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T.A. Cohn

Publications and source records attributed to T.A. Cohn.

9 recordsLinked to original sources
USGS2013

A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series

he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.

Water Resources Research↗
USGS2012

Quantifying uncertainty in discharge measurements: A new approach

The accuracy of discharge measurements using velocity meters and the velocity-area method is typically assessed based on empirical studies that may not correspond to conditions encountered in practice. In this paper, a statistical approach for assessing uncertainty based on interpolated variance estimation (IVE) is introduced. The IVE method quantifies all sources of random uncertainty in the measured data. This paper presents results employing data from sites where substantial over-sampling allowed for the comparison of IVE-estimated uncertainty and observed variability among repeated measurements. These results suggest that the IVE approach can provide approximate estimates of measurement uncertainty. The use of IVE to estimate the uncertainty of a discharge measurement would provide the hydrographer an immediate determination of uncertainty and help determine whether there is a need for additional sampling in problematic river cross sections. ?? 2009 ASCE.

Conference Paper↗
USGS2005

Nature's style: Naturally trendy

Hydroclimatological time series often exhibit trends. While trend magnitude can be determined with little ambiguity, the corresponding statistical significance, sometimes cited to bolster scientific and political argument, is less certain because significance depends critically on the null hypothesis which in turn reflects subjective notions about what one expects to see. We consider statistical trend tests of hydroclimatological data in the presence of long-term persistence (LTP). Monte Carlo experiments employing FARIMA models indicate that trend tests which fail to consider LTP greatly overstate the statistical significance of observed trends when LTP is present. A new test is presented that avoids this problem. From a practical standpoint, however, it may be preferable to acknowledge that the concept of statistical significance is meaningless when discussing poorly understood systems.

Geophysical Research Letters↗
USGS2004

Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments

The recently developed expected moments algorithm (EMA) [ Cohn et al. , 1997 ] does as well as maximum likelihood estimations at estimating log‐Pearson type 3 (LP3) flood quantiles using systematic and historical flood information. Needed extensions include use of a regional skewness estimator and its precision to be consistent with Bulletin 17B . Another issue addressed by Bulletin 17B is the treatment of low outliers. A Monte Carlo study compares the performance of Bulletin 17B using the entire sample with and without regional skew with estimators that use regional skew and censor low outliers, including an extended EMA estimator, the conditional probability adjustment (CPA) from Bulletin 17B , and an estimator that uses probability plot regression (PPR) to compute substitute values for low outliers. Estimators that neglect regional skew information do much worse than estimators that use an informative regional skewness estimator. For LP3 data the low outlier rejection procedure generally results in no loss of overall accuracy, and the differences between the MSEs of the estimators that used an informative regional skew are generally modest in the skewness range of real interest. Samples contaminated to model actual flood data demonstrate that estimators which give special treatment to low outliers significantly outperform estimators that make no such adjustment.

Water Resources Research↗
USGS2003

Extension of EMA to address regional skew and low outliers

The recently developed expected moments algorithm [EMA] (Cohn et al. 1997) does as well as MLEs at estimating LP3 flood quantiles using systematic and historical information. Needed extensions include use of a regional skewness estimator and its precision to be consistent with Bulletin 17B and to make use of such hydrologic information. Another issue addressed by Bulletin 17B is the treatment of low outliers. A Monte Carlo study illustrates the performance of an extended EMA estimator compared to estimators that employ the complete data set with and without use of regional skew, conditional probability adjustment from Bulletin 17B, and an estimator that uses probability plot regression to compute substitute values for low outliers. Estimators that use a regional skew all do better than estimators that fail to use an informative regional skewness estimator. For LP3 data, the low outlier rejection procedure results in no loss of overall accuracy, and the differences between the MSEs of the estimators that used an informative regional skew were generally negligible in the skew range of real interest.

Conference Paper↗
USGS2000

A new look at natural disasters

We are paying a high price for the way we live on our beautiful but dangerous planet. Last year the world experienced deadly earthquakes in Turkey, Taiwan, Colombia and Greece; floods and devastating landslides in Venezuela; hurricanes along the Atlantic Coast that forced evacuation of millions; and numerous smaller disasters. As bad as these events were, they were not extraordinary viewed in the context of previous 20th century occurrences.

News and Trends in the Geosciences↗
USGS1997

An algorithm for computing moments-based flood quantile estimates when historical flood information is available

This paper presents the expected moments algorithm (EMA), a simple and efficient method for incorporating historical and paleoflood information into flood frequency studies. EMA can utilize three types of at-site flood information: systematic stream gage record; information about the magnitude of historical floods; and knowledge of the number of years in the historical period when no large flood occurred. EMA employs an iterative procedure to compute method-of-moments parameter estimates. Initial parameter estimates are calculated from systematic stream gage data. These moments are then updated by including the measured historical peaks and the expected moments, given the previously estimated parameters, of the below-threshold floods from the historical period. The updated moments result in new parameter estimates, and the last two steps are repeated until the algorithm converges. Monte Carlo simulations compare EMA, Bulletin 17B 's [ United States Water Resources Council , 1982] historically weighted moments adjustment, and maximum likelihood estimators when fitting the three parameters of the log-Pearson type III distribution. These simulations demonstrate that EMA is more efficient than the Bulletin 17B method, and that it is nearly as efficient as maximum likelihood estimation (MLE). The experiments also suggest that EMA has two advantages over MLE when dealing with the log-Pearson type III distribution: It appears that EMA estimates always exist and that they are unique, although neither result has been proven. EMA can be used with binomial or interval-censored data and with any distributional family amenable to method-of-moments estimation.

Water Resources Research↗
USGS1987

Use of historical information in a maximum-likelihood framework

This paper discusses flood-quantile estimators which can employ historical and paleoflood information, both when the magnitudes of historical flood peaks are known, and when only threshold-exceedance information is available. Maximum likelihood, quasi-maximum likelihood and curve fitting methods for simultaneous estimation of 1, 2 and 3 unknown parameters are examined. The information contained in a 100 yr record of historical observations, during which the flood perception threshold was near the 10 yr flood level (i.e., on average, one flood in ten is above the threshold and hence is recorded), is equivalent to roughly 43, 64 and 78 years of systematic record in terms of the improvement of the precision of 100 yr flood estimators when estimating 1, 2 and 3 parameters, respectively. With the perception threshold at the 100 yr flood level, the historical data was worth 13, 20 and 46 years of systematic data when estimating 1, 2 and 3 parameters, respectively.

Journal of Hydrology↗