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At least 325 records · Page 18Linked to original sources
USGS2007

ASTER-Derived 30-Meter-Resolution Digital Elevation Models of Afghanistan

INTRODUCTION The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an imaging instrument aboard the Terra satellite, launched on December 19, 1999, as part of the National Aeronautics and Space Administration's (NASA) Earth Observing System (EOS). The ASTER sensor consists of three subsystems: the visible and near infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR), each with a different spatial resolution (VNIR, 15 meters; SWIR, 30 meters, TIR 90 meters). The VNIR system has the capability to generate along-track stereo images that can be used to create digital elevation models (DEMs) at 30-meter resolution. Currently, the only available DEM dataset for Afghanistan is the 90-meter-resolution Shuttle Radar Topography Mission (SRTM) data. This dataset is appropriate for macroscale DEM analysis and mapping. However, ASTER provides a low cost opportunity to generate higher resolution data. For this publication, study areas were identified around populated areas and areas where higher resolution elevation data were desired to assist in natural resource assessments. The higher resolution fidelity of these DEMs can also be used for other terrain analysis including landform classification and geologic structure analysis. For this publication, ASTER scenes were processed and mosaicked to generate 36 DEMs which were created and extracted using PCI Geomatics' OrthoEngine 3D Stereo software. The ASTER images were geographically registered to Landsat data with at least 15 accurate and well distributed ground control points with a root mean square error (RMSE) of less that one pixel (15 meters). An elevation value was then assigned to each ground control point by extracting the elevation from the 90-meter SRTM data. The 36 derived DEMs demonstrate that the software correlated on nearly flat surfaces and smooth slopes accurately. Larger errors occur in cloudy and snow-covered areas, lakes, areas with steep slopes, and southeastern-facing slopes. In these areas, holes, large pits, and spikes were generated by the software during the correlation process and the automatic interpolation method. To eliminate these problems, overlapping DEMs were generated and filtered using a progressive morphologic filter. The quadrangles used to delineate the DEMs in the publication were derived from the Afghan Geodesy and Cartography Head Office's (AGCHO) 1:100,000-scale maps series quadrangles. Each DEM was clipped and assigned a name according to the associated AGCHO quadrangle name. The geospatial data included in this publication are intended to be used with any GIS software packages including, but not limited to, ESRI's ArcGIS and ERDAS IMAGINE.

Data Series↗
USGS2008

High-Resolution Digital Terrain Models of the Sacramento/San Joaquin Delta Region, California

The U.S. Geological Survey (USGS) Western Region Geographic Science Center, in conjunction with the USGS Water Resources Western Branch of Regional Research, has developed a high-resolution elevation dataset covering the Sacramento/San Joaquin Delta region of California. The elevation data were compiled photogrammically from aerial photography (May 2002) with a scale of 1:15,000. The resulting dataset has a 10-meter horizontal resolution grid of elevation values. The vertical accuracy was determined to be 1 meter. Two versions of the elevation data are available: the first dataset has all water coded as zero, whereas the second dataset has bathymetry data merged with the elevation data. The projection of both datasets is set to UTM Zone 10, NAD 1983. The elevation data are clipped into files that spatially approximate 7.5-minute USGS quadrangles, with about 100 meters of overlap to facilitate combining the files into larger regions without data gaps. The files are named after the 7.5-minute USGS quadrangles that cover the same general spatial extent. File names that include a suffix (_b) indicate that the bathymetry data are included (for example, sac_east versus sac_east_b). These files are provided in ESRI Grid format.

Data Series↗
USGS2013

Estimated annual agricultural pesticide use for counties of the conterminous United States, 1992--2009

This report provides estimated annual agricultural pesticide use for counties of the conterminous United States for 459 compounds from 1992 through 2009 following the methods described in Thelin and Stone (2013). As described in Thelin and Stone (2013), U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District (CRD)-level pesticide-use data to estimate county-level pesticide use. Estimated pesticide use (EPest) values were calculated with both the EPest-high and EPest-low methods. The distinction between the EPest-high method and the EPest-low method is that there are more counties with estimated pesticide use for EPest-high compared to EPest-low (Thelin and Stone, 2013). The estimates of annual agricultural pesticide use are provided in tab-delimited files and organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and kg (amount in kilograms). EPest-high county pesticide-use estimates were divided into tables 1 through 7 by pesticide name: Table 1: 2, 4-D through Chlordimeform Table 2: Chlorethoxyfos through Diflufenzopyr Table 3: Dimethenamid through Gibberellic acid Table 4: Glufosinate through Metriam Table 5: Metolachlor through Propazine Table 6: Propiconazole through Triazamate Table 7: Tribenuron methyl through Zoxamide EPest-low county pesticide-use estimates were divided into tables 8 through 14 by pesticide name: Table 8: 2, 4-D through Chlordimeform Table 9: Chlorethoxyfos through Diflufenzopyr Table 10: Dimethenamid through Gibberellic acid Table 11: Glufosinate through Metriam Table 12: Metolachlor through Propazine Table 13: Propiconazole through Triazamate Table 14: Tribenuron methyl through Zoxamide

Data Series↗
USGS2017

Onshore industrial wind turbine locations for the United States

This dataset provides industrial-scale onshore wind turbine locations in the United States, corresponding facility information, and turbine technical specifications. The database has wind turbine records that have been collected, digitized, locationally verified, and internally quality controlled. Turbines from the Federal Aviation Administration Digital Obstacles File, through product release date July 22, 2013, were used as the primary source of turbine data points. The dataset was subsequently revised and reposted as described in the revision histories for the report. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in Environmental Systems Research Institute (Esri) ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacles Repository System numbers were visually identified and point locations were added to the collection. We estimated a locational error of plus or minus 10 meters for turbine locations. Wind farm facility names were identified from publicly available facility datasets. Facility names were then used in a Web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications for turbines were assigned based on the wind turbine make and model as described in literature, specifications listed in the Federal Aviation Administration Digital Obstacles File, and information on the turbine manufacturer’s Web site. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.

Data Series↗
USGS2015

Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12

Annual county-level pesticide use was estimated for 423 herbicides, insecticides, and fungicides applied to agricultural crops grown in the conterminous United States during 2008–12. For all States except California, pesticide-use data were compiled from proprietary surveys of farm operations located within U.S. Department of Agriculture Crop Reporting Districts (CRDs). Surveyed pesticide-use data were used in conjunction with county annual harvested-crop acres reported by the U.S. Department of Agriculture 2007 and 2012 Censuses of Agriculture and the 2008–11 County Agricultural Production Survey to calculate use rates per harvested-crop acre, or an “estimated pesticide use” (EPest) rate, for each crop by year. County-use estimates were then calculated by multiplying EPest rates by harvested-crop acres for each pesticide crop combination. Use estimates for California were obtained from annual Department of Pesticide Regulation-Pesticide Use Reports. Proprietary surveyed pesticide-use data were not available for all CRDs and years. When pesticide-survey data were unavailable for a CRD in a particular year, EPest extrapolated rates were calculated from adjoining or nearby CRDs to ensure that pesticide use was estimated for all counties where harvested-crop acres were reported. Two estimation methods were used—EPest-low and EPest high—and differed in how they treated situations when a CRD was surveyed and pesticide use was not reported for a particular pesticide-by-crop combination. California pesticide-use estimates were not extrapolated; therefore, EPest-low and EPest-high are the same for counties in California. This data series is a continuation of the 1992–2009 pesticide-use estimates reported by Stone (2013). It is an update of estimates for 2008–9 (Stone, 2013), as well as an update of the 2010–11 preliminary estimates reported by Baker and Stone (2013). EPest values from these compilations (1992–2012) are suitable for making national, regional, and watershed assessments of annual pesticide use. County-level estimates are provided to make it easier to compile watershed assessments; however, users should be aware there is a greater degree of uncertainty in individual county-level estimates when compared to CRD or State-level estimates. This report provides EPest-low and EPest-high annual agricultural pesticide use for counties of the conterminous United States for 423 compounds during 2008–12 in tab-delimited files organized by compound, year, State Federal Information Processing Standard (FIPS) code, county FIPS code, and amount in kilograms (kg). EPest-high county pesticide-use estimates were divided into tables 1 through 7 by pesticide name: Table 1: 2, 4-D through Chlorantraniliprole Table 2: Chlorethoxyfos through Diflufenzopyr Table 3: Dimethenamid through Gibberellic acid Table 4: Glufosinate through Metiram Table 5: Metolachlor through Propazine Table 6: Propiconazole through Triasulfuron Table 7: Tribenuron methyl through Zoxamide EPest-low county pesticide-use estimates were divided into tables 8 through 14 by pesticide name: Table 8: 2, 4-D through Chlorantraniliprole Table 9: Chlorethoxyfos through Diflufenzopyr Table 10: Dimethenamid through Gibberellic acid Table 11: Glufosinate through Metiram Table 12: Metolachlor through Propazine Table 13: Propiconazole through Triasulfuron Table 14: Tribenuron methyl through Zoxamide

Data Series↗
USGS2009

Investigating white-nose syndrome in bats

A devastating, emergent disease afflicting hibernating bats has pread from the northeast to the mid-Atlantic region of the United States at an alarming rate. Since the winter of 2006-2007, hundreds of thousands of insect-eating bats from at least nine states have died from this new disease, named White-Nose Syndrome (WNS). The disease is named for the white fungus often seen on the muzzles, ears, and wings of bats. This disease poses a threat to cave hibernating bats of the United States and potentially all temperate regions of the world. USGS scientists from the National Wildlife Health Center (NWHC) and the Fort Collins Science Center (FORT), in collaboration with the New York State Department of Environmental Conservation, the U.S. Fish and Wildlife Service, and others have linked a newly described, cold-loving fungus to WNS.

Fact Sheet↗
USGS2011

Cobalt: for strength and color

Cobalt is a shiny, gray, brittle metal that is best known for creating an intense blue color in glass and paints. It is frequently used in the manufacture of rechargeable batteries and to create alloys that maintain their strength at high temperatures. It is also one of the essential trace elements (or "micronutrients") that humans and many other living creatures require for good health. Cobalt is an important component in many aerospace, defense, and medical applications and is a key element in many clean energy technologies. The name cobalt comes from the German word kobold, meaning goblin. It was given this name by medieval miners who believed that troublesome goblins replaced the valuable metals in their ore with a substance that emitted poisonous fumes when smelted. The Swedish chemist Georg Brandt isolated metallic cobalt-the first new metal to be discovered since ancient times-in about 1735 and identified some of its valuable properties.

Fact Sheet↗
USGS2012

White-nose syndrome in bats: U.S. Geological Survey updates

White-nose syndrome (WNS) is a devastating disease that has killed millions of hibernating bats since it first appeared in New York in 2007 and has spread at an alarming rate from the northeastern to the central United States and Canada. The disease is named for the white fungus Geomyces destructans that infects the skin of the muzzle, ears, and wings of hibernating bats. The U.S. Geological Survey (USGS) National Wildlife Health Center (NWHC), the USGS Fort Collins Science Center, the U.S. Fish and Wildlife Service, and other partners continue to play a primary role in WNS research. Studies conducted at the NWHC led to the discovery (Blehert and others, 2009), characterization, and naming (Gargas and others, 2009) of the cold-loving fungus G. destructans and to the development of standardized criteria for diagnosing the disease (Meteyer and others, 2009). Additionally, scientists at the NWHC have pioneered laboratory techniques for studying the effects of the fungus on hibernating bats (Lorch and others, 2011). To determine if bats are affected by white-nose syndrome, scientists look for a characteristic microscopic pattern of skin erosion caused by G. destructans (Meteyer and others, 2009). Field signs of WNS can include visible white fungal growth on the bat's muzzle, wings, or both, but these signs alone are not a reliable disease indicator - laboratory examination and testing are required for disease confirmation. Infected bats also arouse from hibernation more frequently than uninfected bats (Warnecke and others, 2012) and often display abnormal behaviors in their hibernation sites, such as congregating at or near cave openings and daytime flights during winter. These abnormal behaviors may contribute to the bat's accelerated consumption of stored fat reserves, causing emaciation, a characteristic documented in some of the bats that die with WNS. During hibernation, bats likely have lowered immunity (Bouma and others, 2010), which may facilitate the ability of G. destructans to colonize and damage large areas of wing membrane (fig. 2). A current hypothesis suggests that erosion or ulceration of wing membrane caused by the fungus has the potential to alter the physiology of hibernating bats, resulting in fatal disruption of hydration, electrolyte balance, circulation, and thermoregulation (Cryan and others, 2010). Current estimates of bat population declines in the northeastern United States since the emergence of WNS are over 80 percent (Turner and others, 2011). This sudden and widespread mortality associated with WNS is unprecedented in hibernating bats, among which large-scale disease outbreaks have not been previously documented. It is unlikely that species of bats affected by WNS will recover quickly because most are long-lived and have only a single pup per year. Consequently, repopulation after widespread mortality of breeding adults will be a slow process. Worldwide, bats play essential roles as pollinators, seed dispersers, and as primary consumers of insects. The true ecological consequences of the recent large-scale reductions in populations of hibernating bats are not yet known. However, farmers might feel the impact. A recent economic analysis indicated that insect control services (ecosystem services) provided by bats to U.S. agriculture is valued between 4 to 50 billion dollars nationwide per year (Boyles and others, 2011). The number of North American bats estimated to have died from WNS thus far had the capacity to consume up to 8,000 tons of insects per year (Boyles and others, 2011). The area of North America affected by WNS continues to expand. Within the last 2 years, the disease has been confirmed in several Central States, including Alabama, Indiana, Kentucky, Ohio, Tennessee, and Missouri. High mortality of bats has not yet been reported at these locations, and it remains to be seen if WNS will develop and manifest in other States with the same severity as that in the Northeast.

Alabama;Connecticut;Delaware;Illinois;Indiana;Iowa↗
USGS2013

Gallium--A smart metal

Gallium is a soft, silvery metallic element with an atomic number of 31 and the chemical symbol Ga. The French chemist Paul-Emile Lecoq de Boisbaudran discovered gallium in sphalerite (a zinc-sulfide mineral) in 1875 using spectroscopy. He named the element "gallia" after his native land of France (formerly Gaul; in Latin, Gallia). The existence of gallium had been predicted in 1871 by Dmitri Mendeleev, the Russian chemist who published the first periodic table of the elements. Mendeleev noted a gap in his table and named the missing element "eka-aluminum" because he determined that its location was one place away from aluminum in the table. Mendeleev thought that the missing element (gallium) would be very much like aluminum in its chemical properties, and he was right. Solid gallium has a low melting temperature (~29 degrees Celsius, or °C) and an unusually high boiling point (~2,204 °C). Because of these properties, the earliest uses of gallium were in high-temperature thermometers and in designing metal alloys that melt easily. The development of a gallium-based direct band-gap semiconductor in the 1960s led to what is now one of the most well-known applications for gallium-based products--the manufacture of smartphones and data-centric networks.

Fact Sheet↗
USGS1994

Geologic maps of the Olympus Mons region of Mars

Olympus Mons is one of the broadest volcanoes and certainly the tallest in the Solar System. It has been extensively described and analyzed in scientific publications and frequently noted in the popular and nontechnical literature of Mars. However, the first name given to the feature-Nix Olympica (Schiaparelli, 1879)-was based on its albedo, not its size, because early telescopic observations of Mars revealed only albedo features and not topography (lnge and others, 1971). After Mariner 9 images acquired in 1971 showed that this albedo feature coincides with a giant shield volcano (McCauley and others, 1972), the name Olympus Mons was adopted for the shield to distinguish it from the albedo feature. Olympus Mons is one of the most photographed features on the planet. The Mariner 9 spacecraft obtained 126 images of Olympus Mons with resolutions of 60 m/pixel to 2.5 km/pixel. Later, the two Viking orbiters greatly enlarged this dataset, acquiring more than 2,150 images of the Olympus Mons region at various resolutions and altitudes; 925 images have resolutions of better than 50 m/pixel. More than 150 of the Viking images provide stereoscopic coverage of the shield region (Blasius and others, 1982).

IMAP↗
USGS2002

Controlled photomosaic map of Callisto JC 15M CMN

This sheet is one in a series of maps of the Galilean satellites of Jupiter at a nominal scale of 1:15,000,000. This series is based on data from the Galileo Orbiter Solid-State Imaging (SSI) camera and the cameras of the Voyager 1 and 2 spacecraft. Mercator and Polar Stereographic projections used for this map of Callisto are based on a sphere having a radius of 2,409.3 km. The scale is 1:8,388,000 at ±56° latitude for both projections. Longitude increases to the west in accordance with the International Astronomical Union (1971) (Seidelmann and others, 2002). The geometric control network was computed at the RAND Corporation using RAND's most recent solution as of April 1999 (Davies and Katayama, 1981; Davies and others, 1998). This process involved selecting control points on the individual images, making pixel measurements of their locations, using reseau locations to correct for geometric distortions, and converting the measurements to millimeters in the focal plane. These data are combined with the camera focal lengths and navigation solutions as input to photogrammetric triangulation software that solves for the best-fit sphere, the coordinates of the control points, the three orientation angles of the camera at each exposure (right ascension, declination, and twist), and an angle (W0) which defines the orientation of Callisto in space. W0-in this solution 259.51°-is the angle along the equator to the east, between the 0° meridian and the equator's intersection with the celestial equator at the standard epoch J2000.0. This solution places the crater Saga at its defined longitude of 326° west (Seidelmann and others, 2002). This global map base uses the best image quality and moderate resolution coverage supplied by Galileo SSI and Voyager 1 and 2 (Batson, 1987; Becker and others, 1998; Becker and others, 1999; Becker and others, 2001). The digital map was produced using Integrated Software for Imagers and Spectrometers (ISIS) (Eliason, 1997; Gaddis and others, 1997; Torson and Becker, 1997). The individual images were radiometrically calibrated and photometrically normalized using a Lunar-Lambert function with empirically derived values (McEwen, 1991; Kirk and others, 2000). A linear correction based on the statistics of all overlapping areas was then applied to minimize image brightness variations. The image data were selected on the basis of overall image quality, reasonable original input resolution (from 20 km/pixel for gap fill to as much as 150 m/pixel), and availability of moderate emission/incidence angles for topography. Although consistency was achieved where possible, different filters were included for global image coverage as necessary: clear for Voyager 1 and 2; clear and green (559 nm) for Galileo SSI. Individual images were projected to a Sinusoidal Equal-Area projection at an image resolution of 1.0 kilometer/pixel. The final constructed Sinusoidal projection mosaic was then reprojected to the Mercator and Polar Stereographic projections included on this sheet. The final mosaic was enhanced using commercial software. Names on this sheet are approved by the International Astronomical Union. Names have been applied for features clearly visible at the scale of this map; for a complete list of nomenclature for Callisto, please see the Gazeteer of Planetary Nomenclature. Font color was chosen only for readability.

IMAP↗
USGS1978

Lower Tertiary coal bed distribution and coal resources of the Reno Junction-Antelope Creek area, Campbell, Converse, Niobrara, and Weston counties, Wyoming

The Powder River Basin of Wyoming and Montana contains some of the world's most extensive deposits of low sulfur subbituminous coal. The major coal beds occur in the upper part of the Fort Union and lower part of the Wasatch Formations of early Tertiary age (deposited about 60 to 50 million years ago). Most of the coal beds have been given informal names by local workers; names used in the Reno Junction-Antelope Creek area are shown on the fence diagram and on the sample well logs (fig. 1). Conditions leading to the formation of these coal deposits in the geologic past are discussed by R. W. Brown (1958, 1962).

Wyoming↗
USGS2002

CMGTooL user's manual

During the past several years, the sediment transport group in the Coastal and Marine Geology Program (CMGP) of the U. S. Geological Survey has made major revisions to its methodology of processing, analyzing, and maintaining the variety of oceanographic time-series data. First, CMGP completed the transition of the its oceanographic time-series database to a self-documenting NetCDF (Rew et al., 1997) data format. Second, CMGP’s oceanographic data variety and complexity have been greatly expanded from traditional 2-dimensional, single-point time-series measurements (e.g., Electro-magnetic current meters, transmissometers) to more advanced 3-dimensional and profiling time-series measurements due to many new acquisitions of modern instruments such as Acoustic Doppler Current Profiler (RDI, 1996), Acoustic Doppler Velocitimeter, Pulse-Coherence Acoustic Doppler Profiler (SonTek, 2001), Acoustic Bacscatter Sensor (Aquatec, 1001001001001001001). In order to accommodate the NetCDF format of data from the new instruments, a software package of processing, analyzing, and visualizing time-series oceanographic data was developed. It is named CMGTooL. The CMGTooL package contains two basic components: a user-friendly GUI for NetCDF file analysis, processing and manipulation; and a data analyzing program library. Most of the routines in the library are stand-alone programs suitable for batch processing. CMGTooL is written in MATLAB computing language (The Mathworks, 1997), therefore users must have MATLAB installed on their computer in order to use this software package. In addition, MATLAB’s Signal Processing Toolbox is also required by some CMGTooL’s routines. Like most MATLAB programs, all CMGTooL codes are compatible with different computing platforms including PC, MAC, and UNIX machines (Note: CMGTooL has been tested on different platforms that run MATLAB 5.2 (Release 10) or lower versions. Some of the commands related to MAC may not be compatible with later releases of MATLAB). The GUI and some of the library routines call low-level NetCDF file I/O, variable and attribute functions. These NetCDF exclusive functions are supported by a MATLAB toolbox named NetCDF, created by Dr. Charles Denham . This toolbox has to be installed in order to use the CMGTooL GUI. The CMGTooL GUI calls several routines that were initially developed by others. The authors would like to acknowledge the following scientists for their ideas and codes: Dr. Rich Signell (USGS), Dr. Chris Sherwood (USGS), and Dr. Bob Beardsley (WHOI). Many special terms that carry special meanings in either MATLAB or the NetCDF Toolbox are used in this manual. Users are encouraged to read the documents of MATLAB and NetCDF for references.

Open-File Report↗
USGS2002

Topographic map of Mars M 25M RKN

NOTES ON BASE This map is based on data from the Mars Orbiter Laser Altimeter (MOLA) (Smith and others, 2001), an instrument on NASA's Mars Global Surveyor (MGS) spacecraft (Albee and others, 2001). The image used for the base of this map represents more than 600 million measurements gathered between 1999 and 2001, adjusted for consistency (Neumann and others, 2001, 2002) and converted to planetary radii. These have been converted to elevations above the areoid as determined from a martian gravity field solution GMM2 (Lemoine and others, 2001), truncated to degree and order 50, and oriented according to current standards (see below). The average accuracy of each point is originally ~100 meters in horizontal position, and ~1 meter in radius (Neumann, 2001). However, the total elevation uncertainty is at least +3 m due to the global error in the areoid (+1.8 meters according to Lemoine and others, 2001) and regional uncertainties in its shape (communication from Neumann, 2002). The measurements were converted into a digital elevation model (DEM) (communication from Neumann, 2002; Neumann and others, 2001; Smith and others, 2001) using Generic Mapping Tools software (Wessel and Smith, 1998), with a resolution of 0.015625 degrees per pixel or 64 pixels per degree. In projection, the pixels are 926.17 meters in size at the equator. PROJECTION The Mercator projection is used between latitudes +57°, with a central meridian at 0° and latitude equal to the nominal scale at 0°. The Polar Stereographic projection is used for the polar regions north of the +55° parallel and south of the –55° parallel with a central meridian set for both at 0°. The adopted equatorial radius is 3,396.19 km (Duxbury and others 2002; Seidelmann and others 2002). COORDINATE SYSTEM The MOLA data were initially referenced to an internally consistent inertial coordinate system, derived from tracking of the MGS spacecraft. By adopting appropriate values for the orientation of Mars as defined by the International Astronomical Union (IAU) and the International Association of Geodesy (IAG) (Seidelmann and others, 2002), these inertial coordinates were converted into the planet-fixed coordinates (longitude and latitude) used on this map. These values include the orientation of the north pole of Mars (including the effects of precession), the rotation rate of Mars, and a value for W0 of 176.630°, where W0 is the angle along the equator to the east, between the 0° meridian and the equator's intersection with the celestial equator at the standard epoch J2000.0 (Seidelmann and others, 2002). This value of W0 was chosen (Duxbury and others, 2002) in order to place the 0° meridian through the center of the small (~500 m) crater Airy-0, located in the crater Airy (de Vaucouleurs and others, 1973; Seidelmann and others, 2002). Longitude increases to the east, and latitude is planetocentric as allowed by IAU/IAG standards (Seidelmann and others, 2002) and in accordance with current NASA and USGS standards (Duxbury and others, 2002). A secondary grid (printed in red) has been added to the map as a reference to the west longitude/planetographic latitude system that is also allowed by IAU/IAG standards (Seidelmann and others, 2002) and has also been used for Mars. The figure adopted to compute this secondary grid is an oblate spheroid with an equatorial radius of 3,396.19 km and a polar radius of 3,376.2 km (Duxbury and others, 2002; Seidelmann and others, 2002). MAPPING TECHNIQUES To create the topographic base image, the original DEM produced by the MOLA team in Simple Cylindrical projection with a resolution of 64 pixels per degree was projected into the Mercator and Polar Stereographic pieces. A shaded relief was generated from each DEM with a sun angle of 30° from horizontal and a sun azimuth of 270°, as measured clockwise from north, and a vertical exaggeration of 100%. Illumination is from the west, which follows a long-standing USGS tradition for planetary maps. This allows for continuity in the shading between maps and quadrangles, and most closely resembles lighting conditions found on imagery. The DEM values were then mapped to a smooth global color look-up table. Note that the chosen color scheme simply represents elevation changes and is not intended to imply anything about surface characteristics (e.g., past or current presence of water or ice). These two files were then merged and scaled to 1:25 million for the Mercator portion and 1:15,196,708 for the two Polar Stereographic portions, with a resolution of 300 dots per inch. The projections have a common scale of 1:13,923,113 at +56° latitude. NOMENCLATURE Names on this sheet are approved by the IAU and have been applied for features clearly visible at the scale of this map. For a complete list of the IAU-approved nomenclature for Mars, see the Gazetteer of Planetary Nomenclature at http://planetarynames.wr.usgs.gov/. Font color was chosen for readability. Names followed by an asterisk are provisionally approved. *M 25M RKN: Abbreviation for Mars, 1:25,000,000 series, shaded relief (R), with color (K) and nomenclature (N) (Greeley and Batson, 1990)

Open-File Report↗
USGS2002

Color-coded contour map of Mars M 25M RKN

NOTES ON BASE This map is based on data from the Mars Orbiter Laser Altimeter (MOLA) (Smith and others, 2001), an instrument on NASA's Mars Global Surveyor (MGS) spacecraft (Albee and others, 2001). The image used for the base of this map represents more than 600 million measurements gathered between 1999 and 2001, adjusted for consistency (Neumann and others, 2001, 2002) and converted to planetary radii. These have been converted to elevations above the areoid as determined from a martian gravity field solution GMM2 (Lemoine and others, 2001), truncated to degree and order 50, and oriented according to current standards (see below). The average accuracy of each point is originally ~100 meters in horizontal position, and ~1 meter in radius (Neumann, 2001). However, the total elevation uncertainty is at least +3 m due to the global error in the areoid (+1.8 meters according to Lemoine and others, 2001) and regional uncertainties in its shape (communication from Neumann, 2002). The measurements were converted into a digital elevation model (DEM) (communication from Neumann, 2002; Neumann and others, 2001; Smith and others, 2001) using Generic Mapping Tools software (Wessel and Smith, 1998), with a resolution of 0.015625 degrees per pixel or 64 pixels per degree. In projection, the pixels are 926.17 meters in size at the equator. PROJECTION The Mercator projection is used between latitudes +57°, with a central meridian at 0° and latitude equal to the nominal scale at 0°. The Polar Stereographic projection is used for the polar regions north of the +55° parallel and south of the –55° parallel with a central meridian set for both at 0°. The adopted equatorial radius is 3,396.19 km (Duxbury and others 2002; Seidelmann and others 2002). COORDINATE SYSTEM The MOLA data were initially referenced to an internally consistent inertial coordinate system, derived from tracking of the MGS spacecraft. By adopting appropriate values for the orientation of Mars as defined by the International Astronomical Union (IAU) and the International Association of Geodesy (IAG) (Seidelmann and others, 2002), these inertial coordinates were converted into the planet-fixed coordinates (longitude and latitude) used on this map. These values include the orientation of the north pole of Mars (including the effects of precession), the rotation rate of Mars, and a value for W0 of 176.630°, where W0 is the angle along the equator to the east, between the 0° meridian and the equator's intersection with the celestial equator at the standard epoch J2000.0 (Seidelmann and others, 2002). This value of W0 was chosen (Duxbury and others, 2002) in order to place the 0° meridian through the center of the small (~500 m) crater Airy-0, located in the crater Airy (de Vaucouleurs and others, 1973; Seidelmann and others, 2002). Longitude increases to the east, and latitude is planetocentric as allowed by IAU/IAG standards (Seidelmann and others, 2002) and in accordance with current NASA and USGS standards (Duxbury and others, 2002). A secondary grid (printed in red) has been added to the map as a reference to the west longitude/planetographic latitude system that is also allowed by IAU/IAG standards (Seidelmann and others, 2002) and has also been used for Mars. The figure adopted to compute this secondary grid is an oblate spheroid with an equatorial radius of 3,396.19 km and a polar radius of 3,376.2 km (Duxbury and others, 2002; Seidelmann and others, 2002). MAPPING TECHNIQUES To create the topographic base image, the original DEM produced by the MOLA team in Simple Cylindrical projection with a resolution of 64 pixels per degree was projected into the Mercator and Polar Stereographic pieces. A shaded relief was generated from each DEM with a sun angle of 30° from horizontal and a sun azimuth of 270°, as measured clockwise from north, and a vertical exaggeration of 100%. Illumination is from the west, which follows a long-standing USGS tradition for planetary maps. This allows for continuity in the shading between maps and quadrangles, and most closely resembles lighting conditions found on imagery. The DEM values were then mapped to a smooth global color look-up table. Note that the chosen color scheme simply represents elevation changes and is not intended to imply anything about surface characteristics (e.g., past or current presence of water or ice). These two files were then merged and scaled to 1:25 million for the Mercator portion and 1:15,196,708 for the two Polar Stereographic portions, with a resolution of 300 dots per inch. The projections have a common scale of 1:13,923,113 at +56° latitude. Contours were created from the DEM at a 1-kilometer interval. Contours for features with a diameter of 3 km or less (features too small for this map scale) were removed. The contours were then simplified by removing points along the contours spaced less than 1 km apart. NOMENCLATURE Names on this sheet are approved by the IAU and have been applied for features clearly visible at the scale of this map. For a complete list of the IAU-approved nomenclature for Mars, see the Gazetteer of Planetary Nomenclature at http://planetarynames.wr.usgs.gov/. Font color was chosen for readability. Names followed by an asterisk are provisionally approved. M 25M RKN: Abbreviation for Mars, 1:25,000,000 series, shaded relief (R), with color (K) and nomenclature (N) (Greeley and Batson, 1990).

Open-File Report↗
USGS2003

Correlation of the Klamath Mountains and Sierra Nevada

This report graphically portrays the broadly parallel tectonic development of the Klamath Mountains and Sierra Nevada from early Paleozoic to Early Cretaceous time. It is dedicated to J.S. Diller of the U.S. Geological Survey who, during his pioneer field studies a century ago, recognized significant similarities between these two important provinces. The report is based mainly on the numerous published reports of the field and laboratory studies by various geologists and students during the last century, and to a lesser extent on my own field work which has been substantial in the Klamath Mountains but minimal in the Sierra Nevada. For brevity, required by the format of this report, little of the extensive literature pertaining to these two provinces is referenced. This report is preliminary in nature and was prepared as an aid to further study of the tectonic relations between the Klamath Mountains and Sierra Nevada. This report consists of two sheets: Sheet 1, Map showing accreted terranes and plutons of the Klamath Mountains and Sierra Nevada, and Sheet 2, Successive accretionary episodes of the Klamath mountains and northern part of Sierra Nevada, showing related plutonic, volcanic, and metamorphic events. The map on Sheet 1 was compiled and modified from two Open-File maps (Irwin and Wooden, 1999 and 2001) which had been compiled and modified mainly from Jennings (1977), Harwood (1992), Irwin (1994), Jayko (1988), Graymer and Jones (1994), Edelman and Sharp (1989), Schweickert and others (1999), Saucedo and Wagner(1992), Saleeby and Sharp (1980), Wagner and others (1981), and various other sources. For detailed lists of the sources for the isotopic age data used in Sheets 1 and 2, see Irwin and Wooden (1999 and 2001). On Sheet 2, the accretionary episodes are shown sequentially from left to right in two tiers of figures. Episodes for the Klamath Mountains are in the upper tier; correlative episodes of the Sierra Nevada are directly below in the lower tier. The sequence shown for the Klamath Mountains is modified from Irwin and Mankinen (1998) and Irwin and Wooden (1999). The episodes are named for the accreting terranes of the Klamath Mountains, but those names may not be suitable for reference to the correlative episodes of the Sierra Nevada. In the figure for each episode, a heavy black line represents the active suture that separated oceanic crustal rocks on the left from the earlier accreted terranes on the right. Plutons are particularly useful for timing the accretionary episodes. The preaccretionary plutons, which commonly represent the roots of oceanic volcanic arcs, are shown in the accreting oceanic crustal rocks to the left of the heavy black line. The accretionary plutons consist of rock that has been subducted and remobilized as magma during the accretionary process and injected into an overlying earlier accreted terrane on the right of the heavy black line. Thus, isotopic dating of the accretionary plutons (preferably U/Pb dates measured on zircon extracted from the plutonic rock) provides a useful basis for assigning ages to the accretionary episodes. Many plutons are rootless at depth, as they tend to be truncated by the subduction zone sutures of younger accreting terranes. Volcanic deposits resulting from accretionary episodes apparently are uncommon except for those deposited on the backstop terranes. In the Klamath Mountains, the Eastern Klamath terrane, which consists of the Yreka, Trinity and Redding subterranes, was the backstop for the Central Metamorphic and younger accretionary episodes, and displays a remarkable record of sedimentation, volcanism and plutonism from Silurian-Devonian to Jurassic time. In the Sierra Nevada, the correlative backstop was the Northern Sierra terrane which shows a similar long record of volcanism in the Taylorsville, Permian, and Jurassic volcanic arc sequences. During some accretionary episodes the subducting oceanic rocks were dynamically metamorphosed to schist along the suture zone beneath the overriding accreted terranes. Examples of this in the Klamath Mountains are the Devonian Salmon and Abrams Schists of the Central Metamorphic terrane, the Triassic(?) schist of the Fort Jones terrane , and the Early Cretaceous South Fork Mountain Schist that structurally underlies Klamath Mountains terranes along much of the western edge of the province. The Fort Jones terrane and South Fork Mountains Schist were metamorphosed under blueschist-facies conditions. In the Sierra Nevada, schist that is correlative with the Central Metamorphic terrane is present in patches along the Feather River terrane (see Hacker and Peacock, 1990); the Triassic(?) Red Ant Schist is correlative with the Fort Jones terrane; but a correlative of the South Fork Mountain Schist is not present. In addition to the similarities in the sequences of accretion, plutonism, volcanism, and metamorphism, strong ties between the two provinces are also provided by paleontologic data. The Permian McCloud fusulinid fauna of the Redding subterrane also is present in the Northern Sierra terrane. Rare Tethyan fusulinids are found in Permian limestone of the Eastern Hayfork terrane of the Klamath Mountains and also in limestone blocks in the Central Belt of the Sierra Nevada. Ichthyosaur fossils have been collected from the Triassic of both the Redding subterrane and Northern Sierra terrane. Jurassic ammonites and the pelecypod Buchia concentrica occur in both the Galice Formation of the western Klamath Mountains and the Mariposa Formation of the western Sierra Nevada. Events that preceded the Central Metamorphic episode prior to Silurian-Devonian time are not clearly understood and are not shown in the succession of diagrams on Sheet 2. The oldest rocks of the Klamath Mountains are Neoproterozic and they predate the Central Metamorphic episode by possibly a hundred million years or more. They include ophiolitic rocks of the Trinity subterrane and the Antelope Mountain Quartzite of the Yreka subterrane (see Mankinen and others, 2002). In the Sierra Nevada, correlatives of the ancient ophiolitic rocks may be part of the Feather River terrane. Although Neoproterozoic fossils have not yet been found in the Sierra Nevada, petrologic study shows the quartzite of the Lang sequence is closely similar to the Antelope Mountain Quartzite (see Bond and Devay, 1980). Correlation of the two quartzite formations is also suggested by the similarity of their positions in the accretionary sequence.

Klamath Mountains, Sierra Nevada↗
USGS2003

MODFLOW-2000, the U.S. Geological Survey modular ground-water model -- Documentation of MOD-PREDICT for predictions, prediction sensitivity analysis, and evaluation of uncertainty

This document describes the MOD-PREDICT program, which helps evaluate userdefined sets of observations, prior information, and predictions, using the ground-water model MODFLOW-2000. MOD-PREDICT takes advantage of the existing Observation and Sensitivity Processes (Hill and others, 2000) by initiating runs of MODFLOW-2000 and using the output files produced. The names and formats of the MODFLOW-2000 input files are unchanged, such that full backward compatibility is maintained. A new name file and input files are required for MOD-PREDICT. The performance of MOD-PREDICT has been tested in a variety of applications. Future applications, however, might reveal errors that were not detected in the test simulations. Users are requested to notify the U.S. Geological Survey of any errors found in this document or the computer program using the email address available at the web address below. Updates might occasionally be made to this document, to the MOD-PREDICT program, and to MODFLOW- 2000. Users can check for updates on the Internet at URL http://water.usgs.gov/software/ground water.html/.

Open-File Report↗
USGS2006

Description, Properties, and Degradation of Selected Volatile Organic Compounds Detected in Ground Water--A Review of Selected Literature

This report provides abridged information describing the most salient properties and biodegradation of 27 chlorinated volatile organic compounds detected during ground-water studies in the United States. This information is condensed from an extensive list of reports, papers, and literature published by the U.S. Government, various State governments, and peer-reviewed journals. The list includes literature reviews, compilations, and summaries describing volatile organic compounds in ground water. This report cross-references common names and synonyms associated with volatile organic compounds with the naming conventions supported by the International Union of Pure and Applied Chemistry. In addition, the report describes basic physical characteristics of those compounds such as Henry's Law constant, water solubility, density, octanol-water partition (log Kow), and organic carbon partition (log Koc) coefficients. Descriptions and illustrations are provided for natural and laboratory biodegradation rates, chemical by-products, and degradation pathways.

Open-File Report↗