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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 559 records · Page 31Linked to original sources
USGS2015

Onshore industrial wind turbine locations for the United States up to March 2014

Wind energy is a rapidly growing form of renewable energy in the United States. While summary information on the total amounts of installed capacity are available by state, a free, centralized, national, turbine-level, geospatial dataset useful for scientific research, land and resource management, and other uses did not exist. Available in multiple formats and in a web application, these public domain data provide industrial-scale onshore wind turbine locations in the United States up to March 2014, corresponding facility information, and turbine technical specifications. Wind turbine records have been collected and compiled from various public sources, digitized or position verified from aerial imagery, and quality assured and quality controlled. Technical specifications for turbines were assigned based on the wind turbine make and model as described in public literature. In some cases, turbines were not seen in imagery or turbine information did not exist or was difficult to obtain. Uncertainty associated with these is recorded in a confidence rating.

Scientific Data↗
USGS2013

Ecological prediction with nonlinear multivariate time-frequency functional data models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Lower Missouri River↗
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↗
USGS2025

Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024

Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.

Environmental Microbiome↗
USGS2011

Social network models predict movement and connectivity in ecological landscapes

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Proceedings of the National Academy of Sciences of↗
USGS2013

Petrologic and isotopic data from the Cretaceous (Campanian) Blackhawk Formation and Star Point Sandstone (Mesaverde Group), Wasatch Plateau, Utah

The presence of discrete minerals associated with coal—whether (1) detrital or authigenic constituents of the coals or in thin mudstone or siltstone units interbedded with coals, or (2) authigenic phases that formed along cleats—might influence its utilization as an energy resource. The build-up of sintered ash deposits on the surfaces of heat exchangers in coal-fired power plants, due to the alteration of minerals during combustion of the coal, can seriously affect the functioning of the boiler and enhance corrosion of combustion equipment. In particular, the presence of sodium in coals has been considered a key factor in the fouling of boilers; however, other elements (such as calcium or magnesium) and the amount of discrete minerals burned with coal can also play a significant role in the inefficiency of and damage to boilers. Previous studies of the quality of coals in the Cretaceous (Campanian) Blackhawk Formation of the Wasatch Plateau, Utah, revealed that the sodium content of the coals varied across the region. To better understand the origin and distribution of sodium in these coals, petrologic studies were undertaken within a sedimentological framework to evaluate the timing and geochemical constraints on the emplacement of sodium-bearing minerals, particularly analcime, which previously had been identified in coals in the Blackhawk Formation. Further, the study was broadened to include not just coals in the Blackhawk Formation from various localities across the Wasatch Plateau, but also sandstones interbedded with the coals as well as sandstones in the underlying Star Point Sandstone. The alteration history of the sandstones in both formations was considered a key component of this study because it records the nature and timing of fluids passing through them and the associated precipitation of sodium-bearing minerals; thus, the alteration history could place constraints on the distribution and timing of sodium mineralization in the interbedded or overlying Blackhawk coals. Although some preliminary results were previously presented at scientific meetings, the petrologic and geochemical data have not been fully compiled and reported. The purpose of this report is to present the methods of data acquisition and the results of petrologic and isotopic analyses on coal and sandstone samples from the Blackhawk Formation as well as sandstones of the underlying Star Point Sandstone.

Utah↗
USGS2017

PRISM software—Processing and review interface for strong-motion data

Rapidly available and accurate ground-motion acceleration time series (seismic recordings) and derived data products are essential to quickly providing scientific and engineering analysis and advice after an earthquake. To meet this need, the U.S. Geological Survey National Strong Motion Project has developed a software package called PRISM (Processing and Review Interface for Strong-Motion data). PRISM automatically processes strong-motion acceleration records, producing compatible acceleration, velocity, and displacement time series; acceleration, velocity, and displacement response spectra; Fourier amplitude spectra; and standard earthquake-intensity measures. PRISM is intended to be used by strong-motion seismic networks, as well as by earthquake engineers and seismologists.

Techniques and Methods↗
USGS2001

A data management life-cycle

Documented, reliable, and accessible data and information are essential building blocks supporting scientific research and applications that enhance society's knowledge base (fig. 1). The U.S. Geological Survey (USGS), a leading provider of science data, information, and knowledge, is uniquely positioned to integrate science and natural resource information to address societal needs. The USGS Central Energy Resources Team (USGS-CERT) provides critical information and knowledge on the quantity, quality, and distribution of the Nation's and the world's oil, gas, and coal resources. By using a life-cycle model, the USGS-CERT Data Management Project is developing an integrated data management system to (1) promote access to energy data and information, (2) increase data documentation, and (3) streamline product delivery to the public, scientists, and decision makers. The project incorporates web-based technology, data cataloging systems, data processing routines, and metadata documentation tools to improve data access, enhance data consistency, and increase office efficiency

Fact Sheet↗
USGS2007

Modeling environmental bias and computing velocity field from data of Terra Nova Bay GPS network in Antarctica by means of a quasi-observation processing approach

A semi-permanent GPS network of about 30 vertices has been installed at Terra Nova Bay (TNB) near Ross Sea in Antarctica. A permanent GPS station TNB1 based on an Ashtech Z-XII dual frequency P-code GPS receiver with ASH700936D_M Choke Ring Antenna has been mounted on a reinforced concrete pillar built on bedrock since October 1998 and has recorded continuously up to the present. The semi-permanent network has been routinely surveyed every summer using high quality dual frequency GPS receivers with 24 hour sessions at 15 sec rate; data, metadata and solutions will be available to the scientific community at (http://www.geodant.unimore.it). We present the results of a distributed session approach applied to processing GPS data of the TNB GPS network, and based on Gamit/Globk 10.2-3 GPS analysis software. The results are in good agreement with other authors' computations and with many of the theoretical models.

Open-File Report↗
USGS2020

Sediments and the sea floor of the continental shelves and coastal waters of the United States—About the usSEABED integrated sea-floor-characterization database, built with the dbSEABED processing system

Since the second half of the 20th century, there has been an increase in scientific interest, research effort, and information gathered on the geologic sedimentary character of the continental margins of the United States. Data and information from thousands of sources have increased our scientific understanding of the character of the margin surface, but rarely have those data been combined and integrated. Initially, the U.S. Geological Survey (USGS), in cooperation with the Institute of Arctic and Alpine Research at the University of Colorado Boulder, created the usSEABED database to provide surficial sea-floor-characterization data for USGS assessments of marine-based aggregates and for studies of sea-floor habitat. Since then, the USGS has continued to build up the database as a nationwide resource for many uses and applications. Previously published data derived from the usSEABED database have been released as three USGS data series publications containing data covering the U.S. Atlantic margin, the Gulf of Mexico and Caribbean regions, and the Pacific coast. An updated USGS data release unifies the three publications, incorporates additional data and sources including data from Alaska, Hawaii, and U.S. overseas territories, and provides revised output files that fix known errors and add known or inferred sampling dates. This report accompanies the data release and contains information on the methodology and products of the usSEABED database.

Open-File Report↗
USGS2026

The Climate Hazards Center Infrared Precipitation with Stations, version 3

The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) data stream combines: (1) a high-resolution climatology, (2) thermal infrared (TIR) geostationary satellite observations, and (3) station observations. In the past, CHIRPS version 2 (CHIRPS2) has proven to be valuable for drought monitoring, hydrologic modeling, scientific studies and agricultural decision making. Version 3 (CHIRPS3) improves each of these components. The new version, CHIRPS3 extends to 60°S/N, adopts an improved variance-preserving TIR-to-precipitation estimation method, uses many more stations and station sources than the original CHIRPS2 product, and implements gauge-undercatch correction. In this paper, we evaluate the performance of satellite-only CHIRP3, CHIRP2, IMERG, PERSIANN- CCS, and GPI using high quality interpolated data in twelve regions with dense station coverage. CHIRP3 represents both the observed mean and variance more accurately than CHIRP2. A usage section in Morocco shows that CHIRPS3 better captures the observed rainfall variability when compared to CHIRPS2. This section also demonstrates how station data should be gauge-undercatch-corrected when validating CHIRPS3.

Scientific Data↗
USGS2007

Advances through collaboration: sharing seismic reflection data via the Antarctic Seismic Data Library System for Cooperative Research (SDLS)

The Antarctic Seismic Data Library System for Cooperative Research (SDLS) has served for the past 16 years under the auspices of the Antarctic Treaty (ATCM Recommendation XVI-12) as a role model for collaboration and equitable sharing of Antarctic multichannel seismic reflection (MCS) data for geoscience studies. During this period, collaboration in MCS studies has advanced deciphering the seismic stratigraphy and structure of Antarctica’s continental margin more rapidly than previously. MCS data compilations provided the geologic framework for scientific drilling at several Antarctic locations and for high-resolution seismic and sampling studies to decipher Cenozoic depositional paleoenvironments. The SDLS successes come from cooperation of National Antarctic Programs and individual investigators in “on-time” submissions of their MCS data. Most do, but some do not. The SDLS community has an International Polar Year (IPY) goal of all overdue MCS data being sent to the SDLS by end of IPY. The community science objective is to compile all Antarctic MCS data to derive a unified seismic stratigraphy for the continental margin – a stratigraphy to be used with drilling data to derive Cenozoic circum-Antarctic paleobathymetry maps and local-to-regional scale paleoenvironmental histories.

Open-File Report↗
USGS2017

Can beaches survive climate change?

Anthropogenic climate change is driving sea level rise, leading to numerous impacts on the coastal zone, such as increased coastal flooding, beach erosion, cliff failure, saltwater intrusion in aquifers, and groundwater inundation. Many beaches around the world are currently experiencing chronic erosion as a result of gradual, present-day rates of sea level rise (about 3 mm/year) and human-driven restrictions in sand supply (e.g., harbor dredging and river damming). Accelerated sea level rise threatens to worsen coastal erosion and challenge the very existence of natural beaches throughout the world. Understanding and predicting the rates of sea level rise and coastal erosion depends on integrating data on natural systems with computer simulations. Although many computer modeling approaches are available to simulate shoreline change, few are capable of making reliable long-term predictions needed for full adaption or to enhance resilience. Recent advancements have allowed convincing decadal to centennial-scale predictions of shoreline evolution. For example, along 500 km of the Southern California coast, a new model featuring data assimilation predicts that up to 67% of beaches may completely erode by 2100 without large-scale human interventions. In spite of recent advancements, coastal evolution models must continue to improve in their theoretical framework, quantification of accuracy and uncertainty, computational efficiency, predictive capability, and integration with observed data, in order to meet the scientific and engineering challenges produced by a changing climate.

Journal of Geophysical Research F: Earth Surface↗
USGS2022

Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears ( Ursus maritimus ) in a 28,125 km 2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km 2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

eastern Chukchi Sea↗
USGS2023

Prioritizing science efforts to inform decision making on public lands

Public land management agencies in the US are committed to using science-informed decision making, but there has been little research on the types and topics of science that managers need most to inform their decisions. We used the National Environmental Policy Act to identify four types of science information needed for making decisions relevant to public lands: (1) data on resources of concern, (2) scientific studies relevant to potential effects of proposed actions, (3) methods for quantifying potential effects of proposed actions, and (4) effective mitigation measures. We then used this framework to analyze 70 Environmental Assessments completed by the Bureau of Land Management in Colorado. Commonly proposed actions were oil and gas development, livestock grazing, land transactions, and recreation. Commonly analyzed resources included terrestrial wildlife, protected birds, vegetation, and soils. Focusing research efforts on the intersection of these resources and actions, and on developing and evaluating the effectiveness of mitigation measures to protect these resources, could strengthen the science foundation for public lands decision making.

Frontiers in Ecology and the Environment↗
USGS2016

Chesapeake Bay recovery and factors affecting trends: Long-termmonitoring, indicators, and insights

Monitoring the outcome of restoration efforts is the only way to identify the status of a recovery and the most effective management strategies. In this paper, we discuss Chesapeake Bay and watershed recovery and factors influencing water quality trends. For over 30 years, the Chesapeake Bay Program Partnership’s long-term tidal and watershed water quality monitoring networks have measured physical, chemical and biological parameters throughout the bay and its surrounding watershed underpinning an adaptive management process to drive ecosystem recovery. There are many natural and anthropogenic factors operating and interacting to affect the watershed and bay water quality recovery responses to management actions. Across habitats and indicators, the bay and its watershed continue to express a diverse spatial and temporal fabric of multiscale conditions, stressors and trends that show a range of health conditions and impairment s , as well as evidence of progress and degradation. Recurrent independent reviews of the monitoring program have driven a culture of continued adaptation of the monitoring networks to reflect ever evolving management information needs. The adherence to bay and watershed-wide consistent monitoring protocols provides monitoring data supporting analyses and development of scientific syntheses that underpin indicator and model development, regulatory assessments, targeting of management actions, evaluation of management effectiveness, and directing of priorities and policies.

Delaware, Maryland, Virginia↗
USGS2011

Geoinformatics in the public service: Building a cyberinfrastructure across the geological surveys

Advanced information technology infrastructure is increasingly being employed in the Earth sciences to provide researchers with efficient access to massive central databases and to integrate diversely formatted information from a variety of sources. These geoinformatics initiatives enable manipulation, modeling and visualization of data in a consistent way, and are helping to develop integrated Earth models at various scales, and from the near surface to the deep interior. This book uses a series of case studies to demonstrate computer and database use across the geosciences. Chapters are thematically grouped into sections that cover data collection and management; modeling and community computational codes; visualization and data representation; knowledge management and data integration; and web services and scientific workflows. Geoinformatics is a fascinating and accessible introduction to this emerging field for readers across the solid Earth sciences and an invaluable reference for researchers interested in initiating new cyberinfrastructure projects of their own.

Book chapter↗
USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗