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USGS2024

Are researchers citing their data? A case study from the U.S. Geological Survey

Data citation promotes accessibility and discoverability of data through measures carried out by researchers, publishers, repositories, and the scientific community. This paper examines how a data citation workflow has been implemented by the U.S. Geological Survey (USGS) by evaluating publication and data linkages. Two different methods were used to identify data citations: examining publication structural metadata and examining the full text of the publication. A growing number of USGS researchers are complying with publisher data sharing policies aimed to capture data citation information in a standardized way within associated publications. However, inconsistencies in how data citation information is documented in publications has limited the accessibility and discoverability of the data. This paper demonstrates how organizational evaluations of publication and data linkages can be used to identify obstacles in advancing data citation efforts and improve data citation workflows.

Data Science Journal↗

USGS2017

Water quality data for national-scale aquatic research: The Water Quality Portal

Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

Water Resources Research↗

USGS2024

Estimating ungulate migration corridors from sparse movement data

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal’s movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions, and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse global positioning system (GPS) data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 hours from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-hour data. We then subsampled the 2-hour data to one location every 12 hours (a proxy for sparse data) and fitted BBMMs to the 12-hour data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1,200 m2; elk: 600–1,600 m2) successfully delineated migration corridors similar to the 2-hour baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1,000 m2 (elk) decreased the inflation of the 12-hour corridors relative to the 2-hour corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.

Idaho, Wyoming↗

USGS2025

Too much and not enough data: Challenges and solutions for generating information in freshwater research and monitoring

Evaluating progress toward achieving freshwater conservation and sustainability goals requires transforming diverse types of data into useful information for scientists, managers, and other interest groups. Despite substantial increases in the volume of freshwater data collected worldwide, many regions and ecosystems still lack sufficient data collection and/or data access. We illustrate how these data challenges result from a diverse set of underlying mechanisms and propose solutions that can be applied by individuals or organizations. We discuss creative approaches to address data scarcity, including the use of community science, remote-sensing, environmental sensors, and legacy datasets. We highlight the importance of coordinated data collection efforts among groups and training programs to improve data access. At the institutional level, we emphasize the power of prioritizing data curation, incentivizing data publication, and promoting research that enhances data coverage and representativeness. Some of these strategies involve technological and analytical approaches, but many necessitate shifting the priorities and incentives of organizations such as academic and government research institutions, monitoring groups, journals, and funding agencies. Our overarching goal is to stimulate discussion to narrow the data disparities hindering the understanding of freshwater processes and their change across spatial scales.

Nahuel Huapi Lake, Lake Tahoe↗

USGS2008

Seabed mapping and characterization of sediment variability using the usSEABED data base

We present a methodology for statistical analysis of randomly located marine sediment point data, and apply it to the US continental shelf portions of usSEABED mean grain size records. The usSEABED database, like many modern, large environmental datasets, is heterogeneous and interdisciplinary. We statistically test the database as a source of mean grain size data, and from it provide a first examination of regional seafloor sediment variability across the entire US continental shelf. Data derived from laboratory analyses ("extracted") and from word-based descriptions ("parsed") are treated separately, and they are compared statistically and deterministically. Data records are selected for spatial analysis by their location within sample regions: polygonal areas defined in ArcGIS chosen by geography, water depth, and data sufficiency. We derive isotropic, binned semivariograms from the data, and invert these for estimates of noise variance, field variance, and decorrelation distance. The highly erratic nature of the semivariograms is a result both of the random locations of the data and of the high level of data uncertainty (noise). This decorrelates the data covariance matrix for the inversion, and largely prevents robust estimation of the fractal dimension. Our comparison of the extracted and parsed mean grain size data demonstrates important differences between the two. In particular, extracted measurements generally produce finer mean grain sizes, lower noise variance, and lower field variance than parsed values. Such relationships can be used to derive a regionally dependent conversion factor between the two. Our analysis of sample regions on the US continental shelf revealed considerable geographic variability in the estimated statistical parameters of field variance and decorrelation distance. Some regional relationships are evident, and overall there is a tendency for field variance to be higher where the average mean grain size is finer grained. Surprisingly, parsed and extracted noise magnitudes correlate with each other, which may indicate that some portion of the data variability that we identify as "noise" is caused by real grain size variability at very short scales. Our analyses demonstrate that by applying a bias-correction proxy, usSEABED data can be used to generate reliable interpolated maps of regional mean grain size and sediment character.

Continental Shelf Research↗

USGS2021

Heed the data gap: Guidelines for using incomplete datasets in annual stream temperature analyses

Stream temperature data are useful for deciphering watershed processes important for aquatic ecosystems. Accurately extracting signal trends from stream temperature is essential for predicting responses of environmental and ecological indicators to change. Missing data periods are common for various reasons, and pose a challenge for scientists using temperature signal analysis to support stream research and ecological management objectives. However, the sensitivity of estimated temperature signal patterns to missing data has not been thoroughly evaluated, despite the potentially large impact on interpretation. In this study, we explored the effects of simulated missing daily data on the characterization of annual water temperature signals measured at headwater sites in the Pacific Northwest and Mid-Atlantic regions of the USA. For each site, we used linear regressions of sine-waves fitted to complete (365-d) and partial (7–357 consecutive missing data points) annual datasets of daily mean water temperature and computed three thermal parameters (mean, phase, and amplitude), which together can indicate thermally and ecologically influential watershed processes (e.g., depth and magnitude of groundwater discharge). Expected values (derived from complete datasets) ranged from 7.0 to 12.6 °C, 205 to 254 d, and 1.9 to 9.5 °C for annual mean, phase, and amplitude, respectively. While annual phase and amplitude could be accurately estimated (i.e., within 95–99% confidence intervals of expected values) with up to approximately two months of consecutively missing data, annual mean temperature required more complete datasets. We found that datasets with less than seven weeks of consecutively missing data enabled estimation of all annual signal parameters with reasonable accuracy (>75% probability of being within the 95–99% confidence intervals of expected values). Imputation of missing data expanded this range to approximately 20 weeks, with the greatest improvements in parameter estimation between 9 and 27 weeks of imputed missing data. However, caution should be exercised when applying this technique. For example, imputation improved the accuracy of parameter estimation for most sites, but accuracy decreased for some sites exhibiting strong groundwater influence. The timing of consecutive missing data points within a year had inconsistent effects on annual thermal parameter estimates among regions, years, and individual parameters. Utilizing sites with more than approximately seven consecutive weeks of missing data or 20 weeks of imputed data increases the probability of mischaracterization of annual stream thermal regimes. Understanding this limitation is vital for identifying the potential of streams to serve as climate refugia for ecological indicator species and effective future management of stream systems.

Ecological Indicators↗

USGS1998

Analysis of simulated advanced spaceborne thermal emission and reflection (ASTER) radiometer data of the Iron Hill, Colorado, study area for mapping lithologies

The advanced spaceborne thermal emission and reflection (ASTER) radiometer was designed to record reflected energy in nine channels with 15 or 30 m resolution, including stereoscopic images, and emitted energy in five channels with 90 m resolution from the NASA Earth Observing System AMI platform. A simulated ASTER data set was produced for the Iron Hill, Colorado, study area by resampling calibrated, registered airborne visible/infrared imaging spectrometer (AVIRIS) data, and thermal infrared multispectral scanner (TIMS) data to the appropriate spatial and spectral parameters. A digital elevation model was obtained to simulate ASTER-derived topographic data. The main lithologic units in the area are granitic rocks and felsite into which a carbonatite stock and associated alkalic igneous rocks were intruded; these rocks are locally covered by Jurassic sandstone, Tertiary rhyolitic tuff, and colluvial deposits. Several methods were evaluated for mapping the main lithologic units, including the unsupervised classification and spectral curve-matching techniques. In the five thermalinfrared (TIR) channels, comparison of the results of linear spectral unmixing and unsupervised classification with published geologic maps showed that the main lithologic units were mapped, but large areas with moderate to dense tree cover were not mapped in the TIR data. Compared to TIMS data, simulated ASTER data permitted slightly less discrimination in the mafic alkalic rock series, and carbonatite was not mapped in the TIMS nor in the simulated ASTER TIR data. In the nine visible and near-infrared channels, unsupervised classification did not yield useful results, but both the spectral linear unmixing and the matched filter techniques produced useful results, including mapping calcitic and dolomitic carbonatite exposures, travertine in hot spring deposits, kaolinite in argillized sandstone and tuff, and muscovite in sericitized granite and felsite, as well as commonly occurring illite/muscovite. However, the distinction made in AVIRIS data between calcite and dolomite was not consistently feasible in the simulated ASTER data. Comparison of the lithologie information produced by spectral analysis of the simulated ASTER data to a photogeologic interpretation of a simulated ASTER color image illustrates the high potential of spectral analysis of ASTER data to geologic interpretation.

Journal of Geophysical Research D: Atmospheres↗

USGS2021

OpenET: Filling a critical data gap in water management for the western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

western United States↗

USGS2021

Miniature temperature data loggers increase precision and reduce bias when estimating the daily survival rate for bird nests

Demographic studies of many bird species are challenging because their nests are cryptic, resulting in few nests being found. To maximize statistical power, methods are needed that minimize disturbance while yielding as much information per nest as possible. One way to meet these objectives is to use miniature thermal data loggers to precisely date nest fates. Our objectives, therefore, were to (1) examine the possible effect of thermal data loggers on nest success through hatching by grass- and shrub-nesting songbirds that differed in their parasite egg-accepting and -rejecting behavior, (2) examine the effect of using daily temperature data versus less frequent nest-visit data on statistical power, bias, and precision when estimating the daily survival rate (DSR) for nests, and (3) compare these two approaches using a simulation study and field data. We monitored the survival of nests located in agricultural landscapes and used a binomial logistic regression with main effects for data-loggers and parasite-accepting or -rejecting status and their interaction. We also compared maximum likelihood–derived DSR for differences in estimated rates, precision, and sample sizes with both data collected in the field and simulated with varying sample sizes and visit frequencies. We found no evidence that thermal data loggers had any effect on hatching rates either for all species or for parasite egg-accepting and -rejecting species, separately. Both our simulation and analysis of real nest data indicated that use of data loggers increased the statistical power from each nest studied by increasing effective sample sizes and precision of DSR estimates compared to in-person visits. We also found a negative bias in DSR estimates with longer visit intervals, which use of data-loggers removed. Both the results of simulated- and field-data analyses suggest that future studies of nest survival can be improved by automated nest monitoring by removing a source of bias and providing more time to find additional nests.

Iowa↗

USGS2026

Harmonization of aggregated freshwater biotic data to support continental and global assessment

Biodiversity loss and conservation are increasingly coming into focus in global policy fora, requiring information and assessments at wider spatial and temporal scales than previously considered. However, the monitoring framework required to support such data collection and assessment is lacking in many countries and is not harmonized across countries, hampering these efforts. Aggregation of existing freshwater data offers a solution to the problem of assessing status and trends of ecosystems and biodiversity at large spatial scales in the absence of nationally coordinated monitoring efforts. Analysis of aggregated data from different sources, collected using different protocols and with varying levels of metadata and supporting data, can be challenging and requires decisions regarding data comparability. In this paper, we identify the challenges inherent in harmonizing aggregated freshwater data for analysis, including general concerns related to research goals, spatial and temporal scale, sample selection, sampling effort, and site integrity. We also discuss the challenges related to measured parameters, sampled habitats, sample collection and processing methods, and data integrity for phytoplankton, benthic algae, macrophytes, zooplankton, benthic macroinvertebrates, fish, and supporting variables such as water and sediment chemistry. We provide a workflow to evaluate each of these challenges and make decisions about how best to work with the data. Finally, we review a case study from a large-scale analysis of freshwater data from the circumpolar Arctic region that exemplifies the encountered challenges and the chosen solutions. Through the description of the case study, we provide practical solutions to support aggregation and analysis of existing freshwater data. As global conversations about biodiversity status and trends continue, the demand for large-scale analyses of data from different sources will only grow. In the absence of globally harmonized monitoring, we are faced with the need to ensure comparability of data, making expert judgements where needed to support sound conclusions.

PLOS Water↗

USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗

USGS1988

Land cover mapping of the upper Kuskokwim Resource Managment Area using LANDSAT and a digital data base approach

Digital land cover and terrain data for the Upper Kuskokwim Resource Hanagement Area (UKRMA) were produced by the U.S. Geological Survey, Earth Resources Observation Systems Field Office, Anchorage, Alaska for the Bureau of Land Management. These and other environmental data, were incorporated into a digital data base to assist in the management and planning of the UKRMA. The digital data base includes land cover classifications, elevation, slope, and aspect data centering on the UKRMA boundaries. The data are stored on computer compatible tapes at a 50-m pixel size. Additional digital data in the data base include: (a) summer and winter Landsat multispectral scanner (MSS) data registered to a 50-m Universal Transverse Mercator grid; (b) elevation, slope, aspect, and solar illumination data; (c) soils and surficial geology; and (e) study area boundary. The classification of Landsat MSS data resulted in seven major classes and 24 subclasses. Major classes include: forest, shrubland, dwarf scrub, herbaceous, barren, water, and other. The final data base will be used by resource personnel for management and planning within the UKRMA.

Alaska↗

USGS1998

Availability of Ground-Water Data for California, Water Year 1997

The Water Resources Division of the U.S. Geological Survey, in cooperation with Federal, State, and local water agencies, obtains a large amount of data pertaining to the ground-water resources of California each water year. These data constitute a valuable data base for developing an improved understanding of the water resources of the State. Beginning with the 1985 water year and continuing through 1993, these data were published in a report series entitled 'Water Resources Data for California, Volume 5. Ground-Water Data.' Prior to the introduction of this series, historical ground-water information was published in U.S. Geological Survey Water-Supply Papers. In 1994, the Volume 5 Ground-Water Data report was discontinued, but data continue to be available in our data bases. This Fact Sheet serves as an index to ground-water data for 1997. The 2-page report contains a map of California showing the number of wells (by county) with available water-level and water-quality data for the current water year (fig. 2) and instructions for obtaining this and other ground-water information contained in the data bases of the Water Resources Division, California District.

Fact Sheet↗

USGS1997

Mineral-resource data bases

Data bases are essential for modern scientific research. The new and exciting work being done in the Mineral Resource Program in the U.S. Geological Survey (USGS) usually begins with the question, "Where are the known deposits?" A mineral-resource data base containing this type of information and more can be useful not just to USGS scientists, but to anyone who needs such data. Users of the data bases from outside the USGS include mining and exploration companies, environmental groups, academia, other Federal Agencies, and the general public. At present, the USGS has two large mineral-resource data bases, MRDS (Mineral Resource Data System) and MAS (Minerals Availability System). MRDS was built and is mamtained by the USGS, and MAS was built and maintained by the Bureau of Mines. In 1996, after the Bureau was abolished, MAS was transferred to the USGS. The two data bases were compiled for different purposes and contain very different mformation. For instance, MAS contains information on costs, details of mining methods, and feasibility studies. MRDS has mineralogical and geologic data that are not contained in MAS. Because they are both mineral-resource data bases, however, they contain some information in common, such as location, name(s) of sites, and commodities present. Both data bases are international in scope, and both are quite large. MRDS contains over 110,000 records, while MAS has over 220,000. One reason that MAS has more records is that it contains information on smelters, mill sites, and fossil fuel sites, as well as mineral- resource sites. The USGS is working to combine the information in both data bases. This is a large undertaking that will require some years to complete. In the interim, information from both data bases will still be available

Fact Sheet↗

USGS2000

Availability of Ground-Water Data for California, Water Year 1999

The Water Resources Division of the U.S. Geological Survey, in cooperation with Federal, State, and local water agencies, obtains a large amount of data pertaining to the ground-water resources of California each water year. These data constitute a valuable data base for developing an improved understanding of the water resources of the State. Beginning with the 1985 water year and continuing through 1993, these data were published in a report series entitled ?Water Resources Data for California, Volume 5. Ground-Water Data.? Prior to the introduction of this series, historical ground-water information was published in U.S. Geological Survey Water-Supply Papers. In 1994, the Volume 5 Ground-Water Data report was discontinued, but data continue to be available in our data bases. This Fact Sheet serves as an index to ground-water data for 1999. The 2-page report contains a map of California showing the number of wells (by county) with available water-level and water-quality data for the current water year (fig. 2) and instructions for obtaining this and other ground-water information contained in the data bases of the Water Resources Division, California District.

Fact Sheet↗

USGS1999

Availability of Ground-Water Data For California, Water Year 1998

The Water Resources Division of the U.S. Geological Survey, in cooperation with Federal, State, and local water agencies, obtains a large amount of data pertaining to the ground-water resources of California each water year. These data constitute a valuable data base for developing an improved understanding of the water resources of the State. Beginning with the 1985 water year and continuing through 1993, these data were published in a report series entitled ?Water Resources Data for California, Volume 5. Ground-Water Data.? Prior to the introduction of this series, historical ground-water information was published in U.S. Geological Survey Water-Supply Papers. In 1994, the Volume 5 Ground-Water Data report was discontinued, but data continue to be available in our data bases. This Fact Sheet serves as an index to ground-water data for 1998. The 2-page report contains a map of California showing the number of wells (by county) with available water-level and water-quality data for the current water year (fig. 2) and instructions for obtaining this and other ground-water information contained in the data bases of the Water Resources Division, California District.

Fact Sheet↗

USGS2007

Global Data Toolset (GDT)

According to the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) approximately 60 percent of the data contained in the World Database on Protected Areas (WDPA) has missing or incomplete boundary information. As a result, global analyses based on the WDPA can be inaccurate, and professionals responsible for natural resource planning and priority setting must rely on incomplete geospatial data sets. To begin to address this problem the World Data Center for Biodiversity and Ecology, in cooperation with the U. S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC), the National Biological Information Infrastructure (NBII), the Global Earth Observation System, and the Inter-American Biodiversity Information Network (IABIN) sponsored a Protected Area (PA) workshop in Asuncion, Paraguay, in November 2007. The primary goal of this workshop was to train representatives from eight South American countries on the use of the Global Data Toolset (GDT) for reviewing and editing PA data. Use of the GDT will allow PA experts to compare their national data to other data sets, including non-governmental organization (NGO) and WCMC data, in order to highlight inaccuracies or gaps in the data, and then to apply any needed edits, especially in the delineation of the PA boundaries. In addition, familiarizing the participants with the web-enabled GDT will allow them to maintain and improve their data after the workshop. Once data edits have been completed the GDT will also allow the country authorities to perform any required review and validation processing. Once validated, the data can be used to update the global WDPA and IABIN databases, which will enhance analysis on global and regional levels.

Fact Sheet↗

USGS2002

Ground-water-quality data for Albany and surrounding areas, Southwest Georgia, 1951-99

This report presents ground-water-quality data from the surficial, Upper Floridan, Claiborne, Clayton, and Upper Cretaceous aquifers in the Albany and surrounding areas of southwest Georgia. Water-quality data from about 186 wells in Baker, Calhoun, Dougherty, Lee, Mitchell, Terrell, and Worth Counties are presented for the period from 1951 through 1999. The data include field water-quality parameters collected during 1951-99, volatile and semi-volatile organic compounds collected during 1981-97, inorganic compounds collected during 1951-99, trace metals collected during 1964-99, radiochemicals collected during 1993-95, herbicides and insecticides collected during 1980-97, and recovery data for laboratory surrogate compounds (used for quality control and quality assurance for organic samples) collected during 1993-97. Ground-water quality data are presented in tables by data type and arranged by well number. Illustrations in this report contain information about study area location, well location, stratigraphy, and formation water-bearing properties. Ground-water-quality data are presented in text files and in a data base that includes geographic and tabular data. Data presented in this report provide a base with which to better define and interpret the quality of ground water in Albany, Ga., and surrounding areas. Although some of these data may have been published in previous reports associated with water-resources investigations, water-quality data are compiled as a useful resource.

Georgia↗