Search USGS⌕ Search

SEARCH · Search USGS

Results for “Applied Sciences”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26Linked to original sources

One hundred pressing questions on the future of global fish migration science, conservation, and policy

Migration is a widespread but highly diverse component of many animal life histories. Fish migrate throughout the world's oceans, within lakes and rivers, and between the two realms, transporting matter, energy, and other species (e.g., microbes) across boundaries. Migration is therefore a process responsible for myriad ecosystem services. Many human populations depend on the presence of predictable migrations of fish for their subsistence and livelihoods. Although much research has focused on fish migration, many questions remain in our rapidly changing world. We assembled a diverse team of fundamental and applied scientists who study fish migrations in marine and freshwater environments to identify pressing unanswered questions. Our exercise revealed questions within themes related to understanding the migrating individual's internal state, navigational mechanisms, locomotor capabilities, external drivers of migration, the threats confronting migratory fish including climate change, and the role of migration. In addition, we identified key requirements for aquatic animal management, restoration, policy, and governance. Lessons revealed included the difficulties in generalizing among species and populations, and in understanding the levels of connectivity facilitated by migrating fishes. We conclude by identifying priority research needed for assuring a sustainable future for migratory fishes.

Frontiers in Ecology and Evolution↗

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↗

Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: Application to Chesapeake Bay coastal-plain, USA

Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.

Delaware, Maryland, Virginia↗

Snow processes in mountain forests: Interception modeling for coarse-scale applications

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

Utah↗

Use of recent and historical records to estimate status and trends of a rare and imperiled stream fish, Percina jenkinsi (Percidae)

Rarely encountered animals may be present but undetected, potentially leading to incorrect assumptions about the persistence of a local population or the conservation priority of a particular area. The federally endangered and narrowly endemic Conasauga logperch ( Percina jenkinsi ) is a good example of a rarely encountered fish species of conservation concern, for which basic population statistics are lacking. We evaluated the occurrence frequency for this species using surveys conducted with a repeat-observation sampling approach during the summer of 2008. We also analyzed museum records since the late 1980s to evaluate the trends in detected status through time. The results of these analyses provided support for a declining trend in this species over a portion of its historical range, despite low estimated detection probability. We used the results to identify the expected information return for a given level of monitoring where the sampling approach incorporates incomplete detection. The method applied here may be of value where historic occurrence records are available, provided that the assumption of constant capture efficiency is reasonable.

Canadian Journal of Fisheries and Aquatic Sciences↗

Coastal landforms and processes at the Cape Cod National Seashore, Massachusetts—A primer

Anyone who spends more than a few days on Cape Cod (the Cape) quickly becomes a coastal geologist, quickly learning the rhythms of daily tides and the seasonal cycles of beaches growing and being swept away by storms; swimmers and surfers track how the breakers appear, and dog-walkers notice the hard-packed sand blanketed overnight by an airy layer that leaves deep labored tracks. Careful observers whose paths wander to the ocean’s edge will observe many of the landforms and coastal processes described in this book and if we have done our job well, the stories told here will seem familiar. Watchful experience brings insights; indeed, this is how scientists and perhaps how artists work, describing patterns that explain and predict. When is the next high tide? What will the winter bring? Where do we build, fish, swim? How do wind and waves offshore in the North Atlantic help arrange the plants and dunes and hollows on the beach? And most of all, as human animals drawn to live and play on the edge of the ocean, how do we get the benefits of this complex natural system of geology and biology? How do we affect coastal processes; how is the coast changing now and how is the coast likely to change in years ahead with climate warming and climate change? This book is about the highly dynamic coastal landforms of Cape Cod—the beaches, bluffs, spits, dunes, barrier beaches, estuaries, and salt marshes. What they are, why they are where they are, how they behave with respect to the greater Cape Cod coastal system—how the landforms respond to day-to-day and long-term geologic processes, such as waves and currents, change in sediment transport, relative sea-level rise, and meteorological processes such as hurricanes, nor’easters, and cold front passages. It is also about how the landforms got to be where they are and the way they are and where they are headed in the near future with the predicted effects of global climate warming and change. Our objective is to provide a single source of understandable and readable scientific information for those who live, play, and work on outer Cape Cod and at the Cape Cod National Seashore, as well as to provide an introduction to Cape Cod’s coastal landforms for anyone with an interest in Earth science and nature who wants a better understanding of coastal systems and processes. Basic to an understanding of coastal landforms is the fact that they work together—they interact—as elements of many systems, and therefore our ultimate concern is not the individual landform itself but rather the geologic systems that make up Cape Cod and the Cape Cod National Seashore. Much of this discussion can be applied as well to Nantucket, Martha’s Vineyard, and other coastal regions. The coast of outer Cape Cod, about 15,000 years old and about 30 miles (mi; 50 kilometers [km]) long, is but a tiny piece of the global Earth system that operates within a much larger realm of space and time. Cape Cod’s coastal landforms are temporary holding patterns within a continual interplay of land, sea, atmosphere, climate, ice, and life, including a variety of human activities that both affect and are affected by these processes. These interactions produce the landforms, and the landforms alter the interactions. The resulting landforms provide habitats for a wide variety of coastal plants and animals. The habitats along with their inhabitants and the interacting environmental factors controlling them constitute the Cape’s complex and varied ecosystems. But for now, we are here to enjoy it. We welcome you to delight and wonder at the perpetually changing handshake between the ocean and shore at New England’s Great Beach.

Massachusetts↗

Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta

A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period. Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.

California↗

Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent. We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone. Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations. We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development). Policy implications : Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.

Journal of Applied Ecology↗

Estimating disease prevalence from preferentially sampled, pooled data

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

California↗

Stratification of a cityscape using census and land use variables for inventory of building materials

A cityscape (or any landscape) can be stratified into environmental units using multiple variables of information. For the purposes of sampling building materials, census and land use variables were used to identify similar strata. In the Metropolitan Statistical Area of a cityscape, the census tract is the smallest unit for which census data are summarized and digitized boundaries are available. For purposes of this analysis, census data on total population, total number of housing units, and number of singleunit dwellings were aggregated into variables of persons per square kilometer and proportion of housing units in single-unit dwellings. The level 2 categories of the U.S. Geological Survey's land use and land cover data base were aggregated into variables of proportion of residential land with buildings, proportion of nonresidential land with buildings, and proportion of open land. The cityscape was stratified, from these variables, into environmental strata of Urban Central Business District, Urban Livelihood Industrial Commercial, Urban Multi-Family Residential, Urban Single Family Residential, Non-Urban Suburbanizing, and Non-Urban Rural. The New England region was chosen as a region with commonality of building materials, and a procedure developed for trial classification of census tracts into one of the strata. Final stratification was performed by discriminant analysis using the trial classification and prior probabilities as weights. The procedure was applied to several cities, and the results analyzed by correlation analysis from a field sample of building materials. The methodology developed for stratification of a cityscape using multiple variables has application to many other types of environmental studies, including forest inventory, hydrologic unit management, waste disposal, transportation studies, and other urban studies. Multivariate analysis techniques have recently been used for urban stratification in England. ?? 1987 Annals of Regional Science.

The Annals of Regional Science↗

Spirit rover localization and topographic mapping at the landing site of Gusev crater, Mars

By sol 440, the Spirit rover has traversed a distance of 3.76 km (actual distance traveled instead of odometry). Localization of the lander and the rover along the traverse has been successfully performed at the Gusev crater landing site. We localized the lander in the Gusev crater using two-way Doppler radio positioning and cartographic triangulations through landmarks visible in both orbital and ground images. Additional high-resolution orbital images were used to verify the determined lander position. Visual odometry and bundle adjustment technologies were applied to compensate for wheel slippage, azimuthal angle drift, and other navigation errors (which were as large as 10.5% in the Husband Hill area). We generated topographic products, including 72 ortho maps and three-dimensional (3-D) digital terrain models, 11 horizontal and vertical traverse profiles, and one 3-D crater model (up to sol 440). Also discussed in this paper are uses of the data for science operations planning, geological traverse surveys, surveys of wind-related features, and other science applications.

Journal of Geophysical Research E: Planets↗

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↗

Geomorphological Fieldwork

Geomorphological Fieldwork addresses a topic that always remains popular within the geosciences and environmental science. More specifically, the volume conveys a growing legacy of field-based learning for young geomorphologists that can be used as a student book for field-based university courses and postgraduate research requiring fieldwork or field schools. The editors have much experience of field-based learning within geomorphology and extend this to physical geography. The topics covered are relevant to basic geomorphology as well as applied approaches in environmental and cultural geomorphology. The book integrates a physical-human approach to geography, but focuses on physical geography and geomorphology from an integrated field-based geoscience perspective.

Book↗

Examining spring wet slab and glide avalanche occurrence along the Going-to-the-Sun Road corridor, Glacier National Park, Montana, USA

Wet slab and glide snow avalanches are dangerous and yet can be particularly difficult to predict. Wet slab and glide avalanches are presumably triggered by free water moving through the snowpack and the subsequent interaction with layer or ground interfaces, and typically occur in the spring during warming and subsequent melt periods. In Glacier National Park (GNP), Montana, both types of avalanches can occur in the same year and affect the spring opening operations of the Going-to-the-Sun Road (GTSR). We investigated the timing of wet slab and glide avalanche occurrence along the GTSR from 2003 to 2011 using meteorological and snowpack data from two high-elevation weather stations, one SNOTEL site, and an avalanche database to characterize 55 wet slab and 182 glide avalanches. Daily wet slab and glide avalanche occurrence were combined to represent an avalanche day and were compared to non-avalanche days (no avalanche occurrence) for 60 variables (both direct and derived measurements) using a univariate analysis. A classification tree (CART) was then trained to capture the most important variables for examining specific meteorological and snowpack variables that contribute to these types of wet snow avalanches. The CART was 10-fold cross validated using the data for 2003–2010 seasons and resulted in overall predictive accuracy of 73%. We then used the statistically optimal CART as a predictive model for the spring avalanche season of 2011, which resulted in an overall predictive accuracy of 82% for both avalanche and non-avalanche days, and a predictive accuracy of 91% for avalanche days. The results suggest that the role of air temperature and snowpack settlement appear to be the most important variables in wet slab and glide avalanche occurrence. When applied to the 2011 season, the results of the CART model are encouraging and they enhance our understanding of some of the required meteorological and snowpack conditions for wet slab and glide avalanche occurrence.

Cold Regions Science and Technology↗

Spatially resolved source apportionment of per- and polyfluoroalkyl substances (PFAS) within a post-industrial river catchment

Source apportionment of per- and polyfluoroalkyl substances (PFAS) in rivers is typically based on water concentrations, which cannot quantify PFAS loads or define geographical source areas. This study applied a river catchment-scale approach to identify PFAS source zones and assess the relative importance of industrial PFAS sources in the River Mersey, UK – a post-industrial, densely populated catchment with diverse PFAS sources. Synoptic sampling and PFAS river load analysis identified key sub-catchments and river stretches contributing the majority of PFAS. Notably, the highest PFAS concentrations did not always correspond to the greatest loads. Most PFOS (64 %), PFOA (49 %), 6:2FTS (46 %) and PFHxS (56 %) were exported from the Upper Mersey sub-catchment, despite higher concentrations in northern sub-catchments, emphasising the importance of load-based monitoring. Mass balance analysis of loads highlighted substantial inputs from specific river stretches, notably the Lower Irwell (Bolton to Manchester City Centre), River Tame (Marple Bridge to Stockport), and Upper Mersey (Stockport to Urmston). While PFAS loads generally scaled with catchment area, yield (load per unit area) analysis identified disproportionately high exports from small headwater catchments, notably the upper River Roch (PFOA, PFHpA and PFHxA) and Glaze Brook (PFBS). Industrial sources in these sub-catchments (a waste management facility and landfills, respectively) were confirmed using gadolinium anomaly analysis and consented discharge records. More widely, gadolinium data suggested industrial discharges may contribute to PFAS occurrence at 62 % of our sample sites throughout the catchment. These findings demonstrate that spatial analysis of PFAS loads, rather than concentrations alone, is critical for identifying PFAS source areas. We present a scalable monitoring framework for PFAS source apportionment applied at the river catchment-scale that can be used by environmental managers to target and prioritise PFAS source areas for detailed monitoring and remediation.

River Mersey↗

From drought to deluge: Spatiotemporal variation in migration routing, survival, travel time and floodplain use of an endangered migratory fish

We developed a novel statistical model to relate the daily survival and migration dynamics of an endangered anadromous fish to river flow and water temperature during both extreme drought and severe flooding in an intensively managed river system. Our Bayesian temporally stratified multistate mark recapture model integrates over unobserved travel times and route transitions to efficiently estimate covariate relationships and includes an adjustment for telemetry tag battery failure. We applied the model to acoustic-tagged juvenile Sacramento river winter-run Chinook salmon (Oncorhynchus tshawytscha) and found that survival decreased with decreasing river flows and increased water temperatures. We found that fish were likely to enter at a large floodplain during flood conditions and that survival in floodplain was comparable to the mainstem Sacramento river. Our study demonstrates the response of an endangered anadromous fish population to extreme spatial and temporal variability in habitat accessibility and quality. The general model framework we introduce here can be applied to telemetry of migratory fish through systems with multiple routes to efficiently estimate spatiotemporal variation in survival, travel time, and routing.

California↗

Geologic map of the Palo Alto and part of the Redwood Point 7-1/2' quadrangles, San Mateo and Santa Clara Counties, California

The Palo Alto and southern part of the Redwood Point 7-1/2' quadrangles cover an area on the San Francisco peninsula between San Francisco Bay and the Santa Cruz Mountains. San Francisquito and Los Trancos Creeks, in the southeastern part of the map area, form the boundary between San Mateo and Santa Clara Counties. The area covered by the geologic map extends from tidal and marsh lands at the edge of the bay southward across a gently sloping alluvial plain to the foothills of the northern Santa Cruz Mountains. The foothills are separated from the main mass of the mountains by two northwest-striking faults, the San Andreas and Pilarcitos, that cross the southwest corner of the map area (fig. 1). The map and adjoining areas are here divided into three structural blocks juxtaposed along these faults, adopting the scheme of Nilsen and Brabb (1979): (1) the San Francisco Bay block lying east of the San Andreas Fault Zone; (2) the Pilarcitos block lying between the San Andreas and Pilarcitos Faults; and (3) the La Honda block that includes the main mass of the Santa Cruz Mountains lying west of the Pilarcitos Fault. The west boundary of the La Honda block is the Seal Cove-San Gregorio Fault. Pre-late Pleistocene Cenozoic rocks of the foothills have been compressed into northwest-striking folds, which have been overridden by Mesozoic rocks along southwest-dipping low-angle faults. Coarse- to fine-grained upper Pleistocene and Holocene alluvial and estuarine deposits, eroded from the foothills and composing the alluvial plain, are essentially undeformed. Most of the alluvial plain, including some parts of the marsh land that borders the bay, has been covered by residential and commercial developments, and virtually all of the remaining marsh land has been diked off and used as salt evaporating ponds. The map area includes parts of the municipalities of San Carlos, Redwood City, Atherton, Woodside, Portola Valley, Menlo Park, and East Palo Alto in San Mateo County; and Palo Alto, Stanford University, Los Altos, and Los Altos Hills in Santa Clara County (fig. 2). Much of the university land remains as undeveloped open space surrounded by densely urbanized lands. Geologic maps of all or part of the present map area have been prepared previously by Branner and others (1909), Thomas (1949), Dobbs and Forbes (1960), Dibblee (1966), Page and Tabor (1967), Pampeyan (1970a, 1970b), Beaulieu (1970), Helley and others (1979), and by numerous Stanford University students working on topical earth science problems. In addition, numerous engineering geologic studies have been conducted for site investigations relating to residential and commercial developments and, in particular, for construction of the Stanford Linear Accelerator Center (SLAC). The reports pertaining to SLAC are summarized in Skjei and others (1965) and more recently in a report by Earth Sciences Associates (1983). The interested reader is referred to Brabb and Pampeyan (1983), Brabb and others (1982), Wentworth and others (1985), Wieczorek and others (1985), Thomson and Evernden (1986), Brabb and Olson (1986), Youd and Perkins (1987), Perkins (1987), and Mark and Newman (1988) for information pertaining to geology, history, slope stability, seismic shaking, liquifaction potential, and faulting and seismicity in San Mateo County, some of which can be applied directly to northern Santa Clara County. Field work for the present geologic map was done in 1962-1964 and 1966 when SLAC and Interstate 280 were in early stages of construction. Only minor additions and revisions have been made since this mapping was first released (Pampeyan, 1970a; 1970b) as it was impractical to keep pace with accelerating urban development of the area. Geologic units of the flatlands area are largely adapted from Helley and Lajoie (1979).

California↗

Making USGS information effective in the electronic age

Executive Summary -- The USGS Coastal and Marine Geology Program (CMGP) held a workshop on 'Making USGS Information Effective in the Electronic Age' in Woods Hole, MA, on 6-8 February 2001. The workshop was designed to address broad issues of knowledge and communication, and to help develop the mission, vision, and goals of the National Knowledge Bank called for in the 1999 NRC review of the CMGP. Presentations led by historians and philosophers yield to a wide-ranging review and discussion of the role of USGS science in society: USGS science is important to government to understand certain complicated public policy issues (such as the environment), but we must participate in two-way public dialogs to increase our relevance and usefulness. Presentations led by USGS communications experts reviewed the principles of audience analysis and effective communications: this focused look at audiences, markets, and products provided an introduction to the behaviors, the tools, and the terminology that might be applied to public discourse. Presentations by several information technology experts showed the potential - and pitfalls - of current schemes for Web-based information access. Finally, several brainstorming sessions developed action items, vision, and characteristics of a knowledge bank. Based on the workshop discussions and results, the authors developed the National Knowledge Bank Mission, Vision, and Goals statements.

Open-File Report↗