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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 685 records · Page 38Linked to original sources
USGS1998

Analysis of ground-water-quality data of the Upper Colorado River basin, water years 1972-92

As part of the U.S. Geological Survey's National Water-Quality Assessment program, an analysis of the existing ground-water-quality data in the Upper Colorado River Basin study unit is necessary to provide information on the historic water-quality conditions. Analysis of the historical data provides information on the availability or lack of data and water-quality issues. The information gathered from the historical data will be used in the design of ground-water-quality studies in the basin. This report includes an analysis of the ground-water data (well and spring data) available for the Upper Colorado River Basin study unit from water years 1972 to 1992 for major cations and anions, metals and selected trace elements, and nutrients. The data used in the analysis of the ground-water quality in the Upper Colorado River Basin study unit were predominantly from the U.S. Geological Survey National Water Information System and the Colorado Department of Public Health and Environment data bases. A total of 212 sites representing alluvial aquifers and 187 sites representing bedrock aquifers were used in the analysis. The available data were not ideal for conducting a comprehensive basinwide water-quality assessment because of lack of sufficient geographical coverage. Evaluation of the ground-water data in the Upper Colorado River Basin study unit was based on the regional environmental setting, which describes the natural and human factors that can affect the water quality. In this report, the ground-water-quality information is evaluated on the basis of aquifers or potential aquifers (alluvial, Green River Formation, Mesaverde Group, Mancos Shale, Dakota Sandstone, Morrison Formation, Entrada Sandstone, Leadville Limestone, and Precambrian) and land-use classifications for alluvial aquifers. Most of the ground-water-quality data in the study unit were for major cations and anions and dissolved-solids concentrations. The aquifer with the highest median concentrations of major ions was the Mancos Shale. The U.S. Environmental Protection Agency secondary maximum contaminant level of 500 milligrams per liter for dissolved solids in drinking water was exceeded in about 75 percent of the samples from the Mancos Shale aquifer. The guideline by the Food and Agriculture Organization of the United States for irrigation water of 2,000 milligrams per liter was also exceeded by the median concentration from the Mancos Shale aquifer. For sulfate, the U.S. Environmental Protection Agency proposed maximum contaminant level of 500 milligrams per liter for drinking water was exceeded by the median concentration for the Mancos Shale aquifer. A total of 66 percent of the sites in the Mancos Shale aquifer exceeded the proposed maximum contaminant level. Metal and selected trace-element data were available for some sites, but most of these data also were below the detection limit. The median concentrations for iron for the selected aquifers and land-use classifications were below the U.S. Environmental Protection Agency secondary maximum contaminant level of 300 micrograms per liter in drinking water. Median concentration of manganese for the Mancos Shale exceeded the U.S. Environmental Protection Agency secondary maximum contaminant level of 50 micrograms per liter in drinking water. The highest selenium concentrations were in the alluvial aquifer and were associated with rangeland. However, about 22 percent of the selenium values from the Mancos Shale exceeded the U.S. Environmental Protection Agency maximum contaminant level of 50 micrograms per liter in drinking water. Few nutrient data were available for the study unit. The only nutrient species presented in this report were nitrate-plus-nitrite as nitrogen and orthophosphate. Median concentrations for nitrate-plus-nitrite as nitrogen were below the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter in drinking water except for 0.02 percent of the sites in the alluvial aquifer and 0.03 percent of the sites in the Mancos Shale. Concentrations of orthophosphate did not vary significantly among aquifers or land-use classifications. Historic water-quality data from wells and springs helped to characterize the regional distribution of ground-water quality information in the Upper Colorado River Basin study unit. The historical ground-water data summarized in this report will be used in the design of a ground-water-quality network. Because ground-water-quality issues in the study unit are related to high dissolved solids, sulfate, selenium, and nutrients, this report discusses some of the important findings related to these issues.

Colorado↗
USGS2022

On the documentation, independence, and stability of widely used seismological data products

Earthquake scientists have traditionally relied on relatively small data sets recorded on small numbers of instruments. With advances in both instrumentation and computational resources, the big-data era, including an established norm of open data-sharing, allows seismologists to explore important issues using data volumes that would have been unimaginable in earlier decades. Alongside with these developments, the community has moved towards routine production of interpreted data products such as seismic moment tensor catalogs that have provided an additional boon to earthquake science. As these products have become increasingly familiar and useful, it is important to bear in mind that they are not data, but rather interpreted data products. As such, they differ from data in ways that can be important, but not always appreciated. Important - and sometimes surprising - issues can arise if methodology is not fully described, data from multiple sources are included, or data products are not versioned (time-stamped). The line between data and data products is sometimes blurred, leading to an underappreciation of issues that affect data products. This note illustrates examples from two widely used data products: moment tensor catalogs and Did You Feel It? (DYFI) macroseismic intensity values. These examples show that increasing a data product’s documentation, independence, and stability can make it even more useful. To ensure the reproducibility of studies using data products, time-stamped products should be preserved, for example as electronic supplements to published papers, or, ideally, a more permanent repository.

Frontiers in Earth Science↗
USGS2019

Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a >50% probability of obtaining weekly clear observations.

Minnesota↗
USGS1980

Considerations for blending data from various sensors

A project is being proposed at the EROS Data Center to blend the information from sensors aboard various satellites. The problems of, and considerations for, blending data from several satellite-borne sensors are discussed. System descriptions of the sensors aboard the HCMM, TIROS-N, GOES-D, Landsat 3, Landsat D, Seasat, SPOT, Stereosat, and NOSS satellites, and the quantity, quality, image dimensions, and availability of these data are summaries to define attributes of a multi-sensor satellite data base. Unique configurations of equipment, storage, media, and specialized hardware to meet the data system requirement are described as well as archival media and improved sensors that will be on-line within the next 5 years. Definitions and rigor required for blending various sensor data are given. Problems of merging data from the same sensor (intrasensor comparison) and from different sensors (intersensor comparison), the characteristics and advantages of cross-calibration of data, and integration of data into a product matrix field are addressed. Data processing considerations as affected by formation, resolution, and problems of merging large data sets, and organization of data bases for blending data are presented. Examples utilizing GOES and Landsat data are presented to demonstrate techniques of data blending, and recommendations for future implementation of a set of standard scenes and their characteristics necessary for optimal data blending are discussed.

Sixth Annual Pecora Symposium and Exposition↗
USGS2013

Alaska Geochemical Database, Version 2.0 (AGDB2)–Including “best value” data compilations for rock, sediment, soil, mineral, and concentrate sample mediaI

The Alaska Geochemical Database Version 2.0 (AGDB2) 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 (AGDB, http://pubs.usgs.gov/ds/637/) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future 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 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey personnel and analyzed in U.S. Geological Survey laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various U.S. Geological Survey programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the U.S. Geological Survey Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the U.S. Geological Survey PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB 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. This arduous process of reviewing, verifying and, where necessary, editing all U.S. Geological Survey geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest U.S. Geological Survey geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.

Alaska↗
USGS1997

Water resources data, New York, water year 1996; Volume 1. Eastern New York; excluding Long Island

Introduction Water-resources data for the 1996 water year for New York consist of records of stage, discharge, and water quality of streams; stage, contents, and water quality of lakes and reservoirs; ground-water levels; and precipitation quality. This volume contains records for water discharge at 122 gaging stations; stage only at 7 gaging stations; stage and contents at 4 gaging stations, and 18 other lakes and reservoirs; water quality at 28 gaging stations and 1 precipitation-quality station; and water levels at 3 observation wells. Also included are data for 33 crest-stage partial-record stations. Additional water data were collected at various sites not involved in the systematic data-collection program, and are published as miscellaneous measurements and analyses in this volume. These data together with the data in Volumes 2 and 3 represent that part of the National Water Data System operated by the U.S. Geological Survey in cooperation with State, Municipal, and Federal agencies in New York. Records of discharge and stage of streams, and contents and 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 in an annual series and then in a 5-year series for 1961-65 and 1966-70. Records of water 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 and universities in the United States or may be purchased from the U.S. Geological Survey, Branch of Distribution, 604 South Pickett Street, Alexandria, VA 22304. Since the 1961 water year, streamflow data and since the 1964 water year, water-quality data have been released by the Geological Survey in annual reports on a State-boundary basis. These reports provided rapid release of water data in each state shortly after the end of the water year. Through 1970 the data were also released in the water-supply paper series mentioned above. Streamflow and water-quality data beginning with the 1971 water year, and ground-water data beginning with the 1975 water year are published only in reports on a State-boundary basis. Beginning with the 1975 water year, these Survey reports carry an identification number consisting of the two-letter State abbreviation, the last two digits of the water year, and the volume number. For example, this volume is identified as “U.S. Geological Survey Water-Data Report NY-96-1.” Water-data reports are for sale in paper copy or in microfiche by the National Technical Information Service, U.S. Department of Commerce, Springfield, VA 22161. Additional information, including current prices for ordering specific reports, may be obtained from the District Office at the address given on the back of the title page or by telephone (518) 285-5600.

New York↗
USGS2020

Estimating the drivers of species distributions with opportunistic data using mediation analysis

Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.

eastern United States↗
USGS2021

Spatial data reduction through element -of-interest (EOI) extraction

Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.

Book chapter↗
USGS2025

Data standardization and management to facilitate large-scale and interdisciplinary approaches access

Bringing data related to recreational fishers and fisheries together across large scales can provide tremendous insight. Methods for collecting, analysing, and storing data can vary dramatically, which can have significant implications for the use of these data. Efforts to standardise data within organisations often increase the ability to compare datasets from different areas, monitor changes over time, and increase the utility of the data for management and research. Though employing standardised methodology and data architecture results in the most straightforward and robust opportunities for data integration, doing so may not be possible due to variation in data collection objectives, continuity with historical programs, and resource limitations. Additionally, managing data according to FAIR principles (findability, accessibility, interoperability, and reusability) helps support data sharing and large-scale research efforts. Key elements of integrable data include appropriate data structures, adequate documentation of methodology, interpretable and complete metadata, and accessible storage formats. We document the potential benefits of standardising data and offer example approaches. We also explore best practices regarding the formatting, storage, and transmission of recreational fisher data. Efforts to increase the level of standardisation and integrability of recreational fisher data can create opportunities to better understand fisher behaviour, needs, and fulfilment.

Book chapter↗
USGS2023

Sharing FAIR monitoring program data improves discoverability and reuse

Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.

Enviornmental Monitoring and Assessment↗
USGS2014

Organization of marine phenology data in support of planning and conservation in ocean and coastal ecosystems

Among the many effects of climate change is its influence on the phenology of biota. In marine and coastal ecosystems, phenological shifts have been documented for multiple life forms; however, biological data related to marine species' phenology remain difficult to access and is under-used. We conducted an assessment of potential sources of biological data for marine species and their availability for use in phenological analyses and assessments. Our evaluations showed that data potentially related to understanding marine species' phenology are available through online resources of governmental, academic, and non-governmental organizations, but appropriate datasets are often difficult to discover and access, presenting opportunities for scientific infrastructure improvement. The developing Federal Marine Data Architecture when fully implemented will improve data flow and standardization for marine data within major federal repositories and provide an archival repository for collaborating academic and public data contributors. Another opportunity, largely untapped, is the engagement of citizen scientists in standardized collection of marine phenology data and contribution of these data to established data flows. Use of metadata with marine phenology related keywords could improve discovery and access to appropriate datasets. When data originators choose to self-publish, publication of research datasets with a digital object identifier, linked to metadata, will also improve subsequent discovery and access. Phenological changes in the marine environment will affect human economics, food systems, and recreation. No one source of data will be sufficient to understand these changes. The collective attention of marine data collectors is needed—whether with an agency, an educational institution, or a citizen scientist group—toward adopting the data management processes and standards needed to ensure availability of sufficient and useable marine data to understand marine phenology.

Ecological Informatics↗
USGS2017

Challenges with secondary use of multi-source water-quality data in the United States

Combining water-quality data from multiple sources can help counterbalance diminishing resources for stream monitoring in the United States and lead to important regional and national insights that would not otherwise be possible. Individual monitoring organizations understand their own data very well, but issues can arise when their data are combined with data from other organizations that have used different methods for reporting the same common metadata elements. Such use of multi-source data is termed “secondary use”—the use of data beyond the original intent determined by the organization that collected the data. In this study, we surveyed more than 25 million nutrient records collected by 488 organizations in the United States since 1899 to identify major inconsistencies in metadata elements that limit the secondary use of multi-source data. Nearly 14.5 million of these records had missing or ambiguous information for one or more key metadata elements, including (in decreasing order of records affected) sample fraction, chemical form, parameter name, units of measurement, precise numerical value, and remark codes. As a result, metadata harmonization to make secondary use of these multi-source data will be time consuming, expensive, and inexact. Different data users may make different assumptions about the same ambiguous data, potentially resulting in different conclusions about important environmental issues. The value of these ambiguous data is estimated at \$US12 billion, a substantial collective investment by water-resource organizations in the United States. By comparison, the value of unambiguous data is estimated at \$US8.2 billion. The ambiguous data could be preserved for uses beyond the original intent by developing and implementing standardized metadata practices for future and legacy water-quality data throughout the United States.

Water Research↗
USGS2022

Stakeholder engagement to guide decision-relevant water data delivery

Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.

Journal of the American Water Resources Associatio↗
USGS2010

What you should know about land-cover data

Wildlife biologists are using land-characteristics data sets for a variety of applications. Many kinds of landscape variables have been characterized and the resultant data sets or maps are readily accessible. Often, too little consideration is given to the accuracy or traits of these data sets, most likely because biologists do not know how such data are compiled and rendered, or the potential pitfalls that can be encountered when applying these data. To increase understanding of the nature of land-characteristics data sets, I introduce aspects of source information and data-handling methodology that include the following: ambiguity of land characteristics; temporal considerations and the dynamic nature of the landscape; type of source data versus landscape features of interest; data resolution, scale, and geographic extent; data entry and positional problems; rare landscape features; and interpreter variation. I also include guidance for determining the quality of land-characteristics data sets through metadata or published documentation, visual clues, and independent information. The quality or suitability of the data sets for wildlife applications may be improved with thematic or spatial generalization, avoidance of transitional areas on maps, and merging of multiple data sources. Knowledge of the underlying challenges in compiling such data sets will help wildlife biologists to better assess the strengths and limitations and determine how best to use these data.

Journal of Wildlife Management↗
USGS2000

Digital data sets for map products produced as part of the Black Hills Hydrology Study, western South Dakota

This compact disk contains digital data produced as part of the 1:100,000-scale map products for the Black Hills Hydrology Study conducted in western South Dakota. The digital data include 28 individual Geographic Information System (GIS) data sets: data sets for the hydrogeologic unit map including all mapped hydrogeologic units within the study area (1 data set) and major geologic structure including anticlines and synclines (1 data set); data sets for potentiometric maps including the potentiometric contours for the Inyan Kara, Minnekahta, Minnelusa, Madison, and Deadwood aquifers (5 data sets), wells used as control points for each aquifer (5 data sets), and springs used as control points for the potentiometric contours (1 data set); and data sets for the structure-contour maps including the structure contours for the top of each formation that contains major aquifers (5 data sets), wells and tests holes used as control points for each formation (5 data sets), and surficial deposits (alluvium and terrace deposits) that directly overlie each of the major aquifer outcrops (5 data sets). These data sets were used to produce the maps published by the U.S. Geological Survey.

Open-File Report↗
USGS2011

Global multi-resolution terrain elevation data 2010 (GMTED2010)

In 1996, the U.S. Geological Survey (USGS) developed a global topographic elevation model designated as GTOPO30 at a horizontal resolution of 30 arc-seconds for the entire Earth. Because no single source of topographic information covered the entire land surface, GTOPO30 was derived from eight raster and vector sources that included a substantial amount of U.S. Defense Mapping Agency data. The quality of the elevation data in GTOPO30 varies widely; there are no spatially-referenced metadata, and the major topographic features such as ridgelines and valleys are not well represented. Despite its coarse resolution and limited attributes, GTOPO30 has been widely used for a variety of hydrological, climatological, and geomorphological applications as well as military applications, where a regional, continental, or global scale topographic model is required. These applications have ranged from delineating drainage networks and watersheds to using digital elevation data for the extraction of topographic structure and three-dimensional (3D) visualization exercises (Jenson and Domingue, 1988; Verdin and Greenlee, 1996; Lehner and others, 2008). Many of the fundamental geophysical processes active at the Earth's surface are controlled or strongly influenced by topography, thus the critical need for high-quality terrain data (Gesch, 1994). U.S. Department of Defense requirements for mission planning, geographic registration of remotely sensed imagery, terrain visualization, and map production are similarly dependent on global topographic data. Since the time GTOPO30 was completed, the availability of higher-quality elevation data over large geographic areas has improved markedly. New data sources include global Digital Terrain Elevation Data (DTEDRegistered) from the Shuttle Radar Topography Mission (SRTM), Canadian elevation data, and data from the Ice, Cloud, and land Elevation Satellite (ICESat). Given the widespread use of GTOPO30 and the equivalent 30-arc-second DTEDRegistered level 0, the USGS and the National Geospatial-Intelligence Agency (NGA) have collaborated to produce an enhanced replacement for GTOPO30, the Global Land One-km Base Elevation (GLOBE) model and other comparable 30-arc-second-resolution global models, using the best available data. The new model is called the Global Multi-resolution Terrain Elevation Data 2010, or GMTED2010 for short. This suite of products at three different resolutions (approximately 1,000, 500, and 250 meters) is designed to support many applications directly by providing users with generic products (for example, maximum, minimum, and median elevations) that have been derived directly from the raw input data that would not be available to the general user or would be very costly and time-consuming to produce for individual applications. The source of all the elevation data is captured in metadata for reference purposes. It is also hoped that as better data become available in the future, the GMTED2010 model will be updated.

Open-File Report↗
USGS2013

Defining a data management strategy for USGS Chesapeake Bay studies

The mission of U.S. Geological Survey’s (USGS) Chesapeake Bay studies is to provide integrated science for improved understanding and management of the Chesapeake Bay ecosystem. Collective USGS efforts in the Chesapeake Bay watershed began in the 1980s, and by the mid-1990s the USGS adopted the watershed as one of its national place-based study areas. Great focus and effort by the USGS have been directed toward Chesapeake Bay studies for almost three decades. The USGS plays a key role in using “ecosystem-based adaptive management, which will provide science to improve the efficiency and accountability of Chesapeake Bay Program activities” (Phillips, 2011). Each year USGS Chesapeake Bay studies produce published research, monitoring data, and models addressing aspects of bay restoration such as, but not limited to, fish health, water quality, land-cover change, and habitat loss. The USGS is responsible for collaborating and sharing this information with other Federal agencies and partners as described under the President’s Executive Order 13508—Strategy for Protecting and Restoring the Chesapeake Bay Watershed signed by President Obama in 2009. Historically, the USGS Chesapeake Bay studies have relied on national USGS databases to store only major nationally available sources of data such as streamflow and water-quality data collected through local monitoring programs and projects, leaving a multitude of other important project data out of the data management process. This practice has led to inefficient methods of finding Chesapeake Bay studies data and underutilization of data resources. Data management by definition is “the business functions that develop and execute plans, policies, practices and projects that acquire, control, protect, deliver and enhance the value of data and information.” (Mosley, 2008a). In other words, data management is a way to preserve, integrate, and share data to address the needs of the Chesapeake Bay studies to better manage data resources, work more efficiently with partners, and facilitate holistic watershed science. It is now the goal of the USGS Chesapeake Bay studies to implement an enhanced and all-encompassing approach to data management. This report discusses preliminary efforts to implement a physical data management system for program data that is not replicated nationally through other USGS databases.

Chesapeake Bay↗
USGS2015

Community for Data Integration 2013 Annual Report

The U.S. Geological Survey (USGS) conducts earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups. However by 2009, it became apparent that many members of the council had an interest in developing data integration solutions and sharing expertise in a less formal grassroots perspective, thus transforming the “Council” into a “Community” for Data Integration (CDI). Today, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques provide a forum for people doing work with data integration to come together to share ideas as well as learn new skills and techniques, and grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the Community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical 2 challenges, including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. Today the CDI continues to hold an annual RFP that create data management tools and practices, collaboration tools, and training in support of data integration and delivery.

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