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At least 1,297 records · Page 72Linked to original sources
USGS2017

Integration of vegetation community spatial data into a prescribed fire planning process at Shenandoah National Park, Virginia (USA)

Many eastern forest communities depend on fire for regeneration or are enhanced by fire as a restoration practice. However, the use of prescribed fire in the mesic forested environments and the densely populated regions of the eastern United States has been limited. The objective of our research was to develop a science-based approach to prioritizing the use of prescribed fire in appropriate forest types in the eastern United States based on a set of desired management outcomes. Through a process of expert elicitation and data analysis, we assessed and integrated recent vegetation community mapping results along with other available spatial data layers into a spatial prioritization tool for prescribed fire planning at Shenandoah National Park (Virginia, USA). The integration of vegetation spatial data allowed for development of per-pixel priority rankings and exclusion areas enabling precise targeting of fire management activities on the ground, as well as a park-wide ranking of fire planning compartments. We demonstrate the use and evaluation of this approach through implementation and monitoring of a prescribed burn and show that progress is being made toward desired conditions. Integration of spatial data into the fire planning process has served as a collaborative tool for the implementation of prescribed fire projects, which assures projects will be planned in the most appropriate areas to meet objectives that are supported by current science.

Virginia↗
USGS2009

Long-term variability in Northern Hemisphere snow cover and associations with warmer winters

A monthly snow accumulation and melt model is used with gridded monthly temperature and precipitation data for the Northern Hemisphere to generate time series of March snow-covered area (SCA) for the period 1905 through 2002. The time series of estimated SCA for March is verified by comparison with previously published time series of SCA for the Northern Hemisphere. The time series of estimated Northern Hemisphere March SCA shows a substantial decrease since about 1970, and this decrease corresponds to an increase in mean winter Northern Hemisphere temperature. The increase in winter temperature has caused a decrease in the fraction of precipitation that occurs as snow and an increase in snowmelt for some parts of the Northern Hemisphere, particularly the mid-latitudes, thus reducing snow packs and March SCA. In addition, the increase in winter temperature and the decreases in SCA appear to be associated with a contraction of the circumpolar vortex and a poleward movement of storm tracks, resulting in decreased precipitation (and snow) in the low- to mid-latitudes and an increase in precipitation (and snow) in high latitudes. If Northern Hemisphere winter temperatures continue to warm as they have since the 1970s, then March SCA will likely continue to decrease.

Climatic Change↗
USGS2018

A values-based private landowner typology to improve grassland conservation initiatives

Many conservation initiatives are based on natural science alone, despite an extensive body of literature demonstrating that the incorporation of social science generates more successful and lasting outcomes. The Land Use Value (LUV) scale is an example of a social science tool that grassland conservation practitioners can use to improve their understanding of the land use decisions of private agricultural landowners. Drawing on data from a mail survey, we demonstrated the utility of the LUV scale to segment agricultural producers by four LUV types (Humans First, Nature First, Interconnected, and Disconnected) with significantly different motivations and land use behaviors. This information can be used to evaluate and align grassland conservation practices, policies, and messaging with the LUV types of private agricultural landowners. Tools like the LUV scale are critical to building the social science capacity of conservation professionals and organizations, in order to improve the efficacy of conservation initiatives.

Society and Natural Resources↗
USGS2019

A review of machine learning applications to coastal sediment transport and morphodynamics

A range of computer science methods under the heading of machine learning (ML) enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review some common ML methods and their application to studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’ ML methods contribute, such as ‘what’ science problems ML tools have been used to address, ‘what’ was learned when using ML, and ‘why’ authors used ML methods. We find a variety of research questions have been addressed, ranging from small-scale predictions of sediment transport to larger-scale sand bar morphodynamics and coastal overwash on a developed island. We find various reasons justify the use of ML, including maximize predictability, emulation of model components, smooth and continuous nonlinear regression through data, and explicit inclusion of uncertainty. Overall the expanding use of ML has allowed for an expanding set of questions to be addressed. After reviewing the studies we outline a set of ‘best practices’ for coastal researchers using machine learning methods. Finally we suggest possible areas for future research, including the use of novel machine learning techniques and exploring ‘open data’ that is becoming increasingly available.

Earth-Science Reviews↗
USGS2011

BioData: A national aquatic bioassessment database

BioData is a U.S. Geological Survey (USGS) web-enabled database that for the first time provides for the capture, curation, integration, and delivery of bioassessment data collected by local, regional, and national USGS projects. BioData offers field biologists advanced capabilities for entering, editing, and reviewing the macroinvertebrate, algae, fish, and supporting habitat data from rivers and streams. It offers data archival and curation capabilities that protect and maintain data for the long term. BioData provides the Federal, State, and local governments, as well as the scientific community, resource managers, the private sector, and the public with easy access to tens of thousands of samples collected nationwide from thousands of stream and river sites. BioData also provides the USGS with centralized data storage for delivering data to other systems and applications through automated web services. BioData allows users to combine data sets of known quality from different projects in various locations over time. It provides a nationally aggregated database for users to leverage data from many independent projects that, until now, was not feasible at this scale. For example, from 1991 to 2011, the USGS Idaho Water Science Center collected more than 816 bioassessment samples from 63 sites for the National Water Quality Assessment (NAWQA) Program and more than 477 samples from 39 sites for a cooperative USGS and State of Idaho Statewide Water Quality Network (fig. 1). Using BioData, 20 years of samples collected for both of these projects can be combined for analysis. BioData delivers all of the data using current taxonomic nomenclature, thus relieving users of the difficult and time-consuming task of harmonizing taxonomy among samples collected during different time periods. Fish data are reported using the Integrated Taxonomic Information Service (ITIS) Taxonomic Serial Numbers (TSN's). A simple web-data input interface and self-guided, public data-retrieval web site provides access to bioassessment data. BioData currently accepts data collected using two national protocols: (1) NAWQA and (2) U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). Additional collection protocols are planned for future versions.

Fact Sheet↗
USGS2016

Water isotope systematics: Improving our palaeoclimate interpretations

The stable isotopes of oxygen and hydrogen, measured in a variety of archives, are widely used proxies in Quaternary Science. Understanding the processes that control δ18O change have long been a focus of research (e.g. Shackleton and Opdyke, 1973; Talbot, 1990 ; Leng, 2006). Both the dynamics of water isotope cycling and the appropriate interpretation of geological water-isotope proxy time series remain subjects of active research and debate. It is clear that achieving a complete understanding of the isotope systematics for any given archive type, and ideally each individual archive, is vital if these palaeo-data are to be used to their full potential, including comparison with climate model experiments of the past. Combining information from modern monitoring and process studies, climate models, and proxy data is crucial for improving our statistical constraints on reconstructions of past climate variability. As climate models increasingly incorporate stable water isotope physics, this common language should aid quantitative comparisons between proxy data and climate model output. Water-isotope palaeoclimate data provide crucial metrics for validating GCMs, whereas GCMs provide a tool for exploring the climate variability dominating signals in the proxy data. Several of the studies in this set of papers highlight how collaborations between palaeoclimate experimentalists and modelers may serve to expand the usefulness of palaeoclimate data for climate prediction in future work. This collection of papers follows the session on Water Isotope Systematics held at the 2013 AGU Fall Meeting in San Francisco. Papers in that session, the breadth of which are represented here, discussed such issues as; understanding sub-GNIP scale (Global Network for Isotopes in Precipitation, (IAEA/WMO, 2006)) variability in isotopes in precipitation from different regions, detailed examination of the transfer of isotope signals from precipitation to geological archives, and the implications of advances in understanding in these areas for the interpretation of palaeo records and proxy data – climate model comparison. Here, we briefly review these areas of research, and discuss challenges for the water isotope community in improving our ability to partition climate vs. auxiliary signals in palaeoclimate data.

Quaternary Science Reviews↗
USGS2006

Annual dissolved nitrite plus nitrate and total phosphorous loads for the Susquehanna, St. Lawrence, Mississippi-Atchafalaya, and Columbia River basins, 1968-2004

Annual stream-water loads were calculated near the outlet of four of the larger river basins (Susquehanna, St. Lawrence, Mississippi-Atchafalaya, and Columbia) in the United States for dissolved nitrite plus nitrate (NO2 + NO3) and total phosphorus using LOADEST load estimation software. Loads were estimated for the period 1968-2004; although loads estimated for individual river basins and chemical constituent combinations typically were for shorter time periods due to limitations in data availability. Stream discharge and water-quality data for load estimates were obtained from the U.S. Geological Survey (USGS) with additional stream discharge data for the Mississippi-Atchafalaya River Basin from the U.S. Army Corps of Engineers. The loads were estimated to support national assessments of changes in stream nutrient loads that are periodically conducted by Federal agencies (for example, U.S. Environmental Protection Agency) and other water- and land-resource organizations. Data, methods, and results of load estimates are summarized herein; including World Wide Web links to electronic ASCII text files containing the raw data. The load estimates are compared to dissolved NO2 + NO3 loads for three of the large river basins from 1971 to 1998 that the USGS provided during 2001 to The H. John Heinz III Center for Science, Economics and the Environment (The Heinz Center) for a report The Heinz Center published during 2002. Differences in the load estimates are the result of using the most up-to-date monitoring data since the 2001 analysis, differences in how concentrations less than the reporting limit were handled by the load estimation models, and some errors and exclusions in the 2001 analysis datasets (which resulted in some inaccurate load estimates).

Open-File Report↗
USGS2001

Landsat-7 ETM+ radiometric calibration: Two years on-orbit

Landsat-7 has been in orbit for 2 years as of April 15, 2001 and operationally providing calibrated data products for 2 years as of June 28, 2001. A radiometric calibration team consisting of scientists and analysts from the Landsat Project Science Office, the Landsat-7 Image Assessment System and four universities evaluates the calibration based on on-board and ground-look (vicarious) calibration methodologies. The results are assembled and compared semi-annually and the calibration parameter files are adjusted as necessary. To date the combined results for the reflective bands have not shown any change from pre-launch values. The pre-launch values continue to be used for data processing, with the uncertainty estimated at less than 5%. In the thermal band, the vicarious calibration results indicated a 0.31 W/m/sup 2/ sr /spl mu/m bias in the calibration. This bias results in the ETM+ derived temperatures being about 3K high. The calibration parameter file was updated October 1, 2000 to remove this bias, however the U.S. Landsat Product Generation System (LPGS) software required modification that was not incorporated until December 20, 2000. All LPGS data products generated since this date have the correct thermal band calibration, regardless of image acquisition date, with uncertainties at approximately the 1% level.

Conference Paper↗
USGS2006

Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York-July 1997 through June 1999

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance/quality-control data for the time period addressed in this report were stored in the laboratory's SAS data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality- control samples analyzed from July 1997 through June 1999. Results for the quality-control samples for 18 analytical procedures were evaluated for bias and precision. Control charts indicate that data for eight of the analytical procedures were occasionally biased for either high-concentration and (or) low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, total aluminum, ammonium, calcium, chloride, specific conductance, and sulfate. The data from the potassium and sodium analytical procedures are insufficient for evaluation. Results from the filter-blank and analytical-blank analyses indicate that the procedures for 11 of 13 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. Blank analysis results for chloride showed that 22 percent of blanks did not meet data-quality objectives and results for dissolved organic carbon showed that 31 percent of the blanks did not meet data-quality objectives. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 14 of the 18 analytes. At least 90 percent of the samples met data-quality objectives for all analytes except total aluminum (70 percent of samples met objectives) and potassium (83 percent of samples met objectives). Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated good data quality for most constituents over the time period. The P-sample (low-ionic-strength constituents) analysis had good ratings in two of these studies and a satisfactory rating in the third. The results of the T-sample (trace constituents) analysis indicated high data quality with good ratings in all three studies. The N-sample (nutrient constituents) studies had one each of excellent, good, and satisfactory ratings. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 80 percent of the samples met data-quality objectives for 9 of the 13 analytes; the exceptions were dissolved organic carbon, ammonium, chloride, and specific conductance. Data-quality objectives were not met for dissolved organic carbon in two NWRI studies, but all of the samples were within control limits for the last study. Data-quality objectives were not met in 41 percent of samples analyzed for ammonium, 25 percent of samples analyzed for chloride, and 30 percent of samples analyzed for specific conductance. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 84 percent of the samples analyzed for calcium, chloride, magnesium, pH, and potassium. Data-quality objectives were met by 73 percent of those analyzed for sulfate. The data-quality objective was not met for sodium. The data are insufficient for evaluation of the specific conductance results.

Open-File Report↗
USGS2006

Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York--July 1999 through June 2001

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's LabMaster data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality-control samples analyzed from July 1999 through June 2001. Results for the quality-control samples for 18 analytical procedures were evaluated for bias and precision. Control charts indicate that data for eight of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, total aluminum, calcium, chloride and nitrate (ion chromatography and colormetric method) and sulfate. The total aluminum and dissolved organic carbon procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits. The calcium and specific conductance procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The magnesium procedure was biased for the high-concentration and low concentration samples, but was within control limits. Results from the filter-blank and analytical-blank analyses indicate that the procedures for 14 of 15 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 17 of the 18 analytes. At least 90 percent of the samples met data-quality objectives for all analytes except ammonium (81 percent of samples met objectives), chloride (75 percent of samples met objectives), and sodium (86 percent of samples met objectives). Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated good data quality over the time period, with most ratings for each sample in the good to excellent range. The P-sample (low-ionic-strength constituents) analysis had one satisfactory rating for the specific conductance procedure in one study. The T-sample (trace constituents) analysis had one satisfactory rating for the aluminum procedure in one study and one unsatisfactory rating for the sodium procedure in another. The remainder of the samples had good or excellent ratings for each study. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 89 percent of the samples met data-quality objectives for 10 of the 14 analytes; the exceptions were ammonium, total aluminum, dissolved organic carbon, and sodium. Results indicate a positive bias for the ammonium procedure in all studies. Data-quality objectives were not met in 50 percent of samples analyzed for total aluminum, 38 percent of samples analyzed for dissolved organic carbon, and 27 percent of samples analyzed for sodium. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 91 percent of the samples analyzed for calcium, chloride, fluoride, magnesium, pH, potassium, and sulfate. Data-quality objectives were met by 75 percent of the samples analyzed for sodium and 58 percent of the samples analyzed for specific conductance.

Open-File Report↗
USGS2009

Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2001 Through June 2003

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2001 through June 2003. Results for the quality-control samples for 19 analytical procedures were evaluated for bias and precision. Control charts indicate that data for six of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, chloride, magnesium, nitrate (ion chromatography), potassium, and sodium. The calcium procedure was biased throughout the analysis period for the high-concentration sample, but was within control limits. The total monomeric aluminum and fluoride procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The total aluminum, pH, specific conductance, and sulfate procedures were biased for the high-concentration and low-concentration samples, but were within control limits. Results from the filter-blank and analytical-blank analyses indicate that the procedures for 16 of 18 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for the dissolved organic carbon or specific conductance procedures. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 21 analytes. At least 90 percent of the samples met data-quality objectives for all procedures except total monomeric aluminum (83 percent of samples met objectives), total aluminum (76 percent of samples met objectives), ammonium (73 percent of samples met objectives), dissolved organic carbon (86 percent of samples met objectives), and nitrate (81 percent of samples met objectives). The data-quality objective was not met for the nitrite procedure. Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated satisfactory or above data quality over the time period, with most performance ratings for each sample in the good-to-excellent range. The N-sample (nutrient constituents) analysis had one unsatisfactory rating for the ammonium procedure in one study. The T-sample (trace constituents) analysis had one unsatisfactory rating for the magnesium procedure and one marginal rating for the potassium procedure in one study and one unsatisfactory rating for the sodium procedure in another. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 90 percent of the samples met data-quality objectives for 10 of the 14 analytes; the exceptions were acid-neutralizing capacity, ammonium, dissolved organic carbon, and sodium. Data-quality objectives were not met in 37 percent of samples analyzed for acid-neutralizing capacity, 28 percent of samples analyzed for dissolved organic carbon, and 30 percent of samples analyzed for sodium. Results indicate a positive bias for the ammonium procedure in one study and a negative bias in another. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 90 percent of the samples analyzed for calcium, chloride, magnesium, pH, potassium, and sodium. Data-quality objectives were met by 78 percent of

Open-File Report↗
USGS2009

Quality-assurance data for routine water analyses by the U.S. Geological Survey laboratory in Troy, New York - July 2003 through June 2005

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2003 through June 2005. Results for the quality-control samples for 20 analytical procedures were evaluated for bias and precision. Control charts indicate that data for five of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, pH, silicon, and sodium. Seven of the analytical procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits; these procedures were: dissolved organic carbon, chloride, nitrate (ion chromatograph), nitrite, silicon, sodium, and sulfate. The calcium and magnesium procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The total aluminum and specific conductance procedures were biased for the high-concentration and low-concentration samples, but were within control limits. Results from the filter-blank and analytical-blank analyses indicate that the procedures for 17 of 18 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 22 analytes. At least 85 percent of the samples met data-quality objectives for all analytes except total monomeric aluminum (82 percent of samples met objectives), total aluminum (77 percent of samples met objectives), chloride (80 percent of samples met objectives), fluoride (76 percent of samples met objectives), and nitrate (ion chromatograph) (79 percent of samples met objectives). The ammonium and total dissolved nitrogen did not meet the data-quality objectives. Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated good data quality over the time period, with ratings for each sample in the satisfactory, good, and excellent ranges or less than 10 percent error. The P-sample (low-ionic-strength constituents) analysis had one marginal and two unsatisfactory ratings for the chloride procedure. The T-sample (trace constituents)analysis had two unsatisfactory ratings and one high range percent error for the aluminum procedure. The N-sample (nutrient constituents) analysis had one marginal rating for the nitrate procedure. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 84 percent of the samples met data-quality objectives for 11 of the 14 analytes; the exceptions were ammonium, total aluminum, and acid-neutralizing capacity. The ammonium procedure did not meet data quality objectives in all studies. Data-quality objectives were not met in 23 percent of samples analyzed for total aluminum and 45 percent of samples analyzed acid-neutralizing capacity. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 86 percent of the samples analyzed for calcium, chloride, fluoride, magnesium, pH, potassium, sodium, and sulfate. Data-quality objectives were not met by samples analyzed for fluoride.

Open-File Report↗
USGS2009

Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York - July 2005 through June 2007

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's Lab Master data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality control samples analyzed from July 2005 through June 2007. Results for the quality-control samples for 19 analytical procedures were evaluated for bias and precision. Control charts indicate that data for eight of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: total aluminum, calcium, magnesium, nitrate (colorimetric method), potassium, silicon, sodium, and sulfate. Eight of the analytical procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits; these procedures were: total aluminum, calcium, dissolved organic carbon, chloride, nitrate (ion chromatograph), potassium, silicon, and sulfate. The magnesium and pH procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The acid-neutralizing capacity, total monomeric aluminum, nitrite, and specific conductance procedures were biased for the high-concentration and low-concentration samples, but were within control limits. Results from the filter-blank and analytical-blank analyses indicated that the procedures for 16 of 17 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 18 of the 21 analytes. At least 93 percent of the samples met data-quality objectives for all analytes except acid-neutralizing capacity (85 percent of samples met objectives), total monomeric aluminum (83 percent of samples met objectives), total aluminum (85 percent of samples met objectives), and chloride (85 percent of samples met objectives). The ammonium and total dissolved nitrogen did not meet the data-quality objectives. Results of the USGS interlaboratory Standard Reference Sample (SRS) Project met the Troy Laboratory data-quality objectives for 87 percent of the samples analyzed. The P-sample (low-ionic-strength constituents) analysis had two outliers each in two studies. The T-sample (trace constituents) analysis and the N-sample (nutrient constituents) analysis had one outlier each in two studies. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 85 percent of the samples met data-quality objectives for 11 of the 14 analytes; the exceptions were acid-neutralizing capacity, total aluminum and ammonium. Data-quality objectives were not met in 41 percent of samples analyzed for acid-neutralizing capacity, 50 percent of samples analyzed for total aluminum, and 44 percent of samples analyzed for ammonium. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 86 percent of the samples analyzed for calcium, magnesium, pH, potassium, and sodium. Data-quality objectives were met by 76 percent of the samples analyzed for chloride, 80 percent of the samples analyzed for specific conductance, and 77 percent of the samples analyzed for sulfate.

Open-File Report↗
USGS2024

Observing systems, modeling, and forecasting

Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included: ● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting, ● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels, ● Improve the use of networking technologies for monitoring and modeling efforts, ● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs, ● Develop food web models on the ecosystem fate and effects of toxins, ● Develop and improve species-specific models that link to physical-biological models. Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts. Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.

Report↗
USGS2004

Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activities 93LCA01 and 94LCA01 in Kingsley, Orange, and Lowry Lakes, Northeast Florida, 1993 and 1994

In August and September of 1993 and January of 1994, the U.S. Geological Survey, under a cooperative agreement with the St. Johns River Water Management District (SJRWMD), conducted geophysical surveys of Kingsley Lake, Orange Lake, and Lowry Lake in northeast Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observer's logbook, Field Activity Collection System (FACS) logs, and formal FGDC metadata. A filtered and gained GIF image of each seismic profile is also provided. Refer to the Acronyms page for expansion of acronyms and abbreviations used in this report. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. The data archived here were collected under a cooperative agreement with the St. Johns River Water Management District as part of the USGS Lakes and Coastal Aquifers (LCA) Project. For further information about this study, refer to http://coastal.er.usgs.gov/stjohns, Kindinger and others (1994), and Kindinger and others (2000). The USGS Florida Integrated Science Center (FISC) - Coastal and Watershed Studies in St. Petersburg, Florida, assigns a unique identifier to each cruise or field activity. For example, 93LCA01 tells us the data were collected in 1993 for the Lakes and Coastal Aquifers (LCA) Project and the data were collected during the first field activity for that project in that calendar year. For a detailed description of the method used to assign the field activity ID, see http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html. The boomer is an acoustic energy source that consists of capacitors charged to a high voltage and discharged through a transducer in the water. The transducer is towed on a sled at the sea surface and when discharged emits a short acoustic pulse, or shot, that propagates through the water and sediment column. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), detected by the receiver, and recorded by a PC-based seismic acquisition system. This process is repeated at timed intervals (e.g., 0.5 s) and recorded for specific intervals of time (e.g., 100 ms). In this way, a two-dimensional vertical image of the shallow geologic structure beneath the ship track is produced. Acquisition geometery for 94LCA01 is recorded in the operations logbook. No logbook exists for 93LCA01. Table 1 displays acquisition parameters for both field activities. For more information about the acquisition equipment used, refer to the FACS equipment logs. The unprocessed seismic data are stored in SEG-Y format (Barry and others, 1975). For a detailed description of the data format, refer to the SEG-Y Format page. See the How To Download SEG-Y Data page for more information about these files. Processed profiles can be viewed as GIF images from the Profiles page. Refer to the Software page for details about the processing and examples of the processing scripts. Detailed information about the navigation systems used for each field activity can be found in Table 1 and the FACS equipment logs. To view the trackline maps and navigation files, and for more information about these items, see the Navigation page. The original trace files were recorded in nonstandard ELICS format and later converted to standard SEG-Y format. The original trace files for 94LCA01 lines ORJ127_1, ORJ127_3, and ORJ131_1 were divided into two or more trace files (e.g., ORJ127_1 became ORJ127_1a and ORJ127_1b) because the original total number of traces exceeded the maximum allowed by the processing system. Digital data were not recoverable for 93LCA

Data Series↗
USGS2013

Geophysical logging and geologic mapping data in the vicinity of the GMH Electronics Superfund site near Roxboro, North Carolina

Geologic mapping, the collection of borehole geophysical logs and images, and passive diffusion bag sampling were conducted by the U.S. Geological Survey North Carolina Water Science Center in the vicinity of the GMH Electronics Superfund site near Roxboro, North Carolina, during March through October 2011. The study purpose was to assist the U.S. Environmental Protection Agency in the development of a conceptual groundwater model for the assessment of current contaminant distribution and future migration of contaminants. Data compilation efforts included geologic mapping of more than 250 features, including rock type and secondary joints, delineation of more than 1,300 subsurface features (primarily fracture orientations) in 15 open borehole wells, and the collection of passive diffusion-bag samples from 42 fracture zones at various depths in the 15 wells.

North Carolina↗
USGS1983

Land use and land cover digital data

The discipline of cartography is undergoing a number of profound changesthat center on the emerging influence ofdigital manipulation and analysis ofdata for the preparation of cartographic materials and for use in geographic information systems. Operational requirements have led to the development by the USGS National Mapping Division of several documents that establish in-house digital cartographic standards. In an effort to fulfill lead agency requirements for promulgation of Federal standards in the earth sciences, the documents have been edited and assembled with explanatory text into a USGS Circular. This Circular describes some of the pertinent issues relative to digital cartographic data standards, documents the digital cartographic data standards currently in use within the USGS, and details the efforts of the USGS related to the definition of national digital cartographic data standards. It consists of several chapters; the first is a general overview, and each succeeding chapter is made up from documents that establish in-house standards for one of the various types of digital cartographic data currently produced. This chapter 895-E, describes the Geographic Information Retrieval and Analysis System that is used in conjunction with the USGS land use and land cover classification system to encode, edit, manipuate, and analyze land use and land cover digital data.

Open-File Report↗
USGS2022

Benefits of genetic data for the design of Brook Trout translocation efforts

With wild trout populations in decline, many conservation practitioners are evaluating the feasibility of incorporating reintroduction and genetic rescue into management frameworks. As interest in these conservation tools continues to grow, so too has the need for rigorous science to evaluate translocation success and improve the efficacy of future efforts. From this, it has become increasingly apparent that approaches which consider both demographics and genetics are most likely to result in successful translocations. In particular, while demographic data are often a central component of project designs, they are insufficient for diagnosing genetic threats such as low diversity, maladaptation, and introgression that characterize many wild trout populations. Consideration for these genetic characteristics is important for long-term project success and to reduce the unintended spread of domestic lineages across the landscape. Using a case study of reintroduction of Brook Trout Salvelinus fontinalis into a North Carolina stream, we show how a combined demographic and genetic approach can be used throughout all stages of project design. In particular, we highlight how genetic data were informative for identifying source populations that had the greatest potential to establish a population with the genetic diversity needed for future adaptation. We also discuss how genetic monitoring of the reintroduced population provided insights into reproductive success and genetic diversity that could be indicative of long-term population persistence. While monitoring is ongoing, this combined genetic and demographic approach provides a promising framework for helping meet reintroduction goals and provides more opportunities for adaptive management following translocation.

Conference Paper↗