Ecological characterization atlas of coastal Alabama: map narrative
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FWS/OBS↗
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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.
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Northwestern Florida ecological characterization: an ecological atlas: map narratives
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FWS/OBS↗
Atlas of the spawning and nursery areas of Great Lake fishes
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FWS/OBS↗
Use of miniroutes and Breeding Bird Survey data to estimate abundance
1. Information on relative abundance is easily obtained and adds greatly to the value of an atlas project. 2. The Breeding Bird Survey (BBS) provides annual counts (birds per 50 roadside stops) that can be used to: (1) map relative abundance by physiographic region within a state or province, (2) map relative abundance on a more local scale by using results from individual routes, or (3) compute estimates of total state populations of a species. Where BBS coverage is too scanty to permit mapping, extra temporary routes may be established to provide additional information for the atlas. Or, if continuing coverage is anticipated, additional permanent random routes can be assigned by the U. S. Fish and Wildlife Service. 3. Miniroutes of 15 or more stops can be established in individual atlas blocks to serve the dual purposes of providing efficient uniform coverage and providing information on relative abundance. Miniroutes can also be extracted from BBS routes to supplement special atlas coverage, or vice versa; but the data from the BBS will not be confined to individual atlas blocks. 4. Advantages of 15- or 20-stop Miniroutes over 25-stop Miniroutes are several: the ability to do two per morning and the lower variability among M1niroute results. Also, many 5-km atlas blocks do not have enough secondary roads to accommodate 25 stops at one-half mile intervals. Disadvantages of 15-stop Miniroutes starting at sunrise are the smaller numbers of birds recorded, missing of the very productive dawn chorus period (Robbins 1981), and missing crepuscular species (rails, woodcock, owls, and goatsuckers). 5. Advantages of recording counts of individuals rather than checking only species presence at Miniroute stops are that: (1) relative abundance can be mapped rather than frequency only (a measure of frequency is already available in the number of blocks recording each species); (2) population change can be measured over a period of years when the next atlas is made; and (3) comparative abundance data are available for habitat correlations and other statistical applications (Luis et ale 1983). Disadvantages of recording counts are that: (1) many observers do not feel they can make accurate counts of individuals; and (2) a few species may be missed while observers are counting (an hypothesis that will be field-tested this summer). While counts of individuals may be more subject to observer differences than are frequency counts, both the numbers of birds and their frequencies are available when birds are counted. 6. Roadside counts produce slightly larger samples and more species than off-road counts. The main advantage of off-road coverage is to sample habitats that cannot be sampled from roadsides. 7. Miniroutes can be recommended to individual atlasers as an efficient means of detecting species and upgrading them from Possible to Probable, even when there is no statewide Miniroute program.
Book chapter↗
User’s guide and metada for the PICES Nonindigenous Species Information System
Introduction Welcome to the PICES Nonindigenous Species Information System, a Microsoft Access database that displays the biogeographic distributions, invasion status, vectors, and key life history attributes of the approximately 740 reported nonindigenous species (NIS) in the estuarine and near-coastal habitats of the North Pacific and Hawaii. This database was developed by the U.S. Environmental Protection Agency and the U.S. Geological Survey under the auspices of Working Group 21 (Invasive Species) of the North Pacific Marine Science Organization (PCIES). The PICES database contains the data used to generate the “Atlas of Nonindigenous Marine and Estuarine Species in the North Pacific” (Lee and Reusser, 2012; herein referred to as the “Atlas”). The User’s Guide provides instructions on how to use the PICES database as well as metadata for the database and the Atlas. We note that for most users, the Atlas provides a simpler approach to accessing key information on NIS in the PICES countries than the database, though the database does provide additional information on species and sources as well as allowing users to extract information on specific taxa and/or locations (see Section 4). The PICES database also includes species reported from the PICES Rapid Assessment Surveys (RAS). PICES sponsored four rapid assessment surveys with the objective of quickly characterizing the native, non-native, and cryptogenic species present in different locations. Surveys were sponsored in Dalian, China in 2008, Jeju, Korea in 2009, Newport, Oregon, USA in 2010, and Peter the Great Bay, near Vladivostok and Nakhodka, Russia in 2011 ( http://www.pices.int/publications/pices_press/volume19/v19_n1/pp_30-31_Kobe-WS_f.pdf , http://www.pices.int/publications/pices_press/volume20/v20_n1/pp_26-29_RAS-2011.pdf ). The PICES database contains the RAS species that were made available in time for inclusion. Thus, the database does not capture all the species found in these surveys. In addition, much of the information on the RAS species was provided by the experts conducting the survey, and their distributions, environmental requirements, and life history attributes were not evaluated to the same level of detail by the PICES authors as the North Pacific NIS. In lieu of the more extensive review as conduced with the Atlas species, the information on the RAS species needs to be considered preliminary. Additionally, it is important to use the “Map All Distributions” option (see Section 3.6.6) when mapping their distribution or conducting a query. The general reference for the RAS surveys in the PICES database is “PICES Working Group 21, YEAR SURVEY”. The overall goal of both the database and Atlas was to simplify and standardize the dissemination of distributional, habitat, and life history characteristics of near-coastal and estuarine nonindigenous species. This database provides a means of querying these data and displaying the information in a consistent format. The specific classes of information the database captures include: Regional and global ranges of native and nonindigenous near-coastal and estuarine species at different hierarchical spatial scales. Habitat and physiological requirements of near-coastal and estuarine species. Life history characteristics of near-coastal and estuarine species. Invasion history and vectors for nonindigenous species. This standardized and synthesized data in the database and the Atlas provide the basic information needed to address a number of managerial and scientific needs. Thus, users will be able to: Create a baseline on the extent of invasion by region in order to assess new invasions. Use existing geographical patterns of invasion to gain some insights into potential new invaders. Use existing geographical patters of invasion to gain some insights into mechanisms affecting relative invasibility of different areas. Use life history attributes and environmental requirements of the reported nonindigenous species to evaluate traits of invaders. Understand the potential spread of invaders based on their habitat and environmental requirements. Understand importance of different vectors of introduction of nonindigenous species by region. The data in the Atlas of Nonindigenous Marine and Estuarine Species in the North Pacific (Lee and Reusser, 2012) are up-to-date as of June 2012. Updates to the PICES database were made in September 2012.
Report↗
Structure and development of the southern Moroccan continental shelf
The structure of the continental shelf off southern Morocco was studied by means of 2,100 km of seismic reflection profiles, magnetic and bathymetric surveys, and dredge samples. The research area lies off four geologic divisions adjacent to the coast: the Atlas Mountains; the Souss Trough; the Anti-Atlas Mountains; and the Aaiun Basin. The continental shelf, along with the western Atlas Mountains, the western Souss Trough, and the entire Aaiun Basin, has subsided along a normal fault-flexure system. This system runs along the shore at the Anti-Atlas Mountains, and cuts off this cratonic block from the shelf subsidence. The shelf is narrow and characterized by out-building off the Anti-Atlas range, whereas it is broader and characterized by upbuilding to the north and south. Deposition was essentially continuous at least from Early Cretaceous through Eocene time. Published work suggests that the last cycle of sedimentation began during Permian rifting. After Eocene time, most sediments carried to the shelf must have bypassed it and gone to construct the slope and rise or to the deep sea. Tertiary orogenies caused extensive folding of Mesozoic and early Tertiary deposits off the Atlas Mountains.
Marine Geology↗
Hydrogeology of the Great Basin region of Nevada, Utah, and adjacent states
This atlas is a product of the Great Basin Regional Aquifer-System Analysis (RASA), a study that began in 1981. The study is part of a U.S. Geological Survey program for evaluating regional aquifer systems nationwide. A regional aquifer system is defined as “an areally extensive set of aquifers which are linked in some way, such as hydraulically or economically” (Harrill and others, 1983, p. 2). The purpose of the Great Basin RASA is to evaluate aquifer system in the Great Basin by developing a better understanding of recharge and discharge processes, delineating individual ground-water flow systems, and developing mathematical models of representative flow systems. Harrill and others (1983) provide a more complete background of both the national RASA program and the Great Basin RASA. The purpose of this atlas is to delineate and describe the major hydrogeologic units in the Great Basin region and to identify those units that (1) constitute regional aquifers or (2) act as barriers to the movement of ground water. The scope of this atlas, however, is limited to a brief geologic overview of the Great Basin: lithology and areal extent of units, major structural features, and influence of tectonic events. In addition, the water-bearing characteristics of each unit are briefly summarized. This atlas is Chapter A of the three-part Hydrologic Atlas series. Chapter B shows ground-water levels in the Great Basin region, and Chapter C shows inferred directions of ground-water flow and individual flow systems.
Nevada, Utah↗
Conversion of environmental data to a digital-spatial database, Puget Sound area, Washington
Data and maps from the Puget Sound Environmental Atlas, compiled for the U.S. Environmental Protection Agency, the Puget Sound Water Quality Authority, and the U.S. Army Corps of Engineers, have been converted into a digital-spatial database using a geographic information system. Environmental data for the Puget Sound area,collected from sources other than the Puget SoundEnvironmental Atlas by different Federal, State, andlocal agencies, also have been converted into thisdigital-spatial database. Background on the geographic-information-system planning process, the design and implementation of the geographic information-system database, and the reasons for conversion to this digital-spatial database are included in this report. The Puget Sound Environmental Atlas data layers include information about seabird nesting areas, eelgrass and kelp habitat, marine mammal and fish areas, and shellfish resources and bed certification. Data layers, from sources other than the Puget Sound Environmental Atlas, include the Puget Sound shoreline, the water-body system, shellfish growing areas, recreational shellfish beaches, sewage-treatment outfalls, upland hydrography,watershed and political boundaries, and geographicnames. The sources of data, descriptions of the datalayers, and the steps and errors of processing associated with conversion to a digital-spatial database used in development of the Puget Sound Geographic Information System also are included in this report. The appendixes contain data dictionaries for each of the resource layers and error values for the conversion of Puget SoundEnvironmental Atlas data.
Open-File Report↗
Hydrogeology and hydrogeologic terranes of the Blue Ridge and Piedmont Physiographic Provinces in the eastern United States
Severe and prolonged droughts between 1961 and 1988, combined with increased demands for freshwater supplies in the United States, have resulted in a critical need to assess the potential for development of ground- and surface-water supplies. Rapid industrial growth and urban expansion have caused existing freshwater supplies to be used at or near maximum capacity. Begun in 1978, the Regional Aquifer-System Analysis (RASA) Program of the U.S. Geological Survey (USGS) is a systematic effort to study a number of the Nation's most important aquifer systems, which, in aggregate, underlie much of the country and represent an important component of the Nation's total water supply. The broad objective for each of the 28 studies in the program is to assemble geologic, hydrologic, and geochemical information, to analyze and develop an understanding of the system, and to develop predictive capabilities that will contribute to the effective management of the system. In 1988, as part of the RASA Program, the USGS began a 6-year study of the ground-water resources of parts of 11 States in the Eastern United States (Swain and others, 1991). The study was designated the Appalachian Valley and Piedmont Regional Aquifer-System Analysis (APRASA). The APRASA team investigated ground-water resources primarily in the unglaciated part of the Valley and Ridge, the Blue Ridge, the New England, and the Piedmont Physiographic Provinces (fig. 1). For the purposes of this report, the small area in the New England Physiographic Province that is within the study area in New Jersey and Pennsylvania was considered part of the Piedmont Physiographic Province. The results of the APRASA are contained in about 50 reports and abstracts, including reports on simulation of ground-water flow in three type areas, this atlas, and chapters in Professional Paper 1422. These chapters include the summary (Chapter A), descriptions of recharge rates and surface- and ground-water relations (Chapter B), hydrogeologic terranes in the Valley and Ridge Physiographic Province (Chapter C), and ground-water geochemistry (Chapter D). The purposes of this atlas are to summarize the hydrogeology, to describe an analysis of maps and well records, and to present a classification and map of the hydrogeologic terranes of the Blue Ridge and Piedmont Physiographic Provinces within the APRASA study area. Hydrogeologic terranes are defined for this atlas as regionally mappable areas characterized by similar water-yielding properties of a grouping of selected rock types. The hydrogeologic terranes represent areas of distinct hydrologic character. The terranes are intended to help water users locate and develop adequate water supplies and to help hydrologists interpret the regional hydrogeology. Previous investigations provide maps and descriptions of the geologic units, describe the local quantity and quality of ground water within these units, and establish the statistical methods for comparing the water-yielding properties of these units. State geologic maps show the distribution of geologic units at a scale of 1:500,000 for Alabama (Osborne and others, 1989), Georgia (Lawton and others, 1976), North Carolina (Brown and Parker, 1985), and Virginia (Calver and Hobbs, 1963). State maps show geologic units at a scale of 1:250,000 for Maryland (Cleaves and others, 1968), New Jersey (Lewis and Kummel, 1912), Pennsylvania (Berg and others, 1980), South Carolina (Overstreet and Bell, 1965), Tennessee (Hardeman, 1966), and West Virginia (Cardwell and others, 1968). Quadrangle geologic maps show geologic units at a scale of 1:24,000 for parts of Delaware within the APRASA area (Woodruff and Thompson, 1972, 1975). Many reports have been published describing the groundwater resources of a county, parts of a county, multi-county areas, or river basins. The statistical methods used in this atlas are based largely on those used by Helsel and Hirsch (1992) and by Knopman (1990, p. 7-9). In her analysis of well records in the USGS Ground-Water Site Inventory (GWSI) data base, Knopman (1990) ranked factors that must be taken into account when assessing the water-yielding potential of the rocks in the Valley and Ridge, the Blue Ridge, and the Piedmont Physiographic Provinces in Pennsylvania. Readers are referred to Helsel and Hirsch (1992) and Knopman (1990) for details regarding statistical methods.
Alabama, Delaware, Georgia, Maryland, New Jersey, ↗
Occupancy models for citizen-science data
Large‐scale citizen‐science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early‐warning systems for biodiversity loss. However, citizen‐science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non‐random sampling, false absences, false detections, and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data. We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address: (a) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and (b) the statistical methods should be tailored not only to those questions but also to the specific characteristics of the data. We review the consequences of particular survey design choices that typically need to be made in atlas‐style citizen‐science projects. These include spatial resolution of the sampling units, allocation of effort in space, and collection of information about the observation process. On the analysis side, we review extensions of the basic occupancy models that are frequently necessary with atlas data, including methods for dealing with heterogeneity, non‐independent detections, false detections, and violation of the closure assumption. New technologies, such as cell‐phone apps and fixed remote detection devices, are revolutionizing citizen‐science projects. There is an opportunity to maximize the usefulness of the resulting datasets if the protocols are rooted in robust statistical designs and data analysis issues are being considered. Our review provides guidelines for designing new projects and an overview of the current methods that can be used to analyse data from such projects.
Methods in Ecology and Evolution↗
Locations and attributes of wind turbines in New Mexico, 2009
The New Mexico wind-turbine data series provides geospatial data for all wind turbines established within the State as of August 2009. Attributes specific to each turbine include: turbine location, manufacturer and model, rotor diameter, hub height, rotor height, potential megawatt output, land ownership, and county. Wind energy facility data for each turbine include: facility name, facility power capacity, number of turbines associated with each facility to date, facility developer, facility ownership, year the facility went online, and development status of wind facility. Turbine locations were derived from 1-meter August 2009 true-color aerial photographs produced by the National Agriculture Imagery Program; the photographs have a positional accuracy of about + or - 5 meters. The location of turbines under construction during August 2009 likely will be less accurate than the location of existing turbines. This data series contributes to an Online Interactive Energy Atlas currently (2011) in development by the U.S. Geological Survey. The Energy Atlas will synthesize data on existing and potential energy development in Colorado and New Mexico and will include additional natural resource data layers. This information may be used by decisionmakers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, comprehensive metadata, and decision-support tools will be included in the Energy Atlas. The format of the Energy Atlas will facilitate the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development.
Data Series↗
Locations and attributes of wind turbines in Colorado, 2009
The Colorado wind-turbine data series provides geospatial data for all wind turbines established within the State as of August 2009. Attributes specific to each turbine include: turbine location, manufacturer and model, rotor diameter, hub height, rotor height, potential megawatt output, land ownership, and county. Wind energy facility data for each turbine include: facility name, facility power capacity, number of turbines associated with each facility to date, facility developer, facility ownership, year the facility went online, and development status of wind facility. Turbine locations were derived from August 2009 1-meter true-color aerial photographs produced by the National Agriculture Imagery Program; the photographs have a positional accuracy of about + or - 5 meters. The location of turbines under construction during August 2009 likely will be less accurate than the location of existing turbines. This data series contributes to an Online Interactive Energy Atlas currently (2011) in development by the U.S. Geological Survey. The Energy Atlas will synthesize data on existing and potential energy development in Colorado and New Mexico and will include additional natural resource data layers. This information may be used by decisionmakers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, comprehensive metadata, and decision-support tools will be included in the Energy Atlas. The format of the Energy Atlas will facilitate the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development.
Data Series↗
Locations and attributes of utility-scale solar power facilities in Colorado and New Mexico, 2011
The data series consists of polygonal boundaries for utility-scale solar power facilities (both photovoltaic and concentrating solar power) located within Colorado and New Mexico as of December 2011. Attributes captured for each facility include the following: facility name, size/production capacity (in MW), type of solar technology employed, location, state, operational status, year the facility came online, and source identification information. Facility locations and perimeters were derived from 1-meter true-color aerial photographs (2011) produced by the National Agriculture Imagery Program (NAIP); the photographs have a positional accuracy of about ±5 meters (accessed from the NAIP GIS service: http://gis.apfo.usda.gov/arcgis/services). Solar facility perimeters represent the full extent of each solar facility site, unless otherwise noted. When visible, linear features such as fences or road lines were used to delineate the full extent of the solar facility. All related equipment including buildings, power substations, and other associated infrastructure were included within the solar facility. If solar infrastructure was indistinguishable from adjacent infrastructure, or if solar panels were installed on existing building tops, only the solar collecting equipment was digitized. The "Polygon" field indicates whether the "equipment footprint" or the full "site outline" was digitized. The spatial accuracy of features that represent site perimeters or an equipment footprint is estimated at +/- 10 meters. Facilities under construction or not fully visible in the NAIP imagery at the time of digitization (December 2011) are represented by an approximate site outline based on the best available information and documenting materials. The spatial accuracy of these facilities cannot be estimated without more up-to-date imagery – users are advised to consult more recent imagery as it becomes available. The "Status" field provides information about the operational status of each facility as of December 2011. This data series contributes to an Online Interactive Energy Atlas currently in development by the U.S. Geological Survey. The Energy Atlas will synthesize data on existing and potential energy development in Colorado and New Mexico and will include additional natural resource data layers. This information may be used by decision makers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, metadata, and decision support tools will be included in the Energy Atlas. The format of the Energy Atlas will facilitate the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development activities.
Colorado;New Mexico↗
Locations and attributes of wind turbines in Colorado, 2011
This dataset represents an update to U.S. Geological Survey Data Series 597. Locations and attributes of wind turbines in Colorado, 2009 (available at http://pubs.usgs.gov/ds/597/). This updated Colorado wind turbine Data Series provides geospatial data for all 1,204 wind turbines established within the State of Colorado as of September 2011, an increase of 297 wind turbines from 2009. Attributes specific to each turbine include: turbine location, manufacturer and model, rotor diameter, hub height, rotor height, potential megawatt output, land ownership, county, and development status of the wind turbine. Wind energy facility data for each turbine include: facility name, facility power capacity, number of turbines associated with each facility to date, facility developer, facility ownership, and year the facility went online. The locations of turbines are derived from 1-meter true-color aerial photographs produced by the National Agriculture Imagery Program (NAIP); the photographs have a positional accuracy of about ±5 meters. Locations of turbines constructed during or prior to August 2009 are based on August 2009 NAIP imagery and turbine locations constructed after August 2009 were based on September 2011 NAIP imagery. The location of turbines under construction during September 2011 likely will be less accurate than the location of existing turbines. This data series contributes to an Online Interactive Energy Atlas developed by the U.S. Geological Survey (http://my.usgs.gov/eerma/). The Energy Atlas synthesizes data on existing and potential energy development in Colorado and New Mexico and includes additional natural resource data layers. This information may be used by decisionmakers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, comprehensive metadata, and decision-support tools also are included in the Energy Atlas. The format of the Energy Atlas is designed to facilitate the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development.
Colorado↗
Locations and attributes of wind turbines in New Mexico, 2011
This dataset represents an update to U.S. Geological Survey Data Series 596. Locations and attributes of wind turbines in New Mexico, 2009 (available at http://pubs.usgs.gov/ds/596/).This updated New Mexico wind turbine Data Series provides geospatial data for all 562 wind turbines established within the State of New Mexico as of June 2011, an increase of 155 wind turbines from 2009. Attributes specific to each turbine include: turbine location, manufacturer and model, rotor diameter, hub height, rotor height, potential megawatt output, land ownership, county, and development status of wind turbine. Wind energy facility data for each turbine include: facility name, facility power capacity, number of turbines associated with each facility to date, facility developer, facility ownership, and year the facility went online. The locations of turbines are derived from 1-meter true-color aerial photographs produced by the National Agriculture Imagery Program (NAIP); the photographs have a positional accuracy of about ±5 meters. The locations of turbines constructed during or prior to August 2009 are based on August 2009 NAIP imagery and turbine locations constructed after August 2009 were based June 2011 NAIP imagery. The location of turbines under construction during June 2011 likely will be less accurate than the location of existing turbines. This data series contributes to an Online Interactive Energy Atlas developed by the U.S. Geological Survey (http://my.usgs.gov/eerma/). The Energy Atlas synthesizes data on existing and potential energy development in Colorado and New Mexico and includes additional natural resource data layers. This information may be used by decisionmakers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, comprehensive metadata, and decision-support tools also are included in the Energy Atlas. The format of the Energy Atlas is designed to facilitate the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development.
New Mexico↗
Map showing sediment isopachs in the deep-sea basins of the Pacific continental margin, Point Conception to Point Loma
The U.S. Geological Survey conducted a series of cruises, EEZSCAN 84 (EEZ-SCAN 84 Scientific Staff, 1986), to collect reconnaissance data on the newly proclaimed Exclusive Economic Zone (EEZ), the area out to 200 nautical miles from the coastline of the United States. The cruises systematically surveyed the entire conterminous United States west coast EEZ using the Geological Long-Range Inclined Asdic (GLORIA) side-scan sonar, a 160-in 3 airgun seismic-reflection profiler, a 3.5-kHz high-resolution seismic-reflection profiler, a 10-kHz echo sounder, and a proton-precession magnetometer. The nominal trackline spacing throughout the survey was 30 km. Derivative maps of sediment thickness (I-2089-A, I-2090-A, I-2091-A) and depth to basement (I-2089-B, I-2090-B, I-2091-B) in the basins of the west coast EEZ were compiled from both the sonar-image dala and the deep-penetration seismic data obtained on these cruises. Only EEZ-SCAN 84 data were used for the map compilations because available data from other cruises in this region are sparsely located, have poor navigational control, or were obtained with seismic systems that were not powerful enough to resolve oceanic basement. For this map series, sediment thickness and depth to basement were determined only in the deep-ocean basin regions because the seismic system used on the EEZ-SCAN 84 cruises could not resolve oceanic basement beneath the thick sediments of the continental slope. All the data used to compile the maps are presented in the "Atlas of the Exclusive Economic Zone, Western Conterminous United States" (EEZ-SCAN 84 Scientific Staff, 1986). Basement Outcrops GLORIA imagery was used to locate areas of basement outcrop throughout the region. Where possible, sediment thickness on and immediately adjacent to basement outcrops was measured from seismic data. Where there was no bathymetric or seismic control, a seamount peak was assumed to have no sediment cover. A pattern is used on the map to indicate all basement outcrops. Data Reduction Acoustic basement in the basins, invariably oceanic Layer 2, was observed on all of the seismic records. One-way traveltime was measured from the sea floor to acoustic basement. Because the trackline spacing of about 30 km is relatively large compared to the data density along track, we chose to measure the sediment thickness every 0.5 hour or at an interval of approximately 7.5 km. Water depth was measured with a 10-kHz profiler. Depth to basement was calculated using the sea surface as the zero datum and adding the corrected water depth (Carter, 1980) to the sediment thickness. Acoustic travel times were converted to depths by first calculating a regression equation from the interval velocity versus depth data of Connard and others (1984). Their data base comprises a compilation of all available Deep Sea Drilling Project data plus wide-angle refraction data, which were collected in Cascadia Basin west of Oregon and represents the best data set available for the United States west coast EEZ. The regression equation was integrated to determine sediment thickness as a function of one-way traveltime . The resulting equation is z = 1400t + 0 .5t 2 where z is sediment thickness in meters, and t is one-way traveltime in seconds. Sediment thicknesses calculated using this equation were compared to values calculated from the general equation of Carlson and others (1986). Values for sediment thickness calculated by the two equations differed by no greater than 10 percent throughout the range of travel times. Bathymetry The bathymetry is from Chase and others (1981). The bathymetric data were compiled from a variety of sources, and data quality is inconsistent. Because of differences in navigational precision and density of coverage, the bathymetric contours of a feature may be at a different location than the corresponding isopleths derived from the EEZ-SCAN 84 data, or a feature may not be indicated on the bathymetry at all. These situations occur because either the feature is poorly located in the bathymetric data set or, especially in the outer EEZ, the bathymetric data are too sparse to have defined the existence of a feature. Acknowledgments Topographic digital data bases were corrected and verified by Christina Lief. Gerald Evenden developed the computer software system MAPGEN, used to compose this map. Reviews, suggestions, and technical contributions from Edward C. Escowitz and Florence Wong and advice about cartographic design from Will Stettner substantially improved the quality of this map. References Cited Carlson, R.L., Gangi, A.F., and Snow, K.R., 1986, Empirical reflection-traveltime/ depth and velocity/depth functions for the deep-sea sediment column: Journal of Geophysical Research, v. 91, no. B8, p. 8249-8266. Carter, D.J.T., 1980, Echo-sounding correction tables: Taunton, United Kingdom, Hydrographic Department, Ministry of Defence, 150 p. Chase, T.E., Wilde, Pat, Normark, W.R, Miller, C.P., Seekins, B.A., and Young, J.D., 1981, Offshore topography of the Western United States between 32° and 49° North latitudes: U.S. Geological Survey Open-File Report 81-443, scale 1:864,518 at 38° latitude, 2 sheets. Connard, G., Couch, R., Keeling , K., Roy, J., and Troseth, S., 1984, Abyssal plain and continental net-objective sedimentary thicknesses, in Kulm, L.D., and others, eds., Western North America continental margin and adjacent ocean floor off Oregon and Washington, Atlas 1 of Regional Atlas Series, Ocean Margin Drilling Program: Woods Hole, Mass., Marine Science International, sheet 7. EEZ-SCAN 84 Scientific Staff, 1986, Atlas of the Exclusive Economic Zone, Western Conterminous United States: U.S. Geological Survey Miscellaneous Investigations Series I-1792, scale 1:500,000, 152 p. index map for I-2089-A See Also "U.S. Pacific West Coast Field Activities" (Paskevich and others, 2011; http://pubs.usgs.gov/of/2010/1332/htmldocs/pc/pc_overview.html). Paskevich, V.F., Wong, F.L., O?Malley, J.J., Stevenson, A.J., and Gutmacher, C.E., 2011, GLORIA sidescan-sonar imagery for parts of the U.S. Exclusive Economic Zone and adjacent areas: U.S. Geological Survey Open-File Report 2010?1332, available at http://pubs.usgs.gov/of/2010/1332/.
IMAP↗
Map showing depth to basement in the deep-sea basins of the Pacific continental margin, Point Conception to Point Loma
The U.S. Geological Survey conducted a series of cruises, EEZSCAN 84 (EEZ-SCAN 84 Scientific Staff, 1986), to collect reconnaissance data on the newly proclaimed Exclusive Economic Zone (EEZ), the area out to 200 nautical miles from the coastline of the United States. The cruises systematically surveyed the entire conterminous United States west coast EEZ using the Geological Long-Range Inclined Asdic (GLORIA) side-scan sonar, a 160-in 3 airgun seismic-reflection profiler, a 3.5-kHz high-resolution seismic-reflection profiler, a 10-kHz echo sounder, and a proton-precession magnetometer. The nominal trackline spacing throughout the survey was 30 km. Derivative maps of sediment thickness (I-2089-A, I-2090-A, I-2091-A) and depth to basement (I-2089-B, I-2090-B, I-2091-B) in the basins of the west coast EEZ were compiled from both the sonar-image dala and the deep-penetration seismic data obtained on these cruises. Only EEZ-SCAN 84 data were used for the map compilations because available data from other cruises in this region are sparsely located, have poor navigational control, or were obtained with seismic systems that were not powerful enough to resolve oceanic basement. For this map series, sediment thickness and depth to basement were determined only in the deep-ocean basin regions because the seismic system used on the EEZ-SCAN 84 cruises could not resolve oceanic basement beneath the thick sediments of the continental slope. All the data used to compile the maps are presented in the "Atlas of the Exclusive Economic Zone, Western Conterminous United States" (EEZ-SCAN 84 Scientific Staff, 1986). Basement Outcrops GLORIA imagery was used to locate areas of basement outcrop throughout the region. Where possible, sediment thickness on and immediately adjacent to basement outcrops was measured from seismic data. Where there was no bathymetric or seismic control, a seamount peak was assumed to have no sediment cover. A pattern is used on the map to indicate all basement outcrops. Data Reduction Acoustic basement in the basins, invariably oceanic Layer 2, was observed on all of the seismic records. One-way traveltime was measured from the sea floor to acoustic basement. Because the trackline spacing of about 30 km is relatively large compared to the data density along track, we chose to measure the sediment thickness every 0.5 hour or at an interval of approximately 7.5 km. Water depth was measured with a 10-kHz profiler. Depth to basement was calculated using the sea surface as the zero datum and adding the corrected water depth (Carter, 1980) to the sediment thickness. Acoustic travel times were converted to depths by first calculating a regression equation from the interval velocity versus depth data of Connard and others (1984). Their data base comprises a compilation of all available Deep Sea Drilling Project data plus wide-angle refraction data, which were collected in Cascadia Basin west of Oregon and represents the best data set available for the United States west coast EEZ. The regression equation was integrated to determine sediment thickness as a function of one-way traveltime . The resulting equation is z = 1400t + 0 .5t 2 where z is sediment thickness in meters, and t is one-way traveltime in seconds. Sediment thicknesses calculated using this equation were compared to values calculated from the general equation of Carlson and others (1986). Values for sediment thickness calculated by the two equations differed by no greater than 10 percent throughout the range of travel times. Bathymetry The bathymetry is from Chase and others (1981). The bathymetric data were compiled from a variety of sources, and data quality is inconsistent. Because of differences in navigational precision and density of coverage, the bathymetric contours of a feature may be at a different location than the corresponding isopleths derived from the EEZ-SCAN 84 data, or a feature may not be indicated on the bathymetry at all. These situations occur because either the feature is poorly located in the bathymetric data set or, especially in the outer EEZ, the bathymetric data are too sparse to have defined the existence of a feature. Acknowledgments Topographic digital data bases were corrected and verified by Christina Lief. Gerald Evenden developed the computer software system MAPGEN, used to compose this map. Reviews, suggestions, and technical contributions from Edward C. Escowitz and Florence Wong and advice about cartographic design from Will Stettner substantially improved the quality of this map. References Cited Carlson, R.L., Gangi, A.F., and Snow, K.R., 1986, Empirical reflection-traveltime/ depth and velocity/depth functions for the deep-sea sediment column: Journal of Geophysical Research, v. 91, no. B8, p. 8249-8266. Carter, D.J.T., 1980, Echo-sounding correction tables: Taunton, United Kingdom, Hydrographic Department, Ministry of Defence, 150 p. Chase, T.E., Wilde, Pat, Normark, W.R, Miller, C.P., Seekins, B.A., and Young, J.D., 1981, Offshore topography of the Western United States between 32° and 49° North latitudes: U.S. Geological Survey Open-File Report 81-443, scale 1:864,518 at 38° latitude, 2 sheets. Connard, G., Couch, R., Keeling , K., Roy, J., and Troseth, S., 1984, Abyssal plain and continental net-objective sedimentary thicknesses, in Kulm, L.D., and others, eds., Western North America continental margin and adjacent ocean floor off Oregon and Washington, Atlas 1 of Regional Atlas Series, Ocean Margin Drilling Program: Woods Hole, Mass., Marine Science International, sheet 7. EEZ-SCAN 84 Scientific Staff, 1986, Atlas of the Exclusive Economic Zone, Western Conterminous United States: U.S. Geological Survey Miscellaneous Investigations Series I-1792, scale 1:500,000, 152 p. index map for I-2089-A See Also "U.S. Pacific West Coast Field Activities" (Paskevich and others, 2011; http://pubs.usgs.gov/of/2010/1332/htmldocs/pc/pc_overview.html). Paskevich, V.F., Wong, F.L., O?Malley, J.J., Stevenson, A.J., and Gutmacher, C.E., 2011, GLORIA sidescan-sonar imagery for parts of the U.S. Exclusive Economic Zone and adjacent areas: U.S. Geological Survey Open-File Report 2010?1332, available at http://pubs.usgs.gov/of/2010/1332/.
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