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At least 1,351 records · Page 75Linked to original sources

Digital data sets that describe aquifer characteristics of the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma

ARC/INFO export and nonproprietary format files This diskette contains digitized aquifer boundaries and maps of of hydraulic conductivity, recharge, and ground-water level elevation contours for the alluvial and terrace deposits along the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square miles of the Quaternary-age alluvial and terrace aquifer is an important source of water for irrigation, industrial, municipal, stock, and domestic supplies. The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz sand with minor amounts of clay, silt, and basal gravel. The hydraulically connected alluvial and terrace deposits unconformably overlie the Tertiary-age Ogallala Formation and Permian-age formations. Most of the lines in the aquifer boundary and recharge data sets and some of the lines in the hydraulic conductivity data set were extracted from a published digital surficial geology data set based on a scale of 1:250,000. The ground-water elevation contours and some of the lines for the aquifer boundary, hydraulic conductivity, and recharge data sets were digitized from a ground-water modeling report about the aquifer published at a scale of 1:250,000. The hydraulic conductivity values and recharge rates also are from the ground-water modeling report. The data sets are provided in both nonproprietary and ARC/INFO export file formats. Ground-water flow models are numerical representations that simplify and aggregate natural systems. Models are not unique; different combinations of aquifer characteristics may produce similar results. Therefore, values of hydraulic conductivity and recharge used in the model and presented in this data set are not precise, but are within a reasonable range when compared to independently collected data.

Oklahoma↗

Nitrogen and phosphorus data for surface water in the Upper Colorado River basin, Colorado, 1980-94

This report documents, summarizes, and provides on 3.5-in. diskette the surface-water data collected from January 1980 through August 1994 for nitrogen and phosphorus in the Upper Colorado River Basin from the Colorado-Utah State line to the Continental Divide. Ancillary data for parameters, such as water temperature, streamflow, specific conductance, dissolved oxygen, pH, and alkalinity, also are compiled, if available. Data were retrieved from the U.S. Geological Survey National Water Information System and the U.S. Environmental Protection Agency STORET (STOrage and RETrieval) system. The water-quality data are presented for sites having five or more nutrient analyses that reflect ambient stream conditions. The compiled data base contains 4,927 samples from 123 sites. The median sample period of record for individual sites is 2.5 years, and the seventy-fifth percentile is about 12 years. Sixteen sites have only five samples each. The median number of samples per site is 14 samples, whereas the seventy-fifth percentile is 65 samples. The compiled data set was used in the design of a basinwide sampling network that incorporates sites that lack historic surface-water-quality data.

Colorado↗

Data Base for Assessment of Streambed Scour and Channel Instability at Selected Bridges in Indiana, 1991–95

The U.S. Geological Survey, in cooperation with the Indiana Department of Transportation, has collected data at 5,587 bridges in Indiana built with federal aid. These data, which can be useful for assessing Streambed scour and channel instability, are maintained in a computerized data base at the U.S. Geological Survey in Indianapolis, Indiana. The data elements are grouped under one of five headings: General Site Characteristics, Observed and Calculated Scour Characteristics, Bridge Characteristics, Stream Characteristics, and Debris Characteristics. The description of the data in each group includes the element name; examples of the data from bridge number 89-54 crossing Lick Creek in Wayne County, Indiana; and a brief description of each element. The data already have been used in Indiana to produce an observed-scour index and a potential-scour index and may be useful in other applications as well.

Indiana↗

Summary statistics and graphical comparisons of historical hydrologic and water-quality data, Seco Creek Watershed, South-Central Texas

The U.S. Geological Survey collected hydrologic (rainfall, streamflow, and reservoir content) and water-quality data in the Seco Creek watershed, south-central Texas. Most of the data from 15 sites were collected as part of a study in cooperation with the U.S. Department of Agriculture and the Texas State Soil and Water Conservation Board to evaluate the effects of agricultural best-management practices on surface- and ground-water quantity and quality in the 255-square-mile watershed. Nearly 400 best-management practices at 58 sites were implemented by landowners in the watershed during March 1990-September 1995. Most of the data are from the early 1990s, the period during and after implementation of best-management practices. Data from five sites include water quality and are summarized in tables and graphics in the text; and data from all 15 sites are summarized on a diskette. Maximum annual rainfall among the sites for which data are presented in the text (excluding one site) for the during-and-after-implementation period (March 1990-September 1995) was 53.27 inches in water year 1992. Maximum annual total streamflow among the sites for the period was 63,400 acre-feet, also in water year 1992. At the one site with water-quality data (under base-flow conditions) for both the before-implementation period and the during-and-after implementation period of best-management practices, percentiles (5, 25, 50, 75, 95) for specific conductance, nitrate concentration, and fecal coliform density were less for the during-and-after-implementation period than for the before-implementation period.

Texas↗

Index of hydrologic characteristics and data resources for the Gwynns Falls watershed, Baltimore County and Baltimore City, Maryland

The Gwynns Falls watershed has been selected as the primary study area for the Baltimore Ecosystem Study, as part of the National Science Foundation's Long-Term Ecological Research program. Because the Baltimore Ecosystem Study will be investigating long-term hydrologic changes in the Gwynns Falls watershed, a compilation of existing data resources for the watershed is necessary. This report discusses hydrologic characteristics and presents a compilation of historical data resources for the Gwynns Falls watershed. Tables are presented that list active and discontinued continuous-record streamflow-gaging stations, nontidal crest-stage partial-record stations, low-flow partial-record stations, and water-quality partial-record stations operated by the U.S. Geological Survey, Maryland-Delaware-D. C. District, in the Gwynns Falls watershed. Statistics regarding the number of inventoried ground-water wells in or bordering the Gwynns Falls watershed also are presented. A summary of additional data resources for the Gwynns Falls watershed is provided. This includes (1) an inventory of selected U.S. Geological Survey studies and reports that contain historical data or basin characteristics for streams in the watershed, (2) a listing of indirect flood-discharge measurements that have been made at several monitoring stations in the watershed, (3) a brief discussion of channel-stability and bridge-scour data collected by the U.S. Geological Survey in the watershed during 1990?95, (4) a listing of climatological data stations in the watershed and in the surrounding regional area, and (5) a listing of other selected reports that include data or information on the Gwynns Falls watershed.

Maryland↗

Data sources and compilation: Chapter 27 in Petroleum systems and geologic assessment of oil and gas in the San Joaquin Basin Province, California

Geologic, production, and exploration/discovery-history data are used by the U.S. Geological Survey to aid in the assessment of petroleum resources. These data, as well as the broad knowledge and experience of the assessing geologists, are synthesized to provide, for each assessment unit, geologic and exploration models upon which estimates are made of the number and sizes of undiscovered accumulations for conventional assessment units or number and total recoverable volumes of untested cells for continuous assessment units (input data for resource calculations). Quantified geologic information and trends in production and exploration/discovery-history data with respect to time and exploration effort provide guides for the estimating parameters of variables recorded on the input-data forms (input data) used to calculate petroleum resources. An Assessment Review Team reviews proposed geologic and exploration models and input data for each assessment unit in formal assessment meetings. The Assessment Review Team maintains the accuracy and consistency of the assessment procedure during the formal assessment meetings.

Professional Paper↗

State of Texas - Highlighting low-lying areas derived from USGS Digital Elevation Data

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation of Texas and a grayscale relief of the surrounding areas. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. The NED source data for this map consists of a mixture of 30-meter- and 10-meter-resolution DEMs. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2002. Shaded relief over Mexico was obtained from the USGS National Atlas.

Scientific Investigations Map↗

Summary of suspended-sediment data for streams draining the Chesapeake Bay Watershed, water years 1952-2002

U.S. Geological Survey suspended-sediment data from 1952 to 2002 from selected stream-gaging stations draining the nontidal parts of the Chesapeake Bay Watershed were summarized to identify areas in the Watershed with high suspended-sediment loads, yields, and concentrations. The suspended-sediment load data were separated into two periods, 1952?1984 and 1985?2001. In 1985, the Chesapeake Bay Program began recommending sediment regulations, so 1985 represents an important break in the data. The instantaneous suspended-sediment concentration data were examined for the period 1985?2002. Suspended-sediment load data collected from 43 stations from 1952?1984, with a minimum of 3 years of record, indicated that the two highest average annual suspended-sediment loads were for stations on the main stem of the Potomac and Susquehanna Rivers. The highest average annual sediment yields and discharge-weighted sediment concentrations were for streams draining the metropolitan Washington, D.C. area, possibly related to urbanization. Data from 1985 through 2001 that were collected from 35 stations with a minimum of 3 years of record showed that the highest average annual suspended-sediment loads were also on the main stem of the Potomac and Susquehanna Rivers. Four of the six highest average annual sediment yields and discharge-weighted sediment concentrations for 1985?2001 were for stations draining to the Conestoga River, a tributary of the Susquehanna River. Examination of percentiles (10th, 50th, and 90th) of instantaneous suspended-sediment concentrations for 51 stations with a minimum of 3 years of data and at least 10 samples in a year indicated that streams that drain to the Conestoga River had the highest suspended-sediment concentrations. Sediment-transport curves for the 51 stations were separated into classes by drainage-area size. Five of the eight drainage-area classes showed that streams draining the Susquehanna River Basin had the highest suspended-sediment concentrations. Three of the Susquehanna River Basin drainage-area classes were in the Conestoga River Basin. Agriculture is the dominant land use in the Conestoga River Basin and may be an important source of sediment leading to the high sediment yields and instantaneous suspended-sediment concentrations, but further research is needed to quantify the importance of agriculture in relation to other sources of sediment in the Conestoga River Basin.

Scientific Investigations Report↗

Inventory and Statistical Analysis of Sediment Data for Streams in Kentucky, 1950-2008

Suspended sediment is a constituent of water that is monitored because of concerns about accelerated erosion, nonpoint contamination of water resources, and degradation of aquatic environments. Understanding the availability of monitored sediment data for streams in Kentucky is critical to planning future modeling and monitoring efforts. In order to assess the availability of sediment data for Kentucky, long-term records were obtained from the National Water Information System (NWIS) of the U.S. Geological Survey (USGS). Suspended-sediment concentration (SSC), the parameter traditionally measured and reported by the USGS, was statistically compared to turbidity and total suspended solids (TSS), two parameters that are considered surrogate data. Coincident observations of SSC with either turbidity or TSS were available for 42 sites. In combination with instantaneous streamflow, turbidity and TSS both proved to be significant indicators of SSC when data from all sites were used. Because of the perceived link between sediment and nutrient abundance in streams, sediment-parameter data at these sites were correlated to total-nitrogen and total-phosphorus concentrations. A significant relation (p-value < 0.05) was found between monitored nutrient concentrations and coincident sediment abundance, although there were no clear linear relations. This compilation of data showed that SSC was monitored at 118 sites in Kentucky at some time between 1950 and 2008. As of March 2008, 9 sites were monitored for SSC in Kentucky (8 of which are new) down from a high of more than 60 SSC sites during the 1980s. Of these 118 SSC sites, 21 sites were also monitored for TSS; there are coincident records for both SSC and TSS at 6 sites. Forty-seven of these long-term water-quality sites were also monitored for turbidity; there are coincident records for SSC and turbidity at 42 sites, including all of those at which there are coincident data for TSS. The number of sites at which SSC and at least one other sediment parameter (TSS or turbidity) were monitored decreased from a high of 27 in 1987 to zero during the period 2001-2005.

Scientific Investigations Report↗

Collection, processing, and interpretation of ground-penetrating radar data to determine sediment thickness at selected locations in Deep Creek Lake, Garrett County, Maryland, 2007

The U.S. Geological Survey collected geophysical data in Deep Creek Lake in Garrett County, Maryland, between September 17 through October 4, 2007 to assist the Maryland Department of Natural Resources to better manage resources of the Lake. The objectives of the geophysical surveys were to provide estimates of sediment thickness in shallow areas around the Lake and to test the usefulness of three geophysical methods in this setting. Ground-penetrating radar (GPR), continuous seismic-reflection profiling (CSP), and continuous resistivity profiling (CRP) were attempted. Nearly 90 miles of GPR radar data and over 70 miles of CSP data were collected throughout the study area. During field deployment and testing, CRP was determined not to be practical and was not used on a large scale. Sediment accumulation generally could be observed in the radar profiles in the shallow coves. In some seismic profiles, a thin layer of sediment could be observed at the water bottom. The radar profiles appeared to be better than the seismic profiles for the determination of sediment thickness. Although only selected data profiles were processed, all data were archived for future interpretation. This investigation focused on selected regions of the study area, particularly in the coves where sediment accumulations were presumed to be thickest. GPR was the most useful tool for interpreting sediment thickness, especially in these shallow coves. The radar profiles were interpreted for two surfaces of interest-the water bottom, which was defined as the "2007 horizon," and the interface between Lake sediments and the original Lake bottom, which was defined as the "1925 horizon"-corresponding to the year the Lake was impounded. The ground-penetrating radar data were interpreted on the basis of characteristics of the reflectors. The sediments that had accumulated in the impounded Lake were characterized by laminated, parallel reflections, whereas the subsurface below the original Lake bottom was characterized by more discontinuous and chaotic reflections, often with diffractions indicating cobbles or boulders. The reflectors were picked manually along the water bottom and along the interface between the Lake sediments and the pre-Lake sediments. A simple graphic approach was used to convert traveltimes to depth through water and depth through saturated sediments using velocities of the soundwaves through the water and the saturated sediments. Nineteen cross sections were processed and interpreted in 9 coves around Deep Creek Lake, and the difference between the 2007 horizon and the 1925 horizon was examined. In most areas, GPR data indicate a layer of sediment between 1 and 7 feet thick. When multiple cross sections from a single cove were compared, the cross sections indicated that sediment thickness decreased toward the center of the Lake.

Maryland↗

Historical water-quality data from the Harlem River, New York

Data specific to the Harlem River, New York, have been summarized and are presented in this report. The data illustrate improvements in the quality of water for the past 65 years and emphasize the importance of a continuous water-quality record for establishing trends in environmental conditions. Although there is a paucity of sediment-quality data, the New York City Department of Environmental Protection (NYCDEP) Bureau of Wastewater Treatment has maintained a water-quality monitoring network in the Harlem River (and throughout the harbor of New York City) to which 61 combined sewer outfalls discharge effluent. In cooperation with the NYCDEP, the U.S. Geological Survey evaluated water-quality data collected by the NYCDEP dating back to 1945, which indicate trends in water quality and reveal improvement following the 1972 passage of the Clean Water Act. These improvements are indicated by the steady increase in median dissolved oxygen concentrations and an overall decrease in fecal indicator bacteria concentrations starting in the late 1970s. Further, the magnitude of the highest fecal indicator bacteria concentrations (that is, the 90th percentile) in samples collected from the Harlem River have decreased significantly over the past four decades. Other parameters of water quality used to gauge the health of a water body include total suspended solids and nutrient (inorganic forms of nitrogen and phosphorus) concentrations&mdash;mean concentrations for these indicators have also decreased in the past decades. The limited sediment data available for one sample in the Harlem River indicate concentrations of copper, zinc, and lead are above sediment-quality thresholds set by the New York State Department of Environmental Conservation. However, more data are needed to better understand the changes in both sediment and water quality in the Harlem River, both as the tide cycles and during precipitation events. As a partner in the Urban Waters Federal Partnership, the U.S. Geological Survey has worked to address the chronic water-quality concerns of the Harlem River by compiling relevant data and studies, which is an important component for understanding and rectifying water-quality problems within a watershed.

New York↗

Collection methods and quality assessment for Escherichia coli, water quality, and microbial source tracking data within Tumacácori National Historical Park and the upper Santa Cruz River, Arizona, 2015-16

Tumacácori National Historical Park protects the culturally important Mission, San José de Tumacácori, while also managing a portion of the ecologically diverse riparian corridor of the Santa Cruz River. This report describes the methods and quality assurance procedures used in the collection of water samples for the analysis of Escherichia coli ( E. coli ), microbial source tracking markers, suspended sediment, water-quality parameters, turbidity, and the data collection for discharge and stage; the process for data review and approval is also described. Finally, this report provides a quantitative assessment of the quality of the E. coli , microbial source tracking, and suspended sediment data. The data-quality assessment revealed that bias attributed to field and laboratory contamination was minimal, with E. coli detections in only 3 out of 33 field blank samples analyzed. Concentrations in the field blanks were several orders of magnitude lower than environmental concentrations. The microbial source tracking (MST) field blank was below the detection limit for all MST markers analyzed. Laboratory blanks for E. coli at the USGS Arizona Water Science Center and laboratory blanks for MST markers at the USGS Ohio Water Microbiology Laboratory were all below the detection limit. Irreplicate data for E. coli and suspended sediment indicated that bias was not introduced to the data by combining samples collected using discrete sampling methods with samples collected using automatic sampling methods. The split and sequential E. coli replicate data showed consistent analytical variability and a single equation was developed to explain the variability of E. coli concentrations. An additional analysis of analytical variability for E. coli indicated analytical variability around 18 percent relative standard deviation and no trend was observed in the concentration during the processing and analysis of multiple split-replicates. Two replicate samples were collected for MST and individual markers were compared for a base flow and flood sample. For the markers found in common between the two types of samples, the relative standard deviation for the base flow sample was more than 3 times greater than the markers in the flood sample. Sequential suspended sediment replicates had a relative standard deviation of about 1.3 percent, indicating that environmental and analytical variability was minimal. A holding time review and laboratory study analysis supported the extended holding times required for this investigation. Most concentrations for flood and base-flow samples were within the theoretical variability specified in the most probable number approach suggesting that extended hold times did not overly influence the final concentrations reported.

Arizona↗

An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data

An exploratory discrete Bayesian network (BN) was developed to assess the potential of this type of model for estimating the magnitudes and uncertainties of an arbitrary subset of unmeasured water-quality parameters given the measured complement of parameters historically measured at a U.S. Geological Survey streamgage. Water-quality data for 27 water-quality parameters from 596 discrete measurements at U.S. Geological Survey streamgage 03374100 White River at Hazleton, Indiana, were used to develop this BN. Data for each of the water-quality parameters were discretized into five intervals based on the quintiles of the measured values. The 596 discrete measurements were randomly partitioned into a training set with 80 percent of the data and a testing set with 20 percent of the data to identify, estimate, and assess the training and testing accuracy of the Bayesian network. A BN with 28 nodes was formed from the 27 water-quality parameters and the month of sample collection. Based on data in the training set, a network with 53 directed edges and month as the target node was identified by minimizing the negative log-likelihood function for all nodes treated, in turn, as the target variable. The edge structure determines the number and magnitude of elements in conditional probability tables associated with all nodes. The effectiveness of the BN was assessed on the basis of correct classification rates to one of the five discrete intervals, which were computed separately for the training and testing datasets and for two conditioning variable sets. The selected sets of conditioning variables represent two of many possible sets of measured parameters on which to base estimates of unmeasured parameters. The first set includes only the month of sample collection ( month ), and an expanded set includes month and six other continuously measurable parameters, referred to as the ContMeasSet , all of which were obtained from the discrete data. Results indicated that the training dataset had average correct classification rates of 41.7- and 61.2-percent rates conditioned on the month and ContMeasSet sets, respectively. The testing dataset had somewhat lower average correct classification rates of 40.8 and 56.5 percent for the two conditioning variable sets. When conditioned on month only, the average correct classification rate for the testing dataset was only slightly lower than the average correct classification rate in the training dataset, indicating little model overfitting. When using the ContMeasSet , however, the average decrease in accuracy between training and testing sets was 4.9 percent. The training and testing datasets and both sets of conditioning variables, however, indicate that the BN would substantially outperform a random assignment model, which would be expected to have a 20-percent correct classification rate. In addition, the edge structure of the BN depicts how information can flow through the network, which may help prioritize parameters for measurement to facilitate estimation of unmeasured parameters. Finally, extension of a static BN, like the one developed in this report, to a dynamic BN may provide a basis for using high-frequency or continuous water-quality data to extend information in time between discrete water-quality samples, and this integration could mitigate some of the limitations of high-frequency and discrete water-quality sampling methods.

Indiana↗

Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator

Data collection, along with hydraulic and fluvial egg transport modeling, was completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam in Kentucky and Indiana. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for Hypophthalmichthys nobilis (bighead carp). A copy of an existing step-backwater model of Ohio River flows was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows were selected to represent typical conditions ranging from a high-streamflow event to a seasonal dry-weather event, with two streamflows between these extremes for this reach of the Ohio River. Outputs from the hydraulic model, a range of five water temperatures observed in water-quality data, and four potential spawning locations were used as input to the Fluvial Egg Drift Simulator to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run. Results from the Fluvial Egg Drift Simulator scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical or biological factors such as excess turbulence, fertilization failure, predation, or starvation) indicate that most eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the model reach. The overall mean percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24 to 30 degrees Celsius. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature and high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. Although the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information about conditions favorable to spawning and recruitment habitat for bighead carp in the Ohio River.

Indiana, Kentucky↗

Analysis of Escherichia coli, total recoverable iron, and dissolved selenium concentrations, loading, and identifying data gaps for selected 303(d) listed streams, Grand Valley, western Colorado, 1980–2018

Tributaries to the Colorado River in the Grand Valley in western Colorado (segment COLCLC13b) have been placed on the State of Colorado 303(d) list as impaired for Escherichia coli (E. coli) , total recoverable iron, and dissolved selenium. The Colorado Department of Public Health and Environment Water Quality Control Division is required to develop total maximum daily loads for these constituents in these tributaries. The U.S. Geological Survey, in cooperation with the Grand Valley Drainage District and Colorado Water Conservation Board, conducted a study to (1) characterize concentrations, loads, and load reductions for E. coli , total recoverable iron, and dissolved selenium using existing data and (2) identify water-quality data gaps to inform future monitoring strategies. This study analyzed water-quality and streamflow data for 3 main-stem sites (2 sites along the Colorado River and 1 site along the Gunnison River) and 29 selected sites on tributaries to the Colorado River. Sample data were available at five sites along Adobe Creek and at six sites along Leach Creek, the two tributaries in the study area that are impaired for E. coli . All geometric mean E. coli concentrations at sites along Adobe Creek and Leach Creek exceeded the State recreational use standard of 126 colony forming units per 100 milliliters (CFU/100 mL). In Adobe Creek, E. coli concentrations in samples ranged from 45.7 to more than 2,420 CFU/100 mL (method upper reporting limit for undiluted samples), and geometric mean concentrations at sites ranged from 301 to 1,180 CFU/100 mL. The E. coli concentrations generally increased in the downstream direction in Adobe Creek; however, increases were not seen between all sites. The largest downstream increase in E. coli concentration was measured between the two most upstream sites. In Leach Creek, concentrations of E. coli in samples ranged from 25.9 to more than 2,420 CFU/100 mL, and geometric mean concentrations at sites ranged from 160 to 259 CFU/100 mL. The E. coli concentrations showed no consistent downgradient increase in Leach Creek. In fact, some of the highest E. coli concentrations were measured at the most upstream site, Leach Creek at Summer Hill Drive. Total recoverable iron concentrations and loads were evaluated at 15 tributary sites for samples collected from August 1993 to February 2018. Median total recoverable iron concentrations ranged from 211 to 4,670 micrograms per liter (µg/L). The chronic aquatic-life water-quality standard (1,000 µg/L) was exceeded in most irrigation season (April through October) samples but was rarely exceeded in nonirrigation season (November through March) samples. Concentrations were often an order of magnitude higher in samples collected during irrigation season than in samples collected during nonirrigation season. None of the sites had enough concurrent total recoverable iron and streamflow data to compute annual loads. As with E. coli , the lack of concurrent total recoverable iron and streamflow information represents a data gap, which needs to be addressed to compute annual loads. Dissolved selenium concentrations and loads were evaluated at 20 tributary sites using discrete water-quality data collected 1991–2018. Dissolved selenium concentrations were higher during nonirrigation season than during irrigation season at tributary sites. However, irrigation season dissolved selenium loads were generally higher than nonirrigation selenium loads, because streamflows were higher during irrigation season. Regression analysis was used to estimate daily dissolved selenium concentrations and loads at three main-stem sites for water years (WYs) 1980–2018 (Gunnison River near Grand Junction and Colorado River near Colorado-Utah State Line) and WYs 2002–18 (Colorado River near Cameo). A trend analysis of dissolved selenium concentrations and loads was completed for these sites from the same respective starting dates but ending in 2017. A continuing downward trend in dissolved selenium concentration was observed at all sites and across all seasonal designations of the analysis. The dissolved selenium concentration decreased by 0.12 µg/L from WY 2002 to 2017 at Colorado River near Cameo, representing an 18-percent decrease during the time period. The dissolved selenium concentration at Gunnison River near Grand Junction decreased by 4.2 µg/L from WY 1980 to 2017, representing a 56-percent decrease overall. During the same time period, dissolved selenium concentration at Colorado River near Colorado-Utah State Line decreased by 3.8 µg/L, representing a 56-percent decrease overall. A downward trend in dissolved selenium load was also observed at all sites and across all seasonal designations of the analysis. The relative contribution of dissolved selenium from the Grand Valley near Grand Junction was estimated by comparing loads at main-stem sites bracketing the study area. The two upstream sites, Colorado River near Cameo and Gunnison River near Grand Junction, contributed 60,300 cumulative pounds and 251,000 cumulative pounds, respectively, during WYs 2002–18. At the furthest downstream site, Colorado River near Colorado-Utah State Line, 490,000 cumulative pounds were estimated during the same time period, indicating that the region between Whitewater and State line contributed approximately 179,000 cumulative pounds or a mean annual load of 10,500 lb/yr. Grand Valley dissolved selenium contributions appear to be stable during WYs 2002–18.

Colorado↗

Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

The U.S. Census Bureau provides decadal demographic data collected at the household level and aggregated to larger enumeration units for anonymity purposes. Although this system is appropriate for the dissemination of large amounts of national demographic data, often the boundaries of the enumeration units do not reflect the distribution of the underlying statistical phenomena. Conventional mapping methods such as choropleth mapping, are primarily employed due to their ease of use. However, the analytical drawbacks of choropleth methods are well known ranging from (1) the artificial transition of population at the boundaries of mapping units to (2) the assumption that the phenomena is evenly distributed across the enumeration unit (when in actuality there can be significant variation). Many methods to map population distribution have been practiced in geographic information systems (GIS) and remote sensing fields. Many cartographers prefer dasymetric mapping to map population because of its ability to more accurately distribute data over geographic space. Similar to ?choropleth maps?, a dasymetric map utilizes standardized data (for example, census data). However, rather than using arbitrary enumeration zones to symbolize population distribution, a dasymetric approach introduces ancillary information to redistribute the standardized data into zones relative to land use and land cover (LULC), taking into consideration actual changing densities within the boundaries of the enumeration unit. Thus, new zones are created that correlate to the function of the map, capturing spatial variations in population density. The transfer of data from census enumeration units to ancillary-driven homogenous zones is performed by a process called areal interpolation.

Techniques and Methods↗

Field techniques for the determination of algal pigment fluorescence in environmental waters—Principles and guidelines for instrument and sensor selection, operation, quality assurance, and data reporting

The use of algal fluorometers by the U.S. Geological Survey (USGS) has become increasingly common. The basic principles of algal fluorescence, instrument calibration, interferences, data quantification, data interpretation, and quality control are given in Hambrook Berkman and Canova (2007). Much of the guidance given for instrument maintenance, data storage, and quality assurance in Wagner and others (2006) are also applicable to algal fluorometers, although they are not explicitly discussed. Algal fluorometers have advanced substantially since these guidance documents were published; so that while the basic principles remain unchanged, new guidance is needed. This techniques and methods report is intended to provide additional information on algal fluorescence-sensor calibration, maintenance, measurement, data storage, and quality assurance that meet stated objectives of USGS data-collection efforts. The operations described facilitate and standardize the collection and accurate communication of algal fluorescence data collected by the USGS across studies, sites, and instrument types. This report provides technical background information on algal fluorescence sensors; including specifications, operating principles, key features, and design elements. Maintenance and calibration protocols, quality-assurance techniques, and suggestions for data reporting are presented. Sensor performance issues, common interferences, and strategies for addressing them are also described.

Techniques and Methods↗

OPR-PPR, a computer program for assessing data importance to model predictions using linear statistics

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.

Techniques and Methods↗