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Results for “International Journal for Disaster Risk Reduction”

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

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USGS1995

Profiles of gamma-ray and magnetic data for aerial surveys over parts of the Western United States from longitude 108 to 126 degrees W. and from latitude 34 to 49 degrees N.

This CD-ROM contains images generated from geophysical data, software for displaying and analyzing the images and software for displaying and examining profile data from aerial surveys flown as part of the National Uranium Resource Evaluation (NURE) Program of the U.S. Department of Energy. The images included are of gamma-ray data (uranium, thorium, and potassium channels), Bouguer gravity data, isostatic residual gravity data, aeromagnetic anomalies, topography, and topography with bathymetry. This publication contains image data for the conterminous United States and profile data for the conterminous United States within the area longitude 108 to 126 degrees W. and latitude 34 to 49 degrees N. The profile data include apparent surface concentrations of potassium, uranium, and thorium, the residual magnetic field, and the height above the ground. The images on this CD-ROM include graytone and color images of each data set, color shaded-relief images of the potential-field and topographic data, and color composite images of the gamma-ray data. The image display and analysis software can register images with geographic and geologic overlays. The profile display software permits the user to view the profiles as well as obtain data listings and export ASCII versions of data for selected flight lines.

Data Series↗
USGS1976

Digital telemetry system network installation for the National Earthquake Information Service

A Digital Telemetry System Network, developed by the Albuquerque Seismological Laboratory, was installed for the USGS National Earthquake Information Service (NEIS) during January 1975. NEIS is located in Golden, Colorado. The initial Digital Telemetry System Network consists of four independent digital telemetry systems that provide wide area coverage over most of the United States. The four systems were installed between: Albuquerque, New Mexico to NEIS; St. Louis, Missouri to NEIS; Salt Lake City, Utah to NEIS; and Menlo Park, California to NEIS. Each digital telemetry system will transmit fourteen channels of high accuracy seismic data in a digital format over a standard C-1 grade telephone line. Digitized data words will have a resolution of eleven digital bits plus one sign bit. Each digital telemetry receiver, located at NEIS, will have the capacity to provide fourteen channels of seismic data in a digital format suitable for either on-line digital computer analysis or magnetic tape recording. In addition the data can be presented in an analog format suitable for recording on helicorders or develocorders. This network is the first large scale digital telemetry system used by the U.S. Geological Survey for the transmission of real time seismic data.

Open-File Report↗
USGS2026

Methodology for quantitative spatial sensitivity analysis of volcanic geodetic networks

Introduction This report introduces a methodology for assessing the state of the U.S. Geological Survey Volcano Observatories’ geodetic monitoring networks that measure how volcanoes deform or change shape. This new method uses a model-based approach that considers the uniqueness of the instrument environments at each volcano. This report focuses on simplified volcanic sources, is independent of the shape or size of the volcano, or the network geometry, and thus highlights the strengths and potential vulnerabilities of each volcano’s geodetic network in an actionable visual format. This analysis can help observatories to make informed decisions about whether volcanoes have an adequate level of geodetic monitoring and indicate where improvements are needed.

Lassen Peak, Mount Shasta↗
USGS2025

Capturing the uncertainty of seismicity observations in earthquake rate estimates: Implications for probabilistic seismic hazard analysis and the USGS National Seismic Hazard Model

The rate of earthquakes in a region is a fundamental input to Probabilistic Seismic Hazard Analysis. We present a Monte Carlo method for computing that rate from seismicity catalogs while including a range of data and analysis uncertainties. This method is applied to regions for which the b value is assumed to be spatially invariant. Each region is broken down into epochs for which each epoch is estimated to have a uniform magnitude of completeness ( ⁠⁠ M c ). The distribution of earthquake rates for M ≥ M c is determined for each epoch by considering the Poisson likelihood of rates given the number of observed earthquakes with M ≥ M c ⁠ . We use a Monte Carlo process to include the uncertainty in b , ⁠ M c , and individual event magnitudes. The result for each epoch is the joint distribution of the Poisson rate of earthquakes with magnitudes larger than the minimum value used to calculate hazard ( ⁠⁠ M 1 ) and the Gutenberg–Richter b values, which control the extrapolation to other magnitudes. The rate for each region is either the duration‐weighted average over the epochs or, to better capture temporal variations, we also consider mixture models. The mixture models also provide an avenue to allow temporal variations in b values. To implement this joint distribution in a logic tree, we use the mean and 95% confidence branches, each of which is parameterized with an M ≥ M 1 rate and b value. We explore different ways of defining those branches, as well as non‐Gutenberg–Richter branches, and their impact on hazard estimates. The mean hazard, but not the fractiles, is robust with respect to these choices. To illustrate these new methods, we use synthetic data and catalogs from recent U.S. Geological Survey National Seismic Hazard Models for the Central and Eastern United States and for Puerto Rico and the U.S. Virgin Islands.

central and eastern United States, Puerto Rico, U.↗
USGS1999

Estimation of magnitude and frequency of floods for streams in Puerto Rico: New empirical models

Flood-peak discharges and frequencies are presented for 57 gaged sites in Puerto Rico for recurrence intervals ranging from 2 to 500 years. The log-Pearson Type III distribution, the methodology recommended by the United States Interagency Committee on Water Data, was used to determine the magnitude and frequency of floods at the gaged sites having 10 to 43 years of record. A technique is presented for estimating flood-peak discharges at recurrence intervals ranging from 2 to 500 years for unregulated streams in Puerto Rico with contributing drainage areas ranging from 0.83 to 208 square miles. Loglinear multiple regression analyses, using climatic and basin characteristics and peak-discharge data from the 57 gaged sites, were used to construct regression equations to transfer the magnitude and frequency information from gaged to ungaged sites. The equations have contributing drainage area, depth-to-rock, and mean annual rainfall as the basin and climatic characteristics in estimating flood peak discharges. Examples are given to show a step-by-step procedure in calculating a 100-year flood at a gaged site, an ungaged site, a site near a gaged location, and a site between two gaged sites.

Puerto Rico↗
USGS2024

Flood-inundation maps for the Cuyahoga River at Jaite, Ohio, 2024

Digital flood-inundation maps for a nearly 6-mile reach of the Cuyahoga River at Jaite, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees. The maps depict estimates of the extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 04206425 on the Cuyahoga River at Jaite, Ohio. Water-surface profiles were computed for the stream reach by using a one-dimensional steady-state step-backwater model. The hydraulic model was calibrated to the current USGS streamgage data and then used to compute 15 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 6 to 20 feet, which correspond to below “action stage” to “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used with a geographic information system digital elevation model derived from light detection and ranging data to delineate the areas flooded at each stage. These maps, along with current stage data from the USGS streamgage and forecasted high-flow stages from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.

Ohio↗
USGS1990

Preconditioned Conjugate-Gradient 2 (PCG2), a computer program for solving ground-water flow equations

This report documents PCG2: a numerical code to be used with the U.S. Geological Survey modular three-dimensional, finite-difference, ground-water flow model. PCG2 uses the preconditioned conjugate-gradient method to solve the equations produced by the model for hydraulic head. Linear or nonlinear flow conditions may be simulated. PCG2 includes two reconditioning options: modified incomplete Cholesky preconditioning, which is efficient on scalar computers; and polynomial preconditioning, which requires less computer storage and, with modifications that depend on the computer used, is most efficient on vector computers. Convergence of the solver is determined using both head-change and residual criteria. Nonlinear problems are solved using Picard iterations. This documentation provides a description of the preconditioned conjugate gradient method and the two preconditioners, detailed instructions for linking PCG2 to the modular model, sample data inputs, a brief description of PCG2, and a FORTRAN listing.

Water-Resources Investigations Report↗
USGS2025

Understanding market sensitivity: Estimation of supply and demand elasticities for non-fuel minerals

In today’s rapidly changing economic landscape, understanding market responsiveness to price changes and the factors influencing commodity prices has become increasingly relevant. Price elasticities serve as indicators of how variations in market conditions affect supply and demand, providing insights into the sensitivity of commodity markets to price fluctuations. This paper presents a comprehensive analysis of price elasticities of supply and demand for 74 non-fuel mineral commodities including precious metals, base metals, minor metals, and industrial minerals that are utilized across various industries. We employ various econometric techniques, including fixed effects models for panel data and two-stage dynamic ordinary least squares (2S-DOLS) alongside autoregressive distributed lag (ARDL) models for time series analysis, to derive robust estimates of price elasticities. Our findings reveal variability in elasticities among different commodities and indicate that all studied mineral commodities exhibit price inelastic supply and demand in the short run, which we define as one year for the purposes of our analysis, given that the data is all annual. This research provides original estimates of price responsiveness for a wide range of commodities that have not been previously addressed in the literature, thereby enhancing the understanding of market dynamics in the mineral sector. Given that price elasticities can be influenced by factors such as market structure, technological advancements, mining costs, and industry-specific demand drivers, we use variables that serve as proxies for these factors.

Mineral Economics↗
USGS2026

Efficient physics‐informed ground‐motion simulations with reduced‐order models: CyberShake implications and high‐resolution site terms for southern San Andreas fault earthquakes

Recent advances in Probabilistic Seismic Hazard Analysis (PSHA) leverage physics‐based ground‐motion simulations to estimate seismic hazard, such as the CyberShake project. However, computational costs quickly escalate when performing PSHA for numerous faults or sites and can become prohibitively expensive. To reduce computational demands, CyberShake uses reciprocity and interpolates physics‐informed corrections from simulations conducted at fewer locations, but the accuracy of these interpolations remains poorly quantified. To quantify the interpolation accuracy, we derive high‐resolution, frequency‐dependent site terms for southern California and compare them with interpolated site terms using the CyberShake approach. We accomplish this by performing a set of earthquake point‐source simulations distributed along the nonplanar fault geometry for the southern San Andreas fault (SSAF) extending from Bombay Beach to Lake Hughes. Using SeisSol, we simulate three minutes of viscoelastic seismic wave propagation for these sources and store the horizontal‐component Green’s functions for 480,000 sites. We then use a scientific machine learning approach based on interpolated proper orthogonal decomposition to construct an accurate reduced‐order model of the Green’s functions to efficiently predict effective amplitude spectra (EAS) for finite‐source rupture models of SSAF earthquakes. Using minimum curvature interpolation with tension, as used in CyberShake, we compare the interpolated site terms against our high‐resolution site terms. We identify local discrepancies with EAS differing by up to a factor of approximately three. Furthermore, we identify locations where unexpectedly high or low ground motions are missed when using the interpolated dataset for these earthquakes. We estimate that our approach may be used within CyberShake to reduce the time‐to‐solution by a factor of 336 for the entire earthquake rupture forecast. Our analysis of physics‐based site terms provides more insight into the seismic hazard due to SSAF ruptures and guides future developments by combining high‐performance computing and reduced‐order modeling techniques for PSHA.

California↗
USGS2026

Where will the cat cross the road? Comparing camera and GPS-based models for identifying wildlife corridors

Designing effective wildlife corridors is a critical conservation challenge in fragmented landscapes. GPS-based step selection functions strongly predict dispersal corridors and connectivity, but GPS collaring can be expensive and invasive. Camera-based occupancy models are widely used for connectivity analyses but may involve trade-offs in data resolution. Despite widespread use of both approaches, few studies have directly compared them using concurrent datasets. We developed a stacked single-species, single-season occupancy model and a Circuitscape connectivity surface for mountain lions (Puma concolor) on Washington’s Olympic Peninsula, USA, and compared them with a connectivity surface from an existing integrated step selection function. Both models predicted mountain lion GPS locations well, with binned Spearman rank correlations of 1 for Circuitscape and 0.96 for the step selection function, though step selection better identified habitat use by dispersers. Connectivity predictions were moderately correlated across the landscape ( r = 0.26), but agreement was strongest in human-dominated areas most critical for corridor planning. We conclude that GPS-based approaches are advantageous when data collection is feasible and the focus is on dispersal or fine-scale movement. However, camera-based approaches may be preferable for multi-species monitoring, large spatial and temporal scales, noninvasive sampling, when resources are limited, or when fine-scale or dispersal-specific inference is not required.

Washington↗
USGS2025

Prospectivity mapping for geologic hydrogen

Geologic, or naturally occurring, hydrogen has the potential to become a new, low-carbon, primary energy resource. Often referred to as “white” or “gold” hydrogen, this gas occurs naturally in the Earth’s subsurface, similar to petroleum resources. However, unlike petroleum, which releases carbon dioxide when burned, burning hydrogen only produces water as a byproduct. Exploration for geologic hydrogen remains in an early stage and discoveries of high concentrations of subsurface hydrogen are still relatively rare. To facilitate research and exploration for this potential resource, this report presents the first publicly available prospectivity map of geologic hydrogen accumulations in the conterminous United States. Prospective regions are those regions in which all major components necessary for a hydrogen accumulation likely are present—a source of sufficient hydrogen generation, porous reservoirs for storage, and seals to prevent leakage. The midcontinent region of the United States and the central California coast are revealed as having high prospectivity. This analysis also identifies previously unrecognized prospective regions that may be favorable due to long distance lateral migration of subsurface hydrogen, such as the offshore eastern seaboard of the United States, and can provide a linkage between surface observations of hydrogen degassing and far-field source regions. The methodology developed to create this map is expandable and flexible and may be adapted to incorporate new concepts in the hydrogen system and for application to other regions of the world.

conterminous United States↗
USGS2024

A multidisciplinary approach that considers occurrence, geochemistry, bioavailability, and toxicity to prioritize critical minerals for environmental research

Critical minerals (or critical elements) are minerals or elements that are essential to global security and development and have supply chains vulnerable to disruption. In general, knowledge of the environmental behavior and health effects of critical elements is needed to support the development of safe and environmentally responsible supplies. This knowledge includes identifying potential consequences of increased critical element production and use, alternative critical element sources such as mine wastes, and adverse effects of critical elements on ecosystem condition and organismal health. Here we identify significant data gaps in the understanding of critical elements in surficial and aquatic environments, and the need, given the large number of commodities (50) identified on the 2022 critical minerals list for the United States, for an approach to prioritize them for study of their environmental fate and effects. We propose a multidisciplinary approach for this prioritization, considering measures of occurrence, geochemistry, bioavailability, and toxicity. We describe relatively easy-to-obtain metrics for each of these topic areas and demonstrate the utility of this integrated prioritization approach using indium and zinc as examples. This approach facilitates prioritizing research with a focus on those critical elements that are most mobile in the environment, bioavailable, toxic, or simply lacking data in these categories.

Environmental Science & Technology↗
USGS2025

Airborne geophysical efforts for critical mineral systems mapping in the southern Midcontinent, USA

The increasing global demand for critical minerals to support energy and technological advancement has accelerated exploration and research efforts for these essential resources. Since 2019, the United States Geological Survey (USGS) Earth Mapping Resources Initiative (EMRI) has worked to modernize geologic mapping of the Nation to better understand its critical mineral resources. To further this initiative, the USGS has flown a series of high-resolution airborne magnetic and radiometric surveys over large areas of the southern Midcontinent. The surveys cover known critical mineral deposits and areas with the potential to host additional critical minerals based on the presence of one or more overlapping mineral systems. One aspect of EMRI emphasizes close collaboration between the USGS and the Association of American State Geologists, as well as other government and industry partners to leverage geophysical, geological, and geochemical expertise on both regional and local scales. The EMRI high-resolution airborne survey data provide new insights into the geophysical framework of the southern Midcontinent and its critical mineral endowment. Additionally, discoveries made from the data have directed new studies for critical mineral exploration.

southern Midcontinent↗
USGS2026

From model to action: Identifying the gaps in coastal marsh models for decision making

Coastal salt marsh modeling projects are designed to answer questions about where salt marshes may migrate, how they can migrate, and how they will be impacted by changing water levels. While this information is vital for climate-resilient marsh conservation, translating these findings into actionable results remains a challenge. The authors examined the actionability of marsh modeling efforts in the United States by analyzing data collected from semi-structured interviews of marsh model users ( n = 24) across two ongoing projects along the U.S. East Coast and the Gulf of America (Gulf of Mexico). By qualitatively analyzing the interview data, the authors found that the tasks of practitioners who use coastal marsh model outputs fall into three major themes: (1) marsh restoration, (2) planning with uncertainty, and (3) conserving habitat for marsh-reliant species. For each of these themes, the authors identify unmet needs, including high spatial resolution information for local planning, accessible descriptions of uncertainty to increase user confidence, and the incorporation of human dimensions data (e.g., human alterations to the landscape such as impoundments and culverts) for a comprehensive understanding of the coastal salt marsh. Marsh modeling projects should strategize how to fulfil these unmet user needs so that marsh modeling outputs can better support decisions related to marsh conservation and restoration.

Gulf of America, United States East Coast↗
USGS2025

2023 Earthquake Ground-Motion Workshop for the Central and Eastern United States, with a focus on the Gulf and Atlantic Coastal Plains—Agenda and abstracts

The U.S. Geological Survey held a virtual workshop December 7–8, 2023, to share research and ideas about earthquake ground motions in the Central and Eastern United States, with a focus on the Atlantic and Gulf Coastal Plains. The workshop was organized to learn about potential regionalization of ground-motion characteristics (source, path, and site), consider new explanatory variables for site response, and hear and discuss updates on ground-motion research on the Atlantic and Gulf Coastal Plains. The workshop was organized into a series of contributed presentations and three panel discussions held during 2 days. This report documents the agenda, contributed abstracts, and panel summaries.

Scientific Investigations Report↗
USGS2024

Federal lands greenhouse gas emissions and sequestration in the United States: Estimates for 2005–22

In 2016, the Secretary of the U.S. Department of the Interior requested that the U.S. Geological Survey (USGS) produce a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use of fossil fuels from Federal lands. The first report in this series included emissions estimates from 2005 to 2014 and were reported for 29 States and two offshore areas. Native American and Tribal lands were not included in that analysis. This report recalculates those previous years (2005–14) with updated data and methods and extends the estimates to 2022. Nationwide emissions from fossil fuels produced on Federal lands in 2022 were 1,081.2 million metric tons of carbon dioxide equivalent (MMT CO 2 Eq.) for CO 2 , 33.4 MMT CO 2 Eq. for methane (CH 4 ), and 4.3 MMT CO 2 Eq. for nitrous oxide (N 2 O). Compared to 2005, the 2022 totals represent decreases in emissions for all three greenhouse gases (by 17 percent for CO 2 , 37 percent for CH 4 , and 30 percent for N 2 O). Emissions from fossil fuels produced on Federal lands represent, on average, 21.8 percent of U.S. emissions for CO 2 , 6.1 percent for CH 4 , and 1.3 percent for N 2 O over the 18 years included in this estimate. The trends and relative magnitudes of the greenhouse gas emissions estimated are roughly parallel to the Federal lands fossil fuel production volumes. In 2021, Federal lands of the conterminous United States stored 70,424 MMT CO 2 Eq. in terrestrial ecosystems. Soils stored most of the terrestrial ecosystem carbon (66 percent), followed by live vegetation (25 percent), deadwood (5 percent), and litter (4 percent). Carbon sequestration on Federal lands was highly variable over time, owing primarily to interannual variability in climate and weather, and variability in land use and land cover (LULC) change and disturbances, among these are wildfires and logging. Between 2005 and 2021, Federal lands sequestered an average of 83 MMT CO 2 Eq./yr. By subtracting the cumulative effects of LULC and disturbance-related CO 2 losses to the atmosphere from the total, we estimate that ecosystems at the national level sequestered CO 2 at an annual mean rate of 17 MMT CO 2 Eq./yr in a term called the net ecosystem exchange (NEE). This annual NEE sequestration value represents about 1.4 percent of average fossil fuel emissions over the same period. The USGS estimates presented in this report represent an accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. A combined net CO 2 emissions estimate, which is the difference between the emitted and sequestered CO 2 from both the fossil fuel and ecosystems estimates, provides context for evaluating the greenhouse gas contributions of activities on these lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.

Scientific Investigations Report↗
USGS2026

Family life is critical for migration and survival in a long-lived social bird

Extended parental care is common among birds and particularly well known among long-lived migratory species. Yet, factors influencing the duration of parental care are poorly understood, and empirical evidence for the functional importance of family life for offspring development is scarce. Using unique tracking and genetic data of 55 adult and juvenile migratory Bewick’s swans, among which 18 parent-offspring pairs and 13 sibling pairs, we quantify variation in family bond duration and identify the cause and consequences of their dissolution. Families generally broke up during the second half of the first northward migration of the offspring, when offspring were nearly one year old. Earlier separation occurred on the wintering grounds in response to disturbance ( n = 10 juveniles) and led to delayed migration and, critically, lower survival (40 vs. 86%). Our results provide rare empirical evidence that family bonds, as a pathway for social learning, are not just important, but vital to the migration and survival of young family-living birds.

Behavioral Ecology and Sociobiology↗
USGS2026

Making many out of one: Synthetic geologic deformation model distributions for use in USGS NSHM25‐PRVI Puerto Rico-U.S. Virgin Island update

A key use‐case of geologic slip rates is within deformation models used in probabilistic seismic hazard analyses. Field‐derived geologic slip rates have formed the cornerstone of deformation models in such applications for decades. Recent advancements in seismic hazard analyses have expanded the use of faults for which geologic slip rates are not well constrained using categorical slip rate estimates. Because of these advancements, application of a geologic deformation model for use in 2025 U.S. Geological Survey National Seismic Hazard Model Puerto Rico‐U.S. Virgin Islands (NSHM25‐PRVI) proved challenging due to: (1) a lack of field‐based geologic slip rates, and (2) a lack of epistemic uncertainty distributions within a broad range of estimated slip rates. Preliminary versions of the NSHM25‐PRVI model sampled these slip rate bins in a coincident manner along preferred and extreme value branches, which yielded untenable correlations in mean hazard results. To minimize the influence of correlated uncertainties amid these challenges, we develop a synthetic epistemic uncertainty distribution for deformation rate on each crustal fault. Each fault has a weighting schema across four possible distribution shapes: uniform, normal, triangular favoring local minima, and triangular favoring local maxima. The synthetic distributions are then sampled several times for each logic tree branch. The results provide a more realistic distribution of rates across the study region as compared with using correlated extrema sampling. This exploration of our method in a small region like PRVI can pave the way for larger‐scale, more complicated applications (e.g., western United States).

Puerto Rico, US Virgin Islands↗