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At least 307 records · Page 17Linked to original sources
USGS2021

Integrating telemetry data at several scales with spatial capture–recapture to improve density estimates

Accurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise ( Gopherus agassizii ); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1-km 2 plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio-telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed-model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non-spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.

California, Nevada↗
USGS2008

Statistical Stationarity of Sediment Interbed Thicknesses in a Basalt Aquifer, Idaho National Laboratory, Eastern Snake River Plain, Idaho

The statistical stationarity of distributions of sedimentary interbed thicknesses within the southwestern part of the Idaho National Laboratory (INL) was evaluated within the stratigraphic framework of Quaternary sediments and basalts at the INL site, eastern Snake River Plain, Idaho. The thicknesses of 122 sedimentary interbeds observed in 11 coreholes were documented from lithologic logs and independently inferred from natural-gamma logs. Lithologic information was grouped into composite time-stratigraphic units based on correlations with existing composite-unit stratigraphy near these holes. The assignment of lithologic units to an existing chronostratigraphy on the basis of nearby composite stratigraphic units may introduce error where correlations with nearby holes are ambiguous or the distance between holes is great, but we consider this the best technique for grouping stratigraphic information in this geologic environment at this time. Nonparametric tests of similarity were used to evaluate temporal and spatial stationarity in the distributions of sediment thickness. The following statistical tests were applied to the data: (1) the Kolmogorov-Smirnov (K-S) two-sample test to compare distribution shape, (2) the Mann-Whitney (M-W) test for similarity of two medians, (3) the Kruskal-Wallis (K-W) test for similarity of multiple medians, and (4) Levene's (L) test for the similarity of two variances. Results of these analyses corroborate previous work that concluded the thickness distributions of Quaternary sedimentary interbeds are locally stationary in space and time. The data set used in this study was relatively small, so the results presented should be considered preliminary, pending incorporation of data from more coreholes. Statistical tests also demonstrated that natural-gamma logs consistently fail to detect interbeds less than about 2-3 ft thick, although these interbeds are observable in lithologic logs. This should be taken into consideration when modeling aquifer lithology or hydraulic properties based on lithology.

Scientific Investigations Report↗
USGS2016

Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse

We developed rangewide population and habitat models for Greater Sage-Grouse ( Centrocercus urophasianus ) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4–88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1–85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25–34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.

Ecosphere↗
USGS2022

Power analysis for detecting the effects of best management practices on reducing nitrogen and phosphorus fluxes to the Chesapeake Bay watershed, USA

In 2010 the U.S. Environmental Protection Agency established the Total Maximum Daily Load (TMDL) which is a “pollution diet” that aims to reduce the amount of nitrogen and phosphorus entering the Chesapeake Bay, the largest estuary in the United States, by 25 and 24% percent, respectively. To achieve this goal the TMDL requires the implementation of Best Management Practices (BMPs), which are accepted land management practices for reducing pollutant runoff to nearby bodies of water. While the TMDL requires that the necessary management actions be in place by 2025 to eventually reach targeted nutrient loads, the ability to detect an effect of BMPs while assuming that one has occurred (i.e. statistical power) is still not well understood. The goal of this study was to investigate the power and required timelines to detect nutrient reductions in streams and rivers as the result of BMP implementation at the Chesapeake Watershed scale. Power estimates were produced using SPAtially Referenced Regression On Watershed attributes (SPARROW) models, which offer a flexible statistical framework and were recently extended to allow for modeling multiple time steps. Nitrogen and phosphorus focused models were calibrated to estimate the power to detect reductions in flux from numerous constituent sources. To confidently detect a decrease in constituent flux reaching the Chesapeake Bay’s tidal waters from a specific constituent source, reductions ranging from 30–60% were required for the nitrogen model. In contrast, reductions of up to 80% were not detectable under the phosphorus model. The timelines necessary to detect reductions in nitrogen flux ranged from 11 to several hundred years under different rates-of-change and management scenarios. The approach proposed here can help better understand the ability to detect the effects of BMPs on a regional scale and help guide future management actions and monitoring programs.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS1985

Selected hydrographs and statistical analyses characterizing the water resources of the Arkansas River basin, Colorado

Hydrographs of annual precipitation from 30 stations, April 1 snowpack readings from 18 snow-survey courses, annual discharge from 46 streamflow gaging stations, and instantaneous water levels from 37 wells are presented to illustrate the temporal and spatial variability of the water resources of the Arkansas River basin in Colorado. Statistical analyses indicate no apparent time trends in annual precipitation or April 1 snowpack , but they do indicate declines in annual discharge for locations in the eastern part of the basin. A composite hydrograph indicates a negligible change in groundwater levels between 1930 and 1980 in the alluvial aquifer downstream from Pueblo. Generally poor correlation occurs between precipitation data and snowpack data (less than 0.40 for monthly data and less than 0.61 for annual data). In addition, precipitation data did not correlate very well with discharge (less than 0.57 for monthly data), leading to the conclusion that the typical streamflow conditions are affected little by direct precipitation. Main-stem discharge correlates quite well with snow-pack (as much as 0.85 for annual data), indicating its dependence on snowmelt runoff. (USGS)

Water-Resources Investigations Report↗
USGS2025

The accuracy of capture per unit effort in predicting density of a cryptic snake was more sensitive to reductions in spatial than temporal coverage

A critical component of monitoring wildlife populations is understanding changes in population size or abundance. However, for most populations a complete census is not possible; thus, trends or abundance need to be estimated through alternative means, such as indexes. An important aspect of using indexes, such as capture per unit effort (CPUE), is validating them as accurate or precise predictors of population trends or abundance. We completed such analyses using data collected from visual surveys and trapping for brown treesnakes ( Boiga irregularis ) within a 5-ha enclosure that was undergoing a continuous population decline. During a ~ 6-year period, we censused and marked the snake population to fully enumerated the population, with new individuals resulting from births and removals resulting only from mortality (natural or experimental). From trapping and visual surveys, we were also able to calculate CPUE as a function of trap nights or km surveyed and used regressions to forecast snake density (snakes/km) in the enclosure from CPUE. We also rarefied the true dataset to measure whether reductions in sampling intensity, either temporally or spatially, affected the accuracy or precision in predicting snake density from CPUE. We found that trap CPUE demonstrated no statistical relationship to density based on our study methods. CPUE during visual surveys did predict actual density, with sufficient spatial and temporal sampling intensity. CPUE from visual surveys was relatively robust against reductions in temporal sampling when spatial intensity remained high. However, reductions in the spatial area covered to less than 50% of the enclosure rapidly reduced the accuracy and precision in using CPUE to forecast density. Our results indicate that visual surveys are a relatively accurate measure of true density for brown treesnakes, given sufficient spatial sampling effort. The spatial area of coverage required for CPUE to accurately predict changes in abundance was, however, intense with > 50% of the spatial area required to be sampled on a given sampling night. Our results indicate that CPUE is only reliable as an index of abundance or population trends for cryptic snakes, if sampling effort covers most of the landscape over which populations are being estimated.

Guam↗
USGS2010

A general science-based framework for dynamical spatio-temporal models

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Test↗
USGS2021

Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

California↗
USGS2010

Groundwater-quality data in the South Coast Range-Coastal study unit, 2008: Results from the California GAMA Program

Groundwater quality in the approximately 766-square-mile South Coast Range–Coastal (SCRC) study unit was investigated from May to December 2008, as part of the Priority Basins Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basins Project was developed in response to legislative mandates (Supplemental Report of the 1999 Budget Act 1999-00 Fiscal Year; and, the Groundwater Quality Monitoring Act of 2001 [Sections 10780-10782.3 of the California Water Code, Assembly Bill 599]) to assess and monitor the quality of groundwater in California, and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The SCRC study unit was the 25th study unit to be sampled as part of the GAMA Priority Basins Project. The SCRC study unit was designed to provide a spatially unbiased assessment of untreated groundwater quality in the primary aquifer systems and to facilitate statistically consistent comparisons of untreated groundwater quality throughout California. The primary aquifer systems (hereinafter referred to as primary aquifers) were defined as that part of the aquifer corresponding to the perforation interval of wells listed in the California Department of Public Health (CDPH) database for the SCRC study unit. The quality of groundwater in shallow or deep water-bearing zones may differ from the quality of groundwater in the primary aquifers; shallow groundwater may be more vulnerable to surficial contamination. In the SCRC study unit, groundwater samples were collected from 70 wells in two study areas (Basins and Uplands) in Santa Barbara and San Luis Obispo Counties. Fifty-five of the wells were selected using a spatially distributed, randomized grid-based method to provide statistical representation of the study unit (grid wells), and 15 wells were selected to aid in evaluation of specific water-quality issues (understanding wells). In addition to the 70 wells sampled, 3 surface-water samples were collected in streams near 2 of the sampled wells in order to better comprehend the interaction between groundwater and surface water in the area. The groundwater samples were analyzed for organic constituents (volatile organic compounds [VOC], pesticides and pesticide degradates, polar pesticides and metabolites, and pharmaceutical compounds), constituents of special interest (perchlorate, N-nitrosodimethylamine [NDMA], and 1,2,3-TCP), naturally occurring inorganic constituents (trace elements, nutrients, dissolved organic carbon [DOC], major and minor ions, silica, total dissolved solids [TDS], and alkalinity), and radioactive constituents (gross alpha and gross beta radioactivity). Naturally occurring isotopes (stable isotopes of hydrogen and oxygen in water, stable isotopes of nitrogen and oxygen in dissolved nitrate, stable isotopes of sulfur in dissolved sulfate, stable isotopes of carbon in dissolved inorganic carbon, activities of tritium, and carbon-14 abundance), and dissolved gases (including noble gases) also were measured to help identify the sources and ages of the sampled groundwater. In total, 298 constituents and field water-quality indicators were investigated. Three types of quality-control samples (blanks, replicates, and matrix-spikes) were collected at approximately 3 to 12 percent of the wells in the SCRC study unit, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination from sample collection procedures was not a significant source of bias in the data for the groundwater samples. Differences between replicate samples generally were less than 10 percent relative and/or standard deviation, indicating acceptable analytical reproducibility. Matrix-spike recoveries were within the acceptable range (70 to 130 percent) for approximately 84 percent of the compounds. This study did not attempt to evaluate the quality of drinking water delivered to consumers; after withdrawal from the ground, untreated groundwater typically is treated, disinfected, and/or blended with other waters to maintain water quality. Regulatory thresholds apply to water that is served to the consumer, not to untreated groundwater. However, to provide some context for the results, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and CDPH, and to non-regulatory thresholds established for aesthetic concerns by CDPH. Comparisons between data collected for this study and thresholds for drinking water are for illustrative purposes only and are not indicative of compliance or noncompliance with those thresholds. Most organic and inorganic constituents that were detected in groundwater samples from the 55 grid wells in the SCRC study unit were detected at concentrations less than drinking-water thresholds. In addition, all detections of organic constituents in SCRC grid well samples were less than health-based thresholds. In total, VOCs were detected in 33 percent of the 55 grid wells sampled and pesticides and pesticide degradates were detected in 27 percent of grid wells sampled in the SCRC study unit. In the Basins study area, VOCs and pesticides and pesticide degradates were detected in approximately 33 percent of the 39 grid wells. In the Uplands study area, VOCs were detected in approximately 31 percent and pesticides and pesticide degradates were detected in approximately 13 percent of the 16 grid wells. Trace elements and minor ions were sampled for at 32 grid wells and nutrients at 33 grid wells in the SCRC study unit, and most detections were less than health-based thresholds. Exceptions in the Basins study area include one detection of arsenic greater than the USEPA maximum contaminant level (MCL-US) of 10 µg/L and three detections of nitrite plus nitrate, as nitrogen (NO2-+NO3-) greater than the MCL-US of 10 mg/L. Exceptions in the Uplands study area include two detections of arsenic greater than the MCL-US and eight detections of molybdenum greater than the USEPA lifetime health advisory level (HAL-US) of 40 µg/L. All detections of major and minor ions and gross alpha and gross beta radioactivity from the SCRC grid wells were less than health-based thresholds. Results for trace elements, major ions, and TDS with non-enforceable thresholds set for aesthetic concerns from 16 Basins study area grid wells showed that iron concentrations greater than the CDPH secondary maximum contaminant level (SMCL-CA) of 300 µg/L were detected in grid wells. Manganese concentrations greater than the SMCL-CA of 50 µg/L were detected in six grid wells. Chloride concentrations greater than the recommended SMCL-CA threshold of 250 mg/L were detected in one grid well. Sulfate concentrations greater than the recommended SMCL-CA threshold of 250 mg/L were measured in 12 grid wells and 3 of these wells also were greater than the upper SMCL-CA threshold of 500 mg/L. TDS concentrations greater than the SMCL-CA recommended threshold of 500 mg/L were measured in 14 of the 16 Basins study area grid wells and concentrations in 5 of these wells also were greater than the SMCL-CA upper threshold of 1,000 mg/L. In the Uplands study area, iron concentrations greater than the SMCL-CA were detected in 2 of 16 grid wells and manganese concentrations greater than the SMCL-CA were detected in 3 grid wells. TDS and sulfate concentrations greater than the recommended SMCL-CA thresholds were detected in 11 and 2 grid wells, respectively, but none of these concentrations were greater than the SMCL-CA upper thresholds.

California↗
USGS2023

Gene expression and wildlife health: Varied interpretations based on perspective

We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.

Alaska, British Columbia, California, Oregon, Wash↗
USGS2014

Groundwater-quality data in the North San Francisco Bay Shallow Aquifer study unit, 2012: results from the California GAMA Program

Groundwater quality in the 1,850-square-mile North San Francisco Bay Shallow Aquifer (NSF-SA) study unit was investigated by the U.S. Geological Survey (USGS) from April to August 2012, as part of the California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring and Assessment (GAMA) Program’s Priority Basin Project (PBP). The GAMA-PBP was developed in response to the California Groundwater Quality Monitoring Act of 2001 and is being conducted in collaboration with the SWRCB and Lawrence Livermore National Laboratory (LLNL). The NSF-SA study unit was the first study unit to be sampled as part of the second phase of the GAMA-PBP, which focuses on the shallow aquifer system. The GAMA NSF-SA study was designed to provide a spatially unbiased assessment of untreated-groundwater quality in the shallow aquifer systems and to facilitate statistically consistent comparisons of untreated-groundwater quality throughout California. The shallow aquifer system in the NSF-SA study unit was defined as the part of the aquifer system that is used by many private domestic wells and is shallower than the primary aquifer system used by many public-supply wells. In the NSF-SA study unit located in Marin, Mendocino, Napa, Solano, and Sonoma Counties, groundwater samples were collected from 71 wells. Seventy of the wells were selected by using a spatially distributed, randomized grid-based method to provide statistical representation of the study unit (grid wells), and one well was selected to aid in evaluation of water-quality issues (understanding well). The groundwater samples were analyzed for organic constituents (volatile organic compounds [VOCs], pesticides, and pesticide degradates); constituents of special interest (perchlorate and 1,2,3-trichloropropane [1,2,3-TCP]); naturally occurring inorganic constituents (trace elements, nutrients, major and minor ions, silica, and total dissolved solids [TDS]); and radioactive constituents (radon-222 and gross alpha and gross beta radioactivity). Naturally occurring isotopes (stable isotopes of hydrogen, oxygen, boron, strontium, and inorganic carbon in water, tritium activities, and carbon-14 abundances) were measured to help identify the sources and ages of the sampled groundwater. In total, 207 constituents and water-quality indicators were measured. Three types of quality-control samples (blanks, replicates, and matrix spikes) were collected at up to 13 percent of the wells in the NSF-SA study unit, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Blanks rarely contained detectable concentrations of any constituent, suggesting that contamination from sample-collection procedures was not a significant source of bias in the data for the groundwater samples. Replicate samples generally were within the limits of acceptable analytical reproducibility. Matrix-spike recoveries were within the acceptable range (70 to 130 percent) for approximately 91 percent of the compounds. Most of the wells sampled for this study were private domestic wells. Private domestic wells are not regulated in California, and groundwater from these wells is rarely analyzed for water-quality constituents. Although regulatory benchmarks for drinking-water quality do not apply to private domestic wells, to provide some context for the results, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory health-based benchmarks established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH), to non-regulatory health-based benchmarks established by the USGS in cooperation with the USEPA, and to non-regulatory benchmarks established for aesthetic concerns by the CDPH. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks. Most of the organic and inorganic constituents that were detected in groundwater samples from the 70 grid wells in the NSF-SA study unit were detected at concentrations less than drinking-water benchmarks. Of the 149 organic and special-interest constituents analyzed for in groundwater samples, 31 were detected; concentrations of most detected constituents were less than regulatory and non-regulatory health-based benchmarks. One VOC, benzene, and one insecticide, dieldrin, were detected at concentrations above their respective health-based benchmarks. In total, VOCs were detected in 40 percent of the grid wells sampled, pesticides and pesticide degradates were detected in 13 percent, and perchlorate was detected in 27 percent of the 70 grid wells sampled. Groundwater samples from 70 grid wells were analyzed for trace elements, major and minor ions, nutrients, and radioactive constituents; most detected concentrations were less than health-based benchmarks. Exceptions are 12 detections of manganese greater than the USGS Health-Based Screening Level (HBSL), 7 detections of arsenic greater than the USEPA maximum contaminant level (MCL-US) of 10 micrograms per liter (μg/L), 2 detections of boron greater than the HBSL of 6,000 μg/L, 2 detections of fluoride greater than the CDPH maximum contaminant level (MCL-CA) of 2 milligrams per liter (mg/L), 2 detections of nitrate greater than the MCL-US of 10 mg/L, and two detections of radon-222 greater than the proposed MCL-US of 4,000 picocuries per liter. Results for constituents with non-regulatory benchmarks set for aesthetic concerns from the grid wells showed that iron concentrations greater than the CDPH secondary maximum contaminant level (SMCL-CA) of 300 μg/L were detected in 13 grid wells. Chloride was detected at a concentration greater than the SMCL-CA recommended benchmark of 250 mg/L in two grid wells. Sulfate concentrations greater than the SMCL-CA recommended benchmark of 250 mg/L were measured in two grid wells, and the concentration in one of these wells was also greater than the SMCL-CA upper benchmark of 500 mg/L. TDS concentrations greater than the SMCL-CA recommended benchmark of 500 mg/L were measured in 15 grid wells, and concentrations in 4 of these wells were also greater than the SMCL-CA upper benchmark of 1,000 mg/L.

California↗
USGS2012

Status of groundwater quality in the California Desert Region, 2006-2008: California GAMA Priority Basin Project

Groundwater quality in six areas in the California Desert Region (Owens, Antelope, Mojave, Coachella, Colorado River, and Indian Wells) was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The six Desert studies were designed to provide a spatially unbiased assessment of the quality of untreated groundwater in parts of the Desert and the Basin and Range hydrogeologic provinces, as well as a statistically consistent basis for comparing groundwater quality to other areas in California and across the Nation. Samples were collected by the USGS from September 2006 through April 2008 from 253 wells in Imperial, Inyo, Kern, Los Angeles, Mono, Riverside, and San Bernardino Counties. Two-hundred wells were selected using a spatially distributed, randomized grid-based method to provide a spatially unbiased representation of the study areas (grid wells), and fifty-three wells were sampled to provide additional insight into groundwater conditions (additional wells). The status of the current quality of the groundwater resource was assessed based on data from samples analyzed for volatile organic compounds (VOCs), pesticides, and inorganic constituents such as major ions and trace elements. Water-quality data from the California Department of Public Health (CDPH) database also were incorporated in the assessment. The status assessment is intended to characterize the quality of untreated groundwater resources within the primary aquifer systems of the Desert Region, not the treated drinking water delivered to consumers by water purveyors. The primary aquifer systems (hereinafter, primary aquifers) in the six Desert areas are defined as that part of the aquifer corresponding to the perforation intervals of wells listed in the CDPH database. Relative-concentrations (sample concentration divided by the benchmark concentration) were used as the primary metric for evaluating groundwater quality for those constituents that have Federal and (or) California benchmarks. A relative-concentration (RC) greater than (>) 1.0 indicates a concentration above a benchmark, and an RC less than or equal to (≤) 1.0 indicates a concentration equal to or below a benchmark. Organic and special-interest constituent RCs were classified as “low” (RC ≤ 0.1), “moderate” (0.1 < RC ≤ 1.0), or “high” (RC > 1.0). Inorganic constituent RCs were classified as “low” (RC ≤ 0.5), “moderate” (0.5 < RC ≤ 1.0), or “high” (RC > 1.0). A lower threshold value RC was used to distinguish between low and moderate RCs for organic constituents because these constituents are generally less prevalent and have smaller RCs than inorganic constituents. Aquifer-scale proportion was used as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion was defined as the percentage of the area of the primary aquifers with an RC greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentage of the primary aquifers with moderate and low RCs, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the Desert Region (within 90 percent confidence intervals). The status assessment determined that one or more inorganic constituents with health-based benchmarks had high RCs in 35.4 percent of the Desert Region’s primary aquifers, moderate RCs in 27.4 percent, and low RCs in 37.2 percent. The inorganic constituents with health-based benchmarks having the largest high aquifer-scale proportions were arsenic (17.8 percent), boron (11.4 percent), fluoride (8.9 percent), gross-alpha radioactivity (6.6 percent), molybdenum (5.7 percent), strontium (3.7 percent), vanadium (3.6 percent), uranium (3.2 percent), and perchlorate (2.4 percent). Inorganic constituents with non-health-based benchmarks were also detected at high RCs in 18.6 percent and at moderate RCs in 16.0 percent of the Desert Region’s primary aquifers. In contrast, organic constituents had high RCs in only 0.3 percent of the Desert Region’s primary aquifers, moderate in 2.0 percent, low in 48.0 percent, and were not detected in 49.7 percent of the primary aquifers in the Desert Region. Of 149 organic constituents analyzed for all six study areas, 42 constituents were detected. Six organic constituents, carbon tetrachloride, chloroform, 1,2-dichloropropane, dieldrin, 1,2-dichloroethane, and tetrachloroethene, were found at moderate RCs in one or more of the grid wells. One constituent, N -nitrosodimethylamine, a special-interest VOC, was detected at a high RC in one well. Thirty-nine organic constituents were detected only at low concentrations. Three organic constituents were frequently detected (in more than 10 percent of samples from grid wells): chloroform, simazine, and deethylatrazine.

California↗
USGS2009

Groundwater-quality data in the South Coast Interior Basins study unit, 2008: Results from the California GAMA Program

Groundwater quality in the approximately 653-square-mile South Coast Interior Basins (SCI) study unit was investigated from August to December 2008, as part of the Priority Basins Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basins Project was developed in response to Legislative mandates (Supplemental Report of the 1999 Budget Act 1999-00 Fiscal Year; and, the Groundwater-Quality Monitoring Act of 2001 [Sections 10780-10782.3 of the California Water Code, Assembly Bill 599]) to assess and monitor the quality of groundwater used as public supply for municipalities in California, and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). SCI was the 27th study unit to be sampled as part of the GAMA Priority Basins Project. This study was designed to provide a spatially unbiased assessment of the quality of untreated groundwater used for public water supplies within SCI, and to facilitate statistically consistent comparisons of groundwater quality throughout California. Samples were collected from 54 wells within the three study areas [Livermore, Gilroy, and Cuyama] of SCI in Alameda, Santa Clara, San Benito, Santa Barbara, Ventura, and Kern Counties. Thirty-five of the wells were selected using a spatially distributed, randomized grid-based method to provide statistical representation of the study unit (grid wells), and 19 were selected to aid in evaluation of specific water-quality issues (understanding wells). The groundwater samples were analyzed for organic constituents [volatile organic compounds (VOCs), pesticides and pesticide degradates, polar pesticides and metabolites, and pharmaceutical compounds], constituents of special interest [perchlorate and N-nitrosodimethylamine (NDMA)], naturally occurring inorganic constituents [trace elements, nutrients, major and minor ions, silica, total dissolved solids (TDS), and alkalinity], and radioactive constituents [gross alpha and gross beta radioactivity and radon-222]. Naturally occurring isotopes [stable isotopes of hydrogen, oxygen, and carbon, and activities of tritium and carbon-14] and dissolved noble gases also were measured to help identify the sources and ages of the sampled groundwater. In total, 288 constituents and water-quality indicators (field parameters) were investigated. Three types of quality-control samples (blanks, replicates, and matrix spikes) each were collected at approximately 4–11 percent of the wells, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination was not a significant source of bias in the data obtained from the groundwater samples. Differences between replicate samples generally were less than 10 percent relative standard deviation, indicating acceptable analytical reproducibility. Matrix spike recoveries were within the acceptable range (70 to 130 percent) for most compounds. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, untreated groundwater typically is treated, disinfected, and/or blended with other waters to maintain water quality. Regulatory thresholds apply to water that is served to the consumer, not to untreated groundwater. However, to provide some context for the results, concentrations of constituents measured in the untreated groundwater were compared with regulatory and nonregulatory health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH), and to nonregulatory thresholds established for aesthetic and technical concerns by CDPH. Comparisons between data collected for this study and thresholds for drinking water are for illustrative purposes only, and are not indicative of compliance or noncompliance with those thresholds. Most inorganic constituents that were detected in groundwater samples from the 35 grid wells in the SCI study unit were found at concentrations below drinking-water thresholds; additionally, all detections of organic constituents in SCI grid well samples were below health-based thresholds.

California↗
USGS2012

Spatial patterns and temporal trends in mercury concentrations, precipitation depths, and mercury wet deposition in the North American Great Lakes region, 2002-2008

Annual and weekly mercury (Hg) concentrations, precipitation depths, and Hg wet deposition in the Great Lakes region were analyzed by using data from 5 monitoring networks in the USA and Canada for a 2002-2008 study period. High-resolution maps of calculated annual data, 7-year mean data, and net interannual change for the study period were prepared to assess spatial patterns. Areas with 7-year mean annual Hg concentrations higher than the 12 ng per liter water-quality criterion were mapped in 4 states. Temporal trends in measured weekly data were determined statistically. Monitoring sites with significant 7-year trends in weekly Hg wet deposition were spatially separated and were not sites with trends in weekly Hg concentration. During 2002-2008, Hg wet deposition was found to be unchanged in the Great Lakes region and its subregions. Any small decreases in Hg concentration apparently were offset by increases in precipitation.

Great Lakes Region↗
USGS2020

Hierarchical computing for hierarchical models in ecology

Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade-offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation-assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual- and population-level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi-species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation-assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

Article↗
USGS2014

Groundwater-quality data in the Klamath Mountains study unit, 2010: results from the California GAMA Program

Groundwater quality in the 8,806-square-mile Klamath Mountains (KLAM) study unit was investigated by the U.S. Geological Survey (USGS) from October to December 2010, as part of the California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring and Assessment (GAMA) Program’s Priority Basin Project (PBP). The GAMA-PBP was developed in response to the California Groundwater Quality Monitoring Act of 2001 and is being conducted in collaboration with the SWRCB and Lawrence Livermore National Laboratory (LLNL). The KLAM study unit was the thirty-third study unit to be sampled as part of the GAMA-PBP. The GAMA Klamath Mountains study was designed to provide a spatially unbiased assessment of untreated-groundwater quality in the primary aquifer system and to facilitate statistically consistent comparisons of untreated-groundwater quality throughout California. The primary aquifer system is defined by the perforation intervals of wells listed in the California Department of Public Health (CDPH) database for the KLAM study unit. Groundwater quality in the primary aquifer system may differ from the quality in the shallower or deeper water-bearing zones; shallower groundwater may be more vulnerable to surficial contamination. In the KLAM study unit, groundwater samples were collected from sites in Del Norte, Siskiyou, Humboldt, Trinity, Tehama, and Shasta Counties, California. Of the 39 sites sampled, 38 were selected by using a spatially distributed, randomized grid-based method to provide statistical representation of the primary aquifer system in the study unit (grid sites), and the remaining site was non-randomized (understanding site). The groundwater samples were analyzed for basic field parameters, organic constituents (volatile organic compounds [VOCs] and pesticides and pesticide degradates), inorganic constituents (trace elements, nutrients, major and minor ions, total dissolved solids [TDS]), radon-222, gross alpha and gross beta radioactivity, and microbial indicators (total coliform and Escherichia coli [E. coli]). Isotopic tracers (stable isotopes of hydrogen and oxygen in water, isotopic ratios of dissolved strontium in water, and stable isotopes of carbon in dissolved inorganic carbon), dissolved noble gases, and age-dating tracers (tritium and carbon-14) were measured to help identify sources and ages of sampled groundwater. Quality-control samples (field blanks, replicate sample pairs, and matrix spikes) were collected at 13 percent of the sites in the KLAM study unit, and the results were used to evaluate the quality of the data from the groundwater samples. Field blank samples rarely contained detectable concentrations of any constituent, indicating that contamination from sample collection or analysis was not a significant source of bias in the data for the groundwater samples. More than 99 percent of the replicate pair samples were within acceptable limits of variability. Matrix-spike sample recoveries were within the acceptable range (70 to 130 percent) for approximately 91 percent of the compounds. This study did not evaluate the quality of water delivered to consumers. After withdrawal, groundwater typically is treated, disinfected, and (or) blended with other waters to maintain water quality. Regulatory benchmarks apply to water that is delivered to the consumer, not to untreated groundwater. However, to provide some context for the results, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory health-based benchmarks established by the U.S. Environmental Protection Agency (USEPA) and CDPH, and to non-health-based benchmarks established for aesthetic concerns by the CDPH. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks. All concentrations of organic constituents from grid sites sampled in the KLAM study unit were less than health-based benchmarks. In total, VOCs were detected in 16 of the 38 grid sites sampled (approximately 42 percent), pesticides and pesticide degradates were detected in 8 grid sites (about 21 percent), and microbial indicators were detected in 14 grid sites (approximately 37 percent). Inorganic constituents (trace elements, major and minor ions, nutrients, and uranium and other radioactive constituents) and microbial indicators were sampled for at 38 grid sites, and all concentrations were less than health-based benchmarks, with the exception of one detection of boron greater than the CDPH notification level of 1,000 micrograms per liter (μg/L). Generally, concentrations of inorganic constituents with non-health-based benchmarks (iron, manganese, chloride, and TDS) were less than the CDPH secondary maximum contaminant level (SMCL-CA). Exceptions include three detections of iron greater than the SMCL-CA of 300 μg/L, four detections of manganese greater than the SMCL-CA of 50 μg/L, one detection of chloride greater than the recommended SMCL-CA of 250 μg/L, and one detection of TDS greater than the recommended SMCL-CA of 500 μg/L.

California↗
USGS2009

Groundwater quality data for the Tahoe-Martis study unit, 2007: Results from the California GAMA Program

Groundwater quality in the approximately 460-square-mile Tahoe–Martis study unit was investigated in June through September 2007 as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin 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 study was designed to provide a spatially unbiased assessment of the quality of raw groundwater used for public water supplies within the Tahoe–Martis study unit (Tahoe–Martis) and to facilitate statistically consistent comparisons of groundwater quality throughout California. Samples were collected from 52 wells in El Dorado, Placer, and Nevada Counties. Forty-one of the wells were selected using a spatially distributed, randomized grid-based method to provide statistical representation of the study area (grid wells), and 11 were selected to aid in evaluation of specific water-quality issues (understanding wells). The groundwater samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOC], pesticides and pesticide degradates, and pharmaceutical compounds), constituents of special interest (perchlorate and N -nitrosodimethylamine [NDMA]), naturally occurring inorganic constituents (nutrients, major and minor ions, and trace elements), radioactive constituents, and microbial indicators. Naturally occurring isotopes (tritium, carbon-14, strontium isotope ratio, and stable isotopes of hydrogen and oxygen of water), and dissolved noble gases also were measured to help identify the sources and ages of the sampled groundwater. In total, 240 constituents and water-quality indicators were investigated. Three types of quality-control samples (blanks, replicates, and samples for matrix spikes) each were collected at 12 percent of the wells, and the results obtained from these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that data for the groundwater samples were not compromised by possible contamination during sample collection, handling or analysis. Differences between replicate samples were within acceptable ranges. Matrix spike recoveries were within acceptable ranges for most compounds. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, raw water typically is treated, disinfected, or blended with other waters to maintain water quality. Regulatory thresholds apply to water that is served to the consumer, not to raw groundwater. However, to provide some context for the results, concentrations of constituents measured in the raw groundwater were compared with regulatory and nonregulatory health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and the California Department of Public Health (CDPH), and with aesthetic and technical thresholds established by CDPH. Comparisons between data collected for this study and drinking-water thresholds are for illustrative purposes only and do not indicate of compliance or noncompliance with regulatory thresholds. The concentrations of most constituents detected in groundwater samples from the Tahoe–Martis wells were below drinking-water thresholds. Organic compounds (VOCs and pesticides) were detected in about 40 percent of the samples from grid wells, and most concentrations were less than 1/100th of regulatory and nonregulatory health-based thresholds, although the conentration of perchloroethene in one sample was above the USEPA maximum contaminant level (MCL-US). Concentrations of all trace elements and nutrients in samples from grid wells were below regulatory and nonregulatory health-based thresholds, with five exceptions. Concentrations of arsenic were above the MCL-US in 20 percent of the samples from grid wells. Gross alpha particle activity (MCL-US), boron (CDPH notification level, NL-CA), and molybdenum (USEPA lifetime health advisory, HAL-US) were each detected above thresholds in two of the samples from grid wells, and radon (proposed alternative MCL-US) was detected above the threshold in one sample from a grid well. Most of the samples from Tahoe–Martis grid wells had concentrations of major elements, total dissolved solids, and trace elements below the CDPH secondary maximum contaminant levels, nonenforceable thresholds set for aesthetic and technical concerns. Fifteen percent of the samples from grid wells contained iron, manganese, or total dissolved solids at concentrations above these levels.

California, Nevada↗
USGS2019

The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards

Estimation of ground level geoelectric fields has been identified by the National Space Weather Action Plan as a key component of assessment and mitigation of space weather impacts on critical infrastructure. Estimates of spatially and temporally variable electric fields are used to generate statistically based hazard maps and show promise toward monitoring and responding to geomagnetic disturbances in near real‐time. One approach to geoelectric field estimation is to employ three‐dimensional (3D) Earth conductivity models. These data‐constrained conductivity models are the results of regional magnetotelluric inversions based primarily on NSF’s Earthscope USArray impedances, which to date cover ~60% of the contiguous United States. Here, we present the first‐ever composite conductivity model of the contiguous United States and describe its compilation from 3D regional conductivity models, a global mantle conductivity model, offshore bathymetry, and sediment thickness data. We discuss structures within the conductivity model and how they relate to the complex geologic tapestry of the continent. Finally, we discuss the utility of this synthesis model for estimation and mitigation of geomagnetically induced currents.

Book chapter↗