Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013

Wagner, Daniel M.; Krieger, Joshua D.; Veilleux, Andrea G.

doi:10.3133/sir20165081

Cited by 3 publications

(2 citation statements)

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“…The decision was made based on comparative studies in Australia (Haddad et al, ; Haddad & Rahman, ; Haddad, Rahman, & Ling, ). In the United States, the U.S. Geological Survey (USGS) has recently conducted regional skew studies in several states employing the Bayesian GLS framework suggested in Reis et al () and further developed in Gruber et al () (Eash et al, ; Feaster et al, ; Gotvald et al, ; Gotvald et al, ; Lamontagne et al, ; Mastin et al, ; Olson, ; Over et al, ; Parrett et al, ; Paretti et al, ; Southard & Veilleux, ; Weaver et al, ; Wagner et al, ; Wood et al, ; Zarriello, ).…”

Section: Introductionmentioning

confidence: 99%

“…The decision was made based on comparative studies in Australia Haddad, Rahman, & Ling, 2015). In the United States, the U.S. Geological Survey (USGS) has recently conducted regional skew studies in several states employing the Bayesian GLS framework suggested in and further developed in Gruber et al ( 2007) (Eash et al, 2013;Feaster et al, 2009;Gotvald et al, 2009;Gotvald et al, 2012;Lamontagne et al, 2012;Mastin et al, 2016;Olson, 2014;Over et al, 2016;Parrett et al, 2011;Paretti et al, 2014;Southard & Veilleux, 2014;Weaver et al, 2009;Wagner et al, 2016;Wood et al, 2016;Zarriello, 2017). This paper starts with the Bayesian GLS model error variance estimator in and develops a complete analysis framework including a range of new regression diagnostic statistics for both B-WLS and B-GLS analyses.…”

Section: Introductionmentioning

confidence: 99%

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Operational Bayesian GLS Regression for Regional Hydrologic Analyses

Reis

Veilleux

Lamontagne

et al. 2020

Water Resources Research

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This paper develops the quasi‐analytic Bayesian analysis of the generalized least squares (GLS) (B‐GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R2, pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region‐of‐influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B‐GLS and the new diagnostic statistics are illustrated with a regional log‐space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method‐of‐moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B‐GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140). B‐GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational Bayesian GLS Regression for Regional Hydrologic Analyses

Reis

Veilleux

Lamontagne

et al. 2020

Water Resources Research

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Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware

Hammond¹,

Doheny²,

Dillow³

et al. 2022

Scientific Investigations Report

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Reliable estimates of the magnitude of peak flows in streams are required for the economical and safe design of transportation and water conveyance structures. In addition, reliable estimates of the magnitude of low flows at defined frequencies and durations are needed for meeting regulatory requirements, quantifying base flows in streams and rivers, and evaluating time of travel and dilution of toxic spills. This report, in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey, presents methods for estimating the magnitude of peak flows and low flows at defined frequencies and durations on nontidal streams in Delaware, at locations both monitored by streamflow-gage sites and ungaged. Methods are presented for estimating (1) the magnitude of peak flows for return periods ranging from 2 to 500 years (50-percent to 0.2-percent annual-exceedance probability), and (2) the magnitude of low flows as applied to 7-, 14-, and 30-consecutive day low-flow periods with recurrence intervals of 2, 10, and 20 years (50-, 10-, and 5-percent annual non-exceedance probabilities). These methods are applicable to watersheds that exhibit a full range of development conditions in Delaware. The report also describes StreamStats, a web application that allows users to easily obtain peak-flow and low-flow magnitude estimates for userselected locations in Delaware.Peak-flow and low-flow magnitude estimates for ungaged sites are obtained using statistical regression analysis through a process known as regionalization, where information from a group of streamflow-gage sites within a region forms the basis for estimates for ungaged sites within the same region. Ninetyfour streamflow-gage sites in and near Delaware with at least 10 years of nonregulated annual peak-flow data were used for the peak-flow regression analysis, a subset of the 121 sites for which peak-flow estimates were computed. These sites included both continuous-record streamflow-gage sites as well as partial record sites. Forty-five streamflow-gage sites with

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Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016

Ensminger,

Wagner,

Whaling

2024

Scientific Investigations Report

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To improve flood-frequency estimates for rural streams in the Coastal Plain region of Louisiana, generalized leastsquares regression techniques were used to relate corresponding annual exceedance probability streamflows for 211 streamgages in the region to a suite of explanatory variables that include physical, climatic, pedologic, and land-use characteristics of the streamgage drainage area. The resulting generalized least-squares models can be used to estimate selected annual exceedance probability streamflows for rural ungaged locations in the Coastal Plain region of Louisiana. For the 211 streamgages in the Coastal Plain region of Louisiana and surrounding States, annual peak-streamflow data available through the 2016 water year were used in this study. Two unique flood regions, the Mississippi Alluvial Plain and Coastal Plain, were identified as separate hydrologic regions based on statistical evaluation and significance of categorical variables representing the regions regressed against the 1-percent annual exceedance probability streamflow (the 100-year flood). Regional regression equations for estimating annual exceedance probability streamflow for the Mississippi Alluvial Plain region have been previously published; therefore, the purpose of this study was to generate updated regional regression equations for the Coastal Plain region of Louisiana. The final regression models used drainage area and channel slope as explanatory variables based on performance metrics.

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Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013

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Cited by 3 publications

References 10 publications

Operational Bayesian GLS Regression for Regional Hydrologic Analyses

Operational Bayesian GLS Regression for Regional Hydrologic Analyses

Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware

Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016

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