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USGS2018

Department of the Interior metadata implementation guide—Framework for developing the metadata component for data resource management

The Department of the Interior (DOI) is a Federal agency with over 90,000 employees across 10 bureaus and 8 agency offices. Its primary mission is to protect and manage the Nation’s natural resources and cultural heritage; provide scientific and other information about those resources; and honor its trust responsibilities or special commitments to American Indians, Alaska Natives, and affiliated island communities. Data and information are critical in day-to-day operational decision making and scientific research. DOI is committed to creating, documenting, managing, and sharing high-quality data and metadata in and across its various programs that support its mission. Documenting data through metadata is essential in realizing the value of data as an enterprise asset. The completeness, consistency, and timeliness of metadata affect users’ ability to search for and discover the most relevant data for the intended purpose; and facilitates the interoperability and usability of these data among DOI bureaus and offices. Fully documented metadata describe data usability, quality, accuracy, provenance, and meaning. Across DOI, there are different maturity levels and phases of information and metadata management implementations. The Department has organized a committee consisting of bureau-level points-of-contacts to collaborate on the development of more consistent, standardized, and more effective metadata management practices and guidance to support this shared mission and the information needs of the Department. DOI’s metadata implementation plans establish key roles and responsibilities associated with metadata management processes, procedures, and a series of actions defined in three major metadata implementation phases including: (1) Getting started—Planning Phase, (2) Implementing and Maintaining Operational Metadata Management Phase, and (3) the Next Steps towards Improving Metadata Management Phase. DOI’s phased approach for metadata management addresses some of the major data and metadata management challenges that exist across the diverse missions of the bureaus and offices. All employees who create, modify, or use data are involved with data and metadata management. Identifying, establishing, and formalizing the roles and responsibilities associated with metadata management are key to institutionalizing a framework of best practices, methodologies, processes, and common approaches throughout all levels of the organization; these are the foundation for effective data resource management. For executives and managers, metadata management strengthens their overarching views of data assets, holdings, and data interoperability; and clarifies how metadata management can help accelerate the compliance of multiple policy mandates. For employees, data stewards, and data professionals, formalized metadata management will help with the consistency of definitions, and approaches addressing data discoverability, data quality, and data lineage. In addition to data professionals and others associated with information technology; data stewards and program subject matter experts take on important metadata management roles and responsibilities as data flow through their respective business and science-related workflows. The responsibilities of establishing, practicing, and governing the actions associated with their specific metadata management roles are critical to successful metadata implementation.

Techniques and Methods↗
USGS2019

Alaska Geochemical Database Version 3.0 (AGDB3)—Including “Best Value” Data Compilations for Rock, Sediment, Soil, Mineral, and Concentrate Sample Media

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one “best value” determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from data-bases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these data-bases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical data-bases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

Alaska↗
USGS2008

Data-resolution matrix and model-resolution matrix for Rayleigh-wave inversion using a damped least-squares method

Inversion of multimode surface-wave data is of increasing interest in the near-surface geophysics community. For a given near-surface geophysical problem, it is essential to understand how well the data, calculated according to a layered-earth model, might match the observed data. A data-resolution matrix is a function of the data kernel (determined by a geophysical model and a priori information applied to the problem), not the data. A data-resolution matrix of high-frequency (>2 Hz) Rayleigh-wave phase velocities, therefore, offers a quantitative tool for designing field surveys and predicting the match between calculated and observed data. We employed a data-resolution matrix to select data that would be well predicted and we find that there are advantages of incorporating higher modes in inversion. The resulting discussion using the data-resolution matrix provides insight into the process of inverting Rayleigh-wave phase velocities with higher-mode data to estimate S-wave velocity structure. Discussion also suggested that each near-surface geophysical target can only be resolved using Rayleigh-wave phase velocities within specific frequency ranges, and higher-mode data are normally more accurately predicted than fundamental-mode data because of restrictions on the data kernel for the inversion system. We used synthetic and real-world examples to demonstrate that selected data with the data-resolution matrix can provide better inversion results and to explain with the data-resolution matrix why incorporating higher-mode data in inversion can provide better results. We also calculated model-resolution matrices in these examples to show the potential of increasing model resolution with selected surface-wave data. ?? Birkhaueser 2008.

Pure and Applied Geophysics↗
USGS2022

Ten practical questions to improve data quality

High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood. Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data. We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability. Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.

Rangelands↗
USGS2008

A trade-off between model resolution and variance with selected Rayleigh-wave data

Inversion of multimode surface-wave data is of increasing interest in the near-surface geophysics community. For a given near-surface geophysical problem, it is essential to understand how well the data, calculated according to a layered-earth model, might match the observed data. A data-resolution matrix is a function of the data kernel (determined by a geophysical model and a priori information applied to the problem), not the data. A data-resolution matrix of high-frequency (??? 2 Hz) Rayleigh-wave phase velocities, therefore, offers a quantitative tool for designing field surveys and predicting the match between calculated and observed data. First, we employed a data-resolution matrix to select data that would be well predicted and to explain advantages of incorporating higher modes in inversion. The resulting discussion using the data-resolution matrix provides insight into the process of inverting Rayleigh-wave phase velocities with higher mode data to estimate S-wave velocity structure. Discussion also suggested that each near-surface geophysical target can only be resolved using Rayleigh-wave phase velocities within specific frequency ranges, and higher mode data are normally more accurately predicted than fundamental mode data because of restrictions on the data kernel for the inversion system. Second, we obtained an optimal damping vector in a vicinity of an inverted model by the singular value decomposition of a trade-off function of model resolution and variance. In the end of the paper, we used a real-world example to demonstrate that selected data with the data-resolution matrix can provide better inversion results and to explain with the data-resolution matrix why incorporating higher mode data in inversion can provide better results. We also calculated model-resolution matrices of these examples to show the potential of increasing model resolution with selected surface-wave data. With the optimal damping vector, we can improve and assess an inverted model obtained by a damped least-square method.

Conference Paper↗
USGS2013

Geodatabase compilation of hydrogeologic, remote sensing, and water-budget-component data for the High Plains aquifer, 2011

The High Plains aquifer underlies almost 112 million acres in the central United States. It is one of the largest aquifers in the Nation in terms of annual groundwater withdrawals and provides drinking water for 2.3 million people. The High Plains aquifer has gained national and international attention as a highly stressed groundwater supply primarily because it has been appreciably depleted in some areas. The U.S. Geological Survey has an active program to monitor the changes in groundwater levels for the High Plains aquifer and has documented substantial water-level changes since predevelopment: the High Plains Groundwater Availability Study is part of a series of regional groundwater availability studies conducted to evaluate the availability and sustainability of major aquifers across the Nation. The goals of the regional groundwater studies are to quantify current groundwater resources in an aquifer system, evaluate how these resources have changed over time, and provide tools to better understand a systems response to future demands and environmental stresses. The purpose of this report is to present selected data developed and synthesized for the High Plains aquifer as part of the High Plains Groundwater Availability Study. The High Plains Groundwater Availability Study includes the development of a water-budget-component analysis for the High Plains completed in 2011 and development of a groundwater-flow model for the northern High Plains aquifer. Both of these tasks require large amounts of data about the High Plains aquifer. Data pertaining to the High Plains aquifer were collected, synthesized, and then organized into digital data containers called geodatabases. There are 8 geodatabases, 1 file geodatabase and 7 personal geodatabases, that have been grouped in three categories: hydrogeologic data, remote sensing data, and water-budget-component data. The hydrogeologic data pertaining to the northern High Plains aquifer is included in three separate geodatabases: (1) base data from a groundwater-flow model; (2) hydrogeology and hydraulic properties data; and (3) groundwater-flow model data to be used as calibration targets. The remote sensing data for this study were developed by the U. S. Geological Survey Earth Resources Observation and Science Center and include historical and predicted land-use/land-cover data and actual evapotranspiration data by using remotely sensed temperature data. The water-budget-component data contains selected raster data from maps in the “Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 Through 1949 and 2000 Through 2009” report completed in 2011 ( http://pubs.usgs.gov/sir/2011/5183/ ). Federal Geographic Data Committee compliant metadata were created for each spatial and tabular data layer in the geodatabases.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗
USGS2002

Water resources data, Iowa, water year 2001, Volume 2. surface water--Missouri River basin, and ground water

The Water Resources Division of the U.S. Geological Survey, in cooperation with State, county, municipal, and other Federal agencies, obtains a large amount of data pertaining to the water resources of Iowa each water year. These data, accumulated during many water years, constitute a valuable data base for developing an improved understanding of the water resources of the State. To make this data readily available to interested parties outside of the Geological Survey, the data is published annually in this report series entitled “Water Resources Data - Iowa” as part of the National Water Data System. Water resources data for water year 2001 for Iowa consists of records of stage, discharge, and water quality of streams; stage and contents of lakes and reservoirs; and water levels and water quality of ground water. This report, in two volumes, contains stage or discharge records for 132 gaging stations; stage records for 9 lakes and reservoirs; water-quality records for 4 gaging stations; sediment records for 13 gaging stations; and water levels for 163 ground-water observation wells. Also included are peak-flow data for 92 crest-stage partial-record stations, water-quality data from 86 municipal wells, and precipitation data collected at 6 gaging stations and 2 precipitation sites. Additional water data were collected at various sites not included in the systematic data-collection program, and are published here as miscellaneous measurements and analyses. These data represent that part of the National Water Data System operated by the U.S. Geological Survey and cooperating local, State, and Federal agencies in Iowa. Records of discharge or stage of streams, and contents or stage of lakes and reservoirs were first published in a series of U.S. Geological Survey water-supply papers entitled “Surface Water Supply of the United States.” Through September 30, 1960, these water-supply papers were published in an annual series; during 1961-65 and 1966-70, they were published in 5- year series. Records of chemical quality, water temperatures, and suspended sediment were published from 1941 to 1970 in an annual series of water-supply papers entitled “Quality of Surface Waters of the United States.” Records of ground-water levels were published from 1935 to 1974 in a series of water-supply papers entitled “Ground-Water Levels in the United States.” Water-supply papers may be consulted in the libraries of the principal cities in the United States, or they may be purchased from Books and Open-File Reports Section, Federal Center, Box 25425, Denver, Colorado 80225. For water years 1961 through 1970, streamflow data were released by the Geological Survey in annual reports on a State-boundary basis. Water-quality records for water years 1964 through 1970 were similarly released either in separate reports or in conjunction with streamflow records. Beginning with the 1971 water year, water data for streamflow, water quality, and ground water is published in official U.S. Geological Survey reports on a State-boundary basis. These official reports carry an identification number consisting of the two-letter State postal abbreviation, the last two digits of the water year, and the volume number. For example, this report is identified as “U.S. Geological Survey Water-Data Report IA-01-1.” These water-data reports are for sale by the National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161.

Iowa↗
USGS2016

A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO 2 Atlas (SOCAT) is a synthesis of quality-controlled f CO 2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million f CO 2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million f CO 2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water f CO 2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water f CO 2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014).

Earth System Science Data↗
USGS2010

Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals

ASTER reflectance spectra from Cuprite, Nevada, and Mountain Pass, California, were compared to spectra of field samples and to ASTER-resampled AVIRIS reflectance data to determine spectral accuracy and spectroscopic mapping potential of two new ASTER SWIR reflectance datasets: RefL1b and AST_07XT. RefL1b is a new reflectance dataset produced for this study using ASTER Level 1B data, crosstalk correction, radiance correction factors, and concurrently acquired level 2 MODIS water vapor data. The AST_07XT data product, available from EDC and ERSDAC, incorporates crosstalk correction and non-concurrently acquired MODIS water vapor data for atmospheric correction. Spectral accuracy was determined using difference values which were compiled from ASTER band 5/6 and 9/8 ratios of AST_07XT or RefL1b data subtracted from similar ratios calculated for field sample and AVIRIS reflectance data. In addition, Spectral Analyst, a statistical program that utilizes a Spectral Feature Fitting algorithm, was used to quantitatively assess spectral accuracy of AST_07XT and RefL1b data.Spectral Analyst matched more minerals correctly and had higher scores for the RefL1b data than for AST_07XT data. The radiance correction factors used in the RefL1b data corrected a low band 5 reflectance anomaly observed in the AST_07XT and AST_07 data but also produced anomalously high band 5 reflectance in RefL1b spectra with strong band 5 absorption for minerals, such as alunite. Thus, the band 5 anomaly seen in the RefL1b data cannot be corrected using additional gain adjustments. In addition, the use of concurrent MODIS water vapor data in the atmospheric correction of the RefL1b data produced datasets that had lower band 9 reflectance anomalies than the AST_07XT data. Although assessment of spectral data suggests that RefL1b data are more consistent and spectrally more correct than AST_07XT data, the Spectral Analyst results indicate that spectral discrimination between some minerals, such as alunite and kaolinite, are still not possible unless additional spectral calibration using site specific spectral data are performed. ?? 2010.

Remote Sensing of Environment↗
USGS2011

Data sharing by scientists: Practices and perceptions

Background Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.

PLoS ONE↗
USGS2024

U.S. Geological Survey data strategy 2023–33

The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publications, and data release, ultimately contributes to the transparency and availability of science. Foundational Government directives and laws, such as the Foundations for Evidence-Based Policymaking Act of 2018 (Public Law 115–435, 132 Stat. 5529) as well as Executive Order 13642, “Making Open and Machine Readable the New Default for Government Information,” provide a framework for addressing strategic data management. The USGS Data Strategy builds on that framework by outlining high-level goals and objectives that serve as a long-term, decadal guide toward achieving a broad, data-focused vision. Benefits of the USGS Data Strategy are many. The USGS will contribute to open science by increasing efficiencies in the consistent management of valuable data assets; driving innovation that results in modernized capabilities to ensure data are analysis ready; increasing data skills across the Bureau to enhance workforce data literacy; broadening capacity to understand and address needs of stakeholders; and measuring progress in producing findable, accessible, interoperable, and reusable (FAIR) data products. The major goals and objectives of the USGS Data Strategy promote maximizing the utility of USGS data based on stakeholder needs, promoting data innovation, coordinating common data practices, modernizing our USGS enterprise data architecture, and enhancing our data-centric culture. The goals and objectives in the strategy align with other Bureau strategic plans, guidance, and directives from the Department of the Interior and the Federal Government. This strategy is a key component to strengthen the Bureau’s data ecosystem to ensure a relevant, long-term capacity that supports internal needs and achieves its scientific mission in the most efficient and effective manner.

Circular↗
USGS1994

Topographic data requirements for EOS global change research

This document is a result of Earth Observing System Data and Information System (EOSDIS) Version 0 activities of the Land Processes Distributed Active Archive Center at the U.S. Geological Survey's EROS Data Center. A relatively small part of the Version 0 funding provided by NASA is used to address topographic data issues related to EOS. These issues include identifying and improving access to existing sources of topographic data, data generation, facilitating the use of topographic data in global change research by demonstrating derivative products, and inventorying the specific topographic data requirements of EOS investigators. There is a clear need for global topographic data in EOSDIS. Only 10 percent of the global land surface is covered by high-resolution data that are available to the global change science community. Alternative sources for new data exist or have been proposed; however, none of them alone can fulfill the data requirements by the launch of the first EOS platform in 4 years. There is no operational provider of all the source data that are required. Even if appropriate global source data existed, a concerted production effort would be necessary to ensure the existence of the requisite topographic data before EOS launch. Additionally, no funding from NASA or any other agency has been appropriated for a new mapping mission or for other means of data acquisition. This effort to document requirements is an initial step toward understanding the severity of the data shortage. It is well beyond the scope of Version 0 funding and capabilities to provide the required data in the proper timeframe. The shortage of data and the lack of a plan for providing the necessary topographic data through EOSDIS in time for launch are issues that must be addressed by the EOS program.

Open-File Report↗
USGS1996

Water-quality assessment of the Connecticut, Housatonic, and Thames River Basins study unit: Analysis of available data on nutrients, suspended sediments, and pesticides, 1972-92

This retrospective report examines available nutrient, suspended sediment, and pesticide data in surface and ground water in the Connecticut, Housatonic and Thames Rivers Study Unit of the National Water-Quality Assessment Program. The purpose of this study is to improve the understanding of natural and anthropogenic factors affecting water quality in the study unit. Waterquality data were acquired from various sources, primarily, the U.S. Geological Survey and the U.S. Environmental Protection Agency. The report examines data for water years 1972-92, focusing on 1980-92, although it also includes additional data from as early as 1905. The study unit lies within the New England Physiographic Province and altitudes range from sea level in coastal Connecticut to 6,288 feet above sea level at Mount Washington, New Hampshire. Two major aquifer types underlie the study unit unconsolidated glacial deposits and fractured bedrock. The climate generally is temperate and humid, with four distinct seasons. Average annual precipitation ranges from 34 to 65 inches. The study unit has a population of about 4.5 million, which is most highly concentrated in southwestern Connecticut and along the south-central region of the Connecticut River Valley. Surface-water-quality data were screened to provide information about sites with adequate numbers of analyses (50) over sufficiently long periods (1980-90) to enable valid statistical analyses. In order to compare effects of different types of land use on surface-water quality, examination of data required application of several statistical and graphical techniques, including mapping, histograms, boxplots, concentration-discharge plots, trend analysis, and load estimation. Spatial and temporal analysis of surface-water-quality data indicated that, with a single exception, only stations in the Connecticut water-quality network had sufficient data collected over adequately long time periods to use in detailed analyses. Ground-water nutrient and pesticide data were compiled from several Federal and State agencies, primarily the U.S. Geological Survey, U.S. Environmental Protection Agency, and Connecticut Department of Health Services. Nutrient data were available for several thousand wells; nitrite plus nitrate as nitrogen was the most commonly reported constituent. Most wells with nutrient data are in Massachusetts and Connecticut. Relative to nutrient data in ground and surface water, pesticide data are less common. Pesticide data were available for slightly more than 200 surface-water sites and less than 500 wells; about 95 percent of the wells are completed in stratified-drift or till aquifers. Data for 81 pesticide compounds were available in various data bases. 2,4-D and silvex were the most commonly detected herbicides in surface water and dieldrin and diazinon were the most commonly detected insecticides. Most surface-water pesticide samples and detections are from bed sediment, but much of the data are not recent. Ethylene dibromide (EDB), a soil fumigant used in tobacco farming was detected in 268 wells in a 50 square-mile area of north-central Connecticut; EDB contamination also was detected in wells in Massachusetts. Atrazine, an herbicide commonly used in corn farming, commonly was detected in wells installed in tilled agricultural fields. Corn herbicides were commonly detected in the northern part of the study unit, although the sampling has been less frequent than in the southern part of the study unit. Pesticides were seldom detected in public-supply wells in Connecticut. Urban sites with relatively high population densities and high concentrations of dischargers were characterized by having the highest nutrient concentrations and loads when adjusted for differences in drainage area or population. Particularly, the Pequabuck, Naugatuck, and Quinnipiac River Basins were characterized by high nutrient concentrations median total nitrogen concentrations ranged from 3.3 to 4.2 mg/L (milligrams per liter) and median total phosphorus concentrations ranged from 0.42 to 0.8 mg/L. In contrast, the predominantly forested and low density residential land-use sites, such as Saugatuck and Salmon River Basins, were characterized by low nutrient concentrations median total nitrogen ranged from 0.50 to 0.60 mg/L and median total phosphorus concentrations ranged from 0.01 to 0.02 mg/L. Estimated total nitrogen loadings in median discharge years ranged from 940 kilograms per square mile at the Salmon River near East Hampton, Conn., to 5,800 kilograms per square mile at the Naugatuck River at Beacon Falls, Conn. Water quality, in terms of nutrient concentrations and areally adjusted loadings, for sites with large drainage basins integrating a wide variety of land-use categories fell between the extremes of the urban and forested sites total nitrogen was 1,400 kilograms per square mile per year at the Connecticut River at Thompsonville, Conn. Nitrate concentrations in ground water occasionally exceeded the safe drinking-water standard of 10 mg/L as nitrogen. The greatest number of detections exceeding the standard, however, were not in public-water supplies but in shallow observation wells in agricultural settings (the most frequently sampled type of well). None of the public-supply wells in Massachusetts exceeded the standard. Although nitrate concentrations for Vermont and New Hampshire generally were low, few data were available and those were seldom reported on the basis of drainage basin, making analysis difficult. Trend analysis indicated that flow-adjusted concentrations of total and dissolved phosphorus generally decreased during the period of analysis, however, total nitrogen did not change substantially. Decreases in ammonia concentrations with time were usually accompanied by increases in nitrate, suggesting improvements in sewage treatment. The lack of adequate data from more or less exclusively agricultural areas points to the need for further study of the effects of fanning on surface-water quality in the study unit. Furthermore, additional information is needed on the rates, transformations, and movements of nutrients and other materials through and between the aquatic and terrestrial components of the study unit.

Connecticut, Massachusetts, New Hampshire, Quebec,↗
USGS2015

Validation of geometric accuracy of Global Land Survey (GLS) 2000 data

The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Photogrammetric Engineering and Remote Sensing↗
USGS2002

Selected micrometeorological and soil-moisture data at Amargosa Desert Research Site in Nye County near Beatty, Nevada, 1998-2000

Selected micrometeorological and soil-moisture data were collected at the Amargosa Desert Research Site adjacent to a low-level radioactive waste and hazardous chemical waste facility near Beatty, Nev., 1998-2000. Data were collected in support of ongoing research studies to improve the understanding of hydrologic and contaminant-transport processes in arid environments. Micrometeorological data include precipitation, air temperature, solar radiation, net radiation, relative humidity, ambient vapor pressure, wind speed and direction, barometric pressure, soil temperature, and soil-heat flux. All micrometeorological data were collected using a 10-second sampling interval by data loggers that output daily mean, maximum, and minimum values, and hourly mean values. For precipitation, data output consisted of daily, hourly, and 5-minute totals. Soil-moisture data included periodic measurements of soil-water content at nine neutron-probe access tubes with measurable depths ranging from 5.25 to 29.75 meters. The computer data files included in this report contain the complete micrometeorological and soil-moisture data sets. The computer data consists of seven files with about 14 megabytes of information. The seven files are in tabular format: (1) one file lists daily mean, maximum, and minimum micrometeorological data and daily total precipitation; (2) three files list hourly mean micrometeorological data and hourly precipitation for each year (1998-2000); (3) one file lists 5-minute precipitation data; (4) one file lists mean soil-water content by date and depth at four experimental sites; and (5) one file lists soil-water content by date and depth for each neutron-probe access tube. This report highlights selected data contained in the computer data files using figures, tables, and brief discussions. Instrumentation used for data collection also is described. Water-content profiles are shown to demonstrate variability of water content with depth. Time-series data are plotted to illustrate temporal variations in micrometeorological and soil-water content data. Substantial precipitation at the end of an El Ni?o cycle in early 1998 resulted in measurable water penetration to a depth of 1.25 meters at one of the four experimental soil-monitoring sites.

Nevada↗
USGS2007

Analysis of flood-magnitude and flood-frequency data for streamflow-gaging stations in the Delaware and North Branch Susquehanna River Basins in Pennsylvania

The Delaware and North Branch Susquehanna River Basins in Pennsylvania experienced severe flooding as a result of intense rainfall during June 2006. The height of the flood waters on the rivers and tributaries approached or exceeded the peak of record at many locations. Updated flood-magnitude and flood-frequency data for streamflow-gaging stations on tributaries in the Delaware and North Branch Susquehanna River Basins were analyzed using data through the 2006 water year to determine if there were any major differences in the flood-discharge data. Flood frequencies for return intervals of 2, 5, 10, 50, 100, and 500 years (Q2, Q5, Q10, Q50, Q100, and Q500) were determined from annual maximum series (AMS) data from continuous-record gaging stations (stations) and were compared to flood discharges obtained from previously published Flood Insurance Studies (FIS) and to flood frequencies using partial-duration series (PDS) data. A Wilcoxon signed-rank test was performed to determine any statistically significant differences between flood frequencies computed from updated AMS station data and those obtained from FIS. Percentage differences between flood frequencies computed from updated AMS station data and those obtained from FIS also were determined for the 10, 50, 100, and 500 return intervals. A Mann-Kendall trend test was performed to determine statistically significant trends in the updated AMS peak-flow data for the period of record at the 41 stations. In addition to AMS station data, PDS data were used to determine flood-frequency discharges. The AMS and PDS flood-frequency data were compared to determine any differences between the two data sets. An analysis also was performed on AMS-derived flood frequencies for four stations to evaluate the possible effects of flood-control reservoirs on peak flows. Additionally, flood frequencies for three stations were evaluated to determine possible effects of urbanization on peak flows. The results of the Wilcoxon signed-rank test showed a significant difference at the 95-percent confidence level between the Q100 computed from AMS station data and the Q100 determined from previously published FIS for 97 sites. The flood-frequency discharges computed from AMS station data were consistently larger than the flood discharges from the FIS; mean percentage difference between the two data sets ranged from 14 percent for the Q100 to 20 percent for the Q50. The results of the Mann-Kendall test showed that 8 stations exhibited a positive trend (i.e., increasing annual maximum peaks over time) over their respective periods of record at the 95-percent confidence level, and an additional 7 stations indicated a positive trend, for a total of 15 stations, at a confidence level of greater than or equal to 90 percent. The Q2, Q5, Q10, Q50, and Q100 determined from AMS and PDS data for each station were compared by percentage. The flood magnitudes for the 2-year return period were 16 percent higher when partial-duration peaks were incorporated into the analyses, as opposed to using only the annual maximum peaks. The discharges then tended to converge around the 5-year return period, with a mean collective difference of only 1 percent. At the 10-, 50-, and 100-year return periods, the flood magnitudes based on annual maximum peaks were, on average, 6 percent higher compared to corresponding flood magnitudes based on partial-duration peaks. Possible effects on flood peaks from flood-control reservoirs and urban development within the basin also were examined. Annual maximum peak-flow data from four stations were divided into pre- and post-regulation periods. Comparisons were made between the Q100 determined from AMS station data for the periods of record pre- and post-regulation. Two stations showed a nearly 60- and 20-percent reduction in the 100-year discharges; the other two stations showed negligible differences in discharges. Three stations within urban basins were compared to 38 stations

Pennsylvania↗
USGS1995

Digital aeromagnetic anomaly data from eastern-most Guyana

The Center for Inter-American Mineral Resource Investigations (CIMRI) supported distribution and analysis of geoscientific and mineral resource related information concerning Latin America. CIMRI staff digitized aeromagnetic data for eastern-most Guyana as part of a preliminary regional assessment of minerals in the Guyana Shield, South America. The data were digitized from 145 aeromagnetic contour maps at a scale of 1:50,000 and merged into a single digital data set. The data were used to examine the Precambrian shield, greenstone belts, and other tectonic boundaries as well as explore ideas concerning mineral deposits within the area. A subset of these digital data were presented to the Guyanan government during early 1995 (Pierce, 1994). This Open-File report, consisting of this text and seven (7) 3.5" IBM-PC compatible ASCII magnetic disks, makes the digital data available to the public. Information regarding the source of data and subsequent processing is included below. The data were collected in Guyana by two contractors at different times. The first data were collected from 1962 to 1963. These data are several aeromagnetic surveys covering parts of 12 quadrangles funded by the United Nations and flown by Aero Service Corporation. The second and more extensive data set was collected from 1971 to 1972 by the Canadian International Development Agency flown by Terra Surveys Ltd. under a contract with the Geological Survey of Guyana. The Guyana Government published the data as contour maps that are available in Georgetown through the Guyana Government. Coverage extends from about 2°45*N to 8°30*N latitude and from 60°0'W to 57°0'W longitude (see Figure 1.). The contour maps were digitized at points where the magnetic contours intersect the flight lines. The data files include XYZ ASCII files, XYZ binary files, ASCII grids, and binary "standard USGS" grids. There are four grids consisting of the following data types: unprotected raw data grid unprotected residual or International Geomagnetic Reference Field (IGRF) removed grid UTM projected residual (IGRF removed) grid UTM projected residual with a second order surface removed These data files were transferred to 3.5" 1.44 megabyte floppy disks readable on IBM-compatible personal computers. These data are also available from the Department of Commerce National Geophysical Data Center.

Open-File Report↗
USGS2012

Estimation of evaporation from open water - A review of selected studies, summary of U.S. Army Corps of Engineers data collection and methods, and evaluation of two methods for estimation of evaporation from five reservoirs in Texas

Organizations responsible for the management of water resources, such as the U.S. Army Corps of Engineers (USACE), are tasked with estimation of evaporation for water-budgeting and planning purposes. The USACE has historically used Class A pan evaporation data (pan data) to estimate evaporation from reservoirs but many USACE Districts have been experimenting with other techniques for an alternative to collecting pan data. The energy-budget method generally is considered the preferred method for accurate estimation of open-water evaporation from lakes and reservoirs. Complex equations to estimate evaporation, such as the Penman, DeBruin-Keijman, and Priestley-Taylor, perform well when compared with energy-budget method estimates when all of the important energy terms are included in the equations and ideal data are collected. However, sometimes nonideal data are collected and energy terms, such as the change in the amount of stored energy and advected energy, are not included in the equations. When this is done, the corresponding errors in evaporation estimates are not quantifiable. Much simpler methods, such as the Hamon method and a method developed by the U.S. Weather Bureau (USWB) (renamed the National Weather Service in 1970), have been shown to provide reasonable estimates of evaporation when compared to energy-budget method estimates. Data requirements for the Hamon and USWB methods are minimal and sometimes perform well with remotely collected data. The Hamon method requires average daily air temperature, and the USWB method requires daily averages of air temperature, relative humidity, wind speed, and solar radiation. Estimates of annual lake evaporation from pan data are frequently within 20 percent of energy-budget method estimates. Results of evaporation estimates from the Hamon method and the USWB method were compared against historical pan data at five selected reservoirs in Texas (Benbrook Lake, Canyon Lake, Granger Lake, Hords Creek Lake, and Sam Rayburn Lake) to evaluate their performance and to develop coefficients to minimize bias for the purpose of estimating reservoir evaporation with accuracies similar to estimates of evaporation obtained from pan data. The modified Hamon method estimates of reservoir evaporation were similar to estimates of reservoir evaporation from pan data for daily, monthly, and annual time periods. The modified Hamon method estimates of annual reservoir evaporation were always within 20 percent of annual reservoir evaporation from pan data. Unmodified and modified USWB method estimates of annual reservoir evaporation were within 20 percent of annual reservoir evaporation from pan data for about 91 percent of the years compared. Average daily differences between modified USWB method estimates and estimates from pan data as a percentage of the average amount of daily evaporation from pan data were within 20 percent for 98 percent of the months. Without any modification to the USWB method, average daily differences as a percentage of the average amount of daily evaporation from pan data were within 20 percent for 73 percent of the months. Use of the unmodified USWB method is appealing because it means estimates of average daily reservoir evaporation can be made from air temperature, relative humidity, wind speed, and solar radiation data collected from remote weather stations without the need to develop site-specific coefficients from historical pan data. Site-specific coefficients would need to be developed for the modified version of the Hamon method.

Texas↗