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At least 1,189 records · Page 66Linked to original sources
USGS2013

Hydraulic and water-quality data collection for the investigation of Great Lakes tributaries for Asian carp spawning and egg-transport suitability

If the invasive Asian carps (bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix ) migrate to the Great Lakes, in spite of the efforts to stop their advancement, these species will require the fast-flowing water of the Great Lakes tributaries for spawning and recruitment in order to establish a growing population. Two Lake Michigan tributaries (the Milwaukee and St. Joseph Rivers) and two Lake Erie tributaries (the Maumee and Sandusky Rivers) were investigated to determine if these tributaries possess the hydraulic and water-quality characteristics to allow successful spawning of Asian carps. To examine this issue, standard U.S. Geological Survey sampling protocols and instrumentation for discharge and water-quality measurements were used, together with differential global positioning system data for georeferencing. Non-standard data-processing techniques, combined with detailed laboratory analysis of Asian carp egg characteristics, allowed an assessment of the transport capabilities of each of these four tributaries. This assessment is based solely on analysis of observed data and did not utilize the collected data for detailed transport modeling. All four tributaries exhibited potential settling zones for Asian carp eggs both within the estuaries and river mouths and within the lower 100 kilometers (km) of the river. Dams played a leading role in defining these settling zones, with the exception of dams on the Sandusky River. The impoundments created by many of the larger dams on these rivers acted to sufficiently decelerate the flows and allowed the shear velocity to drop below the settling velocity for Asian carp eggs, which would allow the eggs to fall out of suspension and settle on the bottom where it is thought the eggs would perish. While three rivers exhibited these settling zones upstream of the larger dams, not all settling zones are likely to have such effects on egg transport. The Milwaukee River exhibited only a short settling zone upstream of the Grafton Dam, whereas the St. Joseph and Maumee Rivers both had extensive settling zones (>5 km) behind major dams. These longer settling zones are likely to capture more eggs than shorter settling reaches. All four rivers exhibited settling zones at their river mouths, with the Lake Erie tributaries having much larger settling zones extending more than 10 km up the tributaries. While hydraulic data from all four rivers indicated settling of eggs is possible in some locations, all four rivers also exhibited sufficient temperatures, water-quality characteristics, turbulence, and transport times outside of settling zones for successful suspension and development of Asian carp eggs to the hatching stage before the threat of settlement. These observed data indicate that these four Great Lakes tributaries have sufficient hydraulic and water-quality characteristics to support successful spawning and recruitment of Asian carps. The data indicate that with the right temperature and flow conditions, river reaches as short as 25 km may allow Asian carp eggs sufficient time to develop to hatching. Additionally, examining the relation between critical shear velocity and mean velocity, egg settling appears to take place at mean velocities in the range of 15–25 centimeters per second, a much lower value than is generally cited in the literature. A first-order estimate of the minimum transport velocity for Asian carp eggs in a river can be obtained by using mean flow depth and river substrate data, and curves were constructed to show this relation. These findings would expand the number of possible tributaries suitable for Asian carp spawning and contribute to the understanding of how hydraulic and water-quality information can be used to screen additional rivers in the future.

Great Lakes↗
USGS2014

Extending the turbidity record: making additional use of continuous data from turbidity, acoustic-Doppler, and laser diffraction instruments and suspended-sediment samples in the Colorado River in Grand Canyon

Turbidity is a measure of the scattering and absorption of light in water, which in rivers is primarily caused by particles, usually sediment, suspended in the water. Turbidity varies significantly with differences in the design of the instrument measuring turbidity, a point that is illustrated in this study by side-by-side comparisons of two different models of instruments. Turbidity also varies with changes in the physical parameters of the particles in the water, such as concentration, grain size, grain shape, and color. A turbidity instrument that is commonly used for continuous monitoring of rivers has a light source in the near-infrared range (860±30 nanometers) and a detector oriented 90 degrees from the incident light path. This type of optical turbidity instrument has a limited measurement range (depending on pathlength) that is unable to capture the high turbidity levels of rivers that carry high suspended-sediment loads. The Colorado River in Grand Canyon is one such river, in which approximately 60 percent of the range in suspended-sediment concentration during the study period had unmeasurable turbidity using this type of optical instrument. Although some optical turbidimeters using backscatter or other techniques can measure higher concentrations of suspended sediment than the models used in this study, the maximum turbidity measurable using these other turbidimeters may still be exceeded in conditions of especially high concentrations of suspended silt and clay. In Grand Canyon, the existing optical turbidity instruments remain in use in part to provide consistency over time as new techniques are investigated. As a result, during these periods of high suspended-sediment concentration, turbidity values that could not be measured with the optical turbidity instruments were instead estimated from concurrent acoustic attenuation data collected using side-looking acoustic-Doppler profiler (ADP) instruments. Extending the turbidity record to the full range of sediment concentrations in the study area using data from the ADP instruments is particularly useful for biological studies. In Grand Canyon, turbidity has been correlated with food availability for aquatic organisms (gross primary production) as well as with fish behavior specific to predator-prey interactions. On the basis of the complete “extended” turbidity record and the relation between suspended-sediment concentration and turbidity, levels were higher before the construction of Glen Canyon Dam by a factor of approximately 2,000 at the Lees Ferry monitoring station (15 miles downstream from the dam) and by a factor of approximately 20 at the monitoring station 87 miles downstream from Lees Ferry (102 miles downstream from the dam). A comparison of turbidity data with data from Laser In-Situ Scattering and Transmissometry (LISST) laser-diffraction instruments, suspended-sediment concentration data, and ADP data shows the influence of the physical properties of suspended sediment. Apparent outliers in relations between turbidity, ADP, and suspended-sediment data during two events within the study period, a 2007 tributary flood from a watershed altered by a recent wildfire and a 2008 experimental controlled-flood release from Glen Canyon Dam, are explained in part by atypical grain sizes, shapes, densities, colors, and (or) clay mineral assemblages of suspended sediment occurring in the Colorado River during these two events. These analyses demonstrate the value of using multiple data-collection strategies for turbidity and sediment-transport studies and of continuous monitoring for capturing the full range and duration of turbidity and sediment-transport conditions, identifying the provenance of the sediment causing turbidity, and detecting physical and chemical processes that may be important for management of critical physical and biological resources.

Arizona↗
USGS2017

Estimated fecal coliform bacteria concentrations using near real-time continuous water-quality and streamflow data from five stream sites in Chester County, Pennsylvania, 2007–16

Several streams used for recreational activities, such as fishing, swimming, and boating, in Chester County, Pennsylvania, are known to have periodic elevated concentrations of fecal coliform bacteria, a type of bacteria used to indicate the potential presence of fecally related pathogens that may pose health risks to humans exposed through water contact. The availability of near real-time continuous stream discharge, turbidity, and other water-quality data for some streams in the county presents an opportunity to use surrogates to estimate near real-time concentrations of fecal coliform (FC) bacteria and thus provide some information about associated potential health risks during recreational use of streams. The U.S. Geological Survey (USGS), in cooperation with the Chester County Health Department (CCHD) and the Chester County Water Resources Authority (CCWRA), has collected discrete stream samples for analysis of FC concentrations during March–October annually at or near five gaging stations where near real-time continuous data on stream discharge, turbidity, and water temperature have been collected since 2007 (or since 2012 at 2 of the 5 stations). In 2014, the USGS, in cooperation with the CCWRA and CCHD, began to develop regression equations to estimate FC concentrations using available near real-time continuous data. Regression equations included possible explanatory variables of stream discharge, turbidity, water temperature, and seasonal factors calculated using Julian Day with base-10 logarithmic (log) transformations of selected variables. The regression equations were developed using the data from 2007 to 2015 (101–106 discrete bacteria samples per site) for three gaging stations on Brandywine Creek (West Branch Brandywine Creek at Modena, East Branch Brandywine Creek below Downingtown, and Brandywine Creek at Chadds Ford) and from 2012 to 2015 (37–38 discrete bacteria samples per site) for one station each on French Creek near Phoenixville and White Clay Creek near Strickersville. Fecal coliform bacteria data collected by USGS in 2016 (about nine samples per site) were used to validate the equations. The best-fit regression equations included log turbidity and seasonality factors computed using Julian Day as explanatory variables to estimate log FC concentrations at all five stream sites. The adjusted coefficient of determination for the equations ranged from 0.61 to 0.76, with the strength of the regression equations likely affected in part by the limited amount and variability of FC bacteria data. During summer months, the estimated and measured FC concentrations commonly were greater than the Pennsylvania Department of Environmental Protection established standards of 200 and 400 colonies per 100 milliliters for water contact from May through September at the 5 stream sites, with concentrations typically higher at 2 sites (White Clay Creek and West Branch Brandywine Creek at Modena) than at the other 3 sites. The estimated concentrations of FC bacteria during the summer months commonly were higher than measured concentrations and therefore could be considered cautious estimates of potential human-health risk. Additional water-quality data are needed to maintain and (or) improve the ability of regression equations to estimate FC concentrations by use of surrogate data.

Pennsylvania↗
USGS2018

Data analysis considerations for pesticides determined by National Water Quality Laboratory schedule 2437

In 2013, the U.S. Geological Survey National Water Quality Laboratory (NWQL) made a new method available for the analysis of pesticides in filtered water samples: laboratory schedule 2437. Schedule 2437 is an improvement on previous analytical methods because it determines the concentrations of 225 fungicides, herbicides, insecticides, and associated degradates in one method at similar or lower concentrations than previously available methods. Additionally, the pesticides included in schedule 2437 were strategically identified in a prioritization analysis that assessed likelihood of occurrence, prevalence of use, and potential toxicity. When the NWQL reports pesticide concentrations for analytes in schedule 2437, the laboratory also provides supplemental information useful to data users for assessing method performance and understanding data quality. That supplemental information is discussed in this report, along with an initial analysis of analytical recovery of pesticides in water-quality samples analyzed by schedule 2437 during 2013–2015. A total of 523 field matrix spike samples and their paired environmental samples and 277 laboratory reagent spike samples were analyzed for this report (1,323 samples total). These samples were collected in the field as part of the U.S. Geological Survey National Water-Quality Assessment groundwater and surface-water studies and as part of the NWQL quality-control program. This report reviews how pesticide samples are processed by the NWQL, addresses how to obtain all the data necessary to interpret pesticide concentrations, explains the circumstances that result in a reporting level change or the occurrence of a raised reporting level, and describes the calculation and assessment of recovery. This report also discusses reasons why a data user might choose to exclude data in an interpretive analysis and outlines the approach used to identify the potential for decreased data quality in the assessment of method recovery. The information provided in this report is essential to understanding pesticide data determined by schedule 2437 and should be reviewed before interpretation of these data.

Scientific Investigations Report↗
USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗
USGS2022

Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana

Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements. In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge. A normalized variability index ( V N ) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the V N values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding V N values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error. The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.

Illinois, Indiana↗
USGS2022

Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21

Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses. Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer. Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.

South Dakota↗
USGS2023

Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables

The Mississippi Alluvial Plain, located in the south-central United States, is undergoing long-term groundwater-level declines within the surficial Mississippi River Valley alluvial aquifer (hereinafter referred to as “alluvial aquifer”), which has raised concerns about future groundwater availability. In some parts of the alluvial aquifer, groundwater availability for common uses such as irrigation, public supply, and domestic use is limited by quality (for example, high salinity) rather than quantity of water stored in the aquifer. The Mississippi Alluvial Plain region has an abundance of water-quality measurements in the alluvial aquifer and deeper aquifers; however, large areas lack direct measurements of salinity to evaluate regional groundwater availability. Statistical models can interpolate between wells to fill in spatial data gaps. In 2021, the U.S. Geological Survey trained two boosted regression tree (BRT) machine-learning models on specific conductance data available between 1942 and 2020 to predict spatially continuous surfaces of groundwater salinity at multiple depths for the alluvial aquifer and deeper aquifers. Well construction information, water levels, and surficial variables such as geomorphology and soils were included as explanatory variables in this baseline model. Additionally, subsurface electrical resistivity data from the first aquifer-wide aerial electromagnetic (AEM) survey for the region were incorporated to create a geophysical model. This work expands on prior BRT salinity predictions of the alluvial aquifer and extends predictions south to the Gulf of Mexico, where groundwater salinity is high. AEM survey data were not available for the southern extent of the alluvial aquifer at the time of modeling. A BRT model was trained without (baseline) and with (geophysical) AEM variables to test the ability of the models to predict salinity where explanatory data are missing and response data are sparse. Additionally, model sensitivity to AEM survey data was evaluated to better understand how AEM variables influence specific conductance predictions. Model performance was improved with the addition of geophysical data, which added three-dimensional information, thereby improving salinity predictions at depth. Groundwater specific conductance predictions can help inform other geophysical investigations in the southern extent of the study area, where high groundwater specific conductance can obfuscate changes in aquifer sediment resistivity and could limit groundwater resources for agricultural, public supply, and domestic uses.

Scientific Investigations Report↗
USGS1956

X-ray powder data for uranium and thorium minerals

The U.S. Geological Survey has in preparation a comprehensive volume on the mineralogy of uranium and thorium. This work has been done as part of a continuing systematic survey of data on uranium and thorium minerals on behalf of the Division of Raw Materials, U.S. Atomic Energy Commission. Pending publication of this volume and in response to a widespread demand among workers in uranium and thorium mineralogy, the X-ray powder diffraction data for the known minerals that contain uranium or thorium as an essential constituent are presented here. The coverage is complete except for a few minerals for which there are no reliable data owing to lack of authentic specimens. With the exception of that for ianthinite, the new data either originated in the Geological Survey or in the Mineralogical Laboratory of Harvard University. Data from the literature or other sources were cross-checked against the files of standard patterns of these laboratories; the sources are indicated in the references. Data not accompanied by a reference were obtained from films in the Harvard Standard File and cross-checked as to the identity of the film with the Geological Survey's file. Minor differences can be expected in the d-spacings reported for the same specimens by different investigators because of the manner of preparation of the mount, the conditions of X-ray irradiation, and the method of photography and measurement of the film or chart. The Harvard and Geological Survey data all were obtained from films taken in 114-mm diameter cameras, using either ethyl cellulose and toluene or collodion spindle mounts and Straumanis-type film mounting. Unless otherwise indicated all patterns were taken with copper radiation (Kα 1.5418 A.) and nickel filter and data are given in Angstrom units. The d-spacings are not corrected for film shrinkage. The correction ordinarily is small and in general is less than either the variation in spacing arising from differences in experimental technique of different investigators, including the varying absorption of samples of different thickness and concentration, or the variation attending slight changes in the chemical composition of the mineral. Some uranium minerals give poor diffraction patterns. The best results are generally obtained by using relatively small diameter spindles and long exposures, with a take-off angle from teh X-ray tube of about 4°. It is sometimes advantageous to shield the film from fluorescence in the visible region excited by X-ray irradiation. Copper radiation is preferable. The patterns of a few uranium minerals are greatly impaired by heavy grinding of the sample. Light crushing of the coarse sample after mixing with about one-third its volume of coarsely powdered low-absorption glass is helpful. Many uranium minerals, such as the members of the torbernite group, readily lose zeolithic water or transform to lower hydrates at or near ordinary conditions of temperature and humidity and care should be taken to control this in the manner of preservation and preparation of the sample.

Trace Elements Investigations↗
USGS2015

User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data

Evaluating long-term changes in river conditions (water quality and discharge) is an important use of hydrologic data. To carry out such evaluations, the hydrologist needs tools to facilitate several key steps in the process: acquiring the data records from a variety of sources, structuring it in ways that facilitate the analysis, processing the data with routines that extract information about changes that may be happening, and displaying findings with graphical techniques. A pair of tightly linked R packages, called dataRetrieval and EGRET (Exploration and Graphics for RivEr Trends), have been developed for carrying out each of these steps in an integrated manner. They are designed to easily accept data from three sources: U.S. Geological Survey hydrologic data, U.S. Environmental Protection Agency (EPA) STORET data, and user-supplied flat files. The dataRetrieval package not only serves as a “front end” to the EGRET package, it can also be used to easily download many types of hydrologic data and organize it in ways that facilitate many other hydrologic applications. The EGRET package has components oriented towards the description of long-term changes in streamflow statistics (high flow, average flow, and low flow) as well as changes in water quality. For the water-quality analysis, it uses Weighted Regressions on Time, Discharge and Season (WRTDS) to describe long-term trends in both concentration and flux. EGRET also creates a wide range of graphical presentations of the water-quality data and of the WRTDS results. This report serves as a user guide to these two R packages, providing detailed guidance on installation and use of the software, documentation of the analysis methods used, as well as guidance on some of the kinds of questions and approaches that the software can facilitate.

Techniques and Methods↗
USGS2000

Comparability of suspended-sediment concentration and total suspended solids data

Two laboratory analytical methods — suspended-sediment concentration (SSC) and total suspended solids (TSS) — are predominantly used to quantify concentrations of suspended solid-phase material in surface waters of the United States. The analytical methods differ. SSC data are produced by measuring the dry weight of all the sediment from a known volume of a water-sediment mixture. TSS data are produced by several methods, most of which entail measuring the dry weight of sediment from a known volume of a subsample of the original. An evaluation of 3,235 paired SSC and TSS data, of which 860 SSC values include percentages of sand-size material, shows bias in the relation between SSC and TSS —SSC values tend to increase at a greater rate than their corresponding paired TSS values. As sand-size material in samples exceeds about a quarter of the sediment dry weight, SSC values tend to exceed their corresponding paired TSS values. TSS analyses of three sets of quality-control samples (35 samples) showed unexpectedly small sediment recoveries and relatively large variances in the TSS data. Two quality-control data sets (18 samples) that were analyzed for SSC showed both slightly deficient sediment recoveries, and variances that are characteristic of most other quality-control data compiled as part of the U.S. Geological Survey’s National Sediment Laboratory Quality Assurance Program. The method for determining TSS, which was originally designed for analyses of wastewater samples, is shown to be fundamentally unreliable for the analysis of natural-water samples. In contrast, the method for determining SSC produces relatively reliable results for samples of natural water, regardless of the amount or percentage of sand-size material in the samples. SSC and TSS data collected from natural water are not comparable and should not be used interchangeably. The accuracy and comparability of suspended solid-phase concentrations of the Nation’s natural waters would be greatly enhanced if all these data were produced by the SSC analytical method.

Water-Resources Investigations Report↗
USGS2001

Review and analysis of available streamflow and water-quality data for Park County, Colorado, 1962-98

Information on streamflow and surface-water and ground-water quality in Park County, Colorado, was compiled from several Federal, State, and local agencies. The data were reviewed and analyzed to provide a perspective of recent (1962-98) water-resource conditions and to help identify current and future water-quantity and water-quality concerns. Streamflow has been monitored at more than 40 sites in the county, and data for some sites date back to the early 1900's. Existing data indicate a need for increased archival of streamflow data for future use and analysis. In 1998, streamflow was continuously monitored at about 30 sites, but data were stored in a data base for only 10 sites. Water-quality data were compiled for 125 surface-water sites, 398 wells, and 30 springs. The amount of data varied considerably among sites; however, the available information provided a general indication of where water-quality constituent concentrations met or exceeded water-quality standards. Park County is primarily drained by streams in the South Platte River Basin and to a lesser extent by streams in the Arkansas River Basin. In the South Platte River Basin in Park County, more than one-half the annual streamflow occurs in May, June, and July in response to snowmelt in the mountainous headwaters. The annual snowpack is comparatively less in the Arkansas River Basin in Park County, and mean monthly streamflow is more consistent throughout the year. In some streams, the timing and magnitude of streamflow have been altered by main-stem reservoirs or by interbasin water transfers. Most values of surface-water temperature, dissolved oxygen, and pH were within recommended limits set by the Colorado Department of Public Health and Environment. Specific conductance (an indirect measure of the dissolved-solids concentration) generally was lowest in streams of the upper South Platte River Basin and higher in the southern one-half of the county in the Arkansas River Basin and in the South Platte River downstream from Antero Reservoir. Historical nitrogen concentrations in surface water were small. Nitrite was not detected, most un-ionized ammonia concentrations were less than 0.02 milligram per liter, and all nitrate concentrations were less than 1.2 milligrams per liter. Nitrate concentrations were higher in urban and built-up areas than in rangeland and forest areas. Most median concentrations of total phosphorus at individual sites were less than 0.05 milligram per liter, and concentrations were not significantly different among urban and built-up, rangeland, and forest areas. An upward trend in total phosphorus concentration was determined for flow from the East Portal of the Harold D. Roberts Tunnel, but the slope of the trend line was small and the concentrations were equal or nearly equal to the detection limit of 0.01 milligram per liter. Using median phosphorus loads for two South Platte River sites, the annual phosphorus load transported out of Park County in the South Platte River was calculated to be about 10,000 pounds. Median iron and manganese concentrations for most areas of Park County were less than in-stream water-quality standards, even though several individual concentrations were one to two orders of magnitude larger than the standards. The largest concentrations of aluminum, cadmium, chromium, copper, iron, manganese, nickel, and zinc were from the upper North Fork South Platte River Basin or the Mosquito Creek Basin. All ground-water concentrations of chloride and most ground-water concentrations of sulfate were less than the U.S. Environmental Protection Agency (USEPA) drinking-water standard of 250 milligrams per liter. Median dissolved-solids concentrations in ground water ranged from 160 milligrams per liter in the crystalline-rock aquifers to 257 milligrams per liter in the sedimentary-rock aquifers. Dissolved-solids concentrations greater than the USEPA drinking-water standard of 500 milligrams per liter were detected in abo

Water-Resources Investigations Report↗
USGS1975

Satellite relay and processing of hydrologic data in south Florida

Management of water in south Florida requires current hydrologic data on water levels and rainfall. This need is being met by a data processing system which provides near-real-time data from remote areas. The flow of data is from data-collection platforms at field sites via LANDSAT-1 satellite to the National Aeronautics and Space Administration's (NASA) ground-receiving stations to the NASA Data Processing Facility at Goddard Space Flight Center to the Miami office of the U.S. Geological Survey to data users. The process requires only a few hours, and current data are provided to water-management agencies in several different forms. The system has proven to be dependable.

Florida↗
USGS1985

Evaluation of surface geophysical methods for collection of hydrogeologic data in the Nebraska Sand Hills region

The practicality of using surface geophysical methods for obtaining geohydrologic data in the Nebraska Sand Hills region was studied during the summer of 1984. Seismic refraction and electrical-resistivity equipment were used, because an evaluation of geohydrologic data indicated that results of surveys made with this equipment probably would yield the most useful data. The study area, which included parts of Garfield, Holt, and Wheeler Counties, was selected because it is geohydrologically representative of the eastern part of the Sand Hills region, and because sufficient geohydrologic data were available for use in evaluating the results of geophysical surveys. Geophysical methods were evaluated for their ability to consistently detect selected geohydrologic horizons. These horizons in descending order, are: the water table, the top of Quaternary silt beds, the top of Quaternary sand and gravel beds , the top of the Tertiary Ogallala Formation, and the top of the Cretaceous Pierre Shale. The top of the Pierre Shale generally is the base of the aquifer, which consists of all of the 500 to 700 ft of overlying deposits. Evaluations of the geophysical data indicate that seismic refraction surveys are best suited for determining the depth to the water table, but are not effective in studying beds below the water table. Vertical electrical soundings provided data on the depth to water table and the top of the silt beds. Available geohydrologic data, however, indicate that with some changes in data collection or interpretation techniques, it may be possible to obtain information on the top of the sand and gravel deposits, the top of the Ogallala Formation, and the top of the Pierre Shale with vertical electrical soundings. Use of either geophysical method could enhance the results of geohydrologic investigations in the Nebraska Sand Hills region. (Author 's abstract)

Water-Resources Investigations Report↗
USGS1990

Comparison of accuracy and completeness of data obtained from three types of automatic water-quality monitors

A comparison of data (specific conductance, dissolved-oxygen concentration, temperature, and pH) collected by the U.S. Geological Survey flowthrough monitor, the U.S. Geological Survey minimonitor, and a self-contained commercial 'packaged-sensor' system indicates that the data obtained by means of the most complete of the three systems. The U.S. Geological Survey flowthrough monitor is powered by 120-volt alternating current and in a heated weather-proof shelter. A pumping system brings water from the stream to sensors clustered in a sample clustered in a sample chamber located in the shelter. This instrument measures output from the senors; data are recorded in binary-coded decimal form on a 16-channel punched-paper tape recorder tape recorder housed in the shelter. The U.S. Geological Survey's minimonitor is powered by an external battery and is housed in a weatherproof shelter. This instrument measures output of instream sensors with extension cables having underwater connectors; data are recorded in binary-coded decimal form on a 16-channel punched-paper tape recorder housed in the shelter. The packaged-sensor system also measures output of senors housed in a packages that is submerged in the stream. It has internal power supply, no moving parts, and does not requires a weatherproof shelter; data are stored are stored in solid-state memory. Minimonitors were installed at four sites in Ohio where U.S. Geological survey flowthrough were in operation. Two package-sensor systems also were assigned to each site and were alternated every two weeks. Detailed records were kept of (1) field measurements, for comparison with monitor-system data from each instrument, and (2) equipment problems that resulted in loss of data. Results of the comparisons shows that the flow-through monitor gave the most accurate and the most complete data.

Water-Resources Investigations Report↗
USGS1998

Preliminary estimates of residence times and apparent ages of ground water in the Chesapeake Bay watershed, and water-quality data from a survey of springs

Knowledge of the residence times of the ground-water systems in Chesapeake Bay watershed helps resource managers anticipate potential delays between implementation of land-management practices and any improvements in river and estuary water quality. This report presents preliminary estimates of ground-water residence times and apparent ages of water in the shallow aquifers of the Chesapeake Bay watershed. A simple reservoir model, published data, and analyses of spring water were used to estimate residence times and apparent ages of ground-water discharge. Ranges of aquifer hydraulic characteristics throughout the Bay watershed were derived from published literature and were used to estimate ground-water residence times on the basis of a simple reservoir model. Simple combinations of rock type and physiographic province were used to delineate hydrogeomorphic regions (HGMR's) for the study area. The HGMR's are used to facilitate organization and display of the data and analyses. Illustrations depicting the relation of aquifer characteristics and associated residence times as a continuum for each HGMR were developed. In this way, the natural variation of aquifer characteristics can be seen graphically by use of data from selected representative studies. Water samples collected in September and November 1996, from 46 springs throughout the watershed were analyzed for chlorofluorocarbons (CFC's) to estimate the apparent age of ground water. For comparison purposes, apparent ages of water from springs were calculated assuming piston flow. Additional data are given to estimate apparent ages assuming an exponential distribution of ages in spring discharge. Additionally, results from previous studies of CFC-dating of ground water from other springs and wells in the watershed were compiled. The CFC data, and the data on major ions, nutrients, and nitrogen isotopes in the water collected from the 46 springs are included in this report. The apparent ages of water discharging from 30 of the 46 springs sampled were less than 20 years, including 5 that were "modern" (0-4 years). Four samples had apparent ages of 22 to 34 years, and two others from thermal springs were 40 years or greater. The remaining ten samples were contaminated with local sources of CFC and could not be dated. Nitrate concentrations and nitrate delta 15 nitrogen (d 15 N) values in water from many springs are similar to those in shallow ground water beneath fertilized fields, and some values are high enough to indicate a probable source from animal-waste components. The nitrogen data reported here highlight the significance of the springs sampled during this study as pathways for nutrient transport in the Chesapeake Bay watershed. Ground-water samples were collected from springs during an unusually high flow period and thus may not be representative of low base-flow conditions. Residence times estimated from plausible ranges of aquifer properties and results of previous age-dating analyses generally corroborate the apparent-age analysis made in the current study and suggests that some residence times could be much longer. The shortest residence times tend to be in the Blue Ridge and northern carbonate areas; however, the data are preliminary and not appropriate for statistical tests of significance or variance. Because the age distributions in the aquifer discharging to the springs are not known, and because the apparent ages of water from the springs are based on various combinations of CFC criteria, the apparent ages and calculated residence times are compared for illustrative purposes but are considered preliminary until further work is accomplished.

Maryland, Virginia↗
USGS2022

The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus)

Satellite telemetry (ST) has played a critical role in the management and conservation of polar bears ( Ursus maritimus ) over the last 50 years. ST data provide biological information relevant to subpopulation delineation, movements, habitat use, maternal denning, health, human-bear interactions, and accurate estimates of vital rates and abundance. Given that polar bears are distributed at low densities over vast and remote habitats, much of the information provided by ST data cannot be collected by other means. Obtaining ST data for polar bears requires chemical immobilization and application of a tracking device. Although immobilization has not been found to have negative effects beyond a several-day reduction in activity, over the last few decades opposition to immobilization and deployment of satellite-linked radio collars has resulted in a lack of current ST data in many of the 19 recognized polar bear subpopulations. Here, we review the uses of ST data for polar bears and evaluate its role in addressing 21 st century conservation and management challenges, which include estimation of sustainable harvest rates, understanding the impacts of climate warming, delineating critical habitat, and assessing potential anthropogenic impacts from tourism, resource development and extraction. We found that in subpopulations where ST data have been consistently collected, information was available to estimate vital rates and subpopulation density, document the effects of sea-ice loss, and inform management related to subsistence harvest and regulatory requirements. In contrast, a lack of ST data in some subpopulations resulted in increased bias and uncertainty in ecological and demographic parameters, which has a range of negative consequences. As sea-ice loss due to climate warming continues, there is a greater need to monitor polar bear distribution, habitat use, abundance, and subpopulation connectivity. We conclude that continued collection of ST data will be critically important for polar bear management and conservation in the 21 st century and that the benefits of immobilizing small numbers of individual polar bears in order to deploy ST devices significantly outweigh the risks.

Frontiers in Marine Science↗
USGS2017

A land product characterization system for comparative analysis of satellite data and products

A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature). The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS), and simulated data for the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA) Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Remote Sensing↗