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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 595 records · Page 33Linked to original sources
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↗
USGS2019

Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards

Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.

Colorado↗
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↗
USGS2022

What common-garden experiments tell us about climate responses in plants

Common garden experiments are indoor or outdoor plantings of species or populations collected from multiple distinct geographic locations, grown together under shared conditions. These experiments examine a range of questions for theory and application using a variety of methods for analysis. The eight papers of this special feature comprise a cross section of contemporary approaches, summarized and synthesized here by what they tell us about the relationships between climate-related trait spectra and fitness optima. Four of the eight papers are based on field experiments in prairie, desert, Mediterranean and boreal biomes. Representative of many common garden experiments, these experiments reveal consistent evidence of traits varying with population climate provenance, but evidence of a tradeoff between growth and tolerance traits or of consistent fitness optimization at home is scant, in contrast to trait theory. Two synthesis papers highlight dominant patterns of trait divergence, including for an exotic invasive species. One theoretical paper warned that unknown kinship relationships between populations can result in the misidentification of adaptive trait divergence. A third synthesis paper formulated novel and ambitious goals for common-garden studies through including measurement of response variables at multiple levels of biological organization. The featured papers discuss multiple avenues for improving common garden studies. Genomic analysis, together with the quantification of kinship relationships, will continue to reveal the influence of environmental drivers on gene selection. Measuring a more complete set of fitness traits, especially for traits related to regeneration, will permit the development of projection models to explicitly link trait spectra, climate patterns and fitness consequences. More standardized data reporting will additionally improve abilities to synthesize findings across experiments. Testing population performance in competition with other species will produce more robust fitness comparisons between genotypes, especially for slower-growing genotypes in higher-resource environments. Adding gardens in and beyond climatic edge locations will furthermore strengthen the understanding of population failure and species exclusion. Finally, there is unrealized potential in adding ecosystem-level observations to common-garden studies that will enhance integrative analysis across scales of biological organization and scientific domains. Synthesis . With novel, creative designs, data integration and synthesis, common garden experiments will continue to advance the understanding of trait ensembles interacting with climate across scales of biological organization, provide pivotal data for global change models and guide ecological applications such as restoration of habitats for rare and climate sensitive species.

Journal of Ecology↗
USGS2024

New developments at the Center for Engineering Strong-Motion Data (CESMD)

The Center for Engineering Strong-Motion Data (CESMD), an internationally utilized joint center of the U.S. Geological Survey (USGS) and the California Geological Survey (CGS), provides a single access point for earthquake strong-motion records and station metadata from the CGS California Strong-Motion Instrumentation Program (CSMIP), the USGS National Strong-Motion Project (NSMP), the USGS Advanced National Seismic System, and other affiliates. The CESMD has been continuously improving its webtools to facilitate the access of strong-motion data and metadata for use in post-earthquake response and for scientific and engineering research applications. The Center provides raw and processed strong-motion data via the Engineering Data Center (EDC) and the Virtual Data Center (VDC) web portals. This paper focuses on the strong-motion products provided by the EDC where more than 48,000 records with peak ground accelerations greater than 0.1% g from over 2400 earthquakes are currently hosted. and on the ongoing efforts to develop data access tools and applications. The new developments and ongoing efforts in the EDC include: 1) enhancements to the CESMD webservices to facilitate access to station metadata, earthquake information, and strong motion records 2) new features to the interactive map interface, improving the visualization and access to earthquake, station, and record information, 3) efforts to develop a new web application tool for data format conversion from a number of data formats, 4) efforts to unify varying waveform data formats into a consistent format, 5) ongoing efforts to compile seismic station site geology, measured or inferred Vs30 values, shear-wave profiles, NEHRP site class, and available structural instrument deployment schematics, and 6) a special studies pages for research topic-specific ground motion datasets that offer uniform processing of records from a variety of sources.

Conference Paper↗
USGS2016

Mineral resources of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and Wyoming

Scientific Investigations Report 2016–5089 and accompanying data releases are the products of the U.S. Geological Survey (USGS) Sagebrush Mineral-Resource Assessment (SaMiRA). The assessment was done at the request of the Bureau of Land Management (BLM) to evaluate the mineral-resource potential of some 10 million acres of Federal and adjacent lands in Idaho, Montana, Nevada, Oregon, Utah, and Wyoming. The need for this assessment arose from the decision by the Secretary of the Interior to pursue the protection of large tracts of contiguous habitat for the greater sage-grouse ( Centrocercus urophasianus ) in the Western United States. One component of the Department of the Interior plan to protect the habitat areas includes withdrawing selected lands from future exploration and development of mineral and energy resources, including copper, gold, silver, rare earth elements, and other commodities used in the U.S. economy. The assessment evaluates the potential for locatable minerals such as gold, copper, and lithium and describes the nature and occurrence of leaseable and salable minerals for seven Sagebrush Focal Areas and additional lands in Nevada (“Nevada additions”) delineated by BLM. Supporting data are available in a series of USGS data releases describing mineral occurrences (the USGS Mineral Deposit Database or “USMIN”), oil and gas production and well status, previous mineral-resource assessments that covered parts of the areas studied, and a compilation of mineral-use cases based on data provided by BLM, as well as results of the locatable mineral-resource assessment in a geographic information system. The present assessment of mineral-resource potential will contribute to a better understanding of the economic and environmental trade-offs that would result from closing approximately 1 0 million acres of Federal lands to mineral entry.

Idaho, Montana, Nevada, Oregon, Utah, Wyoming↗
USGS2011

Mount Elbert Gas Hydrate Stratigraphic Test Well, Alaska North Slope: Overview of scientific and technical program

The Mount Elbert Gas Hydrate Stratigraphic Test Well was drilled within the Alaska North Slope (ANS) Milne Point Unit (MPU) from February 3 to 19, 2007. The well was conducted as part of a Cooperative Research Agreement (CRA) project co-sponsored since 2001 by BP Exploration (Alaska), Inc. (BPXA) and the U.S. Department of Energy (DOE) in collaboration with the U.S. Geological Survey (USGS) to help determine whether ANS gas hydrate can become a technically and commercially viable gas resource. Early in the effort, regional reservoir characterization and reservoir simulation modeling studies indicated that up to 0.34 trillion cubic meters (tcm; 12 trillion cubic feet, tcf) gas may be technically recoverable from 0.92 tcm (33 tcf) gas-in-place within the Eileen gas hydrate accumulation near industry infrastructure within ANS MPU, Prudhoe Bay Unit (PBU), and Kuparuk River Unit (KRU) areas. To further constrain these estimates and to enable the selection of a test site for further data acquisition, the USGS reprocessed and interpreted MPU 3D seismic data provided by BPXA to delineate 14 prospects containing significant highly-saturated gas hydrate-bearing sand reservoirs. The “Mount Elbert” site was selected to drill a stratigraphic test well to acquire a full suite of wireline log, core, and formation pressure test data. Drilling results and data interpretation confirmed pre-drill predictions and thus increased confidence in both the prospect interpretation methods and in the wider ANS gas hydrate resource estimates. The interpreted data from the Mount Elbert well provide insight into and reduce uncertainty of key gas hydrate-bearing reservoir properties, enable further refinement and validation of the numerical simulation of the production potential of both MPU and broader ANS gas hydrate resources, and help determine viability of potential field sites for future extended term production testing. Drilling and data acquisition operations demonstrated that gas hydrate scientific research programs can be safely, effectively, and efficiently conducted within ANS infrastructure. The program success resulted in a technical team recommendation to project management to drill and complete a long-term production test within the area of existing ANS infrastructure. If approved by stakeholders, this long-term test would build on prior arctic research efforts to better constrain the potential gas rates and volumes that could be produced from gas hydrate-bearing sand reservoirs.

Alaska↗
USGS2017

The HayWired Earthquake Scenario

Foreword The 1906 Great San Francisco earthquake (magnitude 7.8) and the 1989 Loma Prieta earthquake (magnitude 6.9) each motivated residents of the San Francisco Bay region to build countermeasures to earthquakes into the fabric of the region. Since Loma Prieta, bay-region communities, governments, and utilities have invested tens of billions of dollars in seismic upgrades and retrofits and replacements of older buildings and infrastructure. Innovation and state-of-the-art engineering, informed by science, including novel seismic-hazard assessments, have been applied to the challenge of increasing seismic resilience throughout the bay region. However, as long as people live and work in seismically vulnerable buildings or rely on seismically vulnerable transportation and utilities, more work remains to be done. With that in mind, the U.S. Geological Survey (USGS) and its partners developed the HayWired scenario as a tool to enable further actions that can change the outcome when the next major earthquake strikes. By illuminating the likely impacts to the present-day built environment, well-constructed scenarios can and have spurred officials and citizens to take steps that change the outcomes the scenario describes, whether used to guide more realistic response and recovery exercises or to launch mitigation measures that will reduce future risk. The HayWired scenario is the latest in a series of like-minded efforts to bring a special focus onto the impacts that could occur when the Hayward Fault again ruptures through the east side of the San Francisco Bay region as it last did in 1868. Cities in the east bay along the Richmond, Oakland, and Fremont corridor would be hit hardest by earthquake ground shaking, surface fault rupture, aftershocks, and fault afterslip, but the impacts would reach throughout the bay region and far beyond. The HayWired scenario name reflects our increased reliance on the Internet and telecommunications and also alludes to the interconnectedness of infrastructure, society, and our economy. How would this earthquake scenario, striking close to Silicon Valley, impact our interconnected world in ways and at a scale we have not experienced in any previous domestic earthquake? The area of present-day Contra Costa, Alameda, and Santa Clara Counties contended with a magnitude-6.8 earthquake in 1868 on the Hayward Fault. Although sparsely populated then, about 30 people were killed and extensive property damage resulted. The question of what an earthquake like that would do today has been examined before and is now revisited in the HayWired scenario. Scientists have documented a series of prehistoric earthquakes on the Hayward Fault and are confident that the threat of a future earthquake, like that modeled in the HayWired scenario, is real and could happen at any time. The team assembled to build this scenario has brought innovative new approaches to examining the natural hazards, impacts, and consequences of such an event. Such an earthquake would also be accompanied by widespread liquefaction and landslides, which are treated in greater detail than ever before. The team also considers how the now-prototype ShakeAlert earthquake early warning system could provide useful public alerts and automatic actions. Scientific Investigations Report 2017–5013 and accompanying data releases are the products of an effort led by the USGS, but this body of work was created through the combined efforts of a large team including partners who have come together to form the HayWired Coalition (see chapter A). Use of the HayWired scenario has already begun. More than a full year of intensive partner engagement, beginning in April 2017, is being directed toward producing the most in-depth look ever at the impacts and consequences of a large earthquake on the Hayward Fault. With the HayWired scenario, our hope is to encourage and support the active ongoing engagement of the entire community of the San Francisco Bay region by providing the scientific, engineering, and economic and social science inputs for use in exercises and planning well into the future. As HayWired volumes are published, they will be made available at https://doi.org/10.3133/sir20175013 .

California↗
USGS2004

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: An interim report

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Survey’s Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncertainties may be reduced. Subsequently, the value of using a probabilistic framework for estimating and explicitly stating these uncertainties within a decisionmaking process can be estimated when new data are collected. Future work includes the design and implementation of a Bayesian network decision support system (BN-DSS) to produce mitigation scenarios for offset-project evaluation in the Cache Creek watershed. The decisionmaker, a wastewater-treatment plant, is expected to evaluate potential Hg-offset programs in terms of changes in HgT load changes, MeHg-production potential, project cost, and other suitability criteria. Subsequently, scenarios can be analyzed by performing sensitivity analyses and ranking environmental and economic uncertainties in terms of the decisionmaker’s preferences and risk choices. Such an analysis allows decisionmakers and stakeholders to explore various scenarios and predict the consequences of different stated preferences over outcomes.

Open-File Report↗
USGS2008

The critical role of volcano monitoring in risk reduction

Data from volcano-monitoring studies constitute the only scientifically valid basis for short-term forecasts of a future eruption, or of possible changes during an ongoing eruption. Thus, in any effective hazards-mitigation program, a basic strategy in reducing volcano risk is the initiation or augmentation of volcano monitoring at historically active volcanoes and also at geologically young, but presently dormant, volcanoes with potential for reactivation. Beginning with the 1980s, substantial progress in volcano-monitoring techniques and networks - ground-based as well space-based - has been achieved. Although some geochemical monitoring techniques (e.g., remote measurement of volcanic gas emissions) are being increasingly applied and show considerable promise, seismic and geodetic methods to date remain the techniques of choice and are the most widely used. Availability of comprehensive volcano-monitoring data was a decisive factor in the successful scientific and governmental responses to the reawakening of Mount St. Helens (Washington, USA) in 1980 and, more recently, to the powerful explosive eruptions at Mount Pinatubo (Luzon, Philippines) in 1991. However, even with the ever-improving state-ofthe-art in volcano monitoring and predictive capability, the Mount St. Helens and Pinatubo case histories unfortunately still represent the exceptions, rather than the rule, in successfully forecasting the most likely outcome of volcano unrest.

Advances in Geosciences↗
USGS1999

Sea-floor images and data from multibeam surveys in San Francisco Bay, Southern California, Hawaii, the Gulf of Mexico, and Lake Tahoe, California-Nevada

Accurate base maps are a prerequisite for any geologic study, regardless of the objectives. Land-based studies commonly utilize aerial photographs, USGS 7.5-minute quadrangle maps, and satellite images as base maps. Until now, studies that involve the ocean floor have been at a disadvantage due to an almost complete lack of accurate marine base maps. Many base maps of the sea floor have been constructed over the past century but with a wide range in navigational and depth accuracies. Only in the past few years has marine surveying technology advanced far enough to produce navigational accuracy of 1 meter and depth resolutions of 50 centimeters. The Pacific Seafloor Mapping Project of the U.S. Geological Survey's, Western Coastal and Marine Geology Program, Menlo Park, California, U.S.A., in cooperation with the Ocean Mapping Group, University of New Brunswick, Fredericton, Canada, is using this new technology to systematically map the ocean floor and lakes. This type of marine surveying, called multibeam surveying, collects high-resolution bathymetric and backscatter data that can be used for various base maps, GIS coverages, and scientific visualization methods. This is an interactive CD-ROM that contains images, movies, and data of all the surveys the Pacific Seafloor Mapping Project has completed up to January 1999. The images and movies on this CD-ROM, such as shaded relief of the bathymetry, backscatter, oblique views, 3-D views, and QuickTime movies help the viewer to visualize the multibeam data. This CD-ROM also contains ARC/INFO export (.e00) files and full-resolution TIFF images of all the survey sites that can be downloaded and used in many GIS packages.

Data Series↗