The use of GLS regression in regional hydrologic analyses

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

doi:10.1016/j.jhydrol.2007.06.023

Cited by 115 publications

(97 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Linear or power (often linearised through a log-transformation) forms, with either a multiplicative or additive error term, are the most commonly used functions (see e.g. Stedinger and Tasker, 1985;GRE-HYS, 1996;Pandey and Nguyen, 1999;Brath et al, 2001;Kjeldsen et al, 2001Kjeldsen et al, , 2014Bocchiola et al, 2003;Merz and Bloeschl, 2005;Griffis and Stedinger, 2007;Archfield et al, 2013;Smith et al, 2015).…”

Section: E Toth: Estimation Of Flood Warning Runoff Thresholds In Unmentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often 2 years, which may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever the function form, such models are generally parameterised by minimising the mean square error, which assigns equal importance to overprediction or underprediction errors.Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, which penalises the overpredictions more.The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the country of Italy. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.

show abstract

Section: E Toth: Estimation Of Flood Warning Runoff Thresholds In Unmentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The pseudo-R 2 is a measure of the percentage of the variation explained by the basin characteristics (explanatory variables) included in the model. The pseudo-R 2 value is calculated on the basis of the degrees of freedom in the regression (Griffis and Stedinger, 2007). Final GLS regression models were selected primarily on the basis of minimizing values of the standard error of model (SEM) (Eng and others, 2009) and the average standard error of prediction (SEP) (Griffis and Stedinger, 2007;Eng and others, 2009) …”

Section: Development Of Regional Regression Equationsmentioning

confidence: 99%

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

Eash¹,

Barnes²,

O'Shea³

2016

Scientific Investigations Report

View full text Add to dashboard Cite

For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit http://store.usgs.gov.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. Graphs showing relation between the spring 7-day mean low-flow for a recurrence interval of 10 years discharges computed from observed streamflow and those predicted from regression equations for low-flow regions in Iowa for northeast region, northwest region, and southern region, showing streamgages by map number with some of the largest differences between predicted and observed discharges . AbstractA statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuousrecord streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations.Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final eq...

show abstract

“…Also, improvements were noted when concurrent flows at different streamgages were correlated. GLS regression, as described by , Tasker and Stedinger (1989), and Griffis and Stedinger (2007), is a method that weights data from streamgages in the regression analysis according to differences in streamflow reliability (record lengths) and variability (record variance) and according to spatial cross correlations of concurrent streamflow among streamgages. Compared to OLS regression, GLS regression provides improved estimates of streamflow statistics and improved estimates of the predictive accuracy of the regression equations .…”

Section: Generalized-least-squares Regressionmentioning

confidence: 99%

The use of GLS regression in regional hydrologic analyses

Cited by 115 publications

References 27 publications

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

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

Contact Info

Product

Resources

About