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At least 415 records · Page 23Linked to original sources
USGS2006

Annual trace-metal load estimates and flow-weighted concentrations of cadmium, lead, and zinc, in the Spokane River basin, Idaho and Washington, 1999-2004

Streamflow and trace-metal concentration data collected at 10 locations in the Spokane River basin of northern Idaho and eastern Washington during 1999-2004 were used as input for the U.S. Geological Survey software, LOADEST, to estimate annual loads and mean flow-weighted concentrations of total and dissolved cadmium, lead, and zinc. Cadmium composed less than 1 percent of the total metal load at all stations; lead constituted from 6 to 42 percent of the total load at stations upstream from Coeur d’Alene Lake and from 2 to 4 percent at stations downstream of the lake. Zinc composed more than 90 percent of the total metal load at 6 of the 10 stations examined in this study. Trace-metal loads were lowest at the station on Pine Creek below Amy Gulch, where the mean annual total cadmium load for 1999–2004 was 39 kilograms per year (kg/yr), the mean estimated total lead load was about 1,700 kg/yr, and the mean annual total zinc load was 14,000 kg/yr. The trace-metal loads at stations on North Fork Coeur d’Alene River at Enaville, Ninemile Creek, and Canyon Creek also were relatively low. Trace-metal loads were highest at the station at Coeur d’Alene River near Harrison. The mean annual total cadmium load was 3,400 kg/yr, the mean total lead load was 240,000 kg/yr, and the mean total zinc load was 510,000 kg/yr for 1999–2004. Trace-metal loads at the station at South Fork Coeur d’Alene River near Pinehurst and the three stations on the Spokane River downstream of Coeur d’Alene Lake also were relatively high. Differences in metal loads, particularly lead, between stations upstream and downstream of Coeur d’Alene Lake likely are due to trapping and retention of metals in lakebed sediments. LOADEST software was used to estimate loads for water years 1999–2001 for many of the same sites discussed in this report. Overall, results from this study and those from a previous study are in good agreement. Observed differences between the two studies are attributable to streamflow differences in the two regression models, 1999–2001 and 1999-2004. Flow-weighted concentrations (FWCs) calculated from the estimated loads for 1999–2004 were examined to aid interpretation of metal load estimates, which were influenced by large spatial and temporal variations in streamflow. FWCs of total cadmium ranged from 0.04 micrograms per liter (µg/L) at Enaville to 14 µg/L at Ninemile Creek. Total lead FWCs were lowest at Long Lake (1.3 µg/L) and highest at Ninemile Creek (120 µg/L). Elevated total lead FWCs at Harrison confirmed that the high total lead loads at this station were not simply due to higher streamflow. Conversely, relatively low total lead loads combined with high total lead FWCs at Ninemile and Canyon Creeks reflected low streamflow but high concentrations of total lead. Very low total lead FWCs (1.3 to 2.7 µg/L) at the stations downstream of Coeur d’Alene Lake are a result both of deposition of lead-laden sediments in the lake and dilution by additional streamflow. Total zinc FWCs also demonstrated the effect of streamflow on load calculations, and highlighted source areas for zinc in the basin. Total zinc FWCs at Canyon and Ninemile Creeks, 1,600 µg/L and 2,200 µg/L, respectively, were by far the highest in the basin but contributed among the lowest total zinc loads due to their relatively low streamflow. Total zinc FWCs ranged from 38 to 67 µg/L at stations downstream of Coeur d’Alene Lake, but total zinc load estimates at these stations were relatively high because of high mean streamflow compared to other stations in the basin. Long-term regression models for 1991 to 2003 or 2004 were developed and annual trace-metal loads and FWCs were estimated for Pinehurst, Enaville, Harrison, and Post Falls to better understand the variability of metal loading with time. Long-term load estimates are similar to the results for 1999‑2004 in terms of spatial distribution of metal loads throughout the basin. LOADEST results for 1991-2004 indicated that statistically significant downward temporal trends for dissolved and total cadmium, dissolved zinc, and total lead were occurring at Pinehurst, Enaville, Harrison, and Post Falls. Additionally, data for Enaville and Post Falls showed significant downward trends for dissolved lead and total zinc loads; Harrison total zinc loads also decreased with time. The Mann-Kendall trend test results agreed with the LOADEST trend results in most cases, but gave contradictory results for total zinc at Pinehurst and at Post Falls. Long- and short-term load and flow-weighted concentration estimates yielded valuable information about metal storage and transport processes, and demonstrated that water quality data are a great aid in understanding these processes.

Idaho, Washington↗
USGS2020

Inventory and analysis of groundwater resources: Theodore Roosevelt National Park, North Dakota

Industrial and commercial developments in western North Dakota potentially could affect the sources of water that contribute to wells, spring flow, and seeps within Theodore Roosevelt National Park. Without basic water resources data, accurately predicting the effects of water withdrawals and water quality concerns related to industrial and commercial developments near the park would be challenging. Water resources in the park include surface water and groundwater. The Little Missouri River and its tributaries cross all three Theodore Roosevelt National Park units and are the primary surface-water features in the park. Groundwater resources include well discharges, springs, and seeps. The geology and hydrogeology of Theodore Roosevelt National Park are defined by the surrounding Williston Basin. Four aquifers are sources of groundwater to the park: unconsolidated aquifers including alluvial systems, the upper Fort Union aquifer, the lower Fort Union aquifer, and the Fox Hills-lower Hell Creek aquifer. Data used for wells, springs, seeps, and water quality in this report were compiled from the U.S. Geological Survey National Water Information System or from the North Dakota State Water Commission. An inventory of 16 wells was completed for sites within the boundaries of the park. In addition to well data, an inventory of 11 springs and seeps was completed. The groundwater-quality analysis had two objectives: (1) to characterize the groundwater chemistry in aquifers underlying the park and (2) to spatially map selected physical properties and chemical constituents of interest. Groundwater-quality data from the North Dakota State Water Commission were summarized, mapped, and used to characterize groundwater for each aquifer in the study area. Spatial concentration distribution maps were constructed for selected physical properties and chemical constituents using summary statistics and exceedances. Piper diagrams were used to classify and characterize groundwater for each aquifer. Future research to help fill data gaps in water resources information for Theodore Roosevelt National Park, including recommendations from previous studies, consists of the following: (1) evaluating the variability in discharge from springs and seeps in comparison to changes in precipitation or other recharge sources, (2) evaluating flow control measures for flowing artesian wells, (3) completing a water rights review, and (4) performing routine water-quality monitoring for wells and springs.

North Dakota↗
USGS2001

A new global 1-km dataset of percentage tree cover derived from remote sensing

Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying definitions of 'forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.

Global Change Biology↗
USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗
USGS2017

Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA

This study examined the effects of natural and anthropogenic changes in confining margin width by applying remote sensing techniques – fusing LiDAR topography with image-derived bathymetry – over a large spatial extent: 58 km of the Snake River, Wyoming, USA. Fused digital elevation models from 2007 and 2012 were differenced to quantify changes in the volume of stored sediment, develop morphological sediment budgets, and infer spatial gradients in bed material transport. Our study spanned two similar reaches that were subject to different controls on confining margin width: natural terraces versus artificial levees. Channel planform in reaches with similar slope and confining margin width differed depending on whether the margins were natural or anthropogenic. The effects of tributaries also differed between the two reaches. Generally, the natural reach featured greater confining margin widths and was depositional, whereas artificial lateral constriction in the leveed reach produced a sediment budget that was closer to balanced. Although our remote sensing methods provided topographic data over a large area, net volumetric changes were not statistically significant due to the uncertainty associated with bed elevation estimates. We therefore focused on along-channel spatial differences in bed material transport rather than absolute volumes of sediment. To complement indirect estimates of sediment transport derived by morphological sediment budgeting, we collected field data on bed mobility through a tracer study. Surface and subsurface grain size measurements were combined with bed mobility observations to calculate armoring and dimensionless sediment transport ratios, which indicated that sediment supply exceeded transport capacity in the natural reach and vice versa in the leveed reach. We hypothesize that constriction by levees induced an initial phase of incision and bed armoring. Because levees prevented bank erosion, the channel excavated sediment by migrating rapidly across the restricted braidplain and eroding bars and islands.

Wyoming↗
USGS2013

Relaxing the closure assumption in single-season occupancy models: staggered arrival and departure times

Occupancy statistical models that account for imperfect detection have proved very useful in several areas of ecology, including species distribution and spatial dynamics, disease ecology, and ecological responses to climate change. These models are based on the collection of multiple samples at each of a number of sites within a given season, during which it is assumed the species is either absent or present and available for detection while each sample is taken. However, for some species, individuals are only present or available for detection seasonally. We present a statistical model that relaxes the closure assumption within a season by permitting staggered entry and exit times for the species of interest at each site. Based on simulation, our open model eliminates bias in occupancy estimators and in some cases increases precision. The power to detect the violation of closure is high if detection probability is reasonably high. In addition to providing more robust estimation of occupancy, this model permits comparison of phenology across sites, species, or years, by modeling variation in arrival or departure probabilities. In a comparison of four species of amphibians in Maryland we found that two toad species arrived at breeding sites later in the season than a salamander and frog species, and departed from sites earlier.

Ecology↗
USGS2023

Bayesian hierarchical modeling for probabilistic estimation of tsunami amplitude from far-field earthquake sources

Evaluation of tsunami disaster risk for a coastal region requires reliable estimation of tsunami hazard, for example, wave amplitude close to the shore. Observed tsunami data are scarce and have poor spatial coverage, and for this reason probabilistic tsunami hazard analysis (PTHA) traditionally relies on numerical simulation of “synthetic” tsunami generation and propagation toward the coast. Such an approach has been extensively studied in the past and it is widely recognized as an important disaster-risk mitigation tool. PTHA can not only provide less uncertain and spatially coherent hazard estimates in comparison with classical empirical data analysis which is restricted at the tide gauge stations, but also local inundation information. In this paper, we explore a purely statistical alternative to traditional PTHA for evaluation of tsunami amplitude hazard. Here, we use tide gauge measurements of tsunami amplitude along the western United States, specifically California and Oregon, and develop a spatial Bayesian hierarchical model (BHM) to assess tsunami hazard from far-field earthquake sources at various recurrence intervals. The configuration of our model incorporates latent Gaussian fields that utilize information on the distance between tide gauges as well as on the continental shelf width, that is, a covariate linked to potential dissipative effects on wave energy as the tsunami travels over shallow water. Through our BHM, we produce spatially continuous probabilistic maps of far-field tsunami hazard which can aid comprehensive tsunami disaster risk reduction and management.

California, Oregon, Washington↗
USGS2012

Spatial and temporal trends in runoff at long-term streamgages within and near the Chesapeake Bay Watershed

Long-term streamflow data within the Chesapeake Bay watershed and surrounding area were analyzed in an attempt to identify trends in streamflow. Data from 30 streamgages near and within the Chesapeake Bay watershed were selected from 1930 through 2010 for analysis. Streamflow data were converted to runoff and trend slopes in percent change per decade were calculated. Trend slopes for three runoff statistics (the 7-day minimum, the mean, and the 1-day maximum) were analyzed annually and seasonally. The slopes also were analyzed both spatially and temporally. The spatial results indicated that trend slopes in the northern half of the watershed were generally greater than those in the southern half. The temporal analysis was done by splitting the 80-year flow record into two subsets; records for 28 streamgages were analyzed for 1930 through 1969 and records for 30 streamgages were analyzed for 1970 through 2010. The mean of the data for all sites for each year were plotted so that the following datasets were analyzed: the 7-day minimum runoff for the north, the 7-day minimum runoff for the south, the mean runoff for the north, the mean runoff for the south, the 1-day maximum runoff for the north, and the 1-day maximum runoff for the south. Results indicated that the period 1930 through 1969 was statistically different from the period 1970 through 2010. For the 7-day minimum runoff and the mean runoff, the latter period had significantly higher streamflow than did the earlier period, although within those two periods no significant linear trends were identified. For the 1-day maximum runoff, no step trend or linear trend could be shown to be statistically significant for the north, although the south showed a mixture of an upward step trend accompanied by linear downtrends within the periods. In no case was a change identified that indicated an increasing rate of change over time, and no general pattern was identified of hydrologic conditions becoming "more extreme" over time.

Maryland, New York, Pennsylvania, Virginia, West V↗
USGS2025

An approach to modeling abundance of marine wildlife over space and time using unstructured aerial surveys

Estimating spatial and temporal patterns in abundance is often a goal of ecological studies and can be useful for informing management decisions, such as determining the optimal placement of wildlife protection zones. However, estimating abundance can be difficult in practice, especially over large areas, because of imperfect detection, where individuals are present but not detected because of either availability or observer error. Several methods for estimating abundance that account for imperfect detection exist but can be logistically challenging to implement. We present a simpler approach to some of the more commonly used techniques for estimating the abundance of marine wildlife over space and time from unstructured aerial surveys. This approach combines a spatial model for count data with auxiliary information on detection probability obtained from small-scale or previous studies. We employ generalized linear models and generalized additive models with spatial habitat covariates to illustrate this approach using maximum-likelihood with free, open-source statistical software. This framework is intended to be accessible and flexible, requiring lower survey costs and less computation time than other alternatives for estimating abundance. Indeed, our simulation results show that this approach can reduce computation times, while appropriately characterizing uncertainty, compared to a Bayesian approach. We also present R code for our approach using an example of estimating Florida manatee ( Trichechus manatus latirostris ) abundance in Indian River County in Florida, USA. This approach could be applied to other study systems and marine wildlife species using unstructured aerial surveys.

Florida↗
USGS2020

A mixed length scale model for migrating fluvial bedforms

With the expansion of hydropower, in‐stream converters, flood‐protection infrastructures, and growing concerns on deltas fragile ecosystems, there is a pressing need to evaluate and monitor bedform sediment mass flux. It is critical to estimate real‐time bedform size and migration velocity and provide a theoretical framework to convert easily accessible time histories of bed elevations into spatially evolving patterns. We collected spatiotemporally resolved bathymetries from laboratory flumes and the Colorado River in statistically steady, homogeneous, subcritical flow conditions. Wave number and frequency spectra of bed elevations show compelling evidence of scale‐dependent velocity for the hierarchy of migrating bedforms observed in the laboratory and field. New scaling laws were applied to describe the full range of migration velocities as function of two dimensionless groups based on the bed shear velocity, sediment diameter, and water depth. Further simplification resulted in a mixed length scale model estimating scale‐dependent migration velocities, without requiring bedform classification or identification.

Geophysical Research Letters↗
USGS2023

Bayesian forecasting of disease spread with little or no local data

Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections. As an alternative, we present a long-recognized but underutilized Bayesian technique that uses limited local data and informative priors to make statistically valid predictions and forecasts about disease occurrence and spread. As a case study, we use limited local data that were available after the detection of chronic wasting disease in Michigan, U.S. along with information rich priors obtained from a previous study in a neighboring state. Using these limited local data and informative priors, we generate statistically valid predictions of disease occurrence and spread for the Michigan study area. This Bayesian technique is conceptually and computationally simple, relies on little to no local data, and is competitive with non-statistical distance-based metrics in all performance evaluations. Bayesian modeling has added benefits because it allows practitioners to generate immediate forecasts of future disease conditions and provides a principled framework to incorporate new data as they accumulate. We contend that the Bayesian technique offers broad-scale benefits and opportunities to make statistical inference across a diversity of data-deficient systems, not limited to disease.

Scientific Reports↗
USGS2020

Multi‐species occupancy models: Review, roadmap, and recommendations

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

Ecography↗
USGS2019

Bringing Bayesian models to life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models.

Book↗
USGS2008

Ground-water quality data in the Kern County Subbasin study unit, 2006— Results from the California GAMA Program

Ground-water quality in the approximately 3,000 square-mile Kern County Subbasin study unit (KERN) was investigated from January to March, 2006, as part of the Priority Basin Assessment Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Assessment project was developed in response to the Groundwater Quality Monitoring Act of 2001, and is being conducted by the California State Water Resources Control Board (SWRCB) in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory (LLNL). The Kern County Subbasin study was designed to provide a spatially unbiased assessment of raw (untreated) ground-water quality within KERN, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from 50 wells within the San Joaquin Valley portion of Kern County. Forty-seven of the wells were selected using a randomized grid-based method to provide a statistical representation of the ground-water resources within the study unit. Three additional wells were sampled to aid in the evaluation of changes in water chemistry along regional ground-water flow paths. The ground-water samples were analyzed for a large number of man-made organic constituents (volatile organic compounds [VOCs], pesticides, and pesticide degradates), constituents of special interest (perchlorate, N-nitrosodimethylamine [NDMA], and 1,2,3-trichloropropane [1,2,3-TCP]), naturally occurring inorganic constituents (nutrients, major and minor ions, and trace elements), radioactive constituents, and microbial indicators. Naturally occurring isotopes (tritium, carbon-14, and stable isotopes of hydrogen, oxygen, nitrogen, and carbon) and dissolved noble gases also were measured to help identify the source and age of the sampled ground water. Quality-control samples (blanks, replicates, and laboratory matrix spikes) were collected and analyzed at approximately 10 percent of the wells, and the results for these samples were used to evaluate the quality of the data from the ground-water samples. Assessment of the quality-control information resulted in censoring of less than 0.4 percent of the data collected for ground-water samples. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, raw ground water typically is treated, disinfected, or blended with other waters to maintain acceptable water quality. Regulatory thresholds apply, not to the raw ground water, but to treated water that is served to the consumer. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and the California Department of Public Health (CDPH), and as well as with thresholds established for aesthetic concerns (secondary maximum contaminant levels, SMCL-CA) by CDPH. VOCs and pesticides each were detected in approximately 60 percent of the grid wells, and detections of all compounds but one were below health-based thresholds. The fumigant, 1,2-dibromo-3-chloropropane (DBCP), was detected above the USEPA maximum contaminant level (MCL-US) in one sample. Detections of most inorganic constituents were also below health-based thresholds. Constituents detected above health-based thresholds include: nitrate, (MCL-US, 2 samples), arsenic (MCL-US, 2 samples), and vanadium (California notification level, NL-CA, 1 sample). All detections of radioactive constituents were below health-based thresholds, although nine samples had activities of radon-222 above the lower proposed MCL-US. Most of the samples from KERN wells had concentrations of major elements, total dissolved solids, and trace elements below the non-enforceable thresholds set for aesthetic concerns.

California↗
USGS1996

Snowpack chemistry at selected sites in northwestern Colorado during spring 1995

Samples of the alpine and subalpine snowpack were collected in and near the headwater basins of the Yampa River in northwestern Colorado during maximum annual accumulation of snowpack in spring 1995. Sampling protocol at seven selected sites at more than 2,500 meters above sea level divided the snowpack into two distinct strata to enable separate chemical analyses of upper and lower layers of the annual snow cover. These two layers correspond to the early snow season beginning in September until December 12, 1994, and the remainder of the season from January 1 until the sampling date in spring 1995. At one site these two strata were resampled at closely spaced intervals defining substrata to observe variance within the two strata dividing the snow season. Analytical results of snowpack chemistry are presented in support of investigations of seasonal effects on ion concentrations in the snowpack. Chemical concentrations of major anions and cations, dissolved organic carbon, and alkalinity; measured pH; calculated charge balance between anions and cations; the stable-sulfur isotope ratio (34S/32S); and summary statistics of chemical concentrations are tabulated. Sampling sites are plotted on a map of the area. Spatial distributions of the concentrations of the hydrogen, nitrate, and sulfate ions and stable- sulfur isotope ratios also are mapped. Several unusual, late-season, snowfall events occurred during April and May of 1995 after the snowpack was sampled at most of the seven sites in the study area. Consequently, a considerable fraction of the total annual snowpack was not sampled. At one site, the full snowpack was sampled again in June, after the late-spring storms, for comparison to the chemistry of the snowpack sampled earlier in April. Precipitation chemistry from a National Atmospheric Deposition Program (NADP) collector located near the site is presented for comparison of the chemistry of the late-season snow to that of the typical annual snowpack season.

Open-File Report↗
USGS2003

Irrigation-induced contamination of water, sediment, and biota in the western United States-synthesis of data from the National Irrigation Water Quality Program

In October 1985 the U.S. Department of the Interior (DOI), through the National Irrigation Water Quality Program (NIWQP), began a series of field investigations at 26 areas in the Western United States to determine whether irrigation drainage has had harmful effects on fish, wildlife, and humans or has reduced beneficial uses of water. In 1992 NIWQP initiated the Data Synthesis Project to evaluate data collected during the field investigations. Geologic, climatologic, and hydrologic data were evaluated and water, sediment, and biota from the 26 areas were analyzed to identify commonalities and dominant factors that result in irrigation-induced contamination of water and biota. Data collected for the 26 area investigations have been compiled and merged into a common data base. The structure of the data base is designed to enable assessment of relations between contaminant concentrations in water, sediment, and biota. The data base is available to the scientific community through the World Wide Web at URL http://www.usbr.gov/niwqp. Analysis of the data base for the Data Synthesis included use of summary statistics, factor analysis, and logistic regression. A Geographic Information System was used to store and analyze spatially oriented digital data such as land use, geology and evaporation rates. In the U.S. Department of the Interior (DOI) study areas, samples of water, bottom sediment, and biota were collected for trace-element and pesticide analysis. Contaminants most commonly associated with irrigation drainage were identified by comparing concentrations in water with established criteria. For surface water, the criteria used were typically chronic criteria for the protection of freshwater aquatic life. Because ground water can discharge to the surface where wildlife can be exposed to it, the criteria used for ground water were both the maximum contaminant levels (MCL's) for drinking water and the chronic criteria for the protection of freshwater aquatic life. Data collected by the NIWQP studies indicated that, in surface water, filtered and unfiltered samples had nearly the same concentrations of arsenic, boron, molybdenum, and selenium for concentrations greater than about 10 micrograms per liter. Therefore, in this concentration range, filtered concentrations can be directly compared to biological-effect levels developed for unfiltered samples. In the range of 1 to 10 micrograms per liter there may be a tendency for unfiltered arsenic concentrations to be greater than filtered concentrations. For selenium, however, the data suggest differences from equality in that range result from analytical imprecision and not a general tendency for unfiltered concentrations to be greater than filtered concentrations. This relation may not be true in lentic, nutrient-rich waters because in such settings algae can bioaccumulate large amounts of selenium and other trace elements. Selenium was the trace element in surface water that most commonly exceeded chronic criteria for the protection of freshwater aquatic life; more than 40 percent of the selenium concentrations in surface-water samples exceeded the U.S. Environmental Protection Agency (USEPA) aquatic-life chronic criterion (5 micrograms per liter). In 12 of the 26 areas at least 25 percent of the surface water-samples had selenium concentrations that either equaled or exceeded the chronic criterion (5 micrograms per liter). More than 28 percent of boron concentrations and almost 17 percent of the molybdenum concentrations exceeded the aquatic life criteria established by the State of California (550 and 19 micrograms per liter, respectively). In ground water, more than 22 percent of the arsenic concentrations and more than 35 percent of the selenium concentrations exceeded the MCL (10 and 50 micrograms per liter, respectively). Few samples of uranium in surface water exceeded a criterion for the protection of aquatic life (300 micrograms per liter), but 44 percent

Professional Paper↗
USGS2015

Evaluation of multiple-frequency, active and passive acoustics as surrogates for bedload transport

The use of multiple-frequency, active acoustics through deployment of acoustic Doppler current profilers (ADCPs) shows potential for estimating bedload in selected grain size categories. The U.S. Geological Survey (USGS), in cooperation with the University of Montana (UM), evaluated the use of multiple-frequency, active and passive acoustics as surrogates for bedload transport during a pilot study on the Kootenai River, Idaho, May 17-18, 2012. Four ADCPs with frequencies ranging from 600 to 2000 kHz were used to measure apparent moving bed velocities at 20 stations across the river in conjunction with physical bedload samples. Additionally, UM scientists measured the sound frequencies of moving particles with two hydrophones, considered passive acoustics, along longitudinal transects in the study reach. Some patterns emerged in the preliminary analysis which show promise for future studies. Statistically significant relations were successfully developed between apparent moving bed velocities measured by ADCPs with frequencies 1000 and 1200 kHz and bedload in 0.5 to 2.0 mm grain size categories. The 600 kHz ADCP seemed somewhat sensitive to the movement of gravel bedload in the size range 8.0 to 31.5 mm, but the relation was not statistically significant. The passive hydrophone surveys corroborated the sample results and could be used to map spatial variability in bedload transport and to select a measurement cross-section with moving bedload for active acoustic surveys and physical samples.

Conference Paper↗
USGS1998

Bayesian statistics applied to the location of the source of explosions at Stromboli Volcano, Italy

We present a method for determining the location and spatial extent of the source of explosions at Stromboli Volcano, Italy, based on a Bayesian inversion of the slowness vector derived from frequency-slowness analyses of array data. The method searches for source locations that minimize the error between the expected and observed slowness vectors. For a given set of model parameters, the conditional probability density function of slowness vectors is approximated by a Gaussian distribution of expected errors. The method is tested with synthetics using a five-layer velocity model derived for the north flank of Stromboli and a smoothed velocity model derived from a power-law approximation of the layered structure. Application to data from Stromboli allows for a detailed examination of uncertainties in source location due to experimental errors and incomplete knowledge of the Earth model. Although the solutions are not constrained in the radial direction, excellent resolution is achieved in both transverse and depth directions. Under the assumption that the horizontal extent of the source does not exceed the crater dimension, the 90% confidence region in the estimate of the explosive source location corresponds to a small volume extending from a depth of about 100 m to a maximum depth of about 300 m beneath the active vents, with a maximum likelihood source region located in the 120- to 180-m-depth interval.

Stromboli Volcano↗