Magnitude and Frequency of Rural Floods in the Southeastern United States, 2006: Volume 3, South Carolina

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

doi:10.3133/sir20095156

Cited by 23 publications

(22 citation statements)

References 2 publications

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“…According to Henderson and Grissino-Mayer (2009), the Southeastern Coastal Plain spreads from the Gulf and Atlantic coasts to eastern Texas characterized by low relief ranging from the sea level to about 90 m in elevation. The region has many swamps, marshes, and poorly drained soils (Feaster et al, 2009). The mean annual precipitation is generally higher than the mean annual potential evapotranspiration, giving rise to excess soil moisture.…”

Section: Study Areamentioning

confidence: 99%

“…The gauged station data was selected from a dataset of 943 stream gauges compiled by Feaster et al (2009) for the Southeastern U.S. Two criteria were used to select the gauged sites for regionalization. The first criterion ensured that at least 90% of the selected watershed drained the Southeastern Coastal Plain.…”

Section: Selection Of Gauged Sitesmentioning

confidence: 99%

“…This reduced the total number of gauged watersheds to 51, of which 51% had a record period of at least 40 years. The selected variables were based on readily available data compiled by Feaster et al (2009) for the Southeastern U.S. The preference of such long-term data is because at least 28 years of daily data are required to calculate exceedence probabilities of 0.01 and 99.9% using the Weibull plotting position (Cunnane, 1978).…”

Section: Selection Of Gauged Sitesmentioning

confidence: 99%

“…The downside of rainfall-runoff models is their relatively higher learning curve with respect to large number of parameters, their estimation, and higher computation time in contrast to regionalized empirical models (e.g., Guimares and Bohan, 1992; U.S. Geological Survey, U.S. Dept. of Interior, 2000;Grover et al, 2002;Kroll et al, 2004;Schilling and Wolter, 2005;Castellarin et al, 2007;Verdin and Worstell, 2008;Feaster et al, 2009;Zhu and Day, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The actual U.S. Geological Survey gauge number (USGSID) is preceded with a zero.MAP refers to mean annual precipitation.Watersheds used for validation of RFDCs and validation of estimated daily and monthly streamflow.JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONFor a detailed description and calculation methods for each variable, refer toFeaster et al (2009).Table 2also depicts the 20 watershed descriptors of two first-order coastal streams (WS77 and WS80) at Santee Experimental Forest in Cordesville, South The remaining 47 watersheds were used for development of RFDC.…”

mentioning

confidence: 99%

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Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Ssegane

Amatya

Tollner

et al. 2013

J American Water Resour Assoc

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Commonly used methods to predict streamflow at ungauged watersheds implicitly predict streamflow magnitude and temporal sequence concurrently. An alternative approach that has not been fully explored is the conceptualization of streamflow as a composite of two separable components of magnitude and sequence, where each component is estimated separately and then combined. Magnitude is modeled using the flow duration curve (FDC), whereas sequence is modeled by transferring streamflow sequence of gauged watershed(s). This study tests the applicability of the approach on watersheds ranging in size from about 25‐7,226 km2 in Southeastern Coastal Plain (U.S.) with substantial surface storage of wetlands. A 19‐point regionalized FDC is developed to estimate streamflow magnitude using the three most selected variables (drainage area, hydrologic soil index, and maximum 24‐h precipitation with a recurrence interval of 100 years) by a greedy‐heuristic search process. The results of validation on four watersheds (Trent River, North Carolina: 02092500; Satilla River, Georgia: 02226500; Black River, South Carolina: 02136000; and Coosawhatchie River, South Carolina: 02176500) yielded Nash‐Sutcliffe efficiency values of 0.86‐0.98 for the predicted magnitude and 0.09‐0.84 for the predicted daily streamflow over a simulation period of 1960‐2010. The prediction accuracy of the method on two headwater watersheds at Santee Experimental Forest in coastal South Carolina was weak, but comparable to simulations by MIKE‐SHE.

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Section: Study Areamentioning

confidence: 99%

Section: Selection Of Gauged Sitesmentioning

confidence: 99%

Section: Selection Of Gauged Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Ssegane

Amatya

Tollner

et al. 2013

J American Water Resour Assoc

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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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Morphological Response to Discharge Reduction in a Partially Abandoned Channel of the Catawba River, North Carolina, USA

Reynolds

Royall

2020

Geomorphology

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Magnitude and Frequency of Rural Floods in the Southeastern United States, 2006: Volume 3, South Carolina

Cited by 23 publications

References 2 publications

Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

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

Morphological Response to Discharge Reduction in a Partially Abandoned Channel of the Catawba River, North Carolina, USA

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