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USGS2009

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↗

USGS2011

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↗

USGS2016

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—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↗

USGS2018

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↗

USGS2018

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↗

USGS2021

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↗

USGS2021

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↗

USGS2007

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↗

USGS2022

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↗

USGS2007

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↗

USGS2018

General introduction for the “National Field Manual for the Collection of Water-Quality Data”

Background As part of its mission, the U.S. Geological Survey (USGS) collects data to assess the quality of our Nation’s water resources. A high degree of reliability and standardization of these data are paramount to fulfilling this mission. Documentation of nationally accepted methods used by USGS personnel serves to maintain consistency and technical quality in data-collection activities. “The National Field Manual for the Collection of Water-Quality Data” (NFM) provides documented guidelines and protocols for USGS field personnel who collect water-quality data. The NFM provides detailed, comprehensive, and citable procedures for monitoring the quality of surface water and groundwater. Topics in the NFM include (1) methods and protocols for sampling water resources, (2) methods for processing samples for analysis of water quality, (3) methods for measuring field parameters, and (4) specialized procedures, such as sampling water for low levels of mercury and organic wastewater chemicals, measuring biological indicators, and sampling bottom sediment for chemistry. Personnel who collect water-quality data for national USGS programs and projects, including projects supported by USGS cooperative programs, are mandated to use protocols provided in the NFM per USGS Office of Water Quality Technical Memorandum 2002.13. Formal training, for example, as provided in the USGS class, “Field Water-Quality Methods for Groundwater and Surface Water,” and field apprenticeships supplement the guidance provided in the NFM and ensure that the data collected are high quality, accurate, and scientifically defensible.

Techniques and Methods↗

USGS2002

Design of a real-time ground-water level monitoring network and portrayal of hydrologic data in southern Florida

Ground-water resources in southern Florida are under increasing stress caused a rapid growth in population. As a result of increased demands on aquifers, water managers need more timely and accurate assessments of ground-water conditions in order to avoid or reduce adverse effects such as saltwater intrusion, loss of pumpage in residential water-supply wells, land-surface subsidence, and aquifer compaction. Hydrologic data were analyzed from three aquifer systems in southern Florida: the surficial aquifer system, which includes the Biscayne aquifer; the intermediate aquifer system, which includes the sandstone and mid-Hawthorn aquifers; and the Florida aquifer system represented by the lower Hawthorn producing zone. Long-term water-level trends were analyzed using the Seasonal Kendall trend test in 83 monitoring wells with a daily-value record spanning 26 years (1974-99). The majority of the wells with data for this period were in the Biscayne aquifer in southeastern Florida. Only 14 wells in southwestern Florida aquifers and 9 in the surficial aquifer system of Martin and Palm Beach Counties had data for the full period. Because many monitoring wells did not have data for this full period, several shorter periods were evaluated as well. The trend tests revealed small but statistically significant upward trends in most aquifers, but large and localized downward trends in the sandstone and mid-Hawthorn aquifers. Monthly means of maximum daily water levels from 246 wells were compared to monthly rainfall totals from rainfall stations in southwestern and southeastern Florida in order to determine which monitoring wells most clearly indicated decreases in water levels that corresponded to prolonged rainfall shortages. Of this total, 104 wells had periods of record over 20 years and could be compared against several drought periods. After factors such as lag, seasonal cyclicity, and cumulative functions were considered, the timing of minimum values of water level from 15 ground-water monitoring wells and average minimum rainfall values agreed 57 to 62 percent of the time over a 20 to 26 year period. On average, the timing of water-level minimums and rainfall minimums agreed about 52 percent of the time, and in some cases only agreed 29 percent of the time. A regression analysis was used to evaluate daily water levels from 203 monitoring wells that are currently, or recently had been, part of the network to determine which wells were most representative of each aquifer. The regression also was used to determine which wells provided data that could be used to provide estimations of water levels at other wells in the aquifer with a coefficient of determination (R2 value) from the regression of 0.64 or greater. In all, the regression analysis alone indicated that 35 wells generally had 10 years or more of data and could be used to directly monitor water levels or to estimate water levels at 180 of 203 wells (89 percent of the network). Ultimately, factors such as existing instrumentation, well construction, long-term water-level trends, and variations of water level and chloride concentration were considered together with the R 2 results in designing the final network. The Seasonal Kendall trend test was used to examine trends in ground-water chloride concentrations in 113 wells. Of these wells, 61 showed statistically significant trends. Fifty-six percent (34 of 61 wells) of the observed trends in chloride concentration were upward and 44 percent (27 of 61 wells) were downward. The relation between water level and chloride concentration in 114 ground-water wells was examined using Spearman's r and Pearson's r correlation coefficients. Statistically significant results showed both positive and negative relations. Based on the results of statistical analyses, period of record, well construction, and existing satellite telemetry, 33 monitoring wells were selected that could be used to assess ground-water conditions in 167 monitoring wells in southern Florida on an interim basis. A real-time ground-water level monitoring network ( http://www.sflorida.er.usgs.gov/ddn_data/index.html ) was designed to provide this information, and a prototype website was constructed to provide water managers with daily updates on ground-water conditions in southern Florida. Many of the same analytical tools used to select monitoring wells representative of aquifer conditions are also employed to analyze data for this website. These tools include regression analysis, the Seasonal Kendall trend test, and frequency analysis. The website also includes image maps showing the current conditions for stations in selected geographical areas and aquifers and statistical comparison plots for each station.

Florida↗

USGS1980

Evaluation of remote hydrologic data-acquisition systems, west-central Florida

The study provides an evaluation of the hydrologic applications of a land-line and two satellite data-relay systems operated during 1977-78 in the Southwest Florida Water Management District. These systems were tested to evaluate operational and reliability characteristics. Telephone lines were used to relay data in the land-line system, and the Geostationary Operational Environmental Satellite (GOES) and Land satellite (Landsat) were used in the satellite system. The land-line system was tested for 15 months at a streamflow site. Accurate data were obtained 94% of the time during the test period. Data losses were attributed to telephone-line interference, low-battery voltage, and vandalism. The GOES system was tested at a rainfall site for 17 months. During this period, 79% of the transmissions received from the station were relayed by the GOES system to the U.S. Geological Survey computer, resulting in successful processing of 88% of all possible rainfall observations. On the average, seven data transmissions were completed each day. The Landsat system was tested at a rainfall site for about 17 months and for about 8 months at a streamflow site. During these periods of operation, only about 2% of all data observations for the stations were successfully relayed by the Landsat system to the U.S. Geological Survey computer. An average of about three data transmissions was completed each day for each site. (USGS).

Florida↗

USGS1979

Urban stormwater data management system with applications to south Florida studies

A data management system was developed to store and retrieve urban stormwater data collected from four small urban basins in south Florida. The system is event oriented in that all data from one storm are stored together on the computer file. The data include descriptive information about the storm and data on rainfall, stage, discharge, and water quality. The data management system, composed of about 20 Fortran programs, was developed to store data, retrieve tables for publication, calculate flow discharges and constituent loads, and provide for interfacing with statistical and deterministic model application programs. (Kosco-USGS)

Water-Resources Investigations Report↗

USGS1986

Analysis of fixed-station water-quality data in the Umpqua River basin, Oregon

An appraisal of surface water quality in the Umpqua River basin was made using existing monthly data collected by the Oregon Department of Environmental Quality and the U.S. Geological Survey in cooperation with the Douglas County Water Resources Survey. This appraisal was limited to interpretation of instantaneous monthly water quality data collected in the Umpqua River basin from water years 1974 to 1983. These data were used to compare water quality conditions throughout the basin and to determine if data collected from the NASQAN (National Stream Quality Accounting network) station are representative of upstream basin conditions. In general, data collected at the NASQAN station represent a composite of water quality from the North and South Umpqua Rivers. These river basins account for 82 % of the NASQAN station drainage. Water quality concentrations, loads, yields, and trends were statistically described and related to point source effluent loads and basin characteristics including geohydrology, hydrology, population, land use, and water use. Available point-and nonpoint-source data provided minimal information for determining cause-effect relations and for explaining observed trends in water quality; however, the data did indicate that the largest effluent discharges are located in the South Umpqua River basin in the Roseburg-Winston area. Instantaneous and annual flow weighted levels of specific conductance, phosphorus, organic plus ammonia nitrogen, nitrite plus nitrate, and fecal coliform bacteria are generally highest in the South Umpqua River near Roseburg. These high levels generally occur during the summer months when river flow is extremely low relative to flow in the North Umpqua River. The North Umpqua River has among the lowest constituent concentrations observed in the basin. (Lantz-PTT)

Water-Resources Investigations Report↗

USGS1987

Quality-assurance data for routine water analysis in the laboratories of the US Geological Survey for water-year 1984

The U.S. Geological Survey maintains a quality-assurance program based on the analysis of reference samples for its two water-analysis laboratories located in Atlanta, Georgia, and Denver, Colorado. Reference samples containing selected inorganic constituents are prepared at the U.S. Geological Survey's Ocala, Florida, office, disguised as routine samples, and sent daily or weekly, as appropriate, to each laboratory through other U.S. Geological Survey offices. The results are permanently stored in the National Water Data Storage and Retrieval System (WATSTORE), the U.S. Geological Survey's data base for all water data. These data are analyzed statistically for precision, bias, and comparability. The results of these statistical analyses are presented for data collected during the 1984 water year. Nutrient samples, simulated-precipitation (low-concentration level) samples, and selected pesticide samples also were submitted as samples of unknown concentrations. The results of these determinations were statistically analyzed for comparability and these data are presented. An overall evaluation of the data for water year 1984 indicated a lack of precision in the Atlanta laboratory for the determination of five constituents and in the Denver laboratory for seven constituents. A biased condition existed in the determination of twenty common constituents at both laboratories. Finally, if any constituent determined by ICP indicated bias, it tended to be positive.

Water-Resources Investigations Report↗

USGS1991

Computer software for converting ground-water and water-quality data from the National Water Information System for use in a geographic information system

A geographic information system (GIS) is computer software that provides the capabilities to rapidly display and analyze spatially referenced data. To use a GIS efficiently, software is needed that provides a link between existing spatially referenced data-base systems and the GIS. The U.S. Geological Survey (USGS) stores large amounts of spatially referenced hydrologic data in computerized data-base systems. Currently, these data-base systems are physically located on computers that are installed in many USGS offices nationwide. This report describes software that can be used quickly and easily to convert site-description and water-quality data from these data-base systems to the GIS that is used by the USGS. The software described in this report is designed to be used with the same hardware and software that is available to most USGS personnel.

Water-Resources Investigations Report↗

USGS1995

Water-quality assessment of the South Platte River basin, Colorado, Nebraska, and Wyoming: Analysis of available nutrient, suspended-sediment, and pesticide data, water years 1980-92

Nutrient, suspended-sediment, and pesticide data from surface- and ground-water sites in the South Platte River Basin for water years 1980-92 were compiled, screened, and interpreted. This activity is part of the U.S. Geological Survey's National Water Quality Assessment (NAWQA) program. The analysis of existing water-quality data provides a perspective on recent water-quality conditions in the South Platte River Basin, evaluations of the strengths and weaknesses of available data, and implications for water-quality issues and future study priorities and design. Most data analyzed were collected by the U.S. Geological Survey. Additionally, data from three local agencies were used in the analysis. A total of 3,484 samples from 54 surface-water sites and 107 wells were used in the analysis. The areal distribution of nutrient samples collected from surface-water and ground-water sites were sufficient in number and areal distribution to describe current water- quality conditions throughout the basin, but data were not sufficient to analyze factors and processes affecting water quality. However, suspended- sediment and pesticide data were sparse in their distribution with respect to time, space, and flow regime, and were sufficient only to provide a preliminary description of conditions in the basin.

Colorado, Nebraska, Wyoming↗