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USGS2016

Groundwater-quality data in the Monterey–Salinas shallow aquifer study unit, 2013: Results from the California GAMA Program

Groundwater quality in the 3,016-square-mile Monterey–Salinas Shallow Aquifer study unit was investigated by the U.S. Geological Survey (USGS) from October 2012 to May 2013 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment (GAMA) Program’s Priority Basin Project. The GAMA Monterey–Salinas Shallow Aquifer study was designed to provide a spatially unbiased assessment of untreated-groundwater quality in the shallow-aquifer systems in parts of Monterey and San Luis Obispo Counties and to facilitate statistically consistent comparisons of untreated-groundwater quality throughout California. The shallow-aquifer system in the Monterey–Salinas Shallow Aquifer study unit was defined as those parts of the aquifer system shallower than the perforated depth intervals of public-supply wells, which generally corresponds to the part of the aquifer system used by domestic wells. Groundwater quality in the shallow aquifers can differ from the quality in the deeper water-bearing zones; shallow groundwater can be more vulnerable to surficial contamination. Samples were collected from 170 sites that were selected by using a spatially distributed, randomized grid-based method. The study unit was divided into 4 study areas, each study area was divided into grid cells, and 1 well was sampled in each of the 100 grid cells (grid wells). The grid wells were domestic wells or wells with screen depths similar to those in nearby domestic wells. A greater spatial density of data was achieved in 2 of the study areas by dividing grid cells in those study areas into subcells, and in 70 subcells, samples were collected from exterior faucets at sites where there were domestic wells or wells with screen depths similar to those in nearby domestic wells (shallow-well tap sites). Field water-quality indicators (dissolved oxygen, water temperature, pH, and specific conductance) were measured, and samples for analysis of inorganic constituents (trace elements, nutrients, major and minor ions, silica, total dissolved solids, and alkalinity) were collected at all 170 sites. In addition to these constituents, the samples from grid wells were analyzed for organic constituents (volatile organic compounds, pesticides and pesticide degradates), constituents of special interest (perchlorate and N -nitrosodimethylamine, or NDMA), radioactive constituents (radon-222 and gross-alpha and gross-beta radioactivity), and geochemical and age-dating tracers (stable isotopes of carbon in dissolved inorganic carbon, carbon-14 abundances, stable isotopes of hydrogen and oxygen in water, and tritium activities). Three types of quality-control samples (blanks, replicates, and matrix spikes) were collected at up to 11 percent of the wells in the Monterey–Salinas Shallow Aquifer study unit, and the results for these samples were used to evaluate the quality of the data from the groundwater samples. With the exception of trace elements, blanks rarely contained detectable concentrations of any constituent, indicating that contamination from sample-collection procedures was not a significant source of bias in the data for the groundwater samples. Low concentrations of some trace elements were detected in blanks; therefore, the data were re-censored at higher reporting levels. Replicate samples generally were within the limits of acceptable analytical reproducibility. The median values of matrix-spike recoveries were within the acceptable range (70 to 130 percent) for the volatile organic compounds (VOCs) and N -nitrosodimethylamine (NDMA), but were only approximately 64 percent for pesticides and pesticide degradates. The sample-collection protocols used in this study were designed to obtain representative samples of groundwater. The quality of groundwater can differ from the quality of drinking water because water chemistry can change as a result of contact with plumbing systems or the atmosphere; because of treatment, disinfection, or blending with water from other sources; or some combination of these. Water quality in domestic wells is not regulated in California, however, to provide context for the water-quality data presented in this report, results were compared to benchmarks established for drinking-water quality. The primary comparison benchmarks were maximum contaminant levels established by the U.S. Environmental Protection Agency and the State of California (MCL-US and MCL-CA, respectively). Non-regulatory benchmarks were used for constituents without maximum contaminant levels (MCLs), including Health Based Screening Levels (HBSLs) developed by the USGS and State of California secondary maximum contaminant levels (SMCL-CA) and notification levels. Most constituents detected in samples from the Monterey–Salinas Shallow Aquifer study unit had concentrations less than their respective benchmarks. Of the 148 organic constituents analyzed in the 100 grid-well samples, 38 were detected, and all concentrations were less than the benchmarks. Volatile organic compounds were detected in 26 of the grid wells, and pesticides and pesticide degradates were detected in 28 grid wells. The special-interest constituent NDMA was detected above the HBSL in three samples, one of which also had a perchlorate concentration greater than the MCL-CA. Of the inorganic constituents, 6 were detected at concentrations above their respective MCL benchmarks in grid-well samples: arsenic (5 grid wells above the MCL of 10 micrograms per liter, μg/L), selenium (3 grid wells, MCL of 50 μg/L), uranium (4 grid wells, MCL of 30 μg/L), nitrate (16 grid wells, MCL of 10 milligrams per liter, mg/L), adjusted gross alpha particle activity (10 grid wells, MCL of 15 picocuries per liter, pCi/L), and gross beta particle activity (1 grid well, MCL of 50 pCi/L). An additional 4 inorganic constituents were detected at concentrations above their respective HBSL benchmarks in grid-well samples: boron (1 grid well above the HBSL of 6,000 μg/L), manganese (8 grid wells, HBSL of 300 μg/L), molybdenum (6 grid wells, HBSL of 40 μg/L), and strontium (6 grid wells, HBSL of 4,000 μg/L). Of the inorganic constituents, 4 were detected at concentrations above their non-health based SMCL benchmarks in grid-well samples: iron (9 grid wells above the SMCL of 300 μg/L), chloride (7 grid wells, SMCL of 500 mg/L), sulfate (14 grid wells, SMCL of 500 mg/L), and total dissolved solids (27 grid wells, SMCL of 1,000 mg/L). Of the inorganic constituents analyzed in the 70 shallow-well tap sites, 10 were detected at concentrations above the benchmarks. Of the inorganic constituents, 3 were detected at concentrations above their respective MCL benchmarks in shallow-well tap sites: arsenic (2 shallow-well tap sites above the MCL of 10 μg/L), uranium (2 shallow-well tap sites, MCL of 30 μg/L), and nitrate (24 shallow-well tap sites, MCL of 10 mg/L). An additional 3 inorganic constituents were detected above their respective HBSL benchmarks in shallow-well tap sites: manganese (4 shallow-well tap sites above the HBSL of 300 μg/L), molybdenum (4 shallow-well tap sites, HBSL of 40 μg/L), and zinc (2 shallow-well tap sites, HBSL of 2,000 μg/L). Of the inorganic constituents, 4 were detected at concentrations above their non-health based SMCL benchmarks in shallow-well tap sites: iron (6 shallow-well tap sites above the SMCL of 300 μg/L), chloride (1 shallow-well tap site, SMCL of 500 mg/L), sulfate (9 shallow-well tap sites, SMCL of 500 mg/L), and total dissolved solids (15 shallow-well tap sites, SMCL of 1,000 mg/L).

California↗

USGS2007

Coupling alongshore variations in wave energy to beach morphologic change using the SWAN wave model at Ocean Beach, San Francisco, CA

Coastal managers have faced increasing pressure to manage their resources wisely over the last century as a result of heightened development and changing environmental forcing. It is crucial to understand seasonal changes in beach volume and shape in order to identify areas vulnerable to accelerated erosion. Shepard (1950) was among the first to quantify seasonal beach cycles. Sonu and Van Beek (1971) and Wright et al. (1985) described commonly occurring beach states. Most studies utilize widest spaced 2-D cross shore profiles or shorelines extracted from aerial photographs (e.g. Winant et al. 1975; Aubrey, 1979, Aubrey and Ross, 1985; Larson and Kraus, 1994; Jimenez et al., 1977; Lacey and Peck, 1998; Guillen et al., 1999; Norcorss et al., 2002) to analyzed systematic changes in beach evolution. But with the exception of established field stations, such as Duck, NC (Birkemeier and Mason, 1984), ans Hazaki Oceanographical Research Station (HORS) in Japan (Katoh, 1997), there are very few beach change data sets with high temporal and spatial resolutions (e.g. Dail et al., 2000; Ruggiero et al., 2005; Yates et al., in press). Comprehensive sets of nearshore morphological data and local in situ measurements outside of these field stations are very rare and virtually non-existent high-energy coasts. Studied that have attempted to relate wave statistics to beach morphology change require some knowledge of the nearshore wave climate, and have had limited success using offshore measurement (Sonu and Van Beek, 1971; Dail et al., 2000). The primary objective of this study is to qualitatively compare spatially variable nearshore wave predictions to beach change measurements in order to understand the processes responsible for a persistent erosion 'hotspot' at Ocean Beach, San Francisco, CA. Local wave measurements are used to calibrate and validate a wave model that provides nearshore wave prediction along the beach. The model is run for thousands of binned offshore wave conditions to help isolate the effects of offshore wave direction and period on nearshore wave predictions. Alongshore varying average beach change statistics are computed at specific profile locations from topographic beach surveys and lidar data. The study area is located in the San Francisco Bight in central California. Ocean Beach is a seven kilometer long north-south trending sandy coastline located just south of the entrance to the San Francisco Bay Estuary (Figure 1). It contains an erosion hotspot in the southern part of the beach which has resulted in damage to local infrastructure and is the cause of continued concern. A wide range of field data collection and numerical modeling efforts have been focused here as part of the United States Geological Survey's (USGS) San Francisco Bight Coastal Processes Study, which began in October 2003 and represents the first comprehensive study of coastal processes at the mouth of San Francisco Bay. Ocean Beach is exposed to very strong tidal flows, with measured currents often in excess of 1 m/s at the north end of the beach. Current profiler measurements indicate that current magnitudes are greater in the northern portion of the beach, while wave energy is greater in the southern portion where erosion problems are greatest (Barnard et al., 2007). The sub-aerial beach volume fluctuates seasonally over a maximum envelope of 400,000 m 3 for the seven kilometer stretch (Barnard et al, 2007). The wave climate in the region is dominated by an abundance of low frequency energy (greater than 20 s period) and prevailing northwest incident wave angles. The application of a wave model to the region is further complicated by the presence of the Farallon Islands 40 kilometers west, and a massive ebb tidal delta at the mouth of San Francisco Bay (~150 km 2 ), which creates complicated refraction patterns as wave energy moves from offshore Ocean Beach; however the cost and threat of the energetic nearshore environment have limited the temporal and spatial resolution of these measurements. Applying numerical models to predict wave and current patterns along the beach can help supplement the filed data that exists and provide opportunities to make prediction about the impacts of changing environmental forcing.

California↗

USGS2013

Distance, dams and drift: What structures populations of an endangered, benthic stream fish?

Spatial population structure plays an important role in species persistence, evolution and conservation. Benthic stream fishes are diverse and frequently imperilled, yet the determinants and spatial scaling of their population structure are understudied. We investigated the range-wide population genetic structure of Roanoke logperch ( Percina rex ), an endangered, benthic stream fish of the eastern United States. Fish were sampled from 35 sites and analysed at 11 microsatellite DNA loci. Clustering models were used to sort individuals into genetically cohesive groups and thereby estimate the spatial scaling of population structure. We then used Bayesian generalized linear mixed models (BGLMMs) to test alternative hypotheses about the environmental factors most responsible for generating structure, as measured by the differentiation statistic F ST . Clustering models delineated seven discrete populations, whose boundaries coincided with agents of fragmentation, including hydroelectric dams and tailwaters. In the absence of hydrological barriers, gene flow was extensive throughout catchments, whereas there was no evidence for contemporary dispersal between catchments across barriers. In the best-supported BGLMM, F ST was positively related to the spatial distance and degree of hydrological alteration between sites and negatively related to genetic diversity within sites. Whereas the effect of tailwaters was equivocal, dams strongly influenced differentiation: the effect of a dam on F ST was comparable to that of a between-site distance of over 1200 km of unimpounded river. Overall, the effect of distance-mediated dispersal was negligible compared to the combined effects of fragmentation and genetic drift. The contemporary population structure of P. rex comprises a few geographically extensive ‘islands’ that are fragmented by hydroelectric projects. This information clarifies the importance of a catchment-scale perspective on conserving the species and suggests that its recovery may require genetic and/or demographic reconnection of presently isolated populations.

North Carolina, Virginia↗

USGS2015

A comparison of thermal infrared to fiber-optic distributed temperature sensing for evaluation of groundwater discharge to surface water

Groundwater has a predictable thermal signature that can be used to locate discrete zones of discharge to surface water. As climate warms, surface water with strong groundwater influence will provide habitat stability and refuge for thermally stressed aquatic species, and is therefore critical to locate and protect. Alternatively, these discrete seepage locations may serve as potential point sources of contaminants from polluted aquifers. This study compares two increasingly common heat tracing methods to locate discrete groundwater discharge: direct-contact measurements made with fiber-optic distributed temperature sensing (FO-DTS) and remote sensing measurements collected with thermal infrared (TIR) cameras. FO-DTS is used to make high spatial resolution (typically m) thermal measurements through time within the water column using temperature-sensitive cables. The spatialtemporal data can be analyzed with statistical measures to reveal zones of groundwater influence, however, the personnel requirements, time to install, and time to georeference the cables can be burdensome, and the control units need constant calibration. In contrast, TIR data collection, either from handheld, airborne, or satellite platforms, can quickly capture point-in-time evaluations of groundwater seepage zones across large scales. However the remote nature of TIR measurements means they can be adversely influenced by a number of environmental and physical factors, and the measurements are limited to the surface skin temperature of water features. We present case studies from a range of lentic to lotic aquatic systems to identify capabilities and limitations of both technologies and highlight situations in which one or the other might be a better instrument choice for locating groundwater discharge. FO-DTS performs well in all systems across seasons, but data collection was limited spatially by practical considerations of cable installation. TIR is found to consistently locate groundwater seepage zones above and along the streambank, but submerged seepage zones are only well identified in shallow systems (e.g. <0.5 m depth) with moderate flow. Winter data collection, when groundwater is relatively warm and buoyant, increases the water surface expression of discharge zones in shallow systems.

Massachusetts, Michigan, Montana, New York, Pennsy↗

USGS2004

Spatial variations in the frequency-magnitude distribution of earthquakes at Mount Pinatubo volcano

The frequency-magnitude distribution of earthquakes measured by the b-value is mapped in two and three dimensions at Mount Pinatubo, Philippines, to a depth of 14 km below the summit. We analyzed 1406 well-located earthquakes with magnitudes MD ???0.73, recorded from late June through August 1991, using the maximum likelihood method. We found that b-values are higher than normal (b = 1.0) and range between b = 1.0 and b = 1.8. The computed b-values are lower in the areas adjacent to and west-southwest of the vent, whereas two prominent regions of anomalously high b-values (b ??? 1.7) are resolved, one located 2 km northeast of the vent between 0 and 4 km depth and a second located 5 km southeast of the vent below 8 km depth. The statistical differences between selected regions of low and high b-values are established at the 99% confidence level. The high b-value anomalies are spatially well correlated with low-velocity anomalies derived from earlier P-wave travel-time tomography studies. Our dataset was not suitable for analyzing changes in b-values as a function of time. We infer that the high b-value anomalies around Mount Pinatubo are regions of increased crack density, and/or high pore pressure, related to the presence of nearby magma bodies.

Bulletin of the Seismological Society of America↗

USGS2008

Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multi-chambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO 4 ); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction.

Journal of Environmental Quality↗

USGS2022

Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study

Spatial capture–recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear ( Ursus americanus ) capture–recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km 2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture–recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.

Utah↗

USGS2024

Novel quantitative methods to enable multispectral identification of high-purity water ice exposures on Mars using High Resolution Imaging Science Experiment (HiRISE) images

Reliable detection and characterization of water ice on the Martian surface is pivotal to not only understand its present and past climate, but to also provide valuable information on in-situ resource availability and distribution for future human exploration missions. Ice-rich features are currently identified with visible/near-IR (VNIR), thermal IR and radar data. However, their coarse spatial scale sometimes limits confident characterization of small (i.e., meter-scale) icy exposures resulting from recent activity like new impacts. Water ice bearing materials possess weaker spectral characteristics at wavelengths shorter than ∼1030 nm that may be resolved by VNIR imaging instruments like the High Resolution Imaging Science Experiment (HiRISE) and the Colour and Stereo Surface Imaging System (CaSSIS). Our study assesses the spectral capability of HiRISE colour observations to help distinguish high purity water ice exposures from ice-poor materials. We report detailed methodologies for reliable colour characterization of icy surface using unfiltered HiRISE images. We present the first quantitative approach to uniquely characterize high-purity ice-rich materials through spectral shape and spectral parameterization methods at high spatial resolution (∼50 cm/pixel). We also present three spectral parameters to aid detection of pure water ice features, while also providing statistical constraints to enable a quantitative interpretation scheme. Our methods are observed to work well in characterizing and separating ice-rich features uniquely from ice-poor and ferrous materials. However, we do observe that these methods have a lower grain size detection limit of ∼250–300 μm, and may not be able to uniquely separate frosts from ground ice exposures. We also apply these methods to better constrain the composition of bright materials exposed by recent impacts identified in previous surveys, where substantial evidence for ice-bearing materials was previously unavailable. Overall, our work proposes HiRISE colour-based methods as a novel approach for high-resolution multispectral characterization of ice-rich features on the Martian surface, which is of particular value since the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has ceased operations.

Icarus↗

USGS2005

Estimating size and composition of biological communities by modeling the occurrence of species

We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically important quantities that are easily constructed and estimated using our model-based estimators of species occurrence. In particular, we compute site-specific estimates of species richness that honor classical notions of species-area relationships. We suggest extensions of our model to estimate maps of occurrence of individual species and to compute inferences related to the temporal and spatial dynamics of biological communities.

Journal of the American Statistical Association↗

USGS1986

Does pH affect fish species richness when lake area is considered?

Numerous surveys have shown that fish species richness (number of species) is positively correlated with lake pH. However, species richness of fish communities is also correlated with lake size, and low‐pH lakes are often small. Thus, conclusions drawn from examination of fish community structure relative to spatial (among‐lake) variation in pH have been limited by uncertainties regarding the confounded effects of lake area. We used two statistical methods, analysis of covariance and a nonparametric blocked comparison test, to remove effects of lake area and compare fish species richness in low‐pH and high‐pH lakes. Data from six previous surveys of water chemistry and fish communities in lakes of Ontario and northern Wisconsin were examined. Lakes with low pH (≤6.0) contained significantly fewer fish species than lakes with high pH (>6.0) when the effect of lake area was considered. A simple probabilistic model showed that the ability to detect differences in species richness is low when lake areas and the pool of potential colonizing species are small. We recommend the blocked comparison test for separating the effects of lake area and pH on species richness and urge critical examination of inferences based on analyses of fish communities in small lakes.

Ontario, Wisconsin↗

USGS2018

Use of remote sensing to detect and predict aquatic nuisance vegetation growth in coastal Louisiana: Summary of findings

On an annual basis, federal and state agencies are responsible for mapping and removing large expanses of aquatic nuisance vegetation from navigable waterways. This study set out to achieve four primary objectives: (1) utilize recent advancements in remote sensing techniques to classify the extent and distribution of aquatic vegetation in coastal ecosystems using satellite imagery, (2) assess primary aquatic vegetation growth and management efforts in coastal Louisiana, (3) statistically identify the ecological drivers that promote growth and infestation of aquatic nuisance vegetation, and (4) develop numerical models and a spatial tool to predict the probability of occurrence and growth of aquatic vegetation given ecological drivers. Moderate spatial resolution multispectral satellite imagery were used in conjunction with environmental variables from available data streams to generate regression models that predict aquatic vegetation occurrence in the eastern coastal region of south Louisiana. Geospatial tools were developed to execute the model logic using recent environmental conditions, thereby predicting aquatic vegetation occurrence and producing classified maps for end users. These products provide more efficient and enhanced capabilities for management of aquatic nuisance vegetation.

Louisiana↗

USGS2002

Relationship of epithermal gold deposits to large-scale fractures in northern Nevada

Geophysical maps of northern Nevada reveal at least three and possibly six large-scale arcuate features, one of which corresponds to the northern Nevada rift that possibly extends more than 1,000 km from the Oregon- Idaho border to southern Nevada. These features may reflect deep discontinuities within the earth's crust, possibly related to the impact of the Yellowstone hot spot. Because mid-Miocene epithermal gold deposits have been shown to correlate with the northern Nevada rift, we investigate the association of other epithermal gold deposits to other similar arcuate features in northern Nevada. Mid-Miocene and younger epithermal gold- silver deposits also occur along two prominent aeromagnetic anomalies west of the northern Nevada rift. Here, we speculate that mid-Miocene deposits formed along deep fractures in association with mid-Miocene rift- related magmatism and that younger deposits preferentially followed these preexisting features. Statistical analysis of the proximity of epithermal gold deposits to these features suggests that epithermal gold deposits in northern Nevada are spatially associated with large-scale crustal features interpreted from geophysical data.

Economic Geology↗

USGS2009

Ground-Water Quality Data in the Santa Clara River Valley Study Unit, 2007: Results from the California GAMA Program

Ground-water quality in the approximately 460-square-mile Santa Clara River Valley study unit (SCRV) was investigated from April to June 2007 as part of the statewide Priority Basin project of the Ground-Water 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 ground water used for public water supplies within SCRV, and to facilitate a statistically consistent basis for comparing water quality throughout California. Fifty-seven ground-water samples were collected from 53 wells in Ventura and Los Angeles Counties. Forty-two wells were selected using a randomized grid-based method to provide statistical representation of the study area (grid wells). Eleven wells (understanding wells) were selected to further evaluate water chemistry in particular parts of the study area, and four depth-dependent ground-water samples were collected from one of the eleven understanding wells to help understand the relation between water chemistry and depth. The ground-water samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOC], pesticides and pesticide degradates, potential wastewater-indicator compounds, and pharmaceutical compounds), a constituent of special interest (perchlorate), naturally occurring inorganic constituents (nutrients, major and minor ions, and trace elements), radioactive constituents, and microbial constituents. Naturally occurring isotopes (tritium, carbon-13, carbon-14 [abundance], stable isotopes of hydrogen and oxygen in water, stable isotopes of nitrogen and oxygen in nitrate, chlorine-37, and bromine-81), and dissolved noble gases also were measured to help identify the source and age of the sampled ground water. Quality-control samples (blanks or replicates, or samples for matrix spikes) were collected from approximately 26 percent of the wells, and the analyses of 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 quality of the environmental data was good, with low bias and low variability, and as a result, less than 0.1 percent of the analytes detected in ground-water samples were censored. 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 delivered (or, supplied) 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 regulatory and non-regulatory thresholds established by the U.S. Environmental Protection Agency (USEPA) and the California Department of Public Health (CDPH) and thresholds established for aesthetic concerns (secondary maximum contaminant levels, SMCL-CA) by CDPH. Most constituents that were detected in ground-water samples were reported at concentrations below their established health-based thresholds. VOCs, pesticides and pesticide degradates, and potential wastewater-indicator compounds were detected in about 33 percent or less of the 42 SCRV grid wells. Concentrations of all detected organic constituents were below established health-based thresholds. Perchlorate was detected in approximately 12 percent of the SCRV grid wells; all concentrations reported were below the NL-CA threshold. Additional constituents, including major ions, trace elements, and nutrients were collected at 26 wells (16 grid wells and 10 understanding wells) of the 53 wells sampled f

Data Series↗

USGS2003

Atlas of interoccurrence intervals for selected thresholds of daily precipitation in Texas

A Poisson process model is used to define the distribution of interoccurrence intervals of daily precipitation in Texas. A precipitation interoccurrence interval is the time period between two successive rainfall events. Rainfall events are defined as daily precipitation equaling or exceeding a specified depth threshold. Ten precipitation thresholds are considered: 0.05, 0.10, 0.25, 0.50, 0.75, 1.0, 1.5, 2.0, 2.5, and 3.0 inches. Site-specific mean interoccurrence interval and ancillary statistics are presented for each threshold and for each of 1,306 National Weather Service daily precipitation gages. Maps depicting the spatial variation across Texas of the mean interoccurrence interval for each threshold are presented. The percent change from the statewide standard deviation of the interoccurrence intervals to the root-mean-square error ranges from a magnitude minimum of (negative) -24 to a magnitude maximum of -60 percent for the 0.05- and 2.0-inch thresholds, respectively. Because of the substantial negative percent change, the maps are considered more reliable estimators of the mean interoccurrence interval for most locations in Texas than the statewide mean values.

Texas↗

USGS2011

Chapter 7: Occurrence and abundance of ants, reptiles, and mammals

Sagebrush ( Artemisia spp.)- associated wildlife are threatened by habitat loss and fragmentation and by impacts associated with anthropogenic disturbances, including energy development. Understanding how species of concern as well as other wildlife including insects, reptiles, and mammals respond to type and spatial scale of disturbance is critical to managing future land uses and identifying sites that are important for conservation. We developed statistical models to describe species occurrence or abundance, based on area searches in 7.29-ha survey blocks, across the Wyoming Basins Ecoregional Assessment (WBEA) area for six shrub steppe-associated species: harvester ant ( Pogonomyrmex spp.), thatch ant ( Formica spp.), short-horned lizard ( Phrynosoma hernandesi ), white-tailed jackrabbit ( Lepus townsendii ), cottontail ( Sylvilagus spp.) and least chipmunk ( Tamius minimus ). We modeled patterns in occupancy or abundance relative to multi-scale measures of vegetation type and pattern, abiotic site characteristics, and anthropogenic disturbance factors. Sagebrush habitat was a strong predictor of occurrence for shorthorned lizards and white-tailed jackrabbits, but weak for the other four species. Vegetation and abiotic characteristics were strong determinants of species occurrence, although the scale of response was not consistent among species. All species, with the exception of the short-horned lizard, responded to anthropogenic disturbance, although responses again varied as a function of scale and direction (negative and positive influences). Our results improve our understanding of how environmental and anthropogenic factors affect species distributions across the WBEA area and facilitate a multi-species approach to management of this sagebrush ecosystem.

Wyoming↗

USGS2026

Space-based monitoring of penguin diet links sea ice, food webs, and population change

Rapid environmental change is reshaping Antarctic ecosystems through shifts in temperature and sea ice dynamics, with implications for species such as the iconic Adélie penguin ( Pygoscelis adeliae ). Because Antarctic predators rely on a relatively small number of key prey resources in the region 1 whose life histories are intrinsically linked to sea ice, 2 , 3 these abiotic changes are expected to cascade through food webs. However, the scale and remoteness of Antarctica have limited efforts to link environmental change to ecological responses across the continent. Combining tools from imaging spectroscopy, stable isotope analysis, and hierarchical statistical modeling, we reconstructed Adélie penguin diet across the entirety of the species’ global range over a three-decade period (1984–2013) using satellite imagery from the Landsat program. This approach leveraged the distinct spectral properties (a generalized measure of color) of penguin guano as observed by satellites, 4 , 5 which vary according to penguin diet. 6 , 7 We found pronounced differences in diet across the continent and that year-to-year dietary shifts were strongly associated with sea ice dynamics. Higher sea ice corresponded to more fish-based diets, whereas lower sea ice was associated with a greater reliance on krill. Additionally, spatial differences in penguin diet were associated with long-term trends in penguin abundance, linking food web processes to large-scale population dynamics. This study represents the first use of satellite observations to capture trophic dynamics at continental and decadal scales and highlights how environmental change might restructure Antarctic food webs and influence the future of a key sentinel species.

Current Biology↗

USGS2025

Tracing metal sources and groundwater flow paths in the Upper Animas River watershed using rare earth elements and stable isotopes

Groundwater flow paths and processes that govern metal mobility and transport are difficult to characterize in mountainous bedrock watersheds. Despite the difficulty in holistic characterization, conceptual understanding of subsurface hydrologic and geochemical processes is key to developing remediation plans for locations affected by acid mine drainage, such as the Upper Animas River watershed in southwestern Colorado, USA. Stable isotopes of water and rare earth elements were utilized to evaluate groundwater flow and metal sources within this complex catchment. Stable isotope samples collected from draining mine adits and springs display systematic spatial variation wherein sample sites at higher elevations have greater seasonal variability than sites at lower elevations. The Upper Cement Creek watershed, where multiple draining mines are present, displays the lowest seasonal variation in stable isotopic signatures, potentially indicating the presence of a large, well-mixed volume of groundwater storage or interbasin groundwater flow. Rare earth elements display statistically significant variation between different alteration styles in the catchment. Overprinting of regional propylitic alteration is evident based on enrichment of middle rare earth elements in acidic springs and mines that are not spatially associated with surficial exposures of acid generating alteration styles. Europium anomaly and middle rare earth enrichment signatures from two flooded mine tunnels on opposite sides of a watershed divide indicate connections to the same subsurface flooded mine workings.

Colorado↗

USGS2007

Habitat classification modeling with incomplete data: Pushing the habitat envelope

Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical techniques for HCMs. One option is to generate pseudo-absence points so that the many available statistical modeling tools can be used. Traditional techniques generate pseudoabsence points at random across broadly defined species ranges, often failing to include biological knowledge concerning the species-habitat relationship. We incorporated biological knowledge of the species-habitat relationship into pseudo-absence points by creating habitat envelopes that constrain the region from which points were randomly selected. We define a habitat envelope as an ecological representation of a species, or species feature's (e.g., nest) observed distribution (i.e., realized niche) based on a single attribute, or the spatial intersection of multiple attributes. We created HCMs for Northern Goshawk (Accipiter gentilis atricapillus) nest habitat during the breeding season across Utah forests with extant nest presence points and ecologically based pseudo-absence points using logistic regression. Predictor variables were derived from 30-m USDA Landfire and 250-m Forest Inventory and Analysis (FIA) map products. These habitat-envelope-based models were then compared to null envelope models which use traditional practices for generating pseudo-absences. Models were assessed for fit and predictive capability using metrics such as kappa, thresholdindependent receiver operating characteristic (ROC) plots, adjusted deviance (Dadj2), and cross-validation, and were also assessed for ecological relevance. For all cases, habitat envelope-based models outperformed null envelope models and were more ecologically relevant, suggesting that incorporating biological knowledge into pseudo-absence point generation is a powerful tool for species habitat assessments. Furthermore, given some a priori knowledge of the species-habitat relationship, ecologically based pseudo-absence points can be applied to any species, ecosystem, data resolution, and spatial extent. ?? 2007 by the Ecological Society of America.

Ecological Applications↗