Geology topics

Thomas E. Burley

Publications and source records attributed to Thomas E. Burley.

12 recordsLinked to original sources

USGS2026

Artificial intelligence strategy for the U.S. Geological Survey

Artificial intelligence (AI) can offer opportunities to enhance the science, science delivery, and business operations of the U.S. Geological Survey (USGS). Although USGS staff have proactively adopted AI into our workflows for many years, a comprehensive USGS strategy for AI has not previously been developed. The strategy described here is motivated by the acceleration of AI technological development, the benefits of increased AI adoption to USGS mission delivery as anticipated by USGS staff, rising public concern about the implications and trustworthiness of AI, and emerging Federal directives and guidance about AI. The USGS vision is to continue integrating AI to deliver valuable science for the public good while maintaining high ethical standards, scientific quality and integrity, and compliance with Federal and U.S. Department of the Interior requirements. To realize this vision, the USGS can take steps to (1) develop a strong AI workforce, (2) adapt our organizational approaches to include AI governance and communication, (3) ensure responsible and trustworthy use of AI, (4) modernize our computing and data infrastructure to support AI, and (5) accelerate AI adoption and innovation in the Bureau.

Circular↗

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↗

USGS2022

Water priorities for the Nation—The USGS National Water Dashboard

The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database ( https://doi.org/10.5066/F7P55KJN ) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.

Fact Sheet↗

USGS2022

Visit the U.S. Geological Survey's National Water Dashboard

General Information Product↗

USGS2017

Similarities and differences in occurrence and temporal fluctuations in glyphosate and atrazine in small Midwestern streams (USA) during the 2013 growing season

Glyphosate and atrazine are the most intensively used herbicides in the United States. Although there is abundant spatial and temporal information on atrazine occurrence at regional scales, there are far fewer data for glyphosate, and studies that compare the two herbicides are rare. We investigated temporal patterns in glyphosate and atrazine concentrations measured weekly during the 2013 growing season in 100 small streams in the Midwestern United States. Glyphosate was detected in 44% of samples (method reporting level 0.2 μg/L); atrazine was detected above a threshold of 0.2 μg/L in 54% of samples. Glyphosate was detected more frequently in 12 urban streams than in 88 agricultural streams, and at concentrations similar to those in streams with high agricultural land use (> 40% row crop) in the watershed. In contrast, atrazine was detected more frequently and at higher concentrations in agricultural streams than in urban streams. The maximum concentration of glyphosate measured at most urban sites exceeded the maximum atrazine concentration, whereas at agricultural sites the reverse was true. Measurement at a 2-day interval at 8 sites in northern Missouri revealed that transport of both herbicide compounds appeared to be controlled by spring flush, that peak concentration duration was brief, but that peaks in atrazine concentrations were of longer duration than those of glyphosate. The 2-day sampling also indicated that weekly sampling is unlikely to capture peak concentrations of glyphosate and atrazine.

Science of the Total Environment↗

USGS2014

Data and spatial studies of the USGS Texas Water Science Center

Hydrologists, geographers, geophysicists, and geologists with the U.S. Geological Survey (USGS) Texas Water Science Center (TXWSC) work in the USGS Water Mission Area on a diverse range of projects built on a foundation of spatial data. The TXWSC has developed sophisticated data and spatial-studies-related capabilities that are an integral part of the projects undertaken by the Center.

Texas↗

USGS2014

The United States Geological Survey Science Data Lifecycle Model

U.S. Geological Survey (USGS) data represent corporate assets with potential value beyond any immediate research use, and therefore need to be accounted for and properly managed throughout their lifecycle. Recognizing these motives, a USGS team developed a Science Data Lifecycle Model (SDLM) as a high-level view of data—from conception through preservation and sharing—to illustrate how data management activities relate to project workflows, and to assist with understanding the expectations of proper data management. In applying the Model to research activities, USGS scientists can ensure that data products will be well-described, preserved, accessible, and fit for re-use. The Model also serves as a structure to help the USGS evaluate and improve policies and practices for managing scientific data, and to identify areas in which new tools and standards are needed.

Open-File Report↗

USGS2012

Sound data management as a foundation for natural resources management and science

Effective decision making is closely related to the quality and completeness of available data and information. Data management helps to ensure data quality in any discipline and supports decision making. Managing data as a long-term scientific asset helps to ensure that data will be usable beyond the original intended application. Emerging issues in water-resources management and climate variability require the ability to analyze change in the conditions of natural resources over time. The availability of quality, well-managed, and documented data from the past and present helps support this requirement.

Fact Sheet↗

USGS2011

Spatially pooled depth-dependent reservoir storage, elevation, and water-quality data for selected reservoirs in Texas, January 1965-January 2010

The U.S. Geological Survey (USGS), in cooperation with Texas Tech University, constructed a dataset of selected reservoir storage (daily and instantaneous values), reservoir elevation (daily and instantaneous values), and water-quality data from 59 reservoirs throughout Texas. The period of record for the data is as large as January 1965-January 2010. Data were acquired from existing databases, spreadsheets, delimited text files, and hard-copy reports. The goal was to obtain as much data as possible; therefore, no data acquisition restrictions specifying a particular time window were used. Primary data sources include the USGS National Water Information System, the Texas Commission on Environmental Quality Surface Water-Quality Management Information System, and the Texas Water Development Board monthly Texas Water Condition Reports. Additional water-quality data for six reservoirs were obtained from USGS Texas Annual Water Data Reports. Data were combined from the multiple sources to create as complete a set of properties and constituents as the disparate databases allowed. By devising a unique per-reservoir short name to represent all sites on a reservoir regardless of their source, all sampling sites at a reservoir were spatially pooled by reservoir and temporally combined by date. Reservoir selection was based on various criteria including the availability of water-quality properties and constituents that might affect the trophic status of the reservoir and could also be important for understanding possible effects of climate change in the future. Other considerations in the selection of reservoirs included the general reservoir-specific period of record, the availability of concurrent reservoir storage or elevation data to match with water-quality data, and the availability of sample depth measurements. Additional separate selection criteria included historic information pertaining to blooms of golden algae. Physical properties and constituents were water temperature, reservoir storage, reservoir elevation, specific conductance, dissolved oxygen, pH, unfiltered salinity, unfiltered total nitrogen, filtered total nitrogen, unfiltered nitrate plus nitrite, unfiltered phosphorus, filtered phosphorus, unfiltered carbon, carbon in suspended sediment, total hardness, unfiltered noncarbonate hardness, filtered noncarbonate hardness, unfiltered calcium, filtered calcium, unfiltered magnesium, filtered magnesium, unfiltered sodium, filtered sodium, unfiltered potassium, filtered potassium, filtered chloride, filtered sulfate, unfiltered fluoride, and filtered fluoride. When possible, USGS and Texas Commission on Environmental Quality water-quality properties and constituents were matched using the database parameter codes for individual physical properties and constituents, descriptions of each physical property or constituent, and their reporting units. This report presents a collection of delimited text files of source-aggregated, spatially pooled, depth-dependent, instantaneous water-quality data as well as instantaneous, daily, and monthly storage and elevation reservoir data.

Texas↗

USGS2011

Usage and administration manual for a geodatabase compendium of water-resources data: Rio Grande Basin from the Rio Arriba-Sandoval County line, New Mexico, to Presidio, Texas, 1889-2009

The U.S. Geological Survey, in cooperation with the New Mexico Interstate Stream Commission, developed a geodatabase compendium (hereinafter referred to as the 'geodatabase') of available water-resources data for the reach of the Rio Grande from Rio Arriba-Sandoval County line, New Mexico, to Presidio, Texas. Since 1889, a wealth of water-resources data has been collected in the Rio Grande Basin from Rio Arriba-Sandoval County line, New Mexico, to Presidio, Texas, for a variety of purposes. Collecting agencies, researchers, and organizations have included the U.S. Geological Survey, Bureau of Reclamation, International Boundary and Water Commission, State agencies, irrigation districts, municipal water utilities, universities, and other entities. About 1,750 data records were recently (2010) evaluated to enhance their usability by compiling them into a single geospatial relational database (geodatabase). This report is intended as a user's manual and administration guide for the geodatabase. All data available, including water quality, water level, and discharge data (both instantaneous and daily) from January 1, 1889, through December 17, 2009, were compiled for the study area. A flexible and efficient geodatabase design was used, enhancing the ability of the geodatabase to handle data from diverse sources and helping to ensure sustainability of the geodatabase with long-term maintenance. Geodatabase tables include daily data values, site locations and information, sample event information, and parameters, as well as data sources and collecting agencies. The end products of this effort are a comprehensive water-resources geodatabase that enables the visualization of primary sampling sites for surface discharges, groundwater elevations, and water-quality and associated data for the study area. In addition, repeatable data processing scripts, Structured Query Language queries for loading prepared data sources, and a detailed process for refreshing all data in the compendium have been developed. The geodatabase functionality allows users to explore spatial characteristics of the data, conduct spatial analyses, and pose questions to the geodatabase in the form of queries. Users can also customize and extend the geodatabase, combine it with other databases, or use the geodatabase design for other water-resources applications.

New Mexico, Texas↗

USGS2010

The future of geospatial data

Do you know where your data are or how they came to be? This question has been pondered by nearly everyone working in natural-resource management. Spatial data, in particular, are being collected at a significant rate, and an increasing number of sources are freely available.

GeoWorld↗

USGS2009

NBII-SAIN Data Management Toolkit

The Strategic Plan for the U.S. Geological Survey Biological Informatics Program (2005-2009) recognizes the need for effective data management: Though the Federal government invests more than $600 million per year in biological data collection, it is difficult to address these issues because of limited accessibility and lack of standards for data and information...variable quality, sources, methods, and formats (for example observations in the field, museum specimens, and satellite images) present additional challenges. This is further complicated by the fast-moving target of emerging and changing technologies such as GPS and GIS. Even though these technologies offer new solutions, they also create new informatics challenges (Ruggiero and others, 2005). The USGS National Biological Information Infrastructure program, hereafter referred to as NBII, is charged with the mission to improve the way data and information are gathered, documented, stored, and accessed. The central objective of this project is a direct reflection of the purpose of NBII as described by John Mosesso, Program Manager of the U.S. Geological Survey-Biological Informatics Program-GAP Analysis: At the outset, the reason for bringing about NBII was that there were significant amounts of data and information scattered all over the U.S., not accessible, in incompatible formats, and that NBII was tasked with addressing this problem...NBII's focus is to pull data together that truly matters to someone or communities. Essentially, the core questions are: 1) what are the issues, 2) where is the data, and 3) how can we make it usable and accessible (John Mosesso, U.S. Geological Survey, oral commun., 2006). Redundancy in data collection can be a major issue when multiple stakeholders are involved with a common effort. In 2001 the U.S. General Accounting Office (USGAO) estimated that about 50 percent of the Federal government's geospatial data at the time was redundant. In addition, approximately 80 percent of the cost of a spatial information system is associated with spatial data collection and management (U.S. General Accounting Office, 2003). These figures indicate that the resources (time, personnel, money) of many agencies and organizations could be used more efficiently and effectively. Dedicated and conscientious data management coordination and documentation is critical for reducing such redundancy. Substantial cost savings and increased efficiency are direct results of a pro-active data management approach. In addition, details of projects as well as data and information are frequently lost as a result of real-world occurrences such as the passing of time, job turnover, and equipment changes and failure. A standardized, well documented database allows resource managers to identify issues, analyze options, and ultimately make better decisions in the context of adaptive management (National Land and Water Resources Audit and the Australia New Zealand Land Information Council on behalf of the Australian National Government, 2003). Many environmentally focused, scientific, or natural resource management organizations collect and create both spatial and non-spatial data in some form. Data management appropriate for those data will be contingent upon the project goal(s) and objectives and thus will vary on a case-by-case basis. This project and the resulting Data Management Toolkit, hereafter referred to as the Toolkit, is therefore not intended to be comprehensive in terms of addressing all of the data management needs of all projects that contain biological, geospatial, and other types of data. The Toolkit emphasizes the idea of connecting a project's data and the related management needs to the defined project goals and objectives from the outset. In that context, the Toolkit presents and describes the fundamental components of sound data and information management that are common to projects involving biological, geospatial, and other related data

Open-File Report↗