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usSEABED: Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) offshore surficial sediment data release

Over the past 50 years there has been an explosion in scientific interest, research effort and information gathered on the geologic sedimentary character of the United States continental margin. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the shelf surface but rarely have those data been combined and integrated. This publication is the first release of the Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) coastal and offshore data from the usSEABED database. The report contains a compilation of published and previously unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to bring assorted data together in a unified database. The dbSEABED system is used to process the data. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the USGS Marine Aggregate Resources and Processes Assessment and Benthic Habitats projects, and will be useful to the marine science community for other studies of the Gulf of Mexico and Caribbean continental margins. This publication is divided into ten sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and those to pages outside this report will open in a new browser window.

Caribbean Sea, Gulf of Mexico↗

usSEABED: Pacific Coast (California, Oregon, Washington) Offshore Surficial-Sediment Data Release, version 1

Over the past 50 years there has been an explosion in scientific interest, research effort, and information gathered on the geologic sedimentary character of the continental margin of the United States. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the margin surface but rarely have those data been combined and integrated. This publication is the first release of the Pacific coast data from the usSEABED database. The report contains a compilation of published and unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to unify assorted data, with the data processed by the dbSEABED system. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the U.S. Geological Survey (USGS) Sea floor Mapping and Benthic Habitats project and the Marine Aggregate Resources and Processes assessment project, and will be useful to the marine science community for other studies of the Pacific coast continental margin. The publication is divided into 10 sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments, and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and to those to pages outside this report will open in a new browser window.

California, Oregon, Washington↗

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗

Smartphone technologies and Bayesian networks to assess shorebird habitat selection

Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers ( Charadrius melodus ) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications.

U.S Atlantic Coast↗

Mapping stream habitats with a global positioning system: Accuracy, precision, and comparison with traditional methods

We tested the precision and accuracy of the Trimble GeoXT??? global positioning system (GPS) handheld receiver on point and area features and compared estimates of stream habitat dimensions (e.g., lengths and areas of riffles and pools) that were made in three different Oklahoma streams using the GPS receiver and a tape measure. The precision of differentially corrected GPS (DGPS) points was not affected by the number of GPS position fixes (i.e., geographic location estimates) averaged per DGPS point. Horizontal error of points ranged from 0.03 to 2.77 m and did not differ with the number of position fixes per point. The error of area measurements ranged from 0.1% to 110.1% but decreased as the area increased. Again, error was independent of the number of position fixes averaged per polygon corner. The estimates of habitat lengths, widths, and areas did not differ when measured using two methods of data collection (GPS and a tape measure), nor did the differences among methods change at three stream sites with contrasting morphologies. Measuring features with a GPS receiver was up to 3.3 times faster on average than using a tape measure, although signal interference from high streambanks or overhanging vegetation occasionally limited satellite signal availability and prolonged measurements with a GPS receiver. There were also no differences in precision of habitat dimensions when mapped using a continuous versus a position fix average GPS data collection method. Despite there being some disadvantages to using the GPS in stream habitat studies, measuring stream habitats with a GPS resulted in spatially referenced data that allowed the assessment of relative habitat position and changes in habitats over time, and was often faster than using a tape measure. For most spatial scales of interest, the precision and accuracy of DGPS data are adequate and have logistical advantages when compared to traditional methods of measurement. ?? 2006 Springer Science+Business Media, Inc.

Environmental Management↗

Identifying policy-relevant indicators for assessing landscape vegetation patterns to inform planning and management on multiple use public lands

Understanding the structure and composition of landscapes can empower agencies to effectively manage public lands for multiple uses while sustaining land health. Many landscape metrics exist, but they are not often used in public land decision-making. Our objectives were to (1) develop and (2) apply a process for identifying a core set of indicators that public land managers can use to understand landscape-level resource patterns on and around public lands. We first developed a process for identifying indicators that are grounded in policy, feasible to quantify using existing data and resources, and useful for managers. We surveyed landscape monitoring efforts by other agencies, gathered science and agency input on monitoring goals, and quantified the prevalence of potential indicators in agency land health standards to identify five landscape indicators: amount, distribution, patch size, structural connectivity, and diversity of vegetation types. We then conducted pilot applications in four bureau of land management (BLM) field offices in Arizona, California, and Colorado to refine procedures for quantifying the indicators and assess the utility of the indicators for managers. Results highlighted the dominance of upland and the limited extent of riparian/wetland vegetation communities, moderate connectivity of priority vegetation patches, and lower diversity of native vegetation types on BLM compared to non-BLM lands. Agency staff can use the indicators to inform the development of quantitative resource management objectives in land use plans, evaluate progress in meeting those objectives, quantify potential impacts of proposed actions, and as a foundation for an all-lands approach to landscape-level management across public lands.

Environmental Management↗

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗

A model of Precambrian geology of Kansas derived from gravity and magnetic data

The fabric of the Precambrian geology of Kansas is revealed through inversion of gravity and magnetic data to pseudo-lithology. There are five main steps in the inversion process: (1) reduction of potential-field data to a horizontal plane in the wavenumber domain; (2) separation of the residual anomaly of interest from the regional background, where an assumption is made that the regional anomaly could be represented by some order of polynomial; (3) subtraction of the signal due to the known topography on the Phanerozoic/Precambrian boundary from the residual anomaly (we assume what is left at this stage are the signals due to lateral variation in the Precambrian lithology); (4) inversion of the residual anomaly in the wavenumber domain to density and magnetization distribution in the top part of the Precambrian constrained by the known geologic information; (5) derivation of pseudo-lithology by characterization of density and magnetization. The boundary between the older Central Plains Province to the north and the Southern Granite-Rhyolite Province to the south is clearly delineated. The Midcontinent Rift System appears to widen in central Kansas and involve a considerable portion of southern Kansas. Lithologies in southwestern Kansas appear to change over fairly small areas and include mafic rocks which have not been encountered in drill holes. The texture of the potential field data from southwestern Kansas suggests a history of continental growth by broad extension. Copyright ?? 1996 Elsevier Science Ltd.

Computers & Geosciences↗

Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning

Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.

Water Resources Research↗

An orbital comparison of a late mantling unit on Aeolis Mons with other erosion-resistant strata explored by MSL in Gale crater, Mars

Previous investigations along Curiosity 's traverse in Gale crater have explored the relationship between orbital and in situ observations. This work aims to better understand the geologic environment of units only observable from orbit and compare them to the properties of units examined by Curiosity . Here, we map an erosion-resistant dark-toned mantling unit that overlies the modern topography of Aeolis Mons (informally known as Mt. Sharp) and compare this unit to two other previously mapped dark-toned resistant units, the marker band and the mound skirting unit (MSU), that have been inferred to represent different geologic environments (lacustrine and aeolian, respectively). Visible to short wave infrared spectra from the Compact Reconnaissance Imaging Spectrometer for Mars and visual images from the High Resolution Imaging Science Experiment and Context Cameras aboard the Mars Reconnaissance Orbiter are used for this comparison. Spectral data suggest a mafic composition with minor alteration, although the composition varies more with location around Mt. Sharp rather than between units. Morphologically, the mantling unit has strong similarities to the marker band based on their consistent low-albedo, erosion-resistance, and smooth appearance, contrasting with the highly variable surface texture of the MSU. We hypothesize that all three units had a similar sediment source but experienced aqueous alteration at different times: early ubiquitous cementation in a surface aqueous environment in the mantling unit and marker band versus patchy late diagenesis in the MSU. If true, these results suggest that water activity continued within the Gale crater long after the erosion of Mt. Sharp.

JGR Planets↗

Water-use data in the United States: Challenges and future directions

In the United States, greater attention has been given to developing water supplies and quantifying available waters than determining who uses water, how much they withdraw and consume, and how and where water use occurs. As water supplies are stressed due to an increasingly variable climate, changing land-use, and growing water needs, greater consideration of the demand side of the water balance equation is essential. Data about the spatial and temporal aspects of water use for different purposes are now critical to long-term water supply planning and resource management. We detail the current state of water-use data, the major stakeholders involved in their collection and applications, and the challenges in obtaining high-quality nationally consistent data applicable to a range of scales and purposes. Opportunities to improve access, use, and sharing of water-use data are outlined. We cast a vision for a world-class national water-use data product that is accessible, timely, and spatially detailed. Our vision will leverage the strengths of existing local, state, and federal agencies to facilitate rapid and informed decision-making, modeling, and science for water resources. To inform future decision-making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water-use data.

Journal of the American Water Resources Associatio↗

Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands

If current climate change trends continue, rising sea levels may inundate low-lying islands across the globe, placing island biodiversity at risk. Recent models predict a rise of approximately one meter (1 m) in global sea level by 2100, with larger increases possible in areas of the Pacific Ocean. Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern Hawaiian Islands (NWHI), which extend 1,930 kilometers (km) beyond the main Hawaiian Islands, are a World Heritage Site and part of the Papahanaumokuakea Marine National Monument. These NWHI support the largest tropical seabird rookery in the world, providing breeding habitat for 21 species of seabirds, 4 endemic land bird species and essential foraging, breeding, or haul-out habitat for other resident and migratory wildlife. In recent years, concern has grown about the increasing vulnerability of the NWHI and their wildlife populations to changing climatic patterns, particularly the uncertainty associated with potential impacts from global sea-level rise (SLR) and storms. In response to the need by managers to adapt future resource protection strategies to climate change variability and dynamic island ecosystems, we have synthesized and down scaled analyses for this important region. This report describes a 2-year study of a remote northwestern Pacific atoll ecosystem and identifies wildlife and habitat vulnerable to rising sea levels and changing climate conditions. A lack of high-resolution topographic data for low-lying islands of the NWHI had previously precluded an extensive quantitative model of the potential impacts of SLR on wildlife habitat. The first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahanaumokuakea Marine National Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. Furthermore, this chapter explores the potential effects of SLR on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan Island's breeding bird species to SLR and storms. Using remote sensing and geospatial techniques, we estimated topography, classified vegetation, modeled SLR, and evaluated a range of climate change scenarios. On the basis of high-resolution airborne data collected during 2010-11 (root-mean-squared error = 0.05-0.18 m), we estimated the maximum elevation of 20 individual islands extending from Kure Atoll to French Frigate Shoals (range: 1.8-39.7 m) and computed the mean elevation (1.7 m, standard deviation 1.1 m) across all low-lying islands. We also analyzed general climate models to describe rainfall and temperature scenarios expected to influence adaptation of some plants and animals for this region. Outcomes for the NWHI predicted an increase in temperature of 1.8-2.6 degrees Celsius (°C) and an annual decrease in precipitation of 24.7-76.3 millimeters (mm) across the NWHI by 2100. Our models of passive SLR (excluding wave-driven effects, erosion, and accretion) showed that approximately 4 percent of the total land area in the NWHI will be lost with scenarios of +1.0 m of SLR and 26 percent will be lost with +2.0 m of SLR. Some atolls are especially vulnerable to SLR. For example, at Pearl and Hermes Atoll our analysis indicated substantial habitat losses with 43 percent of the land area inundated at +1.0 m SLR and 92 percent inundated at +2.0 m SLR. Across the NWHI, seven islands will be completely submerged with +2.0 m SLR. The limited global ranges of some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change scenarios and potential SLR impacts presented here emphasize the need for early climate change adaptation and mitigation planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying NWHI: Cyperus pennatiformis var. bryanii , Black-footed Albatross ( Phoebastria nigripes ), Laysan Albatross ( P. immutabilis ), Bonin Petrel ( Pterodroma hypoleuca ), Gray-backed Tern ( Onychoprion lunatus ), Laysan Teal ( Anas laysanensis ), Laysan Finch ( Telespiza cantans ), and Hawaiian monk seal ( Monachus schauinslandi ). Furthermore, SLR scenarios that include the effects of wave dynamics and groundwater rise may indicate amplified vulnerability to climate change driven habitat loss on low-lying islands. In chapter 2, we incorporated the combined effects of SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island ecosystem. This is the first hydrodynamic model to simulate the combined impacts of SLR and wave-driven inundation in the NWHI. We developed a high-resolution digital elevation model (mean vertical accuracy of 0.32 m) for the island. Then using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 m) that may occur over the next century. Additionally, we estimated the carrying capacity of Laysan Island for five species based on the available population monitoring data and described how potential losses in nesting habitat could influence population dynamics for Black-footed Albatross, Laysan Albatross, Red-footed Booby (Sula sula), Laysan Teal, and Laysan Finch. For some other seabird populations (Masked Booby, S. dactylatra ; Brown Booby, S. leucogaster ; Great Frigatebird, Fregata minor ; and Sooty Tern, Onychoprion fuscata ), we used recent colony distribution data, land cover maps, and nesting behavior to estimate potential losses of nesting habitat from SLR and wave-driven inundation. We observed far greater potential impacts of SLR to wildlife with the dynamic wave-driven modeling approach than with the passive modeling approach. Depending on SLR scenario and coastal orientation, during storms under a +2.00 m SLR scenario, the wave-driven inundation model predicted three times more inundation than the passive model (17.2 percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water height to passive inundation surfaces, therefore our dynamic models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven water levels were highest in the northwest quadrant of Laysan Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level provide little protection from wave-driven inundation. When wave-driven inundation was included in the SLR model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. The consequences of habitat loss due to SLR may be worse for species with colonies in the wave-exposed coastal zones (for example, Black-footed Albatross) and for populations already near the island's carrying capacity (for example, Laysan Teal). Species whose peak incubation and chick-rearing periods coincide with seasonally high wave heights also will be increasingly vulnerable, especially those species nesting on the ground in areas vulnerable to inundation, such as Gray-backed Tern and Black-footed Albatross. Other species that have space for population growth, or are not restricted to a narrow range of habitat types on Laysan (for instance, Sooty Terns), may be less sensitive to habitat loss from SLR over the next century. Our assessments of inundation risk, habitat loss, and wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader understanding of the impacts of inundation from SLR and storm-induced waves. Yet, most NWHI and their bird populations lack monitoring data to evaluate adaptations to and impacts of climate change. Exceptions include some data sets from long-term monitoring of wildlife populations, tides, or weather at French Frigate Shoals, Laysan Island, and Midway Atoll. These data sets are potentially valuable baselines, which could be informative for adaptive learning (integrating management and science) to predict, adapt, and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative vulnerability assessment for all of the low-lying NWHI, and results identify biological communities, locales, and resident endangered species of Papahanaumokuakea Marine National Monument expected to be at risk from SLR. This report is also intended as a reference for managers and conservation planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change monitoring priorities and future scientific studies.

Hawai'i↗

U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2016

Introduction 2016 was an exciting year for the Department of the Interior (DOI) Climate Science Centers (CSCs) and the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC). In recognition of our ongoing efforts to raise awareness and provide the scientific data and tools needed to address the impacts of climate change on fish, wildlife, ecosystems, and people, NCCWSC and the CSCs received an honorable mention in the first ever Climate Adaptation Leadership Award for Natural Resources sponsored by the National Fish, Wildlife, and Plant Climate Adaptation Strategy’s Joint Implementation Working Group. The recognition is a reflection of our contribution to numerous scientific workshops and publications, provision of training for students and early career professionals, and work with Tribes and indigenous communities to improve climate change resilience across the Nation. In this report, we highlight some of the activities that took place throughout the NCCWSC and CSC network in 2016.

Open-File Report↗

Earthquake probabilities in the San Francisco Bay region: 2000 to 2030 - A summary of findings

The San Francisco Bay region sits astride a dangerous “earthquake machine,” the tectonic boundary between the Pacific and North American Plates. The region has experienced major and destructive earthquakes in 1838, 1868, 1906, and 1989, and future large earthquakes are a certainty. The ability to prepare for large earthquakes is critical to saving lives and reducing damage to property and infrastructure. An increased understanding of the timing, size, location, and effects of these likely earthquakes is a necessary component in any effective program of preparedness. This study reports on the probabilities of occurrence of major earthquakes in the San Francisco Bay region (SFBR) for the three decades 2000 to 2030. The SFBR extends from Healdsberg on the northwest to Salinas on the southeast and encloses the entire metropolitan area, including its most rapidly expanding urban and suburban areas. In this study a “major” earthquake is defined as one with M≥6.7 (where M is moment magnitude). As experience from the Northridge, California (M6.7, 1994) and Kobe, Japan (M6.9, 1995) earthquakes has shown us, earthquakes of this size can have a disastrous impact on the social and economic fabric of densely urbanized areas. To reevaluate the probability of large earthquakes striking the SFBR, the U.S. Geological Survey solicited data, interpretations, and analyses from dozens of scientists representing a wide crosssection of the Earth-science community (Appendix A). The primary approach of this new Working Group (WG99) was to develop a comprehensive, regional model for the long-term occurrence of earthquakes, founded on geologic and geophysical observations and constrained by plate tectonics. The model considers a broad range of observations and their possible interpretations. Using this model, we estimate the rates of occurrence of earthquakes and 30-year earthquake probabilities. Our study considers a range of magnitudes for earthquakes on the major faults in the region—an innovation over previous studies of the SFBR that considered only a small number of potential earthquakes of fixed magnitude.

California↗

Geoinformatics 2006--Abstracts

In order to facilitate the discovery, integration, and analysis of distributed data, geoscientists-in partnership with information technologists and computer scientists-have established the emerging science of geoinformatics. Geoinformatics 2006, an international conference that was attended by over 300 participants between May 10 and 12, 2006, represents the first nationally organized meeting whose primary goal was to provide a forum for the exchange of the most current research in geoinformatics.

Scientific Investigations Report↗

ChemCam results from the Shaler Outcrop in Gale Crater, Mars

The "Shaler" outcrop in Gale crater is approximately 0.7 m thick and >20 m long, and exhibits multiple well-exposed platy and cross-stratified facies [1] interpreted to be primarily fluvial sandstone deposits. The outcrop is a part of the upper Glenelg member in the Yellowknife Bay (YKB) stratigraphic section [2]. Curiosity first encountered the "Shaler" outcrop on sol 121 of the mission, and returned to the outcrop on sols 309- 324. The rugged nature of the outcrop and short time available for analysis limited opportunities for contact science, but ChemCam’s ability to remotely collect compositional and textural observations resulted in a large data set from Shaler. ChemCam conducted analyses of 29 non-soil targets at Shaler, 26 of which used laser-induced breakdown spectroscopy (LIBS) for a total of 9,180 spectra. Three observations used only the remote micro-imager (RMI). Each of the 26 LIBS targets were analyzed at between 5 and 25 points, providing a measure of the target homogeneity and in some cases transecting fine strata.

Conference Paper↗

High throughput computing: a solution for scientific analysis

Public land management agencies continually face resource management problems that are exacerbated by climate warming, land-use change, and other human activities. As the U.S. Geological Survey (USGS) Fort Collins Science Center (FORT) works with managers in U.S. Department of the Interior (DOI) agencies and other federal, state, and private entities, researchers are finding that the science needed to address these complex ecological questions across time and space produces substantial amounts of data. The additional data and the volume of computations needed to analyze it require expanded computing resources well beyond single- or even multiple-computer workstations. To meet this need for greater computational capacity, FORT investigated how to resolve the many computational shortfalls previously encountered when analyzing data for such projects. Our objectives included finding a solution that would: harness existing Computer Processing Units (CPUs) when they're idle to run multiple jobs concurrently, which reduces the overall processing time without requiring additional hardware; offer an effective, centralized job-management system; handle job failures due to hardware, software, or network interruptions (obviating the need to manually resubmit the job after each stoppage); be affordable; and most importantly, allow us to complete very large, complex analyses that otherwise would not even be possible. In short, we envisioned a job-management system that would take advantage of unused FORT CPUs within a local area network (LAN) to effectively distribute and run highly complex analytical processes. What we found was a solution that uses High Throughput Computing (HTC) and High Performance Computing (HPC) systems to do exactly that (Figure 1).

Report↗

User guide to the FireCLIME Vulnerability Assessment (VA) Tool: A rapid and flexible system for assessing ecosystem vulnerability to climate-fire interactions

Decisionmakers need better methods for identifying critical ecosystem vulnerabilities to changing climate and fire regimes. Climate-wildfire-vegetation interactions are complex and hinder classification and projection necessary for development of management strategies. One such vulnerability assessment (VA) is FireCLIME VA, which allows users to compare management strategies under various climate scenarios and gauge the potential effectiveness of those strategies for reducing undesirable impacts of climate on wildfire regimes and resulting impacts of wildfire on natural ecosystems. Developed as part of the SW FireCLIME science-management partnership, FireCLIME is meant to be quick, flexible, and amendable to a range of data inputs (literature review, expert, and modeling or monitoring activities), allowing users to easily compare various fire-climate outcomes for one or more ecosystems of interest. Users can use literature, hypothetical scenarios, or quantitative data to implement the FireCLIME VA tool. This tool, unlike other vulnerability assessment, is best used iteratively to explore a range of possible scenarios and management strategies.

Report↗