STATISTICAL ANALYSIS OF DAILY WATER QUALITY DATA<sup>1</sup>

Bourodimos, E. L.; Yu, Shaw L.; Hahn, Rita A.

doi:10.1111/j.1752-1688.1974.tb00614.x

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The idea of decomposed series is discussed by Nerlove et al [1979] or Harvey [1984]. See also Bourodimos et al [1974] or Bodo [1989] for application to water quality data. We use a decomposition proposed by Ringnet Bb: Activities which occur in large number but with small effect which disappears rapidly.…”

Section: Summarizing the Modeling Of Exogenous Variables With Transfementioning

confidence: 99%

Statistical estimation and interpretation of trends in water quality time series

Zetterqvist

1991

Water Resources Research

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Three approaches to trend analysis of water quality time series are discussed: (1) seasonal model, with a test for trend based on ranks of observations, with observations assumed to be m dependent; (2) transfer function noise model, in which covariate series may be included by means of transfer functions, with the remaining noise modeled as a seasonal autoregressive moving average process; and (3) component model, with the noise decomposed into series which describe trends, and irregular and seasonal variation. Models are studied with regards to their ability to include covariate series, possibility of interpretation of trends, treatment of seasonal variation and serial dependence, and robustness for outliers. We regard the component model being the most realistic and the most informative of the three approaches. Models are applied to series of monthly phosphorus concentration in the Ljungbyån River in Southern Sweden.

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Section: Summarizing the Modeling Of Exogenous Variables With Transfementioning

confidence: 99%

Statistical estimation and interpretation of trends in water quality time series

Zetterqvist

1991

Water Resources Research

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“…Other investigators have often used DCs in waterquality studies to compute total yields and average load rates on an annual or period of interest basis (e.g., Miller, 1951;Searcy , 1959;Ledbetter and Gloyna, 1964;Bourodimos et al, 1974;Steele et al, 1974;Sherwani and Moreau, 1975;Goolsby et al, 1976;Larson et al, 1976;Lettenmaier, 1977;Simmons and Heath, 1979;Harned et al, 1981;Hirsch, 1981;Smith et al, 1982;Kircher et al, 1984;Liebermann et al, 1987;Leib et al, 1999;Bonta, 2000;Bonta and Dick, 2003). The calculations in these studies result in constituent distributions that are typically integrated to calculate an average concentration or load rate.…”

Section: Duration Curvesmentioning

confidence: 99%

INCORPORATING NATURAL VARIABILITY, UNCERTAINTY, AND RISK INTO WATER QUALITY EVALUATIONS USING DURATION CURVES¹

Bonta¹,

Cleland

2003

J American Water Resour Assoc

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Quantifying natural variability, uncertainty, and risk with minimal data is one of the greatest challenges facing those engaged in water quality evaluations, such as development of total maximum daily loads (TMDL), because of regulatory, natural, and analytical constraints. Quantification of uncertainty and variability in natural systems is illustrated using duration curves (DCs), plots that illustrate the percent of time that a particular flow rate (FDC), concentration (CDC), or load rate (LDC; “TMDL”) is exceeded, and are constructed using simple derived distributions. Duration curves require different construction methods and interpretations, depending on whether there is a statistically significant correlation between concentration (C) and flow (Q), and on the sign of the C‐Q regression slope (positive or negative). Flow DCs computed from annual runoff data vary compared with an FDC developed using all data. Percent exceedance for DCs can correspond to risk; however, DCs are not composed of independent quantities. Confidence intervals of data about a regression line can be used to develop confidence limits for the CDC and LDC. An alternate expression to a fixed TMDL is suggested as the risk of a load rate being exceeded and lying between confidence limits. Averages over partial ranges of DCs are also suggested as an alternative expression of TMDLs. DCs can be used to quantify watershed response in terms of changes in exceedances, concentrations, and load rates after implementation of best management practices.

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Water quality sampling: Some statistical considerations

Liebetrau¹

1979

Water Resources Research

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Typically, a state, regional, or national water quality (WQ) monitoring system has many objectives. Examples include (1) providing a system‐wide synopsis of WQ; (2) determining whether selected WQ parameters show gradual changes over time; (3a) detecting actual or potential WQ problems, (3b) determining the specific causes of actual problems, and (3c) assessing the effect of any corrective action; and (4) enforcing the law. While these purposes have different data requirements, they are interrelated, and data collected for any one can have value for others; for example, data collected for the listed reasons could be used for future long‐range planning. Each objective makes certain demands of a WQ sampling network, and these in turn have implications concerning sampling design and statistical analysis of resulting data. In a sampling scheme flexible enough to encompass multiple objectives, data sets collected for answering questions arising under points 1–4 need not all have similar characteristics; consequently, a variety of statistical methods are needed. A good sampling design is important for achieving the first two objectives. A working definition of ‘water quality’ is given initially, and this is followed by the definition of a sampling population. The set of all segments of the drainage network serves as the population for several sampling plans. In particular, the use of probability sampling to design a network of synoptic sampling stations is discussed in detail. Finally, sequential statistical procedures are applied to a specific problem of type 3c. In terms of the amount of data required, sequential methods are quite efficient for detecting changes of a specified magnitude in some WQ parameter.

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STATISTICAL ANALYSIS OF DAILY WATER QUALITY DATA¹

Cited by 5 publications

References 11 publications

Statistical estimation and interpretation of trends in water quality time series

Statistical estimation and interpretation of trends in water quality time series

INCORPORATING NATURAL VARIABILITY, UNCERTAINTY, AND RISK INTO WATER QUALITY EVALUATIONS USING DURATION CURVES¹

Water quality sampling: Some statistical considerations

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STATISTICAL ANALYSIS OF DAILY WATER QUALITY DATA1

Cited by 5 publications

References 11 publications

Statistical estimation and interpretation of trends in water quality time series

Statistical estimation and interpretation of trends in water quality time series

INCORPORATING NATURAL VARIABILITY, UNCERTAINTY, AND RISK INTO WATER QUALITY EVALUATIONS USING DURATION CURVES1

Water quality sampling: Some statistical considerations

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STATISTICAL ANALYSIS OF DAILY WATER QUALITY DATA¹

INCORPORATING NATURAL VARIABILITY, UNCERTAINTY, AND RISK INTO WATER QUALITY EVALUATIONS USING DURATION CURVES¹