Identification of Outliers in Flood Frequency Analysis: Comparison of Original and Multiple Grubbs-Beck Test
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Identification of Outliers in Flood Frequency Analysis: Comparison of Original and Multiple Grubbs-Beck Test

Authors: Ayesha S. Rahman, Khaled Haddad, Ataur Rahman

Abstract:

At-site flood frequency analysis is used to estimate flood quantiles when at-site record length is reasonably long. In Australia, FLIKE software has been introduced for at-site flood frequency analysis. The advantage of FLIKE is that, for a given application, the user can compare a number of most commonly adopted probability distributions and parameter estimation methods relatively quickly using a windows interface. The new version of FLIKE has been incorporated with the multiple Grubbs and Beck test which can identify multiple numbers of potentially influential low flows. This paper presents a case study considering six catchments in eastern Australia which compares two outlier identification tests (original Grubbs and Beck test and multiple Grubbs and Beck test) and two commonly applied probability distributions (Generalized Extreme Value (GEV) and Log Pearson type 3 (LP3)) using FLIKE software. It has been found that the multiple Grubbs and Beck test when used with LP3 distribution provides more accurate flood quantile estimates than when LP3 distribution is used with the original Grubbs and Beck test. Between these two methods, the differences in flood quantile estimates have been found to be up to 61% for the six study catchments. It has also been found that GEV distribution (with L moments) and LP3 distribution with the multiple Grubbs and Beck test provide quite similar results in most of the cases; however, a difference up to 38% has been noted for flood quantiles for annual exceedance probability (AEP) of 1 in 100 for one catchment. This finding needs to be confirmed with a greater number of stations across other Australian states.

Keywords: Floods, FLIKE, probability distributions, flood frequency, outlier.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097235

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3318

References:


[1] V. W. Griffis, andJ. R.Stedinger,“The LP3 distribution and its application in flood frequency analysis, 2. Parameter estimation methods,” J. of Hydrol. Engineering, vol. 12(5), pp. 492–500, 2007.
[2] B. Bobée, G. Cavidas, F. Ashkar, J. Bernier, and P. Rasmussen, “Towards a systematic approach to comparing distributions used in flood frequency analysis,” J Hydrol,vol. 142, pp. 121–136, 1993.
[3] T. A. McMahon, and R. Srikanthan, “Log Pearson III distribution is it applicable to flood frequency analysis of Australian streams?,” J Hydrol, vol. 52(139), pp. 147, 1981.
[4] R. M. Vogel, T. A. McMahon, and F. H. S.Chiew,“Flood flow frequency model selection in Australia,” J Hydrol,vol. 146(421), pp. 449, 1993.
[5] B. C. Bates, A. Rahman, R. G. Mein, and P. E. Weinmann, “Climatic and physical factors that influence thehomogeneity of regional floods in south-eastern Australia,” Water Resour Res, vol. 34(12), pp. 3369–3381, 1998.
[6] R. Merz, G. Bloschil, and G. Humer,“National flood discharge mapping in Austria,” Nat Hazards, vol. 46, pp. 53–72, 2008.
[7] A. Meshgi, and D. Khalili, “Comprehensive evaluation of regional flood frequency analysis by L-abdLHmoments. 1. A re-visit to regional homogeneity,”Stoch Environ Res Risk Assess, vol. 23, pp. 119–135, 2009a.
[8] A. Meshgi, and D. Khalili, “Comprehensive evaluation of regional flood frequency analysis by L-abdLHmoments. II. Devewlopment of LHmoments parameters for the generalized Pareto and generalizedlogistic distributions,”Stoch Environ Res Risk Asses,vol. 23, pp. 137–152, 2009b.
[9] E. H. Ishak,A. Rahman, S. Westra, A. Sharma, and G. Kuczera, “Preliminary analysis of trends in Australian flood data,” in Proc.World Environmental and Water Resources Congress, American Society of Civil Engineers (ASCE), Providence, Rhode Island, USA, 2010, pp. 120- 124.
[10] E. H. Ishak, K. Haddad, M. Zaman, and A. Rahman,“Scaling property of regional floods in New South Wales Australia,”Nat Hazards, vol. 58, pp. 1155–1167, 2011.
[11] K. Haddad, A.Rahman, andG. Kuczera,“Comparison of ordinary and generalised least squares regression models in regional flood frequency analysis: a case study for New South Wales,”Aust J Water Resour,vol. 15(2), pp. 59–70, 2011.
[12] K. Haddad, A. Rahman, and J. R. Stedinger,“Regional Flood Frequency Analysis using Bayesian Generalized Least Squares: a Comparison between Quantile and Parameter Regression Techniques,”Hydrol Process,vol. 25(1), pp. 14, 2012.
[13] K. Haddad, and A. Rahman,“Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework—quantile regression versus parameter regression technique,”J Hydrol,vol. 430–431, pp. 142–161, 2012.
[14] A. Rahman, K. Haddad, M. Zaman, G. Kuczera, P. E. Weinmann, and W. Weeks, “Regional flood estimation in Australia: an overview of the study for the upgrade of Australian Rainfall and Runoff,”in Proc.Hydrology and Water Resources Symposium, Engineers Australia, Sydney, Australia, 2012, pp. 1441–1448.
[15] K. Haddad, A. Rahman, M. Zaman, and S. Shrestha,“Applicability of Monte Carlo cross validation technique for model development and validation in hydrologic regression analysis using ordinary and generalised least squares regression,”J Hydrol,vol. 482, pp. 119–128, 2013.
[16] A. S. Rahman, A. Rahman, M. Zaman, K. Haddad, A. Ashan, and M. A. Imteaz, “A Study on Selection of Probability Distributions for At-site Flood Frequency Analysis in Australia,”Natural Hazards,vol. 69, pp. 1803-1813, 2013.
[17] C. Cunnane,“Factors affecting choice of distribution for flood series,”HydrolSci J,vol. 30, pp. 25–36, 1985.
[18] C. Cunnane,“Statistical distributions for flood frequency analysis,”in Proc.Operational hydrological Report No. 5/33, World Meteorological Organization (WMO), Geneva, Switzerland, 1989.
[19] K. M. Conway,“Flood frequency analysis of some NSW coastal rivers,”Thesis (M. Eng. Sc.), University of New South Wales, Australia, 1970
[20] R. A. Kopittke, B. J. Stewart, andK. S. Tickle,“Frequency analysis of flood data in Queensland,” inProc. Hydrological Symposium, Institution of Engineers Australia, National Conference, 1976, Publication No. 76/2, 20:24.
[21] Institution of Engineers Australia (I.E. Aust.), Australian Rainfall and Runoff: A Guide to Flood Estimation, 2001.
[22] Institution of Engineers Australia (I.E. Aust.), Australian Rainfall and Runoff: A Guide to Flood Estimation, 1987.
[23] Interagency Advisory Committee on Water Data (IAWCD),Guidelines for Determining Flood Flow Frequency: Bulletin 17-B.Hydrol. Subcomm., Washington, DC, 1982.
[24] A. Rahman, P. E. Weinmann,and R. G. Mein,“At-site flood frequency analysis: LP3-product moment, GEV-L moment and GEV-LH moment procedures compared,” in Proc. Hydrology and Water Resource Symposium, Brisbane, 1999, pp. 715–720.
[25] G. Kuczera,“Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference,” Water Resour Res,vol. 35(5), pp. 1551–1557, 1999.
[26] R. J. Nathan, and P. E. Weinmann,“Application of at-site and regional flood frequency analyses,”in Proc. International Hydrology Water Resources Symposium, Perth, 1991, pp. 769-774.
[27] K. Haddad, and A. Rahman, “Investigation on at-site flood frequency analysis in south-east Australia,”IEM Journal, The Journal of The Institution of Engineers, Malaysia, vol. 69(3), pp. 59-64, 2008.
[28] K. Haddad, A. Rahman,“Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia,”StochEnv Res Risk Assess,vol. 25, pp. 415–428, 2010.
[29] T. A. Cohn, J. F. England, C. E. Berenbroc, R. R. Mason,J. R. Stedinger, and J. R. Lamontagne,“A generalized Grubbs-Beck test statistic for detecting multiple potentially influential outliers in flood series,”Water Resources Research, vol. 49, pp. 5047–5058, 2013.
[30] W. O. Thomas,“A uniform technique for flood frequency analysis,”J. Water Resour. Plann. Manage,vol. 111(3), pp. 321–337, 1985.
[31] B. Saf,“Assessment of the effects of discordant sites on regional flood frequency analysis,”J Hydrol,vol. 380 (3–4), pp. 362–375, 2010
[32] R. J. Beckman, and R. D. Cook, “Outlier … … ….s.” Technometrics,vol. 25(2), pp. 119-149, 1983.
[33] J. R. Stedinger, R. M. Vogel, and E. Foufoula-Georgiou, Frequency Analysis of Extreme Events, chap. 18, pp. 99, McGraw Hill, New York, 1993.
[34] V. Barnett, and T. Lewis,Outliers in Statistical Data, John Wiley, New York, 1994.
[35] W. Thompson,“On a criterion for the rejection of observations and the distribution of the ratio of deviation to sample standard deviation,”Ann. Math. Stat.,vol. 6, pp. 214–219, 1935.
[36] F. E. Grubbs, “Procedures for Detecting Outlying Observations in Samples,”Technometrics,vol. 11(1), pp. 1-21, 1969.
[37] F. E. Grubbs, G. Beck,“Extension of sample sizes and percentage points for significance tests of outlying observations,”Technometrics,vol. 4(14), pp. 847–853, 1972.
[38] G. Tietjen, and R. Moore,“Some Grubbs-type statistics for the detection of several outliers,”Technometrics,vol. 14(3), pp. 583–597, 1972.
[39] B. Rosner,“On the detection of many outliers,”Technometrics,vol. 17(2), pp. 221–227, 1975.
[40] B. Rosner,“Percentage points for a generalized ESD many outlier procedure,”Technometrics,vol. 25(2), pp. 165–172, 1983.
[41] P. Prescott, “An approximate test for PILFs in linear models,’Technometrics,vol. 17(1), pp. 129–132, 1975.
[42] P. Prescott,“Examination of the behaviour of tests for outliers when more than one outlier is present,”Appl. Stat.,vol. 27, pp. 10–25, 1978.
[43] J. Gentleman, and M.Wilk, “Detecting PILFs. II. Supplementing the direct analysis of residuals,”Biometrics,vol. 31(2), pp. 387–410, 1975.
[44] M. Marasinghe,“A multistage procedure for detecting several PILFs in linear regression,”Technometrics,vol. 27(4), pp. 395–399, 1985.
[45] P. Rousseeuw, and B. V. Zomeren,“Unmasking multivariate PILFs and leverage points,”J. Am. Stat. Assoc., vol. 85(411), pp. 633–639, 1990.
[46] A. Hadi, and J. Simonoff,“Procedures for the identification of multiple outliers in linear models,”J. Am. Stat. Assoc.,vol. 88(424), pp. 1264– 1272, 1993.
[47] C. Spencer, and R.McCuen,“Detection of PILFs in Pearson type III data,”J. Hydrol. Eng.,vol. 1, pp. 2–10, 1996.
[48] P. Rousseeuw, and A. Leroy,Robust Regression and Outlier Detection, John Wiley, Hoboken, N. J, 2003
[49] S. Verma, and A. Quiroz-Ruiz,“Critical values for six Dixon tests for outliers in normal samples up to sizes 100, and applications in science and engineering,”Rev. Mex. Cienc. Geol.,vol. 23(2), pp. 133–161, 2006.
[50] W. J. Dixon, “Analysis of extreme values,”Ann. Math. Stat.,vol. 21(1), pp. 488–506, 1950.
[51] W. J. Dixon, “Ratios involving extreme values.”Ann. Math. Stat.,vol. 22(1), pp. 68–78, 1951.
[52] Interagency Advisory Committee on Water Data (IAWCD), Robust National Flood Frequency Guidelines: What is an Outlier? Bulletin 17- C, 2013.
[53] J. R. Lamontagne, J. R. Stedinger, T. A. Cohn and N. A. Barth “Robust national flood frequency guidelines: What is an outlier?”In Proc.World Environmental and Water Resources Congress,ASCE, 2013.
[54] A. J. Gotvald, N. A. Barth, A. G.Veilleux, and C. Parrett C,“Methods for determining magnitude and frequency of floods in California, based on data through water year 2006,”U.S. Geological Survey, Reston, Virginia, 2012.
[55] R. H. McCuen, B. M.Ayyub,“Probability, statistics, and reliability for engineers and scientists,”A Chapman & Hall book, USA, 1996.
[56] L. R. Beard, “Statistical methods in hydrology, Civil works investigation project CW-151,”U.S. Army Corps of Engineers, Sacramento, California, 1962.
[57] M. A. Benson,“Uniform flood-frequency estimating methods for federal agencies,”Water Resour. Res.,vol. 4(5), pp. 891–908, 1968.
[58] W. Kirby, “Computer-oriented Wilson–Hilferty transformation that preserves the first three moments and the lower bound of the Pearson type 3 distribution,”Water Resour. Res.,vol. 8(5), pp. 125l–l254, 1972.