During 2017, as part of the National Water-Quality Assessment Project, the U.S. Geological Survey conducted the California Stream Quality Assessment to investigate the quality of streams in the Central California Foothills and Coastal Mountains ecoregion, United States. The goal of the California Stream Quality Assessment study was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic biota and by evaluating the relation between these stressors and biological indicators of stream health. Urbanization, agriculture, and modifications to streamflow are anthropogenic changes that affect water quality in the region; consequently, the study design primarily targeted sites and specific stressors associated with these activities. For the study, 85 stream sites were selected to represent the types and intensity of land use in the watershed; categories of site types were undeveloped, urban (low, medium, high), agriculture (low, high), and mixed (urban and agriculture). Most sites (about 70 percent) represent a gradient of urbanization from undeveloped to 99-percent urbanized. At most of the sites, streamgages or pressure transducers were used to monitor stream discharge and stage, as well as temperature. Water-quality samples were collected routinely at all sites and were analyzed for major ions, organic contaminants, nutrients, and suspended sediment. Sampling frequency varied on the basis of site type and location. Discrete water samples were collected weekly and generally 6 times per site, except for 11 undeveloped sites that were sampled only 4 times (during the last 4 weeks). Water sampling began at sites in the southern part of the study on March 13, 2017, and at sites in the northern part of the study on April 3, 2017. Passive samplers were deployed at most sites for measurement of polar organic contaminants (pesticides and pharmaceuticals). In May 2017, coincident with completion of water-quality sampling, an ecological survey was conducted at each site to assess benthic algal and macroinvertebrate communities and instream habitat. During the ecological surveys, a single composite streambed-sediment sample was collected for chemical analysis and toxicity testing. In addition, a few focused studies were done at subsets of sites, namely, measuring pesticides using small-volume automated samplers, measuring pesticides in biofilms, and sampling suspended sediments using passive samplers. This report describes the various study components and methods of the California Stream Quality Assessment, including measurements of water quality, sediment chemistry, habitat assessments, and ecological surveys, as well as procedures for sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201023","collaboration":"National Water Quality Program","usgsCitation":"May, J.T., Nowell, L.H., Coles, J.F., Button, D.T., Bell, A.H., Qi, S.L., and Van Metre, P.C., 2020, Design and methods of the California stream quality assessment, 2017: U.S. Geological Survey Open-File Report 2020–1023, 88 p.,","productDescription":"Report: x, 88 p.; 1 Table","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-105445","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1023/coverthb.jpg"},{"id":374197,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023_app_table_1.1.xlsx","text":"Table 1.1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Sampling matrix for the 85 sites used in the U.S. Geological Survey California Stream Quality Assessment in 2017."}],"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 -120.34423828125,\n 34.50655662164561\n ],\n [\n -119.267578125,\n 34.95799531086792\n ],\n [\n -121.4208984375,\n 37.78808138412046\n ],\n [\n -122.10205078125,\n 39.14710270770074\n ],\n [\n -122.25585937500001,\n 39.41922073655956\n ],\n [\n -123.48632812499999,\n 38.85682013474361\n ],\n [\n -122.67333984374999,\n 37.87485339352928\n ],\n [\n -122.05810546875,\n 37.055177106660814\n ],\n [\n -121.79443359375,\n 36.65079252503471\n ],\n [\n -121.9482421875,\n 36.61552763134925\n ],\n [\n -121.83837890625,\n 36.155617833818525\n ],\n [\n -121.26708984374999,\n 35.585851593232356\n ],\n [\n -120.58593749999999,\n 35.04798673426734\n ],\n [\n -120.5419921875,\n 34.488447837809304\n ],\n [\n -120.34423828125,\n 34.50655662164561\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This five-year science plan outlines short-term and long-term goals for improving three-dimensional seismic velocity models in the greater San Francisco Bay region as well as how to foster a community effort in reaching those goals. The short-term goals focus on improving the current U.S. Geological Survey San Francisco Bay region geologic and seismic velocity model using existing data. The long-term goals focus on acquiring new data and leveraging better analytic tools to improve the model and characterize the uncertainty. The plan describes opportunities for contributions by members of the community to develop these seismic velocity models, provides current and potential users with general information on where efforts will likely be focused to improve these models and how new versions of the models will be released, and outlines funding needs and obstacles for improving and maintaining such models. Several aspects of this plan, including how to foster a community effort, are independent of the geographic region and apply to other similar efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201019","collaboration":"","usgsCitation":"Aagaard, B.T., Graymer, R.W., Thurber, C.H., Rodgers, A.J., Taira, T., Catchings, R.D., Goulet, C.A., and Plesch, A., 2020, Science plan for improving three-dimensional seismic velocity models in the San Francisco Bay region, 2019–24: U.S. Geological Survey Open-File Report 2020–1019, 37 p., https://doi.org/10.3133/ofr20201019.","productDescription":"vi, 37 p.","onlineOnly":"Y","ipdsId":"IP-112680","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":374023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1019/coverthb.jpg"},{"id":374024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1019/ofr20201019.pdf","text":"Report","size":"4.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1019"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.74999999999999,\n 36.527294814546245\n ],\n [\n -120.498046875,\n 36.527294814546245\n ],\n [\n -120.498046875,\n 39.11301365149975\n ],\n [\n -123.74999999999999,\n 39.11301365149975\n ],\n [\n -123.74999999999999,\n 36.527294814546245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
MS 966, Box 25046
Denver, CO 80225
The North Carolina Turnpike Authority plans to improve transportation in the Currituck Sound area by constructing a two-lane bridge—the Mid-Currituck Bridge—across Currituck Sound from the mainland to the Outer Banks, North Carolina. The results of the final environmental impact statement for the project indicate potential water-quality and habitat effects for Currituck Sound associated with the bridge and roadway improvements.
The primary objective of this study is to characterize water-quality conditions and bed-sediment chemistry in the vicinity of the planned Mid-Currituck Bridge, providing a baseline for evaluating the potential effects of bridge construction and bridge deck runoff on environmental conditions in Currituck Sound. From August 2011 through January 2018, water-quality and bed-sediment samples were collected from five sampling stations along the planned bridge alignment. Samples were analyzed for numerous characteristics, including physical properties and constituents that are associated with bridge deck stormwater runoff and are important to estuarine waters. The analyzed characteristics included dissolved oxygen, pH, specific conductance, turbidity, suspended solids, metals, nutrients, semi-volatile organic compounds, bacteria, chlorophyll a, cyanotoxins, and phytoplankton abundance. The most common constituents with concentrations above applicable State and Federal water-quality thresholds included chlorophyll a, pH, turbidity, Enterococci, and pentachlorophenol. Few bed-sediment samples had constituent concentrations that exceeded applicable sediment-quality guidelines.
Results indicated that water sampled along the planned bridge alignment was well mixed vertically and horizontally but varied temporally. Seasonal changes in water quality best explained the variations in water-quality conditions in Currituck Sound during the study. Wind conditions also influenced water levels and water-quality conditions. Turbidity and concentrations of particle-associated constituents tended to be higher when water levels were lower, possibly reflecting the increased resuspension of bottom materials from wind-driven wave action.
Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
We evaluate three approaches to accounting for incidental carcasses when estimating an upper bound on total mortality (\uD835\uDC40) as \uD835\uDC40∗ using the Evidence of Absence model (EoA; Dalthorp and others, 2017) to assess compliance with an Incidental Take Permit (ITP) (Dalthorp & Huso, 2015) under a monitoring protocol that includes formal, dedicated carcass surveys that achieve an overall detection probability of \uD835\uDC54\uD835\uDC60=0.15 in the first year, followed by 4 years with no formal monitoring but with carcasses potentially discovered incidentally by operations and maintenance crews in their normal course of activity or otherwise discovered outside the formal searches. We refer to carcasses discovered incidentally as “incidentals” and define \uD835\uDC65\uD835\uDC56 as the count of incidentals. Similarly, we define \uD835\uDC65\uD835\uDC60 as the number of carcasses found during the formal searches conducted the first year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201027","usgsCitation":"Dalthorp, Daniel, Rabie, Paul, Huso, Manuela, and Tredennick, Andrew, 2020, Some approaches to accounting for incidental carcass discoveries in non-monitored years using the Evidence of Absence model: U.S. Geological Survey Open-File Report 2020-1027, 24 p., https://doi.org/10.3133/ofr20201027.","productDescription":"iv, 22 p.","onlineOnly":"Y","ipdsId":"IP-114583","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":373890,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1027/ofr20201027.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1027"},{"id":373889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1027/coverthb.jpg"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The collective set of decisions involved with the restoration of degraded wetlands is often more complex than considering only ecological responses and outcomes. Restoration is commonly driven by a complex interaction of social, economic, and ecological factors representing the mandate of resource stewards and the values of stakeholders. The authors worked with the Herring River Restoration Committee (HRRC) to develop a decision framework to understand the implications of complex tradeoffs and to guide decision making for the restoration of the 1,100-acre Herring River estuary within Cape Cod National Seashore, which has been restricted from tidal influence for more than 100 years. The HRRC represents decision maker and stakeholder interests in the restoration process. For a 25-year planning horizon, decisions involve the rate at which newly constructed water-control structures allow tidal exchange, and the timing and location of implementing numerous secondary management options. Decisions affect multiple stakeholders, including residents of two adjacent towns who value the watershed for numerous benefits and whose economy relies on seasonal activities and aquaculture. System response to management decisions is characterized by a high degree of uncertainty and risk with positive and negative outcomes possible. Decision policies will affect biophysical (for example, sediment transport, discharge of fecal coliform bacteria) and ecological (for example, vegetation response, fish passage, effects on shellfish) processes, as well as socioeconomic interests (for example, effects on property, viewscapes, recreation). The framework provides a structured approach for evaluating tradeoffs among multiple objectives (ecological and social) while appropriately characterizing relevant uncertainties and accounting for levels of risk tolerances and the values of decision makers and stakeholders. Consequences of tide-gate management options are predicted using a range of methods from quantitative physical process models to elicited expert judgement. The decision framework is presented, and the software developed to implement the tradeoff analysis is introduced. The results from an initial prototype analysis using a software application developed for analyses of tradeoffs and of sensitivity of the decision to risk and uncertainty are presented. The next step is to use the decision-support application to analyze options using improved predictions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191115","collaboration":"Prepared in cooperation with National Park Service and U.S. Fish and Wildlife Service","usgsCitation":"Smith, D.R., Eaton, M.J., Gannon, J.J., Smith, T.P., Derleth, E.L., Katz, J., Bosma, K.F., and Leduc, E., 2020, A decision framework to analyze tide-gate options for restoration of the Herring River Estuary, Massachusetts: U.S. Geological Survey Open-File Report 2019–1115, 42 p., https://doi.org/10.3133/ofr20191115.","productDescription":"viii, 42 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101813","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":373779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1115/ofr20191115.pdf","text":"Report","size":"3.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1115"},{"id":373778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1115/coverthb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Herring River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.07286071777344,\n 41.92961289444422\n ],\n [\n -70.02462387084961,\n 41.92961289444422\n ],\n [\n -70.02462387084961,\n 41.96357478222518\n ],\n [\n -70.07286071777344,\n 41.96357478222518\n ],\n [\n -70.07286071777344,\n 41.92961289444422\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The Vermont Department of Health and the U.S. Geological Survey analyzed the concentrations of chloride in groundwater samples collected from 4,319 domestic wells across Vermont between 2011 and 2018. Ninety of these wells were sampled twice and the remaining 4,229 were sampled once. This sample size represents approximately 4 percent of all wells in the State of Vermont. More than half of the wells sampled statewide had groundwater chloride concentrations less than 5 milligrams per liter, whereas more than 1 percent had groundwater concentrations greater than 250 milligrams per liter. Statistical analysis of this dataset revealed distinct patterns in the distribution of chloride in domestic wells. Wells closer (less than 100 meters) to a paved road had significantly higher concentrations of chloride than wells farther ( more than 100 meters) away. Also, wells in urban and in high population density areas, particularly Chittenden and Grand Isle Counties, had significantly higher concentrations of chloride and exhibited greater change in concentrations of chloride over time than wells in less populated areas. This evaluation addresses the distribution of chloride concentrations across the State, which may have adverse health impacts from water infrastructure corrosion and implications for deicing salt application at the State and local levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191148","collaboration":"Prepared in cooperation with the Vermont Department of Health","usgsCitation":"Levitt, J.P., and Larsen, S.L., 2020, Groundwater chloride concentrations in domestic wells and proximity to roadways in Vermont, 2011–2018: U.S. Geological Survey Open-File Report 2019–1148, 12 p., https://doi.org/10.3133/ofr20191148.","productDescription":"Report: 12 p.; Data Release","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104230","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":373835,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1148/ofr20191148.pdf","text":"Report","size":"77.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1148"},{"id":373355,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The 3D Elevation Program (3DEP) is an acquisition strategy that uses data from commercial remote sensing technologies to create three-dimensional maps of the United States and U.S. territories. Currently, light detection and ranging and interferometric synthetic aperture radar are the two commercial technologies being used to provide three-dimensional information to meet the program’s operational requirements. This is because there is not a well-established process for vendors of new and novel instruments to know when and how 3DEP will accept their technologies into the 3DEP portfolio. The purpose of this plan is to provide a strategy and rules for communication between 3DEP and commercial partners interested in proposing their modalities for use in the program. To accomplish this, 3DEP will also consider how it invests in new technologies and how it disseminates data to and categorizes data for the broader community and the public.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201015","collaboration":"","usgsCitation":"Stoker, J.M., 2020, Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption: U.S. Geological Survey Open-File Report 2020–1015, 7 p., \nhttps://doi.org/ 10.3133/ ofr20201015.","productDescription":"iv, 7 p.","onlineOnly":"Y","ipdsId":"IP-110726","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":373798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1015/ofr20201015.pdf","text":"Report","size":"932 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1015"},{"id":373797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1015/coverthb2.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
MS-511
12201 Sunrise Valley Drive
Reston, VA 20192
We systematically evaluate datasets, functional forms, independent parameters of estimation, and resulting ground-motion predictions (as median and aleatory variability) of the Graizer and Kalkan (2015, 2016) (GK15) ground-motion prediction equation (GMPE) with the next generation of attenuation project (NGA-West2) models of Abrahamson and others (2014) (ASK14) and Boore and others (2014) (BSSA14) for application to earthquakes in California. This evaluation is performed in three stages: (1) by comparing attenuation, magnitude scaling, style-of-faulting effects, site response, response-spectral shape and amplitude, and standard deviations; (2) by comparing median predictions, standard deviations, and analyses of residuals with respect to near-field (within 20 kilometers [km] of the fault) and intermediate-field (50 to 70 km from the fault) records from major earthquakes in California, and (3) by comparing total, intra-event, and inter-event residual distributions among the GMPEs with respect to a near-source (within 80 km of the fault) subset of the NGA-West2 database covering 975 ground motions from 73 events in California ranging from moment magnitude 5 to 7.36. The results reveal that the scaling features of the GK15 GMPE and the ASK14 and BSSA14 GMPEs are, in general, similar in terms of distance attenuation but differ in terms of scaling with magnitude, style of faulting, and site effects. The original standard deviations of GMPEs are also different. For the near-source California subset, the three GMPEs result in standard deviations that are similar to each other. The mixed-effect residuals analysis shows that the GK15 GMPE has no perceptible trend with respect to the independent predictors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201028","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Kalkan, E., and Graizer, V., 2020, Ground-motion predictions for California — Comparisons of three prediction equations: U.S. Geological Survey Open-File Report 2020–1028, 28 p., https://doi.org/10.3133/ofr20201028.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-087031","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":373754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1028/coverthb.jpg"},{"id":373755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1028/ofr20201028.pdf","text":"Report"},{"id":399470,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109905.htm"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 42.16340342422401\n ],\n [\n -124.91455078125,\n 41.02964338716638\n ],\n [\n -124.78271484375,\n 39.690280594818034\n ],\n [\n -123.96972656249999,\n 38.839707613545144\n ],\n [\n -122.51953124999999,\n 36.96744946416934\n ],\n [\n -121.70654296874999,\n 35.11990857099681\n ],\n [\n -118.564453125,\n 33.55970664841198\n ],\n [\n -117.22412109375,\n 32.63937487360669\n ],\n [\n -114.63134765625001,\n 32.7503226078097\n ],\n [\n -114.14794921875,\n 34.21634468843463\n ],\n [\n -114.41162109375,\n 34.77771580360469\n ],\n [\n -119.90478515625,\n 39.18117526158749\n ],\n [\n -119.86083984375,\n 42.01665183556825\n ],\n [\n -124.4091796875,\n 42.16340342422401\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035
A study was conducted in the lower Yakima River, Washington, during June–October 2019 to evaluate water temperature effects on adult sockeye salmon (Oncorhynchus nerka) behavior. A total of 60 sockeye salmon adults were tagged with radio transmitters and monitored during the study. Fourteen of the fish were collected and tagged at Prosser Dam in late June and the remainder were collected and tagged at the mouth of the Yakima River in late July. Water temperature exceeded 20 degrees Celsius (°C), conditions shown to block upstream migration of adult sockeye salmon in other river systems, from June 9, 2019 to September 15, 2019. These elevated temperatures seemed to affect the behavior of tagged fish during this study. Fish that were collected and tagged at Prosser Dam left the Yakima River within days of release and tagged fish that were collected and released at the mouth of the Yakima River failed to enter and move upstream until mid-September when water temperature decreased to less than (<) 20 °C. Monitoring sites were located adjacent to several known areas of cool-water inputs that may provide thermal refuge for fish in the lower Yakima River to determine if tagged fish spent time in these areas. Although several tagged fish moved repeatedly past these sites, most fish spent <30 minutes at any given site, indicating that fish were actively migrating past the sites rather than holding near cool-water inputs. A single tagged fish moved upstream to Roza Dam and was collected for upstream transport to Cle Elum Reservoir during our study. Additional research in subsequent years likely will be required to better understand how water temperature affects adult sockeye salmon in the lower Yakima River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201033","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, and Washington Department of Fish and Wildlife","usgsCitation":"Kock, T.J., Evans., S.D., Hansen, A.C., Ekstrom, B.K., Visser, R., Saluskin, B., and Hoffarth, P., 2020, Evaluation of water temperature effects on adult sockeye salmon (Oncorhynchus nerka) behavior in the Yakima River, Washington, 2019: U.S. Geological Survey Open-File Report 2020-1033, 15 p., https://doi.org/10.3133/ofr20201033.","productDescription":"iv, 15 p.","onlineOnly":"Y","ipdsId":"IP-115963","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373693,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1033/ofr20201033.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1033"},{"id":373692,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1033/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.59417724609375,\n 46.96338498544958\n ],\n [\n -120.5804443359375,\n 46.916503267244835\n ],\n [\n -120.52001953124999,\n 46.837649560937464\n ],\n [\n -120.50628662109374,\n 46.77184961467733\n ],\n [\n -120.57220458984376,\n 46.67394106549699\n ],\n [\n -120.56671142578124,\n 46.590956573124544\n ],\n [\n -120.51727294921875,\n 46.44731998341687\n ],\n [\n -120.31402587890625,\n 46.3127900695348\n ],\n [\n -120.17120361328125,\n 46.219752144776876\n ],\n [\n -119.92401123046875,\n 46.181732100439916\n ],\n [\n -119.34997558593749,\n 46.20834889395228\n ],\n [\n -119.19342041015624,\n 46.26534147068603\n ],\n [\n -119.16595458984374,\n 46.42271253466717\n ],\n [\n -119.37469482421875,\n 46.67205646734499\n ],\n [\n -119.47631835937499,\n 46.741742832123364\n ],\n [\n -119.66308593749999,\n 46.71161922789268\n ],\n [\n -119.83612060546874,\n 46.69278343251575\n ],\n [\n -119.84710693359375,\n 46.98399993718925\n ],\n [\n -120.59417724609375,\n 46.96338498544958\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
In an effort to determine whether fish populations in an area affected by wood tar waste exhibited health effects, fish were collected and analyzed with histopathology. Multiple species, including Mottled Sculpin (Cottus bairdii), Creek Chub (Semotilus atromaculatus), White Sucker (Catostumus commersonii), Redside Dace (Clinostomus elongatus), Common Shiner (Luxilus cornutus), and Western Blacknose Dace (Rhinichthys obtusus) were sampled from a reference site, Meade Run, and potentially affected streams, Kinzua Creek and Threemile Run, in northwestern Pennsylvania. A full histopathological evaluation was conducted to identify microscopic abnormalities potentially associated with wood tar exposure. The evaluation identified primarily parasites associated with tissue changes. These included microsporidian parasites in the ovaries of Common Shiner and Western Blacknose Dace; myxozoan cysts in the muscle of Common Shiner, Creek Chub, and Western Blacknose Dace; trematode cysts in the muscle of Creek Chub, Redside Dace and Common Shiner; and coccidia in spleen or pancreas of Creek Chub and Common Shiner. Microscopic abnormalities potentially associated with chemical exposure included ceroid/lipofuscin deposits in the meninges of the olfactory lobe of the brain in Common Shiner, Western Blacknose Dace, and Creek Chub, as well as bile duct proliferation and a biliary tumor in Creek Chub. Overall, the findings did not reveal significant microscopic pathology consistent with exposure to wood tar waste.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201024","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Walsh, H.L., Blazer, V.S., Mazik, P.M., Sperry, A.J., and Pavlick, D., 2020, Assessment of microscopic pathology in fishes collected at sites impacted by wood tar in Pennsylvania: U.S. Geological Survey Open-File Report 2020–1024, 14 p., https://doi.org/10.3133/ofr20201024.","productDescription":"vi, 14 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116287","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science 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\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter referred to as Clear Lake; fig. 1), California, are experiencing long-term declines in abundance. Upper Klamath Lake populations are decreasing because juvenile suckers are not surviving and recruiting into the adult population. Most juvenile sucker mortality occurs within the first year of life in Upper Klamath Lake. Annual production of juvenile suckers in Clear Lake appear to be highly variable and may not occur at all in very dry years. However, juvenile sucker survival is much higher in Clear Lake, with some suckers surviving to join spawning aggregations. Long-term monitoring of juvenile sucker populations is needed to 1) determine if there are annual and species-specific differences in production, survival, and growth; 2) better understand when juvenile sucker mortality is greatest; 3) help identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake; and 4) monitor for successful juvenile survival in Upper Klamath Lake.
The U.S. Geological Survey (USGS) began a summer juvenile sucker monitoring program in 2015 to track cohorts over time in Upper Klamath and Clear Lakes. The juvenile sucker monitoring program involved using trap net data at fixed sites to determine the status of juvenile suckers. Annual variability in apparent age-0 sucker production, juvenile sucker survival, and growth were tracked. Using genetic markers, suckers were classified as one of three taxa; shortnose (combinations of shortnose and Klamath largescale suckers), Lost River, or suckers with genetic markers of both species (Intermediate [Prob]). By using catch data, we generated taxa-specific indices of year-class strength, August–September apparent survival, and overwinter apparent survival. We also examined the prevalence and severity of afflictions such as parasites, wounds, and deformities.
The Upper Klamath Lake year-class strength indices for both Lost River and shortnose suckers were slightly lower in 2015 and 2017 than in 2016. The ratios of age-0 Lost River suckers to age-0 shortnose suckers captured in August in Upper Klamath Lake were low in 2015 and 2017, given that adult Lost River suckers are more abundant and more fecund than adult shortnose suckers. This may indicate lower egg, larval, or juvenile survival or poorer spawning success for Lost River suckers than shortnose suckers in these two years. Apparent relative age-0 survival indices for Lost River suckers from August to September in Upper Klamath Lake were greater in 2015 (0.29) than in 2016 (0.16) or 2017 (0.14). Age-0 shortnose sucker catch rates increased between August and September in 2015, possibly indicating new individuals of this species were still recruiting to the lake between the two sampling periods. August to September relative survival indices for Upper Klamath Lake shortnose suckers were 0.35 in 2016 and 0.00 in 2017.
We predicted year-class strength would be greater in Clear Lake in years when high spring-time lake elevations and instream flow allowed adult suckers access to spawning habitat in the Willow Creek drainage. Instream flows and lake elevations were sufficient to allow adult suckers to access Willow Creek during the 2016 and 2017 spawning seasons, and age-0 suckers were detected in Clear Lake both years. Higher lake surface elevations and instream flows in 2017 than in 2016 were not associated with higher year-class strength indices in 2017 than in 2016. Low lake surface elevations appeared to limit access by adults to Willow Creek during the 2014 and 2015 spawning seasons and age-0 suckers were not detected in Clear Lake during these years. Nineteen shortnose suckers from the 2014 cohort were captured in Clear Lake in 2017. A 2015 cohort of shortnose suckers was captured as age-1 in 2016 and as age-2 in 2017. The most likely explanation for increasing catch rates of the 2015 cohort is that the higher Willow Creek flows in 2016 and 2017 facilitated the movement of stream-resident suckers, spawned in 2014 and 2015 downstream into Clear Lake. Due to uncertainty in the genetic identification of non-Lost River suckers, these fish are equally likely to be Klamath largescale or shortnose suckers (Hoy and Ostberg, 2015).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201025","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Bart, R.J., Burdick, S.M., Hoy, M.S., and Ostberg, C.O., 2020, Juvenile Lost River and shortnose sucker year-class formation, survival, and growth in Upper Klamath Lake, Oregon and Clear Lake Reservoir, California—2017 Monitoring Report: U.S. Geological Survey Open-File Report 2020–1025, 36 p., https://doi.org/10.3133/ofr20201025.","productDescription":"v, 36 p.","onlineOnly":"Y","ipdsId":"IP-112875","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373492,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1025/ofr20201025.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1025"},{"id":373491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1025/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.77197265625,\n 40.763901280945866\n ],\n [\n -121.22314453124999,\n 40.763901280945866\n ],\n [\n -121.22314453124999,\n 43.08493742707592\n ],\n [\n -123.77197265625,\n 43.08493742707592\n ],\n [\n -123.77197265625,\n 40.763901280945866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Larval Pacific lamprey live for several years burrowed in nearshore sediments where they filter feed on detritus and organic matter. Dewatering of larval habitat can occur as a result of flow-management practices, construction projects, or seasonal closures of irrigation diversions. Effective management of dewatering events requires guidance on approaches to protect lamprey, such as dewatering rates and light conditions (day or night) that allow lamprey the best opportunity to relocate water and avoid being stranded. We conducted controlled laboratory experiments comparing five dewatering rates (1, 1.8, 4, 8, and 16 inches per hour [in/h]) and two light conditions (light and dark) to evaluate their effectiveness in protecting larval lamprey. We used a tank with a simulated shoreline at a 10-percent slope filled with river sediment and manipulated the outflow to control the rate of dewatering until water was covering only the sediment in the lowest tank section, at the bottom of the slope. Following dewatering, larvae were classified as either stranded (in or on the substrate outside the watered area) or safe (relocated to the wetted area at the lower end of the tank). All study groups experienced high rates of stranding. The lowest stranding rates were for 1 in/h, in both light (77 percent) and dark (80 percent). Faster dewatering rates generally produced higher percentages of stranded fish, and both the dark and light trials at 16 in/h stranded all larvae. At each of the five dewatering rates, trials conducted in the dark stranded the same or higher proportions of fish than the corresponding trial conducted in the light, so there was no clear advantage to dewatering during dark conditions. The largest contribution to stranding rates for all study groups was the high number of larvae (50–80 percent) that did not initiate movement in response to dewatering and remained in the uppermost tank section where they were stocked at the start of the trials. The proportion of larvae that emerged from the sediment during dewatering trials was approximately 30 percent, and fish that emerged were consistently smaller than those that remained burrowed. Combining all dewatering rates, emergence was 31.3 percent for groups under dark conditions and 30.7 percent for groups under light conditions. We recorded the timing of emergence for 58 larvae and their median time to emerge (after the surface of the sediment in the uppermost tank section was dewatered) was 0.62 hour (h) (range 0–4.5 h). We measured larval movement rates and found that large fish moved faster than small fish. Differences in larval movement rate based on light condition were significant only for large fish, which had a significantly faster rate during light conditions. Larval lamprey moved, over short distances, at rates that exceeded the fastest dewatering rate we tested. The mean movement rates for groups ranged from 19.0 to 44.4 centimeters per minute [cm/min]) and the fastest dewatering rate (16 in/h) is equivalent to less than 1 cm/min. Only the slowest movement rate measured, 6.6 cm/min for one individual lamprey, was slower than the fastest dewatering rate.
We also investigated lamprey responses to a series of dewatering and rewatering events. Individual larvae were held in cylinders and exposed to four cycles of dewatering and rewatering using dewatering rates of 1 and 16 in/h and a rewatering rate of 2 in/h. Each dewatering rate was tested under both dark and light conditions. The location of fish, either on the surface of the sediment or burrowed, was recorded after each dewatering event for four rounds. The most common individual fish response for all study groups was to remain burrowed through all four rounds, and there were large differences in response between small and large larvae. Overall for small larvae, combining all groups, 14 of 28 fish emerged, and of those, 8 died and 1 was lethargic. The 1-in/h rate had 7 of the 8 mortalities, split about equally between the dark (3 fish) and light (4 fish) trials. All but one fish that died emerged from the sediment at some point during the four rounds of dewatering. Large larvae predominantly remained burrowed in all four rounds and did not experience any mortality. None of the large fish emerged for more than a single round, and emergence occurred only in the first and second rounds. Larvae emerged more quickly as the number of dewatering events increased. The mean time to emerge after the surface of the sediment in the tube was dewatered, combing all four groups, was 42 minutes (min) in round 1 (14 fish), 16 min in round 2 (5 fish), 11 min in round 3 (3 fish), and 8 minutes in round 4 (3 fish). When all groups and rounds of dewatering were combined, the overall mean time to emerge was 29 min (25 fish) and ranged from 1 min to 2 hours after the surface of the sediment was dewatered. Larvae burrowed deeper during the 1-in/h trials than the 16-in/h trials, and few fish were deeper than about 23 centimeters (cm). Large larvae burrowed deeper than small larvae. Small larvae were most concentrated from 0 to 7.6 cm (83.7 percent), and large fish were concentrated from 15.2 to 22.8 cm (43.3 percent). The second dewatering event resulted in greater mean burrowing depth than the first event, but trends after the second event were less clear.
Larval size played a role in lamprey responses to dewatering, having a significant effect on emergence, movement rate, and vertical distribution. The sediment used for laboratory testing or occupied by lamprey in the field appears to affect lamprey response to dewatering and deserves greater attention in future studies. Larvae were more active in the dark, but darkness did not consistently provide better outcomes (e.g., more emergence or reduced stranding) compared to daylight. An improved understanding of the cues that prompt larvae to emerge from the sediment, combined with the ability to manage dewatering rates, would be useful to guide future dewatering events to minimize negative effects to lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201026","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Fish and Wildlife Office, Portland, Oregon; and Columbia River Fish and Wildlife Conservation Office, Vancouver, Washington","usgsCitation":"Liedtke, T.L., Weiland, L.K., Skalicky. J.J., and Gray, A.E., 2020, Evaluating dewatering approaches to protect larval Pacific lamprey: U.S. Geological Survey Open-File Report 2020–1026, 32 p., https://doi.org/10.3133/ofr20201026.","productDescription":"iv, 32 p.","onlineOnly":"Y","ipdsId":"IP-113959","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1026/coverthb.jpg"},{"id":373418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1026/ofr20201026.pdf","text":"Report","size":"992 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1026"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Taupo Volcanic Zone (TVZ), New Zealand, is the most productive area of explosive silicic volcanism in the world. Faulted early and middle Pleistocene volcanic products are generally concealed beneath voluminous, generally unfaulted, younger volcanic products. An exception is the southeast margin of the TVZ where the two parallel, northeast-trending Paeroa and Te Weta Fault blocks expose Quaternary volcanic products consisting predominantly of caldera-related, rhyolitic ignimbrites and lacustrine sediments. The Taupo-Reporoa Basin is situated along the eastern part of the map area, and its northernmost part underwent collapse to form Reporoa Caldera.
The Paeroa Fault block is the largest exposed fault block within the TVZ, and it encompasses early and middle Pleistocene ignimbrites and sedimentary deposits that are buried throughout the Taupo-Reporoa Basin to the east. This map displays the volcanic and sedimentary geology of ~430 km2 of the Paeroa Fault block and the adjacent Te Weta Fault block at a scale of 1:50,000. Volcanic and sedimentary rocks are divided into the Reporoa Group, Whakamaru Group, and Huka Group (from oldest to youngest), which are overlain by relatively unfaulted late Pleistocene and Holocene surficial volcanic and sedimentary deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201021","usgsCitation":"Downs, D.T., Leonard, G.S., Wilson, C.J.N., and Rowland, J.V., 2020, Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand: U.S. Geological Survey Open-File Report 2020–1021, scale 1:50,000, https://doi.org/10.3133/ofr20201021.","productDescription":"1 Map: 50.22 x 34.79 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-107861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":373326,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DYBBGX","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Database for the geologic map of the Paeroa fault block and surrounding area, Taupo Volcanic Zone, New Zealand"},{"id":373324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1021/coverthb.jpg"},{"id":373325,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1021/ofr20201021.pdf","text":"Sheet","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1021"}],"country":"New Zealand ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 165.948486328125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -46.72103466129568\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory staff
Alaska Volcano Observatory
4210 University Drive
Anchorage, AK 99508
Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English language"},{"id":373195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1022/coverthb.jpg"},{"id":383760,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet_esp.pdf","text":"Reporte","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet Spanish language","linkHelpText":"En Español"},{"id":373196,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VK2FAL","text":"USGS data release","linkHelpText":"Results from frequency-ratio analyses of soil classification and land use related to landslide locations in Puerto Rico following Hurricane Maria"},{"id":388108,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990ZP4C","text":"USGS data release","linkHelpText":"Geographic Information System Layer of a Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":373241,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_LandslideGuide_2020.pdf","text":"Landslide Guide for Residents of Puerto Rico"},{"id":373242,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_GuiaDerrumbe_2020.pdf","text":"Guía sobre deslizamientos de tierra para residentes de Puerto Rico"},{"id":373256,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_kmz.zip","text":"Landslide susceptibility map as a Google Earth file","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020-1022 kmz"},{"id":373246,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/SI_raster_for_SMAP.zip","text":"SI raster for SMAP","linkFileType":{"id":6,"text":"zip"},"description":"SI raster for SMAP"},{"id":373247,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":376481,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico"},{"id":376485,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.tif","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado (Geotiff)","description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado (Geotiff)"},{"id":399457,"rank":19,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109788.htm"},{"id":373261,"rank":14,"type":{"id":4,"text":"Application Site"},"url":"https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=10506ecc7f15491daee17647f19248ee","text":"Landslide susceptibility map interactive web viewer"},{"id":373262,"rank":15,"type":{"id":4,"text":"Application Site"},"url":"https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=9378c6a890164d378a2e66da4b7970c0","text":"Mapa de susceptibilidad a deslizamientos de tierra visor web interactivo"},{"id":373248,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_geospatial.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced"},{"id":373249,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_georeferenced.tif","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced (Geotiff)","description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico (Geotiff)"},{"id":373260,"rank":13,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2020-1022 readme file"},{"id":376484,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3736572265625,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 17.832374329567518\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Barrier islands, such as Dauphin Island, Alabama, provide numerous invaluable ecosystem services including storm damage reduction and erosion control to the mainland, habitat for fish and wildlife, carbon sequestration in marshes, water catchment and purification, recreation, and tourism. These islands are dynamic environments that are gradually shaped by currents, waves, and tides under quiescent conditions yet can evolve in the time scale of hours to days during hurricanes and other extreme storms. The ecosystems associated with these islands also face numerous other hazards, including accelerated sea-level rise, oil spills, and anthropogenic stressors.
Hurricane Katrina in 2005 and the Deepwater Horizon oil spill in 2010 are two major events that have affected habitats and natural resources on Dauphin Island, Ala. The latter event prompted a cooperative effort between the U.S. Geological Survey and the U.S. Army Corps of Engineers to investigate viable, sustainable restoration measures that reduce degradation and enhance the natural resources of Dauphin Island, Ala. In collaboration with the State of Alabama and the National Fish and Wildlife Foundation, the overarching goal of the Alabama Barrier Island Restoration Feasibility Assessment project was to document baseline conditions and forecast potential conditions under varying sea-level change and storm scenarios for a no-action alternative along with a variety of restoration measures including beach and dune restoration, marsh and back-barrier restoration, and placement of sand in the littoral zone. The modeling component of this project used decadal hydrodynamic geomorphic, water quality, and habitat modeling to better understand how the various restoration measures may influence the habitat composition, sustainability, and resiliency of Dauphin Island under potential future conditions, benchmarked against the no-action case.
The report covers the habitat modeling efforts associated with the Alabama Barrier Island Restoration Feasibility Assessment project. For various potential future island configurations for Dauphin Island, we predicted coverage of habitat types (for example, beach, dune, intertidal marsh, and woody vegetation) using a spatially explicit habitat model based on landscape-position information (for example, elevation and distance from shore) extracted from the hydrodynamic geomorphic outputs. Similarly, we forecasted habitat suitability for oysters and seagrass using habitat suitability index models. Another component of the Alabama Barrier Island Restoration Feasibility Assessment project, presented separately, integrates these habitat model results into a structured decision-making framework that accounts for competing objectives. Collectively, this information provides insights to natural resource managers and planners on how a restoration measure may maintain or impede natural coastal processes and provide information critical for making future-focused decisions regarding barrier island restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201003","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and in collaboration with the State of Alabama and the National Fish and Wildlife Foundation","usgsCitation":"Enwright, N.M., Wang, H., Dalyander, P.S., and Godsey, E., eds., 2020, Predicting barrier island habitats and oyster and seagrass habitat suitability for various restoration measures and future conditions for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1003, 99 p., https://doi.org/10.3133/ofr20201003.","productDescription":"Report: x, 99 p.; 3 Data Releases","numberOfPages":"114","onlineOnly":"Y","ipdsId":"IP-113342","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":372971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1003/ofr20201003.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1003"},{"id":372973,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O30XMZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Oyster habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":399449,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109756.htm"},{"id":372974,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B32VTE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Seagrass habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":372972,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PK0EH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Landscape position-based habitat modeling for the Alabama Barrier Island feasibility assessment at Dauphin Island"},{"id":372970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1003/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.341064453125,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.166500980766052\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
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
Forecasting barrier island evolution provides coastal managers and stakeholders the ability to assess the resiliency of these important coastal environments that are home to both established communities and existing natural habitats. This study uses an established coupled model framework to assess how Dauphin Island, Alabama, responds to various storm and sea-level change scenarios, along with a suite of restoration measures, over the course of a decade. The coupled model framework uses validated models for long-term alongshore sediment transport (Delft 3D), short-term storm induced impacts (XBeach), as well as dune building and recovery (empirical dune growth model). This model framework was simulated with the various storm and sea-level change scenarios on a non-restored Dauphin Island, then a subset of the storm and sea-level change scenarios were applied to a suite of seven different restoration measures to determine how they would influence the morphologic evolution over a decadal period. Topographic and bathymetric changes captured in post-simulation digital elevation models were then passed on to partners for various simulations to determine the effects on habitat evolution and water quality as it relates to oyster reef and submerged aquatic vegetation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, The Water Institute of the Gulf, and the University of North Carolina at Wilmington","usgsCitation":"Mickey, R.C., Godsey, E., Dalyander, P.S., Gonzalez, V., Jenkins, R.L., III, Long, J.W., Thompson, D.M., and Plant, N.G., 2020, Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1001, 45 p., https://doi.org/10.3133/ofr20201001.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113301","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399440,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109734.htm"},{"id":372467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2020/1001/ofr20201001.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1001"},{"id":372442,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDM1OJ","text":"USGS data release","linkHelpText":"Dauphin Island decadal forecast evolution model inputs and results"},{"id":372303,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191139","text":"Open-File Report 2019-1139","linkHelpText":"- Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution"},{"id":372301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2020/1001/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3355712890625,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.180747605060766\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
Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.36715698242186,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.210421455819937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Since the 2018 eruption of Kīlauea Volcano, the topic of whether commercial developments not only caused the eruption to occur in the lower East Rift Zone (LERZ), but also caused its high eruption rate has been a subject of public discussion. We review Kīlauea Volcano publications from the past several decades and show that the eruptive behavior of the volcano has varied and that the 2018 eruption was similar to past eruptions in many ways. We find no evidence to support any human influence on Kīlauea Volcano.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201017","usgsCitation":"Kauahikaua, J. and Trusdell, F., 2020, Have humans influenced volcanic activity on the lower East Rift Zone of Kīlauea Volcano? A publication review: U.S. Geological Survey Open-File Report 2020–1017, 17 p., https://doi.org/10.3133/ofr20201017.","productDescription":"iv, 17","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-111187","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":372511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1017/ofr20201017.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1017"},{"id":372510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1017/coverthb.jpg"},{"id":399456,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109729.htm"}],"country":"United States","state":"Hawaii","otherGeospatial":"Lower East Rift Zone of Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3741455078125,\n 19.19835036116298\n ],\n [\n -155.26016235351562,\n 19.281332062593734\n ],\n [\n -155.20523071289062,\n 19.26059057084779\n ],\n [\n -155.14892578125,\n 19.264479800497103\n ],\n [\n -155.0665283203125,\n 19.30466310133747\n ],\n [\n -154.9456787109375,\n 19.37334071336406\n ],\n [\n -154.82070922851562,\n 19.474360774988355\n ],\n [\n -154.80422973632812,\n 19.530024424775405\n ],\n [\n -154.92233276367188,\n 19.596019240312494\n ],\n [\n -154.9456787109375,\n 19.621892180319374\n ],\n [\n -154.97177124023438,\n 19.641294152538578\n ],\n [\n -154.97177124023438,\n 19.676211792974332\n ],\n [\n -155.050048828125,\n 19.590844152960923\n ],\n [\n -155.12832641601562,\n 19.520964205879825\n ],\n [\n -155.18875122070312,\n 19.449759112405612\n ],\n [\n -155.26565551757812,\n 19.42256346067618\n ],\n [\n -155.35629272460938,\n 19.359089245934307\n ],\n [\n -155.38787841796872,\n 19.299478713495898\n ],\n [\n -155.4071044921875,\n 19.23206673568465\n ],\n [\n -155.4071044921875,\n 19.19186565046399\n ],\n [\n -155.3741455078125,\n 19.19835036116298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contacts, Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue, Suite A-8
Hilo, HI 96720
From 2017 to 2018, the U.S. Geological Survey (USGS) Hawaiian Volcano Observatory (HVO) responded to ongoing and changing eruptions at Kīlauea Volcano as part of its mission to monitor volcanic processes, issue warnings of dangerous activity, and assess volcanic hazards. To formalize short-term hazards assessments—and, in some cases, issue prognoses for future activity—and make results discoverable to both the public and the authorities, HVO released reports online. These reports were published rapidly, received peer review under the USGS’s Fundamental Science Practice guidelines, and were intended to address a focused question posed by one or more cooperating agencies—for this reason, they were called “cooperator reports.” This Open-File Report concatenates four such products issued in 2017 and 2018 into a single publication. These reports have been reformatted and lightly edited for clarity, but the content has not otherwise been changed from the versions first publicly released.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201002","usgsCitation":"Neal, C.A., and Anderson, K.R., 2020, Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018: U.S. Geological Survey Open-File Report 2020–1002, 34 p., https://doi.org/10.3133/ofr20201002.","productDescription":"iv, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-114660","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399444,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109722.htm"},{"id":372588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1002/coverthb.jpg"},{"id":372589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1002/ofr20201002.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3114,\n 19.1644\n ],\n [\n -154.8036,\n 19.1644\n ],\n [\n -154.8036,\n 19.4433\n ],\n [\n -155.3114,\n 19.4433\n ],\n [\n -155.3114,\n 19.1644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue, Suite A-8
Hilo, HI 96720
The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations and groundwater and the geologic materials between them; (3) the location of evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2019). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley near Lost Hills, California, adjacent to the Lost Hills oil field. Data collected at the Lost Hills multiple-well monitoring site (LHSP) provide information about the geology, hydrology, geophysics, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the Lost Hills oil field in an area where there is little groundwater data. This report presents construction information for the LHSP and initial geohydrologic data collected from the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191114","collaboration":"Prepared in cooperation with California State Water Resources Control Board","usgsCitation":"Everett, R.R., Kjos, A., Brown, A.A., Gillespie, J.M., and McMahon, P.B., 2020, Multiple-well monitoring site adjacent to the Lost Hills oil field, Kern County, California: U.S. Geological Survey Open-File Report 2019–1114, 8 p., https://doi.org/10.3133/ofr20191114.","productDescription":"8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-104714","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437100,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXIN8","text":"USGS data release","linkHelpText":"Aquifer test data for multiple-well monitoring site LHSP, Kern County, California"},{"id":399418,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109721.htm"},{"id":372427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1114/coverthb.jpg"},{"id":372428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1114/ofr20191114.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Lost Hills Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.0167,\n 35.5294\n ],\n [\n -119.4833,\n 35.5294\n ],\n [\n -119.4833,\n 35.7667\n ],\n [\n -120.0167,\n 35.7667\n ],\n [\n -120.0167,\n 35.5294\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
In 2014, groundwater samples from residential and public supply wells in the vicinity of two former U.S. Navy bases at Willow Grove and Warminster, and an active Air National Guard Station at Horsham, Bucks and Montgomery Counties, Pennsylvania, were found to have concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which are per- and polyfluoroalkyl substances (PFAS), above U.S. Environmental Protection Agency (EPA) provisional health advisory (HA) levels for drinking water. Five supply wells near the bases were shut down because of PFAS contamination. In 2016, after EPA established a Lifetime HA for PFAS in drinking water that is lower than the provisional HA in place in 2014, at least 13 additional supply wells near the bases were shut down because of PFAS contamination. At the request of the U.S. Navy, and in consultation with other Federal and State agencies and local stakeholders, the U.S. Geological Survey used historical and recent data on well withdrawals, recharge rates, aquifer properties, groundwater levels, and stream base flow to evaluate regional groundwater-flow paths from identified areas of PFAS groundwater contamination or potential PFAS sources at the bases. Groundwater withdrawals near the bases from public supply and other large wells decreased substantially from the 1990s to 2017, increasing the proportion of groundwater recharge that discharged to local streams. A preliminary groundwater-flow model, calibrated using 1,009 groundwater levels and 17 stream base flow estimates, simulated regional flow paths from the bases and showed that recharge at the bases discharged to withdrawal wells and local streams, generally within a mile or two of the bases. Supply and remediation wells at the bases captured some of the recharge on base areas of possible PFAS contamination, whereas other base recharge was simulated to flow to nearby public supply wells and streams, depending on water use and aquifer recharge conditions between 1999 and 2017. The locations of many residential wells near the bases that were identified by the Navy and Air National Guard as having elevated PFAS concentrations were generally consistent with the simulated flow paths from possible sources at the bases. However, there are some areas of observed PFAS contamination where no flow paths from base sources were simulated. Additionally, no data were available on PFAS concentrations in groundwater in some areas of simulated flow paths from base sources. Data and models used for this study are provided in this report and in digital data releases to support further investigations and model revisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191137","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Goode, D.J., and Senior, L.A., 2020, Groundwater withdrawals and regional flow paths at and near Willow Grove and Warminster, Pennsylvania—Data compilation and preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017: U.S. Geological Survey Open-File Report 2019–1137, 127 p., https://doi.org/10.3133/ofr20191137.","productDescription":"Report: x, 127 p.; 2 Data Releases","numberOfPages":"138","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113639","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":399427,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109664.htm"},{"id":371906,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZGEI67","text":"USGS data release","linkHelpText":"Groundwater levels, groundwater withdrawals, and point-source discharges to streams in the vicinity of Willow Grove and Warminster, Bucks and Montgomery Counties, Pennsylvania, for selected years during 1999–2017"},{"id":371905,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K36P5S","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used to evaluate groundwater flow in the vicinity of Horsham and Warminster, Bucks and Montgomery Counties, Pennsylvania—Preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017"},{"id":372113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1137/ofr20191137.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1137"},{"id":371903,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1137/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Bucks County, Montgomery County","city":"Warminster, Willow Grove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.3536,\n 40.0678\n ],\n [\n -74.9167,\n 40.0678\n ],\n [\n -74.9167,\n 40.2967\n ],\n [\n -75.3536,\n 40.2967\n ],\n [\n -75.3536,\n 40.0678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Estimating the number of breeding pairs in a mixed-species waterbird colony is difficult because colonial waterbirds are vulnerable to human intrusion and their colonies are often in remote areas with limited access. We investigated methods to estimate the number of nests of waterbirds at a large, mixed-species colony at Chase Lake National Wildlife Refuge in south-central North Dakota. The primary goals of this study were to evaluate survey methods for shrub- and ground-nesting colonial waterbirds at Chase Lake National Wildlife Refuge and to develop protocols for estimating abundance of the different species. The specific objectives were (1) to assess visible-nest counts for ciconiiform species from the perimeter of nesting areas (hereafter, perimeter counts) and observational surveys from fixed points outside the colony to count flights of adult ciconiiforms in and out of the colony (hereafter, flightline surveys) as alternatives to within-colony counts of ciconiiform nests, and (2) to assess semiautomated, pixel-based image-analysis techniques to estimate abundance of American White Pelicans (Pelecanus erythrorhynchos) as an alternative to traditional manual counts from aerial photographs.
For shrub-nesting ciconiiform species, observers counted 2,259 and 1,759 active ciconiiform nests in 2012 and 2013, respectively, during within-colony counts of ciconiiform nests. Results from within-colony counts of ciconiiform nests indicated a positive relation between the number of nests and the area of the shrub subcolony for the three most common ciconiiform species and all ciconiiform species combined. The perimeter nest counts of ciconiiform nests at Chase Lake represented only 18.8 percent of the total active ciconiiform nests counted in 11 subcolonies in 2012, which was well below the recommended target of 50 percent. Although we found a positive relationship between the number of nests counted during perimeter counts and the number of nests counted during within-colony counts for the three most common ciconiiform species and all ciconiiform species combined, perimeter counts at Chase Lake were hampered by disturbance to nesting birds. Thus, we discontinued the perimeter counts before they were completed. We did not develop predictive models from these perimeter counts in 2012 because these models could be misleading due to inconsistent application of the survey methods, which likely would have provided inaccurate perimeter counts. The extent of this issue is unknown. Flightline surveys at Chase Lake documented patterns of ciconiiform activity that were unknown for this region. For the common ciconiiform species, the number of flights to and from the South Island at Chase Lake were greatest in the morning (7:00−12:00 central daylight time [CDT]) and least in the afternoon (12:00−17:00), and least early in the breeding season (May 29–June 20, 2013) and greatest later in the breeding season (June 24–August 1, 2013). Flightline surveys are an index but lacked comparability with within-colony nest counts because the two methods provide measures of different things (that is, adult activity away from the colony as compared to the number of nests within the colony). The overall proportions of flights generally reflected the proportions of the within-colony nest counts for the four most common species: Black-crowned Night-Heron (Nycticorax nycticorax), Cattle Egret (Bubulcus ibis), Great Egret (Ardea alba), and Snowy Egret (Egretta thula). Flightline surveys at Chase Lake indicated apparent variation related to the time of day and season, as well as a variation in detection of inbound and outbound adult ciconiiforms. For ciconiiforms at Chase Lake, the most appropriate combination of survey approaches will depend on the need for annual estimates of nest abundance of ciconiiform species, balanced with the financial, personnel, and logistical constraints associated with the survey methods.
For ground-nesting American White Pelicans, the results from this study indicated that digital-image processing using remote-sensing software provides an accurate estimate of the number of American White Pelican nests. Estimates of the number of pelican nests from digital-image processing, using two commercially available remote-sensing software packages, produced nest estimates that were comparable to those of traditional manual counts from aerial photographs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201008","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Igl, L.D., Bartos, A.J., Woodward, R.O., Scherr, P., and Sovada, M.A, 2020, Evaluation of survey methods for colonial waterbirds at Chase Lake National Wildlife Refuge, North Dakota: U.S. Geological Survey Open-File Report 2020–1008, 44 p., https://doi.org/10.3133/ofr20201008. ","productDescription":"Report: viii, 44 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-112516","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":371775,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1008/ofr20201008.pdf","text":"Report","size":"7.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1008"},{"id":371774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1008/coverthb.jpg"},{"id":371776,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90NK31K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evaluation of Survey Methods for Colonial Waterbirds at Chase Lake National Wildlife Refuge, North Dakota, data release"}],"country":"United States","state":"North Dakota ","otherGeospatial":"Chase Lake National Wildlife Refuge","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.481105,46.983794 ], [ -99.481105,47.030693 ], [ -99.417191,47.030693 ], [ -99.417191,46.983794 ], [ -99.481105,46.983794 ] ] ] } } ] }","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401