Jorge Ramirez","contributorId":196919,"corporation":false,"usgs":false,"family":"Hernandez","given":"Jorge","email":"","middleInitial":"Ramirez","affiliations":[],"preferred":false,"id":711933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711927,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191324,"text":"70191324 - 2017 - A comparison of three federal datasets for thermoelectric water withdrawals in the United States for 2010","interactions":[],"lastModifiedDate":"2017-10-04T11:08:52","indexId":"70191324","displayToPublicDate":"2017-10-04T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of three federal datasets for thermoelectric water withdrawals in the United States for 2010","docAbstract":"Historically, thermoelectric water withdrawal has been estimated by the Energy Information Administration (EIA) and the U.S. Geological Survey's (USGS) water-use compilations. Recently, the USGS developed models for estimating withdrawal at thermoelectric plants to provide estimates independent from plant operator-reported withdrawal data. This article compares three federal datasets of thermoelectric withdrawals for the United States in 2010: one based on the USGS water-use compilation, another based on EIA data, and the third based on USGS model-estimated data. The withdrawal data varied widely. Many plants had three different withdrawal values, and for approximately 54% of the plants the largest withdrawal value was twice the smallest, or larger. The causes of discrepancies among withdrawal estimates included definitional differences, definitional noise, and various nondefinitional causes. The uncertainty in national totals can be characterized by the range among the three datasets, from 5,640 m3/s (129 billion gallons per day [bgd]) to 6,954 m3/s (158 bgd), or by the aggregate difference between the smallest and largest values at each plant, from 4,014 m3/s (92 bgd) to 8,590 m3/s (196 bgd). When used to assess the accuracy of reported values, the USGS model estimates identify plants that need to be reviewed.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12551","usgsCitation":"Harris, M.A., and Diehl, T.H., 2017, A comparison of three federal datasets for thermoelectric water withdrawals in the United States for 2010: Journal of the American Water Resources Association, v. 53, no. 5, p. 1062-1080, https://doi.org/10.1111/1752-1688.12551.","productDescription":"19 p.","startPage":"1062","endPage":"1080","ipdsId":"IP-072613","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":469458,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12551","text":"Publisher Index Page"},{"id":438193,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HX19VW","text":"USGS data release","linkHelpText":"Thermoelectric power plant water withdrawals and associated attributes for three Federal datasets in the United States, 2010"},{"id":346380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-09","publicationStatus":"PW","scienceBaseUri":"59d5f344e4b05fe04cc652c1","contributors":{"authors":[{"text":"Harris, Melissa A. 0000-0003-2659-9763 mharris@usgs.gov","orcid":"https://orcid.org/0000-0003-2659-9763","contributorId":1903,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","email":"mharris@usgs.gov","middleInitial":"A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diehl, Timothy H. 0000-0001-9691-2212 thdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9691-2212","contributorId":546,"corporation":false,"usgs":true,"family":"Diehl","given":"Timothy","email":"thdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711925,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191067,"text":"70191067 - 2017 - Program MAMO: Models for avian management optimization-user guide","interactions":[],"lastModifiedDate":"2018-01-04T09:32:10","indexId":"70191067","displayToPublicDate":"2017-10-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"TR-HCSU-077","title":"Program MAMO: Models for avian management optimization-user guide","docAbstract":"The following chapters describe the structure and code of MAMO, and walk the reader through running the different components of the program with sample data. This manual should be used alongside a computer running R, so that the reader can copy and paste code into R, observe the output, and follow along interactively. Taken together, chapters 2–4 will allow the user to replicate a simulation study investigating the consequences of climate change and two potential management actions on the population dynamics of a vulnerable and iconic Hawaiian forest bird, the ‘I‘iwi (Drepanis coccinea; hereafter IIWI).
","language":"English","publisher":"University of Hawaii at Hilo","usgsCitation":"Guillaumet, A., and Paxton, E., 2017, Program MAMO: Models for avian management optimization-user guide: Technical Report TR-HCSU-077, Report: iii, 84 p.; Code.","productDescription":"Report: iii, 84 p.; Code","ipdsId":"IP-079991","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":346356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346037,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/3312"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d4a1a4e4b05fe04cc4e0e2","contributors":{"authors":[{"text":"Guillaumet, Alban","contributorId":150397,"corporation":false,"usgs":false,"family":"Guillaumet","given":"Alban","email":"","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":711083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":false,"id":711082,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191276,"text":"70191276 - 2017 - UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery","interactions":[],"lastModifiedDate":"2017-10-03T10:46:10","indexId":"70191276","displayToPublicDate":"2017-10-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery","docAbstract":"The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9101020","usgsCitation":"Sturdivant, E.J., Lentz, E.E., Thieler, E.R., Farris, A.S., Weber, K.M., Remsen, D.P., Miner, S., and Henderson, R.E., 2017, UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery: Remote Sensing, v. 9, no. 10, p. 1-20, https://doi.org/10.3390/rs9101020.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-090271","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469461,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9101020","text":"Publisher Index Page"},{"id":438194,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KW5F04","text":"USGS data release","linkHelpText":"Topographic, imagery, and raw data associated with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016"},{"id":346346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Buzzards Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.95451354980469,\n 41.51834634058004\n ],\n [\n -70.60089111328125,\n 41.51834634058004\n ],\n [\n -70.60089111328125,\n 41.77028745790557\n ],\n [\n -70.95451354980469,\n 41.77028745790557\n ],\n [\n -70.95451354980469,\n 41.51834634058004\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"10","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-03","publicationStatus":"PW","scienceBaseUri":"59d4a1a3e4b05fe04cc4e0da","contributors":{"authors":[{"text":"Sturdivant, Emily J. 0000-0002-2420-3115 esturdivant@usgs.gov","orcid":"https://orcid.org/0000-0002-2420-3115","contributorId":175325,"corporation":false,"usgs":true,"family":"Sturdivant","given":"Emily","email":"esturdivant@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711830,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farris, Amy S. 0000-0002-4668-7261 afarris@usgs.gov","orcid":"https://orcid.org/0000-0002-4668-7261","contributorId":196866,"corporation":false,"usgs":true,"family":"Farris","given":"Amy","email":"afarris@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711831,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Kathryn M. 0000-0002-5498-7117 kweber@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-7117","contributorId":196867,"corporation":false,"usgs":true,"family":"Weber","given":"Kathryn","email":"kweber@usgs.gov","middleInitial":"M.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711832,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Remsen, David P.","contributorId":196868,"corporation":false,"usgs":false,"family":"Remsen","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":711833,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miner, Simon","contributorId":196869,"corporation":false,"usgs":false,"family":"Miner","given":"Simon","email":"","affiliations":[],"preferred":false,"id":711834,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Rachel E. 0000-0001-5810-7941 rehenderson@contractor.usgs.gov","orcid":"https://orcid.org/0000-0001-5810-7941","contributorId":196870,"corporation":false,"usgs":true,"family":"Henderson","given":"Rachel","email":"rehenderson@contractor.usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":711835,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70189421,"text":"ds1057 - 2017 - Water-quality data from an earthen dam site in southern Westchester County, New York, 2015","interactions":[],"lastModifiedDate":"2017-10-12T10:00:51","indexId":"ds1057","displayToPublicDate":"2017-10-02T15:00:00","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":"1057","title":"Water-quality data from an earthen dam site in southern Westchester County, New York, 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the New York City Department of Environmental Protection, sampled 37 sites in the reservoir area for nutrients, major ions, metals, pesticides and their degradates, volatile organic compounds, temperature, pH, and specific conductance during fall 2015. Data collection was done to characterize the local groundwater-flow system and identify potential sources of seeps from the southern embankment at the Hillview Reservoir. Water-quality samples were collected in accordance with standard U.S. Geological Survey methods at 37 sites in and adjacent to Hillview Reservoir. These 37 sites were sampled to determine (1) baseline water-quality conditions of the saturated, low-permeability sediments that compose the earthen embankment that surrounds the reservoir, (2) water-quality conditions in the southwestern part of the study area in relation to the seeps on the embankment, and (3) temporal variation of water-quality conditions between 2006 and 2015 (not included in this report). The physical parameters and the results of the water-quality analysis from the 37 sites are included in this report and can be downloaded from the U.S. Geological Survey National Water Information System website.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1057","issn":"2327-0271","isbn":"978-1-4113-4179-1","collaboration":" Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Chu, Anthony, and Noll, M.L., 2017, Water-quality data from an earthen dam site in southern Westchester County, New York, 2015: U.S. Geological Survey Data Series 1057, 83 p., https://doi.org/10.3133/ds1057.","productDescription":"Report: vi, 83 p.; Table 2","numberOfPages":"94","onlineOnly":"N","ipdsId":"IP-084827","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":346167,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/ds1057/coverthb.jpg"},{"id":346486,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/ds1057/ds1057_table02.csv","text":"Table 2","size":"114 KB","linkFileType":{"id":7,"text":"csv"},"description":"DS 1057, Table 2","linkHelpText":"Concentrations of water-quality constituents"},{"id":346168,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/ds1057/ds1057.pdf","text":"Report","size":"2.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1057"}],"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 -73.87413024902342,\n 40.9055340480462\n ],\n [\n -73.86451721191406,\n 40.9055340480462\n ],\n [\n -73.86451721191406,\n 40.91818248731055\n ],\n [\n -73.87413024902342,\n 40.91818248731055\n ],\n [\n -73.87413024902342,\n 40.9055340480462\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
(518) 285–5602
or visit our website at
https://ny.water.usgs.gov
The influence of subduction zone earthquake cycle processes on permanent forearc deformation is poorly understood. In the Cascadia subduction zone forearc of Washington State, USA, deformed and incised fluvial terraces serve as archives of longer-term (103–104 yr) strain manifest as both fluvial incision and slip on upper-plate faults. We focus on comparing these geomorphic records in the Wynoochee River valley in the southern Olympic Mountains with short-term (101 yr) deformation driven by interseismic subduction zone coupling. We use optically stimulated luminescence dating and high-resolution elevation data to characterize strath terrace incision and differential uplift across the Canyon River fault, which cuts Wynoochee River terraces. This analysis demonstrates reverse slip rates of ∼0.1–0.3 mm/yr over the past ∼12–37 k.y., which agree with rates predicted by a GPS-constrained boundary element model of interseismic stress from Cascadia subduction zone coupling. Similarly, model-predicted patterns of interseismic uplift mimic the overall pattern of incision in the lower Wynoochee River valley, as revealed by strath elevations dated at 14.1 ± 1.2 ka. Agreement between modeled short-term and observed long-term records of forearc strain suggests that interseismic stress drives slip on upper-plate faults and fluvial incision in Cascadia. Consistency over multiple time scales may indicate relative stability in spatial patterns of subduction zone coupling over at least ∼104 yr intervals.
The Conservation Efforts Database (CED) is a secure, cloud-based tool that can be used to document and track conservation actions across landscapes. A recently released factsheet describes this tool ahead of the rollout of CED version 2.0. The CED was developed by the U.S. Fish and Wildlife Service, the USGS, and the Great Northern Landscape Conservation Cooperative to support the 2015 Endangered Species Act status review for greater sage-grouse. Currently, the CED accepts policy-level data, such as Land Use Plans, and treatment level data, such as conifer removals and post-fire recovery efforts, as custom spatial and non-spatial records. In addition to a species assessment tool, the CED can also be used to summarize the extent of restoration efforts within a specific area or to strategically site conservation actions based on the location of other implemented actions. The CED can be an important tool, along with post-conservation monitoring, for implementing landscape-scale adaptive management.
","language":"English","publisher":"Great Northern Landscape Conservation Cooperative ","usgsCitation":"Heller, M.M., Welty, J.L., and Wiechman, L.A., 2017, The Conservation Efforts Database: Improving our knowledge of landscape conservation actions, 2 p.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-087922","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":349328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349327,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://conservationefforts.org"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb39e4b06e28e9c22e08","contributors":{"authors":[{"text":"Heller, Matthew M.","contributorId":192963,"corporation":false,"usgs":false,"family":"Heller","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":698331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welty, Justin L. 0000-0001-7829-7324 jwelty@usgs.gov","orcid":"https://orcid.org/0000-0001-7829-7324","contributorId":4206,"corporation":false,"usgs":true,"family":"Welty","given":"Justin","email":"jwelty@usgs.gov","middleInitial":"L.","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":698330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiechman, Lief A. 0000-0002-3804-4426","orcid":"https://orcid.org/0000-0002-3804-4426","contributorId":184047,"corporation":false,"usgs":true,"family":"Wiechman","given":"Lief","email":"","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":698332,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191271,"text":"70191271 - 2017 - Detection and characterization of pulses in broadband seismometers","interactions":[],"lastModifiedDate":"2017-10-02T18:00:51","indexId":"70191271","displayToPublicDate":"2017-10-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Detection and characterization of pulses in broadband seismometers","docAbstract":"Pulsing - caused either by mechanical or electrical glitches, or by microtilt local to a seismometer - can significantly compromise the long‐period noise performance of broadband seismometers. High‐fidelity long‐period recordings are needed for accurate calculation of quantities such as moment tensors, fault‐slip models, and normal‐mode measurements. Such pulses have long been recognized in accelerometers, and methods have been developed to correct these acceleration steps, but considerable work remains to be done in order to detect and correct similar pulses in broadband seismic data. We present a method for detecting and characterizing the pulses using data from a range of broadband sensor types installed in the Global Seismographic Network. The technique relies on accurate instrument response removal and employs a moving‐window approach looking for acceleration baseline shifts. We find that pulses are present at varying levels in all sensor types studied. Pulse‐detection results compared with average daily station noise values are consistent with predicted noise levels of acceleration steps. This indicates that we can calculate maximum pulse amplitude allowed per time window that would be acceptable without compromising long‐period data analysis.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120170089","usgsCitation":"Wilson, D.C., Ringler, A.T., and Hutt, C.R., 2017, Detection and characterization of pulses in broadband seismometers: Bulletin of the Seismological Society of America, v. 107, no. 4, p. 1173-1180, https://doi.org/10.1785/0120170089.","productDescription":"8 p.","startPage":"1173","endPage":"1180","ipdsId":"IP-085630","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":346341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-04","publicationStatus":"PW","scienceBaseUri":"59d35024e4b05fe04cc34d42","contributors":{"authors":[{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutt, Charles R. 0000-0001-9033-9195 bhutt@usgs.gov","orcid":"https://orcid.org/0000-0001-9033-9195","contributorId":1622,"corporation":false,"usgs":true,"family":"Hutt","given":"Charles","email":"bhutt@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711806,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191268,"text":"70191268 - 2017 - Model parameters for representative wetland plant functional groups","interactions":[],"lastModifiedDate":"2017-10-08T12:16:12","indexId":"70191268","displayToPublicDate":"2017-10-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Model parameters for representative wetland plant functional groups","docAbstract":"Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in adjacent areas as they affect wetlands.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1958","usgsCitation":"Williams, A.S., Kiniry, J.R., Mushet, D.M., Smith, L., McMurry, S.T., Attebury, K., Lang, M., McCarty, G.W., Shaffer, J.A., Effland, W.R., and Johnson, M., 2017, Model parameters for representative wetland plant functional groups: Ecosphere, v. 8, no. 10, p. 1-14, https://doi.org/10.1002/ecs2.1958.","productDescription":"Article e01958; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-075940","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1958","text":"Publisher Index Page"},{"id":346339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-02","publicationStatus":"PW","scienceBaseUri":"59d35025e4b05fe04cc34d45","contributors":{"authors":[{"text":"Williams, Amber S.","contributorId":196855,"corporation":false,"usgs":false,"family":"Williams","given":"Amber","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":711793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiniry, James R.","contributorId":66918,"corporation":false,"usgs":true,"family":"Kiniry","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":711795,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Loren M.","contributorId":88876,"corporation":false,"usgs":true,"family":"Smith","given":"Loren M.","affiliations":[],"preferred":false,"id":711796,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMurry, Scott T.","contributorId":191876,"corporation":false,"usgs":false,"family":"McMurry","given":"Scott","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":711797,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Attebury, Kelly","contributorId":196857,"corporation":false,"usgs":false,"family":"Attebury","given":"Kelly","email":"","affiliations":[],"preferred":false,"id":711798,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lang, Megan","contributorId":156431,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":711799,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":711800,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":711801,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Effland, William R.","contributorId":196858,"corporation":false,"usgs":false,"family":"Effland","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711802,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Johnson, Mari-Vaughn V.","contributorId":196859,"corporation":false,"usgs":false,"family":"Johnson","given":"Mari-Vaughn V.","affiliations":[],"preferred":false,"id":711803,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ]}