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At least 397 records · Page 22Linked to original sources
USGS2010

Land-use pressure and a transition to forest-cover loss in the Eastern United States

Contemporary land-use pressures have a significant impact on the extent and condition of forests in the eastern United States, causing a regional-scale decline in forest cover. Earlier in the 20th century, land cover was on a trajectory of forest expansion that followed agricultural abandonment. However, the potential for forest regeneration has slowed, and the extent of regional forest cover has declined by more than 4.0%. Using remote-sensing data, statistical sampling, and change-detection methods, this research shows how land conversion varies spatially and temporally across the East from 1973–2000, and how those changes affect regional land-change dynamics. The analysis shows that agricultural land use has continued to decline, and that this enables forest recovery; however, an important land-cover transition has occurred, from a mode of regional forest-cover gain to one of forest-cover loss caused by timber cutting cycles, urbanization, and other land-use demands.

BioScience↗
USGS2010

Social values for ecosystem services (SolVES): A GIS application for assessing, mapping, and quantifying the social values of ecosystem services-Documentation and user manual, version 1.0

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center, in collaboration with Colorado State University, has developed a geographic information system application, Social Values for Ecosystem Services (SolVES). SolVES can be used to assess, map, and quantify the perceived social values of ecosystem services. SolVES derives a quantitative social values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys. SolVES also generates landscape metrics, such as average elevation and distance to water, calculated from spatial data layers describing the underlying physical environment. Using kernel density calculations and zonal statistics, SolVES derives and maps the 10-point Value Index and reports landscape metrics associated with each index value for social value types such as aesthetics, biodiversity, and recreation. This can be repeated for various survey subgroups as distinguished by their attitudes and preferences regarding public uses of the forests such as motorized recreation and logging for fuels reduction. The Value Index provides a basis of comparison within and among survey subgroups to consider the effect of social contexts on the valuation of ecosystem services. SolVES includes regression coefficients linking the predicted value (the Value Index) to landscape metrics. These coefficients are used to generate predicted social value maps using value transfer techniques for areas where primary survey data are not available. SolVES was developed, and will continue to be enhanced through future versions, as a public domain tool to enable decision makers and researchers to map the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding tradeoffs between different ecosystem services in a variety of physical and social contexts.

Open-File Report↗
USGS2016

Report from the workshop on climate downscaling and its application in high Hawaiian Islands, September 16–17, 2015

In the subtropical and tropical Pacific islands, changing climate is predicted to influence precipitation and freshwater availability, and thus is predicted to impact ecosystems goods and services available to ecosystems and human communities. The small size of high Hawaiian Islands, plus their complex microlandscapes, require downscaling of global climate models to provide future projections of greater skill and spatial resolution. Two different climate modeling approaches (physics-based dynamical downscaling and statistics-based downscaling) have produced dissimilar projections. Because of these disparities, natural resource managers and decision makers have low confidence in using the modeling results and are therefore are unwilling to include climate-related projections in their decisions. In September 2015, the Pacific Islands Climate Science Center (PICSC), the Pacific Islands Climate Change Cooperative (PICCC), and the Pacific Regional Integrated Sciences and Assessments (Pacific RISA) program convened a 2-day facilitated workshop in which the two modeling teams, plus key model users and resource managers, were brought together for a comparison of the two approaches, culminating with a discussion of how to provide predictions that are useable by resource managers. The proceedings, discussions, and outcomes of this Workshop are summarized in this Open-File Report.

Hawaii↗
USGS2022

A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level

Robust, proxy-based reconstructions of relative sea-level (RSL) change are critical to distinguishing the processes that drive spatial and temporal sea-level variability. The relationships between individual proxies and RSL can be complex and are often poorly represented by traditional methods that assume Gaussian likelihood distributions. We develop a new statistical framework to estimate past RSL change based on nonparametric, empirical modern distributions of proxies in relation to RSL, applying the framework to corals and mangroves as an illustrative example. We validate our model by comparing its skill in reconstructing RSL and rates of change to two previous RSL models using synthetic time-series datasets based on Holocene sea-level data from South Florida. The new framework results in lower bias, better model fit, and greater accuracy and precision than the two previous RSL models. We also perform sensitivity tests using sea-level scenarios based on two periods of interest – meltwater pulses (MWPs) and the Holocene – to analyze the sensitivity of the statistical reconstructions to the quantity and precision of proxy data; we define high-precision indicators, such as mangroves and the reef-crest coral Acropora palmata , with 2 σ vertical uncertainties within ± 3 m and lower-precision indicators, such as Orbicella spp., with 2 σ vertical uncertainties within ± 10 m. For reconstructing rapid rates of change in RSL of up to ∼ 40 m kyr −1 , such as those that may have characterized MWPs during deglacial periods, we find that employing the nonparametric model with 5 to 10 high-precision data points per kiloyear enables us to constrain rates to within ± 3 m kyr −1 (1 σ ). For reconstructing RSL with rates of up to ∼ 15 m kyr −1 , as observed during the Holocene, we conclude that employing the model with 5 to 10 high-precision (or a combination of high- and low-precision) data points per kiloyear enables precise estimates of RSL within ± ∼ "> 2 m (2 σ ) and accurate RSL reconstructions with errors ≲ 0.7 m. Employing the nonparametric model with only lower-precision indicators also produces fairly accurate estimates of RSL with errors ≲ 1.50 m, although with less precision, only constraining RSL to ± ∼ "> 3–4 m (2 σ ). Although the model performs better than previous models in terms of bias, model fit, accuracy, and precision, it is computationally expensive to run because it requires inverting large matrices for every sample. The new model also provides minimal gains over similar models when a large quantity of high-precision data are available. Therefore, we recommend incorporating the nonparametric likelihood distributions when no other information (e.g., reef facies or epibionts indicative of shallow-water environments to refine coral elevational uncertainties) or no high-precision data are available at a location or during a given time period of interest.

Advances in Statistical Climatology, Meteorology a↗
USGS2024

Predicting Lake Huron Dreissena spp. spatial distribution patterns from environmental characteristics

Invasive dreissenid mussels ( Dreissena polymorpha and Dreissena rostriformis bugensis ) have altered Great Lakes ecosystems through a multitude of effects on benthic habitats, food web structure, and nutrient cycling. This study explores whether spatially continuous geographic data of environmental factors can be utilized to predict Dreissena spp. spatial distributions on a lake-wide scale. Categorical variables were also assessed for significant relationships with Dreissena spp. biomass. Point observations from the 2017 Lake Huron benthic survey under the Cooperative Science and Monitoring Initiative (CSMI) were utilized for in situ measurements of dreissenid presence and biomass at 119 sites across Lake Huron. Basin, bathymetric zone, and tributary influence were found to have statistically significant relationships to dreissenid biomass. A boosted regression tree (BRT) model (ROC score 0.707) was developed to spatially predict dreissenid presence probability across Lake Huron from six environmental explanatory variables: April, May, and October chlorophyll, June dissolved organic carbon, January bottom temperature, and May bottom temperature. The importance of food availability and bottom temperature illuminated relationships between dreissenid mussels and periods of benthic-pelagic mixing in the spring and fall seasons. Future models could be improved through advancements in survey technology for improved geographic characterization of mussel habitat characteristics and environmental constraints.

Lake Huron↗
USGS2016

The statistical power to detect cross-scale interactions at macroscales

Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.

Ecosphere↗
USGS2015

Floodplain complexity and surface metrics: influences of scale and geomorphology

Many studies of fluvial geomorphology and landscape ecology examine a single river or landscape, thus lack generality, making it difficult to develop a general understanding of the linkages between landscape patterns and larger-scale driving variables. We examined the spatial complexity of eight floodplain surfaces in widely different geographic settings and determined how patterns measured at different scales relate to different environmental drivers. Floodplain surface complexity is defined as having highly variable surface conditions that are also highly organised in space. These two components of floodplain surface complexity were measured across multiple sampling scales from LiDAR-derived DEMs. The surface character and variability of each floodplain were measured using four surface metrics; namely, standard deviation, skewness, coefficient of variation, and standard deviation of curvature from a series of moving window analyses ranging from 50 to 1000 m in radius. The spatial organisation of each floodplain surface was measured using spatial correlograms of the four surface metrics. Surface character, variability, and spatial organisation differed among the eight floodplains; and random, fragmented, highly patchy, and simple gradient spatial patterns were exhibited, depending upon the metric and window size. Differences in surface character and variability among the floodplains became statistically stronger with increasing sampling scale (window size), as did their associations with environmental variables. Sediment yield was consistently associated with differences in surface character and variability, as were flow discharge and variability at smaller sampling scales. Floodplain width was associated with differences in the spatial organization of surface conditions at smaller sampling scales, while valley slope was weakly associated with differences in spatial organisation at larger scales. A comparison of floodplain landscape patterns measured at different scales would improve our understanding of the role that different environmental variables play at different scales and in different geomorphic settings.

Geomorphology↗
USGS2001

Resource materials for a GIS spatial analysis course

This report consists of materials prepared for a GIS spatial analysis course offered as part of the Geography curriculum at the University of Nevada, Reno and the University of California at Santa Barbara in the spring of 2000. The report is intended to share information with instructors preparing spatial-modeling training and scientists with advanced GIS expertise. The students taking this class had completed each universities GIS curriculum and had a foundation in statistics as part of a science major. This report is organized into chapters that contain the following: Slides used during lectures, Guidance on the use of Arcview, Introduction to filtering in Arcview, Conventional and spatial correlation in Arcview, Tools for fuzzification in Arcview, Data and instructions for creating using ArcSDM for simple weights-of-evidence, fuzzy logic, and neural network models for Carlin-type gold deposits in central Nevada, Reading list on spatial modeling, and Selected student spatial-modeling posters from the laboratory exercises.

Open-File Report↗
USGS2010

Hydraulic modeling of mussel habitat at a bridge-replacement site, Allegheny River, Pennsylvania, USA

The Allegheny River in Pennsylvania supports a large and diverse freshwater-mussel community, including two federally listed endangered species, Pleurobema clava (Clubshell) and Epioblasma torulosa rangiana (Northern Riffleshell). It is recognized that river hydraulics and morphology play important roles in mussel distribution. To assess the hydraulic influences of bridge replacement on mussel habitat, metrics such as depth, velocity, and their derivatives (shear stress, Froude number) were collected or computed. The objectives of the project were to evaluate mussel and hydraulic data at a reference site and to compare those findings to a bridge-replacement site. The findings were used to support a statistical analysis, which establishes correlations between mussel count and hydraulics, and a numerical model to forecast habitat based on the statistics. ArcGIS was selected to manage the data and generate a grid to compute area statistics for 3319, 4.9-m × 4.9-m cells (cell) for total mussel count, depth, velocity, shear stress, and Froude number. The Wilcoxon Rank Sum test indicated no statistical significance between the total mussel count and the hydraulic variables; however, trellis graphs were used to account for the spatial variability in the data set. For the flow conditions measured, the total mussel count per cell is greatest at sections where (1) velocities range from 0.061 to 0.21 m/s, (2) shear stresses range from 0.48 to 3.8 dyne/cm 2 , and (3) Froude numbers range from 0.006 to 0.04. Based on the statistical targets established, the hydraulic model results suggest that an additional 2428 m 2 or a 30-percent increase in suitable mussel habitat could be generated at the replacement-bridge site when compared to the baseline condition associated with the existing bridge at that same location. The study did not address the influences of substrate, acid mine drainage, sediment loads from tributaries, and surface-water/ground-water exchange on mussel habitat. Future studies could include methods for quantifying (1) channel–substrate composition and distribution using tools such as hydroacoustic echosounders specifically designed and calibrated to identify bed composition and mussel populations, (2) surface-water and ground-water interactions, and (3) a high-streamflow event.

Pennsylvania↗
USGS1999

Characterizing fish community diversity across Virginia landscapes: Prerequisite for conservation

The number of community types occurring within landscapes is an important, but often unprotected, component of biological diversity. Generally applicable protocols for characterizing community diversity need to be developed to facilitate conservation. We used several multivariate techniques to analyze geographic variation in the composition of fish communities in Virginia streams. We examined relationships between community composition and six landscape variables: drainage basin, physiography, stream order, elevation, channel slope, and map coordinates. We compared patterns at two scales (statewide and subdrainage-specific) to assess sensitivity of community classification to spatial scale. We also compared patterns based on characterizing communities by species composition vs. ecological composition. All landscape variables explained significant proportions of the variance in community composition. Statewide, they explained 32% of the variance in species composition and 48% of the variance in ecological composition. Typical communities in each drainage or physiography were statistically distinctive. Communities in different combinations of drainage, physiography, and stream size were even more distinctive, but composition was strongly spatially autocorrelated. Ecological similarity and species similarity of community pairs were strongly related, but replacement by ecologically similar species was common among drainage–physiography combinations. Landscape variables explained significant proportions of variance in community composition within selected subdrainages, but proportions were less than at the statewide scale, and the explanatory power of individual variables varied considerably among subdrainages. Community variation within subdrainages appeared to be much more closely related to environmental variation than to replacement among ecologically similar species. Our results suggest that taxonomic and ecological characterizations of community composition are complementary; both are useful in a conservation context. Landscape features such as drainage, physiography, and water body size generally may provide a basis for assessing aquatic community diversity, especially in regions where the biota is poorly known. Systematic conservation of community types would be a major advance relative to most current conservation programs, which typically focus narrowly on populations of imperiled species. More effective conservation of aquatic biodiversity will require new approaches that recognize the value of both species and assemblages, and that emphasize protection of key landscape-scale processes.

Ecological Applications↗
USGS2023

Efficient modeling of wave generation and propagation in a semi-enclosed estuary

Accurate, and high-resolution wave statistics are critical for regional hazard mapping and planning. However, long-term simulations at high spatial resolution are often computationally prohibitive. Here, multiple rapid frameworks including fetch-limited, look-up-table (LUT), and linear propagation are combined and tested in a large estuary exposed to both remotely (swell) and locally generated waves. Predictions are compared with observations and a traditional SWAN implementation coupled to a regional hydrodynamic model. Fetch-limited and LUT approaches both perform well where local winds dominate with errors about 10%–20% larger than traditional SWAN predictions. Combinations of these rapid approaches with linear propagation methods where remotely generated energy is present also perform well with errors 0%–20% larger than traditional SWAN predictions. Model–model comparisons exhibit lower variance than comparisons to observations suggesting that, while model implementation impacts prediction skill, model boundary conditions (winds, offshore waves) may be a dominant source of error. Overall results suggest that with a relatively small loss in prediction accuracy, simulations computation cost can be significantly reduced (by 2–4 orders of magnitude) allowing for high resolution and long-term predictions to adequately define regional wave statistics.

British Columbia, Washington↗
USGS2024

Modeling nearshore total phosphorus in Lake Michigan using linked hydrodynamic and water quality models

Although the offshore water of Lake Michigan has been below the Great Lakes Water Quality Agreement (GLWQA) total phosphorus (TP) spring target concentration of 7 µg L −1 for several decades, higher TP concentrations occur in the nearshore, contributing to the resurgence of Cladophora and other nutrient related issues. The most recent update of the GLWQA specifically calls for the assessment of current nearshore conditions. Such assessment would require an intensive monitoring program supported by water quality models. Here we applied multiple versions of a phosphorus-based model linked to an unstructured-grid hydrodynamic model. We focus on the nearshore regions of Lake Michigan around the Grand and Muskegon rivers – a region with strong riverine TP influence and intensive monitoring. Results from a TP model were compared to observational data and to a previously published Phosphorus-based Nutrient–Phytoplankton–Zooplankton–Detrital–Mussel (NPZDM) model. Model results and observational data show that parts of the nearshore can be well above the target TP concentrations but, due to the dynamic nature of this region, the TP concentrations can change rapidly. The models’ skill statistics in predicting individual observations were variable, but it was able to simulate temporal and spatial trends and captured the distribution of observations in our study area. The similarity between the results of the TP and NPZDM models demonstrated the TP concentrations in this nearshore area are driven by hydrodynamics and river TP loads, which are likely the reasons for the higher observed TP concentrations. Simplicity, transparency, and ease of use of the TP model make it a useful tool for supporting nearshore assessments and estimating existing and future nearshore TP concentrations.

Michigan↗
USGS2010

Predicting coastal cliff erosion using a Bayesian probabilistic model

Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.

Marine Geology↗
USGS2010

Space, time, and the third dimension (model error)

The space-time tradeoff of hydrologic data collection (the ability to substitute spatial coverage for temporal extension of records or vice versa) is controlled jointly by the statistical properties of the phenomena that are being measured and by the model that is used to meld the information sources. The control exerted on the space-time tradeoff by the model and its accompanying errors has seldom been studied explicitly. The technique, known as Network Analyses for Regional Information (NARI), permits such a study of the regional regression model that is used to relate streamflow parameters to the physical and climatic characteristics of the drainage basin. The NARI technique shows that model improvement is a viable and sometimes necessary means of improving regional data collection systems. Model improvement provides an immediate increase in the accuracy of regional parameter estimation and also increases the information potential of future data collection. Model improvement, which can only be measured in a statistical sense, cannot be quantitatively estimated prior to its achievement; thus an attempt to upgrade a particular model entails a certain degree of risk on the part of the hydrologist.

Water Resources Research↗
USGS2008

Ground-water quality data in the Southern Sacramento Valley, California, 2005 — Results from the California GAMA Program

Ground-water quality in the approximately 2,100 square-mile Southern Sacramento Valley study unit (SSACV) was investigated from March to June 2005 as part of the Statewide Basin Assessment Project of Ground-Water Ambient Monitoring and Assessment (GAMA) Program. This study was designed to provide a spatially unbiased assessment of raw ground-water quality within SSACV, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from 83 wells in Placer, Sacramento, Solano, Sutter, and Yolo Counties. Sixty-seven of the wells were selected using a randomized grid-based method to provide statistical representation of the study area. Sixteen of the wells were sampled to evaluate changes in water chemistry along ground-water flow paths. Four additional samples were collected at one of the wells to evaluate water-quality changes with depth. The GAMA Statewide Basin Assessment project was developed in response to the Ground-Water Quality Monitoring Act of 2001 and is being conducted by the California State Water Resources Control Board (SWRCB) in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory (LLNL). The ground-water samples were analyzed for a large number of man-made organic constituents (volatile organic compounds [VOCs], pesticides and pesticide degradates, pharmaceutical compounds, and wastewater-indicator constituents), constituents of special interest (perchlorate, N -nitrosodimethylamine [NDMA], and 1,2,3-trichloropropane [1,2,3-TCP]), naturally occurring inorganic constituents (nutrients, major and minor ions, and trace elements), radioactive constituents, and microbial indicators. Naturally occurring isotopes (tritium, and carbon-14, and stable isotopes of hydrogen, oxygen, and carbon), and dissolved noble gases also were measured to help identify the source and age of the sampled ground water. Quality-control samples (blanks, replicates, matrix spikes) were collected at ten percent of the wells, and the results for these samples were used to evaluate the quality of the data for the ground-water samples. Assessment of the quality-control data resulted in censoring of less than 0.03 percent of the analyses of ground-water samples. This study did not evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, and (or) blended with other waters to maintain acceptable water quality. Regulatory thresholds apply to treated water that is served to the consumer, not to raw ground water. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Health Services (CADHS) (Maximum Contaminant Levels [MCLs], notification levels [NLs], or lifetime health advisories [HA-Ls]) and thresholds established for aesthetic concerns (Secondary Maximum Contaminant Levels [SMCLs]). All wells were sampled for organic constituents and selected general water quality parameters; subsets of wells were sampled for inorganic constituents, nutrients, and radioactive constituents. Volatile organic compounds were detected in 49 out of 83 wells sampled and pesticides were detected in 34 out of 82 wells; all detections were below health-based thresholds, with the exception of 1 detection of 1,2,3-trichloropropane above a NL. Of the 43 wells sampled for trace elements, 27 had no detections of a trace element above a health-based threshold and 16 had at least one detection above. Of the 18 trace elements with health-based thresholds, 3 (arsenic, barium, and boron) were detected at concentrations higher an MCL. Of the 43 wells sampled for nitrate, only 1 well had a detection above the MCL. Twenty wells were sampled for radioactive constituents; only 1 (radon-222) was measured at activities higher than the proposed MCL. Radon-222 was detected below the threshold in 7 wells and above the threshold in 13 wells. SMCLs have been established for nine constituents or parameters analyzed in SSACV. Six were measured at levels higher than an SMCL: chloride, iron, manganese, pH, specific conductance, and total dissolved solids. Chloride, iron, manganese, pH, and total dissolved solids were measured in 43 wells: 27 wells had no measurements above a threshold and 16 wells had a measurement above a threshold. Specific conductance was measured in 83 wells. In 68 wells, specific conductance was measured lower than the threshold and in 15 wells it was measured above the threshold.

California↗
USGS2007

Ground-water quality data in the Southern Sierra study unit, 2006— Results from the California GAMA program

Ground-water quality in the approximately 1,800 square-mile Southern Sierra study unit (SOSA) was investigated in June 2006 as part of the Statewide Basin Assessment Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Statewide Basin Assessment Project was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The Southern Sierra study was designed to provide a spatially unbiased assessment of raw ground-water quality within SOSA, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from fifty wells in Kern and Tulare Counties. Thirty-five of the wells were selected using a randomized grid-based method to provide statistical representation of the study area, and fifteen were selected to evaluate changes in water chemistry along ground-water flow paths. The ground-water samples were analyzed for a large number of synthetic organic constituents [volatile organic compounds (VOCs), pesticides and pesticide degradates, pharmaceutical compounds, and wastewater-indicator compounds], constituents of special interest [perchlorate, N-nitrosodimethylamine (NDMA), and 1,2,3-trichloropropane (1,2,3-TCP)], naturally occurring inorganic constituents [nutrients, major and minor ions, and trace elements], radioactive constituents, and microbial indicators. Naturally occurring isotopes [tritium, and carbon-14, and stable isotopes of hydrogen and oxygen in water], and dissolved noble gases also were measured to help identify the source and age of the sampled ground water. Quality-control samples (blanks, replicates, and samples for matrix spikes) were collected for approximately one-eighth of the wells, and the results for these samples were used to evaluate the quality of the data for the ground-water samples. Assessment of the quality-control information resulted in censoring of less than 0.2 percent of the data collected for ground-water samples. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, or blended with other waters to maintain acceptable water quality. Regulatory thresholds apply to treated water that is served to the consumer, not to raw ground water. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH) and thresholds established for aesthetic concerns (secondary maximum contaminant levels, SMCL-CA) by CDPH. VOCs and pesticides were detected in less than one-third of the grid wells, and all detections in samples from SOSA wells were below health-based thresholds. All detections of trace elements and nutrients in samples from SOSA wells were below health-based thresholds, with the exception of four detections of arsenic that were above the USEPA maximum contaminant level (MCL-US) and one detection of boron that was above the CDPH notification level (NL-CA). All detections of radioactive constituents were below health-based thresholds, although four samples had activities of radon-222 above the proposed MCL-US. Most of the samples from SOSA wells had concentrations of major elements, total dissolved solids, and trace elements below the non-enforceable thresholds set for aesthetic concerns. A few samples contained iron, manganese, or total dissolved solids at concentrations above the SMCL-CA thresholds.

California↗
USGS2008

Ground-water quality data in the Central Eastside San Joaquin Basin 2006: Results from the California GAMA program

Ground-water quality in the approximately 1,695-square-mile Central Eastside study unit (CESJO) was investigated from March through June 2006 as part of the Statewide Basin Assessment Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Statewide Basin Assessment project was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the California State Water Resources Control Board (SWRCB) in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory (LLNL). The study was designed to provide a spatially unbiased assessment of raw ground-water quality within CESJO, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from 78 wells in Merced and Stanislaus Counties. Fifty-eight of the 78 wells were selected using a randomized grid-based method to provide statistical representation of the study unit (grid wells). Twenty of the wells were selected to evaluate changes in water chemistry along selected lateral or vertical ground-water flow paths in the aquifer (flow-path wells). The ground-water samples were analyzed for a large number of synthetic organic constituents [volatile organic compounds (VOCs), gasoline oxygenates and their degradates, pesticides and pesticide degradates], constituents of special interest [perchlorate, N-nitrosodimethylamine (NDMA), and 1,2,3-trichloropropane (1,2,3-TCP)], inorganic constituents that can occur naturally [nutrients, major and minor ions, and trace elements], radioactive constituents, and microbial indicators. Naturally occurring isotopes [tritium, carbon-14, and uranium isotopes and stable isotopes of hydrogen, oxygen, nitrogen, sulfur, and carbon], and dissolved noble and other gases also were measured to help identify the source and age of the sampled ground water. Quality-control samples (blanks, replicates, samples for matrix spikes) were collected for approximately one-sixth of the wells, and the results for these samples were used to evaluate the quality of the data for the ground-water samples. Assessment of the quality-control results showed that the environmental data were of good quality, with low bias and low variability, and resulted in censoring of less than 0.3 percent of the detections found in ground-water samples. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, and (or) blended with other waters to maintain acceptable water quality. Regulatory thresholds apply to treated water that is served to the consumer, not to raw ground water. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CADPH) and thresholds established for aesthetic concerns (secondary maximum contaminant levels, SMCL-CA) by CADPH. VOCs and pesticides were detected in approximately half of the grid wells, and all detections in samples from CESJO wells were below health-based thresholds. All detections of nutrients and major elements in grid wells also were below health-based thresholds. Most detections of constituents of special interest, trace elements, and radioactive constituents in samples from grid wells were below health-based thresholds. Exceptions included two detections of arsenic that were above the USEPA maximum contaminant level (MCL-US), one detection of lead above the USEPA action level (AL-US), and one detection of vanadium and three detections of 1,2,3-TCP that were above the CADPH notification levels (NL-CA). All detections of radioactive constituents were below health-based thresholds, although fourteen samples had activities of radon-222 above the lower proposed MCL-US. Most of the samples from CESJO grid wells had concentrations of major elements, total dissolved solids, and trace elements below the non-enforceable thresholds set for aesthetic concerns. A few samples contained manganese or total dissolved solids at concentrations above the SMCL-CA thresholds.

California↗
USGS2008

Ground-water quality data in the middle Sacramento Valley study unit, 2006— Results from the California GAMA program

Ground-water quality in the approximately 3,340 mi 2 Middle Sacramento Valley study unit (MSACV) was investigated from June through September, 2006, as part of the California Groundwater Ambient Monitoring and Assessment (GAMA) program. The GAMA Priority Basin Assessment project was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The Middle Sacramento Valley study was designed to provide a spatially unbiased assessment of raw ground-water quality within MSACV, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from 108 wells in Butte, Colusa, Glenn, Sutter, Tehama, Yolo, and Yuba Counties. Seventy-one wells were selected using a randomized grid-based method to provide statistical representation of the study unit (grid wells), 15 wells were selected to evaluate changes in water chemistry along ground-water flow paths (flow-path wells), and 22 were shallow monitoring wells selected to assess the effects of rice agriculture, a major land use in the study unit, on ground-water chemistry (RICE wells). The ground-water samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOCs], gasoline oxygenates and degradates, pesticides and pesticide degradates, and pharmaceutical compounds), constituents of special interest (perchlorate, N-nitrosodimethylamine [NDMA], and 1,2,3-trichloropropane [1,2,3-TCP]), inorganic constituents (nutrients, major and minor ions, and trace elements), radioactive constituents, and microbial indicators. Naturally occurring isotopes (tritium, and carbon-14, and stable isotopes of hydrogen, oxygen, nitrogen, and carbon), and dissolved noble gases also were measured to help identify the sources and ages of the sampled ground water. Quality-control samples (blanks, replicates, laboratory matrix spikes) were collected at approximately 10 percent of the wells, and the results for these samples were used to evaluate the quality of the data for the ground-water samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination was not a noticeable source of bias in the data for the ground-water samples. Differences between replicate samples were within acceptable ranges, indicating acceptably low variability. Matrix spike recoveries were within acceptable ranges for most constituents. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, or blended with other waters to maintain acceptable water quality. Regulatory thresholds apply to treated water that is served to the consumer, not to raw ground water. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH) and thresholds established for aesthetic concerns (secondary maximum contaminant levels, SMCL-CA) by CDPH. Comparisons between data collected for this study and drinking-water thresholds are for illustrative purposes only and are not indicative of compliance or noncompliance with regulatory thresholds. Most constituents that were detected in ground-water samples were found at concentrations below drinking-water thresholds. VOCs were detected in less than one-third and pesticides and pesticide degradates in just over one-half of the grid wells, and all detections of these constituents in samples from all wells of the MSACV study unit were below health-based thresholds. All detections of trace elements in samples from MSACV grid wells were below health-based thresholds, with the exceptions of arsenic and boron. Arsenic concentrations were above the USEPA maximum contaminant level (MCL-US) threshold in eight grid wells, and boron concentrations were above the CDPH notification level (NL-CA) in two grid wells. Arsenic was detected above the MCL-US in two flow-path wells. Arsenic, barium, boron, molybdenum, strontium, and vanadium were detected above health-based thresholds in a few of the RICE wells; these wells are not used to supply drinking water. All detections of radioactive constituents were below health-based thresholds, although six samples had activities of radon-222 above the lower proposed MCL-US threshold. Most of the samples from the MSACV wells had concentrations of major elements, total dissolved solids, and trace elements below the non-enforceable thresholds set for aesthetic concerns. Chloride and sulfate concentrations exceeded SMCL-CA thresholds in two and one grid well, respectively. Iron, manganese, and total dissolved solids concentrations were above the SMCL-CA thresholds in 1, 12, and 6 grid wells, respectively. Nitrate (nitrite plus nitrate, as dissolved nitrogen) concentrations from two grid wells were above the MCL-US threshold. There were no detections of microbial indicators in MSACV.

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