Results for “Water Use Map Series” — Page 11

USGS2020

Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi ?

In Lake Erie, cisco Coregonus artedi once supported one of the most valuable freshwater fisheries on earth, yet overfishing caused their eventual extirpation from the lake. With warming lake temperatures, some have questioned whether Lake Erie still contains suitable oxythermal conditions for cisco. Using published oxythermal thresholds for cisco and oxythermal profiles from Lake Erie, we sought to answer two questions critical to cisco restoration science. First, is cisco habitat still available during the most restrictive periods? Second, what is the distribution of cisco habitat during these times? Beta regression was used to determine that cisco habitat was most limited during the month of August, and that August of 2010 was the most restrictive period in the time series. We then used Empirical Bayesian Kriging (EBK) to map the spatial extent of cisco habitat during these times. EBK maps revealed large areas of summer refugia for cisco in Lake Erie, even during the least favorable periods. Most of the Central and East Basins contain suitable habitat during the average August, yet during August of 2010, suitable conditions were limited to the eastern edge of the Central Basin and the deep waters of the East Basin. These findings align well with historical accounts of cisco landings. While suitable oxythermal habitat still exists for cisco in Lake Erie, future restoration efforts, if attempted, will partially depend on: 1) better management of nutrient inputs, 2) the realization of future climate scenarios, and 3) the ability of cisco to adapt to a changing lake.

Lake Erie↗

USGS2021

Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation

Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).

California↗

USGS1981

Geohydrology of the Schenectady Aquifer, Schenectady County, New York

This report is the fourth in a series of 11 map sets depicting geohydrologic conditions in selected aquifers in upstate New York. Geohydrologic data are compiled on six maps at 1:24,000 scale. Together the maps provide a comprehensive overview of the Schenectady aquifer, the principal aquifer in Schenectady County and the most heavily pumped aquifer in upstate New York. The maps include surficial geology, geologic sections, soil permeability, water-table altitude, aquifer thickness, estimated well yield, and land use. The maps are designed to enable planners to evaluate underground waste-disposal practices that could adversely affect the aquifer. (USGS)

Open-File Report↗

USGS1987

Hydrology and water quality of East Lake Tohopekaliga, Osceola County, Florida

East Lake Tohopekaliga, one of the major lakes in central Florida, is located in the upper Kissimmee River basin in north-east Osceola County. It is one of numerous lakes in the upper basin used for flood control, in addition to recreation and some irrigation of surrounding pasture. This report is the fourth in a series of lake reconnaissance studies in the Kissimmee River basin prepared in cooperation with the South Florida Water Management District. The purpose of the report is to provide government agencies and the public with a brief summary of the lake 's hydrology and water quality. Site information is given and includes map number, site name, location, and type of data available (specific conductivity, pH, alkalinity, turbidity, color, dissolved oxygen, hardness, dissolved chlorides, dissolved sodium, dissolved calcium, dissolved magnesium, dissolved potassium, nitrogen, ammonia, nitrates, carbon and phosphorus). The U.S. Geological Survey (USGS) maintained a lake stage gaging station on East Lake Tohopekaliga from 1942 to 1968. The South Florida Water Management District has recorded lake stage since 1963. Periodic water quality samples have been collected from the lake by the South Florida Water Management District and USGS. Water quality and discharge data have been collected for one major tributary to the lake, Boggy Creek. Although few groundwater data are available for the study area, results of previous studies of the groundwater resources of Osceola County are included in this report. To supplement the water quality data for East Lake Tohopekaliga, water samples were collected at selected sites in November 1982 (dry season) and in August 1983 (rainy season). Samples were taken at inflow points, and in the lake, and vertical profiles of dissolved oxygen and temperature were measured in the lake. A water budget from an EPA report on the lake is also included. (Lantz-PTT)

Water-Resources Investigations Report↗

USGS2011

Digitized generalized areas where surface-water resources likely or potentially are susceptible to groundwater withdrawals in adjacent valleys, Great Basin National Park area, Nevada

Abstract Polygons delineate generalized areas in and around Great Basin National Park where surface-water resources likely or potentially are susceptible to groundwater withdrawals in adjacent valleys. Purpose This data set was created as part of a U.S. Geological Survey study, done in cooperation with the National Park Service, to characterize surface-water resources in and around Great Basin National Park. The intended uses of this data set include, but are not limited to, natural resource modeling, mapping, and visualization applications. Source Information SIR 2006-5099, Plate 1: Generalized areas where surface-water resources likely or potentially are susceptible to ground-water withdrawals in adjacent valleys, Great Basin National Park area, Nevada.

Nevada↗

USGS2013

Hydrographs showing groundwater levels for selected wells in the Puyallup River watershed and vicinity, Pierce and King Counties, Washington

Hydrographs of groundwater levels for selected wells in and adjacent to the Puyallup River watershed in Pierce and King Counties, Washington, are presented using an interactive Web-based map of the study area to illustrate changes in groundwater levels on a monthly and seasonal basis. The interactive map displays well locations that link to the hydrographs, which in turn link to the U.S. Geological Survey National Water Information System, Groundwater Site Inventory System.

Washington↗

USGS2004

Topographic Map of the Northwest Ascraeus Mons Region of Mars - MTM 500k 15/252E OMKT

This map is part of a series of topographic maps of areas of special scientific interest on Mars. The topography was compiled photogrammetically using Viking Orbiter stereo image pairs. The contour interval is 250 meters. Horizontal and vertical control was established using the USGS Mars Digital Image Model 2.0 (MDIM 2.0) and data from the Mars Orbiter Laser Altimeter (MOLA).

IMAP↗

USGS2004

Topographic Map of the Northeast Ascraeus Mons Region of Mars - MTM 500k 15/257E OMKT

This map is part of a series of topographic maps of areas of special scientific interest on Mars. The topography was compiled photogrammetically using Viking Orbiter stereo image pairs. The contour interval is 250 meters. Horizontal and vertical control was established using the USGS Mars Digital Image Model 2.0 (MDIM 2.0) and data from the Mars Orbiter Laser Altimeter (MOLA).

IMAP↗

USGS2004

Topographic Map of the Southeast Ascraeus Mons Region of Mars - MTM 500k 10/257E OMKT

This map is part of a series of topographic maps of areas of special scientific interest on Mars. The topography was compiled photogrammetically using Viking Orbiter stereo image pairs. The contour interval is 250 meters. Horizontal and vertical control was established using the USGS Mars Digital Image Model 2.0 (MDIM 2.0) and data from the Mars Orbiter Laser Altimeter (MOLA).

IMAP↗

USGS2004

Topographic Map of the Southwest Ascraeus Mons Region of Mars - MTM 500k 10/252E OMKT

This map is part of a series of topographic maps of areas of special scientific interest on Mars. The topography was compiled photogrammetically using Viking Orbiter stereo image pairs. The contour interval is 250 meters. Horizontal and vertical control was established using the USGS Mars Digital Image Model 2.0 (MDIM 2.0) and data from the Mars Orbiter Laser Altimeter (MOLA).

IMAP↗

USGS2012

Recent wetland land loss due to hurricanes: Improved estimates based upon multiple source images

The objective of this study was to provide a moderate resolution 30-m fractional water map of the Chenier Plain for 2003, 2006 and 2009 by using information contained in high-resolution satellite imagery of a subset of the study area. Indices and transforms pertaining to vegetation and water were created using the high-resolution imagery, and a threshold was applied to obtain a categorical land/water map. The high-resolution data was used to train a decision-tree classifier to estimate percent water in a lower resolution (Landsat) image. Two new water indices based on the tasseled cap transformation were proposed for IKONOS imagery in wetland environments and more than 700 input parameter combinations were considered for each Landsat image classified. Final selection and thresholding of the resulting percent water maps involved over 5,000 unambiguous classified random points using corresponding 1-m resolution aerial photographs, and a statistical optimization procedure to determine the threshold at which the maximum Kappa coefficient occurs. Each selected dataset has a Kappa coefficient, percent correctly classified (PCC) water, land and total greater than 90%. An accuracy assessment using 1,000 independent random points was performed. Using the validation points, the PCC values decreased to around 90%. The time series change analysis indicated that due to Hurricane Rita, the study area lost 6.5% of marsh area, and transient changes were less than 3% for either land or water. Hurricane Ike resulted in an additional 8% land loss, although not enough time has passed to discriminate between persistent and transient changes.

Louisana↗

USGS2013

Hyperspectral surface materials map of quadrangle 3468, Chak-e Wardak-Siyahgird (509) and Kabul (510) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials

This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangle 3468, Chak-e Wardak-Siyahgird (509) and Kabul (510) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangles 3664 and 3764, Char Shengo (123), Shibirghan (124), Jalajin (117), and Kham-Ab (118) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials

This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangles 3664 and 3764, Char Shengo (123), Shibirghan (124), Jalajin (117), and Kham-Ab (118) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials

This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗

USGS2013

Hyperspectral surface materials map of quadrangle 3162, Chakhansur (603) and Kotalak (604) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials

This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Open-File Report↗