SEARCH · USGS Science

Results for “Scientific Data - Nature”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41Linked to original sources

USGS2011

Potential water-quality effects of coal-bed methane production water discharged along the upper Tongue River, Wyoming and Montana

Water quality in the upper Tongue River from Monarch, Wyoming, downstream to just upstream from the Tongue River Reservoir in Montana potentially could be affected by discharge of coal-bed methane (CBM) production water (hereinafter referred to as CBM discharge). CBM discharge typically contains high concentrations of sodium and other ions that could increase dissolved-solids (salt) concentrations, specific conductance (SC), and sodium-adsorption ratio (SAR) in the river. Increased inputs of sodium and other ions have the potential to alter the river's suitability for agricultural irrigation and aquatic ecosystems. Data from two large tributaries, Goose Creek and Prairie Dog Creek, indicate that these tributaries were large contributors to the increase in SC and SAR in the Tongue River. However, water-quality data were not available for most of the smaller inflows, such as small tributaries, irrigation-return flows, and CBM discharges. Thus, effects of these inflows on the water quality of the Tongue River were not well documented. Effects of these small inflows might be subtle and difficult to determine without more extensive data collection to describe spatial patterns. Therefore, synoptic water-quality sampling trips were conducted in September 2005 and April 2006 to provide a spatially detailed profile of the downstream changes in water quality in this reach of the Tongue River. The purpose of this report is to describe these downstream changes in water quality and to estimate the potential water-quality effects of CBM discharge in the upper Tongue River. Specific conductance of the Tongue River through the study reach increased from 420 to 625 microsiemens per centimeter (.μS/cm; or 49 percent) in the downstream direction in September 2005 and from 373 to 543 .μS/cm (46 percent) in April 2006. Large increases (12 to 24 percent) were measured immediately downstream from Goose Creek and Prairie Dog Creek during both sampling trips. Increases attributed to direct CBM discharges were smaller. In September 2005, the SC of 12 measured CBM discharges ranged from 1,750 to 2,440 .μS/cm, and the combined discharges increased SC in the river by an estimated 4.5 percent. In April 2006, the SC of eight measured CBM discharges ranged from 1,720 to 2,070 μS/cm; the largest of these discharges likely increased SC in the river by 5.8 percent. Estimates of potential effects of the CBM discharges on the SC of the Tongue River near the Tongue River Reservoir were calculated using a two-step process involving linear regression and mass-balance calculations for a range of streamflow and CBM-discharge conditions. Potential effects from CBM discharges are larger increases of SC and SAR at lower flows than at higher flows and relative increases that are substantially smaller for SC than for SAR. For example, if the streamflow was 100 cubic feet per second (ft 3 /s) in the Tongue River near the Tongue River Reservoir and CBM discharge ranged from 1,250 to 5,000 gallons per minute, the projected increases would range from 4.4 to 16 percent for SC and from 39 to 151 percent for SAR. In comparison, if the streamflow was 600 ft 3 /s, the projected increases would range from 2.2 to 8.4 percent for SC and from 21 to 79 percent for SAR. This analysis of potential water-quality effects on the SC and SAR of the Tongue River in the study area assumes that the quantity and quality of water flowing into the study reach at the time of this study was the same as during the period before CBM development (data from water years 1985-99).

Wyoming;Montana↗

USGS2013

Methods for estimating selected low-flow statistics and development of annual flow-duration statistics for Ohio

This report presents the results of a study to develop methods for estimating selected low-flow statistics and for determining annual flow-duration statistics for Ohio streams. Regression techniques were used to develop equations for estimating 10-year recurrence-interval (10-percent annual-nonexceedance probability) low-flow yields, in cubic feet per second per square mile, with averaging periods of 1, 7, 30, and 90-day(s), and for estimating the yield corresponding to the long-term 80-percent duration flow. These equations, which estimate low-flow yields as a function of a streamflow-variability index, are based on previously published low-flow statistics for 79 long-term continuous-record streamgages with at least 10 years of data collected through water year 1997. When applied to the calibration dataset, average absolute percent errors for the regression equations ranged from 15.8 to 42.0 percent. The regression results have been incorporated into the U.S. Geological Survey (USGS) StreamStats application for Ohio (http://water.usgs.gov/osw/streamstats/ohio.html) in the form of a yield grid to facilitate estimation of the corresponding streamflow statistics in cubic feet per second. Logistic-regression equations also were developed and incorporated into the USGS StreamStats application for Ohio for selected low-flow statistics to help identify occurrences of zero-valued statistics. Quantiles of daily and 7-day mean streamflows were determined for annual and annual-seasonal (September–November) periods for each complete climatic year of streamflow-gaging station record for 110 selected streamflow-gaging stations with 20 or more years of record. The quantiles determined for each climatic year were the 99-, 98-, 95-, 90-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 25-, 20-, 10-, 5-, 2-, and 1-percent exceedance streamflows. Selected exceedance percentiles of the annual-exceedance percentiles were subsequently computed and tabulated to help facilitate consideration of the annual risk of exceedance or nonexceedance of annual and annual-seasonal-period flow-duration values. The quantiles are based on streamflow data collected through climatic year 2008.

Ohio↗

USGS2014

Three-dimensional geologic mapping of the Cenozoic basin fill, Amargosa Desert basin, Nevada and California

Understanding the subsurface geologic framework of the Cenozoic basin fill that underlies the Amargosa Desert in southern Nevada and southeastern California has been improved by using borehole data to construct three-dimensional lithologic and interpreted facies models. Lithologic data from 210 boreholes from a 20-kilometer (km) by 90-km area were reduced to a limited suite of descriptors based on geologic knowledge of the basin and distributed in three-dimensional space using interpolation methods. The resulting lithologic model of the Amargosa Desert basin portrays a complex system of interfingered coarse- to fine-grained alluvium, playa and palustrine deposits, eolian sands, and interbedded volcanic units. Lithologic units could not be represented in the model as a stacked stratigraphic sequence due to the complex interfingering of lithologic units and the absence of available time-stratigraphic markers. Instead, lithologic units were grouped into interpreted genetic classes, such as playa or alluvial fan, to create a three-dimensional model of the interpreted facies data. Three-dimensional facies models computed from these data portray the alluvial infilling of a tectonically formed basin with intermittent internal drainage and localized regional groundwater discharge. The lithologic and interpreted facies models compare favorably to resistivity, aeromagnetic, and geologic map data, lending confidence to the interpretation.

California;Nevada↗

USGS2014

Discovery and analysis of time delay sources in the USGS personal computer data collection platform (PCDCP) system

Intermagnet is an international oversight group which exists to establish a global network for geomagnetic observatories. This group establishes data standards and standard operating procedures for members and prospective members. Intermagnet has proposed a new One-Second Data Standard, for that emerging geomagnetic product. The standard specifies that all data collected must have a time stamp accuracy of ±10 milliseconds of the top-of-the-second Coordinated Universal Time. Therefore, the U.S. Geological Survey Geomagnetism Program has designed and executed several tests on its current data collection system, the Personal Computer Data Collection Platform. Tests are designed to measure the time shifts introduced by individual components within the data collection system, as well as to measure the time shift introduced by the entire Personal Computer Data Collection Platform. Additional testing designed for Intermagnet will be used to validate further such measurements. Current results of the measurements showed a 5.0–19.9 millisecond lag for the vertical channel (Z) of the Personal Computer Data Collection Platform and a 13.0–25.8 millisecond lag for horizontal channels (H and D) of the collection system. These measurements represent a dynamically changing delay introduced within the U.S. Geological Survey Personal Computer Data Collection Platform.

Scientific Investigations Report↗

USGS2015

Characterization of hydrology and water quality of Piceance Creek in the Alkali Flat area, Rio Blanco County, Colorado, March 2012

Previous studies by the U.S. Geological Survey identified Alkali Flat as an area of groundwater upwelling, with increases in concentrations of total dissolved solids, and streamflow loss, but additional study was needed to better characterize these observations. The U.S. Geological Survey, in cooperation with the Bureau of Land Management, White River Field Office, conducted a study to characterize the hydrology and water quality of Piceance Creek in the Alkali Flat area of Rio Blanco County, Colorado. Water-quality samples were collected at five springs on March 27, 2012, to determine field properties, major ions, trace elements, and stable isotopes of water. Major-ion and trace-element chemistry indicated that the springs sampled as part of this study were likely recharged by the bedrock aquifer. Isotopic values for the springs plotted close to that of groundwater from the Parachute Creek Member of the Green River Formation, and the isotopic values from both of these sources are similar to the values for Grand Mesa snow. Based on fluoride, lithium, and strontium concentrations, one spring appeared to have different source water than the other four springs. The spring also had higher concentrations of calcium, magnesium, and sulfate relative to the other four springs. Trace-element and major-ion data indicate that this spring was sourced from the Uinta Formation. It was likely the other four springs were primarily sourced from the lower part of the Parachute Creek Member of the Green River Formation as indicated by low sulfate concentrations and high fluoride, lithium, and boron concentrations. Water-quality samples were collected at 16 surface-water-quality sites on March 14, 2012, to determine field properties, major ions, and trace elements. Sodium was the dominant cation and concentrations increased steadily from upstream to downstream along the study reach. Calcium, magnesium, and potassium concentrations remained relatively stable along the study reach. Strontium concentrations were relatively stable along the study reach, whereas boron and lithium concentrations increased appreciably at site PC22031 and remained elevated to the end of the study reach. Loading profiles were used to further refine areas of spring and groundwater input and streamflow gains and losses. Although there was a minor gain in streamflow from sites PC21543 to PC21816 (58 to 59 cubic feet per second (ft 3 /s) during March 2014), the observed increase in dissolved solids load indicated groundwater contribution to Piceance Creek between these two sites. From sites PC22737 to PC22980, dissolved solids load decreased, which was not observed in concentration profiles and indicated that streamflow loss occurred between these two sites. Barium, boron, lithium, and strontium loads showed similar patterns to that of the major ions along the study reach and indicated similar areas of groundwater gain and loss. Boron and lithium load were not observed to decrease in a similar pattern to that of barium and strontium load which would suggest the contribution to the stream from sources with similar chemistry to that of spring sites PCSP2 through PCSP5. Sodium, chloride, and bicarbonate loads increased and decreased along the study reach in a pattern similar to that of dissolved solids load. A chemical mass balance was used to estimate the amount of groundwater and (or) spring water that contributed to the observed changes in water quality along Piceance Creek. This analysis indicated only 5 percent spring water would need to reach Piceance Creek to result in the observed changes in water quality. Instantaneous streamflow was measured from sites PC20133 to PC23721 during field reconnaissance (February 2012) and during synoptic sampling (March 2012). During both February and March, the study reach from sites PC20133 to PC23721 was a losing reach with net losses that ranged from 0.5 ft 3 /s (February) to 3 ft 3 /s (March). Observed changes in streamflow along the study reach helped to depict interactions between groundwater and surface water in the Alkali Flat area. Water-quality samples were collected at five surface-water sites in December 2010 that were sampled as part of a previous USGS study in 2000. Water-quality data collected during December 2010 showed no appreciable difference from water-quality data collected during December 2000 at the five sites.

Colorado↗

USGS2019

Catalog of microscopic organisms of the Everglades, part 2—The desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge

The Arthur R. Marshall Loxahatchee National Wildlife Refuge (refuge), Boynton Beach, Florida, contains approximately 147,000 acres southeast of Lake Okeechobee. Water quality in the interior portion of the refuge is strongly influenced by rainfall, resulting in slightly acidic waters with low dissolved ions. Desmids, a unique, ornate group of green algae loosely associated with submerged vascular plants, were photo-documented for the first time in samples from the refuge. The canal system surrounding the refuge contains a high level of ions from agricultural runoff, and intrusion of this water into the refuge interior during high canal water levels may have altered some of the desmid population. A transect from the canal to the interior was sampled every 3 months, and the species present were photographed, identified, and catalogued. Approximately 260 unique taxa from 29 genera were encountered. The interior of the refuge had the greatest diversity of desmids; however, the areas of the refuge adjacent to the canals still contained a rich population of desmids. We postulate that the diversity of desmids indicates that the pristine portions of the refuge may be an important refugium for desmids, particularly for those species restricted to the subtropical parts of the United States. This collection of taxa, identified to species with most specimens, will allow a more detailed examination of water quality issues when co-located water quality data are collected.

Florida↗

USGS2021

Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18

Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated. Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method. Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun. Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours. Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.

Colorado↗

USGS2024

Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin

Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. This work is part of that reexamination. The stationarity assumption is a concern because flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done to assess potential nonstationarity in peak flows in the north-central United States. This report summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.

Scientific Investigations Report↗

USGS2026

Effect of mineral deposit data on predictions from the three-part approach to quantitative mineral resource assessment—A study of 16 previous U.S. Geological Survey assessments

The three-part approach to quantitative mineral resource assessment requires information about the properties of undiscovered mineral deposits in an assessment area. These properties are unknown, so the properties of discovered mineral deposits of the same mineral deposit type are used instead. In the three-part approach, these discovered mineral deposits come from around the world, and their properties constitute the pooled data for that mineral deposit type. Alternatively, these discovered mineral deposits could come from the assessment area, and their properties constitute the tract data for that mineral deposit type. Tract data may be more representative of the undiscovered mineral deposits in the assessment area than the pooled data. The goal of this study was to determine whether resource predictions using pooled data are equivalent to resource predictions using tract data. To this end, 16 previous U.S Geological Survey assessments were studied. For each assessment, resources were predicted for one undiscovered mineral deposit in the assessment area. One set of predictions used pooled data, and another used tract data. The two sets of predictions were compared with an equivalence test, using the six assessment statistics that are commonly reported for mineral resource assessments. Practical equivalence is the condition that two corresponding assessment statistics are within a factor of 1.5 of one another. For each of 2 assessments, all 6 assessment statistics were practically equivalent. For both assessments, the assessment statistics from the pooled data, relative to the corresponding assessment statistics from the tract data, ranged from 1.30 times smaller to 1.03 times larger. For each of 14 assessments, 1 or more of the 6 assessment statistics were not practically equivalent. The assessment statistics from the pooled data, relative to the corresponding assessment statistics from the tract data, ranged from 26.6 times smaller to 5.53 times larger. The use of pooled data has been a standard procedure in the three-part approach since at least 1986. The 16 assessments in this study are not a representative sample of those prior assessments that used pooled data. So, it is inappropriate to use the study results to infer whether pooled data affected the resource predictions for those prior assessments.

Scientific Investigations Report↗

USGS2006

Environmental Effects of Agricultural Practices - Summary of Workshop Held on June 14-16, 2005

A meeting between the U.S. Geological Survey (USGS) and its partners was held June 14-16, 2005, in Denver, CO, to discuss science issues and needs related to agricultural practices. The goals of the meeting were to learn about the (1) effects of agricultural practices on the environment and (2) tools for identifying and quantifying those effects. Achieving these goals required defining the environmental concerns, developing scientific actions to address assessment of environmental effects, and creating collaborations to identify future research requirements and technical gaps. Five areas of concern were discussed-emerging compounds; water availability; genetically modified organisms; effects of conservation practices on ecosystems; and data, methods, and tools for assessing effects of agricultural practices.

Scientific Investigations Report↗

USGS2021

Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.

ACM/IMS Transactions on Data Science↗

USGS2023

Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM)

The Stochastic Empirical Loading and Dilution Model (SELDM) was designed to help quantify the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. SELDM is calibrated using representative hydrological and water-quality input statistics. This report by the U.S. Geological Survey, in cooperation with the Federal Highway Administration and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation, documents approaches for assessing flows, concentrations, and loads of highway- and urban-runoff and receiving-stream stormwater in southern New England with SELDM. In this report, the term “urban runoff” is used to identify stormwater flows from developed areas with impervious fractions ranging from 10 to 100 percent without regard to the U.S. Census Bureau designation for any given location. There are more than 48,000 delineated road-stream crossings in southern New England, but because there are relatively few precipitation, streamflow, and water-quality monitoring sites in this area, methods were needed to simulate conditions at unmonitored sites. This report documents simulation methods, methods for interpreting stochastic model results, sensitivity analyses to identify the most critical variables of concern, and examples demonstrating how simulation results can be used to inform scientific decision-making processes. Results of 7,511 SELDM simulations were used to do the sensitivity analyses and provide information decisionmakers can use to address runoff-quality issues in southern New England and other areas of the Nation. The sensitivity analyses indicate the relatively strong effect of input variables on variations in output results. These analyses indicate that highway and urban runoff quality and upstream water-quality statistics that vary considerably from site to site have the greatest effect on simulated results. Further data are needed to improve available water-quality statistics, and because the number of monitored sites will never approach the number of sites of interest for water-quality management, research is needed to identify methods to select statistics for unmonitored sites and quantify the uncertainties in the selection process. Hydrologically, prestorm streamflows with and without zero flows are the most sensitive and therefore the most important hydrologic variables to quantify. Results of analyses also are sensitive to statistics used for simulating structural best management practices. Although the focus of the report is on data, statistics, simulation methods, and methods to interpret stochastic simulations, the examples in this report provide results that can be used to inform scientific decision-making processes. The results of 441 simulations that provide regional and site-specific highway and urban runoff yields across southern New England can be used for total maximum daily load analyses. The example stormwater load analysis done for 16 tributaries of the Narragansett Bay demonstrates that highway nitrogen loads are a small fraction of stormwater loads (about 3.6 percent), and a much smaller fraction of all nitrogen loads to the bay, primarily because highways have a small footprint on the land. Examples evaluating the potential effectiveness of end-of-pipe treatment indicate that offsite treatment is warranted in developed areas, and land conservation may be an effective mitigation strategy. The results of these analyses are consistent with conclusions from other simulation and monitoring studies.

Connecticut, Massachusetts, New Hampshire, Rhode I↗

USGS2019

From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows

Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.

Frontiers in Marine Science↗

USGS2026

Semantic segmentation of light-toned veins in multimodal ChemCam data

Since the Mars Science Laboratory landed in 2012, the ChemCam instrument aboard the rover has collected in-situ laser-induced breakdown spectroscopy (LIBS) data and context images along more than 35 km of the Gale Crater traverse, providing valuable observations including diagenetic features such as light-toned veins. These veins are of particular scientific interest because they are interpreted as indicators of past fluid circulation on Mars and provide insights into the evolution of habitability on Mars. Their identification, however, currently relies on manual visual inspection of Remote Micro Imager (RMI) images, a process that is time-consuming and sensitive to differences in human interpretation. To address this issue, in this paper we introduce a novel pixel-level labeled, multimodal dataset of ChemCam observations specifically tailored for vein detection, along with customized U-Net models to integrate both textural (RMI) and chemical (LIBS) modalities. To further ensure trustworthy scientific use, we incorporate the Learn-Then-Test (LTT) framework to provide statistical control of the false discovery rate without requiring model retraining. The experimental results demonstrate that the proposed customized U-Net models trained on the developed dataset, combined with risk-controlled prediction, increases the efficiency of pixel-level vein identification through automation and produces statistically reliable predictions for multimodal ChemCam data.

Scientific Reports↗

USGS2020

Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington

The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.

Washington↗

USGS1992

Integrating quality assurance in project work plans of the U.S. Geological Survey

The U.S. Geological Survey's objectives for including quality assurance procedures in a project work plan are to ensure that the quality of the data collected is defined and is appropriate for the objectives of the investigation. The data- quality information can be used in the interpre- tation of the data. A project work plan that includes quality assessment provides definable benefits such as clarity of expectations, a method for obtaining a set of data that is expected and has been proven valid, a documentation trail, products that are produced on time and that meet project objectives, and a decrease in work that is lost or redone. Project chiefs must prepare and can publish the work plan for scientific investigations. An expanded outline of a framework that can be used to prepare a project work plan that includes quality assurance is described in this report and contains the following topics: data-quality objectives; project organization and responsibilities; data collection; data processing; project reviews; data analysis; remedial actions; project progress reports and quality assurance reports to management.

Open-File Report↗

USGS2022

Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie

In the last year, we became aware that data used in our above-referenced manuscript from 2018 published in Limnology and Oceanography contained significant errors. In the 2018 manuscript, we found that indices of secondary production were negatively correlated to indices of cyanobacterial abundance and toxicity. Unfortunately, one of our indices of cyanobacterial abundance (biovolume) and our measurement of toxicity (microcystin concentration) were inaccurate in the data we used in the 2018 manuscript. Upon discovering these errors, we immediately began correcting the repositories where these data were available. Having corrected those data repositories, we are now reporting on the re-analysis of the data using the methods previously described in the 2018 manuscript. Although the relationships are slightly different, our interpretation is that the conclusions of the 2018 manuscript are still valid using the corrected data. The data errors we experienced were traced to a spreadsheet that was used to share data among the research team, which had errors caused by mistakes in copy-pasting formulas instead of data and ‘inadvertent’ edits that were saved by the spreadsheet's auto-save function. We apologize to the scientific community for these errors.

Lake Erie↗

USGS2020

Pervasive shifts in forest dynamics in a changing world

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.

Science↗