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At least 73 records · Page 4Linked to original sources
USGS1998

Data from selected U.S. Geological Survey National Stream Water Quality Monitoring Networks

A nationally consistent and well-documented collection of water quality and quantity data compiled during the past 30 years for streams and rivers in the United States is now available on CD-ROM and accessible over the World Wide Web. The data include measurements from two U.S. Geological Survey (USGS) national networks for 122 physical, chemical, and biological properties of water collected at 680 monitoring stations from 1962 to 1995, quality assurance information that describes the sample collection agencies, laboratories, analytical methods, and estimates of laboratory measurement error (bias and variance), and information on selected cultural and natural characteristics of the station watersheds. The data are easily accessed via user-supplied software including Web browser, spreadsheet, and word processor, or may be queried and printed according to user-specified criteria using the supplied retrieval software on CD-ROM. The water quality data serve a variety of scientific uses including research and educational applications related to trend detection, flux estimation, investigations of the effects of the natural environment and cultural sources on water quality, and the development of statistical methods for designing efficient monitoring networks and interpreting water resources data.

Water Resources Research↗
USGS2005

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

Computers & Geosciences↗
USGS2016

Model selection and assessment for multi­-species occupancy models

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

Ecology↗
USGS1960

Double-mass curves, with a section fitting curves to cyclic data

The double.-mass curve is used to check the consistency of many kinds of hydrologic data by comparing data for a single station with that of a pattern composed of the data from several other stations in the area The double-mass curve can be used to adjust inconsistent precipitation data. The graph of the cumulative data of one variable versus the cumulative data of a related variable is a straight line so long as the relation between the variables is a fixed ratio. Breaks in the double-mass curve of such variables are caused by changes in the relation between the variables. These changes may be due to changes in the method of data collection or to physical changes that affect the relation. Applications of the double-mass curve to precipitation, streamflow, and sediment data, and to precipitation-runoff relations are described. A statistical test for significance of an apparent break in the slope of the double-mass curve is described by an example. Poor correlation between the variables can prevent detection of inconsistencies in a record, but an increase in the length of record tends to offset the effect of poor correlation. The residual-mass curve, which is a modification of the double-mass curve, magnifies imperceptible breaks in the double-mass curve for detailed study. Of the several methods of fitting a smooth curve to cyclic or periodic data, the moving-arc method and the double-integration method deserve greater use in hydrology. Both methods are described in this manual. The moving-arc method has general applicability, and the double integration method is useful in fitting a curve to cycles of sinusoidal form.

Water Supply Paper↗
USGS2025

A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modelling framework called Streamflow Rank Estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modelled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall’s Tau rank correlation 0.75 with range 0.6–0.83) than after it (median Kendall’s Tau 0.59 with range 0.34 – 0.74). We found that annotation performance was generally consistent across the eleven camera sites and two individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1,000 annotation pairs. Our model’s estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modelling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

EGUSphere↗
USGS1983

Electrical and magnetic properties of rock and soil

Field and laboratory measurements have been made to determine the electrical conductivity, dielectric constant, and magnetic permeability of rock and soil in areas of interest in studies of electromagnetic pulse propagation. Conductivity is determined by making field measurements of apparent resisitivity at very low frequencies (0-20 cps), and interpreting the true resistivity of layers at various depths by curve-matching methods. Interpreted resistivity values are converted to corresponding conductivity values which are assumed to be applicable at 10^2 cps, an assumption which is considered valid because the conductivity of rock and soil is nearly constant at frequencies below 10^2 cps. Conductivity is estimated at higher frequencies (up to 10^6 cps) by using statistical correlations of three parameters obtained from laboratory measurements of rock and soil samples: conductivity at 10^2 cps, frequency and conductivity measured over the range 10^2 to 10^6 cps. Conductivity may also be estimated in this frequency range by using field measurements of water content and correlations of laboratory sample measurements of the three parameters: water content, frequency, and conductivity measured over the range 10^2 to 10^6 cps. This method is less accurate because nonrandom variation of ion concentration in natural pore water introduces error. Dielectric constant is estimated in a similar manner from field-derived conductivity values applicable at 10^2 cps and statistical correlations of three parameters obtained from laboratory measurements of samples: conductivity measured at 10^2 cps, frequency, and dielectric constant measured over the frequency range 10^2 to 10^6 cps. Dielectric constant may also be estimated from field measurements of water content and correlations of laboratory sample measurements of the three parameters: water content, frequency, and dielectric constant measured from 10^2 to 10^6 cps, but again, this method is less accurate because of variation of ion concentration of pore water. Special laboratory procedures are used to measure conductivity and dielectric constant of rock and soil samples. Electrode polarization errors are minimized by using an electrode system that is electrochemically reversible-with ions in pore water.

Open-File Report↗
USGS2013

Identifying when tagged fishes have been consumed by piscivorous predators: application of multivariate mixture models to movement parameters of telemetered fishes

Background Consumption of telemetered fishes by piscivores is problematic for telemetry studies because tag detections from the piscivore could introduce bias into the analysis of telemetry data. We illustrate the use of multivariate mixture models to estimate group membership (smolt or predator) of telemetered juvenile Chinook salmon ( Oncorhynchus tshawytscha ), juvenile steelhead trout ( O. mykiss ), striped bass ( Morone saxatilis ), smallmouth bass ( Micropterus dolomieu ) and spotted bass ( M. punctulatus ) in the Sacramento River, CA, USA. First, we estimated two types of track statistics from spatially explicit two-dimensional movement tracks of telemetered fishes: the Lévy exponent ( b ) and tortuosity ( τ ). Second, we hypothesized that the distribution of each track statistic would differ between predators and smolts. To estimate the distribution of track statistics for putative predators and smolts, we fitted a bivariate normal mixture model to the mixed distribution of track statistics. Lastly, we classified each track as a smolt or predator using parameter estimates from the mixture model to estimate the probability that each track was that of a predator or smolt. Results Tracks classified as predators exhibited movement that was tortuous and consistent with prey searching tactics, whereas tracks classified as smolts were characterized by directed, linear downstream movement. The estimated mean tortuosity was 0.565 (SD = 0.07) for predators and 0.944 (SD = 0.001) for smolts. The estimated mean Lévy exponent was 1.84 (SD = 1.23) for predators and -0.304 (SD = 1.46) for smolts. We correctly classified 90% of the Micropterus species and 72% of the striped bass as predators. For tagged smolts, 80% of Chinook salmon and 74% of steelhead trout were not classified as predators. Conclusions Mixture models proved valuable as a means to differentiate between salmonid smolts and predators that consumed salmonid smolts. However, successful application of this method requires that telemetered fishes and their predators exhibit measurable differences in movement behavior. Our approach is flexible, allows inclusion of multiple track statistics and improves upon rule-based manual classification methods.

California↗
USGS2025

Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under-monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor relative streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modeling framework called streamflow rank estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modeled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall's Tau rank correlation of 0.75, with a range of 0.6–0.83) than after it (median Kendall's Tau of 0.59, with range 0.34–0.74). We found that annotation performance was generally consistent across the 11 camera sites and 2 individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1000 annotation pairs. Our model's estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modeling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate that this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

Massachusetts↗
USGS2002

SHE analysis for biozonation of benthic foraminiferal assemblages from western arctic ocean

Benthic foraminiferal species abundance in samples from three Mendeleyev Ridge box cores were analyzed by cluster analysis and the newer method of SHE analysis. Previously, the latter technique only has been used on foraminiferal data from depth transects of modern surface sediment samples. Unlike most methods, which initially compare all possible pairs of samples, the SHE procedure results in a linear pattern if a sequence of samples are from the same statistical distribution. A change in slope indicates a statistical change in community structure and/or a change in species composition. The research reported herein is the first application of SHE for the purpose of identifying biozones in sediment core samples for the purpose of stratigraphic correlation. Both cluster analysis and the SHE method provided zonation within cores. However, the cluster method often produced clusters that were difficult to identify and also contained a mixture of samples without stratigraphic continuity. In contrast, SHE resulted in easily identifiable biozones and ensured temporal continuity within them. In general, the cluster analysis produced more zones than the SHE analysis. About 87% of the cluster zones and 64% of the SHE zones were correlated across more than one core. The average age range for correlated biozone boundaries among the three cores, based on radiocarbon dates, was 821 years using cluster analysis and 296 years using SHE. The sequential nature of the analysis, ease in choosing boundaries, and correlation of these boundaries across cores makes SHE the preferred technique.

Palaios↗
USGS2006

Magnitude and Frequency of Floods on Nontidal Streams in Delaware

Reliable estimates of the magnitude and frequency of annual peak flows are required for the economical and safe design of transportation and water-conveyance structures. This report, done in cooperation with the Delaware Department of Transportation (DelDOT) and the Delaware Geological Survey (DGS), presents methods for estimating the magnitude and frequency of floods on nontidal streams in Delaware at locations where streamgaging stations monitor streamflow continuously and at ungaged sites. Methods are presented for estimating the magnitude of floods for return frequencies ranging from 2 through 500 years. These methods are applicable to watersheds exhibiting a full range of urban development conditions. The report also describes StreamStats, a web application that makes it easy to obtain flood-frequency estimates for user-selected locations on Delaware streams. Flood-frequency estimates for ungaged sites are obtained through a process known as regionalization, using statistical regression analysis, where information determined for a group of streamgaging stations within a region forms the basis for estimates for ungaged sites within the region. One hundred and sixteen streamgaging stations in and near Delaware with at least 10 years of non-regulated annual peak-flow data available were used in the regional analysis. Estimates for gaged sites are obtained by combining the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates with regional estimates of skew and flood-frequency magnitudes. Example flood-frequency estimate calculations using the methods presented in the report are given for: (1) ungaged sites, (2) gaged locations, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Regional regression equations applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces of Delaware are presented. The equations incorporate drainage area, forest cover, impervious area, basin storage, housing density, soil type A, and mean basin slope as explanatory variables, and have average standard errors of prediction ranging from 28 to 72 percent. Additional regression equations that incorporate drainage area and housing density as explanatory variables are presented for use in defining the effects of urbanization on peak-flow estimates throughout Delaware for the 2-year through 500-year recurrence intervals, along with suggestions for their appropriate use in predicting development-affected peak flows. Additional topics associated with the analyses performed during the study are also discussed, including: (1) the availability and description of more than 30 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites, with respect to their relations with maximum 24-hour precipitation and housing density, and their use in the regional analysis; (3) calculation of the 90-percent confidence interval associated with peak-flow estimates from the regional regression equations; and (4) a comparison of flood-frequency estimates at gages used in a previous study, highlighting the effects of various improved analytical techniques.

Scientific Investigations Report↗
USGS2013

extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements

Selection of the appropriate extrapolation methods for computing the discharge in the unmeasured top and bottom parts of a moving-boat acoustic Doppler current profiler (ADCP) streamflow measurement is critical to the total discharge computation. The software tool, extrap, combines normalized velocity profiles from the entire cross section and multiple transects to determine a mean profile for the measurement. The use of an exponent derived from normalized data from the entire cross section is shown to be valid for application of the power velocity distribution law in the computation of the unmeasured discharge in a cross section. Selected statistics are combined with empirically derived criteria to automatically select the appropriate extrapolation methods. A graphical user interface (GUI) provides the user tools to visually evaluate the automatically selected extrapolation methods and manually change them, as necessary. The sensitivity of the total discharge to available extrapolation methods is presented in the GUI. Use of extrap by field hydrographers has demonstrated that extrap is a more accurate and efficient method of determining the appropriate extrapolation methods compared with tools currently (2012) provided in the ADCP manufacturers’ software.

Computers & Geosciences↗
USGS2016

A review of single-sample-based models and other approaches for radiocarbon dating of dissolved inorganic carbon in groundwater

Numerous methods have been proposed to estimate the pre-nuclear-detonation 14 C content of dissolved inorganic carbon (DIC) recharged to groundwater that has been corrected/adjusted for geochemical processes in the absence of radioactive decay ( 14 C 0 ) - a quantity that is essential for estimation of radiocarbon age of DIC in groundwater. The models/approaches most commonly used are grouped as follows: (1) single-sample-based models, (2) a statistical approach based on the observed (curved) relationship between 14 C and δ 13 C data for the aquifer, and (3) the geochemical mass-balance approach that constructs adjustment models accounting for all the geochemical reactions known to occur along a groundwater flow path. This review discusses first the geochemical processes behind each of the single-sample-based models, followed by discussions of the statistical approach and the geochemical mass-balance approach. Finally, the applications, advantages and limitations of the three groups of models/approaches are discussed. The single-sample-based models constitute the prevailing use of 14 C data in hydrogeology and hydrological studies. This is in part because the models are applied to an individual water sample to estimate the 14 C age, therefore the measurement data are easily available. These models have been shown to provide realistic radiocarbon ages in many studies. However, they usually are limited to simple carbonate aquifers and selection of model may have significant effects on 14 C 0 often resulting in a wide range of estimates of 14 C ages. Of the single-sample-based models, four are recommended for the estimation of 14 C 0 of DIC in groundwater: Pearson's model, (Ingerson and Pearson, 1964; Pearson and White, 1967), Han & Plummer's model (Han and Plummer, 2013), the IAEA model (Gonfiantini, 1972; Salem et al., 1980), and Oeschger's model (Geyh, 2000). These four models include all processes considered in single-sample-based models, and can be used in different ranges of 13 C values. In contrast to the single-sample-based models, the extended Gonfiantini & Zuppi model (Gonfiantini and Zuppi, 2003; Han et al., 2014) is a statistical approach. This approach can be used to estimate 14 C ages when a curved relationship between the 14 C and 13 C values of the DIC data is observed. In addition to estimation of groundwater ages, the relationship between 14 C and δ 13 C data can be used to interpret hydrogeological characteristics of the aquifer, e.g. estimating apparent rates of geochemical reactions and revealing the complexity of the geochemical environment, and identify samples that are not affected by the same set of reactions/processes as the rest of the dataset. The investigated water samples may have a wide range of ages, and for waters with very low values of 14 C, the model based on statistics may give more reliable age estimates than those obtained from single-sample-based models. In the extended Gonfiantini & Zuppi model, a representative system-wide value of the initial 14 C content is derived from the 14 C and δ 13 C data of DIC and can differ from that used in single-sample-based models. Therefore, the extended Gonfiantini & Zuppi model usually avoids the effect of modern water components which might retain ‘bomb’ pulse signatures. The geochemical mass-balance approach constructs an adjustment model that accounts for all the geochemical reactions known to occur along an aquifer flow path (Plummer et al., 1983; Wigley et al., 1978; Plummer et al., 1994; Plummer and Glynn, 2013), and includes, in addition to DIC, dissolved organic carbon (DOC) and methane (CH 4 ). If sufficient chemical, mineralogical and isotopic data are available, the geochemical mass-balance method can yield the most accurate estimates of the adjusted radiocarbon age. The main limitation of this approach is that complete information is necessary on chemical, mineralogical and isotopic data and these data are often limited. Failure to recognize the limitations and underlying assumptions on which the various models and approaches are based can result in a wide range of estimates of 14 C 0 and limit the usefulness of radiocarbon as a dating tool for groundwater. In each of the three generalized approaches (single-sample-based models, statistical approach, and geochemical mass-balance approach), successful application depends on scrutiny of the isotopic ( 14 C and 13 C) and chemical data to conceptualize the reactions and processes that affect the 14 C content of DIC in aquifers. The recently developed graphical analysis method is shown to aid in determining which approach is most appropriate for the isotopic and chemical data from a groundwater system.

Earth-Science Reviews↗
USGS2000

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

California, Oregon↗
USGS1998

Water-quality assessment of the Ozark Plateaus study unit, Arkansas, Kansas, Missouri, and Oklahoma — Nutrients, bacteria, organic carbon, and suspended sediment in surface water, 1993-95

Nutrient, bacteria, organic carbon, and suspended- sediment samples were collected from 1993-95 at 43 surface-water-quality sampling sites within the Ozark Plateaus National Water- Quality Assessment Program study unit. Most surface-water-quality sites have small or medium drainage basins, near-homogenous land uses (primarily agricultural or forest), and are located predominantly in the Springfield and Salem Plateaus. The water-quality data were analyzed using selected descriptive and statistical methods to determine factors affecting occurrence in streams in the study unit. Nitrogen and phosphorus fertilizer use increased in the Ozark Plateaus study unit for the period 1965-85, but the application rates are well below the national median. Fertilizer use differed substantially among the major river basins and physiographic areas in the study unit. Livestock and poultry waste is a major source of nutrient loading in parts of the study unit. The quantity of nitrogen and phosphorus from livestock and poultry wastes differed substantially among the river basins of the study unit's sampling network. Eighty six municipal sewage-treatment plants in the study unit have effluents of 0.5 million gallons per day or more (for the years 1985-91). Statistically significant differences existed in surface-water quality that can be attributed to land use, physiography, and drainage basin size. Dissolved nitrite plus nitrate, total phosphorus, fecal coliform bacteria, and dissolved organic carbon concentrations generally were larger at sites associated with agricultural basins than at sites associated with forested basins. A large difference in dissolved nitrite plus nitrate concentrations occurred between streams draining basins with agricultural land use in the Springfield and Salem Plateaus. Streams draining both small and medium agricultural basins in the Springfield Plateau had much larger concentrations than their counterparts in the Salem Plateau. Drainage basin size was not a significant factor in affecting total phosphorus, fecal coliform bacteria, or dissolved organic carbon concentrations. Suspended-sediment concentrations generally were small and indicative of the clear water in streams in the Ozark Plateaus. A comparison of the dissolved nitrite plus nitrate, total phosphorus, and fecal coliform data collected at the fixed and synoptic sites indicates that generally the data for streams draining basins of similar physiography, land-use setting, and drainage basin size group together. Many of the variations are most likely the result of differences in percent agricultural land use between the sites being compared or are discharge related. The relation of dissolved nitrite plus nitrate, total phosphorus, and fecal coliform concentration to percent agricultural land use has a strong positive 2 Water-Quality Assessment-Nutrients, Bacteria, Organic Carbon, and Suspended Sediment in Surface Water, 1993-95 correlation, with percent agricultural land use accounting for between 42 and 60 percent of the variation in the observed concentrations.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2002

Swimming performance of upstream migrant fishes: New methods, new perspectives

The ability to traverse barriers of high water velocity limits the distributions of many diadromous and other migratory fish species, and is central to effective fishway design. This dissertation provides a detailed analysis of volitional sprinting behavior of six migratory fish species (American shad Alosa sapidissima, alewife A. pseudoharengus, blueback herring A. aestivalis, striped bass Morone saxatilis , walleye Stizostedion vitreum, and white sucker Catostomus commersoni), against controlled water velocities of 1.5–4.5 m · s−1 in a large, open-channel flume. In Chapter 1, I develop models of maximum distance traversed ( Dmax) by fish ascending these flows, accounting for water velocity and other covariate effects. I then demonstrate the application of these models, using them to predict proportions of active migrants capable of traversing a range of distances and flow velocities. Chapter 2 focuses on behavior and swimming performance of American shad, analyzing covariate effects on attempt rate as well as Dmax, and formalizing how rate and distance jointly affect overall rates of passage. Models describe a complex pattern of varying responses of attempt rate and Dmax to hydraulics, temperature, effort expended on and recovery time since the previous attempt. In Chapter 3, I use the effect of swimming speed on fatigue time to calculate an optimal swimming speed that maximizes the over-ground distance fish can traverse, and hence defines their maximum ability to traverse velocity barriers. This speed reduces to a constant groundspeed within a given gait, regardless of the speed of flow. Data from all six species support this view, although only American shad exhibit a clear shift from the optimum prolonged speed to the optimum sprint speed at the predicted critical flow velocity. Throughout this dissertation I make extensive and novel use of statistical techniques developed for survival analysis to analyze and model behavioral data, both with respect to attempt rate and to D max. Chapter 4 provides an overview of these methods and demonstrates their application to a fish passage study of downstream-migrating Atlantic salmon (Salmo salar) smolts. An understanding of the principles described here will help the reader to better understand the findings of the previous three chapters.

Thesis↗
USGS2018

Statistical approach to neural network imaging of karst systems in 3D seismic reflection data

The current lack of a robust, standardized technique for geophysical mapping of karst systems can be attributed to both the complexity of the environment and prior technological limitations. Abrupt lateral variations in physical properties that are inherent to karst systems generate significant geophysical noise, challenging conventional seismic signal processing and interpretation. Modern application of neural networks to multi-attribute seismic interpretation now provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambiguity generally associated with designing neural networks for seismic object detection can be reduced via statistical analysis of the extracted attribute data. A data-driven approach to selecting the appropriate set of input seismic attributes, as well as the locations and minimum number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized neural network technique is thoroughly demonstrated using three-dimensional seismic reflection data collected from the southeastern portion of the Florida carbonate platform. Several dimensionality reduction methods are applied and the resulting karst probability models are evaluated relative to one another based on both quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, to previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multi-attribute workflow presented is capable of approximating the structural boundaries of karst systems with more accuracy and efficiency than a human counterpart or previously presented seismic interpretation schemes. This objective technique, using solely three-dimensional seismic reflection data, likely represents the most practical approach to mapping karst systems for subsequent hydrogeological modeling.

Interpretation↗
USGS1987

Resource assessments, geologic deposit models, and offshore minerals with an example of heavy-mineral sands

A resource assessment method for offshore minerals based on descriptive and grade-tonnage models is proposed. Historical development and applications of this method are summarized. Based on this approach, descriptive and quantitative deposit models for strand-line titanium placer deposits have been developed. Descriptive statistics were also computed using the worldwide deposit data set upon which the grade-tonnage models are based. Certain guidelines and limitations in applying onshore titanium deposit models to offshore assessment and exploration must, however, be observed. The descriptive model points out the specific features of strandline titanium placer deposits which can be of use in selection of areas for exploration; the grade-tonnage models display the expected size distribution for this type of deposit. Used with an estimate of expected number of deposits, this information can be applied to quantify probable values associated with deposits of this type within a given area.

NATO Science Series↗