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At least 721 records · Page 40Linked to original sources
USGS1995

Evaluation of nonpoint-source contamination, Wisconsin: Land-use and Best-Management-Practices inventory, selected streamwater-quality data, urban-watershed quality assurance and quality control, constituent loads in rural streams, and snowmelt-runoff analysis, water year 1994

The objective of the watershed-management evaluation monitoring program in Wisconsin is to evaluate the effectiveness of best-management practices (BMP) for controlling nonpoint-source contamination in rural and urban watersheds. This report is an annual summary of the data collected for the program by the U.S Geological Survey and a report of the results of several different detailed analyses of the data. A land-use and BMP inventory is ongoing for 12 evaluation monitoring projects to track the sources of nonpoint-source pollution in each watershed and to document implementation of BMP's that may cause changes in the water quality of streams. Updated information is gathered each year, mapped, and stored in a geographic-information-system data base. Summaries of data collected during water years 1989-94 are presented. A water year is the period beginning October 1 and ending September 30; the water year is designated by the calendar year in which it ends. Suspended-sediment and total-phosphorus data (storm loads and annual loads) are summarized for eight rural sites. For all sites, the annual suspended-sediment or suspended-solids load for water year 1993 exceeded the average for the period of data collection; the minimum annual loads were transported in water year 1991 or 1992. Continuous dissolved-oxygen data were collected at seven rural sites during water year 1994. Data for water years 1990-93 are summarized and plotted in terms of percentage of time that a particular concentration is equaled or exceeded. Dissolved-oxygen concentrations in four streams were less than 9 mg/L at least 50 percent of the time, a condition that fails to meet suggested criterion for coldwater streams. The dissolved-oxygen probability curve for one of the coldwater streams is markedly different than the curves for the other streams, perhaps because of differences in aquatic biomass. Blank quality-assurance samples were collected at two of the urban evaluation monitoring sites to isolate contamination in the sample bottle, the automatic sampler and splitter, and the filtration system. Significant contamination caused excessive concentrations of dissolved chloride, alkalinity, and biochemical oxygen demand. The level of contamination may be large enough to affect data for water samples in which these analytes are present at low concentration. Further investigation is being done to determine the source of contamination and take measures to minimize its effect on the sampling. A preliminary regression analysis was done for the rural sites using data collected during water years 1989-93. Loads of suspended solids and total phosphorus in stormflow were regressed against various precipitation-related measures. The results indicate that, for most sites, changes in constituent load on the order of 40 to 50 percent could be detected with a statistical test. For two sites, the change would have to be 60 to 70 percent to be detected. A detailed comparison of snowmelt runoff and rainfall stormflow in urban and rural areas was done using data collected during water years 1985-93. For the rural sites where statistically significant differences were found between constituent loads in snowmelt and storm runoff, the loads of suspended solids and total phosphorus in snowmelt runoff were greater than those in storm runoff. For the urban sites where statistically significant differences were found between snowmelt and storm runoff, the loads of suspended solids and total phosphorus in storm runoff were greater than those in snowmelt runoff. The importance of including snowmelt runoff in designing and analyzing the effects of BMP's on streamwater quality, particularly in rural areas, is emphasized by these results.

Wisconsin↗
USGS1997

Digital data set that describe aquifer characteristics of the Antlers aquifer in southeastern Oklahoma

The data sets in this report include digitized aquifer boundaries and maps of hydraulic conductivity, recharge, and ground-water level elevation contours for the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in an area of about 1,800-square miles (Morton, 1992). The recharge, hydraulic conductivity, and aquifer boundaries data sets include the outcrop area of the Antlers Sandstone in Oklahoma and areas where the Antlers is overlain by alluvial and terrace deposits and a few small thin outcrops of the Goodland Limestone. Most of the lines in the data sets are from the digital data of the surficial geology of the Antlers Sandstone from Cederstrand (1996a, 1996b) except where the Antlers aquifer is overlain by alluvial and terrace deposits near streams and rivers. Morton (1992) interpolated the Antlers aquifer boundaries under the alluvial and terrace deposits where streams cross the aquifer outcrop. Aquifer boundary lines for areas where the aquifer is overlain by alluvial and terrace deposits were digitized and are similar to the aquifer boundaries shown in Morton (1992). A few polygons in this data set represent small and thin outcrops of the Goodland Limestone overlying the Antlers aquifer. The Antlers aquifer in Texas (Morton, 1992) is not included in this data set. The maps from which this data set was derived were scanned or digitized from maps published at a scale of 1:250,000. Hydraulic conductivity and recharge values were used as input to the ground-water model report for the Antlers aquifer by Morton (1992). The water-level elevation contours were digitized from a mylar map at a scale of 1:250,000 that was used to prepare the final map that was published as plate 1, map A, "Observed potentiometric surface, 1970, Antlers aquifer" at a scale of 1:500,000 in Morton (1992). Morton (1992) used water levels measured in wells in 1970 to construct the map. The water-level elevation contours for the Antlers aquifer in Texas (Morton, 1992) are not included in this data set. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. The hydraulic conductivity and recharge are closely interrelated. As long as these two model inputs are in balance the model has a small mean residual; it represents the natural system numerically. If the hydraulic conductivity is accurately known, the model can be used to accurately determine recharge. Likewise, if the hydraulic conductivity is poorly known, then the recharge will be poorly determined. Therefore, values of hydraulic conductivity and recharge used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data. In most aquifers, hydraulic conductivity measurements made in wells or in cores will range over several orders of magnitude, even over short horizontal and vertical distances. Hydraulic conductivity values derived from ground-water flow models represent areal generalizations and do not reflect the large local variance in well or core measurements. Recharge probably varies considerably over the local area, and model recharge is at best an average over an area at least as large as the model grid (and probably much larger than a single cell). Compilation of the data sets was funded under a cooperative Joint Funding Agreement between the U.S. Geological Survey and the State of Oklahoma, Office of the Secretary of Environment.

Oklahoma↗
USGS1996

Hydrologic data for 1994-96 for the Huron Project of the High Plains Ground-Water Demonstration Program

This report presents data on precipitation, water levels, and water quality that have been collected or compiled for water years 1994 through 1996 for the Huron Project of the High Plains Ground-Water Demonstration Program, under the guidance of the Bureau of Reclamation. This is the second report for the project. The first report (Carter, 1995) presented data collected through water year 1993. The purpose of the Huron Project is to demonstrate the artificial recharge potential of glacial aquifers in eastern South Dakota. High flows from the James River during spring runoff were used as a source of supplemental recharge for the Warren aquifer, which is a buried, glacial aquifer. In 1990, 70 observation wells were installed by the South Dakota Department of Environment and Natural Resources (DENR) specifically for this study, and 15 existing DENR observation wells were incorporated into the study. In 1993, the recharge well was installed. After a trial injection of recharge water in April 1994, continuous injection began in June 1994. Many sites were monitored to obtain information before, during, and after recharging the aquifer. This report presents data that were collected during the three phases of recharge. Precipitation data are collected at two sites within the study area. A site description and daily precipitation for water years 1994-95 are presented for one precipitation site. Water-level hydrographs are presented for the 85 observation wells and the recharge well. Hydrographs are shown for the period from October 1, 1993, through November 29, 1995. Recharge water was injected from June 2, 1994, through July 29, 1994, and from June 14, 1995, through September 13, 1995. The cumulative volume of injected water and the injection rates into the aquifer are presented for the periods of recharge. Water-quality data were collected from screening, detailed, and plume-monitoring sampling programs. Screening water-quality data for six observation wells are presented. These data include primarily field parameters and common ions. The four detailed sampling sites represent the quality of untreated water, treated water, and ground water from the Warren aquifer. Data presented for the detailed sampling program include field parameters, bacteria counts, and concentrations of common ions, solids, nutrients, trace elements, radiometrics, total organic carbon, herbicides, insecticides, and volatile organic compounds. Water-quality data for the plume-monitoring sampling program were collected from 25 sites during injection of recharge water into the Warren aquifer in 1994 and 1995. The data for the plume-monitoring program include primarily field parameters and common ions. Data for quality-assurance samples also are presented.

Open-File Report↗
USGS1998

Data Quality Objectives and Criteria for Basic Information, Acceptable Uncertainty, and Quality-Assurance and Quality-Control Documentation

The Federal Highway Administration and State transportation agencies have the responsibility of determining and minimizing the effects of highway runoff on water quality; therefore, they have been conducting an extensive program of water-quality monitoring and research during the last 25 years. The objectives and monitoring goals of highway runoff studies have been diverse, because the highway community must address many different questions about the characteristics and impacts of highway runoff. The Federal Highway Administration must establish that available data and procedures that are used to assess and predict pollutant loadings and impacts from highway stormwater runoff are valid, current, and technically supportable. This report examines criteria for evaluating water-quality data and resultant interpretations. The criteria used to determine if data are valid (useful for intended purposes), current, and technically supportable are derived from published materials from the Federal Highway Administration, the U.S. Environmental Protection Agency, the Intergovernmental Task Force on Monitoring Water Quality, the U.S. Geological Survey and from technical experts throughout the U.S. Geological Survey. Water-quality data that are documented to be meaningful, representative, complete, precise, accurate, comparable, and admissible as legal evidence will meet the scientific, engineering, and regulatory needs of highway agencies. Documentation of basic information, such as compatible monitoring objectives and program design features; metadata (when, where, and how data were collected as well as who collected and analyzed the data); ancillary information (explanatory variables and study-site characteristics); and legal requirements are needed to evaluate data. Documentation of sufficient quality-assurance and quality-control information to establish the quality and uncertainty in the data and interpretations also are needed to determine the comparability and utility of data sets for intended uses. The fact that a program's data may not meet screening criteria for a national synthesis does not mean that the data are not useful for meeting that program's objectives or that they could not be used for water-quality studies with different objectives.

Open-File Report↗
USGS2018

Preliminary groundwater salinity mapping near selected oil fields using historical water-sample data, central and southern California

The distribution of groundwater salinity was mapped for 31 oil fields and adjacent aquifers and summarized by 8 subregions across major oil-producing areas of central and southern California. The objectives of this study were to describe the distribution of groundwater near oil fields having total dissolved solids less than 10,000 milligrams per liter (mg/L) based on available data and to document where data gaps exist. Salinity was represented by the measured or calculated concentration of total dissolved solids (TDS) in samples of produced water obtained from petroleum wells and groundwater obtained from water wells. The water chemistry data were used to estimate the minimum depths of TDS greater than 3,000 mg/L and greater than 10,000 mg/L in areas near selected oil fields using historical water-chemistry data coupled with available well-location and construction information. The 10,000 mg/L threshold, representing the highest level of TDS concentration of water that could be considered as a potential source of drinking water, was present in all but 4 (Jasmin, Kern Bluff, Kern Front, and Mount Poso) of the 31 individual oil fields. Among petroleum wells, the median TDS concentration of produced water ranged from 500 mg/L for the Jasmin field to 32,636 mg/L for the Elk Hills field. Among water wells, median TDS concentrations, either reported or calculated from specific conductance, ranged from 151 mg/L for wells within 2 miles of the Ten Section field to 9,750 mg/L for wells within 2 miles of the combined North and South Belridge fields. In general, TDS across the eight geographic subregions increased with depth, but the relation of TDS with depth varied regionally. The most pronounced increases in TDS with depth were across the West Kern Valley Floor and West Kern Valley Margin subregions on the west side of the San Joaquin Valley, and in the vicinity of the Wilmington field in the Los Angeles Basin subregion; in these areas, relatively high TDS concentrations greater than 10,000 mg/L were present within the upper few hundred to several thousand feet of land surface. Total dissolved solids concentrations increased more gradually with depth in the Middle Kern Valley Floor subregion, in the South Kern Valley Margin subregion, in the vicinity of the Montebello and Santa Fe Springs fields in the Los Angeles Basin subregion, and in the Central Coast Basin subregion. The Kern Sierran Foothills and East Kern Valley Floor subregions, on the east side of the San Joaquin Valley, had the most gradual increases in TDS with depth. Fields in the East Kern Valley Floor subregion generally had groundwater and produced water with TDS less than 10,000 mg/L that extended to a large depth compared to most other subregions. Overall, the west side of the San Joaquin Valley in Kern County and the Wilmington field in Los Angeles County generally have the highest TDS values and the shallowest depths to high TDS. High TDS at relatively shallow depths on the west side of the San Joaquin Valley may be because of a combination of natural conditions and anthropogenic factors. In the vicinity of the Wilmington field in the Los Angeles Basin subregion, high TDS at relatively shallow depths is attributable at least in part to seawater intrusion. Fields on the east side of the San Joaquin Valley in Kern County have the lowest TDS and greatest depths to TDS greater than 10,000 mg/L because of their geologic setting adjacent to Sierra Nevada recharge areas. Reconnaissance salinity mapping was limited by several factors. The primary limitation was the lack of well-construction data for a significant number of water wells. Bottom perforation, well depth, or hole depth were not available for 35 percent of wells used for salinity mapping. A second limitation was variability in data quality. Total dissolved solids and specific conductance data were compiled from different data sources with varying degrees of documentation that ranged from comprehensive to very little or none. As a result, it was not always possible to assess the quality of the provided data with respect to either conditions at each well during sampling or the methodology used for sample collection and analysis. A third limitation was the lack of wells, either petroleum or water, and associated TDS data over large vertical intervals for some fields. As a result, the distribution of salinity and the depths at which TDS concentration exceeds the 3,000 and 10,000 mg/L thresholds could not always be precisely determined. This analysis highlights key gaps that need to be filled with additional analysis of other sources of information, such as borehole geophysical logs and new water sample or geophysical data collection.

California↗
USGS2000

Analysis of nitrate and volatile organic compound data for ground water in the Great Salt Lake Basins, Utah, Idaho, and Wyoming, 1980-98

In 1995, ground water was the source of drinking water to about 52 percent of the population served by public drinking water systems in the Great Salt Lake Basins study unit, which includes parts of Utah, Idaho, and Wyoming. Existing nitrate and volatile organic compound data for ground water collected in the study unit were compiled and summarized as part of the National Water-Quality Assessment Program’s objective to describe water-quality conditions in the Nation’s aquifers. Prerequisites for the inclusion of nitrate and volatile organic compound data into this retrospective analysis are that the data set is available in electronic form, the data were collected during 1980-98, the data set is somewhat regional in coverage, and the locations of the sampled sites are known. Ground-water data stored in the U.S. Geological Survey’s National Water Information System and the Idaho and Utah Public Drinking Water Systems databases were reviewed. Only the most recent analysis was included in the data sets if more than one analysis was available for a site. The National Water Information System data set contained nitrate analyses for water from 480 wells. The median concentration of nitrate was 1.30 milligrams per liter for the 388 values above minimum reporting limits. The maximum contaminant level for nitrate as established by the U.S. Environmental Protection Agency was exceeded in water from 10 of the 200 wells less than or equal to 150 feet deep and in water from 3 of 280 wells greater than 150 feet deep. The Public Drinking Water Systems data set contained nitrate analyses for water from 587 wells. The median concentration of nitrate was 1.12 milligrams per liter for the 548 values above minimum reporting limits. The maximum contaminant level for nitrate was exceeded at 1 site and 22 sites had concentrations equal to or greater than 5 milligrams per liter. The types of land use surrounding a well and the well depth were related to measured nitrate concentrations in the sampled ground water. Overall, water sampled from wells in rangeland areas had a lower median measured nitrate concentration (0.76 milligrams per liter) than water from areas with an agricultural or urban/residential land use (1.41 and 1.20 milligrams per liter, respectively). In the National Water Information System data set, the median measured nitrate concentration in water from urban/residential areas varied from 1.00 milligrams per liter for wells greater than 150 feet deep to 1.84 milligrams per liter for wells less than or equal to 150 feet deep. The Public Drinking Water Systems and the National Water Information System data sets contained analyses for most of the State and Federally regulated volatile organic compounds in water from about 368 and 74 wells, respectively. Fifteen different volatile organic compounds were detected at least once in ground water sampled from the Great Salt Lake Basins study unit. Water from 21 wells contained at least 1 volatile organic compound at detectable concentrations. About 68 percent of the volatile organic compounds detected were in water sampled from wells in Salt Lake County, Utah. Tetrachloroethylene was the most commonly detected volatile organic compound in ground water sampled from the study unit, present in 8 out of 442 samples. Maximum contaminant levels for tetrachloroethylene and 1,1-dichloroethylene as established by the U.S. Environmental Protection Agency were exceeded in water from one well each.

Idaho,Utah, Wyoming↗
USGS2022

A call to action: Standardizing white-tailed deer harvest data in the Midwestern United States and implications for quantitative analysis and disease management

Recreational hunting has been the dominant game management and conservation mechanism in the United States for the past century. However, there are numerous modern-day issues that reduce the viability and efficacy of hunting-based management, such as fewer hunters, overabundant wildlife populations, limited access, and emerging infectious diseases in wildlife. Quantifying the drivers of recreational harvest by hunters could inform potential management actions to address these issues, but this is seldom comprehensively accomplished because data collection practices limit some analytical applications (e.g., differing spatial scales of harvest regulations and harvest data). Additionally, managing large-scale issues, such as infectious diseases, requires collaborations across management agencies, which is challenging or impossible if data are not standardized. Here we discuss modern issues with the prevailing wildlife management framework in the United States from an analytical point of view with a case study of white-tailed deer ( Odocoileus virginianus ) in the Midwest. We have four aims: (1) describe the interrelated processes that comprise hunting and suggest improvements to current data collections systems, (2) summarize data collection systems employed by state wildlife management agencies in the Midwestern United States and discuss potential for large-scale data standardization, (3) assess how aims 1 and 2 influence managing infectious diseases in hunted wildlife, and (4) suggest actionable steps to help guide data collection standards and management practices. To achieve these goals, Wisconsin Department of Natural Resources disseminated a questionnaire to state wildlife agencies (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin), and we report and compare their harvest management structures, data collection practices, and responses to chronic wasting disease. We hope our “call to action” encourages re-evaluation, coordination, and improvement of harvest and management data collection practices with the goal of improving the analytical potential of these data. A deeper understanding of the strengths and deficiencies of our current management systems in relation to harvest and management data collection methods could benefit the future development of comprehensive and collaborative management and research initiatives (e.g., adaptive management) for wildlife and their diseases.

Illinois, Indiana, Iowa, Kentucky, Michigan, Minne↗
USGS2019

Optimizing an inner-continental shelf geologic framework investigation through data repurposing and machine learning

The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5,400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 groundtruth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study.

Delaware, Maryland, Virginia↗
USGS2014

Advances in a distributed approach for ocean model data interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

Journal of Marine Science and Engineering↗
USGS2019

Analysis and visualization of coastal ocean model data in the cloud

The traditional flow of coastal ocean model data is from High Performance Computing (HPC) centers to the local desktop, or to a file server where just the data needed can be extracted via services such as OPeNDAP. Analysis and visualization is then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC to the Cloud and perform interactive, scalable, data-proximate analysis and visualization, with simply a web browser user interface. We use the framework advanced by the NSF-funded Pangeo project, a free, open-source Python system which provides multi-user login via JupyterHub and parallel analysis via Dask, both running in Docker containers orchestrated by Kubernetes. Data is stored in the Zarr format, a Cloud-friendly ndarray format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on massive model grids is made possible by new tools in the Python PyViz ecosystem, which can render maps at screen resolution, dynamically updating on pan and zoom operations. Two example are given: (1) calculating the maximum water level at each grid cell from a 53GB, 720 time step, 9 million node triangular mesh ADCIRC simulation of Hurricane Ike; (2) creating a dashboard for visualizing data from the curvilinear orthogonal COAWST/ROMS forecast model.

Journal of Marine Science and Engineering↗
USGS2016

Deriving habitat models for northern long-eared bats from historical detection data: A case study using the Fernow Experimental Forest

The listing of the northern long-eared bat ( Myotis septentrionalis ) as federally threatened under the Endangered Species Act following severe population declines from white-nose syndrome presents considerable challenges to natural resource managers. Because the northern long-eared bat is a forest habitat generalist, development of effective conservation measures will depend on appropriate understanding of its habitat relationships at individual locations. However, severely reduced population sizes make gathering data for such models difficult. As a result, historical data may be essential in development of habitat models. To date, there has been little evaluation of how effective historical bat presence data, such as data derived from mist-net captures, acoustic detection, and day-roost locations, may be in developing habitat models, nor is it clear how models created using different data sources may differ. We explored this issue by creating presence probability models for the northern long-eared bat on the Fernow Experimental Forest in the central Appalachian Mountains of West Virginia using a historical, presence-only data set. Each presence data type produced outputs that were dissimilar but that still corresponded with known traits of the northern long-eared bat or are easily explained in the context of the particular data collection protocol. However, our results also highlight potential limitations of individual data types. For example, models from mist-net capture data only showed high probability of presence along the dendritic network of riparian areas, an obvious artifact of sampling methodology. Development of ecological niche and presence models for northern long-eared bat populations could be highly valuable for resource managers going forward with this species. We caution, however, that efforts to create such models should consider the substantial limitations of models derived from historical data, and address model assumptions.

West Virginia↗
USGS1998

Regional characterization of land cover using multiple sources of data

Many organizations require accurate intermediate-scale land-cover information for many applications, including modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land-use planning, and policy development. While many techniques have been successfully used to classify land cover in relatively small regions, there are substantial obstacles in applying these methods to large, multiscene regions. The purpose of this study was to generate and evaluate a large region land-cover classification product using a multiple-layer land-characteristics database approach. To derive land-cover information, mosaicked Landsat thematic mapper (TM) scenes were analyzed in conjunction with digital elevation data (and derived slope, aspect, and shaded relief), population census information, Defense Meteorological Satellite Program city lights data, prior land-use and land-cover data, digital line graph data, and National Wetlands Inventory data. Both leaf-on and leaf-off TM data sets were analyzed. The study area was U.S. Federal Region III, which includes the states of Pennsylvania, Virginia, Maryland, Delaware, and West Virginia. The general procedure involved (1) generating mosaics of multiple scenes of leaves-on TM data using histogram equalization methods; (2) clustering mosaics into 100 spectral classes using unsupervised classification; (3) interpreting and labeling spectral classes into approximately 15 land-cover categories (analogous to Anderson Level 1 and 2 classes) using aerial photographs; (4) developing decision-making rules and models using from one to several ancillary data layers to resolve confusion in spectral classes that represented two or more targeted land-cover categories; and (5) incorporating data from other sources (for example, leaf-off TM data and National Wetlands Inventory data) to yield a final land-cover product. Although standard accuracy assessments were not done, a series of consistency checks using available sources of land-cover information were conducted to evaluate the effectiveness of this approach for generating accurate land-cover information for large regions.

Photogrammetric Engineering and Remote Sensing↗
USGS2010

Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and watersurface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The generation of EDEN waterlevel surfaces is derived from real-time data. Real-time data are automatically checked for outliers using minimum, maximum, and rate-of-change thresholds for each station. Smaller errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the gages. Correcting smaller errors in the data often is time consuming and water-level data may not be finalized for several months. To provide water-level surfaces on a daily basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous waterlevel data. A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Florida↗
USGS1981

The role of change data in a land use and land cover map updating program

An assessment of current land use and a process for identifying and measuring change are needed to evaluate trends and problems associated with the use of our Nation's land resources. The U. S. Geological Survey is designing a program to maintain the currency of its land use and land cover maps and digital data base and to provide data on changes in our Nation's land use and land cover. Ways to produce and use change data in a map updating program are being evaluated. A dual role for change data is suggested. For users whose applications require specific polygon data on land use change, showing the locations of all individual category changes and detailed statistical data on these changes can be provided as byproducts of the map-revision process. Such products can be produced quickly and inexpensively either by conventional mapmaking methods or as specialized output from a computerized geographic information system. Secondly, spatial data on land use change are used directly for updating existing maps and statistical data. By incorporating only selected change data, maps and digital data can be updated in an efficient and timely manner without the need for complete and costly detailed remapping and redigitization of polygon data.

Pecora VII Symposium↗
USGS2015

The upper bound of abutment scour defined by selected laboratory and field data

The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, conducted a field investigation of abutment scour in South Carolina and used that data to develop envelope curves defining the upper bound of abutment scour. To expand upon this previous work, an additional cooperative investigation was initiated to combine the South Carolina data with abutment-scour data from other sources and evaluate the upper bound of abutment scour with the larger data set. To facilitate this analysis, a literature review was made to identify potential sources of published abutment-scour data, and selected data, consisting of 446 laboratory and 331 field measurements, were compiled for the analysis. These data encompassed a wide range of laboratory and field conditions and represent field data from 6 states within the United States. The data set was used to evaluate the South Carolina abutment-scour envelope curves. Additionally, the data were used to evaluate a dimensionless abutment-scour envelope curve developed by Melville (1992), highlighting the distinct difference in the upper bound for laboratory and field data. The envelope curves evaluated in this investigation provide simple but useful tools for assessing the potential maximum abutment-scour depth in the field setting.

South Carolina↗
USGS2012

Quantification of water-level variability effect on plant species populations using paleoecological and hydrological time series data

Soil cores provide valuable data on historical changes in vegetation and hydrologic conditions. Empirical models were developed to quantify the effect of meteorological and hydrologic forcing on plant species distributions over a 110-year period in Water Conservation Area 1 (WCA1) in the Florida Everglades, also known as the Arthur R. Marshall Loxahatchee National Wildlife Refuge. Empirical models that predict plant species distributions at sites within WCA1 were developed by linking temporally sparse seed bank data from soil cores with continuous multi-decadal daily meteorological and hydrologic time series data. The meteorological data included rainfall and maximum daily temperatures that spanned the entire study period of 110 years. The hydrologic data included stage data from two gages in WCA1 established in 1954. These stage data were hindcasted to be concurrent with the meteorological data by using correlation models that fit measured stages as a function of the meteorological parameters. The historical plant species data came from seven peat cores from WCA1. Different depths from each core were carbon-dated and assayed for relative percentages of 83 plant species using pollen counts. The oldest dates were more than 1,000 years old; however, only core data that overlapped the study period were used, for a total of 67 assays among the seven cores. Twenty-three of the species had ratios of at least 5 percent for one or more of the 67 assays, hereafter referred to as the "top23". Using the assays as input vectors, the top23 were grouped using the k-means clustering into four plant classes that represented the extent to which the various species have historically appeared together. This reduced the modeling problem to one of predicting the relative ratios of the four plant classes from the hindcasted stage time-series data. A separate empirical model was developed for each class using a multi-layer perceptron artificial neural network, which provides multivariate, nonlinear curve fitting. The models predicted the relative ratios of the classes, and the sums of the predictions are near 1. The coefficient of determination (R2) of the models varied from 0.87 to 0.96, indicating that the relative ratios of the plant classes are predictable, and therefore controllable, from stage forcing. Similar soil cores are available for the Coastal Plain of North Carolina and are planned for the Congaree National Park in South Carolina.

Florida↗
USGS2016

Mercury in fish and macroinvertebrates from New York's streams and rivers: A compendium of data sources

The U.S. Geological Survey has compiled a list of existing data sets, from selected sources, containing mercury (Hg) concentration data in fish and macroinvertebrate samples that were collected from flowing waters of New York State from 1970 through 2014. Data sets selected for inclusion in this report were limited to those that contain fish and (or) macroinvertebrate data that were collected across broad areas, cover relatively long time periods, and (or) were collected as part of a broader-scale (e.g. national) study or program. In addition, all data sets listed were collected, processed, and analyzed with documented methods, and contain critical sample information (e.g. fish species, fish size, Hg species) that is needed to analyze and interpret the reported Hg concentration data. Fourteen data sets, all from state or federal agencies, are listed in this report, along with selected descriptive information regarding each data source and data set contents. Together, these 14 data sets contain Hg and related data for more than 7,000 biological samples collected from more than 700 unique stream and river locations between 1970 and 2014.

New York↗
USGS2001

Transboundary movement of airborne pollutants — A methodology for integrating spaceborne images and ground based data

The availability of relevant and accurate environmental information is essential for environmental policy-makers. Recent improvements in satellite remote sensing technologies, groundbased monitors, and data access have resulted in the ability to observe and assess major atmospheric and ecological events around the world on a timely basis Each of these monitoring technologies reveals different and useful information, yet rarely are the resulting data sets used together in an integrated manner. The U.S. Environmental Protection Agency (EPA) and the United Nations Environment Programme (UNEP) Global Resource Information Database (GRID) office in Sioux Falls identified an environmental issue of global interest as a test case for applying an integrated approach: the transboundary movement of atmospheric pollutants. Transboundary movement of atmospheric pollutants has ramifications for human and environmental health, as well as economic impacts. As a result, it is the focus of many bilateral, regional, and international policy efforts. A central question with atmospheric pollutant transport is how to monitor pollutant movement and how to merge different monitoring datasets into useful information. Highly visible regional plumes of dust, smoke, and urban haze can be seen with satellite sensors, while ground-based monitoring of air pollutants such as fine particulates, SO2 , and toxics occurs at the local level. Integration of these two kinds of measurements allows the user to remotely observe large environmental effects in many areas of the world, while obtaining more detailed information from ground-based monitors. Hence, the combination of satellite-based sensor data and ground-based monitoring data promotes greater understanding of the movement of pollutants than either data set alone. Combined data sets are important for use by both scientists and international policy-makers. A standard methodology did not exist to guide and encourage integrated use of satellite images and ground-based data to monitor and understand major pollution events, such as air pollution. Thus, a small team was assembled to develop a methodology for the integration of satellite images and ground-based data. First, we conducted a literature and project review covering past and current integrated remote and ground-based data projects, a literature search of published work, and a search of data sets and technologies that could be used in a combined form. Second, based on this search and documentation, a general methodology was developed for using integrated spaceborne and ground-based data sets, intended as a guide for general scientists and policy-makers. Third, we found an existing project that was willing to be a pilot for testing the methodology: a U.S. EPANOAA project that was using aerial and ground-based sampling to learn more about the airborne sources of mercury deposition in the Florida Everglades. This document presents the results of the literature and project review, the complete methodology, and the outcome of the Florida Everglades pilot project.

Report↗