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J.R. Stedinger

Publications and source records attributed to J.R. Stedinger.

7 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↗
USGS2011

Regional skew for California, and flood frequency for selected sites in the Sacramento-San Joaquin River Basin, based on data through water year 2006

Improved flood-frequency information is important throughout California in general and in the Sacramento-San Joaquin River Basin in particular, because of an extensive network of flood-control levees and the risk of catastrophic flooding. A key first step in updating flood-frequency information is determining regional skew. A Bayesian generalized least squares (GLS) regression method was used to derive a regional-skew model based on annual peak-discharge data for 158 long-term (30 or more years of record) stations throughout most of California. The desert areas in southeastern California had too few long-term stations to reliably determine regional skew for that hydrologically distinct region; therefore, the desert areas were excluded from the regional skew analysis for California. Of the 158 long-term stations used to determine regional skew, 145 have minimally regulated annual-peak discharges, and 13 stations are dam sites for which unregulated peak discharges were estimated from unregulated daily maximum discharge data furnished by the U.S. Army Corp of Engineers. Station skew was determined by using an expected moments algorithm (EMA) program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual peak-discharge data. The Bayesian GLS regression method previously developed was modified because of the large cross correlations among concurrent recorded peak discharges in California and the use of censored data and historical flood information with the new expected moments algorithm. In particular, to properly account for these cross-correlation problems and develop a suitable regression model and regression diagnostics, a combination of Bayesian weighted least squares and generalized least squares regression was adopted. This new methodology identified a nonlinear function relating regional skew to mean basin elevation. The regional skew values ranged from -0.62 for a mean basin elevation of zero to 0.61 for a mean basin elevation of 11,000 feet. This relation between skew and elevation reflects the interaction of snow with rain, which increases with increased elevation. The equivalent record length for the new regional skew ranges from 52 to 65 years of record, depending upon mean basin elevation. The old regional skew map in Bulletin 17B, published by the Hydrology Subcommittee of the Interagency Advisory Committee on Water Data (1982), reported an equivalent record length of only 17 years. The newly developed regional skew relation for California was used to update flood frequency for the 158 sites used in the regional skew analysis as well as 206 selected sites in the Sacramento-San Joaquin River Basin. For these sites, annual-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years were determined on the basis of data through water year 2006. The expected moments algorithm was used for determining the magnitude and frequency of floods at gaged sites by using regional skew values and using the basic approach outlined in Bulletin

Scientific Investigations Report↗
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↗
USGS1989

An operational GLS model for hydrologic regression

Recent Monte Carlo studies have documented the value of generalized least squares (GLS) procedures to estimate empirical relationships between streamflow statistics and physiographic basin characteristics. This paper presents a number of extensions of the GLS method that deal with realities and complexities of regional hydrologic data sets that were not addressed in the simulation studies. These extensions include: (1) a more realistic model of the underlying model errors; (2) smoothed estimates of cross correlation of flows; (3) procedures for including historical flow data; (4) diagnostic statistics describing leverage and influence for GLS regression; and (5) the formulation of a mathematical program for evaluating future gaging activities.

Journal of Hydrology↗
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↗
USGS1987

Regional regression of flood characteristics employing historical information

Streamflow gauging networks provide hydrologic information for use in estimating the parameters of regional regression models. The regional regression models can be used to estimate flood statistics, such as the 100 yr peak, at ungauged sites as functions of drainage basin characteristics. A recent innovation in regional regression is the use of a generalized least squares (GLS) estimator that accounts for unequal station record lengths and sample cross correlation among the flows. However, this technique does not account for historical flood information. A method is proposed here to adjust this generalized least squares estimator to account for possible information about historical floods available at some stations in a region. The historical information is assumed to be in the form of observations of all peaks above a threshold during a long period outside the systematic record period. A Monte Carlo simulation experiment was performed to compare the GLS estimator adjusted for historical floods with the unadjusted GLS estimator and the ordinary least squares estimator. Results indicate that using the GLS estimator adjusted for historical information significantly improves the regression model.

Journal of Hydrology↗