Geology topics

Janet M. Carter

Publications and source records attributed to Janet M. Carter.

At least 19 recordsLinked to original sources

USGS2026

Artificial intelligence strategy for the U.S. Geological Survey

Artificial intelligence (AI) can offer opportunities to enhance the science, science delivery, and business operations of the U.S. Geological Survey (USGS). Although USGS staff have proactively adopted AI into our workflows for many years, a comprehensive USGS strategy for AI has not previously been developed. The strategy described here is motivated by the acceleration of AI technological development, the benefits of increased AI adoption to USGS mission delivery as anticipated by USGS staff, rising public concern about the implications and trustworthiness of AI, and emerging Federal directives and guidance about AI. The USGS vision is to continue integrating AI to deliver valuable science for the public good while maintaining high ethical standards, scientific quality and integrity, and compliance with Federal and U.S. Department of the Interior requirements. To realize this vision, the USGS can take steps to (1) develop a strong AI workforce, (2) adapt our organizational approaches to include AI governance and communication, (3) ensure responsible and trustworthy use of AI, (4) modernize our computing and data infrastructure to support AI, and (5) accelerate AI adoption and innovation in the Bureau.

Circular↗

USGS2020

Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17

This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use. Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.

Montana, North Dakota, South Dakota↗

USGS2019

Groundwater characterization of the Madison aquifer near Jewel Cave National Monument, South Dakota

Jewel Cave National Monument in the Black Hills of southwestern South Dakota has more than 200 miles of mapped cave passages and several subterranean lakes that have been discovered since 2015. Jewel Cave is one of the world’s longest known caves and its natural beauty and unique natural cave features led U.S. President Theodore Roosevelt to designate the cave as a national monument in 1908. Jewel Cave was naturally formed in the regionally extensive Madison Limestone, which is characterized as a carbonate karst environment (containing caves and sinkholes) with extensive subterranean cave networks and losing streams at the land surface. Preserving and protecting the cave is an important element of the National Park Service mission, and understanding the hydrogeologic connection between the surface and the subsurface is essential for ensuring the preservation and protection of the cave for future generations. A component in preserving and protecting the park includes the improved understanding of groundwater flow and vulnerability of the subsurface, which allows scientists, park managers, the visiting public, and the surrounding communities to better manage, protect, and preserve the site and its unique natural features.

South Dakota↗

USGS2019

Analytical framework to estimate water use associated with continuous oil and gas development

An analytical framework was designed to estimate water use associated with continuous oil and gas (COG) development in support of the U.S. Geological Survey Water Availability and Use Science Program. This framework was developed to better understand the relation between the production of COG resources for energy and the amount of water needed to sustain this type of energy development in the United States. The total mean undiscovered, technically recoverable volume of COG has increased, highlighting the continued need to develop approaches to better characterize water use associated with COG development. The analytical framework can be used to estimate water use associated with COG development for three water-use components—direct, indirect, and ancillary water use—that are related to the life cycle of COG development. Direct water use is defined as water used in a wellbore to complete a well, including the water used for drilling, cementing, stimulating, and maintaining the well during production. Indirect water use is the water used at or near the well site, including water used for dust abatement, for cleaning equipment, and for crew and staff use. Ancillary water use is all other water used during the life cycle of COG development that is not categorized as direct or indirect, such as additional local or regional water use resulting from a change (for example, population) related to COG development. The analytical framework includes the data inputs, the processes involved in estimating the water-use coefficients and analyzing their uncertainties, and the outputs. The analytical framework was developed as an R script, which contains the statistical models used to estimate water-use components. The availability of data across COG reservoirs in the United States is variable and presents challenges for estimating water use for extracting COG from their reservoirs; thus, the R script can be modified for the types of data available within a COG reservoir, the extent and resolution of data available for each water-use component, and the desired output of the water-use assessment. The script was written so that the units of the data in the script were standardized. Water-use estimates were simulated for the mean and 10th, 50th, and 90th percentiles of the data distributions. Uncertainties were quantified with confidence intervals for the estimated coefficients. Uncertainty for estimated or simulated data can be calculated with the R script by providing a range of representative values that are within the appropriate confidence intervals of the mean of the data.

Lower 48 states↗

USGS2019

Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015

Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends. Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature ( T min ), and daily maximum air temperature ( T max ). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses. Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in T min and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in T max were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and T min , and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior. Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction. Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend. Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual T min . The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin. A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing. Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.

Scientific Investigations Report↗

USGS2018

Quality-assurance plan for groundwater activities, U.S. Geological Survey Dakota Water Science Center

As the Nation’s principal earth-science information agency, the U.S. Geological Survey (USGS) is depended upon to collect accurate data and produce factual and impartial interpretive reports. Methods for data collection and analysis that were developed by the USGS have become standard techniques used by numerous Federal, State, and local agencies and by private enterprises. The USGS has implemented a program designed to ensure that all scientific work done by or for USGS Water Science Centers is done in accordance with a quality-assurance plan. The implementation of a groundwater quality-assurance plan will enhance groundwater data collected by the USGS. This report is a quality-assurance plan for groundwater activities conducted by the USGS Dakota Water Science Center and is meant to complement qualityassurance plans for surface-water and water-quality activities and similar plans for the Dakota Water Science Center and general project activities throughout the USGS.

Open-File Report↗

USGS2018

Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development

As the demand for energy increases in the United States, so does the demand for water used to produce many forms of that energy. Technological advances, limited access to conventional oil and gas accumulations, and the rise of oil and gas prices resulted in increased development of unconventional oil and gas (UOG) accumulations. Unconventional oil and gas is developed using a method that combines directional drilling and hydraulic fracturing techniques, allowing for greater oil and gas production from previously unrecoverable reservoirs. Quantification of the water resources required for UOG development and production is difficult because of disparate data sources, variable reporting requirements across boundaries (local, State, and national), and incomplete or proprietary datasets. A topical study was started in 2015 under the U.S. Geological Survey’s Water Availability and Use Science Program, as part of the directive in the Secure Water Act for the U.S. Geological Survey to conduct a National Water Census, to better understand the relation between production of UOG resources for energy and the amount of water needed to produce and sustain this type of energy development in the United States. The Water Availability and Use Science Program goal for this topical study is to develop and apply a statistical model to better estimate the water use associated with UOG development, regardless of the location and target geologic formation. As a first step, a conceptual model has been developed to characterize the life cycle of water use in areas of UOG development. Categories of water use and the way water-use data are collected might change over time; therefore, a generic approach was used in developing the conceptual model to allow for greater flexibility in adapting to future changes or newly available data. UOG development can be summarized into four stages: predrilling construction, drilling, hydraulic fracturing, and ongoing production. The water used in UOG production can be categorized further as direct, indirect, or ancillary water use. Direct water use is defined as the water used for drilling and hydraulic fracturing a well and for maintaining the well during ongoing production. Indirect water use is defined as the water used at or near a well pad. The water used for dust abatement also is considered an indirect use but may be applied away from the well pad. Ancillary water use is defined as the additional local or regional water use resulting from a change (for example, population) directly related to UOG development throughout the life cycle that is not used directly in the well or indirectly for any other purpose at the well pad. The conceptual model presented in this report consists of five elements: (1) input data, (2) processes, (3) decisions, (4) output data, and (5) outcomes. The input data requirements for estimating water use associated with UOG development are somewhat onerous, and obtaining suitable datasets can be challenging because local, State, and Federal agencies do not collect data similarly. The quality of a water-use assessment that uses the conceptual model presented in this report is dependent on the quality and quantity of data that are available for a UOG play. The conceptual model can be used for an assessment with sparse data; however, having sparse data likely will result in greater uncertainty in the water-use estimates. The conceptual model presented in this report is designed to be robust to characterize and simulate the data processing to estimate water use associated with UOG development. Although the results of an analysis that includes missing data have greater uncertainty, the analysis still can be insightful because it can establish a baseline estimate of UOG water use that may be refined further as more data become available. Analysis of models that include missing data also could aid in identifying the data most needed for future water-use estimates. Characterizing individual model limitations is important because the conceptual model can be used in future water-use studies to facilitate data compiling, data processing, estimating, and assessing UOG activities regardless of location.

Scientific Investigations Report↗

USGS2018

Hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia

Armenia is a landlocked country located in the mountainous Caucasus region between Asia and Europe. It shares borders with the countries of Georgia on the north, Azerbaijan on the east, Iran on the south, and Turkey and Azerbaijan on the west. The Ararat Basin is a transboundary basin in Armenia and Turkey. The Ararat Basin (or Ararat Valley) is an intermountain depression that contains the Aras River and its tributaries, which also form the border between Armenia and Turkey and divide the basin into northern and southern regions. The Ararat Basin also contains Armenia’s largest agricultural and fish farming zone that is supplied by high-quality water from wells completed in the artesian aquifers that underlie the basin. Groundwater constitutes about 40 percent of all water use, and groundwater provides 96 percent of the water used for drinking purposes in Armenia. Since 2000, groundwater withdrawals and consumption in the Ararat Basin of Armenia have increased because of the growth of aquaculture and other uses. Increased groundwater withdrawals caused decreased springflow, reduced well discharges, falling water levels, and a reduction of the number of flowing artesian wells in the southern part of Ararat Basin in Armenia. In 2016, the U.S. Geological Survey and the U.S. Agency for International Development (USAID) began a cooperative study in Armenia to share science and field techniques to increase the country’s capabilities for groundwater study and modeling. The purpose of this report is to describe the hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia based on data collected in 2016 and previous hydrogeologic studies. The study area includes the Ararat Basin in Armenia. This report was completed through a partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships effort through the Advanced Science and Partnerships for Integrated Resource Development program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter. The hydrogeologic framework of the Ararat Basin includes several basin-fill stratigraphic units consisting of interbedded dense clays, gravels, sands, volcanic basalts, and andesite deposits. Previously published cross sections and well lithologic logs were used to map nine general hydrogeologic units. Hydrogeologic units were mapped based on lithology and water-bearing potential. Water-level data measured in the water-bearing hydrogeologic units 2, 4, 6, and 8 in 2016 were used to create potentiometric surface maps. In hydrogeologic unit 2, the estimated direction of groundwater flow is from the west to north in the western part of the basin (away from the Aras River) and from north to south (toward the Aras River) in the eastern part of the basin. In hydrogeologic unit 4, the direction of groundwater flow is generally from west to east and north to south (toward the Aras River) except in the western part of the basin where groundwater flow is toward the north or northwest. Hydrogeologic unit 6 has the same general pattern of groundwater flow as unit 4. Hydrogeologic unit 8 is the deepest of the water-bearing units and is confined in the basin. Groundwater flow generally is from the south to north (away from the Aras River) in the western part of the basin and from west to east and north to south (toward the Aras River) elsewhere in the basin. In addition to water levels, personnel from Armenia’s Hydrogeological Monitoring Center also measured specific conductance at 540 wells and temperature at 2,470 wells in the Ararat Basin using U.S. Geological Survey protocols in 2016. The minimum specific conductance was 377 microsiemens per centimeter (μS/cm), the maximum value was 4,000 μS/cm, and the mean was 998 μS/cm. The maximum water temperature was 24.2 degrees Celsius. An analysis between water temperature and well depth indicated no relation; however, spatially, most wells with cooler water temperatures were within the 2016 pressure boundary or in the western part of the basin. Wells with generally warmer water temperatures were in the eastern part of the basin. Samples were collected from four groundwater sites and one surface-water site by the U.S. Geological Survey in 2016. The stable-isotope values were similar for all five sites, indicating similar recharge sources for the sampled wells. The Hrazdan River sample was consistent with the groundwater samples, indicating the river could serve as a source of recharge to the Ararat artesian aquifer.

Ararat Basin↗

USGS2016

Construction of a groundwater-flow model for the Big Sioux Aquifer using airborne electromagnetic methods, Sioux Falls, South Dakota

The city of Sioux Falls is the fastest growing community in South Dakota. In response to this continued growth and planning for future development, Sioux Falls requires a sustainable supply of municipal water. Planning and managing sustainable groundwater supplies requires a thorough understanding of local groundwater resources. The Big Sioux aquifer consists of glacial outwash sands and gravels and is hydraulically connected to the Big Sioux River, which provided about 90 percent of the city’s source-water production in 2015. Managing sustainable groundwater supplies also requires an understanding of groundwater availability. An effective mechanism to inform water management decisions is the development and utilization of a groundwater-flow model. A groundwater-flow model provides a quantitative framework for synthesizing field information and conceptualizing hydrogeologic processes. These groundwater-flow models can support decision making processes by mapping and characterizing the aquifer. Accordingly, the city of Sioux Falls partnered with the U.S. Geological Survey to construct a groundwater-flow model. Model inputs will include data from advanced geophysical techniques, specifically airborne electromagnetic methods.

South Dakota↗

USGS2016

Building groundwater modeling capacity in Mongolia

Ulaanbaatar, the capital city of Mongolia (fig. 1), is dependent on groundwater for its municipal and industrial water supply. The population of Mongolia is about 3 million people, with about one-half the population residing in or near Ulaanbaatar (World Population Review, 2016). Groundwater is drawn from a network of shallow wells in an alluvial aquifer along the Tuul River. Evidence indicates that current water use may not be sustainable from existing water sources, especially when factoring the projected water demand from a rapidly growing urban population (Ministry of Environment and Green Development, 2013). In response, the Government of Mongolia Ministry of Environment, Green Development, and Tourism (MEGDT) and the Freshwater Institute, Mongolia, requested technical assistance on groundwater modeling through the U.S. Army Corps of Engineers (USACE) to the U.S. Geological Survey (USGS). Scientists from the USGS and USACE provided two workshops in 2015 to Mongolian hydrology experts on basic principles of groundwater modeling using the USGS groundwater modeling program MODFLOW-2005 (Harbaugh, 2005). The purpose of the workshops was to bring together representatives from the Government of Mongolia, local universities, technical experts, and other key stakeholders to build in-country capacity in hydrogeology and groundwater modeling. A preliminary steady-state groundwater-flow model was developed as part of the workshops to demonstrate groundwater modeling techniques to simulate groundwater conditions in alluvial deposits along the Tuul River in the vicinity of Ulaanbaatar. ModelMuse (Winston, 2009) was used as the graphical user interface for MODFLOW for training purposes during the workshops. Basic and advanced groundwater modeling concepts included in the workshops were groundwater principles; estimating hydraulic properties; developing model grids, data sets, and MODFLOW input files; and viewing and evaluating MODFLOW output files. A key to success was developing in-country technical capacity and partnerships with the Mongolian University of Science and Technology; Freshwater Institute, Mongolia, a non-profit organization; United Nations Educational, Scientific and Cultural Organization (UNESCO); the Government of Mongolia; and the USACE.

Open-File Report↗

USGS2016

Estimating national water use associated with unconventional oil and gas development

The U.S. Geological Survey’s (USGS) Water Availability and Use Science Program (WAUSP) goals are to provide a more accurate assessment of the status of the water resources of the United States and assist in the determination of the quantity and quality of water that is available for beneficial uses. These assessments would identify long-term trends or changes in water availability since the 1950s in the United States and help to develop the basis for an improved ability to forecast water avail- ability for future economic, energy-production, and environmental uses. The National Water Census ( http://water.usgs.gov/watercensus/ ), a research program of the WAUSP, supports studies to develop new water accounting tools and assess water availability at the regional and national scales. Studies supported by this program target focus areas with identified water availability concerns and topical science themes related to the use of water within a specific type of environmental setting. The topical study described in this fact sheet will focus on understanding the relation between production of unconventional oil and gas (UOG) for energy and the water needed to produce and sustain this type of energy development. This relation applies to the life-cycle of renewable and nonrenewable forms of UOG energy and includes extraction, production, refinement, delivery, and disposal of waste byproducts. Water-use data and models derived from this topical study will be applied to other similar oil and gas plays within the United States to help resource managers assess and account for water used or needed in these areas. Additionally, the results from this topical study will be used to further refine the methods used in compiling water-use data for selected categories (for example, mining, domestic self-supplied, public supply, and wastewater) in the USGS’s 5-year national water-use estimates reports ( http://water.usgs.gov/watuse/ ).

Fact Sheet↗

USGS2016

Building science-based groundwater tools and capacity in Armenia for the Ararat Basin

The U.S. Geological Survey (USGS) and U.S. Agency for International Development (USAID) began a study in 2016 to help build science-based groundwater tools and capacity for the Ararat Basin in Armenia. The growth of aquaculture and other uses in the Ararat Basin has been accompanied by increased withdrawals of groundwater, which has resulted in a reduction of artesian conditions (decreased springflow, well discharges, and water levels) including loss of flowing wells in many places (Armenia Branch of Mendez England and Associates, 2014; Yu and others, 2015). This study is in partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships (STIP) effort through the Advanced Science and Partnerships for Integrated Resource Development (ASPIRED) program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter. Scientific tools will be developed through this study that groundwater-resource managers, such as those in the Ministry of Nature Protection, in Armenia can use to understand and predict the consequences of their resource management decisions.

Ararat Basin↗

USGS2016

Pesticide concentrations in wetlands on the Lake Traverse Indian Reservation, South and North Dakota, July 2015

During July 2015, water samples were collected from 18 wetlands on the Lake Traverse Indian Reservation in northeastern South Dakota and southeastern North Dakota and analyzed for physical properties and 54 pesticides. This study by the U.S. Geological Survey in cooperation with the Sisseton-Wahpeton Oyate was designed to provide an update on pesticide concentrations of the same 18 wetlands that were sampled for a reconnaissance-level assessment during July 2006. The purpose of this report is to present the results of the assessment of pesticide concentrations in selected Lake Traverse Indian Reservation wetlands during July 2015 and provide a comparison of pesticide concentrations between 2006 and 2015. Of the 54 pesticides that were analyzed for in the samples collected during July 2015, 47 pesticides were not detected in any samples. Seven pesticides—2-chloro-4-isopropylamino-6-amino-s-triazine (CIAT); 2,4–D; acetachlor; atrazine; glyphosate; metolachlor; and prometon—were detected in the 2015 samples with estimated concentrations or concentrations greater than the laboratory reporting level, and most pesticides were detected at low concentrations in only a few samples. Samples from all wetlands contained at least one detected pesticide. The maximum number of pesticides detected in a wetland sample was six, and the median number of pesticides detected was three. The most commonly detected pesticides in the 2015 samples were atrazine and the atrazine degradate CIAT (also known as deethylatrazine), which were detected in 14 and 13 of the wetlands sampled, respectively. Glyphosate was detected in samples from 11 wetlands, and metolachlor was detected in samples from 10 wetlands. The other detected pesticides were 2,4–D (4 wetlands), acetochlor (3 wetlands), and prometon (1 wetland). The same pesticides that were detected in the 2006 samples were detected in the 2015 samples, with the exception of simazine, which was detected only in one sample in 2006. Atrazine and CIAT were the most commonly detected pesticides in both sampling years; however, atrazine and CIAT were detected in fewer wetlands in 2015 (14 and 13 wetlands, respectively) than in 2006 (17 wetlands for both pesticides). The pesticides 2,4–D and prometon also were detected in fewer wetlands in 2015 than 2006, and simazine was only detected in 2006. In contrast, acetochlor, glyphosate, and metolachlor were detected in samples from more wetlands in 2015 than in 2006. In samples from individual wetlands, the number of pesticides detected was similar between 2006 and 2015. At least one pesticide was detected in all wetlands in 2015, and all but one wetland had pesticide detections in 2006. Concentrations of pesticides detected in samples from wetlands were compared to selected water-quality (human-health and aquatic-life) benchmarks. None of the concentrations in either 2006 or 2015 were greater than water-quality benchmarks, with the exception of atrazine. All detections of atrazine in the 2006 and 2015 samples were greater than the acute benchmark of 0.001 microgram per liter (μg/L) for vascular plants. In addition, some concentrations of 2,4–D and atrazine were within an order of magnitude of a water-quality benchmark. The 2,4–D concentrations in the 2015 samples from three wetlands were within an order of magnitude of the U.S. Environmental Protection Agency’s Maximum Contaminant Level of 70 μg/L (that is, sample concentrations were greater than 7.0 μg/L). The maximum dissolved atrazine concentration of 0.185 μg/L in the 2015 samples along with the concentrations in 2006 samples from two wetlands were within an order of magnitude of the acute benchmark of less than 1 μg/L for nonvascular plants (that is, concentrations were greater than 0.1 μg/L).

North Dakota, South Dakota↗

USGS2016

Modern (1992–2011) and projected (2012–99) peak snowpack and May–July runoff for the Fort Peck Lake and Lake Sakakawea watersheds in the Upper Missouri River Basin

Mountain snowpack is an important contributor to runoff in the Upper Missouri River Basin; for example, high amounts of winter and spring precipitation in the mountains and plains in 2010–11 were associated with the peak runoff of record in 2011 in the Upper Missouri River Basin. To project trends in peak mountain snowpack and runoff in the upcoming decades, multiple linear regression models of peak mountain snowpack and total May–July runoff were developed for the Fort Peck Lake (above Fort Peck Dam) and lower Lake Sakakawea watersheds (between Fort Peck and Garrison Dams) in the Upper Missouri River Basin. Input to regression models included seasonal estimates of precipitation, air temperature, and total reference evapotranspiration stratified by elevation. Calibration was based on records from 107 weather stations from 1991 to 2011. Regressed annual peak mountain snowpack was used as input to the transfer function of May–July runoff. Peak snowpack and May–July runoff were projected for 2012–99 on the basis of air temperature and precipitation from the Community Climate System Model (CCSM) output. Two estimates of projected peak snowpack and May–July runoff for 2012–99 were computed: one estimate was based on output from the CCSM, version 3.0 (CCSM3), and the second estimate was based on output from the CCSM, version 4.0 (CCSM4). The significance of projected trends was based on the Kendall’s tau nonparametric test. Annual peak snowpack was projected to have a downward trend for the Fort Peck Lake watershed and an upward trend for the lower Lake Sakakawea watershed. Projections of May–July runoff had a significant downward trend for the Fort Peck Lake, lower Lake Sakakawea, and Lake Sakakawea (combination of Fort Peck Lake and lower Lake Sakakawea) watersheds. Downward trends in projected May–July runoff indicated that power production at Fort Peck Dam might be affected particularly in the later part of the simulation (2061–99); however, confidence in projected May–July runoff for the later part of the simulation was less certain because bias-corrected air temperatures from CCSM3 and CCSM4 commonly fell outside of the observed range used for calibration. Projected May–July runoff combined for the Fort Peck Lake and lower Lake Sakakawea watersheds were on the order of magnitude of the 2011 flood for 1 simulation year for each of the CCSM-based simulations. High peak snowpack and precipitation in April, May, and June in the plains was associated with large May–July runoff events; therefore, high precipitation at lower elevations in the Fort Peck Lake and lower Lake Sakakawea watersheds was a factor in the simulation of extreme runoff events at the magnitude of the 2011 flood.

Montana, North Dakota, Wyoming↗

USGS2013

Prioritization of constituents for national- and regional-scale ambient monitoring of water and sediment in the United States

A total of 2,541 constituents were evaluated and prioritized for national- and regional-scale ambient monitoring of water and sediment in the United States. This prioritization was done by the U.S. Geological Survey (USGS) in preparation for the upcoming third decade (Cycle 3; 2013–23) of the National Water-Quality Assessment (NAWQA) Program. This report provides the methods used to prioritize the constituents and the results of that prioritization. Constituents were prioritized by the NAWQA National Target Analyte Strategy (NTAS) work group on the basis of available information on physical and chemical properties, observed or predicted environmental occurrence and fate, and observed or anticipated adverse effects on human health or aquatic life. Constituents were evaluated within constituent groups that were determined on the basis of physical or chemical properties or on uses or sources. Some constituents were evaluated within more than one constituent group. Although comparable objectives were used in the prioritization of constituents within the different constituent groups, differences in the availability of information accessed for each constituent group led to the development of separate prioritization approaches adapted to each constituent group to make best use of available resources. Constituents were assigned to one of three prioritization tiers: Tier 1, those having the highest priority for inclusion in ambient monitoring of water or sediment on a national or regional scale (including NAWQA Cycle 3 monitoring) on the basis of their likelihood of environmental occurrence in ambient water or sediment, or likelihood of effects on human health or aquatic life; Tier 2, those having intermediate priority for monitoring on the basis of their lower likelihood of environmental occurrence or lower likelihood of effects on human health or aquatic life; and Tier 3, those having low or no priority for monitoring on the basis of evidence of nonoccurrence or lack of effects on human health or aquatic life, or of having insufficient evidence of potential occurrence or effects to justify placement into Tier 2. Of the 1,081 constituents determined to be of highest priority for ambient monitoring (Tier 1), 602 were identified for water and 686 were identified for sediment (note that some constituents were evaluated for both water and sediment). These constituents included various types of organic compounds, trace elements and other inorganic constituents, and radionuclides. Some of these constituents are difficult to analyze, whereas others are mixtures, isomers, congeners, salts, and acids of other constituents; therefore, modifications to the list of high-priority constituents for ambient monitoring could be made on the basis of the availability of suitable methods for preparation, extraction, or analysis. An additional 1,460 constituents were placed into Tiers 2 or 3 for water or sediment, including some constituents that had been placed into Tier 1 for a different matrix; 436 constituents were placed into Tier 2 for water and 246 constituents into Tier 2 for sediment; 979 constituents were placed into Tier 3 for water and 779 constituents into Tier 3 for sediment.

Scientific Investigations Report↗

USGS2012

Factors associated with sources, transport, and fate of chloroform and three other trihalomethanes in untreated groundwater used for drinking water

Multiple lines of evidence for indicating factors associated with the sources, transport, and fate of chloroform and three other trihalomethanes (THMs) in untreated groundwater were revealed by evaluating low-level analytical results and logistic regression results for THMs. Samples of untreated groundwater from wells used for drinking water were collected from 1996-2007 from 2492 wells across the United States and analyzed for chloroform, bromodichloromethane, dibromochloromethane, and bromoform by a low-level analytical method implemented in April 1996. Using an assessment level of 0.02 μg/L, chloroform was detected in 36.5% of public-well samples and 17.6% of domestic-well samples, with most concentrations less than 1 μg/L. Brominated THMs occurred less frequently than chloroform but more frequently in public-well samples than domestic-well samples. For both public and domestic wells, THMs occurred most frequently in urban areas. Logistic regression analyses showed that the occurrence of THMs was related to nonpoint sources such as urban land use and to point sources like septic systems. The frequent occurrence and concentration distribution pattern of THMs, as well as their frequent co-occurrence with other organic compounds and nitrate, all known to have anthropogenic sources, and the positive associations between THM occurrence and dissolved oxygen and recharge indicate the recycling of water that contains THMs and other anthropogenic contaminants.

Environmental Science & Technology↗

USGS2011

USGS library for S-PLUS for Windows -- Release 4.0

Release 4.0 of the U.S. Geological Survey S-PLUS library supercedes release 2.1. It comprises functions, dialogs, and datasets used in the U.S. Geological Survey for the analysis of water-resources data. This version does not contain ESTREND, which was in version 2.1. See Release 2.1 for information and access to that version. This library requires Release 8.1 or later of S-PLUS for Windows. S-PLUS is a commercial statistical and graphical analysis software package produced by TIBCO corporation(http://www.tibco.com/). The USGS library is not supported by TIBCO or its technical support staff.

Open-File Report↗

USGS2010

Concentration data for anthropogenic organic compounds in groundwater, surface water, and finished water of selected community water systems in the United States, 2002-10

The National Water-Quality Assessment Program of the U.S. Geological Survey began implementing Source Water-Quality Assessments (SWQAs) in 2001 that focus on characterizing the quality of source water and finished water of aquifers and major rivers used by some of the larger community water systems in the United States. As used in SWQA studies, source water is the raw (ambient) water collected at the supply well before water treatment (for groundwater) or the raw (ambient) water collected from the river near the intake (for surface water), and finished water is the water that has been treated and is ready to be delivered to consumers. Finished-water samples are collected before the water enters the distribution system. The primary objective of SWQAs is to determine the occurrence of more than 250 anthropogenic organic compounds in source water used by community water systems, many of which currently are unregulated in drinking water by the U.S. Environmental Protection Agency. A secondary objective is to understand recurrence patterns in source water and determine if these patterns also occur in finished water before distribution. SWQA studies were conducted in two phases for most studies completed by 2005, and in one phase for most studies completed since 2005. Analytical results are reported for a total of 295 different anthropogenic organic compounds monitored in source-water and finished-water samples collected during 2002-10. The 295 compounds were classified according to the following 13 primary use or source groups: (1) disinfection by-products; (2) fumigant-related compounds; (3) fungicides; (4) gasoline hydrocarbons, oxygenates, and oxygenate degradates; (5) herbicides and herbicide degradates; (6) insecticides and insecticide degradates; (7) manufacturing additives; (8) organic synthesis compounds; (9) pavement- and combustion-derived compounds; (10) personal-care and domestic-use products; (11) plant- or animal-derived biochemicals; (12) refrigerants and propellants; and (13) solvents. This report presents the analytical results of source- water samples from 448 community water system wells and 21 surface-water sites. This report also presents the analytical results of finished-water samples from 285 wells and 20 surface-water sites from community water systems. Results of quality-assurance/quality-control samples also are presented including data for equipment blanks, field blanks, source solution blanks, and replicate samples.

Data Series↗