{"pageNumber":"254","pageRowStart":"6325","pageSize":"25","recordCount":46679,"records":[{"id":70228433,"text":"70228433 - 2020 - Estimating population persistence for at-risk species using citizen science data","interactions":[],"lastModifiedDate":"2022-02-10T13:24:33.608697","indexId":"70228433","displayToPublicDate":"2020-03-03T07:22:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population persistence for at-risk species using citizen science data","docAbstract":"

Population persistence probability is valuable for characterizing risk to species and informing listing and conservation decisions but is challenging to estimate through traditional methods for rare, data-limited species. Modeling approaches have used citizen science data to mitigate data limitations of focal species and better estimate parameters such as occupancy and detection, but their use to estimate persistence and inform conservation decisions is limited. We developed an approach to estimate persistence using only occurrence records of the target species and citizen science occurrence data of non-target species to account for search effort and imperfect detection. We applied the approach to a highly cryptic and data-limited species, the southern hognose snake (Heterodon simus), as part of its USFWS Species Status Assessment, and estimated current (in 2018) and future persistence under plausible scenarios of varying levels of urbanization, sea level rise, and management. Of 222 known populations, 133 (60%) are likely extirpated currently (persistence probability < 50%), and 165 (74%) populations are likely to be extirpated by 2080 with no additional management. Future management scenarios that included strategies to acquire and improve habitat on currently unprotected lands with existing populations lessened the estimated rate of population declines. These results can directly inform listing decisions and conservation planning for the southern hognose snake by Federal, State, and other partners. Our approach – using occurrence records and auxiliary data from non-target species to estimate population persistence – is applicable across rare and at-risk species for evaluating extinction risk with limited data and prioritizing management actions.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108489","usgsCitation":"Crawford, B., Olds, M., Maerz, J., and Moore, C.T., 2020, Estimating population persistence for at-risk species using citizen science data: Biological Conservation, v. 243, 108489, 13 p., https://doi.org/10.1016/j.biocon.2020.108489.","productDescription":"108489, 13 p.","ipdsId":"IP-111355","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457518,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2020.108489","text":"Publisher Index Page"},{"id":395763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.34374999999999,\n 39.027718840211605\n ],\n [\n -79.98046875,\n 37.43997405227057\n ],\n [\n -83.84765625,\n 33.797408767572485\n ],\n [\n -87.5390625,\n 32.91648534731439\n ],\n [\n -90,\n 31.42866311735861\n ],\n [\n -89.82421875,\n 30.06909396443887\n ],\n [\n -87.36328125,\n 30.221101852485987\n ],\n [\n -84.375,\n 29.458731185355344\n ],\n [\n -82.705078125,\n 26.745610382199022\n ],\n [\n -80.771484375,\n 24.926294766395593\n ],\n [\n -79.27734374999999,\n 25.562265014427492\n ],\n [\n -79.89257812499999,\n 28.536274512989916\n ],\n [\n -80.5078125,\n 30.826780904779774\n ],\n [\n -78.75,\n 32.32427558887655\n ],\n [\n -75.322265625,\n 35.17380831799959\n ],\n [\n -75.41015624999999,\n 36.66841891894786\n ],\n [\n -75.673828125,\n 37.85750715625203\n ],\n [\n -76.46484375,\n 38.95940879245423\n ],\n [\n -77.34374999999999,\n 39.027718840211605\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"243","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crawford, B.A.","contributorId":275273,"corporation":false,"usgs":false,"family":"Crawford","given":"B.A.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olds, M.","contributorId":275789,"corporation":false,"usgs":false,"family":"Olds","given":"M.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maerz, J.C.","contributorId":275274,"corporation":false,"usgs":false,"family":"Maerz","given":"J.C.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208879,"text":"70208879 - 2020 - Gas hydrate petroleum systems: What constitutes the “seal”?","interactions":[],"lastModifiedDate":"2020-06-04T16:58:08.036025","indexId":"70208879","displayToPublicDate":"2020-03-02T15:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Gas hydrate petroleum systems: What constitutes the “seal”?","docAbstract":"The gas hydrate petroleum system (GHPS) approach, which has been used to characterize gas hydrates in nature, utilizes three distinct components: a methane source, a methane migration pathway, and a reservoir that not only contains gas hydrate, but also acts as a seal to prevent methane loss. Unlike GHPS, a traditional petroleum system (PS) approach further distinguishes between the reservoir, a unit with generally coarser sediment grains, and a separate overlying seal unit with generally finer sediment grains. Adopting this traditional PS distinction in the GHPS approach facilitates assessments of reservoir growth and production potential. The significance of the seal for the formation of a gas hydrate reservoir as well as for the efficiency in methane extraction from the reservoir as an energy resource is evident in the findings from recent offshore field expeditions, such as India’s second National Gas Hydrate Program expedition (NGHP-02). In regards to gas hydrate-bearing reservoir formation, the NGHP-02 gas chemistry data indicate a primarily microbial methane source. Fine-grained seal sediment in contact with coarser-grained reservoir sediment can facilitate that microbial methane production. Logging-while-drilling and sediment core data also indicate that the overlying fine-grained seal sediment is less permeable than the underlying, highly gas hydrate-saturated reservoir sediment. The overlying seal’s capacity to act as a low-permeability boundary is important not only for preventing methane migration out of the reservoir over time, but for also preventing water invasion into the reservoir during methane extraction from the reservoir. Ultimately, the presence of an overlying, fine-grained, low-permeability “Seal”? influences how gas hydrate initially forms in a coarse-grained reservoir and dictates how efficiently methane can be extracted as an energy resource from the gas hydrate reservoir via depressurization.","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/int-2019-0026.1","usgsCitation":"Jang, J., Waite, W., and Stern, L.A., 2020, Gas hydrate petroleum systems: What constitutes the “seal”?: Interpretation, v. 8, no. 2, p. T231-T248, https://doi.org/10.1190/int-2019-0026.1.","productDescription":"18 p.","startPage":"T231","endPage":"T248","ipdsId":"IP-104479","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Bay of Bengal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 84.44091796875,\n 18.47960905583197\n ],\n [\n 82.41943359375,\n 17.11979250078707\n ],\n [\n 82.44140625,\n 16.720385051694\n ],\n [\n 82.1337890625,\n 16.172472808397515\n ],\n [\n 81.40869140625,\n 16.25686733062344\n ],\n [\n 81.10107421874999,\n 15.665354182093287\n ],\n [\n 82.90283203125,\n 14.817370620155254\n ],\n [\n 86.396484375,\n 17.434510551522894\n ],\n [\n 84.44091796875,\n 18.47960905583197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jang, Junbong 0000-0001-5500-7558 jjang@usgs.gov","orcid":"https://orcid.org/0000-0001-5500-7558","contributorId":189400,"corporation":false,"usgs":true,"family":"Jang","given":"Junbong","email":"jjang@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":783812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209070,"text":"70209070 - 2020 - Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","interactions":[],"lastModifiedDate":"2020-03-16T17:06:02","indexId":"70209070","displayToPublicDate":"2020-03-02T14:40:52","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","docAbstract":"

We sampled juvenile wild Steelhead Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize populations and investigate life-history metrics, particularly migratory patterns. We used Passive Integrated Transponder (PIT)-tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead have been planted in the Wind River subbasin since 1997, and hatchery adults are estimated to be less than one percent of spawners in most years (pers comm. Thomas Buehrens, Washington Department of Fish and Wildlife). Our repeated headwater sampling of consistent sites in the Wind River subbasin has also allowed us to track relative abundance of Brook Trout, a non-native species to the Wind River. Our work is contributing to understanding of Steelhead population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, where our PTISs are helping to quantify adult response.

Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research Monitoring and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (www.cbfish.org/ProgramStrategy.mvc/ViewProgramStrategySummary/1). Specifically this work addresses the sub-strategies of: 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and to Uncertainties Research regarding differing life histories of a wild Steelhead population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. Our headwaters parr PIT tagging, WDFW parr, smolt, and adult tagging and our instream PTISs are providing data on movements and life histories of parr, smolt, and adult Steelhead.

During summer 2018, we PIT-tagged 1,592 age-0 and age-1 Steelhead parr in headwater areas of the Wind River subbasin to characterize population traits and investigate life-history diversity, including growth and pre-smolt downstream movement. Repeat headwater sampling and smolt trap operations provide opportunities for recapture, and instream PTISs and Columbia River infrastructure provide opportunity for detection of PIT-tagged fish. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead parr, smolts, and adults.

Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. Substantial numbers of parr make downstream movements as age-1 fish. We have estimated that from 15 to 33 percent of parr tagged as age-0 fish make downstream migrations at age-1 for additional rearing. We have estimated that from 1 to 27 percent of parr tagged as age-1 fish make downstream migrations during fall. These findings raise many questions about parr rearing strategies, habitat use, and success of these migrants and suggest a need for broader monitoring of juvenile Steelhead in some river systems to fully document juvenile production. Long-term monitoring of PIT-tagged fish is providing information on contribution of various life-history strategies to smolt production and adult returns.

Movements of PIT-tagged adult Steelhead were recorded at instream PTISs. These data have allowed assessment of adult returns to tributary watersheds within the Wind River subbasin. Detection efficiency of adult PIT-tagged Steelhead at our primary adult-monitoring PTIS in Trout Creek has been greater than 92 percent during 6 of the past 7 years. This is providing excellent data to estimate adult returns to this watershed. Determination of adult use of tributary watersheds is providing data to help evaluate the efficacy of the removal of Hemlock Dam on Trout Creek. Hemlock Dam, located at rkm 2.0 of Trout Creek, was removed in summer 2009. The dam contributed to hydrologic impairment of Trout Creek and had potential negative effects on Steelhead. The improvements made to the upper Wind River PTIS (site code WRU at rkm 28.3; better site characteristics and grid power) during 2016 and 2017, and a planned new site in the Mine Reach of the upper Wind River, will allow estimates of subbasin adult escapement like those in Trout Creek.

During 2018, we also completed planning and permitting with U.S. Forest Service for a new PTIS site at rkm 36 of the Wind River (the Mine Reach, mentioned above). This site will replace two sites (one in Paradise Creek and one at rkm 41 of the Wind River), which had operational challenges due to lack of adequate solar power and winter difficulties. The new Mine Reach PTIS site at rkm 36, will have better solar exposure, fewer winter operations difficulties, and provide opportunity to detect fish from juvenile sampling sites that were downstream of the previous two PTISs. The more consistent operation of the new Mine Reach PTIS site will increase our ability to estimate migrant abundance as all the juveniles tagged upstream of it will be subject to the same potential detection history, instead of three different potential detection histories as before. Additionally, with the new Mine Reach PTIS site lower in the watershed, it will subject more PIT-tagged adult Steelhead to detection and provide ability to generate a nonbiased adult-detection efficiency estimate for the WRU PTIS at rkm 28.3 of the Wind River. This will provide the opportunity to estimate yearly adult Steelhead abundance to the upper Wind watershed area. Permitting is complete and some supplies have been purchased to build and install this new site in 2019.

Repeat sampling at consistent locations in the subbasin has allowed investigation into juvenile Steelhead growth patterns. Growth rates (relative change in weight) of age-0 PIT-tagged parr during summer are similar across the subbasin, but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly growth for parr tagged at age-0 is similar across the subbasin. Yearly growth for parr tagged at age-1 is lowest in Martha Creek, but similar elsewhere.

Non-native Brook Trout are present in portions of the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Percentage of catch that is Brook Trout at each of four sample sites in Trout Creek have declined from the period 1998 – 2003 to the period 2011 – 2018. There was a pattern of decline in percent of catch and number of Brook Trout at the Trout Creek sites from 2011 through 2016, though a slight upward trend during 2017 and 2018 has been evident. 

Evaluating and planning restoration efforts are of interest to many managers and agencies to ensure efficient use of resources. The evaluation of various life-histories of Steelhead within the Wind River subbasin will provide information to better track populations, and to direct habitat restoration and water allocation planning. Movement of Steelhead parr raises many questions regarding estimating juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead parr are increasingly allowing us to investigate such questions. Increasingly detailed Viable Salmonid Population information, such as that provided by PIT-tagging and instream PTISs networks like those in the Wind River subbasin, provide data to inform policy and management, as life-history strategies and production bottlenecks are identified and understood.

","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I.G., 2020, Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018, 74 p.","productDescription":"74 p.","ipdsId":"IP-115314","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373226,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P170098"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.34374999999999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 45.69083283645816\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205095,"text":"sir20195080 - 2020 - Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","interactions":[],"lastModifiedDate":"2022-04-22T21:26:12.93031","indexId":"sir20195080","displayToPublicDate":"2020-03-02T10:35:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5080","displayTitle":"Assessment of Bridge Scour Countermeasures at Selected Bridges in the United States, 2014–18","title":"Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","docAbstract":"

Erosion of the streambed, known also as scour, around pier 3 of the New York State Thruway bridge over Schoharie Creek caused the pier to fail, which ultimately resulted in bridge failure during the flooding event of April 5, 1987. The Federal Highway Administration (FHWA) responded to the need for better guidance on the evaluation of bridge scour and the selection and installation of scour countermeasures with the release of several Hydraulic Engineering Circulars. Although this information has been available, used, and updated over the years, an evaluation of the current conditions of scour countermeasures has not been performed. Therefore, the U.S. Geological Survey, in cooperation with the FHWA, began a study in 2013 to assess the current conditions of bridge scour countermeasures at selected sites around the country. The bridge scour countermeasure site assessments included reviewing countermeasure design plans, field inspections, traditional surveys, motion-compensated terrestrial light detection and ranging technology (lidar), high-resolution multi-beam bathymetry scanning, underwater video imaging, and a review of the peak and daily streamflow history for the associated river or stream. A total of 34 bridge scour countermeasure sites were selected in 11 states for this study. The types of countermeasures installed at the bridge scour study sites ranged from riprap, the most common countermeasure in the study, to A-Jacks and cabled-concrete mattresses.

The installed countermeasures were generally exposed to hydraulic forces from floods that equaled or exceeded the 1-percent, and even the 0.2-percent, annual exceedance probability at some of the study sites, but not all. The field inspections and countermeasure evaluations identified areas of shifting, slumping, and some scour holes and damage or washouts to the countermeasures, but generally most remained in place. The high-resolution laser scanner data, photo imaging and traditional survey data, and field notes were provided to the FHWA for expert evaluation of the bridge scour countermeasure performance.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195080","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Suro, T.P., Huizinga, R.J., Fosness, R.L., and Dudunake, T.J., 2020, Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18: U.S. Geological Survey Scientific Investigations Report 2019–5080, 29 p., https://doi.org/10.3133/sir20195080.","productDescription":"Report: ix, 29 p.; 2 Data Releases","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108279","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science 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\"}}]}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ 08648

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-03-02","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Suro, Thomas P. 0000-0002-9476-6829 tsuro@usgs.gov","orcid":"https://orcid.org/0000-0002-9476-6829","contributorId":2841,"corporation":false,"usgs":true,"family":"Suro","given":"Thomas","email":"tsuro@usgs.gov","middleInitial":"P.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huizinga, Richard J. 0000-0002-2940-2324 huizinga@usgs.gov","orcid":"https://orcid.org/0000-0002-2940-2324","contributorId":2089,"corporation":false,"usgs":true,"family":"Huizinga","given":"Richard","email":"huizinga@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fosness, Ryan L. 0000-0003-4089-2704 rfosness@usgs.gov","orcid":"https://orcid.org/0000-0003-4089-2704","contributorId":2703,"corporation":false,"usgs":true,"family":"Fosness","given":"Ryan","email":"rfosness@usgs.gov","middleInitial":"L.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dudunake, Taylor 0000-0001-7650-2419 tdudunake@usgs.gov","orcid":"https://orcid.org/0000-0001-7650-2419","contributorId":191564,"corporation":false,"usgs":true,"family":"Dudunake","given":"Taylor","email":"tdudunake@usgs.gov","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770004,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208373,"text":"ofr20191139 - 2020 - Development of a modeling framework for predicting decadal barrier island evolution","interactions":[],"lastModifiedDate":"2022-04-21T19:50:07.768456","indexId":"ofr20191139","displayToPublicDate":"2020-03-02T08:30:00","publicationYear":"2020","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":"2019-1139","displayTitle":"Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution","title":"Development of a modeling framework for predicting decadal barrier island evolution","docAbstract":"

Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191139","usgsCitation":"Mickey, R.C., Long, J.W., Dalyander, P.S., Jenkins, R.L., III, Thompson, D.M., Passeri, D.L., and Plant, N.G., 2019, Development of a modeling framework for predicting decadal barrier island evolution: U.S. Geological Survey Open-File Report 2019–1139, 46 p., https://doi.org/10.3133/ofr20191139.","productDescription":"Report: vi, 46 p.; Data Release","ipdsId":"IP-111247","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399428,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109735.htm"},{"id":372441,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ALL6C","text":"USGS data release","linkHelpText":"Dauphin Island decadal hindcast model inputs and results"},{"id":372678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2019/1139/ofr20191139.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1139"},{"id":372308,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201001","text":"Open-File Report 2020-1001","linkHelpText":"- Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama"},{"id":372306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2019/1139/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37677001953125,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.18905718468536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-03-02","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":781647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":222095,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy ","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":781648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781651,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781652,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227806,"text":"70227806 - 2020 - Use of multiple temperature logger models can alter conclusions","interactions":[],"lastModifiedDate":"2022-02-01T20:38:40.959549","indexId":"70227806","displayToPublicDate":"2020-03-01T15:37:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Use of multiple temperature logger models can alter conclusions","docAbstract":"

Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.

","language":"English","publisher":"MDPI","doi":"10.3390/w12030668","usgsCitation":"Whittier, J.B., Westhoff, J.T., Paukert, C.P., and Rotman, R.M., 2020, Use of multiple temperature logger models can alter conclusions: Water, v. 12, no. 3, 9 p., https://doi.org/10.3390/w12030668.","productDescription":"9 p.","ipdsId":"IP-092924","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12030668","text":"Publisher Index Page"},{"id":395243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westhoff, Jacob T.","contributorId":58106,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":879,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rotman, Robin M.","contributorId":272858,"corporation":false,"usgs":false,"family":"Rotman","given":"Robin","email":"","middleInitial":"M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":832347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208889,"text":"70208889 - 2020 - Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","interactions":[],"lastModifiedDate":"2020-03-04T15:12:43","indexId":"70208889","displayToPublicDate":"2020-03-01T15:07:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","docAbstract":"Hydrology and water quality (suspended sediment, total nitrogen, ammonia, total Kjeldahl nitrogen, nitrate plus nitrite, and total phosphorus (TP)) were monitored in two small agricultural drainages in northwestern Mississippi to document changes in water quality that coincided with the implementation of BMPs in upstream drainages. Using an event-based dataset and bootstrapping techniques, we tested for difference and equivalence in median event concentration and differences in concentration-discharge (C-Q) relationships between an early and late period at each site, where most of the major BMP implementation occurred during the early period. Results for one site were inconclusive. None of the constituents had statistically different or equivalent event concentrations between the periods, indicating a lack of evidence to tell whether water quality had changed or stayed the same, and only TP had a significantly higher C-Q slope during the late period. At the other site, more than half the constituents had a significantly different median, slope, or intercept between periods, indicating a 35% or more decrease in event concentration following a period of intense BMP implementation. These mixed results could be due to variety of differences between the sites including BMP implementation, production practices, and crops. We also used the monitoring data to generate synthetic data and perform a simulation-based power analysis to explore the ability to detect change under 25 scenarios of sampled event counts and hypothetical percent changes. The simulation-based power analysis indicated that high natural variability in event concentration and flow hindered our ability to detect change. Based on our monitoring, data analysis, and power analysis, we provide recommendations for future monitoring.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.2.218","usgsCitation":"Murphy, J.C., Hicks, M.B., and Stocks, S.J., 2020, Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses: Journal of Soil and Water Conservation, v. 75, no. 2, p. 218-230, https://doi.org/10.2489/jswc.75.2.218.","productDescription":"13 p.","startPage":"218","endPage":"230","ipdsId":"IP-091590","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.2.218","text":"Publisher Index Page"},{"id":437076,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FJJ","text":"USGS data release","linkHelpText":"Hydrologic event-based water-quality and streamflow data for three oxbow tributaries in northwestern Mississippi, 2007-2016"},{"id":372917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Bee Lake, Lake Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.889892578125,\n 32.35212281198644\n ],\n [\n -90.186767578125,\n 33.15594830078649\n ],\n [\n -90.098876953125,\n 33.93424531117312\n ],\n [\n -90.208740234375,\n 34.96699890670367\n ],\n [\n -90.54931640625,\n 34.67839374011646\n ],\n [\n -90.802001953125,\n 34.27083595165\n ],\n [\n -91.0546875,\n 33.925129700072\n ],\n [\n -91.1865234375,\n 33.63291573870479\n ],\n [\n -91.153564453125,\n 33.27543541298162\n ],\n [\n -91.131591796875,\n 32.80574473290688\n ],\n [\n -91.043701171875,\n 32.44488496716713\n ],\n [\n -90.889892578125,\n 32.35212281198644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":783845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stocks, Shane J. 0000-0003-1711-3071 sjstocks@usgs.gov","orcid":"https://orcid.org/0000-0003-1711-3071","contributorId":3811,"corporation":false,"usgs":true,"family":"Stocks","given":"Shane","email":"sjstocks@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208171,"text":"70208171 - 2020 - Preserving meander bend geometry through scale","interactions":[],"lastModifiedDate":"2020-08-28T12:39:57.506553","indexId":"70208171","displayToPublicDate":"2020-03-01T13:01:09","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preserving meander bend geometry through scale","docAbstract":"

Stream meander geometry is a function of hydrologic, geologic, and anthropogenic forces. Meander morphometrics are used in geomorphic classification, ecological characterization, and tectonic and hydrologic change detection. Thus, detailed measurement and classification of meander geometry is imperative to multiscale representation of hydrographic features, which raises important questions. What meander geometries are important to preserve in multi-scale databases? How are geometries measured? How are they preserved? Is the choice between preservation of geometry or use of classification attributes? Questions related to multiscale measurement and representation of hydrographic features continue to emerge with increased spatial and temporal data collection.

A key metric for understanding meander bend geometry is sinuosity. The most common measure of sinuosity is the length of a feature divided by the distance between stream head and mouth. The measure relays deviation from a straight line but nothing about meander wavelength. There is not a clear consensus on methods for measuring meander geometry, much less efficiently, at scales made viable with increased data resolution. Here we propose a method for automated characterization of meander wavelength or bend radius. The method, termed Scale-Specific Sinuosity (S3), is a derivation from the Richardson plot. The Richardson (1961) plot is a classic means of calculating fractal dimension of natural line features and describes feature length (ℓ) given increasing vertex spacing, or step size (S), plotted on a log-log plot. The S3 metric is defined as negative one times the slope of a Richardson plot for a given stride length. This paper demonstrates utility of S3 for estimating changes in sinuosity with scale change.

","conferenceTitle":"Second Annual SPARC Workshop, Scale and Spatial Analytics","conferenceDate":"February 10-11, 2020","conferenceLocation":"Tempe, AZ","language":"English","publisher":"Arizona State University","usgsCitation":"Shavers, E.J., Stanislawski, L., Buttenfield, B.P., and Kronenfeld, B.J., 2020, Preserving meander bend geometry through scale, Second Annual SPARC Workshop, Scale and Spatial Analytics, Tempe, AZ, February 10-11, 2020, 3 p.","productDescription":"3 p.","ipdsId":"IP-113570","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":377952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377950,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sgsup.asu.edu/sparc/ScaleWorkshop"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":780797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":780798,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227680,"text":"70227680 - 2020 - Testing prediction accuracy in short-term ecological studies","interactions":[],"lastModifiedDate":"2022-01-26T17:27:52.033911","indexId":"70227680","displayToPublicDate":"2020-03-01T11:13:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":970,"text":"Basic and Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Testing prediction accuracy in short-term ecological studies","docAbstract":"

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.baae.2020.01.003","usgsCitation":"Wood, C.M., Loman, Z., McKinney, S.T., and Loftin, C., 2020, Testing prediction accuracy in short-term ecological studies: Basic and Applied Ecology, v. 43, p. 77-85, https://doi.org/10.1016/j.baae.2020.01.003.","productDescription":"9 p.","startPage":"77","endPage":"85","ipdsId":"IP-073394","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.baae.2020.01.003","text":"Publisher Index Page"},{"id":394885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New Hampshire","otherGeospatial":"Appalachian Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.08129882812499,\n 44.190082025040525\n ],\n [\n -72.00439453125,\n 43.739352079154706\n ],\n [\n -71.52099609375,\n 43.58834891179792\n ],\n [\n -69.66430664062499,\n 45.127804527473224\n ],\n [\n -70.125732421875,\n 45.598665689820635\n ],\n [\n -70.86181640625,\n 45.22848059584359\n ],\n [\n -71.817626953125,\n 44.72332018895825\n ],\n [\n -72.08129882812499,\n 44.190082025040525\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Connor M.","contributorId":167785,"corporation":false,"usgs":false,"family":"Wood","given":"Connor","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":831705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":831788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinney, Shawn T. smckinney@usgs.gov","contributorId":5175,"corporation":false,"usgs":true,"family":"McKinney","given":"Shawn","email":"smckinney@usgs.gov","middleInitial":"T.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":831707,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204123,"text":"70204123 - 2020 - Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","interactions":[],"lastModifiedDate":"2024-05-17T15:49:38.223294","indexId":"70204123","displayToPublicDate":"2020-03-01T11:07:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","docAbstract":"The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as \"duplicate interpretations\"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111261","usgsCitation":"Pengra, B., Stehman, S.V., Horton, J., Dockter, D., Schroeder, T.A., Yang, Z., Cohen, W.B., Healey, S.P., and Loveland, T., 2020, Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program: Remote Sensing of Environment, v. 238, 111261, 10 p., https://doi.org/10.1016/j.rse.2019.111261.","productDescription":"111261, 10 p.","ipdsId":"IP-101422","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457550,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111261","text":"Publisher Index Page"},{"id":437077,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QA5Q25","text":"USGS data release","linkHelpText":"LCMAP CONUS Intensification Reference Data Product 1984&ndash;2019 land cover, land use and change process attributes"},{"id":414788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@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":765622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":765623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":216813,"corporation":false,"usgs":true,"family":"Horton","given":"Josephine","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":765626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":765627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cohen, Warren B 0000-0003-3144-9532","orcid":"https://orcid.org/0000-0003-3144-9532","contributorId":216815,"corporation":false,"usgs":false,"family":"Cohen","given":"Warren","email":"","middleInitial":"B","affiliations":[{"id":39525,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331","active":true,"usgs":false}],"preferred":false,"id":765628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Healey, Sean P.","contributorId":216816,"corporation":false,"usgs":false,"family":"Healey","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":39526,"text":"USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 84401","active":true,"usgs":false}],"preferred":false,"id":765629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":202518,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":765630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249358,"text":"70249358 - 2020 - Transitioning from change detection to monitoring with remote sensing: A paradigm shift","interactions":[],"lastModifiedDate":"2023-10-04T23:41:21.337275","indexId":"70249358","displayToPublicDate":"2020-03-01T09:55:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Transitioning from change detection to monitoring with remote sensing: A paradigm shift","docAbstract":"The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111558","usgsCitation":"Woodcock, C.E., Loveland, T., Herold, M., and Bauer, M.E., 2020, Transitioning from change detection to monitoring with remote sensing: A paradigm shift: Remote Sensing of Environment, v. 238, 111558, 5 p., https://doi.org/10.1016/j.rse.2019.111558.","productDescription":"111558, 5 p.","ipdsId":"IP-113612","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457553,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111558","text":"Publisher Index Page"},{"id":421598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodcock, Curtis E.","contributorId":294423,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":885300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herold, Martin","contributorId":330558,"corporation":false,"usgs":false,"family":"Herold","given":"Martin","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":885302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Marvin E.","contributorId":330559,"corporation":false,"usgs":false,"family":"Bauer","given":"Marvin","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":885303,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215305,"text":"70215305 - 2020 - Planetary sensor models interoperability using the community sensor model specification","interactions":[],"lastModifiedDate":"2020-10-15T14:38:30.665861","indexId":"70215305","displayToPublicDate":"2020-03-01T09:35:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Planetary sensor models interoperability using the community sensor model specification","docAbstract":"

This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation (https://github.com/USGS-Astrogeology/usgscsm). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.

","language":"English","publisher":"Wiley","doi":"10.1029/2019EA000713","usgsCitation":"Laura, J., Mapel, J., and Hare, T.M., 2020, Planetary sensor models interoperability using the community sensor model specification: Earth and Space Science, v. 7, no. 6, e2019EA000713, 17 p., https://doi.org/10.1029/2019EA000713.","productDescription":"e2019EA000713, 17 p.","ipdsId":"IP-108414","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":457556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019ea000713","text":"Publisher Index Page"},{"id":379404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mapel, Jesse 0000-0001-5756-0373","orcid":"https://orcid.org/0000-0001-5756-0373","contributorId":206344,"corporation":false,"usgs":true,"family":"Mapel","given":"Jesse","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227142,"text":"70227142 - 2020 - Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","interactions":[],"lastModifiedDate":"2022-01-03T16:02:02.227914","indexId":"70227142","displayToPublicDate":"2020-03-01T08:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","docAbstract":"

Eurasian zebra and quagga mussels were likely introduced to the Laurentian Great Lakes via ballast water release in the 1980s, and their range has since expanded across the US, including some of their southernmost occurrences in Texas. Their spread into the state has resulted in a need to revise previous delimitations of suitable dreissenid habitat. We therefore assessed invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent species distribution models based upon global occurrence and climate data; and 2) refining lake-specific predictions via collection and analysis of physicochemical data. Maxent models predicted a lack of suitable habitat for quagga mussels within Texas. However, models did predict the presence of suitable zebra mussel habitat, with hotspots of suitable habitat occurring along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Although predicted suitable habitat extended further west than in previous models, most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor zebra mussel habitat suitability. Collection of physicochemical data (i.e., dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel-invaded lakes and a subset of uninvaded but high-risk lakes of North and Central Texas, did not refine model predictions because there was no apparent distinction between invaded and uninvaded lakes. Overall, we demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, abundant suitable habitat for continuing zebra mussel invasion exists within the state. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.

","language":"English","publisher":"REABIC","usgsCitation":"Barnes, M., and Patino, R., 2020, Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics: Management of Biological Invasions, v. 11, no. 1, p. 63-79.","productDescription":"17 p.","startPage":"63","endPage":"79","ipdsId":"IP-107295","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":393733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393746,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/mbi/2020/Issue1.aspx"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.30541992187499,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 29.334298230315675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, M. A.","contributorId":270689,"corporation":false,"usgs":false,"family":"Barnes","given":"M. A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":829770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211874,"text":"70211874 - 2020 - Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis","interactions":[],"lastModifiedDate":"2020-08-12T15:03:49.11224","indexId":"70211874","displayToPublicDate":"2020-02-29T10:53:30","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis","docAbstract":"We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Faulds, J., Brown, S.C., Coolbaugh, M.F., Queen, J.H., Treitel, S., Fehler, M., Mlawsky, E., Glen, J.M., Lindsey, C., Burns, E., Smith, C.M., Gu, C., and Ayling, B.F., 2020, Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis, in Proceedings: 45th workshop on geothermal reservoir engineering, 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Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","interactions":[],"lastModifiedDate":"2020-08-12T15:04:28.21349","indexId":"70211876","displayToPublicDate":"2020-02-29T10:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","docAbstract":"The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. The success of play fairway analysis in geothermal exploration depends critically on defining a systematic methodology that is grounded in theory and adapted to the geologic and hydrologic framework of real geothermal systems. \nThis study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the Snake River Plain: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for Heat were heat flow, the distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Permeability was derived from stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetic and gravity data. Data for Seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment (CRS) maps for Heat, Permeability and Seal, which were combined to create a Composite Common Risk Segment (CCRS) map for all of southern Idaho that reflects the risk associated with geothermal resource exploration and helps to identify favorable resource tracks. \nOur data suggests that important undiscovered geothermal resources may be located in several areas of the SRP, including the western SRP (associated with buried lineaments capped by lacustrine sediment), at lineament intersections in the central SRP, and along the margins of the eastern SRP. These blind resources are associated with temperatures sufficient to support electricity production, and may be exploitable with existing deep drilling technology. We are testing our methodology by drilling a geothermal test well in Camas Prairie, ID, confirm our predictions of permeability and reservoir temperature.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Shervais, J., Glen, J.M., Siler, D.L., Liberty, L., Nielson, D., Garg, S., Dobson, P., Gasperikova, E., Sonnenthal, E., Newell, D., Evans, J.E., DeAngelo, J., Peacock, J., Earney, T.E., Schermerhorn, W.D., and Neupane, G., 2020, Play fairway analysis in geothermal exploration: The Snake River plain volcanic province, in Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 186-194.","productDescription":"9 p.","startPage":"186","endPage":"194","ipdsId":"IP-115891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377334,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.proceedings.com/53283.html"}],"country":"United States","state":"Idaho","otherGeospatial":"Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5936279296875,\n 43.36512572875844\n ],\n [\n -111.544189453125,\n 44.15462243076731\n ],\n [\n -112.587890625,\n 44.33956524809713\n ],\n [\n -113.192138671875,\n 43.47285413777968\n ],\n [\n -114.60937499999999,\n 43.32517767999296\n ],\n [\n -115.7464599609375,\n 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jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liberty, Lee","contributorId":189113,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","affiliations":[],"preferred":false,"id":795550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Dennis","contributorId":237918,"corporation":false,"usgs":false,"family":"Nielson","given":"Dennis","affiliations":[{"id":47642,"text":"DOSECC Exploration Services","active":true,"usgs":false}],"preferred":false,"id":795551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garg, Sabodh","contributorId":193564,"corporation":false,"usgs":false,"family":"Garg","given":"Sabodh","email":"","affiliations":[],"preferred":false,"id":795552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dobson, Patrick","contributorId":193558,"corporation":false,"usgs":false,"family":"Dobson","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":795553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":795554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":795555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Newell, Dennis","contributorId":237921,"corporation":false,"usgs":false,"family":"Newell","given":"Dennis","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795556,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":795557,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795558,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795559,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795560,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795561,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Neupane, Ghanashyam","contributorId":237924,"corporation":false,"usgs":false,"family":"Neupane","given":"Ghanashyam","email":"","affiliations":[{"id":27243,"text":"Idaho National Laboratory","active":true,"usgs":false}],"preferred":false,"id":795562,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70205260,"text":"fs20193051 - 2020 - The 3D Elevation Program and energy for the Nation","interactions":[],"lastModifiedDate":"2020-03-02T06:19:52","indexId":"fs20193051","displayToPublicDate":"2020-02-28T16:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3051","displayTitle":"The 3D Elevation Program and Energy for the Nation","title":"The 3D Elevation Program and energy for the Nation","docAbstract":"

High-resolution light detection and ranging (lidar) data are used in energy infrastructure siting, design, permitting, construction, and monitoring to promote public safety through the reduction of risks. For example, lidar data are used to identify safe locations for energy infrastructure by analyzing terrain parameters and identifying and evaluating geologic hazards (for example, landslide and fault locations) and their potential public safety effects on the location or design of infrastructure. Increasingly, engineering companies and regulatory agencies are using lidar and other remote sensing techniques as an efficient method to collect accurate, comprehensive data while reducing risks to field personnel.

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) is collecting lidar data nationwide (interferometric synthetic aperture radar [IfSAR] data in Alaska) to support a wide range of applications, including projects related to energy infrastructure construction and safety. Renewable energy resources, resource mining, and oil and gas resources were identified by the National Enhanced Elevation Assessment as business uses requiring three-dimensional (3D) elevation data.

Elevation data are critical in assessing potential sites for energy infrastructure, such as pipelines, refineries and other facilities, to mitigate risks from natural hazards. For example, the Federal Energy Regulatory Commission (FERC), an independent agency that regulates the interstate transmission of electricity, natural gas, and oil, uses enhanced elevation data to conduct National Environmental Policy Act (NEPA) compliance assessments. The acquisition of high-resolution lidar data by the USGS 3DEP initiative helps the FERC and NEPA permit applicants by providing accurate and consistent data for hazards analysis. The use of these data accelerates the application and review process and avoids the much higher costs of acquiring elevation data along proposed energy facility locations and pipeline corridors.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193051","usgsCitation":"Thatcher, C.A., Lukas, Vicki, and Stoker, J.M., 2020, The 3D Elevation Program and energy for the Nation: U.S. Geological Survey Fact Sheet 2019–3051, 2 p., https://doi.org/10.3133/fs20193051.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107266","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":372745,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3051/coverthb.jpg"},{"id":372746,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3051/fs20193051.pdf","text":"Report","size":"566 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3051"}],"contact":"

Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Thatcher, Cindy A. 0000-0003-0331-071X","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":218872,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"","middleInitial":"A.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukas, Vicki 0000-0002-3151-6689 vlukas@usgs.gov","orcid":"https://orcid.org/0000-0002-3151-6689","contributorId":2890,"corporation":false,"usgs":true,"family":"Lukas","given":"Vicki","email":"vlukas@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770592,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208827,"text":"70208827 - 2020 - Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","interactions":[],"lastModifiedDate":"2020-03-03T09:05:19","indexId":"70208827","displayToPublicDate":"2020-02-28T09:04:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","docAbstract":"Recreational activities can negatively affect protected area landscapes and resources and soil erosion is frequently cited as the most significant long-term impact to recreational trails. Comprehensive modeling of soil loss on trails can identify influential factors that managers can manipulate to design and manage more sustainable trails. Field measurements assessed soil loss as the mean vertical depth along 135 trail transects across the Appalachian Trail sampled along three 5km trail segments in the White Mountains National Forest of New Hampshire. Using LiDAR data to accurately measure terrain characteristics that influence trail erosion can improve predictive models of trail system soil loss. Borrowing from geomorphic and agricultural soil erosion models, this study evaluated a variety of terrain and hydrology characteristics to model trail soil loss at three spatial scales: transect, trail corridor, and watershed. The model for each spatial scale and a combined model are presented. The adjusted R2 explaining variation in soil loss is 0.57 using variables from all spatial scales, a substantial improvement on previous trail erosion models. Environmental and trail design factors such as slope and watershed flow length were found to be significantly correlated to soil loss and have implications for sustainable trail design and management.","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2020.103765","usgsCitation":"Eagleston, H., and Marion, J.L., 2020, Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA: Landscape and Urban Planning, v. 198, 103765, 9 p., https://doi.org/10.1016/j.landurbplan.2020.103765.","productDescription":"103765, 9 p.","ipdsId":"IP-088107","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457565,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/98678","text":"External 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loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","interactions":[],"lastModifiedDate":"2022-04-22T21:35:38.301278","indexId":"sir20195092","displayToPublicDate":"2020-02-28T08:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5092","displayTitle":"Sediment and Chemical Contaminant Loads in Tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","title":"Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","docAbstract":"

A study was conducted by the U.S. Geological Survey (USGS) in cooperation with the Washington, D.C., Department of Energy & Environment to estimate the loads of suspended-sediment-bound chemical compounds in five gaged tributaries and four ungaged tributaries of the Anacostia River (known locally as “Lower Anacostia River”) in Washington, D.C. Tributaries whose discharge is measured by the USGS are the Northeast and Northwest Branches of the Anacostia River, referred to in this report as “Northeast Branch” (NEB) and “Northwest Branch” (NWB), respectively; Watts Branch (WB); and Hickey Run (HR). A USGS streamflow-gaging station was established in 2016 on Beaverdam Creek (known locally as “Lower Beaverdam Creek” [LBDC]) to support this study. The ungaged streams studied include Nash Run; Pope Branch; an unnamed stream at Fort DuPont, referred to in this report as “Fort DuPont Creek”; and an unnamed stream at Fort Stanton, referred to in this report as “Fort Stanton Creek.” The gaged streams were sampled during four to five storms and two low-flow events during January, March, May, and July 2017. The ungaged streams were sampled during one storm and one low-flow event during July 2017. Storm sampling involved collecting large-volume (60- to 70-liter) composite samples, then removing sediment by filtration in the laboratory. Low-flow samples were obtained by filtering streamwater directly in the field. Continuously recording data sondes were deployed throughout the study to measure turbidity and other water-quality characteristics. During sampling, multiple discrete samples of streamwater were collected to determine suspended-sediment concentration (SSC) and particulate organic carbon (POC) concentration. Shortly after each storm, bed sediment was collected for chemical analysis.

Sediment samples were analyzed for 209 polychlorinated biphenyl (PCB) congeners; 35 polyaromatic hydrocarbon (PAH) compounds, including 20 nonalkylated and 15 alkylated species; and 20 organochlorine pesticide (OP) compounds. Sediment from one storm was analyzed for 23 metals.

Relations were developed among turbidity, discharge, and measured SSC by using multiple linear regression of log-transformed data. These relations were used to estimate SSC from continuous records of discharge and turbidity and were subsequently used to estimate sediment loads for the 2017 calendar year. USGS continuous records of turbidity in NEB, NWB, Watts Branch, and Hickey Run were available for 2013–17, which allowed sediment loads to be calculated for these years. Sediment loads for the ungaged streams were estimated by using loads measured in Watts Branch adjusted on the basis of stream-basin areas.

Sediment loads for 2017 total 3.10×107 kilograms (kg), with 1.02×107 kg (33 percent of total) from the NEB, 1.55×107 kg (50 percent) from the NWB, 4.45×106 kg (14 percent) from LBDC, 5.62×105 kg (2 percent) from Watts Branch, and 2.82×105 kg (1 percent) from Hickey Run. Sediment yields were highest from NWB and LBDC (3.13×105 kilograms per year per square mile [kg/yr/mi2] and 3.01 kg/yr/mi2, respectively). As a result of gaps in turbidity and discharge data, the load for LBDC reported here was calculated from measurements representing only 88 percent of the year (2017), and thus underestimates the actual load. All other gaged tributaries had datasets covering 100 percent of the year and are considered to fully represent actual loads. Estimated sediment loads for the ungaged streams during 2017 total 3.5×105 kg, with 1.2×105 kg from Nash Run, 6.2×104 kg from Pope Branch, 1.1×105 kg from Fort DuPont Creek, and 5.6×104 kg from Fort Stanton Creek.

Concentrations of PCBs, PAHs, and chlorinated pesticides in streamwater are presented for stormflow and low-flow conditions. Average concentrations (in stormflow and low-flow samples) of total PCBs (sum of all congeners, including coelutions) are 5.9 micrograms per kilogram (µg/kg) for NEB, 6.6 µg/kg for NWB, 130 µg/kg for LBDC, 34 µg/kg for Watts Branch, and 69 µg/kg for Hickey Run. Average concentrations of total PAHs (tPAH) (total of nonalkylated and alkylated species) are 2,000 µg/kg for NEB, 3,300 µg/kg for NWB, 2,200 µg/kg for LBDC, 2,400 µg/kg for Watts Branch, and 18,000 µg/kg for Hickey Run. tPAH concentrations among the ungaged streams were highest in Nash Run (5,500 µg/kg); concentrations in the other ungaged streams were less than (<) 700 µg/kg.

The general magnitude of tPCB and tPAH concentrations in streamwater samples was low-flow samples greater than (>) stormflow samples greater than or equal to (≥) bed-sediment samples. PCB congener profiles in the three types of samples were nearly identical in each stream and were similar in all streams except for LBDC, where the dominant PCBs shifted to the lighter di- through tetra- homologs. LBDC showed higher tPCB concentrations and a distinct congener profile from the other streams. The similarity in congener makeup supported that averaging PCB concentrations in stormflow and low-flow samples was appropriate for calculating chemical loads.

Loads of tPCB, tPAH (total of alkylated and nonalkylated forms), and pesticides were estimated for each stream by multiplying average contaminant concentrations by the respective sediment loads. Total PCB loads for 2017 were estimated to be 820 grams (g) with 8 percent (60 g) from NEB, 12 percent (95 g) from NWB, 75 percent (590 g) from LBDC, 3 percent (25 g) from Watts Branch, and 2.5 percent (19 g) from Hickey Run. PCB toxicity totaled 3.8×10−3 µg/kg, with the largest contribution (47 percent) derived from LBDC. Total PAH loads (sum of alkylated and nonalkylated forms) for 2017 were estimated to be 89,000 g, with 23 percent (20,000 g) from NEB, 59 percent (52,000 g) from NWB, 11 percent (9,800 g) from LBDC, 2 percent (1,400 g) from Watts Branch, and 6 percent (5,200 g) from Hickey Run. These results indicate that the largest contributor (75 percent) of PCBs to the Anacostia River is LBDC, although it contributes only 15 percent of the sediment and its basin area represents only 10 percent of the area of the Anacostia River watershed. The majority of the PAH load originates from NWB (59 percent of total) and NEB (22 percent). The ungaged tributaries contribute extremely small loads of PCBs and PAHs, totaling 8.1 g and 765 kg, respectively. More than 94 percent of the total load from the ungaged tributaries is derived from the Nash Run Basin.

Various organochlorine pesticides were present in suspended and bed sediment from all gaged and ungaged tributaries; however, elevated detection levels associated with the analytical methods resulted in numerous unquantifiable concentrations in the suspended-sediment samples. Only the pesticide chlordane was found in measurable concentrations in all gaged tributaries. As a result, in this report, a combination of analytical data from suspended-sediment and bed-sediment samples was used to estimate the maximum pesticide loading for each tributary. Chlordane was the principal compound present in the gaged tributaries; the highest average concentration (average of stormflow and low-flow samples from each stream) was 62 µg/kg in sediment from Watts Branch. Chlordane loads for 2017 totaled 1,100 g, of which 7 percent (430 g) was from NEB, 28 percent (320 g) was from NWB, 28 percent (310 g) was from LBDC, 5 percent (56 g) was from Watts Branch, and 1 percent (11 g) was from Hickey Run. Chlordane was not present in suspended or bed sediment from any of the ungaged tributaries. Loads of the other pesticides were estimated by using the highest concentration measured in the combined suspended-sediment and bed-sediment data for each stream. Notable loads include dieldrin (860 g from NWB), methoxychlor (205 g from LBDC), endrin aldehyde (150 g from NWB), and 4,4-DDT (79 g from Watts Branch). Compared with pesticide loads from the gaged streams, those from the ungaged streams were minimal, with only the Pope Branch contribution exceeding 1 gram per year for 4,4-DDE (1.05 g) and 4,4’-DDT (1.3 g).

The results of this study show that the dominant source of PCBs and chlordane is LBDC, despite its relatively small basin area. PAHs are ubiquitous throughout the study area, with the largest sources being NEB and NWB; this finding is a result of the large sediment load originating from these basins. The small, ungaged streams supply only minimal PCB and PAH loads, with Nash Run being the largest contributor.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195092","collaboration":"Prepared in cooperation with the Washington, D.C., Department of Energy & Environment","usgsCitation":"Wilson, T.P., 2019, Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5092, 146 p., https://doi.org/10.3133/sir20195092.","productDescription":"Report: x, 146 p.; Data Release","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099743","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":399540,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109730.htm"},{"id":372690,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RUZSMV","text":"USGS data release","linkHelpText":"Discharge and sediment data for selected tributaries to the Anacostia River, Washington, District of Columbia, 2003–18"},{"id":372692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5092/sir20195092.pdf","text":"Report","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5092"},{"id":372691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5092/coverthb.jpg"}],"country":"United States","state":"District of Columbia","county":"Washington","otherGeospatial":"Anacostia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.0797,\n 38.8447\n ],\n [\n -76.7689,\n 38.8447\n ],\n [\n -76.7689,\n 39.1611\n ],\n [\n -77.0797,\n 39.1611\n ],\n [\n -77.0797,\n 38.8447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Timothy P. 0000-0003-1914-6344","orcid":"https://orcid.org/0000-0003-1914-6344","contributorId":219174,"corporation":false,"usgs":true,"family":"Wilson","given":"Timothy P.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771489,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209422,"text":"70209422 - 2020 - Foreward: Geology Field Trips in and around the U.S. Capital","interactions":[],"lastModifiedDate":"2020-04-28T20:30:35.335374","indexId":"70209422","displayToPublicDate":"2020-02-26T12:11:17","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Foreward: Geology Field Trips in and around the U.S. Capital","docAbstract":"The first annual meeting of the Geological Society of America (GSA) was held in 1888 in Ithaca, New York (Fairchild, 1932), but official Sections of GSA formed much later. During the spring of 1949, a symposium in Knoxville, Tennessee, on mineral resources of the southeastern United States became the catalyst for the creation of the Southeastern Section of the Geological Society of America (King, 1964), and the first annual meeting of the Southeastern Section was held in 1952 in Roanoke, Virginia (Wilson, 1954). The Northeastern Section formed much later, and its first annual meeting was held in 1966 in Philadelphia, Pennsylvania (Socolow, 1968). At all of these section meetings, field trips have been important venues for geologists and especially students to gather together, examine rocks in the field, and discuss ideas. These field trips have been especially important at combined section meetings because they provide settings for geologists who are experienced in one geographic region to examine and compare the geology of other regions. The first combined meeting of the Southeastern and Northeastern sections occurred in 1976 in Arlington, Virginia. Since then, the Southeastern and Northeastern sections have met together on numerous occasions, including 1982 in Washington, DC; 1991 in Baltimore, Maryland; 2004 in Tysons Corner, Virginia; and 2010 in Baltimore, Maryland. \n Since the first combined section meeting in 1976, there has been a gradual increase in the role of technology in geology field studies. In fact, during the past several decades there has been an increase in emphasis in our society on the instrumental component of science, the goal of which is operational techniques to do or control things, and a corresponding decrease in emphasis on the natural philosophy component of science, the goal of which is a greater understanding of the natural world (Dear, 2006). The modern education acronym STEM (Science, Technology, Engineering, and Mathematics), for example, is often used as a catch-all term that implies that science and technology are relatively synonymous, and implies that greater technology leads automatically to greater understanding of the natural world. This assumption, however, is not always valid (Dear, 2006), and technology should not be promoted as a substitute for field experiences. Technology can be a tool that leads to greater understanding of the natural world, but not all Science uses technology as a means of providing greater understanding. The benefits of new technologies include: (1) data of greater resolution; and (2) greater efficiency of capturing, storing, and visualizing data. The risks of new technologies include: (1) an overabundance of data, some of which may be of little value; (2) less time available for analysis of data, if geologists become occupied primarily with capturing and storing data; and (3) errors that arise from complacency and the perception that field-checking may not be necessary. In other words, there is a risk that a glut of data and vast amounts of time devoted to the capturing and storing of data may result in a reduced interest and (or) willingness to field-check data. \nIn the spirit of the early GSA section meetings, we feel that there are still enormous advantages to conducting geology field trips in conjunction with traditional meeting presentations and posters. In 2020, with this current combined Southeastern and Northeastern section meeting in Reston, Virginia, we have assembled eight different field trips that cover a wide range of territory in and around the Nation’s capital. These field trip localities include the immediate vicinity of Washington, DC, as well as various locations in nearby areas of Virginia, Maryland, and West Virginia. The physiographic provinces include Mesozoic Rift Basins, the Piedmont, the Blue Ridge, the Valley and Ridge, and the Allegheny Plateau of the Appalachian Basin. The field trip sites exhibit a wide range of igneous, metamorphic, and sedimentary rocks, as well as rocks with a wide range of geologic ages from the Mesoproterozoic to the Holocene. We hope that this guidebook provides new motivation for geologists to examine rocks in the field, to discuss ideas with colleagues in the field, and to avoid becoming complacent. \n The editors of this volume would like to thank the authors of the different field trip guides, the field trip leaders, and all of the reviewers who made suggestions for improving the field trip manuscripts. The editors would also like to thank Elle Derwent of GSA for her logistical help and guidance regarding the field trips, and April Leo and the staff of the GSA Publications Department for seeing this book through to publication.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society of America Field Guide","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0057(00)","collaboration":"","usgsCitation":"Swezey, C.S., and Carter, M.W., 2020, Foreward: Geology Field Trips in and around the U.S. Capital, chap. of Geological Society of America Field Guide, v. 57, p. v-vi, https://doi.org/10.1130/2020.0057(00).","productDescription":"2 p.","startPage":"v","endPage":"vi","ipdsId":"IP-113973","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":373862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.870361328125,\n 36.84006462037767\n ],\n [\n -75.8056640625,\n 36.84006462037767\n ],\n [\n -75.8056640625,\n 39.65222681530652\n ],\n [\n -80.870361328125,\n 39.65222681530652\n ],\n [\n -80.870361328125,\n 36.84006462037767\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":786450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":786451,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223337,"text":"70223337 - 2020 - Trends in cheetah Acinonyx jubatus density in north-central Namibia","interactions":[],"lastModifiedDate":"2021-08-24T13:13:40.57478","indexId":"70223337","displayToPublicDate":"2020-02-26T08:09:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Trends in cheetah Acinonyx jubatus density in north-central Namibia","title":"Trends in cheetah Acinonyx jubatus density in north-central Namibia","docAbstract":"

Assessing trends in abundance and density of species of conservation concern is vital to inform conservation and management strategies. The remaining population of the cheetah (Acinonyx jubatus) largely exists outside of protected areas, where they are often in conflict with humans. Despite this, the population status and dynamics of cheetah outside of protected areas have received relatively limited attention across its range. We analyzed remote camera trapping data of nine surveys conducted from 2005 to 2014 in the Waterberg Conservancy, north-central Namibia, which included detections of 74 individuals (52 adult males, 7 adult females and 15 dependents). Using spatial capture–recapture methods, we assessed annual and seasonal trends in cheetah density. We found evidence of a stable trend in cheetah density over the study period, with an average density of 1.94/100 km2 (95% confidence interval 1.33–2.84). This apparent stability of cheetah density is likely the result of stable and abundant prey availability, a high tolerance to carnivores by farmers and low turnover rates in home range tenure. This study highlights the importance of promoting long-term surveys that capture a broad range of environmental variation that may influence species density and the importance of nonprotected areas for cheetah conservation.

","language":"English","publisher":"Wiley","doi":"10.1002/1438-390X.12045","usgsCitation":"Fabiano, E.C., Sutherland, C., Fuller, A.K., Nghikembua, M., Eizirik, E., and Marker, L., 2020, Trends in cheetah Acinonyx jubatus density in north-central Namibia: Population Ecology, v. 62, no. 2, p. 233-243, https://doi.org/10.1002/1438-390X.12045.","productDescription":"11 p.","startPage":"233","endPage":"243","ipdsId":"IP-102240","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Namibia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[16.34498,-28.57671],[15.60182,-27.82125],[15.21047,-27.09096],[14.98971,-26.11737],[14.74321,-25.39292],[14.40814,-23.85301],[14.38572,-22.65665],[14.25771,-22.11121],[13.86864,-21.69904],[13.3525,-20.87283],[12.82685,-19.67317],[12.60856,-19.04535],[11.79492,-18.06913],[11.7342,-17.30189],[12.21546,-17.11167],[12.81408,-16.94134],[13.46236,-16.97121],[14.0585,-17.42338],[14.20971,-17.3531],[18.26331,-17.30995],[18.95619,-17.78909],[21.37718,-17.93064],[23.21505,-17.52312],[24.03386,-17.29584],[24.68235,-17.35341],[25.07695,-17.57882],[25.08444,-17.66182],[24.52071,-17.88712],[24.21736,-17.88935],[23.57901,-18.28126],[23.19686,-17.86904],[21.65504,-18.21915],[20.91064,-18.25222],[20.88113,-21.81433],[19.89546,-21.84916],[19.89577,-24.76779],[19.89473,-28.4611],[19.00213,-28.97244],[18.4649,-29.04546],[17.83615,-28.85638],[17.3875,-28.78351],[17.21893,-28.35594],[16.82402,-28.08216],[16.34498,-28.57671]]]},\"properties\":{\"name\":\"Namibia\"}}]}","volume":"62","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Fabiano, Ezequiel Chimbioputo","contributorId":264636,"corporation":false,"usgs":false,"family":"Fabiano","given":"Ezequiel","email":"","middleInitial":"Chimbioputo","affiliations":[{"id":54520,"text":"University of Nambia","active":true,"usgs":false}],"preferred":false,"id":821801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutherland, Chris","contributorId":264637,"corporation":false,"usgs":false,"family":"Sutherland","given":"Chris","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":821802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nghikembua, Matti","contributorId":264638,"corporation":false,"usgs":false,"family":"Nghikembua","given":"Matti","email":"","affiliations":[{"id":54521,"text":"Cheetah Conservation Fund","active":true,"usgs":false}],"preferred":false,"id":821803,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eizirik, Eduardo","contributorId":264639,"corporation":false,"usgs":false,"family":"Eizirik","given":"Eduardo","affiliations":[{"id":54522,"text":"Pontifícia UniversidadeCatólicadoRio Grandedo Sul","active":true,"usgs":false}],"preferred":false,"id":821804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marker, Laurie","contributorId":264640,"corporation":false,"usgs":false,"family":"Marker","given":"Laurie","email":"","affiliations":[{"id":54521,"text":"Cheetah Conservation Fund","active":true,"usgs":false}],"preferred":false,"id":821805,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207592,"text":"sir20195147 - 2020 - Water withdrawals, uses, and trends in Florida, 2015","interactions":[],"lastModifiedDate":"2022-04-25T20:27:23.096539","indexId":"sir20195147","displayToPublicDate":"2020-02-26T07:43:08","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5147","displayTitle":"Water Withdrawals, Uses, and Trends in Florida, 2015","title":"Water withdrawals, uses, and trends in Florida, 2015","docAbstract":"

In 2015, the total amount of water withdrawn in Florida was estimated to be 15,319 million gallons per day (Mgal/d). Saline water accounted for 9,598 Mgal/d (63 percent) and freshwater accounted for 5,721 Mgal/d (37 percent) of the total. Groundwater accounted for 3,604 Mgal/d (63 percent) of freshwater withdrawals and surface water accounted for the remaining 2,117 Mgal/d (37 percent). Surface-water sources accounted for 9,401 Mgal/d (98 percent) of the saline-water withdrawals, and groundwater sources accounted for the remaining 198 Mgal/d (2 percent). The majority of groundwater withdrawals (almost 62 percent) in 2015 were from the Floridan aquifer system, which is used throughout most of the State while the majority of fresh surface-water withdrawals (52 percent) occurred in the Southern Florida Subregion, a hydrologic unit that includes Lake Okeechobee and canals in the Everglades Agricultural Area. Groundwater provided drinking water (public supplied and self-supplied) for 18.324 million people (92 percent of Florida’s population), and fresh surface water provided drinking water for 1.491 million people (8 percent).

Overall, public supply accounted for 39 percent of the total freshwater withdrawals (ground and surface) and 53 percent of groundwater withdrawals, followed by agricultural self-supplied uses, which accounted for 37 percent of the total freshwater withdrawals and 28 percent of groundwater withdrawals. Other self-supplied groundwater withdrawals include commercial-industrial-mining self-supplied (8 percent), recreational-landscape irrigation and domestic self-supplied (5 percent each), and power generation (less than 1 percent). Agricultural self-supplied withdrawals accounted for 51 percent of fresh surface-water withdrawals, followed by power generation (19 percent), public supply (15 percent), recreational-landscape irrigation (10 percent), and commercial-industrial-mining self-supplied (5 percent).

In 1975, agricultural water withdrawals accounted for 43 percent of the total freshwater withdrawals, followed by power generation (24 percent) and public supply (17 percent). By 2000, agricultural withdrawals increased to 48 percent of the total freshwater withdrawals, followed by public supply (30 percent). For 2015, agricultural self-supplied decreased to 37 percent of total freshwater withdrawals, and was surpassed by public supply at 39 percent. Over the 40-year period between 1975 and 2015, increases in freshwater withdrawals caused by large gains in population and the expansion of irrigated acreage were offset by decreases in water used for power generation and commercial-industrial-mining withdrawals. Since 2000, however, irrigated acreage has decreased statewide because of crop disease, storm damage, and urbanization. This decline, coupled with large gains in water conservation measures in the farming industry, has led to agricultural withdrawals in Florida being less than public-supply withdrawals for the first time since water-use data were first reported in 1965.

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\"}}]}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-02-26","noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Marella, Richard L. 0000-0003-4861-9841","orcid":"https://orcid.org/0000-0003-4861-9841","contributorId":221550,"corporation":false,"usgs":true,"family":"Marella","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":778628,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219560,"text":"70219560 - 2020 - Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America","interactions":[],"lastModifiedDate":"2021-04-13T12:33:13.855719","indexId":"70219560","displayToPublicDate":"2020-02-26T07:31:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America","docAbstract":"

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long‐distance migrants where the use of landscapes varies by season and, sometimes, age cohort. We tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age‐structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion‐sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between‐year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first‐winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age‐related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex‐based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.

","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12325","usgsCitation":"Kenney, M.L., Belthoff, J.R., Carling, M., Miller, T.A., and Katzner, T., 2020, Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America: Journal of Field Ornithology, v. 91, no. 1, p. 92-101, https://doi.org/10.1111/jofo.12325.","productDescription":"10 p.","startPage":"92","endPage":"101","ipdsId":"IP-113723","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457594,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jofo.12325","text":"External Repository"},{"id":385049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, North Carolina, Pennsylvania, West 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Carolina\",\"nation\":\"USA \"}}]}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenney, Macy L","contributorId":257372,"corporation":false,"usgs":false,"family":"Kenney","given":"Macy","email":"","middleInitial":"L","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carling, Matthew","contributorId":257375,"corporation":false,"usgs":false,"family":"Carling","given":"Matthew","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":814141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":814142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814143,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211485,"text":"70211485 - 2020 - Multi‐species occupancy models: Review, roadmap, and recommendations","interactions":[],"lastModifiedDate":"2020-11-13T15:46:27.710581","indexId":"70211485","displayToPublicDate":"2020-02-25T19:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Multi‐species occupancy models: Review, roadmap, and recommendations","docAbstract":"

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04957","usgsCitation":"Devarajan, K., Tenan, S., and Morelli, T.L., 2020, Multi‐species occupancy models: Review, roadmap, and recommendations: Ecography, v. 43, no. 11, p. 1612-1624, https://doi.org/10.1111/ecog.04957.","productDescription":"14 p.","startPage":"1612","endPage":"1624","ipdsId":"IP-114395","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.04957","text":"Publisher Index Page"},{"id":376821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Devarajan, Kadambari","contributorId":236828,"corporation":false,"usgs":false,"family":"Devarajan","given":"Kadambari","email":"","affiliations":[],"preferred":false,"id":794271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tenan, Simone","contributorId":177519,"corporation":false,"usgs":false,"family":"Tenan","given":"Simone","email":"","affiliations":[],"preferred":false,"id":794272,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":794273,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208409,"text":"sir20205011 - 2020 - Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","interactions":[],"lastModifiedDate":"2022-04-25T21:37:51.100623","indexId":"sir20205011","displayToPublicDate":"2020-02-24T12:42:30","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5011","displayTitle":"Hydrologic and Hydraulic Analyses of Selected Streams in Stark County, Ohio","title":"Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","docAbstract":"

To update and expand a part of the Federal Emergency Management Agency Flood Insurance Study, the U.S. Geological Survey, the Muskingum Watershed Conservancy District, and the Stark County Commissioners began a cooperative study. The study consisted of hydrologic and hydraulic analyses for selected reaches of 14 streams in Stark County, Ohio: Broad-Monter Creek, Chatham Ditch, East Branch Nimishillen Creek, Fairhope Ditch, Firestone Ditch, Hayden Ditch, Middle Branch Nimishillen Creek, Middle Branch Nimishillen Creek Tributary Number 1, Nimishillen Creek, Reemsnyder Ditch, Sherrick Run, unnamed stream, West Branch Nimishillen Creek, and Zimber Ditch. The study totaled nearly 50 miles of stream reaches.

Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from the streamgages Nimishillen Creek at North Industry, Ohio (U.S. Geological Survey station number 03118500), and Middle Branch Nimishillen Creek at Canton, Ohio (U.S. Geological Survey station number 03118000), regional flood regression equations, and streamflow urbanization techniques.

The annual exceedance probability streamflows were then used in a Hydrologic Engineering Center-River Analysis System step-backwater model to determine water-surface profiles, flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probability floods, and a regulatory floodway along a selected reach of each stream. Model input included DEM-derived cross sections supplemented with field surveys of open channel cross sections and hydraulic structures, field estimates of roughness values, and annual exceedance probability flood estimates from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway for each stream reach.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205011","collaboration":"Prepared in cooperation with Stark County and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J. and Whitehead, M.T, 2020, Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio: U.S. Geological Survey Scientific Investigations Report 2020–5011, 15 p., https://doi.org/10.3133/sir20205011.","productDescription":"Report: iv, 15 p.; 4 Appendixes; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399632,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109726.htm"},{"id":372523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5011/coverthb.jpg"},{"id":372525,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix1.pdf","text":"Appendix 1","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 1","linkHelpText":"– Technical Support Data Notebook"},{"id":372526,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix2.pdf","text":"Appendix 2","size":"780 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 2","linkHelpText":"– Floodway data tables"},{"id":372524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011"},{"id":372527,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix3.pdf","text":"Appendix 3","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 3","linkHelpText":"– Water-surface profiles"},{"id":372528,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix4.pdf","text":"Appendix 4","size":"8.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 4","linkHelpText":"– Flood-inundation maps"},{"id":372529,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YQJ8B7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic models for selected streams in Stark County, Ohio"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"

Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Suite 100
Columbus, OH 43229–1737

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ostheimer, Chad J. 0000-0002-4528-8867","orcid":"https://orcid.org/0000-0002-4528-8867","contributorId":213950,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitehead, Matthew T. 0000-0002-4888-2597 mtwhiteh@usgs.gov","orcid":"https://orcid.org/0000-0002-4888-2597","contributorId":218036,"corporation":false,"usgs":true,"family":"Whitehead","given":"Matthew T.","email":"mtwhiteh@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222500,"text":"70222500 - 2020 - A machine learning approach to developing ground motion models from simulated ground motions","interactions":[],"lastModifiedDate":"2021-07-30T12:47:26.178465","indexId":"70222500","displayToPublicDate":"2020-02-24T07:44:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A machine learning approach to developing ground motion models from simulated ground motions","docAbstract":"

We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic-based GMM with empirically based GMMs derived from the globally based Next Generation Attenuation West2 data set, finding near-zero median residuals and similar amplitude and trends (with period) of total variability. Additionally, we find that the artificial neural network GMM has similar bias and variability to empirical GMMs from records of the recent \"urn:x-wiley:grl:media:grl60306:grl60306-math-0001\" Ridgecrest event, which neither GMM has included in its formulation. As simulations continue to better model broadband ground motions, machine learning provides a way to utilize the vast amount of synthetically generated data and guide future parameterization of GMMs.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL086690","usgsCitation":"Withers, K., Moschetti, M.P., and Thompson, E.M., 2020, A machine learning approach to developing ground motion models from simulated ground motions: Geophysical Research Letters, v. 47, no. 6, e2019GL086690, 9 p., https://doi.org/10.1029/2019GL086690.","productDescription":"e2019GL086690, 9 p.","ipdsId":"IP-116117","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.091796875,\n 33.815666308702774\n ],\n [\n -117.31201171875001,\n 32.34284135639302\n ],\n [\n -114.345703125,\n 32.69486597787505\n ],\n [\n -113.7744140625,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 33.815666308702774\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}