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At least 253 records · Page 14Linked to original sources
USGS2008

Simulation of the Quantity, Variability, and Timing of Streamflow in the Dennys River Basin, Maine, by Use of a Precipitation-Runoff Watershed Model

The U.S. Geological Survey (USGS), in cooperation with the Maine Department of Marine Resources Bureau of Sea Run Fisheries and Habitat, began a study in 2004 to characterize the quantity, variability, and timing of streamflow in the Dennys River. The study included a synoptic summary of historical streamflow data at a long-term streamflow gage, collecting data from an additional four short-term streamflow gages, and the development and evaluation of a distributed-parameter watershed model for the Dennys River Basin. The watershed model used in this investigation was the USGS Precipitation-Runoff Modeling System (PRMS). The Geographic Information System (GIS) Weasel was used to delineate the Dennys River Basin and subbasins and derive parameters for their physical geographic features. Calibration of the models used in this investigation involved a four-step procedure in which model output was evaluated against four calibration data sets using computed objective functions for solar radiation, potential evapotranspiration, annual and seasonal water budgets, and daily streamflows. The calibration procedure involved thousands of model runs and was carried out using the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The SCE method reliably produces satisfactory solutions for large, complex optimization problems. The primary calibration effort went into the Dennys main stem watershed model. Calibrated parameter values obtained for the Dennys main stem model were transferred to the Cathance Stream model, and a similar four-step SCE calibration procedure was performed; this effort was undertaken to determine the potential to transfer modeling information to a nearby basin in the same region. The calibrated Dennys main stem watershed model performed with Nash-Sutcliffe efficiency (NSE) statistic values for the calibration period and evaluation period of 0.79 and 0.76, respectively. The Cathance Stream model had an NSE value of 0.68. The Dennys River Basin models make use of limited streamflow-gaging station data and provide information to characterize subbasin hydrology. The calibrated PRMS watershed models of the Dennys River Basin provide simulated daily streamflow time series from October 1, 1985, through September 30, 2006, for nearly any location within the basin. These models enable natural-resources managers to characterize the timing and quantity of water moving through the basin to support many endeavors including geochemical calculations, water-use assessment, Atlantic salmon population dynamics and migration modeling, habitat modeling and assessment, and other resource-management scenario evaluations. Characterizing streamflow contributions from subbasins in the basin and the relative amounts of surface- and ground-water contributions to streamflow throughout the basin will lead to a better understanding of water quantity and quality in the basin. Improved water-resources information will support Atlantic salmon protection efforts.

Scientific Investigations Report↗
USGS2015

Estimation of unaltered daily mean streamflow at ungaged streams of New York, excluding Long Island, water years 1961-2010

The lakes, rivers, and streams of New York State provide an essential water resource for the State. The information provided by time series hydrologic data is essential to understanding ways to promote healthy instream ecology and to strengthen the scientific basis for sound water management decision making in New York. The U.S. Geological Survey, in cooperation with The Nature Conservancy and the New York State Energy Research and Development Authority, has developed the New York Streamflow Estimation Tool to estimate a daily mean hydrograph for the period from October 1, 1960, to September 30, 2010, at ungaged locations across the State. The New York Streamflow Estimation Tool produces a complete estimated daily mean time series from which daily flow statistics can be estimated. In addition, the New York Streamflow Estimation Tool provides a means for quantitative flow assessments at ungaged locations that can be used to address the objectives of the Clean Water Act—to restore and maintain the chemical, physical, and biological integrity of the Nation’s waters. The New York Streamflow Estimation Tool uses data from the U.S. Geological Survey streamflow network for selected streamgages in New York (excluding Long Island) and surrounding States with shared hydrologic boundaries, and physical and climate basin characteristics to estimate the natural unaltered streamflow at ungaged stream locations. The unaltered streamflow is representative of flows that are minimally altered by regulation, diversion, or mining, and other anthropogenic activities. With the streamflow network data, flow-duration exceedance probability equations were developed to estimate unaltered streamflow exceedance probabilities at an ungaged location using a methodology that equates streamflow as a percentile from a flow-duration curve for a particular day at a hydrologically similar reference streamgage with streamflow as a percentile from the flow-duration curve for the same day at an ungaged location. The reference streamgage is selected using map correlation, a geostatistical method in which variogram models are developed that correlate streamflow at one streamgage with streamflows at all other locations in the study area. Regression equations used to predict 17 flow-duration exceedance probabilities were developed to estimate the flow-duration curves at ungaged locations for New York using geographic information system-derived basin characteristics. A graphical user interface, with an integrated spreadsheet summary report, has been developed to estimate and display the daily mean streamflows and statistics and to evaluate different water management or water withdrawal scenarios with the estimated monthly data. This package of regression equations, U.S. Geological Survey streamgage data, and spreadsheet application produces an interactive tool to estimate an unaltered daily streamflow hydrograph and streamflow statistics at ungaged sites in New York. Among other uses, the New York Streamflow Estimation Tool can assist water managers with permitting water withdrawals, implementing habitat protection, estimating contaminant loads, or determining the potential affect from chemical spills.

New York↗
USGS2002

Techniques for estimating the magnitude and frequency of floods in rural basins of South Carolina, 1999

Data from 167 streamflow-gaging stations in or near South Carolina with 10 or more years of record through September 30, 1999, were used to develop two methods for estimating the magnitude and frequency of floods in South Carolina for rural ungaged basins that are not significantly affected by regulation. Floodfrequency estimates for 54 gaged sites in South Carolina were computed by fitting the water-year peak flows for each site to a log-Pearson Type III distribution. As part of the computation of flood-frequency estimates for gaged sites, new values for generalized skew coefficients were developed. Flood-frequency analyses also were made for gaging stations that drain basins from more than one physiographic province. The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, updated these data from previous flood-frequency reports to aid officials who are active in floodplain management as well as those who design bridges, culverts, and levees, or other structures near streams where flooding is likely to occur. Regional regression analysis, using generalized least squares regression, was used to develop a set of predictive equations that can be used to estimate the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence- interval flows for rural ungaged basins in the Blue Ridge, Piedmont, upper Coastal Plain, and lower Coastal Plain physiographic provinces of South Carolina. The predictive equations are all functions of drainage area. Average errors of prediction for these regression equations ranged from -16 to 19 percent for the 2-year recurrence-interval flow in the upper Coastal Plain to -34 to 52 percent for the 500-year recurrenceinterval flow in the lower Coastal Plain. A region-of-influence method also was developed that interactively estimates recurrence- interval flows for rural ungaged basins in the Blue Ridge of South Carolina. The region-of-influence method uses regression techniques to develop a unique relation between flow and basin characteristics for an individual watershed. This, then, can be used to estimate flows at ungaged sites. Because the computations required for this method are somewhat complex, a computer application was developed that performs the computations and compares the predictive errors for this method. The computer application includes the option of using the region-of-influence method, or the generalized least squares regression equations from this report to compute estimated flows and errors of prediction specific to each ungaged site. From a comparison of predictive errors using the region-of-influence method with those computed using the regional regression method, the region-of-influence method performed systematically better only in the Blue Ridge and is, therefore, not recommended for use in the other physiographic provinces. Peak-flow data for the South Carolina stations used in the regionalization study are provided in appendix A, which contains gaging station information, log- Pearson Type III statistics, information on stage-flow relations, and water-year peak stages and flows. For informational purposes, water-year peak-flow data for stations on regulated streams in South Carolina also are provided in appendix D. Other information pertaining to the regulated streams is provided in the text of the report.

South Carolina↗
USGS2010

Environmental conditions affecting the efficiency and efficacy of piscicides for use in nonnative fish eradication

Conservation of native fish is a pressing issue for fisheries managers. Conservation efforts often require eliminating threats posed by nonnative fish by eradicating them with piscicides. The piscicides rotenone and antimycin are used for eradication but their application is often inefficient or ineffective. My goal was to increase the efficiency and efficacy of nonnative fish eradication using piscicides. I identified environmental conditions affecting piscicide application, researched methods to overcome these problems, and provided tools that piscicide applicators can use to make piscicide application more efficient and effective. Rotenone and antimycin were exposed to varying levels of sunlight, turbulence, and dissolved organic matter (DOM) to determine the effect these environmental conditions have on piscicides. Bioassay fish were used to determine the toxicity of the piscicides. Sunlight and turbulence affected rotenone and antimycin but DOM did not. Increasing the concentration of chemical can increase the resistance to the effects of these environmental conditions; however, the effects of these conditions are considerable in natural settings. Observations of bioassay fish in stream applications of rotenone were used to develop a statistical model to predict the persistence of the piscicide. The model can be used to predict rotenone persistence in small montane streams and to estimate where rotenone concentrations need to be fortified. I measured the mixing rate of a chemical plume in different channel morphologies and at center or edge applications. Center application had a significantly shorter mixing distance than edge application, but mixing distance was not different among meandering, straight, and riffle/pool morphologies. Application of my findings will increase the efficiency and efficacy of native fish conservation using piscicides.

Thesis↗
USGS1980

Chemical and physical characteristics of precipitation at selected sites in Florida

Infrequent sampling of precipitation in Florida has been conducted by the U.S. Geological Survey since 1965. A summary of the historical data from 24 sites throughout Florida indicate that the principal ionic composition of atmospheric precipitation samples is calcium-sodium and bicarbonate-chloride with an average specific conductance of 32 micromhos per centimeter at 25 C. Historically, much of the sampling focused on primary nutrients and selected trace elements. Historical data indicate that nitrogen and phosphorus concentrations averaged 1.1 and 0.1 milligrams per liter, respectively. The limited trace metal data indicate that motor-vehicle activity may have a significant impact on local precipitation quality. Lead, for example, was measured in concentrations of as much as 2,400 micrograms per liter in samples collected in a highly populated, commerical area in south Florida. Statistical testing indicated that most major inorganic constituents, primary nutrients, and trace metals were significantly differnet among the sampling sites. The pH data indicated a range of about 5.0 to 7.0, but only limited pH data were collected and analyzed at the historical sites in such a timely manner as to represent pH conditions of the atmospheric precipitation during active rainfall. A critical review of the historical data suggested that while they may reflect local atmospheric quality conditions they likely do not define baseline conditions from a regional perspective. The application of these data were generally limited regarding regional extrapolation due to lack of standardization of sampling techniques, variable methods of sample preservation, nonuniform sampling intervals, variable sample sizes, and different periods of record. (USGS)

Water-Resources Investigations Report↗
USGS2014

A probabilistic method for constructing wave time-series at inshore locations using model scenarios

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

Coastal Engineering↗
USGS2018

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data

Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an “as-received” basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.

Indiana↗
USGS2013

Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red–blue ratio (673 nm/434 nm) and Fe 3+ -bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400–1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows that the relationship between SWIR multispectral imaging data and APXS- and Mössbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of μm) and MB measurements (hundreds of μm). Results from the upcoming Mars Science Laboratory (MSL) rover’s ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes.

Icarus↗
USGS2022

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height ( H s ), mean wave period ( T m ) and mean wave direction ( θ m ) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications.

Scientific Data↗
USGS1984

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Conference Paper↗
USGS1998

Bayesian statistics applied to the location of the source of explosions at Stromboli Volcano, Italy

We present a method for determining the location and spatial extent of the source of explosions at Stromboli Volcano, Italy, based on a Bayesian inversion of the slowness vector derived from frequency-slowness analyses of array data. The method searches for source locations that minimize the error between the expected and observed slowness vectors. For a given set of model parameters, the conditional probability density function of slowness vectors is approximated by a Gaussian distribution of expected errors. The method is tested with synthetics using a five-layer velocity model derived for the north flank of Stromboli and a smoothed velocity model derived from a power-law approximation of the layered structure. Application to data from Stromboli allows for a detailed examination of uncertainties in source location due to experimental errors and incomplete knowledge of the Earth model. Although the solutions are not constrained in the radial direction, excellent resolution is achieved in both transverse and depth directions. Under the assumption that the horizontal extent of the source does not exceed the crater dimension, the 90% confidence region in the estimate of the explosive source location corresponds to a small volume extending from a depth of about 100 m to a maximum depth of about 300 m beneath the active vents, with a maximum likelihood source region located in the 120- to 180-m-depth interval.

Stromboli Volcano↗
USGS2023

A generalized application of the catch-curve regression with comparisons of adult mortality and year-class strength between hatchery-stocked and wild-reared lake trout in US waters of Lake Huron

The recently developed approach to estimating the instantaneous total mortality of coded-wire-tagged lake trout ( Salvelinus namaycush ) is generally applicable to catch-at-age data. We further formalized the technique to objectively incorporate the year-class and year effects into the model structure of catch-curve regression. We used this new method to compare adult mortality and year-class strength between the hatchery-stocked and wild-reared lake trout in US waters of Lake Huron, one of the Laurentian Great Lakes. Model comparisons showed no difference in adult mortality between the hatchery-stocked and wild-reared lake trout. Based on 95% confidence intervals, the estimate of adult mortality using the simple catch-curve regression with average number-at-age was not statistically different from the estimate using the linear mixed model with individual number-at-age of multiple year-classes and sampling years. The linear mixed model, however, also quantified lake trout year-class strength and indicated that since 2003, the increases in recruitment of wild-reared lake trout did not fully compensate for the rapid declines in recruitment of hatchery-stocked lake trout in Lake Huron.

Lake Huron↗
USGS2009

A Comparison of Turbidity-Based and Streamflow-Based Estimates of Suspended-Sediment Concentrations in Three Chesapeake Bay Tributaries

Fluvial transport of sediment into the Chesapeake Bay estuary is a persistent water-quality issue with major implications for the overall health of the bay ecosystem. Accurately and precisely estimating the suspended-sediment concentrations (SSC) and loads that are delivered to the bay, however, remains challenging. Although manual sampling of SSC produces an accurate series of point-in-time measurements, robust extrapolation to unmeasured periods (especially highflow periods) has proven to be difficult. Sediment concentrations typically have been estimated using regression relations between individual SSC values and associated streamflow values; however, suspended-sediment transport during storm events is extremely variable, and it is often difficult to relate a unique SSC to a given streamflow. With this limitation for estimating SSC, innovative approaches for generating detailed records of suspended-sediment transport are needed. One effective method for improved suspended-sediment determination involves the continuous monitoring of turbidity as a surrogate for SSC. Turbidity measurements are theoretically well correlated to SSC because turbidity represents a measure of water clarity that is directly influenced by suspended sediments; thus, turbidity-based estimation models typically are effective tools for generating SSC data. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program and Virginia Department of Environmental Quality, initiated continuous turbidity monitoring on three major tributaries of the bay - the James, Rappahannock, and North Fork Shenandoah Rivers - to evaluate the use of turbidity as a sediment surrogate in rivers that deliver sediment to the bay. Results of this surrogate approach were compared to the traditionally applied streamflow-based approach for estimating SSC. Additionally, evaluation and comparison of these two approaches were conducted for nutrient estimations. Results demonstrate that the application of turbidity-based estimation models provides an improved method for generating a continuous record of SSC, relative to the classical approach that uses streamflow as a surrogate for SSC. Turbidity-based estimates of SSC were found to be more accurate and precise than SSC estimates from streamflow-based approaches. The turbidity-based SSC estimation models explained 92 to 98 percent of the variability in SSC, while streamflow-based models explained 74 to 88 percent of the variability in SSC. Furthermore, the mean absolute error of turbidity-based SSC estimates was 50 to 87 percent less than the corresponding values from the streamflow-based models. Statistically significant differences were detected between the distributions of residual errors and estimates from the two approaches, indicating that the turbidity-based approach yields estimates of SSC with greater precision than the streamflow-based approach. Similar improvements were identified for turbidity-based estimates of total phosphorus, which is strongly related to turbidity because total phosphorus occurs predominantly in particulate form. Total nitrogen estimation models based on turbidity and streamflow generated estimates of similar quality, with the turbidity-based models providing slight improvements in the quality of estimations. This result is attributed to the understanding that nitrogen transport is dominated by dissolved forms that relate less directly to streamflow and turbidity. Improvements in concentration estimation resulted in improved estimates of load. Turbidity-based suspended-sediment loads estimated for the James River at Cartersville, VA, monitoring station exhibited tighter confidence interval bounds and a coefficient of variation of 12 percent, compared with a coefficient of variation of 38 percent for the streamflow-based load.

Scientific Investigations Report↗
USGS2007

Application of acoustic doppler velocimeters for streamflow measurements

The U.S. Geological Survey (USGS) principally has used Price AA and Price pygmy mechanical current meters for measurement of discharge. New technologies have resulted in the introduction of alternatives to the Price meters. One alternative, the FlowTracker acoustic Doppler velocimeter, was designed by SonTek/YSI to make streamflow measurements in wadeable conditions. The device measures a point velocity and can be used with standard midsection method algorithms to compute streamflow. The USGS collected 55 quality-assurance measurements with the FlowTracker at 43 different USGS streamflow-gaging stations across the United States, with mean depths from 0.05to0.67m, mean velocities from 13 to 60 cm/s, and discharges from 0.02 to 12.4m3/s. These measurements were compared with Price mechanical current meter measurements. Analysis of the comparisons shows that the FlowTracker discharges were not statistically different from the Price meter discharges at a 95% confidence level. ?? 2007 ASCE.

Journal of Hydraulic Engineering↗
USGS2014

A landscape-based reconnaissance survey of estrogenic activity in streams of the upper Potomac, upper James,and Shenandoah Rivers, USA

Endocrine-disrupting compounds (EDCs) are becoming of increasing concern in waterways of the USA and worldwide. What remains poorly understood, however, is how prevalent these emerging contaminants are in the environment and what methods are best able to determine landscape sources of EDCs. We describe the development of a spatially structured sampling design and a reconnaissance survey of estrogenic activity along gradients of land use within sub-watersheds. We present this example as a useful approach for state and federal agencies with an interest in identifying locations potentially impacted by EDCs that warrant more intensive, focused research. Our study confirms the importance of agricultural activities on levels of a measured estrogenic equivalent (E2Eq) and also highlights the importance of other potential sources of E2Eq in areas where intensive agriculture is not the dominant land use. Through application of readily available geographic information system (GIS) data, coupled with spatial statistical analysis, we demonstrate the correlation of specific land use types to levels of estrogenic activity across a large area in a consistent and unbiased manner.

Environmental Monitoring and Assessment↗
USGS2016

Statistical comparison of methods for estimating sediment thickness from Horizontal-to-Vertical Spectral Ratio (HVSR) seismic methods: An example from Tylerville, Connecticut, USA

Determining sediment thickness and delineating bedrock topography are important for assessing groundwater availability and characterizing contamination sites. In recent years, the horizontal-to-vertical spectral ratio (HVSR) seismic method has emerged as a non-invasive, cost-effective approach for estimating the thickness of unconsolidated sediments above bedrock. Using a three-component seismometer, this method uses the ratio of the average horizontal- and vertical-component amplitude spectrums to produce a spectral ratio curve with a peak at the fundamental resonance frequency. The HVSR method produces clear and repeatable resonance frequency peaks when there is a sharp contrast (>2:1) in acoustic impedance at the sediment/bedrock boundary. Given the resonant frequency, sediment thickness can be determined either by (1) using an estimate of average local sediment shear-wave velocity or by (2) application of a power-law regression equation developed from resonance frequency observations at sites with a range of known depths to bedrock. Two frequently asked questions about the HVSR method are (1) how accurate are the sediment thickness estimates? and (2) how much do sediment thickness/bedrock depth estimates change when using different published regression equations? This paper compares and contrasts different approaches for generating HVSR depth estimates, through analysis of HVSR data acquired in the vicinity of Tylerville, Connecticut, USA.

Connecticut↗
USGS2000

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

Water-Resources Investigations Report↗
USGS2006

A Streamflow Statistics (StreamStats) Web Application for Ohio

A StreamStats Web application was developed for Ohio that implements equations for estimating a variety of streamflow statistics including the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year peak streamflows, mean annual streamflow, mean monthly streamflows, harmonic mean streamflow, and 25th-, 50th-, and 75th-percentile streamflows. StreamStats is a Web-based geographic information system application designed to facilitate the estimation of streamflow statistics at ungaged locations on streams. StreamStats can also serve precomputed streamflow statistics determined from streamflow-gaging station data. The basic structure, use, and limitations of StreamStats are described in this report. To facilitate the level of automation required for Ohio's StreamStats application, the technique used by Koltun (2003)1 for computing main-channel slope was replaced with a new computationally robust technique. The new channel-slope characteristic, referred to as SL10-85, differed from the National Hydrography Data based channel slope values (SL) reported by Koltun (2003)1 by an average of -28.3 percent, with the median change being -13.2 percent. In spite of the differences, the two slope measures are strongly correlated. The change in channel slope values resulting from the change in computational method necessitated revision of the full-model equations for flood-peak discharges originally presented by Koltun (2003)1. Average standard errors of prediction for the revised full-model equations presented in this report increased by a small amount over those reported by Koltun (2003)1, with increases ranging from 0.7 to 0.9 percent. Mean percentage changes in the revised regression and weighted flood-frequency estimates relative to regression and weighted estimates reported by Koltun (2003)1 were small, ranging from -0.72 to -0.25 percent and -0.22 to 0.07 percent, respectively.

Scientific Investigations Report↗