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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 415 records · Page 23Linked to original sources
USGS2023

Estimating streamflow for base flow conditions at partial-record streamgaging stations at Acadia National Park, Maine

The objective of the work presented in this report is to develop equations that can be used to extend the base flow record at multiple partial-record streamgaging stations at Acadia National Park in eastern coastal Maine based on nearby continuous-record streamgaging stations. Daily mean streamflow values at U.S. Geological Survey continuous-record streamgaging station Otter Creek near Bar Harbor, Maine (station 01022840) had stronger correlations with instantaneous measurements during base flow conditions from 2006 to 2020 at 14 partial-record streamgaging stations at Acadia National Park than the other four continuous-record streamgaging stations tested for use as index stations. Index stations are continuous-record stations on hydrologically similar streams that have the potential to be used to extend the record at the partial-record station. Base flow is that part of streamflow that is sustained primarily by groundwater discharge. It is not attributable to direct precipitation or melting snow. Five of the partial-record stations had strong correlations with Otter Creek (correlation coefficient greater than 0.90) and relatively low root mean square errors (from 0.04 to 0.19). An additional four partial-record stations had fair correlations with Otter Creek (correlation coefficient from 0.79 to 0.9) and relatively low root mean square errors (from 0.05 to 0.19). For these 10 stations, maintenance of variance extension type 1 (MOVE.1) record extension equations computed in this report provide a reasonable method for extending the partial record, estimating summer monthly means and medians, and estimating daily mean streamflow values at these sites on days with no streamflow (discharge) measurements. Four of the partial-record stations have weak correlations (less than 0.78) or high root mean square error values (greater than 9) or both, indicating that record extension techniques are not appropriate for these partial-record stations using currently [2022] available data.

Maine↗
USGS2024

A case for improved global coordination of volcano observatories

The distribution of volcano monitoring networks and volcano expertise does not correlate well with the global distribution of volcanic risk. All countries have cultural, financial, bureaucratic, political, and logistical barriers to effective risk reduction. The lack of parity amongst volcano observatories jeopardizes public safety and curtails scientific research and understanding. Having global data compiled daily to retain a full record of volcanic unrest would lead to large and meaningful improvements in future eruption forecasts. To make progress on these issues, the volcanological community needs greater collaboration, standardization, and support..

Annals of Geophysics↗
USGS2010

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

Community Online Resource for Statistical Seismici↗
USGS2025

Estimating disease prevalence from preferentially sampled, pooled data

After the onset of the COVID-19 pandemic, scientific interest in coronaviruses endemic in animal populations has increased dramatically. However, investigating the prevalence of disease in animal populations across the landscape, which requires finding and capturing animals can be difficult. Spatial random sampling over a grid could be extremely inefficient because animals can be hard to locate, and the total number of samples may be small. Alternatively, preferential sampling, using existing knowledge to inform sample location, can guarantee larger numbers of samples, but estimates derived from this sampling scheme may exhibit bias if there is a relationship between higher probability sampling locations and the disease prevalence. Sample specimens are commonly grouped and tested in pools which can also be an added challenge when combined with preferential sampling. Here we present a Bayesian method for estimating disease prevalence with preferential sampling in pooled presence-absence data motivated by estimating factors related to coronavirus infection among Mexican free-tailed bats ( Tadarida brasiliensis ) in California. We demonstrate the efficacy of our approach in a simulation study, where a naive model, not accounting for preferential sampling, returns biased estimates of parameter values; however, our model returns unbiased results regardless of the degree of preferential sampling. Our model framework is then applied to data from California to estimate factors related to coronavirus prevalence. After accounting for preferential sampling impacts, our model suggests small prevalence differences between male and female bats.

California↗
USGS1977

A digital file of the lunar normal Albedo

A digital file of the normal albedo of the Moon has been produced at a resolution of about 1/550 of a lunar diameter (about 6.3 km). The file was produced from five photographs taken with the 61-cm reflector of the Northern Arizona University Astrophysical Observatory. No mosaicking was necessary. Spatial control is selenodetic rather than landmark-morphologic. Photometric control is provided through a combination of electrography and regular photoelectric photometry. Pixel photometric function corrections are employed. The file was provided as data base for the Lunar Consortium. Brief discussion of the scientific implications of the frequency histogram is offered, and the negligibility of lunar limb darkening below e{open} = 77?? is affirmed. It is specifically desired not to withhold these data from publication while more significant and detailed scientific interpretation is carried on. ?? 1977 D. Reidel Publishing Company, Dordrecht-Holland.

The Moon↗
USGS2013

Volcano crisis response at Yellowstone volcanic complex - after-action report for exercise held at Salt Lake City, Utah, November 15, 2011

A functional tabletop exercise was run on November 14-15, 2011 in Salt Lake City, Utah, to test crisis response capabilities, communication protocols, and decision-making by the staff of the multi-agency Yellowstone Volcano Observatory (YVO) as they reacted to a hypothetical exercise scenario of accelerating volcanic unrest at the Yellowstone caldera. The exercise simulated a rapid build-up of seismic activity, ground deformation, and hot-spring water-chemistry and temperature anomalies that culminated in a small- to moderate-size phreatomagmatic eruption within Yellowstone National Park. The YVO scientific team's responses to the unfolding events in the scenario and to simulated requests for information by stakeholders and the media were assessed by (a) the exercise organizers; (b) several non-YVO scientists, who observed and queried participants, and took notes throughout the exercise; and (c) the participants themselves, who kept logs of their actions during the exercise and later participated in a group debriefing session and filled out detailed questionnaires. These evaluations were tabulated, interpreted, and summarized for this report, and on the basis of this information, recommendations have been made. Overall, the YVO teams performed their jobs very well. The exercise revealed that YVO scientists were able to successfully provide critical hazards information, issue information statements, and appropriately raise alert levels during a fast-moving crisis. Based on the exercise, it is recommended that several measures be taken to increase YVO effectiveness during a crisis: 1. Improve role clarification within and between YVO science teams. 2. Improve communications tools and protocols for data-sharing and consensus-building among YVO scientists, who are geographically and administratively dispersed among various institutions across the United States. 3. Familiarize YVO staff with Incident Command System (ICS) procedures and protocols, and provide more in-depth training to appropriate staff members, as needed. 4. Train all science team members in the use of all analytical and computational tools available to them, in order to maximize effectiveness of teams in tracking and interpreting possible accelerating unrest at Yellowstone. Desirable pre-crisis preparations include: (a) updating a catalog of existing map and information products (and identifying additional products) that would be helpful during a crisis; (b) creating "to do" lists of early-crisis tasks for each scientific team; (c) coordinating radio frequencies among partner agencies; and (d) brief training on and promotion of the internal YVO Web log as a repository for scientific observations, data, photographs, and other material to be shared among YVO scientific teams during a crisis. This exercise was designed as an opportunity to practice response to a fast-developing volcano crisis and to test for organizational and procedural weaknesses that could emerge during a real crisis. This report is based upon the observations of the exercise organizers during the one-day exercise and upon written evaluations by the participants. It does not attempt to evaluate any other aspect of YVO or the scientific expertise of any of the highly competent YVO staff. Participants unanimously found the exercise to be helpful for improving their response capabilities, and it is our hope that the report will be a starting point for internal discussions that will make YVO even better-prepared for some future volcano crisis.

Utah↗
USGS2013

Overcoming the momentum of anachronism: American geologic mapping in a twenty-first-century world

The practice of geologic mapping is undergoing conceptual and methodological transformation. Profound changes in digital technology in the past 10 yr have potential to impact all aspects of geologic mapping. The future of geologic mapping as a relevant scientific enterprise depends on widespread adoption of new technology and ideas about the collection, meaning, and utility of geologic map data. It is critical that the geologic community redefine the primary elements of the traditional paper geologic map and improve the integration of the practice of making maps in the field and office with the new ways to record, manage, share, and visualize their underlying data. A modern digital geologic mapping model will enhance scientific discovery, meet elevated expectations of modern geologic map users, and accommodate inevitable future changes in technology.

GSA Special Papers↗
USGS2017

Best practices for virtual participation in meetings: Experiences from synthesis centers

The earth environment is a complex system, in which collaborative scientific approaches can provide major benefits by bringing together diverse perspectives, methods, and data, to achieve robust, synthetic understanding (Fig. 1). Face-to-face scientific meetings remain extremely valuable because of the opportunity to build deep mutual trust and understanding, and develop new collaborations and sometimes even lifelong friendships (Alberts 2013, Cooke and Hilton 2015). However, it has been argued that ecologists should be particularly sensitive to the environmental footprint of travel (Fox et al. 2009); such concerns, along with the time demands for travel, particularly for multi-national working groups, provide strong motivation for exploring virtual attendance. While not replacing the richness of face-to-face interactions entirely, it is now feasible to virtually participate in meetings through services that allow video, audio, and file sharing, as well as other Web-enabled communication.

Bulletin of the Ecological Society of America↗
USGS2011

A working environment for digital planetary data processing and mapping using ISIS and GRASS GIS

Since the beginning of planetary exploration, mapping has been fundamental to summarize observations returned by scientific missions. Sensor-based mapping has been used to highlight specific features from the planetary surfaces by means of processing. Interpretative mapping makes use of instrumental observations to produce thematic maps that summarize observations of actual data into a specific theme. Geologic maps, for example, are thematic interpretative maps that focus on the representation of materials and processes and their relative timing. The advancements in technology of the last 30 years have allowed us to develop specialized systems where the mapping process can be made entirely in the digital domain. The spread of networked computers on a global scale allowed the rapid propagation of software and digital data such that every researcher can now access digital mapping facilities on his desktop. The efforts to maintain planetary missions data accessible to the scientific community have led to the creation of standardized digital archives that facilitate the access to different datasets by software capable of processing these data from the raw level to the map projected one. Geographic Information Systems (GIS) have been developed to optimize the storage, the analysis, and the retrieval of spatially referenced Earth based environmental geodata; since the last decade these computer programs have become popular among the planetary science community, and recent mission data start to be distributed in formats compatible with these systems. Among all the systems developed for the analysis of planetary and spatially referenced data, we have created a working environment combining two software suites that have similar characteristics in their modular design, their development history, their policy of distribution and their support system. The first, the Integrated Software for Imagers and Spectrometers (ISIS) developed by the United States Geological Survey, represents the state of the art for processing planetary remote sensing data, from the raw unprocessed state to the map projected product. The second, the Geographic Resources Analysis Support System (GRASS) is a Geographic Information System developed by an international team of developers, and one of the core projects promoted by the Open Source Geospatial Foundation (OSGeo). We have worked on enabling the combined use of these software systems throughout the set-up of a common user interface, the unification of the cartographic reference system nomenclature and the minimization of data conversion. Both software packages are distributed with free open source licenses, as well as the source code, scripts and configuration files hereafter presented. In this paper we describe our work done to merge these working environments into a common one, where the user benefits from functionalities of both systems without the need to switch or transfer data from one software suite to the other one. Thereafter we provide an example of its usage in the handling of planetary data and the crafting of a digital geologic map. ?? 2010 Elsevier Ltd. All rights reserved.

Conference Paper↗
USGS1999

The USGS Abandoned Mine Lands Initiative: Protecting and restoring the environment near abandoned mine lands

The Abandoned Mine Lands (AML) Initiative is part of a larger strategy of the U.S. Department of the Interior and the U.S. Department of Agriculture to clean up Federal lands contaminated by abandoned mines. Thousands of abandond hard-rock metal mines (such as gold, copper, lead, and zinc) have left a dual legacy across the Western United States. They reflect the historic development of the west, yet at the same time represent a possible threat to human health and local ecosystems. Abandoned Mine Lands (AML) are areas adjacent to or affected by abandoned mines. AML's often contain unmined mineral deposits, mine dumps (the ore and rock removed to get to the ore deposits), and tailings (the material left over from the ore processing) that contaminate the surrounding watershed and ecosystem. For example, streams near AML's can contain metals and (or) be so acidic that fish and aquatic insects cannot live in them. Many of these abandoned hard-rock mines are located on or adjacent to public lands administered by the Bureau of Land Management, National Park Service, and U.S. Forest Service. These federal land management agencies and the USGS are committed to mitigating the adverse effects that AML's can have on water quality and stream habitats. The USGS AML Initiative began in 1997 and will continue through 2001 in two pilot watersheds - the Boulder River basin in southwestern Montana and the upper Animas River basin in southwestern Colorado. The USGS is providing a wide range of scientific expertise to help land managers minimize and, where possible, eliminate the adverse environmental effects of AML's. USGS ecologists, geologists, water quality experts, hydrologists, geochemists, and mapping and digital data collection experts are collaborating to provide the scientific knowledge needed for an effective cleanup of AML's.

Arizona, California, Colorado, Idaho, Montana, New↗
USGS2020

U.S. Geological Survey science in support of the North American Bat Monitoring Program (NABat)

Bats make up one-fifth of all mammalian species worldwide and are found on every continent except Antarctica. They contribute to overall ecosystem health by suppressing pest insects and pollinating plants and spreading seeds. Eight North American bat species are listed as federally endangered or threatened, and more than one-half are of current conservation concern in the United States, Canada, or Mexico. The U.S. Geological Survey (USGS) leads, manages, and coordinates the multinational North American Bat Monitoring Program (NABat) as well as conducts scientific research on bats. USGS and NABat partners help resource managers and policymakers make informed decisions regarding the conservation of bats across North America. USGS science also helps inform decision making with respect to WNS surveillance and bat vulnerability; mitigation of potential impacts of energy development on bats; prelisting conservation efforts for regulatory agencies; and land management practices. Partners are essential to the success of NABat. The data contributed by NABat partners provide baseline knowledge on the distribution and abundance of bats, which is used to evaluate the impacts of the threats to bats across North America. These data are also the foundation for the scientific information used to set conservation priorities and evaluate the effectiveness of management actions.

Fact Sheet↗
USGS2017

A dynamic spatio-temporal model for spatial data

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Wisconsin↗
USGS2022

Physics-guided machine learning from simulation data: An application in modeling lake and river systems

This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, these models often produce biased simulations due to inaccurate parameterizations or approximations used to represent the true physics. In this paper, we aim to build a new data-driven framework to monitor dynamical systems by extracting general scientific knowledge embodied in simulation data generated by the physics-based model. To handle the bias in simulation data caused by imperfect parameterization, we propose to extract general physical relations jointly from multiple sets of simulations generated by a physics-based model under different physical parameters. In particular, we develop a spatio-temporal network architecture that uses its gating variables to capture the variation of physical parameters. We initialize this model using a pre-training strategy that helps discover common physical patterns shared by different sets of simulation data. Then we fine-tune it using limited observation data via a contrastive learning process. By leveraging the complementary strength of machine learning and domain knowledge, our method has been shown to produce accurate predictions, use less training samples and generalize to out-of-sample scenarios. We further show that the method can provide insights about the variation of physical parameters over space and time in two domain applications: predicting temperature in streams and predicting temperature in lakes.

Conference Paper↗
USGS2007

Concerns regarding a call for pluralism of information theory and hypothesis testing

1. Stephens et al. (2005) argue for 'pluralism' in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example. 2. I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches. 3. We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. 'no difference') is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past. 4. We clarify misconceptions presented by Stephens et al. (2005) . 5. We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj| data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data |H0). 6. Synthesis and applications . I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.

Journal of Applied Ecology↗
USGS2016

TopoLens: Building a cyberGIS community data service for enhancing the usability of high-resolution National Topographic datasets

Geospatial data, often embedded with geographic references, are important to many application and science domains, and represent a major type of big data. The increased volume and diversity of geospatial data have caused serious usability issues for researchers in various scientific domains, which call for innovative cyberGIS solutions. To address these issues, this paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Through the collaboration between the CyberGIS Center at the University of Illinois at Urbana-Champaign (UIUC) and the U.S. Geological Survey (USGS), a community data service for accessing, customizing, and sharing digital elevation model (DEM) and its derived datasets from the 10-meter national elevation dataset, namely TopoLens, is created to demonstrate the workflow integration of geospatial big data sources, computation, analysis needed for customizing the original dataset for end user needs, and a friendly online user environment. TopoLens provides online access to precomputed and on-demand computed high-resolution elevation data by exploiting the ROGER supercomputer. The usability of this prototype service has been acknowledged in community evaluation.

Conference Paper↗
USGS2016

Compilation of V S 30 Data for the United States

V S 30 , the time-averaged shear-wave velocity ( V S ) to a depth of 30 meters, is a key index adopted by the earthquake engineering community to account for seismic site conditions. V S 30 is typically based on geophysical measurements of V S derived from invasive and noninvasive techniques at sites of interest. Owing to cost considerations, as well as logistical and environmental concerns, V S 30 data are sparse or not readily available for most areas. Where data are available, V S 30 values are often assembled in assorted formats that are accessible from disparate and (or) impermanent Web sites. To help remedy this situation, we compiled V S 30 measurements obtained by studies funded by the U.S. Geological Survey (USGS) and other governmental agencies. Thus far, we have compiled V S 30 values for 2,997 sites in the United States, along with metadata for each measurement from government-sponsored reports, Web sites, and scientific and engineering journals. Most of the data in our V S 30 compilation originated from publications directly reporting the work of field investigators. A small subset (less than 20 percent) of V S 30 values was previously compiled by the USGS and other research institutions. Whenever possible, V S 30 originating from these earlier compilations were crosschecked against published reports. Both downhole and surface-based V S 30 estimates are represented in our V S 30 compilation. Most of the V S 30 data are for sites in the western contiguous United States (2,141 sites), whereas 786 V S 30 values are for sites in the Central and Eastern United States; 70 values are for sites in other parts of the United States, including Alaska (15 sites), Hawaii (30 sites), and Puerto Rico (25 sites). An interactive map is hosted on the primary USGS Web site for accessing V S 30 data ( https://earthquake.usgs.gov/data/vs30/us/ ).

Data Series↗
USGS2022

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Environmental Evidence↗
USGS2019

Black Carp in North America: A description of range, habitats, time of year, and methods of reported captures

Black Carp Mylopharyngodon piceus are considered invasive in North America. Since the first wild capture in 2003, collection records have increased, yet information summarizing successful collection methods is lacking. Reported capture methods throughout the Black Carp's native and introduced ranges vary providing minimal aid for determining control and monitoring methods. Here, we describe the current species range and the spatial and temporal variation among captures. The size of fish can affect capture; thus, we report captured fish and gear dimensions and recommend appropriate scientific collection methods. We focus on collection data from 302 Black Carp ranging from 410 to 1,607 mm total length received from 2011 to February 2019. The reported range of Black Carp has expanded in the Cumberland, Illinois, Mississippi, Missouri, Ohio, and Tennessee rivers. Captures most frequently occurred in channel (41%), side‐channel (24%), and backwater (22%) habitat types, with increased records in May, June, and July. Most records were commercial captures, of which hoop net (51%) and gill net (26%) were most common. Results suggest that standard scientific methods for sampling fish in large rivers and standing water by hoop net and gill net may be used to monitor Black Carp, but a robust study design needs to be applied to determine gear selectivity and to determine if catch rates are density dependent or incidental.

North American Journal of Fisheries Management↗