Biological conservation often requires an understanding of how environmental conditions affect species occurrence and detection probabilities. We used a hierarchical framework to evaluate these effects for several Appalachian stream fish species of conservation concern: Chrosomus cumberlandensis (BSD; blackside dace), Etheostoma sagitta (CAD; Cumberland arrow darter), and Etheostoma spilotum (KAD; Kentucky arrow darter). Etheostoma susanae (Cumberland darter) also is present in the study area but was too rare to model in this analysis. In this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, fish and habitat data were collected from 205 randomly selected stream sites in the upper Cumberland and Kentucky River Basins (120 and 85 sites, respectively) of Kentucky and Tennessee. Sites were sampled with 10 spatial replicates (2 meter x 5 meter electrofishing zones) to enable estimation of detection probabilities and environmental effects. The best models (that is, lowest Akaike information criterion scores) showed the effects of agriculture (negative) on occurrence of BSD and stream conductivity (negative) on occurrence of CAD and KAD. These effects were statistically more important than measures of basin area, elevation, and substrate size. Conductivity and agriculture showed nonlinear effects on species occurrence, and effects of conductivity were more precise above 400 microsiemens per centimeter than below this threshold. Models incorporated detection-level effects of electrofishing time (positive), flow velocity (negative), sand substrate (positive), and gravel/cobble substrate (negative). Models accounting for detection of BSD estimated occupancy rates similar to the observed proportion of occupied sites (0.10), but the best-supported models for CAD and KAD increased expected occupancy by about 4 percent for each species (from 0.17 to 0.21 for CAD and from 0.07 to 0.11 for KAD). Results of this study provide new inferences for modeling stream fish occurrence and detection processes and highlight the importance of continued monitoring and assessment of rare fish species in Appalachian headwater streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201100","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Hitt, N.P., Rogers, K.M., Kessler, K., and Macmillan, H., 2020, Modeling occupancy of rare stream fish species in the upper Cumberland and Kentucky River Basins: U.S. Geological Survey Open-File Report 2020–1100, 22 p., https://doi.org/10.3133/ofr20201100.","productDescription":"vi, 22 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118746","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":378605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1100/ofr20201100.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1100"},{"id":378604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1100/coverthb.jpg"}],"country":"United States","state":"Kentucky, Tennessee, Virginia","otherGeospatial":"Cumberland River basin, Kentucky River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 38.77121637244273\n ],\n [\n -87.1875,\n 38.77121637244273\n ],\n [\n -87.1875,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, used surface and borehole geophysical methods to delineate the freshwater/saltwater interface in coastal plain aquifers along the southwestern part of Long Island, New York. Over pumping of groundwater in the early 20th century combined with freshwater/saltwater interfaces at the coastline created saltwater intrusion in the upper glacial, Jameco, Magothy, and Lloyd aquifers. This study documents, for the first time, extensive saltwater intrusion of the Lloyd aquifer along the southwestern coast of Long Island, N.Y. Several public-supply wells in the southern parts of Nassau, Queens, and Kings Counties have been adversely affected by saltwater intrusion causing supply wells to be shutdown and abandoned. Due to the ongoing groundwater pumping in southern Nassau County, the freshwater/saltwater interface requires delineation and monitoring for any inland movement.
In 2015–17, the U.S. Geological Survey collected time domain electromagnetic soundings at 12 locations and borehole electromagnetic induction conductivity logs at 9 wells within the study area to delineate several saltwater intrusion wedges. The upper glacial, Jameco, and Magothy aquifers were grouped into one aquifer complex within the study area to simplify interpretations. The coastal plain sediments increase in thickness from west to east and north to south because of their regional dip toward the southeast. Three separate wedges, shallow, intermediate, and deep, of saltwater intrusion were delineated in the upper glacial, Jameco, and Magothy aquifer complex. In addition, analysis of geophysical logs collected in an open borehole of a test well in southern Queens County in 1989 revealed the Lloyd aquifer was nearly completely intruded by saltwater with an estimated chloride concentration of 15,000 milligrams per liter. The geophysical logs from this well provides, for the first time, definitive proof of saltwater intrusion of the Lloyd aquifer on Long Island’s south shore, suggesting the freshwater/saltwater interface was at the coastline and not miles offshore as theorized by previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201093","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Como, M.D., and Zuck, M.A., 2020, Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17: U.S. Geological Survey Open-File Report 2020–1093, 27 p., https://doi.org/10.3133/ofr20201093.","productDescription":"Report: vi, 27 p.; Data Release; Database","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118303","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378439,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7X63KT0","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS GeoLog Locator"},{"id":378438,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens County, New York, October–November 2017"},{"id":378437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1093/ofr20201093.pdf","text":"Report","size":"3.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1093"},{"id":378436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1093/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03961181640625,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.54406959045767\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan (HCP/NCCP) Preserve System was designed to protect and enhance ecological diversity and function in eastern Contra Costa County, California. Aquila chrysaetos (golden eagle) is a special-status species expected to benefit from biological goals of the HCP/NCCP. As part of a broader study, we estimated site-occupancy, abundance, and reproduction of golden eagles in the HCP/NCCP inventory area in 2019. We completed 99 surveys and recorded a total of 50 detections of territorial pairs of eagles at 20 (67 percent) of 30 sites (13.9-square-kilometer [km2] plots). Detection probability of territorial pairs was highest in January and February (≥0.75) and lowest in mid-June to late July (<0.50). After correcting for imperfect detection, the expected probability of site-occupancy was 0.69 (standard error [SE] = 0.09), and mean expected abundance was 0.76 pairs per site (SE = 0.16), or 27.4 pairs per 500 km2. We found evidence of successful nesting (≥1 young fledged) for 3 (14 percent) of 22 pairs of eagles monitored in 2019. Our study design and baseline results should be useful for future monitoring and conservation of golden eagles in the HCP/NCCP area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201107","collaboration":"Prepared in cooperation with East Contra Costa County Habitat Conservancy Science and Research Grant Program, East Bay Regional Parks District, Save Mount Diablo’s Mary Bowerman Science and Research Grant Program, and NextEra Energy","usgsCitation":"Wiens, J.D., Kolar, P.S., and Bell, D.A., 2020, Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California: U.S. Geological Survey Open-File Report 2020-1107, 11 p., https://doi.org/10.3133/ofr20201107.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-119617","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":378434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1107/coverthb.jpg"},{"id":378435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1107/ofr20201107.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1107"}],"country":"United States","state":"California","county":"Contra Costa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75048828124999,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 37.37015718405753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Because of its importance for species covered under Federal Fishery Management Plans (FMPs), the San Francisco Bay (SFB) estuary has been designated as Essential Fish Habitat (EFH) under the Magnuson-Stevens Fishery Conservation and Management Act (MSA; 16 United States Code §18559b). Within this estuary, benthic macroinvertebrate communities provide important prey resources for many economically significant fish species that rely on EFH. Periodic maintenance dredging can impact benthic communities; however, there is a lack of scientific information specific to SFB regarding dredging effects on macroinvertebrates in fish foraging areas. In addition, rates of benthic community recolonization and recovery following dredging and subsequent effects on foraging fish are unknown. For this reason, it is difficult for regulatory and resource agencies to determine the impacts of maintenance dredging. Thus, the National Marine Fisheries Service (NMFS) and the consortium of agencies (U.S. Environmental Protection Agency [EPA], U.S. Army Corp of Engineers [USACE], San Francisco Regional Water Quality Control Board [SFRWQCB], and San Francisco Bay Conservation and Development Commission [BCDC]) that make up the San Francisco Bay Long Term Management Strategy for Dredging (LTMS) identified a study of dredging impacts on SFB fish foraging habitat as one of their highest priorities in their 2011 Programmatic EFH Agreement (U.S. Army Corp of Engineers and U.S. Environmental Protection Agency, 2011).
The LTMS agencies identified the region of interest as shallow (<13 feet [<4 meters (m)] mean lower low water [MLLW]), soft-bottom (silt/clay soil texture) areas in the Central Bay of SFB that were periodically dredged (every 1–3 years). Fish species of interest were compiled by NMFS and included those managed by the Pacific Groundfish, Pacific Salmon, and Coastal Pelagic FMPs (pursuant to the MSA) as well as those listed under the California State or Federal Endangered Species Act (ESA; 16 U.S.C. §1531–1544) as threatened or endangered. Target species included leopard shark (Triakis semifasciata), big skate (Raja binoculata), English sole (Parophrys vetulus), starry flounder (Platichthys stellatus), brown rockfish (Sebastes auriculatus), green sturgeon (Acipenser medirostris; threatened species under Federal ESA), northern anchovy (Engraulis mordax), longfin smelt (Spirinchus thaleichthys, threatened under California ESA), and Pacific sardine (Sardinops sagax). In addition, Dungeness crab (Cancer magister), California halibut (Paralichthys californicus), and white sturgeon (Acipenser transmontanus) also were included because they are substantial contributors to the California State fishery.
To address LTMS priorities, U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station (hereafter USGS) conducted a multi-phased project including an initial literature review, study design, pilot study, and implementation of a full study. The overarching goal was to assess the effects of periodic dredge operations (every 1–3 years) on benthic habitat for foraging fish in the Central Bay, with emphasis on the foraging requirements of target fish species and analyses of benthic macroinvertebrates in dredged areas compared to adjacent undredged reference areas. The USGS partnered with University of California, Davis, fisheries expert James Hobbs to synthesize existing knowledge of fish foraging ecology and review benthic infauna community composition in SFB with a focus on the Central Bay. The literature review (Phase I; De La Cruz and others, 2016) addressed key questions identified by the LTMS on benthic foraging fish in the study area, including the following: (1) What are target fish eating? (2) What are the seasonal differences in prey items and macroinvertebrate assemblages? (3) What are the annual differences in prey items and macroinvertebrate assemblages? (4) What are the predominant macroinvertebrate functional groups from the perspective of fish foraging? Phase II consisted of creating a framework for a functional assessment of maintenance dredging effects on foraging fish and drafting a full study design (De La Cruz and others, 2017), which was then tested in the Phase III pilot study. The Phase IV full study incorporated lessons learned from the pilot study. Here we focus on the results of the full study and implications for benthic foraging fishes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201086","usgsCitation":"De La Cruz, S.E.W., Woo, I., Hall, L., Flanagan, A., and Mittelstaedt, H., 2020, Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California: U.S. Geological Survey Open-File Report 2020–1086, 96 p., https://doi.org/10.3133/ofr20201086.","productDescription":"x, 96 p.","onlineOnly":"Y","ipdsId":"IP-112237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":378273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1086/coverthb.jpg"},{"id":378274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1086/ofr20201086.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1086"}],"country":"United States","state":"California","otherGeospatial":"Central San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65411376953125,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.75334401310656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
We assessed the history of flow and riparian ecosystem change along the Wind River using cottonwood tree-ring data, streamgage records, historical temperature and precipitation data, drought indices, and local observations and Traditional Ecological Knowledge from elders of the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming. This assessment identified impacts that have occurred to riparian resources of concern to the Tribes, which will assist in prioritizing drought planning efforts. Impacts included reduced abundance, reduced regeneration, and increased mortality in cottonwoods (Populus deltoides and P. angustifolia); an increase in invasive species, especially Russian olive (Elaeagnus angustifolia), that are gradually replacing cottonwoods and other native woody plants; decreased abundance of native and culturally important plants; reduced abundance of culturally important fish; reduced volume and changes to the timing of flows; and changes in river course. This assessment documented the biophysical and social factors that have contributed to riparian ecosystem change and to reduced water availability and flows, including agricultural diversion, drought, and fire. Cottonwoods along the Wind River are as much as 300 years old. By relating tree-ring width to recorded streamflows, we were able to reconstruct streamflows confidently back to the 1850s and speculatively back to the mid-1700s. Extending the historical record of streamflows allows for a more-complete understanding of hydroclimatic variability and provides a foundation for developing preparedness and response strategies for drought management. Ring width of cottonwood trees at the Boysen Site was more strongly correlated to river flow than to local precipitation or temperature, indicating that growth of trees is controlled more by montane snowmelt than by local weather. Therefore, tree rings are a better indicator of water supply than of the local conditions controlling water demand. The extended flow record from tree rings revealed the occurrence of a major period of low flow from 1870 to 1910 that was not evident in the shorter instrumental records of flow and weather. Information from tree rings, streamflow measurements, drought indices, and elder observations all suggest that the early 2000s drought was the most severe, sustained drought in the last century. Our results illustrate how drought is experienced in different ways across locations and sectors, which underscores the importance of using multiple indicators for drought management. These results will contribute to ongoing assessment, monitoring, and planning efforts at the Wind River Reservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201072","collaboration":"Prepared in cooperation with the Eastern Shoshone Tribe of the Wind River Reservation, Wyoming, the Northern Arapaho Tribe of the Wind River Reservation, Wyoming, and Colorado State University","usgsCitation":"McNeeley, S.M., Friedman, J.M., Beeton, T.A., and Thaxton, R.D., 2020, Cottonwoods, water, and people—Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming: U.S. Geological Survey Open-File Report 2020–1072, 33 p., https://doi.org/10.3133/ofr20201072.","productDescription":"Report: iv, 33 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113563","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378034,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S1UIAL","text":"USGS data release","linkHelpText":"Tree-Ring Data Collected in 2017 and 2018 From Cottonwood Trees Along the Wind River in Wind River Indian Reservation, Wyoming"},{"id":377898,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1072/coverthb.jpg"},{"id":377899,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1072/ofr20201072.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1072"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.44442749023438,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 42.83569550641452\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Avenue, Bldg. C
Fort Collins, CO 80526
The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Coastal cliffs constitute 80 percent of the world’s coastline, with seacliffs fronting a large proportion of the U.S. West Coast shoreline, particularly in California. Erosion of coastal cliffs can threaten infrastructure and human life, yet the spatial and temporal scope of cliff studies have been limited by cumbersome traditional methods that rely on the manual interpretation of seacliff features—especially seacliff toes and top edges. The Cliff Feature Delineation Tool (CFDT) and the Baseline Builder Tool are designed to increase the efficiency of deriving seacliff features from remote sensing datasets by utilizing an automated, quantitative approach that eliminates traditional interpretive methods and ensures reproducibility. This document functions as a user guide for operating the Cliff Feature Delineation Tool and Baseline Builder Tool and includes a walkthrough of data-visualization and data-review workflows for the tools’ three-dimensional (3D) cliff feature outputs. Also included is a brief overview of cliff feature delineation at the U.S. Geological Survey (USGS) and a detailed description of the tools’ algorithmic logic.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201070","usgsCitation":"Seymour, A.C., Hapke, C.J., and Warrick, J., 2020, Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide: U.S. Geological Survey Open File Report 2020–1070, 54 p.,\nhttps://doi.org/10.3133/ofr20201070.","productDescription":"Report: vi, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112057","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377578,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1070/ofr20201070.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1070"},{"id":377535,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1070/coverthb2.jpg"},{"id":377532,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UKW7PO","text":"USGS software release","linkHelpText":"Cliff Feature Delineation Tool and Baseline Builder version 1.0"}],"contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025
Kelp forests and rocky reefs are among the most recognized marine ecosystems and provide the primary habitat for several species of fishes, invertebrates, and algal assemblages (Stephens and others, 2006). In addition, kelp forests have been shown to be important carbon dioxide sinks (Wilmers and others, 2012) and are an important source of nearshore marine primary production (Duggins and others, 1989). These highly dynamic ecosystems are extremely variable, and both top-down and bottom-up ecological controls drive this rich trophic environment. Giant kelp (Macrocystis pyrifera) forests and the species that inhabit these ecosystems are influenced by several environmental conditions, such as wave exposure, water temperature, water clarity, bottom depth and composition, species composition, and the density of kelp and other algal assemblages (Schiel and Foster, 2015). However, in addition to “normal” variability, kelp forests can undergo extreme regime shifts from kelp canopy forested areas to barrens characterized by high densities of urchins and encrusting coralline algae (Harrold and Reed, 1985).
San Nicolas Island (SNI), outermost of the California Channel Islands, is home to a diverse group of terrestrial and marine organisms and includes kelp bed and rocky reef habitats (fig. 1). The SNI kelp forests not only provide food and shelter for fishes and invertebrates within the habitat, but also they support higher trophic level consumers such as marine birds and several marine mammal species including the southern sea otter (Enhydra lutris nereis), a major predator on sea urchins and other marine invertebrates.
Owing to concern about the vulnerability of the California population, the U.S. Fish and Wildlife Service (USFWS) translocated 140 southern sea otters from the central California coast to SNI between 1987 and 1990. Although only approximately 14 translocated otters are thought to have remained at SNI (U.S. Fish and Wildlife Service, 2012), their population at the island has increased and is currently greater than 120 individuals (Hatfield and others, 2019). Sea otters are a natural part of the kelp forest ecosystem, but their presence has implications for community dynamics as they repopulate a region from which they were extirpated in the 19th century. At SNI, sea otters have been concentrated mostly around the west end of the island, with some use of the south side and very little, but expanding, use of the northeast side. An ecosystem shift from urchin dominated to kelp dominated, that occurred at a site at the west end of the island in the early 2000s, though initiated by sea urchin disease, was likely facilitated to some degree by sea otter foraging (Kenner and Tinker, 2018).
These ecosystems also are the target of many fisheries, including urchin and lobster. Urchin fisheries, which target the larger red sea urchin, may release the smaller but more mobile purple sea urchin from competitive control (Dayton and others, 1998). Lobster fisheries may release purple sea urchins from predatory control (Lafferty, 2004). Owing to the distance from the mainland, however, SNI kelp forests and reefs have been somewhat protected from the degree of harvest and other anthropogenic impacts experienced by the southern California mainland. Invasive species are another issue, and there are a few invasive subtidal macroalgae of concern in southern California waters. Although the brown alga Sargassum muticum has been established at the island for decades, S. horneri has only recently been seen at SNI and, so far, the invasive kelp Undaria pinnatifida and the green alga Caulerpa taxifolia have not been observed there. Sargassum horneri, in particular, has demonstrated a capability to outcompete native kelps at some of the other Channel Islands but it is unclear what indirect effects it may have on community structure (Marks and others, 2015).
Because the surrounding kelp forests fall within the management boundary of the SNI Integrated Natural Resources Management Plan (INRMP; U.S. Navy, 2015), USGS works with the Navy to provide surveys of this ecologically important ecosystem that inform natural resource managers of trends in the population abundance of particular species. In addition, long-term surveys allow for an understanding of potential changes in species diversity and community composition as a result of trophic or other interactions.
The U.S. Geological Survey (USGS) implemented a kelp forest monitoring program for the U.S. Navy at San Nicolas Island in 2014, building on sites and methods established by USFWS scientists in 1980 (appendix 1). This report focuses on data collected during sampling expeditions to these sites in fall 2018 (October 2–5) and spring 2019 (April 3–6). Together they will be herein referred to as year 5 because, although the trips were made in different calendar years, they were approximately 6 months apart and were conducted under the fifth year of this contract. The previous sampling year (fall 2017 and spring 2018) is referred to as year 4. The year 5 data are compared with data collected during eight trips from fall 2014 through spring 2018. Differences in counts between these expeditions can result from seasonal factors, stochastic variation, or sampling error, but temporal comparison can reveal population trends. Where appropriate, long-term data collected during the 33 years prior to the implementation of these slightly revised protocols will be presented in order to lend some context to the observations reported here.
Genus and species names used in this report are those currently recognized as valid in the Integrated Taxonomic Information System (ITIS.gov). Upon first use, the name recognized as valid by the World Register of Marine Species (WoRMS; marinespecies.org) is shown in brackets if different. The exception is Sargassum horneri which does not show up in any discernable form in ITIS.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201091","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J.A., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report: U.S. Geological Survey Open-File Report 2020–1091, 93 p., https://doi.org/10.3133/ofr20201091.","productDescription":"ix, 93 p.","onlineOnly":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377300,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1091/coverthb.jpg"},{"id":377301,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1091/ofr20201091.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1091"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Naval Facility San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.60197448730467,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.19675310661128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559
Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled \"Building Capacity for Applied Short-Term Ecological Forecasting\" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS.
Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201073","usgsCitation":"Bradford, J.B., Weltzin, J.F., McCormick, M., Baron, J., Bowen, Z., Bristol, S., Carlisle, D., Crimmins, T., Cross, P., DeVivo, J., Dietze, M., Freeman, M., Goldberg, J., Hooten, M., Hsu, L., Jenni, K., Keisman, J., Kennen, J., Lee, K., Lesmes, D., Loftin, K., Miller, B.W., Murdoch, P., Newman, J., Prentice, K.L., Rangwala, I., Read, J., Sieracki, J., Sofaer, H., Thur, S., Toevs, G., Werner, F., White, C.L., White, T., and Wiltermuth, M., 2020, Ecological forecasting—21st century science for 21st century management: U.S. Geological Survey Open-File Report 2020–1073, 54 p., https://doi.org/10.3133/ofr20201073.","productDescription":"vii, 54 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114740","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1073/ofr20201073.pdf","text":"Report","size":"598 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1073"},{"id":376786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1073/coverthb.jpg"}],"contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits. Information from MRDS, USMIN, and MDI records and from the extensive literature describing MRS mineral deposits was used to classify each entry by deposit type, host rock age and type, and estimated mineralization age. Most deposits were readily classified because of unique mineralogy, location, or well-constrained host rock. These deposits were then put into a tectonic evolutionary framework for the MRS, which showed that many deposits formed within discrete spatial and temporal stages of rift evolution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201069","usgsCitation":"Woodruff, L.G., Schulz, K.J., Dicken, C.L., and Nicholson, S.W., 2020, Mineral resource database for deposits related to the Mesoproterozoic Midcontinent Rift System, United States and Canada: U.S. Geological Survey Open-File Report 2020–1069, 20 p., https://doi.org/10.3133/ofr20201069.","productDescription":"Report: vi, 20 p.; 2 Tables","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113694","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":436840,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HTATKY","text":"USGS data release","linkHelpText":"Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376912,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.csv","text":"Table 1","size":"171 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376911,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.xlsx","text":"Table 1","size":"124 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376909,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1069/coverthb.jpg"},{"id":376910,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1069"}],"country":"United States, Canada","otherGeospatial":"Mesoproterozoic Midcontinent Rift System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.2216796875,\n 40.245991504199026\n ],\n [\n -81.8701171875,\n 50.792047064406866\n ],\n [\n -96.6357421875,\n 51.23440735163459\n ],\n [\n -96.1083984375,\n 43.45291889355465\n ],\n [\n -97.5146484375,\n 43.739352079154706\n ],\n [\n -97.55859375,\n 41.541477666790286\n ],\n [\n -99.931640625,\n 41.376808565702355\n ],\n [\n -100.1513671875,\n 37.16031654673677\n ],\n [\n -94.7021484375,\n 37.09023980307208\n ],\n [\n -94.833984375,\n 39.53793974517628\n ],\n [\n -82.2216796875,\n 40.245991504199026\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator, Mineral Resources Program
U.S. Geological Survey
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The U.S. Geological Survey National Crustal Model (NCM) is being developed to include spatially varying estimates of site response in seismic hazard assessments. Primary outputs of the NCM are continuous velocity and density profiles from the Earth’s surface to the mantle transition zone at 410-kilometer (km) depth for each location on a 1-km grid across the conterminous United States. Datasets used to produce the NCM may have a resolution of better than 1 km near the Earth’s surface in some regions, but, with increasing depth, NCM resolution decreases to tens to hundreds of kilometers in the mantle. Basic subsurface information is provided by the NCM geologic framework, thermal model, and petrologic and mineral physics database. In this report, the velocities and densities that can be extracted from the NCM are calibrated through the development of a porosity model based on Biot-Gassmann theory and more than 2,000 compressional- and (or) shear-wave velocity profiles less than 10 km deep from across the conterminous United States and southwestern Canada.
Sediment and rock porosities are derived from shear-wave velocity and are found to depend on effective pressure, rock type, and age (for sedimentary and extrusive volcanic deposits). Porosity-effective pressure functions are then estimated for each rock type (and age for sedimentary and extrusive volcanic deposits). Unconsolidated sediments are found to have higher porosities than consolidated units, which have higher porosities than unweathered igneous units; young sedimentary units (for example, Quaternary age units) tend to have higher porosities than older sedimentary units (for example, pre-Cenozoic age units); porosity decreases with increasing effective pressure; and porosities can decrease quickly through the weathered layer of intrusive rocks.
Comparing two Los Angeles area velocity models and the U.S. Geological Survey Bay Area velocity model with the NCM, the NCM does a better job on average of reproducing observed shear-wave velocities below 1 km per second because it has less bias and uncertainty. Approaching and above 1 km per second, the NCM tends to underpredict observed shear-wave velocity. Whereas several factors could contribute to this, the primary factor is probably bias in the NCM geologic framework. For example, the NCM will predict lower velocities in places where the depth to bedrock and basement appear shallower in the measured velocity profiles than specified in the NCM geologic framework. With regard to observed compressional-wave velocity and density, the NCM has significantly less bias than California models for the former, especially below 2 km per second, and all models tend to overpredict density for densities less than about 2,200 kilograms per cubic meter.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201052","usgsCitation":"Boyd, O.S., 2020, Calibration of the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2020–1052, 23 p., https://doi.org/10.3133/ofr20201052.","productDescription":"Report: vi, 23 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115717","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":436847,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQ5LNU","text":"USGS data release","linkHelpText":"GeoPhys"},{"id":376928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1052/ofr20201052.pdf","text":"Report","size":"6.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1052"},{"id":376929,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GO3CP8","text":"USGS 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U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
The seawaveQ R package provides functionality and help to fit a parametric regression model, SEAWAVE-Q, to pesticide concentration data from stream-water samples to assess trends. The model incorporates the strong seasonality and high degree of censoring common in pesticide data, and users can incorporate numerous ancillary variables such as streamflow anomalies. The model is fitted to pesticide data using maximum likelihood methods for censored data and is robust in terms of pesticide, stream location, and degree of censoring of the concentration data. This R package standardizes this methodology for trend analysis, documents the code, and provides help and tutorial information.
In previous investigations, the SEAWAVE-Q model assumed a linear trend across the period analyzed. For short trend periods, this assumption of a linear trend is adequate. However, as the period of record analyzed becomes longer, the assumption of linearity is problematic because of changes in pesticide regulation and use, some of which can be abrupt. In this update to the model, a restricted cubic spline option was added for long trend periods. This option allows for more flexibility in the time component of the model. Bootstrap functionality is included to determine statistical significance. Model results with the new restricted cubic spline option are compared to the linear trend option for two pesticide-site combinations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201082","collaboration":"National Water Quality Program","usgsCitation":"Ryberg, K.R., and York, B.C., 2020, seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0: U.S. Geological Survey Open-File Report 2020–1082, 25 p., https://doi.org/10.3133/ofr20201082.","productDescription":"Report: vi, 25; 3 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101011","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376796,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_1.pdf","text":"Appendix 1.","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 1","linkHelpText":"— Vignette for seawaveQ—An R Package Providing a Model and Utilities for Analyzing Trends in Chemical Concentrations in Streams with a Seasonal Wave (seawave) and Adjustment for Streamflow (Q) and Other Ancillary Variables"},{"id":376797,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_2.pdf","text":"Appendix 2.","size":"228 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 2","linkHelpText":"— R Documentation"},{"id":376798,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_4.pdf","text":"Appendix 4.","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 4","linkHelpText":"— Model Comparisons Using seawaveQ"},{"id":376794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1082/coverthb.jpg"},{"id":376795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082"}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD
The U.S. Army Fort Belvoir (FTBL) installation is on the banks of the Potomac River in Fairfax County, northeastern Virginia. The installation was founded by the U.S. Army during World War I. It has been home to a variety of military organizations over the course of its more than 100-year history and currently houses more than 145 mission partners. The installation consists of two noncontiguous units, the Main Post, and a smaller area to the northwest, Fort Belvoir North Area (FTNA). FTBL encompasses 8.91 square miles.
There is concern that activities on FTBL, including a long history of training, operations, and maintenance, may have resulted in contamination of stream water, streambed sediment, and (or) soils. Of particular concern is the U.S. Environmental Protection Agency (EPA) Target Analyte List (TAL). TAL refers to “the list of inorganic compounds/elements designated for analysis as contained in the version of the EPA Contract Laboratory Program Statement of Work for Inorganics Analysis, Multi-Media, Multi-Concentration in effect as of the date on which the laboratory is performing the analysis” (https://www.nj.gov/dep/srp/guidance/tcl_tal/). Because of the potential for TAL contamination at FTBL, the U.S. Geological Survey (USGS), in cooperation with U.S. Army Fort Belvoir, conducted a survey of FTBL’s stream water, streambed sediment, and soils during calendar year 2019.
The terminology “ambient concentrations” is used in this report to represent the concentrations of the TAL and other constituents at the time of sampling. This is in contrast to “background concentrations,” a term that “refers to areas in which the concentrations of chemicals have not been elevated by site activities”. Although some of the samples collected for this project may represent “background concentrations,” there is no assurance that they do, so all data collected are described as having “ambient concentrations.”
The purpose of the study was to obtain environmental data to characterize ambient concentrations of EPA TAL constituents in stream water, streambed sediment, and soils in FTBL, Virginia. This report describes methods and results of sampling stream water, streambed sediment, and soils during 2019. The purpose of this report is four-fold: (1) to describe the field sampling methods used to collect stream water, streambed sediment, and soils; (2) to describe the laboratory methods used to analyze the samples; (3) to report summaries of the field and laboratory results; and (4) to report the quality assurance and quality control results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201059","collaboration":"Prepared in cooperation with the U.S. Army, Fort Belvoir","usgsCitation":"Rice, K.C., and Chambers, D.B., 2020, Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019: U.S. Geological Survey Open-File Report 2020–1059, 20 p., https://doi.org/10.3133/ofr20201059.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116519","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":376670,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91P7OZJ","text":"USGS data release","linkHelpText":"Fort Belvoir, Virginia, stream-water, streambed-sediment, and soil data collected in 2019"},{"id":376668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1059/coverthb.jpg"},{"id":376669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1059/ofr20201059.pdf","text":"Report","size":"6.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1059"}],"country":"United States","state":"Virginia","city":"Fort Belvoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.19388961791992,\n 38.71096464102174\n ],\n [\n -77.20212936401367,\n 38.69033348573663\n ],\n [\n -77.12900161743163,\n 38.67117065551123\n ],\n [\n -77.11492538452148,\n 38.695290864945804\n ],\n [\n -77.12608337402344,\n 38.741766321754575\n ],\n [\n -77.14050292968749,\n 38.753012320665185\n ],\n [\n -77.16659545898438,\n 38.74390855335671\n ],\n [\n -77.18238830566406,\n 38.73051855149164\n ],\n [\n -77.19388961791992,\n 38.71096464102174\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2184371948242,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.73935623438332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
This document describes a standard operating procedure (SOP) for the preparation of purposely killed juvenile salmon, implanted with telemetry transmitters, to be released into rivers, lakes, or streams to test one of the survival model assumptions. Procedures for releases of purposely killed fish (hereinafter dead fish releases) were developed by staff from the U.S. Geological Survey’s Columbia River Research Laboratory, on the basis of laboratory experiments and practical experience with telemetry studies in the Columbia River Basin. Initially, we used extended exposure to high dose anesthetic baths to euthanize fish for dead fish releases. This approach was selected on the basis of euthanization procedures described in the literature for studies that required an effective and rapid procedure, such as stress physiology assessments. Ultimately, this technique was deemed insufficient because detection records suggested that some fish seemed to revive and continue their migration with limited effect. That is, the detection histories of dead fish were very similar to those of live fish. To overcome this challenge, we adapted our procedures to require a combination of euthanization procedures on individual fish to ensure that there was no opportunity for revival. A combination of euthanization procedures for dead fish releases was used in one study in Germany. This SOP has been used by the U.S. Geological Survey to test survival model assumptions in several field studies and has consistently performed well. In addition, limited laboratory tests were completed to ensure that no live juvenile salmon were found in holding tanks for 24 hours following the procedures described in this SOP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201083","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Tomka, R.G., Liedtke, T.L., Frost, C., and Smith, C.D., 2020, A standard operating procedure for the preparation of purposely killed juvenile salmon used to test survival model assumptions: U.S. Geological Survey Open-File Report 2020–1083, 11 p., https://doi.org/10.3133/ofr20201083.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-116988","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1083/coverthb.jpg"},{"id":376755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1083/ofr20201083.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1083"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Chattahoochee River National Recreation Area (CRNRA) is a National Park Service unit/park with 48 miles of urban waterway in the Atlanta metropolitan area. The Chattahoochee River within the CRNRA is a popular place for water-based recreation but is known to periodically experience elevated levels of fecal-coliform bacteria associated with warm-blooded animals that can result in a variety of pathogen-related human illnesses. In 2000, the National Park Service entered into a public-private partnership with the U.S. Geological Survey (USGS) and the Chattahoochee Riverkeeper, called the Chattahoochee River BacteriALERT program, to monitor Escherichia coli (E. coli), which is a fecal indicator bacteria and a proxy for human health risk from waterborne pathogens. The BacteriALERT network monitors E. coli densities at three stations on the Chattahoochee River within the CRNRA, at Norcross (USGS station 02335000), Powers Ferry (USGS station 02335880), and Atlanta (USGS station 02336000). E. coli densities determined from water samples were compared to the U.S. Environmental Protection Agency’s Beach Action Value (BAV) of 235 colony forming units per 100 milliliters to assess whether conditions were considered safe for freshwater, primary contact recreational use. Sample E. coli densities exceeded the BAV for 15.5 percent of the samples collected at Norcross (n = 1,969) and 30.3 percent of the samples at Atlanta (n = 1,938) for the study period October 23, 2000, to May 23, 2019, and 33.6 percent of the samples from Powers Ferry (n = 134) for the study period May 5, 2016, to May 23, 2019.
Models to predict E. coli densities in near real-time were developed for the three BacteriALERT stations. Models were developed using forward-stepwise multiple linear regression with the Bayesian Information Criteria and were calibrated with samples collected between October 4, 2007, and May 23, 2019. Explanatory variables included season, turbidity, water temperature, streamflow, upstream tributary streamflows, and temporal trend. The most statistically significant explanatory variables in the models were turbidity, upstream tributary streamflows, and season. The Norcross model had an increasing trend in E. coli densities of 2.3 percent per year. A significant trend was not detected for the Atlanta station, while trends were not assessed for Powers Ferry models due to the short (3-year) calibration period. Model adjusted R2s ranged from 0.686 (Atlanta) to 0.795 (Norcross with time trend) indicating that the models explained a substantial portion of the variations in E. coli densities. Evaluation of model predictions and residuals indicated that models were well posed and exhibited little bias. The models performed well in accurately determining compliance and exceedance of the BAV with low misidentification rates ranging from 3.5 percent (Norcross) to 11.3 percent (Powers Ferry). Misidentification was most common for densities near the BAV, and misidentification rates in the study were low despite fairly low model precisions because E. coli densities were infrequently near the BAV. The precisions of the models developed herein were comparable to the more complex models developed by Lawrence (2012) that were never implemented in the BacteriALERT program due to their computational complexity. The predictive E. coli models developed herein will improve the ability to assess the health risks of water-based recreational activities in the CRNRA in near real-time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201048","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Aulenbach, B.T., and McKee, A.M., 2020, Monitoring and real-time modeling of Escherichia coli bacteria for the Chattahoochee River, Chattahoochee River National Recreation Area, Georgia, 2000–2019: U.S. Geological Survey Open-File Report 2020–1048, 43 p., https://doi.org/10.3133/ofr20201048.","productDescription":"x, 43 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113124","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1048/coverthb.jpg"},{"id":376663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1048/ofr20201048.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1048"}],"country":"United States","state":"Georgia","city":"Atlanta","otherGeospatial":"Chattahoochee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1168212890625,\n 34.17772537282446\n ],\n [\n -84.48211669921875,\n 34.01396527491264\n ],\n [\n -84.44915771484375,\n 33.747180448149855\n ],\n [\n -84.29809570312499,\n 33.76544869849223\n ],\n [\n -84.1387939453125,\n 33.902336404480685\n ],\n [\n -84.0509033203125,\n 34.075412438417395\n ],\n [\n -84.04541015625,\n 34.14363482031264\n ],\n [\n -84.1168212890625,\n 34.17772537282446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Surface-water and suspended-sediment samples were collected and analyzed by the U.S. Geological Survey for multiple current-use pesticides and pesticide degradates approximately every 2 weeks at up to five sites in the Yolo Bypass and Cache Slough Complex before, during, and after augmented flow pulses in summer and fall 2016 and 2018 as well as during ambient flow conditions in summer and fall 2017 (no flow pulse). In 2016, augmented flows occurred during the summer (July) and required the pumping of Sacramento River water by local Reclamation Districts into the Colusa Basin Drain and Yolo Bypass Toe Drain. In contrast, augmented flows in 2018 occurred in the fall (August–September) and used agricultural tailwater (primarily rice field discharge water) to create the flow pulse. Water samples were analyzed by the U.S. Geological Survey for a suite of 175 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry laboratory methods. Suspended sediments filtered from the water samples were analyzed for 143 pesticides and degradates by gas chromatography with mass spectrometry.
During the study, 53 pesticides were detected, and all the samples contained mixtures of multiple pesticides at concentrations ranging from below method detection limits to 8,780 nanograms per liter. Pesticides used in growing rice were the dominant pesticides present at four of the five sites sampled and urban-use pesticides dominated at the remaining site. Overall, total pesticide concentrations tended to be higher at sites in the northern part of the Yolo Bypass and lower at southern sites, except for the farthest downstream site which received additional pesticide inputs from the Sacramento River. Flow-pulse water source influenced total pesticide concentrations in the Yolo Bypass and Cache Slough Complex, and the highest total pesticide concentrations at each site were detected either immediately before or during the flow pulse generated with agricultural tailwater in 2018. Data gathered during this study will aid the California Department of Water Resources and other agencies working in the region in adaptively managing pulse flows in the Yolo Bypass and Cache Slough Complex, as one of several California Natural Resources Agency’s Delta Smelt Resiliency strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201076","collaboration":"Prepared in cooperation with the California Department of Water Resources and the State and Federal Contractors Water Agency","usgsCitation":"Orlando, J.L., De Parsia, M., Sanders, C., Hladik, M., and Frantzich, J., 2020, Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California: U.S. Geological Survey Open-File Report 2020–1076, 101 p., https://doi.org/10.3133/ofr20201076.","productDescription":"Report: vi, 101 p.; Data release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-109449","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"U.S. Geological Survey, 2019, National Water Information System: U.S. Geological Survey Web interface"},{"id":376628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1076/ofr20201076.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1076/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85760498046875,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 38.45789034424927\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
Forster’s terns (Sterna forsteri), historically one of the most numerous colonial-breeding waterbirds in South San Francisco Bay, California, have experienced recent decreases in the number of nesting colonies and overall breeding population size. The South Bay Salt Pond Restoration Project aims to restore 50–90 percent of former salt evaporation ponds to tidal marsh habitat in South San Francisco Bay. During phase 1 of the South Bay Salt Pond Restoration Project, the breaching of several pond levees to begin the process of tidal marsh restoration inundated island nesting habitat that had been used by Forster’s terns, American avocets (Recurvirostra americana), and other waterbirds. Additional nesting habitat could be lost as more managed ponds are converted to tidal marsh in the future. To address this issue, the South Bay Salt Pond Restoration Project organized the construction of new nesting islands in managed ponds that will not be restored to tidal marsh, thereby providing enduring island nesting habitat for waterbirds. In 2012, 16 new islands were constructed in Pond A16 in the Alviso complex of the Don Edwards San Francisco Bay National Wildlife Refuge, which increased the number of islands in this pond from 4 to 20. However, despite a long history of nesting on the four islands in Pond A16 before the 2012 construction activities, no Forster’s terns have nested in Pond A16 during the 7-year period (2012–18) after island construction.
During the 2017 and 2019 breeding seasons, we used social attraction measures (decoys and colony call playback systems) to attract Forster’s terns to islands within Pond A16 to re-establish nesting colonies. We maintained these systems from March through August in each year. To evaluate the effect of these social attraction measures, we completed surveys (between April and August) where we recorded the number and location of all Forster’s terns and other waterbirds using Pond A16, and we monitored waterbird nests. We compared bird survey and nest monitoring data collected in 2017 and 2019 to data collected in 2015 and 2016, prior to the implementation of social attraction measures, allowing for direct evaluation of the effect of social attraction efforts on Forster’s terns.
To increase the visibility and stakeholder involvement of this project, we engaged in multiple outreach activities in 2017, 2019, and 2020, including the development of a project website and educational video; publication of popular articles in 2017 and 2020; the development of outreach materials describing the project to the general public; and public presentations to relay findings to managers, stakeholders, and the general public.
The relative abundance of Forster’s terns in Pond A16, after adjusting for the overall South San Francisco Bay breeding population each year, was higher during the nesting period in 2017 and 2019 (when social attraction was used) than in 2015 and 2016 (before social attraction was used). Furthermore, more Forster’s terns were observed during the pre-nesting and nesting periods in the areas of Pond A16 where decoys and call systems were deployed. Although no Forster’s tern nests were observed in Pond A16 before social attraction was implemented (2015, 2016), or during the first-year social attraction was implemented (2017), 35 Forster’s tern nests were recorded during the second year of social attraction implementation in 2019. These 35 nests represent a re-establishment of a Forster’s tern nesting colony to Pond A16 for the first time in 8 years. As social attraction efforts often benefit from multiple years of decoy and call system deployment, results from 2017 and 2019 suggest that continued implementation of social attraction measures could help to ensure Forster’s tern breeding colonies persist in Pond A16 and other areas of South San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201081","collaboration":"Prepared in cooperation with the San Francisco Bay Bird Observatory","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Wang, Y., and Strong, C., 2020, Establishing Forster’s Tern (Sterna forsteri) nesting sites at pond A16 using social attraction for the South Bay Salt Pond restoration project: U.S. Geological Survey Open-File Report 2020–1081, 28 p., https://doi.org/10.3133/ofr20201081.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118152","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":376595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1081/covrthb.jpg"},{"id":376596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1081/ofr20201081.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.28057861328124,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.40452830389465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Strix occidentalis caurina (northern spotted owl; hereinafter referred to as spotted owl) have rapidly declined throughout the subspecies’ geographic range. Competition with invading Strix varia (barred owl) has been identified as an immediate cause of those declines. A pilot study in California showed that removal of barred owls coupled with conservation of suitable habitat conditions can slow or even reverse population declines of spotted owls. It is unknown, however, whether similar results can be obtained in areas with different forest conditions, greater densities of barred owls, and fewer remaining spotted owls. We used a before-after-control-impact experimental design on three study areas with long-term demographic information on spotted owls to determine if removal of barred owls can improve population trends of spotted owls. This report summarizes research accomplishments and initial results from the first 4.5 years (from March 2015 to August 2019) of implementing barred owl removal experiments in Washington and Oregon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201089","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Wiens, J.D., Dugger, K.M., Lesmeister, D.B., Dilione, K.E., and Simon, D.C., 2020, Effects of barred owl (Strix varia) removal on population demography of northern spotted owls (Strix occidentalis caurina) in Washington and Oregon—2019 annual report: U.S. Geological Survey Open-File Report 2020–1089, 19 p., https://doi.org/10.3133/ofr20201089.","productDescription":"iv, 19 p.","onlineOnly":"Y","ipdsId":"IP-117976","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":376560,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1089/coverthb2.jpg"},{"id":376531,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1089/ofr20201089.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1089"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.28906250000001,\n 46.483264729155586\n ],\n [\n -119.59716796875,\n 46.483264729155586\n ],\n [\n -119.59716796875,\n 47.54687159892238\n ],\n [\n -121.28906250000001,\n 47.54687159892238\n ],\n [\n -121.28906250000001,\n 46.483264729155586\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.99169921875,\n 43.723474896114794\n ],\n [\n -122.98095703125,\n 43.723474896114794\n ],\n [\n -122.98095703125,\n 44.824708282300236\n ],\n [\n -123.99169921875,\n 44.824708282300236\n ],\n [\n -123.99169921875,\n 43.723474896114794\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21142578125,\n 42.47209690919285\n ],\n [\n -122.23388671874999,\n 42.47209690919285\n ],\n [\n -122.23388671874999,\n 43.24520272203356\n ],\n [\n -124.21142578125,\n 43.24520272203356\n ],\n [\n -124.21142578125,\n 42.47209690919285\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330