Data Analysis for Scientists and Engineers.

Morgan, Ryan; Meyer, S. L.

doi:10.2307/2346528

Cited by 4 publications

(8 citation statements)

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“…(2002):

\mathrm{Q}=\frac{{\mathrm{Q}}_{Inj}\left({C}_{Inj}-{C}_{o}\right)}{\left({C}_{Site}-{C}_{o}\right)}

where Q is the discharge at a given stream sampling location, Q Inj is the volumetric rate of injection of the NaBr or KBr solution to the stream, C Inj is the Br − concentration of the injected tracer solution, and C o is the background Br − concentration in the stream. The uncertainty of Br − derived stream discharge calculations are based on standard methods of propagating uncertainty (Meyer, 1975). Additional information on methods and data can be found in supporting information (Text S3 in Supporting Information , Data Set ).…”

Section: Methodsmentioning

confidence: 99%

“…Stream discharge change (i.e., groundwater flux) per unit stream length was calculated as the slope of a linear regression through at least five Br − measurement stations along a given study reach using Q (m 3 d −1 ) as the y component and stream distance, L (m), as the x component such that the slope is equal to dQ/dL [m 3 d −1 m −1 ]. The uncertainty (error) of seepage flux from ASM transects was evaluated using standard methods considering error propagation (Kline, 1985; Meyer, 1975; Peters et al., 1974). The uncertainty (error) of seepage flux from stream discharge measurements was calculated as the uncertainty of the slope using the LINEST function in Excel (Morrison, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…The linear actuator lowers the probe until it contacts the water and the water level is determined with a precision of ±0.05 mm. The seepage flux measured from a single ASM point is the mean of three tests conducted over a two-hour period and the uncertainty (error) for a given point is the error of all three tests propagated using standard methods (Meyer, 1975). The seepage meter and the uncertainty of calculated q is discussed in detail in Solomon et al (2020).…”

Section: Measuring Groundwater Seepage Flux (Q)mentioning

confidence: 99%

“…Stream discharge was calculated from measured Br − concentrations through a mass-balance equation modified from Kimball et al (2002): (Meyer, 1975). Additional information on methods and data can be found in supporting information (Text S3 in Supporting Information S1, Data Set S1).…”

Section: Stream Discharge From Bromide Tracer Dilutionmentioning

confidence: 99%

“…The side hole was plugged with a rubber stopper, the tube was filled with stream water, and the water level decline was measured along the clear, graduated tube a minimum of five times during a single falling head test. Tests generally lasted for approximately 10 min, but up to 1 hr for points with lower K. The uncertainty (error) of K was evaluated using standard methods considering error propagation (Kline, 1985;Meyer, 1975;Peters et al, 1974) and discussed by Genereux et al (2008) and Leahy (2007).…”

Section: Measuring Hydraulic Conductivitymentioning

confidence: 99%

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Using Automated Seepage Meters to Quantify the Spatial Variability and Net Flux of Groundwater to a Stream

Humphrey

Solomon

Genereux

et al. 2022

Water Resources Research

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We utilized 251 measurements from a recently developed automated seepage meter (ASM) in streambeds in the Nebraska Sand Hills, USA to investigate the small‐scale spatial variability of groundwater seepage flux (q) and the ability of the ASM to estimate mean q at larger scales. Small‐scale spatial variability of q was analyzed in five dense arrays, each covering an area of 13.5–28.0 m2 (169 total point measurements). Streambed vertical hydraulic conductivity (K) was also measured. Results provided: (a) high‐resolution contour plots of q and K, (b) anisotropic semi‐variograms demonstrating greater correlation scales of q and K along the stream length than across the stream width, and (c) the number of rows of points (perpendicular to streamflow) needed to represent the groundwater flux of areas up to 28.0 m2. The findings suggest that representative streambed measurements are best conducted perpendicular to streamflow to accommodate larger seepage flux heterogeneity in this direction and minimize sampling redundancy. To investigate the ASM's ability to produce accurate mean q at larger scales, seepage meters were deployed in four stream reaches (170–890 m), arranged in three to six transects (three to eight points each) per reach across the channel. In each reach, the mean seepage flux from ASMs was compared to the seepage flux from bromide tracer dilution. Agreement between the two methods indicates the viability of a modest number of seepage meter measurements to determine the overall groundwater flux to the stream and can guide sampling for solutes and environmental tracers.

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“…(2002):

\mathrm{Q}=\frac{{\mathrm{Q}}_{Inj}\left({C}_{Inj}-{C}_{o}\right)}{\left({C}_{Site}-{C}_{o}\right)}

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Measuring Groundwater Seepage Flux (Q)mentioning

confidence: 99%

Section: Stream Discharge From Bromide Tracer Dilutionmentioning

confidence: 99%

Section: Measuring Hydraulic Conductivitymentioning

confidence: 99%

See 3 more Smart Citations

Using Automated Seepage Meters to Quantify the Spatial Variability and Net Flux of Groundwater to a Stream

Humphrey

Solomon

Genereux

et al. 2022

Water Resources Research

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Personality of place: Regional psychosocial characteristics of economic activity

Cuomo

Davis

Shapiro

et al. 2020

The Social Science Journal

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Increasingly, social scientists are recognizing the limitations of traditional measures (e.g., geographic, demographic, economic) when trying to explain differing regional prosperity outcomes. This research seeks to understand how regions' differing personalities can help describe economic variance. We test this by employing least squares linear regression on an exploratory battery of 16 psychosocial variables (the "Big 5" personality profiles, plus other General Social Survey items) and four dependent variables of economic output: per capita income, employment rates, income mobility, and rates of entrepreneurship. All items, aggregated at the county level across the US, exhibited a unique constellation of relationships, emphasizing the great need for more work on the economic impact of what we coin the personality of place.

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Analysis and Interpretation of the Cramér-Rao Lower-Bound in Astrometry: One-Dimensional Case

Mendez

Silva

Lobos

2013

Publications of the Astronomical Society of the Pacific

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In this paper we explore the maximum precision attainable in the location of a point source imaged by a pixel array detector in the presence of a background, as a function of the detector properties. For this we use a well-known result from parametric estimation theory, the so-called Cramér-Rao lower bound. We develop the expressions in the 1-dimensional case of a linear array detector in which the only unknown parameter is the source position. If the object is oversampled by the detector, analytical expressions can be obtained for the Cramér-Rao limit that can be readily used to estimate the limiting precision of an imaging system, and which are very useful for experimental (detector) design, observational planning, or performance estimation of data analysis software: In particular, we demonstrate that for background-dominated sources, the maximum astrometric precision goes as B/F 2 , where B is the background in one pixel, and F is the total flux of the source, while when the background is negligible, this precision goes as F −1 . We also explore the dependency of the astrometric precision on: (1) the size of the source (as imaged by the detector), (2) the pixel detector size, and (3) the effect of source de-centering. Putting these results into context, the theoretical Cramér-Rao lower bound is compared to both groundas well as spaced-based astrometric results, indicating that current techniques approach this limit very closely. It is furthermore demonstrated that practical astrometric estimators like maximum likelihood or least-squares techniques can not formally reach the Cramér-Rao bound, but that they approach this limit in the 1-dimensional case very tightly, for a wide range of S/N of the source. Our results indicate that we have found in the Cramér-Rao lower variance bound a very powerful astrometric "benchmark" estimator concerning the maximum expected positional precision for a point source, given a prescription for the source, the background, the detector characteristics, and the detection process.

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Data Analysis for Scientists and Engineers.

Abstract: Data Analysis for Scientists and Engineers. By Stuart L. Meyer. New York and London, Wiley, 1975. 513 p. 1014″. £9·15.

Cited by 4 publications

References 0 publications

Using Automated Seepage Meters to Quantify the Spatial Variability and Net Flux of Groundwater to a Stream

Using Automated Seepage Meters to Quantify the Spatial Variability and Net Flux of Groundwater to a Stream

Personality of place: Regional psychosocial characteristics of economic activity

Analysis and Interpretation of the Cramér-Rao Lower-Bound in Astrometry: One-Dimensional Case

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