Search USGS⌕ Search

SEARCH · Search USGS

Results for “Data Science in Science”

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 1,261 records · Page 70Linked to original sources
USGS2024

System characterization report on Vision-1

Executive Summary This report addresses system characterization of the Airbus Vision-1 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Vision-1 is a high-resolution Earth observation satellite launched in September 2018 as a collaborative effort between Airbus and Surrey Satellite Technology Ltd. It features a Newtonian telescope with a refractive relay, capturing images in panchromatic and multispectral bands. Operating in a Sun-synchronous orbit at an altitude of 583 kilometers, Vision-1 ensures consistent illumination conditions during image acquisition. It has a revisit time of 1 to 8 days depending on latitude and viewing angle, and it features an off-pointing agility of plus or minus 45 degrees, allowing for multiple target captures in a single pass using spot, strip, and mosaic imaging modes. The panchromatic band offers a resolution of 0.87 meter (m), whereas the multispectral bands (blue, green, red, and near infrared) provide a resolution of 3.48 m. These capabilities support a variety of applications including urban planning, agricultural monitoring, land classification, natural resource management, and disaster response. More information on the Vision-1 satellite and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.” The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the Vision-1 satellite has an interior geometric performance in the range of 0 to 0.02 m in easting and −0.01 to 0.03 m in northing in band-to-band registration, an exterior geometric performance of 1.7 to 2.2 m in easting and −1.1 to −0.7 m in northing offset with a 90-percent circular error of 3.4 to 3.7 m, a radiometric performance in the range of −0.029 to 0.017 in offset and 0.884 to 0.984 in slope, and a spatial performance in the range of 0.992 to 1.092 pixels for multispectral full width at half maximum and 1.895 pixels for the panchromatic band full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.29 to 0.36 for the multispectral bands and 0.05 for the panchromatic band.

Open-File Report↗
USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗
USGS2012

The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration

Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policy-makers are faced with countless decisions each year at local, state, tribal, territorial, and national levels on issues as diverse as renewable and non-renewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decision-making is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and well-being of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decision-makers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that USGS scientists and partners can play in building scientific understanding and providing timely information to decision-makers. The strategy underscores the connection between scientific discoveries and the application of new knowledge. The strategy integrates ecosystem science and decision-making, producing new scientific outcomes to assist resource managers and providing public benefits. The USGS is uniquely positioned to play an important role in ecosystem science. With its wide range of expertise, the agency can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the DOI and other partners in the natural sciences, in both conducting science and its application. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework and vision for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected and reinforcing components: • Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. • Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. • Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. • Develop tools, technologies, and capacities to inform decision-making about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. • Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: • Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. • Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. • Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. • Support decision-making. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the strategic approaches are a set of proposed actions that represent a sampling of specific activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct both discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role over the next decade in advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Open-File Report↗
USGS2021

USGS CEOS analysis ready data for land achievements and future plans

The efforts of the Committee on Earth Observation Satellites (CEOS) to bring CEOS Analysis Ready Data for Land (CARD4L) products to countries and international organizations quickly and easily continues to receive important support from the U.S. Geological Survey (USGS). As part of its engagement with CARD4L, the USGS worked to address specific Threshold and Target Product Family Specification (PFS) requirements for its Landsat Collection 2 Level-2 science products and in July 2020, received formal CEOS endorsement for 100 percent CARD4L-compliance at the Threshold level for Collection 2 surface reflectance and surface temperature. This endorsement ensures these products meet a level of interoperability with data from other Earth-observing platforms, such as Europe's Sentinel-2 satellites, as the European Space Agency also works toward CARD4L-compliant products. In addition to the Collection 2 Level-2 land surface data products, the USGS recognizes Landsat's potential to make a valuable contribution to aquatic science and environmental monitoring capabilities for aquatic ecosystems, especially in coastal and inland waters. Working with subject matter experts, the USGS has been coordinating an international agency effort to establish a new CARD4L PFS for aquatic reflectance to be considered for CEOS endorsement in 2021.

Conference Paper↗
USGS2021

System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)

Executive Summary This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999. The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “ 2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium ” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.

Open-File Report↗
USGS2002

Characterization and copper binding of humic and nonhumic organic matter isolated from the South Platte River: Evidence for the presence of nitrogenous binding site

Humic substances typically constitute 40−60% of the dissolved organic matter (DOM) in surface waters. However, little information is available regarding the metal binding properties of the nonhumic hydrophilic portion of the DOM. In this study, humic and nonhumic DOM samples were isolated from the South Platte River (Colorado, DOC = 2.6 mg·L - 1 , SUVA 254 = 2.4 L/mg·m) using a two-column array of XAD-8 and XAD-4 resins. The three major isolated fractions of DOM, which accounted for 57% of the bulk DOM, were characterized using a variety of analytical tools. Proton and copper binding properties were studied for each fraction. The main objective of this work was to compare the structural and chemical characteristics of the isolated fractions and test models describing DOM reactivity toward metal ions. The characterization work showed significant structural differences between the three isolated fractions of DOM. The hydrophobic acid fraction (i.e., humic substances isolated from the XAD-8 resin) gave the largest C/H, C/O, and C/N ratios and aromatic carbon content among the three isolated fractions. The transphilic acid (TPHA) fraction (“transphilic” meaning fraction of intermediate polarity isolated from the XAD-4 resin) was found to incorporate the highest proportion of polysaccharides, whereas the transphilic neutral (TPHN) fraction was almost entirely proteinaceous. The gradual increase of the charge with pH for the three DOM fractions is most likely caused by a large distribution of proton affinity constants for the carboxylic groups, as well as a second type of group more generally considered to be phenolic. In the case of the DOM fraction enriched in proteinaceous material (i.e., TPHN fraction), the results showed that the amino groups are responsible for the charge reversal. For low copper concentrations, nitrogen-containing functional groups similar to those of amino acids are likely to be involved in complexation, in agreement with previously published data.

Colorado↗
USGS1997

Analysis of environmental data with censored observations

The potential threats to humans and to terrestrial and aquatic ecosystems from environmental contamination could depend on the sum of the concentrations of different chemicals. However, direct summation of environmental data is not generally feasible because it is common for some chemical concentrations to be recorded as being below the analytical reporting limit. This creates special problems in the analysis of the data. A new model selection procedure, named forward censored regression, is introduced for selecting an appropriate model for environmental data with censored observations. The procedure is demonstrated using concentrations of atrazine (2-chloro-4-ethylamino-6-isopropylamino- s -triazine), deethylatrazine (DEA, 2-amino-4-chloro-6-isopropylamino- s -triazine), and deisopropylatrazine (DIA, 2-amino-4-chloro-6-ethylamino- s -triazine) in groundwater in the midwestern United States by using the data derived from a previous study conducted by the U.S. Geological Survey. More than 80% of the observations for each compound for this study were left censored at 0.05 μg/L. The values for censored observations of atrazine, DEA, and DIA are imputed with the selected models. The summation of atrazine residue (atrazine + DEA + DIA) can then be calculated using the combination of observed and imputed values to generate a pseudo-complete data set. The all-subsets regression procedure is applied to the pseudo-complete data to select the final model for atrazine residue. The methodology presented can be used to analyze similar cases of environmental contamination involving censored data.

Environmental Science & Technology↗
USGS1989

Problems of snowmelt runoff modelling for a variety of physiographic and climatic conditions

Problems include: a) definition of the spatial and temporal distribution of model input; b) measurement or estimation of snow accumulation, snowmelt, and runoff process parameters for a range of applications and scales; and c) development of accurate short term and long term snowmelt runoff forecasts. Procedures being investigated to solve these problems include: a) integrating conventional and remote-sensing data to improve estimates of input data; b) developing snowmelt process algorithms which have parameters that are closely related to measurable basin and climatic characteristics; and c) updating model paramters and components using measured data or knowledge of past uncertainty. -from Author

Hydrological Sciences Journal↗
USGS2008

Anticorrelated seismic velocity anomalies from post-perovskite in the lowermost mantle

Earth's lowermost mantle has thermal, chemical, and mineralogical complexities that require precise seismological characterization. Stacking, migration, and modeling of over 10,000 P and S waves that traverse the deep mantle under the Cocos plate resolve structures above the core-mantle boundary. A small -0.07 ?? 0.15% decrease of P wave velocity (Vp) is accompanied by a 1.5 ?? 0.5% increase in S wave velocity (Vs) near a depth of 2570 km. Bulk-sound velocity [Vb = (V p2 - 4/3Vs2)1/2] decreases by -1.0 ?? 0.5% at this depth. Transition of the primary lower-mantle mineral, (Mg1-x-y FexAly)(Si,Al) O3 perovskite, to denser post-perovskite is expected to have a negligible effect on the bulk modulus while increasing the shear modulus by ???6%, resulting in local anticorrelation of Vb and Vs anomalies; this behavior explains the data well.

Science↗
USGS2003

The National Map: from geography to mapping and back again

When the means of production for national base mapping were capital intensive, required large production facilities, and had ill-defined markets, Federal Government mapping agencies were the primary providers of the spatial data needed for economic development, environmental management, and national defense. With desktop geographic information systems now ubiquitous, source data available as a commodity from private industry, and the realization that many complex problems faced by society need far more and different kinds of spatial data for their solutions, national mapping organizations must realign their business strategies to meet growing demand and anticipate the needs of a rapidly changing geographic information environment. The National Map of the United States builds on a sound historic foundation of describing and monitoring the land surface and adds a focused effort to produce improved understanding, modeling, and prediction of land-surface change. These added dimensions bring to bear a broader spectrum of geographic science to address extant and emerging issues. Within the overarching construct of The National Map, the U.S. Geological Survey (USGS) is making a transition from data collector to guarantor of national data completeness; from producing paper maps to supporting an online, seamless, integrated database; and from simply describing the Nation’s landscape to linking these descriptions with increased scientific understanding. Implementing the full spectrum of geographic science addresses a myriad of public policy issues, including land and natural resource management, recreation, urban growth, human health, and emergency planning, response, and recovery. Neither these issues nor the science and technologies needed to deal with them are static. A robust research agenda is needed to understand these changes and realize The National Map vision. Initial successes have been achieved. These accomplishments demonstrate the utility of The National Map to the Nation and give confidence in evolving its future applications.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Daniel Goodman’s empirical approach to Bayesian statistics

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

PeerJ↗
USGS2016

Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013

From April 13 to 20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of Field Activity 13BIM01 . The objectives of the study were to quantify inorganic and organic accretion rates in back-barrier and mainland marsh and estuarine environments. Various field and laboratory methods were used to achieve these objectives, including subsurface imaging using Ground Penetrating Radar (GPR), sediment sampling, lithologic and microfossil analyses, and geochronology techniques to produce barrier island stratigraphic cross sections to help interpret the recent (last 2000 years) geologic evolution of the island. This data series report is an archive of GPR and associated Global Positioning System (GPS) data collected in April 2013 from Dauphin Island and adjacent barrier-island environments. In addition to GPR data, marsh core and vibracore data were also collected collected but are not reported (or included) in the current report. Data products, including elevation-corrected subsurface profile images of the processed GPR data, unprocessed digital GPR trace data, post-processed GPS data, Geographic Information System (GIS) files and accompanying Federal Geographic Data Committee (FGDC) metadata, can be downloaded from the Data Downloads page.

Alabama↗
USGS2018

Collection methods and quality assessment for Escherichia coli, water quality, and microbial source tracking data within Tumacácori National Historical Park and the upper Santa Cruz River, Arizona, 2015-16

Tumacácori National Historical Park protects the culturally important Mission, San José de Tumacácori, while also managing a portion of the ecologically diverse riparian corridor of the Santa Cruz River. This report describes the methods and quality assurance procedures used in the collection of water samples for the analysis of Escherichia coli ( E. coli ), microbial source tracking markers, suspended sediment, water-quality parameters, turbidity, and the data collection for discharge and stage; the process for data review and approval is also described. Finally, this report provides a quantitative assessment of the quality of the E. coli , microbial source tracking, and suspended sediment data. The data-quality assessment revealed that bias attributed to field and laboratory contamination was minimal, with E. coli detections in only 3 out of 33 field blank samples analyzed. Concentrations in the field blanks were several orders of magnitude lower than environmental concentrations. The microbial source tracking (MST) field blank was below the detection limit for all MST markers analyzed. Laboratory blanks for E. coli at the USGS Arizona Water Science Center and laboratory blanks for MST markers at the USGS Ohio Water Microbiology Laboratory were all below the detection limit. Irreplicate data for E. coli and suspended sediment indicated that bias was not introduced to the data by combining samples collected using discrete sampling methods with samples collected using automatic sampling methods. The split and sequential E. coli replicate data showed consistent analytical variability and a single equation was developed to explain the variability of E. coli concentrations. An additional analysis of analytical variability for E. coli indicated analytical variability around 18 percent relative standard deviation and no trend was observed in the concentration during the processing and analysis of multiple split-replicates. Two replicate samples were collected for MST and individual markers were compared for a base flow and flood sample. For the markers found in common between the two types of samples, the relative standard deviation for the base flow sample was more than 3 times greater than the markers in the flood sample. Sequential suspended sediment replicates had a relative standard deviation of about 1.3 percent, indicating that environmental and analytical variability was minimal. A holding time review and laboratory study analysis supported the extended holding times required for this investigation. Most concentrations for flood and base-flow samples were within the theoretical variability specified in the most probable number approach suggesting that extended hold times did not overly influence the final concentrations reported.

Arizona↗
USGS2021

Documentation of methods and inventory of irrigation information collected for the 2015 U.S. Geological Survey estimated use of water in the United States

The U.S. Geological Survey (USGS) National Water-Use Science Project strives to report water-use estimates using the best available information for the period of the estimates. The information available on water used for irrigation activities varies from State to State and in some areas from county to county within a State, which results in many information sources and methods being used to estimate water withdrawals and consumption for the Nation. The variety of estimation methods makes it difficult to compare information across States and makes it difficult to understand how different methods or data sources bias irrigation water-use estimates and trends over time. The sources of information and methods used by USGS Water Science Centers to estimate irrigation water use (the number of irrigated acres by irrigation system type, withdrawal values by water source type, and consumed-water values) for 2015 are compiled and described herein to assist with interpreting the water-use estimates. State-level summaries of information sources and methods are compiled in appendix 1, and a dataset of calendar-year, county-level estimates of actual evapotranspiration for the conterminous United States and Hawaii is provided in an associated USGS data release.

Scientific Investigations Report↗
USGS2016

Archive of bathymetry and backscatter data collected in 2014 nearshore Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana

As part of the Barrier Island Monitoring Project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted nearshore geophysical surveys off Breton and Gosier Islands, Louisiana, in July and August of 2014. To assist the United States Fish and Wildlife Service (USFWS) with restoration planning efforts, the USGS was tasked with answering fundamental questions about the physical environment of the southern Chandeleur Islands, including the geology, morphology, and oceanography. Baseline data needed to answer these questions were either insufficient or missing. The USGS conducted a comprehensive geologic investigation in the summer of 2014, collecting geophysical and sedimentological data. Breton Island, located at the southern end of the Chandeleur Island chain in southeastern Louisiana, was recognized as a natural, globally significant nesting sanctuary for several bird species and was established as the Breton National Wildlife Refuge (NWR) in 1904. The areal extent of Breton Island has diminished 90 percent since 1920. Land loss is attributed to ongoing relative sea-level rise, diminished sediment supply, and storm impacts. The bird population on Breton Island has also declined over the years, most notably after Hurricane George in 1998 and after Hurricane Katrina in 2015; the latter completely submerged the island. Despite decreasing habitable acreage, migratory seabirds continue to return and nest on Breton Island. To prevent the island from being submerged in the future, and to protect, stabilize, and provide more nesting and foraging areas for the bird population, the USFWS proposed a restoration effort to rebuild Breton Island to its pre-Katrina footprint. This data series serves as an archive of processed interferometric swath and single-beam bathymetry data, and side-scan sonar data, collected in the nearshore of Breton and Gosier Islands, NWR, Louisiana. The data were collected during two USGS cruises (USGS Field Activity Numbers 2014-314-FA and 2014-317-FA) in July and August 2014. Geographic information system data products include a 100-meter-cell-size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata. NOTE: These data are scientific in nature and are not to be used for navigation. Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Louisiana↗
USGS2020

Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km 2 . Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

contiguous United States↗
USGS2012

Data resources for the Wyoming Landscape Conservation Initiative (WLCI) Integrated Assessment (IA)

The data contained in this report were compiled, modified, and analyzed for the Wyoming Landscape Conservation Initiative (WLCI) Integrated Assessment (IA). The WLCI is a long-term science based effort to assess and enhance aquatic and terrestrial habitats at a landscape scale in southwest Wyoming while facilitating responsible energy development through local collaboration and partnerships. The IA is an integrated synthesis and analysis of WLCI resource values based on best available data and information collected from multiple agencies and organizations. It is a support tool for landscape-scale conservation planning and evaluation, and a data and analysis resource that can be used for addressing specific management questions. The IA analysis was conducted using a Geographic Information System in a raster (that is, a grid) environment using a cell size of 30 meters. To facilitate the interpretation of the data in a regional context, mean values were summarized and displayed at the subwatershed unit (WLCI subwatersheds were subset from the National Hydrography Dataset, Hydrologic Unit Code 12/Level 6). A dynamic mapping platform, accessed via the WLCI webpage at http://www.wlci.gov is used to display the mapped information, and to access underlying resource values that were combined to produce the final mapped results. The raster data used in the IA are provided here for use by interested parties to conduct additional analyses and can be accessed via the WLCI webpage. This series contains 74 spatial data sets: WLCI subwatersheds (vector) and 73 geotiffs (raster) that are segregated into the major categories of Multicriteria Index (including Resource Index and Condition), Change Agents, and Future Change. The Total Multicriteria Index is composed of the Aquatic Multicriteria Index and the Terrestrial Multicriteria Index. The Aquatic Multicriteria Index is composed of the Aquatic Resource Index and the Aquatic Condition. The Aquatic Resource Index is composed of the following components: Groundwater, Special Management Areas, and Priority Areas. The Aquatic Condition is composed of the following components: Focal Species, Species of Concern, Focal Ecosystems, and Proper Functioning Condition. The Terrestrial Multicriteria Index is composed of the Terrestrial Resource Index and the Terrestrial Condition. The Terrestrial Resource Index is composed of the following components: Special Management Areas, Agriculture, and Priority Areas. The Terrestrial Condition is composed of the following components: Focal Species, Big Game, Species of Concern, Rare Plants, and Focal Ecosystems. The Change Agents are composed the following components: Roads, Energy, Mines, and Urban. The Future Change is composed of the following components: Oil-Gas-Coal, Wind, Minerals, Climate-Temperature, Invasive Species, and Urban.

Wyoming↗
USGS2019

Confronting uncertainty: Contributions of the wildlife profession to the broader scientific community

Most wildlife professionals are engaged in 1 or both of 2 basic endeavors: science and management. These endeavors are a focus of many other disciplines, leading to widespread sharing of general methodologies. Wildlife professionals have appropriately borrowed and assimilated many methods developed primarily in other disciplines but have also led the development of one class of quantitative methods, those that confront and incorporate uncertainty. Uncertainty arises in counts of focal entities, for which wildlife professionals have developed effective methods to deal with the common problems of nondetection and misclassification. These methods have been borrowed by disciplines as varied as paleobiology, medicine, human epidemiology, industrial quality control, military target acquisition, remote sensing, and human census. Uncertainty also arises in the modeling of those counts, specifically the observation and ecological processes that generated them. Wildlife professionals recognized the fundamental importance of model selection and rapidly assimilated methods for selecting the most appropriate model for a given data set. These methods for dealing with uncertainty inherent to counting and modeling are critical to the conduct of science and management. Wildlife professionals have developed additional methods for incorporating uncertainty in the accumulation of knowledge and the development of optimal decisions in an environment of learning. In some cases, professionals in other disciplines are using methods developed and popularized in the wildlife profession, but there is much potential for greater use. In this essay, I describe these areas of wildlife leadership, document their assimilation by other disciplines, and emphasize the potential for more interdisciplinary use of these methods.

Journal of Wildlife Management↗