Sediment and nutrient transport in the School Branch watershed (in central Indiana west of Indianapolis) is considered to be heavily affected by agricultural land use throughout the watershed. In 2015, the U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, deployed continuous water-quality monitors and began collecting discrete water-quality samples at the streamflow-gaging station School Branch at CR750N at Brownsburg, Indiana (U.S. Geological Survey station 03353420). Regression models that estimate concentrations of suspended sediment, total nitrogen, and total phosphorus were developed by relating streamflow and continuously monitored water-quality data to concentrations measured in discrete water-quality samples collected from June 2015 through February 2019. Regression model diagnostics indicated that streamflow and sensor-measured turbidity concentrations explained about 95 percent of the variation in suspended-sediment concentration and 73 percent of the variation in total phosphorus concentration. Similarly, streamflow and sensor-measured nitrate plus nitrite concentrations explained about 97 percent of the variation in total nitrogen concentrations.
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed from regression model concentrations and instantaneous streamflow. The estimated mean daily suspended-sediment discharge (June 2015 through February 2019) was 1.184 tons per day; the estimated median suspended-sediment discharge was 0.053 tons per day. The estimated mean daily total nitrogen discharge (June 2015 through February 2019) was 127.50 pounds per day; the estimated median total nitrogen discharge was 28.49 pounds per day. The estimated mean daily total phosphorus discharge (June 2015 through February 2019) was 12.08 pounds per day; the estimated median total-phosphorus discharge was 1.208 pounds per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215099","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Downhour, M.S., Bunch, A.R., and Lathrop, T.R., 2021, Regression models for estimating sediment, nutrient concentrations and loads at School Branch at Brownsburg, Indiana, June 2015 through February 2019: U.S. Geological Survey Scientific Investigations Report 2021–5099, 15 p., https://doi.org/10.3133/sir20215099.","productDescription":"Report: v, 14 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-119874","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391136,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":391135,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YWNBAQ","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Data and regression model for suspended sediment for School Branch at CR750N at Brownsburg, Indiana June 23, 2015, to February 6, 2019"},{"id":391133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5099/coverthb.jpg"},{"id":391134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5099/sir20215099.pdf","text":"Report","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5099"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.44866943359375,\n 39.81170080625297\n ],\n [\n -86.23306274414062,\n 39.81170080625297\n ],\n [\n -86.23306274414062,\n 40.01604611654875\n ],\n [\n -86.44866943359375,\n 40.01604611654875\n ],\n [\n -86.44866943359375,\n 39.81170080625297\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
The Partridge River Basin (PRB) covers 156 square miles in northeastern Minnesota with headwaters in the Mesabi Iron Range. The basin is characterized by extensive wetlands, lakes, and streams in poorly drained and often thin glacial material overlying Proterozoic bedrock. To better understand the interaction between these extensive surface water features and the groundwater system, a three-dimensional, steady-state, groundwater-flow model of the PRB was developed by the U.S. Geological Survey in cooperation with the Great Lakes Indian Fish & Wildlife Commission using the finite-difference computer code MODFLOW-NWT. The model simulates steady-state base flow in streams and groundwater interactions using the streamflow routing (SFR2) package. Existing mining features including tailings basins, stockpiles, pumped mine pits, and flooded mine pits were simulated using either high hydraulic conductivity zones or the drain (DRN) package. The unsaturated zone flow (UZF) package was used to better represent the groundwater system in areas with a high water table and for wetlands often associated with such areas. UZF typically is used to represent unsaturated zone processes but also can simulate the rejection of recharge and groundwater discharge to the land surface when the water table is near land surface. The steady-state model used data from the 2011 to 2013 period when 2011 high-resolution land surface (light detecting and ranging [lidar]) data were available that reflected land-surface and water elevations from mining activity in the basin. The parameter-estimation software suite PEST_HP was used to obtain a best fit of the modeled to measured groundwater levels, streamflow, pit inflow rates, and mapped peat deposits. The PEST calibration used the target residuals from two models with the same model parameters and targets from two separate periods: (1) a 1995–2015 calibration model, which provided a larger number of calibration targets, and (2) a 2011–2013 mining conditions model, which included calibration targets that reflected conditions consistent with the modeled mine-workings topography.
Calibration of the PRB model resulted in ranges of glacial horizontal hydraulic conductivity parameters that generally agreed with literature values and other models of the region. Horizontal hydraulic conductivity of the bedrock was higher in the upper bedrock layers where numerous and continuous fractures have been observed and lower in the deeper bedrock layers. Average basin-wide calibrated infiltration was 5.3 inches per year. An average of 4.6 inches per year of infiltration crosses the water table and becomes recharge and 0.7 inch per year is rejected by UZF due to saturated conditions at the land surface. Simulated groundwater runoff (the sum of rejected recharge and groundwater seepage to the land surface) can either be routed to streams or removed from the model as evapotranspiration. The calibrated model indicates relatively shallow groundwater-flow paths dominating and approximately 50 percent of the stream base flow coming from groundwater runoff.
The 2011–2013 mining conditions model was then used to develop five model scenarios simulating the response of the groundwater and surface-water system to potential hydrologic stress. The purpose of these mine pit scenarios is to present a possible workflow to quantify a model’s uncertainty for a given model forecast and serve as a possible guide for initial data collection that may improve a future model’s ability to make such a forecast. The scenarios included one scenario with the currently existing Peter Mitchell pit at final buildout and flooded to an elevation of 1,500 feet, and four scenarios with a hypothetical, new mine pit plus the flooded Peter Mitchell at final buildout. The five model scenarios were used to forecast streamflow at six locations in the PRB, pit inflow rates for the new mine pits and the flooded Peter Mitchell pit, and the average depth to water in 12 wetlands. A linear uncertainty analysis was performed using information from the PEST calibration and tools in the PyEMU python package to assess model uncertainty propagation to the model forecasts. Streamflows generally were reduced with future mining and the greatest streamflow reductions occurred from the flooded Peter Mitchell Pit, probably due to its large size. Average depth to groundwater in wetlands was most affected the closer the wetland was to a new mine pit.
Linear uncertainty methods were also used to evaluate data worth, which is the ability for potential new groundwater elevation observations to reduce the uncertainty in scenario forecasts. Data worth was performed for a grid of new hydraulic head observations. Overall, areas with nonnegligible data worth generally corresponded to wetland areas with no groundwater seepage to land surface from UZF. These model behaviors indicated that the land-surface boundary condition simulated by the UZF package was pinning the groundwater elevations to the land surface in areas with groundwater seepage (33 percent of the 2011–2013 base conditions model) such that the sensitivity to new observations in these areas was minimal. Therefore, representing wetlands as boundary conditions minimized the usefulness of data worth calculations because wetland areas were present over a large part of the model domain.
Probabilistic capture zones were estimated for each of the mines in the model scenarios. A capture zone represents the area contributing recharge to a model feature, like a well or a mine pit, and can be calculated by forward tracking particles from the water table. By using Monte Carlo techniques, it is possible to generate estimated capture zones that include the probability of recharge capture given the uncertainty present in the model. Monte Carlo techniques use randomly generated model parameter sets sampled from a plausible parameter range to create many possible realizations. The resulting capture zone arrays were calculated by tallying the total number of realizations in which a particle from a model cell was captured by the feature. Probabilities from the Monte Carlo runs ranged from 1 (captured in 100 percent of the runs) near the pits to 0 (captured in 0 percent of the runs) at the edges of the capture zone. Capture zones were not always spatially continuous; for example, the capture zone for the proposed mine pits south of the flooded Peter Mitchell pit was discontinuous with capture surrounding the proposed mine pit and north of the flooded Peter Mitchell pit. This northern section represents deeper groundwater flow paths that originate in the topographic high, move under the flooded pit, and discharge into the proposed pit. This pattern of capture indicates the possibility of some deeper flow through the upper fractured bedrock when the shallow groundwater flow system is modified. These results underscore that future site-specific applications of the base condition model require the input of site-specific data and recalibration to focus on the site of interest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215038","collaboration":"Prepared in cooperation with the Great Lakes Indian Fish & Wildlife Commission","usgsCitation":"Haserodt, M.J., Hunt, R.J., Fienen, M.N., and Feinstein, D.T., 2021, Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota: U.S. Geological Survey Scientific Investigations Report 2021–5038, 94 p., https://doi.org/10.3133/sir20215038.","productDescription":"Report: ix, 87 p.; Data Release; Dataset","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-123210","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391131,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.xml","text":"Report xml","size":"277 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5038 xml"},{"id":391130,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":391132,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5038/images"},{"id":391129,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VODOU8","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT and MODPATH models, capture zones and uncertainty data analysis for the Partridge River Basin, Minnesota"},{"id":391127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5038/coverthb.jpg"},{"id":391128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.pdf","text":"Report","size":"69.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5038"}],"country":"United States","state":"Minnesota","otherGeospatial":"Partridge River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.25,\n 47.4\n ],\n [\n -91.75,\n 47.4\n ],\n [\n -91.75,\n 47.8\n ],\n [\n -92.25,\n 47.8\n ],\n [\n -92.25,\n 47.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive,
Madison, WI 53726
As the site of the first permanent English settlement in North America in 1607, Jamestown Island, Colonial National Historical Park (COLO), Virginia, contains a rich archaeological record that extends from the Paleoindian period (15,000 to 8,000 years ago) through the 20th century. The island is located on the lower James River near the mouth of Chesapeake Bay. Jamestown Island vegetation is dominated by upland forests surrounded by tidal, freshwater-to-oligohaline marshes. Along the Virginia coast, relative sea-level rise was more than 2.5 times the global average during the 20th century. Consequently, the National Park Service (NPS) has identified COLO as one of the 25 national parks most threatened by climate change.
Surface waters across the island are hydraulically connected to the laterally continuous Surficial aquifer. The land-surface altitude of the island is low, with two-thirds of the island less than 5 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Consequently, sea-level rise, combined with tides and storm surges, threatens the island and its resources as surface-water and groundwater levels rise, saltwater enters the Surficial aquifer, and groundwater chemistry changes. The impact of sea-level rise on the island’s surface-water resources has been well studied, but groundwater effects have been largely ignored. Quantifying the effects of tides, storm surges, and sea-level rise on groundwater levels and chemistry is essential to developing an effective strategy for managing climate-induced changes. The first step in developing a response strategy includes a parkwide general risk assessment for archaeological sites on the island, so that sites can be prioritized for management actions. The U.S. Geological Survey and the NPS began a study in 2015 to develop a long-term groundwater-monitoring program to evaluate this risk and to develop an updated management strategy.
The groundwater-monitoring program consists of 45 wells and piezometers in two individual clusters and three transects across the island in different hydrologic and chemical settings. Samples for water quality were collected from the wells and piezometers from October 2015 through September 2018 at variable time intervals. Results of the monitoring identified disparate hydrologic and chemical responses to saltwater intrusion across the island. Specific conductance (an indicator of salinity) of groundwater beneath several marshes responded differently to changes in James River salinity. Groundwater response to changes in James River specific conductance appeared to be controlled by land-surface altitude and slope, differences in lateral and vertical sediment characteristics, distance from surface waters, and the degree of surface water/groundwater connectivity between channels and the aquifer.
Groundwater chemistry data from monitoring wells at Black Point, a low-altitude, upland setting, are in contrast with conditions observed in Island House observation wells, a high-altitude, upland setting. Specific conductance (less than 200 microsiemens per centimeter [μS/cm]) and pH (greater than 5.0) of groundwater beneath much of the uplands that characterize the Island House observation wells are typical of groundwater in noncarbonate sedimentary aquifers recharged by precipitation. At Black Point, specific conductance ranged from 2,490 to 15,200 μS/cm, and pH ranged from 3.1 to 6.6 standard units. At the Black Point observation wells, the most saline and dense water was at the water table rather than deeper in the aquifer, causing a density inversion that persisted throughout the study. The density inversion likely resulted from differences in permeability between the shallow clay and fine-grained sands and the deeper coarse-grained sand and gravel. Groundwater with the lowest pH was at the water table. As saline groundwater flows through organic sediment beneath the marshes, bacterial biodegradation of organic matter creates anoxic conditions. Continued biodegradation concomitantly reduces iron-oxide minerals in the sediment and sulfate in saline water. When oxygen is reintroduced into groundwater, iron and sulfur can reoxidize to form sulfuric acid, locally lowering the pH of the water.
This report describes the groundwater monitoring network design, rationale for site selection, monitoring approach, and results of monitoring from October 2015 through September 2018. Maps of inundation at selected water-level altitudes are included to identify the risk to archaeological, cultural, and ecological resources. The monitoring results of the hydrology and chemistry data are interpreted, and the different hydrologic and chemical settings are described. The implications of the study results for management decisions are presented, and suggestions for improving the monitoring network are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215117","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"McCoy, K.J., Rice, K.C., Rickles, E., Frederick, D., Cramer, J., and Geyer, D., 2021, Groundwater hydrology and chemistry of Jamestown Island, Virginia—Potential effects of tides, storm surges, and sea-level rise on archaeological, cultural, and ecological resources: U.S. Geological Survey Scientific Investigations Report 2021–5117, 50 p., https://doi.org/10.3133/sir20215117.","productDescription":"Report: x, 50 p.; Data Release","numberOfPages":"50","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-115948","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":391337,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7X61F","text":"USGS data release","linkHelpText":"Field parameters and water levels from monitoring sites at Jamestown Island, Virginia, 2016 - 2018"},{"id":391336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5117/sir20215117.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5117"},{"id":391335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5117/coverthb2.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Jamestown Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.86309814453125,\n 37.16797725379289\n ],\n [\n -76.48544311523436,\n 37.16797725379289\n ],\n [\n -76.48544311523436,\n 37.36033397019125\n ],\n [\n -76.86309814453125,\n 37.36033397019125\n ],\n [\n -76.86309814453125,\n 37.16797725379289\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
This report provides a step-by-step method for compiling hydraulic fracturing data in the United States from the IHS Markit, 2019, U.S. Well History and Production Relational Database. Data on hydraulically fractured wells include their location (geologic province, State, county), well type (oil or gas), orientation (directional, horizontal, or vertical), spud date, completion date and the hydraulic fracturing treatments, treatment fluids types, treatment fluid volumes, additive types, agent types (“proppants”), and proppant amounts injected. This method also describes how to associate each unique well with the hydraulic fracturing treatments to provide an indication of the total amount of all treatment fluids injected into a well for hydraulic fracturing and the volume of each individual treatment fluid type injected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211090","usgsCitation":"Varela, B.A., and Gallegos, T.J., 2021, Method for compiling temporally and spatially aggregated data on hydraulic fracturing—Treatments and wells: U.S. Geological Survey Open-File Report 2021–1090, 30 p., https://doi.org/10.3133/ofr20211090.","productDescription":"vi, 30 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118145","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":436123,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933SF16","text":"USGS data release","linkHelpText":"Spatial and Temporal Data on Hydraulic Fracturing Fluid Types and Amounts Injected into Oil and Gas Wells Across the U.S., 2015-2019"},{"id":391203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1090/coverthb.jpg"},{"id":391204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1090/ofr20211090.pdf","text":"Report","size":"730 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1090"}],"contact":"Geology, Energy and Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
A concern about statistics in wildlife studies, particularly of endangered and threatened species, is whether the data collected meet the assumptions necessary for the use of parametric statistics. This study identified published papers on the nine endangered and six threatened species found only on Puerto Rico using five different databases. The results from the Zoological Record database identified the most articles, including all identified by the other databases. Of the 222 identified articles, 108 included some form of statistics, 26 used only descriptive statistics, 34 included only parametric statistics, 26 used only nonparametric statistics, and 22 reported both parametric and nonparametric statistical analyses. This meta-study showed that the percentage of articles with no statistical treatment decreased in the most recent 20 years, and that although parametric statistics continue to be the most commonly used in published wildlife studies of Puerto Rican wildlife, there has been a distinct increase in the use of nonparametric statistics over time.
","language":"English","publisher":"College of Arts and Sciences of the University of Puerto Rico, Mayagüez","doi":"10.18475/cjos.v51i2.a10","usgsCitation":"Rivera, S., Alpi, K., Collazo, J.A., and Stoskopf, M., 2021, Statistical methods used in research concerning endangered and threatened animal species of Puerto Rico: A meta-study: Caribbean Journal of Science, v. 51, no. 2, p. 225-241, https://doi.org/10.18475/cjos.v51i2.a10.","productDescription":"17 p.","startPage":"225","endPage":"241","ipdsId":"IP-130807","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.27875878556765,\n 18.57988531442932\n ],\n [\n -67.27875878556765,\n 17.89880355641135\n ],\n [\n -65.58075886661939,\n 17.89880355641135\n ],\n [\n -65.58075886661939,\n 18.57988531442932\n ],\n [\n -67.27875878556765,\n 18.57988531442932\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivera, S.J.","contributorId":341725,"corporation":false,"usgs":false,"family":"Rivera","given":"S.J.","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":908833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpi, K.M.","contributorId":341726,"corporation":false,"usgs":false,"family":"Alpi","given":"K.M.","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":908834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoskopf, M.K.","contributorId":341728,"corporation":false,"usgs":false,"family":"Stoskopf","given":"M.K.","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":908836,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226135,"text":"70226135 - 2021 - Expanding freshwater biologger studies to view fish as environmental sensing platforms","interactions":[],"lastModifiedDate":"2022-01-06T17:30:38.654159","indexId":"70226135","displayToPublicDate":"2021-11-03T07:01:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2681,"text":"Marine and Freshwater Research","active":true,"publicationSubtype":{"id":10}},"title":"Expanding freshwater biologger studies to view fish as environmental sensing platforms","docAbstract":"While recording fish habitat use by electronic sensors, biologgers can also be viewed as autonomous environmental monitoring systems with the organism as a vehicle. This dual perspective has provided novel results from marine ecosystems, but has not been applied to freshwater ecosystems. To understand limitations in fresh water, we evaluated miniature depth and temperature recorders as aquatic monitoring systems in a Laurentian Great Lake: Erie. As part of an acoustic telemetry study, biologgers were opportunistically implanted in a subsample of walleye Sander vitreus. Biologgers recorded temperature and depth at half-hour intervals for up to 1 year. Recaptures provided six biologgers for analysis of seasonal temperature patterns and lake stratification, key variables for understanding dimictic lakes. Depth-resolved temperature patterns showed close correspondence with independent weather buoy measurements. Because the buoy was deployed late in the season, biologger data provided improved estimates of the start of stratification, which had important implications for understanding development of hypoxia in the hypolimnion. Drawbacks to biologger data included imprecise knowledge of fish location and reliance on tag recoveries from the fishery. Optimistically, our results show how biologgers could be part of a monitoring approach that integrates limnological surveys with fisheries science.
This geologic map and its digital databases present the geology of the eight major islands of the State of Hawaiʻi. The map should serve as a useful guide to anyone studying the geologic setting and history of Hawai‘i, including ground- and surface-water resources, economic deposits, and landslide or volcanic hazards. Its presentation in digital format allows the rapid application of geologic knowledge when conducting field work; analyzing land-use or engineering problems; studying onshore or nearshore biologic communities; or simply understanding the relation between the geology, scenery, and cultural history of the Hawaiian paradise.
The map includes a Description of Map Units, which describes the lithologic characteristics and distribution of the geologic deposits. A Correlation of Map Units shows how the different geologic formations are related to each other stratigraphically. A fairly complete geospatial database of the radiometric ages and geochemical analyses has been compiled from findings published over the past 100 years by numerous Earth scientists working across the island chain.
Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
Species invasions can have substantial impacts on native species and ecosystems, with important consequences for biodiversity. How these disturbances drive changes in the trophic structure of native food webs through time is poorly understood. Here, we quantify trophic disruption in freshwater food webs to invasion by an apex fish predator, lake trout, using an extensive stable isotope dataset across a natural gradient of uninvaded and invaded lakes in the northern Rocky Mountains, USA. Lake trout invasion increased fish diet variability (trophic dispersion), displaced native fishes from their reference diets (trophic displacement), and reorganized macroinvertebrate communities, indicating strong food web disruption. Trophic dispersion was greatest 25 to 50 y after colonization and dissipated as food webs stabilized in later stages of invasion (>50 y). For the native apex predator, bull trout, trophic dispersion preceded trophic displacement, leading to their functional loss in late-invasion food webs. Our results demonstrate how invasive species progressively disrupt native food webs via trophic dispersion and displacement, ultimately yielding biological communities strongly divergent from those in uninvaded ecosystems.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2102179118","usgsCitation":"Wainright, C., Muhlfeld, C.C., Elser, J.J., Bourret, S., and Devlin, S.P., 2021, Species invasion progressively disrupts the trophic structure of native food webs: Proceedings of the National Academy of Sciences, v. 118, no. 45, e2102179118, 5 p., https://doi.org/10.1073/pnas.2102179118.","productDescription":"e2102179118, 5 p.","ipdsId":"IP-125794","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2102179118","text":"Publisher Index Page"},{"id":399671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.05957031249999,\n 46.73986059969267\n ],\n [\n -113.367919921875,\n 46.73986059969267\n ],\n [\n -113.367919921875,\n 49.01625665778159\n ],\n [\n -116.05957031249999,\n 49.01625665778159\n ],\n [\n -116.05957031249999,\n 46.73986059969267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","issue":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wainright, Charles","contributorId":290594,"corporation":false,"usgs":false,"family":"Wainright","given":"Charles","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":841352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":841353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elser, James J. 0000-0002-1460-2155","orcid":"https://orcid.org/0000-0002-1460-2155","contributorId":224787,"corporation":false,"usgs":false,"family":"Elser","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":40941,"text":"University of Montana Flathead Lake Biological Station","active":true,"usgs":false}],"preferred":false,"id":841354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bourret, Samuel 0000-0002-8521-1020","orcid":"https://orcid.org/0000-0002-8521-1020","contributorId":290597,"corporation":false,"usgs":false,"family":"Bourret","given":"Samuel","email":"","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":841355,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Devlin, Shawn P.","contributorId":202757,"corporation":false,"usgs":false,"family":"Devlin","given":"Shawn","email":"","middleInitial":"P.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":841356,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225710,"text":"70225710 - 2021 - Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010","interactions":[],"lastModifiedDate":"2021-11-04T13:55:02.047378","indexId":"70225710","displayToPublicDate":"2021-11-01T08:51:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010","docAbstract":"The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human well-being.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2021.104283","usgsCitation":"Mockrin, M.H., Helmers, D., Martinuzzi, S., Hawbaker, T., and Radeloff, V.C., 2021, Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010: Landscape and Urban Planning, v. 218, 104283, 13 p., https://doi.org/10.1016/j.landurbplan.2021.104283.","productDescription":"104283, 13 p.","ipdsId":"IP-121616","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":391382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n 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Madison","active":true,"usgs":false}],"preferred":false,"id":826377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinuzzi, Sebastian","contributorId":268298,"corporation":false,"usgs":false,"family":"Martinuzzi","given":"Sebastian","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":826376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":826378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Radeloff, Volker C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":826379,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226476,"text":"70226476 - 2021 - Synthesis of data and studies relating to Delta Smelt biology in the San Francisco Estuary, emphasizing water year 2017","interactions":[],"lastModifiedDate":"2021-11-19T13:59:19.266473","indexId":"70226476","displayToPublicDate":"2021-11-01T07:47:21","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":5573,"text":"Interagency Ecological Program Technical Report","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"95","title":"Synthesis of data and studies relating to Delta Smelt biology in the San Francisco Estuary, emphasizing water year 2017","docAbstract":"In the San Francisco Estuary (SFE), the effects of freshwater flow on the aquatic ecosystem have been studied extensively over the years and remains a contentious management issue. It is especially contentious with regards to the Delta Smelt (Hypomesus transpacificus), a species endemic to the SFE that has been listed as threatened under the Federal Endangered Species Act and endangered by the State of California. Early studies of Delta Smelt distribution within the SFE suggested that Delta Smelt habitat is determined largely by freshwater flow; however, the exact mechanisms and processes producing such benefits remained unclear. In the summer of 2017, the Flow Alteration Management, Analysis, and Synthesis Team (FLOAT-MAST) was established to analyze, synthesize, and summarize the data collected from the various flow-related monitoring and special studies occurring in 2017(see Table Intro 4). This report will focus on the 2017 summer-fall status of Delta Smelt and its habitat following a record wet year.
There has been a long-term decline in the abundance of Delta Smelt associated with a decline in other pelagic fishes. Investigators concluded that the decline has likely been caused by the interactive effects of several causes, including changes in both physical and biotic habitats, many of which are tied to amount and timing of freshwater flow. For this report, we formulated a number of basic predictions about the likely effects of high flows in 2017 on Delta Smelt and their habitat (Table 3). We use a qualitative weight of evidence approach to evaluate whether these predictions were supported by available data. Data sources included a variety of long-term monitoring surveys conducted by Interagency Ecological Program (IEP) agencies, as well as model outputs.
Delta Smelt population, health, and life history metrics rarely responded as predicted. Water temperature appears to have a stronger effect on Delta Smelt growth rate and some metrics of life history diversity than outflow or X2 position. Other life history diversity attributes varied but did not appear to be driven by outflow or temperature. Health status was difficult to interpret. Low prevalence of lesions and improved nutritional condition during the drought was contradicted by declining overall population levels. Because of the sparse catches of Delta Smelt in the post-POD years, we consider the data insufficient to reach firm conclusions about the predictions concerning range and distribution of Delta Smelt, especially in the fall. The prediction of high survival was not supported. The 2017 Delta Smelt year class began with poor recruitment in spring of 2017 and below average survival for spring to summer and summer to fall. Thus, low production and low survival led to low abundance of all life stages. During the fall to winter period survival improved, yet the resulting adults were low in number. Foraging success of the fish captured, as measured by stomach fullness, was high for juveniles and adults in 2017 relative to recent years associated with the higher densities of common zooplankton prey that occurred in 2017.
The stratigraphy, water-bearing zones, and quality of groundwater were characterized in a 1,400-ft-deep test hole drilled during 2013 in fractured bedrock in Sullivan County, Pa., by collection and analysis of measurements made during drilling, geophysical logs, and depth-specific hydraulic tests and water samples. The multidisciplinary characterization of the test hole was a cooperative effort between the Pennsylvania Department of Natural Resources, Bureau of Geological Survey (BGS), and the U.S. Geological Survey (USGS). The study provided information to aid the bedrock mapping of the Laporte 7.5-minute quad-rangle by BGS to help quantify the depth and character of fresh and saline groundwater in an area of shale-gas exploration (described in this report), which could help gas operators protect groundwater resources.
The Laporte test hole was drilled with air-hammer methods in an upland setting in the headwaters of Loyalsock Creek in the Glaciated High Plateau section of the Appalachian Plateaus physiographic province. Bedrock residuum and till were penetrated from land surface to 8.5 ft, the Huntley Mountain Formation of Mississippian and Devonian age was penetrated from 8.5 to 540 ft, and the Catskill Formation of Devonian age was penetrated from 540 to 1,400 ft. Fractures, determined from optical televiewer, acoustic televiewer, and video logs, were commonly encountered to 200 ft bls (below land surface), then decreased exponentially with depth, except at a highly fractured zone from 637 to 644 ft bls. Most fractures were along bedding planes and had a strike of about 243 degrees and dip about 4 degrees to the northwest, consistent with the test-hole location on the north limb of the Muncy Creek anticline. Few fractures were noted below 650 ft.
The depths of fresh and saline water-bearing fracture zones were identified in the test hole by geophysical-log analysis and were verified by pumping samples from zones isolated with packers and by collecting samples in the open hole with a wire-line point sampler. Six water-bearing zones associated with single or multiple fractures were identified at depths of 130–135, 180, 267–275, 425, 637–644, and 1,003 ft bls. Under ambient conditions, fresh water entered the hole from fractures at 130-135 and 180 ft bls, flowed downward and exited at fractures from 267–275, 425, and 637–644 ft. When pumped at 16.2 gal/min, most of the water from the open test hole was contributed from the fracture at 180 ft bls. Transmissivity, estimated from analysis of the specific-capacity data and flowmeter logs, is about 850 ft2/d for the entire open hole, and about 60 percent of the transmissivity is contributed from the fracture zone at 180 ft bls. The hydraulic heads in the deep water-bearing zones at 425 and 637–644 ft were about 100 ft lower than hydraulic heads in shallow water-bearing zones at 180 ft bls and above, indicating a large downward vertical hydraulic gradient.
Water samples pumped from fracture zones isolated by packers at and above the water-bearing zone at 450 ft bls were fresh with dissolved-solids contents of 105 mg/L or less. The sample isolated at 637–644 ft bls was probably affected by leakage around packers, but the specific-conductance samples collected during drilling that were believed to be representa-tive of the fracture zone at 637–644 ft bls indicated slightly saline water. Below the 637–644 ft zone, a flowmeter log in the open hole did not detect any vertical flow, and the temperature log approached the geothermal gradient, indicating little ambient fluid flow and minimal fracture transmissivity below this depth. A petrophysical-log analysis using estimates of formation water resistivity from Archie’s Equation indicated an apparent transition from fresh to saline water in the sandstones occurs between 450 to 900 ft bls, with saline water indicated below 900 ft.
Small seeps of saline water were delineated at 958, 989, and 1,003 ft bls by a time series of specific-conductance logs, and a discrete-point water sample at 990 ft bls with total dissolved-solids concentration of 19,900 mg/L verified that highly saline water was present below 900 ft bls. Occurrence of saline water at a depth of about 900 ft bls is below altitude of streams within 3 to 5 miles of the test hole but is about 930 ft above the altitude at the mouth of Loyalsock Creek where is enters the West Branch Susquehanna River at Montours-ville, Pa. The depth to saline water in this test hole is close to depths estimated at two other deep test holes drilled by the BGS in upland settings in Bradford and Tioga Counties in north-ern Pennsylvania.
The saline water from 990 ft bls had a chemical composition similar to Appalachian Basin brines that had been diluted with fresh water. Predominant ions in the saline water were sodium, chloride, and calcium. Trace constituents of strontium, bromide, barium, lithium, and molybdenum were all more than 5,000 times greater than in freshwater samples from 167 or 270 ft bls. Methane concentration in the saline water sample from 990 ft was 120 mg/L. The concentration ratios of methane to higher-chain hydrocarbon gases and isotopic ratios of 13C/12C and 2H/1H of methane indicate that the gases are likely of thermogenic origin. In the sample from 990 ft bls, the 13C/12C of methane was less negative (-34.81 per mil) than 13C/12C of ethane (-37.1 per mil). Isotopic reversals such as this are generally found in gases from rocks older than the Catskill Formation, so its recognition in a natural upland setting at relatively shallow depth could be important when interpreting isotopic results to identify the origin of stray gas in the area.
","language":"English","publisher":"Pennsylvania Geological Survey","usgsCitation":"Risser, D.W., Williams, J., and Bierly, A.D., 2021, Geohydrologic and water-quality characterization of a fractured-bedrock test hole in an area of Marcellus Shale gas development, Sullivan County, Pennsylvania: Open-File Report OFMI 21-02.0, xii, 56 p.","productDescription":"xii, 56 p.","ipdsId":"IP-107313","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":393593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393564,"type":{"id":15,"text":"Index Page"},"url":"https://maps.dcnr.pa.gov/publications/Default.aspx?id=995"}],"country":"United States","state":"Pennsylvania","county":"Sullivan County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.61453247070312,\n 41.28967402411714\n ],\n [\n -76.37832641601562,\n 41.28967402411714\n ],\n [\n -76.37832641601562,\n 41.46742831254425\n ],\n [\n -76.61453247070312,\n 41.46742831254425\n ],\n [\n -76.61453247070312,\n 41.28967402411714\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Risser, Dennis W. 0000-0001-9597-5406 dwrisser@usgs.gov","orcid":"https://orcid.org/0000-0001-9597-5406","contributorId":898,"corporation":false,"usgs":true,"family":"Risser","given":"Dennis","email":"dwrisser@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, John H. 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","middleInitial":"H.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bierly, Aaron D.","contributorId":270527,"corporation":false,"usgs":false,"family":"Bierly","given":"Aaron","email":"","middleInitial":"D.","affiliations":[{"id":16182,"text":"Pennsylvania Geological Survey","active":true,"usgs":false}],"preferred":false,"id":829530,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225634,"text":"sim3481 - 2021 - Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015-20","interactions":[],"lastModifiedDate":"2021-11-01T11:47:09.108555","indexId":"sim3481","displayToPublicDate":"2021-10-29T11:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3481","displayTitle":"Elevation and Elevation-Change Maps of Fountain Creek, Southeastern Colorado, 2015–20","title":"Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015-20","docAbstract":"The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, has collected topographic data annually since 2012 at 10 study areas along Fountain Creek, southeastern Colorado. The 10 study areas were located between Colorado Springs and the terminus of Fountain Creek at the Arkansas River in Pueblo. The purpose of this report is to present elevation maps based on topographic surveys collected in 2020 and to present maps of elevation change that occurred between 2015 and 2020 at all 10 study areas. Elevation and elevation-change maps were developed in Global Mapper, R, and ArcGIS from topographic surveys collected at each study area during the winters of 2015 and 2020. Topographic surveys in 2015 were completed using real-time kinematic Global Navigation Satellite Systems. Topographic surveys in 2020 were completed using both real-time kinematic Global Navigation Satellite Systems and light detection and ranging. Elevation-change maps were created using propagated uncertainties associated with the 95-percent confidence limit. Study areas along Fountain Creek underwent a range of geomorphic responses between 2015 and 2020 that were often related to the dominant channel planform pattern of the study area. The results of this ongoing monitoring effort can be used to assess long-term changes in land-surface elevation and to advance understanding of the geomorphic response to possible changes in flow conditions on Fountain Creek.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sim3481","collaboration":"Prepared in cooperation with Colorado Springs Utilities","usgsCitation":"Hempel, L.A., Creighton, A.L., and Bock, A.R., 2021, Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–20: U.S. Geological Survey Scientific Investigations Map 3481, 10 sheets, 12-p. pamphlet, https://doi.org/10.3133/sim3481.","productDescription":"Report: vii, 12 p.; 10 Sheets: 12.19 x 13.44 inches or smaller; Data Release; Read Me; Related Work","onlineOnly":"Y","ipdsId":"IP-124273","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":391163,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3456","text":"Elevation and Elevation-Change Maps of Fountain Creek, Southeastern Colorado, 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layers."},{"id":391159,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3481/sim3481_sheets1to10.pdf","text":"Elevation (2015, 2020) and Elevation-Change (2015−20) Map—Study Areas 1- 10","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3581 Sheets 1-10","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":391162,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98J7DRO","text":"USGS data release","linkHelpText":"Elevation Data from Fountain Creek between Colorado Springs and the Confluence of Fountain Creek at the Arkansas River, Colorado, 2020 (ver 2.0, May 2021)"},{"id":391090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3481/coverthb.jpg"},{"id":391091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3481/sim3481_pamphlet.pdf","text":"Report","size":"2.61 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2020) and Elevation-Change (2015−20) Map—Study Area 05","size":"1.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3581 Sheet 5","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."}],"country":"United States","state":"Colorado","otherGeospatial":"Fountain Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.0567626953125,\n 38.09998264736481\n ],\n [\n -104.2108154296875,\n 38.09998264736481\n ],\n [\n -104.2108154296875,\n 38.9807627650163\n ],\n [\n -105.0567626953125,\n 38.9807627650163\n ],\n [\n -105.0567626953125,\n 38.09998264736481\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
This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that AWiFS has an interior geometric performance in the range of −16.080 (−0.268 pixel) to 35.520 meters (m; 0.592 pixel) in easting and −25.680 (−0.428 pixel) to 23.400 m (0.390 pixel) in northing in band-to-band registration, an exterior geometric error of −64.262 (−1.071 pixels) to −19.059 m (−0.318 pixel) in easting and −29.028 (−0.484 pixel) to 41.249 m (0.687 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 2.29–2.36 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.030–0.035.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030G","usgsCitation":"Ramaseri Chandra, S.N., Kim, M., Christopherson, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on Resourcesat-2 Advanced Wide Field Sensor, chap. G of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors (ver. 1.2, August 2024): U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030G.","productDescription":"Report: iv, 17 p.; Version History","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126658","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":392291,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/g/versionHist.txt","text":"Version History","size":"1.8 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2021–1030G Version History"},{"id":391064,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/g/images"},{"id":391063,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/g/ofr20211030g.xml","text":"Report","size":"79.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1030G xml"},{"id":433255,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/g/ofr20211030g.pdf","text":"Report","size":"2.2 MB","description":"OFR 2021–1030G"},{"id":391061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/g/coverthb3.jpg"}],"edition":"Version 1.0: September 28, 2021; Version 1.1: November 30, 2021; Version 1.2: August 29, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A telemetry study was conducted during August–December 2020 to evaluate behavior and movement patterns of adult smallmouth bass (Micropterus dolomieu) in the forebay of Bonneville Dam, Washington. A total of 40 smallmouth bass were collected, tagged, and released during August–September in seven distinct areas of the dam forebay and monitored until mid-December. Movement data from 36 tagged smallmouth bass were used in behavior analyses with an average detection duration (elapsed time from release to last detection) of 53.3 days. Nine smallmouth bass eventually moved upstream out of the array and sixteen smallmouth bass moved downstream out of the array. Smallmouth bass showed high site fidelity, primarily remaining within their zone of release or moving into nearby adjacent zones. Tagged smallmouth bass spent the greatest percentage of time in their zone of release in all zones except the Boat Rock zone; the five smallmouth bass released in the Boat Rock zone moved to the Goose Island zone, where they stayed most of their time. Smallmouth bass movements to zones farthest away from their zone of release were not common and smallmouth bass residence time in those zones was short. A large percentage of tagged smallmouth bass moved among three zones located immediately upstream from the Bonneville Dam spillway, which was not operated during the study. Results from the study provided new insights into smallmouth bass behavior patterns during fall months in the forebay of Bonneville Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211099","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Kock, T.J., Hansen, G.S., and Evans, S.D., 2021, Behavior and movement of smallmouth bass (Micropterus dolomieu) in the forebay of Bonneville Dam, Columbia River, August–December 2020: U.S. Geological Survey Open-File Report 2021–1099, 13 p., https://doi.org/10.3133/ofr20211099.","productDescription":"vii, 13 p.","onlineOnly":"Y","ipdsId":"IP-127395","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":403444,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211099/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1099"},{"id":391094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1099/coverthb.jpg"},{"id":391095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1099/ofr20211099.pdf","text":"Report","size":"26.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1099"},{"id":397378,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1099/images"},{"id":397379,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1099/ofr20211099.XML"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Bonneville Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.97381973266602,\n 45.624362920967556\n ],\n [\n -121.91717147827148,\n 45.624362920967556\n ],\n [\n -121.91717147827148,\n 45.65736777757339\n ],\n [\n -121.97381973266602,\n 45.65736777757339\n ],\n [\n -121.97381973266602,\n 45.624362920967556\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Northern bobwhite Colinus virginianus populations have been rapidly declining in the eastern, central, and southern United States for decades. Land use change and an incompatibility between northern bobwhite resource needs and human land use practices have driven declines. Here, we applied occupancy analyses on two spatial scales (state level and ecoregion level) to more than 5,000 northern bobwhite surveys conducted over 6 y across the entire state of Arkansas to explore patterns in occupancy and land use variables, and to identify priority areas for management and conservation. At the state level, northern bobwhite occupied 29% of sites and northern bobwhite were most likely to occur in areas with a high percentage of early successional habitat (grassland, pasture, and shrubland). The statewide model predicted that northern bobwhite were likely to occur (≥ 75% predicted occupancy) in < 20% of the state. Arkansas is comprised of five distinct ecoregions, and analyses at the ecoregion spatial scale showed that habitat associations of northern bobwhite could vary between ecoregions. For example, early successional habitat best predicted northern bobwhite occupancy in both the Arkansas River Valley and Ozark Mountains ecoregions, and other habitat associations such as the proportion of herbaceous habitat and hay-pasture habitat, respectively, further refined predictions. Contrastingly, richness of land cover classes alone best predicted northern bobwhite occupancy in the Ouachita Mountains ecoregion. Ecoregion-level models were thus more discerning than the state-level model and should be more helpful to managers in identifying priority conservation areas. However, in two of five ecoregions, surveys too rarely encountered northern bobwhite to accurately predict their occurrence. We found that likely occupied northern bobwhite habitat lay primarily on private properties (95%), but that numerous public entities own and manage land identified as suitable or likely occupied. We conclude that management of northern bobwhite in Arkansas could benefit from cooperation among state, federal, and military partners, as well as surrounding private landowners and that ecoregion-specific models may be more useful in identifying priority areas for management. Our approach incorporates multiple landscape scales when using remote sensing technology in conjunction with monitoring data and could have important application for the management of northern bobwhite and other grassland bird species.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-21-002","usgsCitation":"Lassiter, E.V., Asher, M., Christie, G., Gale, C., Massey, A., Massery, C., MIddaugh, C., Veon, J., and DeGregorio, B.A., 2021, Northern bobwhite occupancy patterns on multiple spatial scales across Arkansas: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 502-512, https://doi.org/10.3996/JFWM-21-002.","productDescription":"11 p.","startPage":"502","endPage":"512","ipdsId":"IP-125981","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-002","text":"Publisher Index Page"},{"id":396907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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R.","contributorId":288241,"corporation":false,"usgs":false,"family":"MIddaugh","given":"C. R.","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":837584,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Veon, J.","contributorId":288245,"corporation":false,"usgs":false,"family":"Veon","given":"J.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":837585,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837586,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225596,"text":"sir20215114 - 2021 - Assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio, 2018–20","interactions":[],"lastModifiedDate":"2023-10-23T20:07:42.537005","indexId":"sir20215114","displayToPublicDate":"2021-10-27T13:11:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5114","displayTitle":"Assessment of Runoff Volume Reduction Associated with Soil Amendments Added to Portions of Highway Median-Strip Catchments in Ohio, 2018–20","title":"Assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio, 2018–20","docAbstract":"The U.S. Geological Survey installed 10 rain gages and 12 calibrated H-flumes to measure rainfall and runoff volumes at 10 locations in Ohio Department of Transportation highway median-strip catchments. Data were collected to facilitate comparisons of rainfall and runoff volumes at study sites before and after stormwater best management practices (BMPs) were installed and between sites with different BMPs. The BMP treatments comprised removing the top layer of the existing soil, rototilling the remaining soil to a 6-inch depth, mixing the soils with one of two soil amendments (compost with sand or shale) at one of two thicknesses (4 inches or 6 inches), topping with a compost blanket, seeding, and installing erosion control matting. The overall treatment used at a given study site is referred to as “BMP.” At two locations where soil amendments were installed, a second “control” site was installed to measure runoff from an adjacent catchment in the same median strip where no soil amendment was installed. This no-treatment option (no soil amendment) was considered its own class of BMP.
Rainfall and runoff data were collected during periods when air temperatures were above freezing (including all months except January, February, and parts of December and March) from 2018 to 2020. The data collection period for each study site was divided into “pre-BMP” and “post-BMP” periods. Equipment to measure rainfall and runoff was installed and data were collected from April to December 2018 before installation of soil amendments (the pre-BMP period). The post-BMP period started between April and May of 2019 at the first measured rainfall after soil amendments were installed. Rainfall and runoff monitoring continued through September 2020. For control sites, the post-BMP periods were assigned to start with the first measured rainfall in the 2019 data collection season.
A rainfall-runoff “event” was defined as beginning at the time of the first measured rainfall and ending when rainfall and runoff (if any) ceased and remained ceased for at least 3 hours. A value referred to as “event runoff percentage,” defined as the total volume of runoff during an event expressed as a percentage of the total volume of rainfall falling over the catchment, was computed for each event. The distribution of rainfall totals associated with events was similar between the pre-BMP and post-BMP periods; however, there were appreciable between-site differences in the distribution of event runoff percentages during the pre-BMP and post-BMP periods.
Empirical distribution function (EDF) tests were performed with and without data from events that resulted in no runoff to determine whether the distribution of event runoff percentages changed from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal in the pre-BMP and post-BMP periods was rejected (α=0.05) in at least one of the two tests for four sites (one site with a shale amendment and three sites with sand amendments). Mean event runoff percentages at each of those four sites decreased from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal was not rejected for the other six sites’ draining catchments with soil amendments or the two control sites. EDF tests performed on event rainfall totals indicated no statistically significant changes between the pre-BMP and post-BMP period distributions for any of the sites.
Double-mass analyses of cumulative runoff were performed for two pairs of closely spaced sites (each pair located in a common median strip): one site in each pair drained a catchment where soil amendments were installed, and the other (a control) drained a catchment without soil amendments. Those double-mass analyses indicated a small reduction in runoff from the pre-BMP to post-BMP period at the site whose catchment received the sand and compost amendment, but no perceptible reduction in runoff at the site whose catchment received the shale and compost amendment.
Regression analyses indicated that (a) three rainfall factors (event rainfall totals, total rainfall for the previous 7 days, and a cross product of the factors) and the intercept term were the four most important factors explaining event runoff percentages, (b) the effect of amendment type on event runoff percentage was small in comparison to the rainfall and intercept terms, (c) event runoff percentages tended to be lower for sites with shale amendments than sites with sand amendments; however, event runoff percentages tended to be lower for control sites than for sites with shale or sand amendments, and (d) event runoff percentages increased with increasing amendment thickness. The counterintuitive results that event runoff percentages increased with increasing amendment thickness and that control sites tended to have lower event runoff percentages than sites draining soil-amended catchments likely reflects unmeasured factors that existed at the sites before BMPs were installed rather than the effect of the BMP treatments.
Although not definitive, some support for the conclusion that the sand amendment was generally more effective at reducing runoff than the shale amendment was provided by results from the EDF tests, double-mass analyses, and runoff statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215114","collaboration":"Prepared in cooperation with ms consultants","usgsCitation":"Whitehead, M.T., and Koltun, G.F., 2021, Assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio, 2018–20 (ver. 1.1, December 2021): U.S. Geological Survey Scientific Investigations Report 2021–5114, 27 p., https://doi.org/10.3133/sir20215114.","productDescription":"Report: vii, 27 p.; Data Release; Version History","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-118944","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":390957,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P945PKJ7","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Dataset for analyses in assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio"},{"id":390955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5114/coverthb2.jpg"},{"id":392682,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.pdf","text":"Report","size":"5.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5114"},{"id":390958,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5114 xml"},{"id":392683,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5114/versionHist.txt","text":"Version History","size":"3.07 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":390959,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5114/images"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.6279296875,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 39.639537564366684\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2021; Version 1.1: December 2021","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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Identifying the physical habitat characteristics associated with riverine freshwater mussel assemblages is challenging but crucial for understanding the causes of mussel declines. The occurrence of mussels in multispecies beds suggests that common physical factors influence or limit their occurrence. Fine-scale geomorphic and hydraulic factors (e.g., scour, bed stability) are predictive of mussel-bed occurrence, but they are computationally challenging to represent at intermediate or riverscape scales. We used maximum entropy (MaxEnt) modeling to evaluate associations between riverscape-scale hydrogeomorphic variables and mussel-bed presence along 530 river km of the Meramec River basin, USA, to identify river reaches that are fundamentally suitable for mussels as well as those that are not. We obtained the locations of mussel beds from an existing, multiyear dataset, and we derived river variables from high-resolution, open-source datasets of aerial imagery and topography. Mussel beds occurred almost exclusively in reaches identified by our model as suitable; these were characterized by laterally stable channels, absence of adjacent bluffs, proximity to gravel bars, higher stream power, and larger areas of contiguous water (a proxy for drought vulnerability). We validated our model findings based on model sensitivity using a set of mussel-bed locations not used in model development. These findings can inform how resource managers allocate survey, monitoring, and conservation efforts.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc-d-20-00002","usgsCitation":"Keymanesh, K., Rosenberger, A.E., Lindner, G., Bouska, K.L., and McMurray, S.E., 2021, Riverscape-scale modeling of fundamentally suitable habitat for mussel assemblages in an Ozark River system, Missouri: Freshwater Mollusk Biology and Conservation, v. 24, no. 2, p. 43-58, https://doi.org/10.31931/fmbc-d-20-00002.","productDescription":"16 p.","startPage":"43","endPage":"58","ipdsId":"IP-113472","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc-d-20-00002","text":"Publisher Index Page"},{"id":433559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.23145192205784,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 38.57316922340365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keymanesh, K.","contributorId":317234,"corporation":false,"usgs":false,"family":"Keymanesh","given":"K.","email":"","affiliations":[],"preferred":false,"id":908903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindner, G.","contributorId":341798,"corporation":false,"usgs":false,"family":"Lindner","given":"G.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMurray, Stephen E.","contributorId":206918,"corporation":false,"usgs":false,"family":"McMurray","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246522,"text":"70246522 - 2021 - Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska","interactions":[],"lastModifiedDate":"2023-07-10T13:20:53.475537","indexId":"70246522","displayToPublicDate":"2021-10-27T06:37:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska","docAbstract":"Mineralogical and geochemical studies of interbedded black and gray mudstones in the Triassic part of the Triassic-Jurassic Otuk Formation (northern Alaska) document locally abundant barite and pyrite plus diverse redox signatures. These strata, deposited in an outer shelf setting at paleolatitudes of ~45 to 60°N, show widespread sedimentological evidence for bioturbation. Barite occurs preferentially in black mudstones (TOC = 0.93–6.46 wt%), forming displacive euhedral crystals with pyrite inclusions and rims, and late albite inclusions or intergrowths. Pyrite also occurs as small (3–20 μm) framboids, discontinuous laminae, euhedral and anhedral crystals, and replacements of barite and fossils (mainly radiolarians). Paragenetically early wurtzite is present as clusters of very small (1–3 μm) aggregates of radiating crystals 0.5 to 1.0 μm long with cores of organic matter that overgrow framboidal pyrite; later wurtzite forms 10- to 30-μm bladed crystals. Equant grains (3–30 μm) and small (20 μm) angular clusters of zinc sulfide that include <1-μm-long, comb-like structures are sphalerite or wurtzite, or both. Minor siderite forms euhedral crystals intergrown with albite that enclose wurtzite and barite. Illite shows intergrowths with sphalerite; rare K-feldspar is intergrown with barite. Formation of these minerals and assemblages is attributed to early diagenetic processes.
Whole-rock geochemical data for 15 samples show large ranges in redox proxies including Post Archean Average Shale (PAAS)-normalized enrichment factors (EFs) for V, U, Mo, and Re, and Al-normalized ratios for V, U, and Mo. Results for most black mudstones, with or without abundant barite and/or pyrite, suggest deposition within an oxygen minimum zone. Cerium anomalies, PAAS-normalized and calculated on a detrital-free basis, range widely from 0.49 to 0.96 and may reflect diagenetic overprinting by Ce-depleted fluids. Considering data for both black and gray mudstones, the overall geochemical pattern together with evidence from pyrite framboid sizes suggest that redox conditions fluctuated greatly from euxinic to oxic, like the redox profiles reported for modern shelf sediments offshore Peru and Namibia. The euxinic redox signatures in some Otuk black mudstones may correlate with widespread Early to Middle Triassic ocean anoxic events proposed for other regions.
Calculations of median EFs for trace elements in Otuk black mudstones reveal both enrichments and depletions. Normalizations to the median composition of the three least-mineralized black mudstones show that barite- and/or pyrite-rich samples display large (>50%) positive changes for Li (+80.4%), V (+75.6%), Sr (+75.9%), Ba (+790%), Cu (+92.1%), Ni (+169%), Ag (+156%), Au (+3091%), As (+109%), Sb (+476%), and Se (+205%); Zn shows a moderate positive change of +42.1%. Moderate negative changes are evident only for Ge (−47.2%) and W (−30.6%). The local enrichments may reflect one or more factors including redox variations in bottom waters and pore fluids, element mobility during diagenesis, and selective fractionation into minerals such as barite, pyrite, and wurtzite. Anomalously low U/Al and UEF values, compared to those for other modern and ancient organic-rich sediments and sedimentary rocks, are attributed to increased solubility and loss of U during bioturbation-related oxygenation in the subsurface.
Physicochemical constraints on barite, pyrite, and wurtzite formation are informed by use of a pH-fO2 plot constructed at 10 °C. Based on paragenetic evidence for multistage deposition of these three minerals, together with the presence of illite intergrown with ZnS and K-feldspar with barite, proposed diagenetic trends involve an increase in pH and fO2 related to the ingress of sulfate-rich pore fluids during bioturbation, followed by a return to lower then higher pH and fO2 conditions linked to carbon, sulfur, barium, and iron cycling during diagenesis. Labile Ba of marine pelagic origin was mobilized from organic-rich sediment upward to the sulfate-methane transition zone where barite precipitated during the interaction of reduced Ba- and CH4-rich fluids with sulfate-bearing pore fluids. The formation of paragenetically early wurtzite (ZnS) crystals, as well as locally high EF values for Cu, Ni, Ag, and Au, is attributed to metal enrichment of pore fluids, with sources being derived in part from water-column deposition from hydrothermal plumes related to coeval Triassic seafloor vent systems including a volcanogenic massive sulfide deposit in British Columbia and the Wrangellia Large Igneous Province in Alaska.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120568","usgsCitation":"Slack, J.F., McAleer, R.J., Shanks, W., and Dumoulin, J.A., 2021, Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska: Chemical Geology, v. 585, 120568, 22 p., https://doi.org/10.1016/j.chemgeo.2021.120568.","productDescription":"120568, 22 p.","ipdsId":"IP-130237","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450344,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120568","text":"Publisher Index Page"},{"id":418739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.604267717169,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 71.70733094087223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"585","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":877040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":877041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanks, Wayne (Pat)","contributorId":240838,"corporation":false,"usgs":true,"family":"Shanks","given":"Wayne (Pat)","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":877042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":877043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225547,"text":"sir20215096 - 2021 - Effects of culvert construction on streams and macroinvertebrate communities at selected sites in the East Gulf Coastal Plain of Alabama, 2010–19","interactions":[],"lastModifiedDate":"2023-10-23T20:08:40.929938","indexId":"sir20215096","displayToPublicDate":"2021-10-25T20:04:17","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5096","displayTitle":"Effects of Culvert Construction on Streams and Macroinvertebrate Communities at Selected Sites in the East Gulf Coastal Plain of Alabama, 2010–19","title":"Effects of culvert construction on streams and macroinvertebrate communities at selected sites in the East Gulf Coastal Plain of Alabama, 2010–19","docAbstract":"The U.S. Geological Survey, in cooperation with the Alabama Department of Transportation, evaluated the role of culvert construction in altering streams and habitats of benthic macroinvertebrate communities at selected study sites in the northern East Gulf Coastal Plain of Alabama during 2011–19. Analysis included examinations of changes in stream channel geometry, suspended sediment, turbidity, and benthic macroinvertebrate communities.
Topographic surveys of stream channel cross sections, upstream and downstream of the culvert, were conducted before and after construction. Changes in channel geometry (cross-sectional area, top width, mean depth, and thalweg slope) were assessed by using paired sample t-tests to compare before- and after-construction channel geometry measurements. Statistically significant changes in stream channel geometry between the before- and after-construction measurements were observed at four of the six study sites. Analysis of the channel geometry data indicates that 1 site had no measured changes, and thalweg reach slopes were inverted at 4 of the 12 study reaches—2 measured in before-construction reaches and 2 measured in after-construction reaches.
Surface-water samples were collected during selected storm events for suspended sediment and turbidity analyses. Samples were simultaneously collected upstream and downstream of the culvert construction reaches during all three phases of construction (before, during, and after). Analysis focused on the parity of upstream to downstream simultaneous samples. The mean upstream to downstream paired ratios of sediment concentrations and turbidity from the after-construction phase indicate that colloidal and noncolloidal sediments were passing through the construction reaches at two of the six sites, noncolloidal sediments were being trapped in the construction reaches at two sites, and colloidal and noncolloidal sediments were being removed from the construction reach at two sites.
Benthic macroinvertebrates were collected and identified at five of the six sites from instream habitats that were available in sampled areas both upstream and downstream of the culvert construction reaches. Differences between upstream and downstream reaches and the Wilcoxon rank sum statistic were used to examine changes in metrics of benthic macroinvertebrate communities between before- and after-construction phases. Benthic macroinvertebrate sampling results did not indicate that culvert construction caused impairment to communities at study sites. No tolerance metrics suggested a major change in the pollution tolerance of the communities. The same upstream to downstream patterns in abundance-weighted tolerance values were observed in the before- and after-construction periods at each site. At one site, the difference between upstream and downstream richness-based tolerance values increased, but the after-construction upstream and downstream richness-based tolerance values were lower (indicating a less pollution-tolerant macroinvertebrate community) than in the before-construction period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215096","collaboration":"Prepared in cooperation with the Alabama Department of Transportation","usgsCitation":"Pugh, A.L., and Gill, A.C., 2021, Effects of culvert construction on streams and macroinvertebrate communities at selected sites in the East Gulf Coastal Plain of Alabama, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5096, 52 p., https://doi.org/10.3133/sir20215096.","productDescription":"Report: vii, 52 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-097029","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":390797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P906BOVO","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Aerial imagery, benthic macroinvertebrate, topographic survey, and soil survey datasets collected for a study of effects of culverts on the natural conditions of streams in the East Gulf Coastal Plain of Alabama, 2010–2019"},{"id":390796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5096/sir20215096.pdf","text":"Report","size":"15.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5096"},{"id":390795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5096/coverthb.jpg"},{"id":390798,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Alabama","otherGeospatial":"East Gulf Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.08837890625,\n 34.69646117272349\n ],\n [\n -88.165283203125,\n 34.69646117272349\n ],\n [\n -88.505859375,\n 31.98012335736804\n ],\n [\n -88.363037109375,\n 30.315987718557867\n ],\n [\n -88.121337890625,\n 30.268556249047727\n ],\n [\n -87.747802734375,\n 30.173624550358536\n ],\n [\n -87.418212890625,\n 30.35391637229704\n ],\n [\n -87.264404296875,\n 30.477082932837682\n ],\n [\n -87.4072265625,\n 30.581179257386985\n ],\n [\n -87.451171875,\n 30.741835717889792\n ],\n [\n -87.593994140625,\n 30.86451022625836\n ],\n [\n -87.5390625,\n 31.005862904624205\n ],\n [\n -84.979248046875,\n 30.996445897426373\n ],\n [\n -85.067138671875,\n 31.175209828310845\n ],\n [\n -85.045166015625,\n 31.306715155075167\n ],\n [\n -85.067138671875,\n 31.456782472114313\n ],\n [\n -85.078125,\n 31.71882222408327\n ],\n [\n -85.0341796875,\n 31.970803930433096\n ],\n [\n -84.88037109375,\n 32.25926542645933\n ],\n [\n -84.9462890625,\n 32.35212281198644\n ],\n [\n -85.045166015625,\n 32.46342595776104\n ],\n [\n -85.23193359375,\n 32.48196313217176\n ],\n [\n -85.6494140625,\n 32.54681317351514\n ],\n [\n -86.044921875,\n 32.57459172113418\n ],\n [\n -86.693115234375,\n 32.648625783736726\n ],\n [\n -87.132568359375,\n 32.685619853722\n ],\n [\n -87.4951171875,\n 32.85190345738802\n ],\n [\n -87.73681640625,\n 33.109948297894285\n ],\n [\n -87.82470703125,\n 33.53223722395908\n ],\n [\n -87.91259765625,\n 34.10725639663118\n ],\n [\n -87.95654296875,\n 34.379712580462204\n ],\n [\n -88.08837890625,\n 34.69646117272349\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.737416","usgsCitation":"Satterthwaite, E.V., Bax, N.J., Miloslavich, P., Ratnarajah, L., Canonico, G., Dunn, D., Simmons, S.E., Carini, R., Evans, K., Allain, V., Appeltans, W., Batten, S., Benedetti-Cecchi, L., Bernard, A.T., Bristol, R., Benson, A., Buttigieg, P.L., Gerhardinger, L.C., Chiba, S., Davies, T.E., Duffy, J., Giron-Nava, A., Hsu, A.J., Kraberg, A.C., Kudela, R.M., Lear, D., Montes, E., Muller-Karger, F., O’Brien, T.D., Obura, D., Provoost, P., Pruckner, S., Rebelo, L., Selig, E.R., Kjesbu, O.S., Starger, C., Stuart-Smith, R.D., Vierros, M., Waller, J.S., Weatherdon, L.V., Wellman, T., and Zivian, A., 2021, Establishing the foundation for the global observing system for marine life: Frontiers in Marine Science, v. 8, 737416, 19 p., https://doi.org/10.3389/fmars.2021.737416.","productDescription":"737416, 19 p.","ipdsId":"IP-127529","costCenters":[{"id":191,"text":"Colorado Water Science 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monitoring","docAbstract":"The Long Term Resource Monitoring (LTRM) element of the Upper Mississippi River Restoration program employs a harvest method for sampling submersed aquatic vegetation (SAV) whereby a rake is dragged ~1.5 m over the substrate and plant materials are retrieved. “Plant density” (PD) scores indicate SAV abundance and are based on the amount of plant material collected on the teeth of the rake. Standard PD scores are ordered, whole numbers from 0 (no SAV on the rake) to 5 (80-100% of rake teeth full) and are assigned at each subsite for all species combined and for each individual species.
In LTRM monitoring between 1998 and 2018, ~73% of non-zero, all-species-combined PD scores were 1s, and ~89% of individual SAV species were 1s. The preponderance of PD = 1 scores along with the wide range of fresh mass represented by PD = 1 (quantified in Drake and Lund 2020) limits inference about SAV abundance from LTRM monitoring data.
Field personnel noted that small plant fragments comprised a substantial fraction of PD = 1 observations and proposed a modification of the existing LTRM methods where PD = 1 was subdivided to include “trace” scores to represent such small fragments. Trace was defined as PD = 0.08, indicating a maximum of 1 of 13 gaps in the sampling rake filled to the level of an original PD = 1. Amounts of plant material greater than PD = 0.08 and up to the original score of 1 were defined PD = +1. This study used field data collected in 2018 (scoring and fresh weights of scored plant materials) from 136 vegetated sites in Pools 4, 8 and 13 to evaluate the proposed subdivision and to examine among-pool differences in PD data. In the study data, 33% of all-species-combined observations and 69% of species (grouped by morphology) that would previously have received a score of 1 were classified as PD = 0.08. PD scores of 0.08, +1, and 2-3 represented statistically distinct amounts of fresh mass in rake samples. There were systematic differences in the mass of SAV reflected by PD score based on plant morphology and species composition. The mean fresh mass of plant materials assigned a given PD score varied among the three pools, suggesting bias attributable to personnel. To reduce this bias in future data collection efforts, the field crews incorporated a calibration of plant density scores in annual field training. The results presented here describe how including a trace PD score in LTRM data collection improves the description of SAV abundance and consequently estimates of biomass from those PD scores. LTRM vegetation crews have recorded trace scores in annual sampling since 2019 as extra information (i.e. which does not change the LTRM data stream as 0.08 and +1 scores can still be combined for PD=1). Trace data are not currently available to outside users through the LTRM data browser but are available from vegetation component personnel upon request.
","language":"English","publisher":"U.S. Army Corps of Engineers, Upper Mississippi River Restoration Program","usgsCitation":"Drake, D.C., Lund, E., and Bales, K., 2021, Evaluation of a “trace” plant density score in LTRM vegetation monitoring: Long Term Resource Monitoring Technical Report LTRM-2018BI03a, 32 p.","productDescription":"32 p.","ipdsId":"IP-106633","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391319,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2021/drake_a_2021.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Lowenberg, Carol 0000-0002-2961-6808","orcid":"https://orcid.org/0000-0002-2961-6808","contributorId":221012,"corporation":false,"usgs":true,"family":"Lowenberg","given":"Carol","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825693,"contributorType":{"id":2,"text":"Editors"},"rank":0}],"authors":[{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":826584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Kyle","contributorId":267952,"corporation":false,"usgs":false,"family":"Bales","given":"Kyle","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825692,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230971,"text":"70230971 - 2021 - Improving the usability of Galileo and Voyager images of Jupiter’s moon, Europa","interactions":[],"lastModifiedDate":"2022-04-29T12:01:12.364248","indexId":"70230971","displayToPublicDate":"2021-10-25T06:58:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Improving the usability of Galileo and Voyager images of Jupiter’s moon, Europa","docAbstract":"NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.