Log-Pearson Type 3 Distribution and Its Application in Flood Frequency Analysis. III: Sample Skew and Weighted Skew Estimators

Griffis, V. W.; Stedinger, Jery R.

doi:10.1061/(asce)1084-0699(2009)14:2(121)

Cited by 50 publications

(57 citation statements)

References 20 publications

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“…The corresponding record lengths (RL) are calculated using equations from Griffis and others (2004) and Griffis and Stedinger (2009): (A2) where RL and Ĝ use appropriate subscripts for RL C and RL sys ; for example, RL C uses G Ĉ and MSE(G Ĉ ), and RL sys uses G syŝ and MSE(G syŝ ), and a(RL), b(RL), and c(RL) are variables described in the equation A2.…”

Section: Methods For Estimating Annual Exceedance-probability Discharmentioning

confidence: 99%

“…These estimated regional skewness values OLS y  are then used to calculate unbiased station-regional skewness variances using the equations reported in Griffis and Stedinger (2009). These station-regional skewness variances are based on the regional OLS estimator of the skewness coefficient instead of the station skewness estimator, which makes the weights in the subsequent steps relatively independent of the station skewness estimates.…”

Section: Ordinary Least Squares Analysismentioning

confidence: 99%

“…The approach used by EMA (Cohn and others (2001;equation 55) generates a first order estimate of the MSE G S , which should perform well when interval data are used. Another option is to use the Griffis and Stedinger (2009) formula in equation A8 (the variance is equal to the MSE), using either the systematic record length or the length of the whole historical period. However, this method does not account for censored data, and thus, can lead to inaccurate and underestimated.…”

Section: Estimating the Mean Square Error Of The Skewness Estimatormentioning

confidence: 99%

See 2 more Smart Citations

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Southard¹,

Veilleux²

2014

Scientific Investigations Report

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Section: Methods For Estimating Annual Exceedance-probability Discharmentioning

confidence: 99%

Section: Ordinary Least Squares Analysismentioning

confidence: 99%

Section: Estimating the Mean Square Error Of The Skewness Estimatormentioning

confidence: 99%

See 1 more Smart Citation

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Southard¹,

Veilleux²

2014

Scientific Investigations Report

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“…There has been an extended discussion of possible improvements in the methodology of Bulletin 17B, see [7][8][9][10][11][12]. The issue here, however, is in the possible changes due to additional data when applying the methodology used in analyzing the original data.…”

Section: Methodsmentioning

confidence: 99%

“…Basic to the procedures of [5] and [7] is the assumption that the logarithms of yearly maximal discharges have a Pearson III distributions; the mathematics of such distributions are discussed in [15] and in [9][10][11].…”

Section: Methodsmentioning

confidence: 99%

Stability of Confidence Levels for Flood Frequencies Using Additional Data

Hromadka¹,

Whitley²,

Smith

2011

JWARP

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A comparison of flood values based on prior data and new additional data at various confidence levels is given for five representative sites in Los Angeles, California. The methodology uses computer simulations to give the confidence level values and flood frequency curves. These calculations show confidence level increases roughly in the range of 5% to 10% using some quarter of a century of additional data.

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Robust flood frequency analysis: Performance of EMA with multiple Grubbs‐Beck outlier tests

Lamontagne

Stedinger

et al. 2016

Water Resources Research

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Flood frequency analysis generally involves the use of simple parametric probability distributions to smooth and extrapolate the information provided by short flood records to estimate extreme flood flow quantiles. Parametric probability distributions can have difficulty simultaneously fitting both the largest and smallest floods. A danger is that the smallest observations in a record can distort the exceedance probabilities assigned to the large floods of interest. The identification and treatment of such Potentially Influential Low Floods (PILFs) frees a fitting algorithm to describe the distribution of the larger observations. This can allow parametric flood frequency analysis to be both efficient, and also robust to deviations from the proposed probability model's lower tail. Historically, PILF identification involved subjective judgement. We propose a new multiple Grubbs‐Beck outlier test (MGBT) for objective PILF identification. MGBT PILF identification rates (akin to Type I errors) are reported for the lognormal (LN) distribution and the log‐Pearson Type III (LP3) distribution with a variety of skew coefficients. MGBT PILF identification generally matched subjective identification from a recent California flood frequency study. Monte Carlo results show that censoring of PILFs identified by the MGBT algorithm improves the extreme quantile estimator efficiency of the expected moments algorithm (EMA) for negatively skewed LP3 distributions and has little effect for zero or positive skews; simultaneously it protects against deviations from the LP3 in the lower tail, as illustrated by distorted LN examples. Thus, MGBT generally makes flood frequency analysis based on the LP3 distribution with EMA both more accurate and more robust.

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Log-Pearson Type 3 Distribution and Its Application in Flood Frequency Analysis. III: Sample Skew and Weighted Skew Estimators

Cited by 50 publications

References 20 publications

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Stability of Confidence Levels for Flood Frequencies Using Additional Data

Robust flood frequency analysis: Performance of EMA with multiple Grubbs‐Beck outlier tests

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