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Are parsimonious FF models more reliable than true ones? I. Accuracy of Quantiles and Moments Estimation (AQME) - method of assessment

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Applying the methodology described in Strupczewski et al. (2005a; this is-sue), the performance of various parsimonious models combined with three estimation methods versus Flood Parent Distributions is comparatively assessed by simulation experiments. Moments (MOM), L-moments (LMM) and maximum likelihood (MLM) are used as alternative methods. Five four-parameter Specific Wakeby Distributions (SWaD) are employed to serve as Flood Parent Distributions and forty Distribution/Estimation (D/E) procedures are included in respect to the estimation of upper quantiles. The relative bias (RB), relative root mean square error (RRMSE) and reliability of procedures are used for the assessment of the relative performance of alternative procedures. Parsimonious two-parameter models generally perform better for hydrological sample sizes than their three-parameter counterparts with respect to RRMSE. How-ever, the best performing procedures differ for various SWaDs. As far as estimation methods are concerned, MOM usually produces the smallest values of both RB and RRMSE of upper quantiles for all competing methods. The second place in rank is occupied by LMM, whereas, MLM produces usually the highest values. Considerable influence of sampling bias on the value of the total bias has been ascertained. The improper choice of a model fitted to SWaD samples causes that the reli-ability of some three-parameter parsimonious D/E procedures does not always rise with the sample size. Also odd is that True model does not always give one hundred percent reliability for very large samples, as it should. This means that estimating algorithms still require improvements.
Rocznik
Strony
419--436
Opis fizyczny
Bibliogr. 36 poz.
Twórcy
  • Institute of Geophysics, Polish Academy of Sciences, Księcia Janusza 64, 01-452 Warszawa, Poland
autor
  • Institute of Geophysics, Polish Academy of Sciences, Księcia Janusza 64, 01-452 Warszawa, Poland
autor
  • Department of Civil and Environmental Engineering, Louisiana State University Baton Rouge, LA 70803-6405, USA
  • Institute of Water Engineering and Water Management, Cracow University of Technology ul. Warszawska 24, 31-155 Kraków, Poland
Bibliografia
  • Akaike, H., 1974: "A new look at the statistical model identification", IEEE Trans. Automat Contr. AC-19 (16), pp. 716-722.
  • Beven, K.J., 1993: "Prophecy, reality and uncertainty in distributed hydrological modelling" Advances in Water Resources 16, pp. 41 -51.
  • Cunnane, C, 1985: "Factors affecting choice of distribution for flood series", Hydrol. Sci. J. 30 (l), pp. 25-36.
  • Cunnane, C, 1989: "Statistical distribution for flood frequency analysis". World Meteorological Organisation, Operational Hydrology, Report No. 33. pp. 73 + Appendices.
  • Eagleson, P.S., 1972: "Dynamics of flood frequency". Water Resour. Res. 8, pp. 878-898. Flood Estimation Handbook, 1999, Institute of Hydrology, Wallingford, UK.
  • Gan, T.Y., E.M. Dlamini and G.F. Biftu, 1997: "Effects of model complexity and structure. Data quality and objective function on hydrologic modelling", J. of Hydrol. 192, pp. 81-103.
  • Greenwood, J.A., J.M. Landwehr, N.C. Matalas and J.R. Wallis, 1979: "Probability weighted moments: definition and relation to parameters of several distributions expressible in inverse form". Water Resour. Res. 15 (5), pp. 1049-54.
  • Hosking, J.R.M., 1991, Fortran routines for use with the method of L-moments, Version 2, Res. Rep. RC 17079, IBM Research Division, York-town Heights, NY 10598.
  • Houghton, J.C., 1978: "Birth of a parent: The Wakeby distribution for modelling flood flows". Water Resour. Res. 14 (6), pp. 1105-1109.
  • Jakeman, A.J., and G.M. Hornberger, 1993: "How much complexity is warranted in a rainfall-runoff model?" Water Resour. Res. 29 (8), pp. 2637-2649.
  • Johnson, N.L., and S. Kotz, 1970, Continuous Univariate Distribution. Vol. 1: Distribution in Statistics, John Wiley, New York.
  • Katz, R.W., M.B. Padange and P. Naveau, 2002: "Statistics of extremes in hydrology". Adv. in Water Resour. 25, pp. 1287-1304.
  • Kendall, M.G., and A. Stuart, 1973, The advanced theory of statistics. Vol. 2: Inference and relationship, Chapt. 29, Charles Griffin. London.
  • Kite, G.W., 1988, Frequency and risk analysis in hydrology. Water Res. Pubis., Littleton, Co., pp. 257.
  • Klemeš, v., 2000a: "Tall tales about tails of hydrological distributions, II”, J. Hydrol. Eng. 5 (3), pp. 227-231.
  • Klemeš, v., 2000b: "Tall tales about tails of hydrological distributions, 11", J. Hydrol. Eng. 5 (3), pp. 232-239.
  • Kochanek, K., W.G. Strupczewski, V.P. Singh and S. W?glarczyk, 2005: "Are parsimonious FF models more reliable than True ones? II. Comparative assessment of the performance of simple models versus the parent distribution", Acta Geophys Pol. (this issue).
  • Kuczera, G., 1982: "Robust flood frequency models". Water Resour. Res. 18 (2), pp. 315-324.
  • Perrin, C, C. Michel and V. Andreassian, 2001: "Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments", J. of Hydrol. 242, pp. 275-301.
  • Rao, A.R., and K.H. Hamed, 2000, Flood frequency analysis, CRC Press LLC, Boca Ratou, USA, pp. 350.
  • Rowiński, P., W.G. Strupczewski and V.P. Singh, 2002: "A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: I. Known pdf, Hydrol. Sc. J. 47 (1), pp. 107-122.
  • Stedinger, J.R., M.V. Vogel and E. Foufoula-Georgiou, 1993: "Frequency analysis of extreme events". In: D.R. Maidment (ed.). Handbook of Hydrology, Mc Graw-Hill, New York, Chap. 18.
  • Stmpczewski, W.G., and V.P. Singh, 2002: "On frontiers of at-site statistical flood frequency estimation". In: ICUH CD-Proc, Kuala Lumpur.
  • Strupczewski, W.G., and V.P. Singh, 2003: "On limitations of at-site statistical flood frequency estimation". In: K. Haman, B. Jakubiak and J. Zabczyk (eds.). Proc. of Int. Workshop on Probabilistic Problems in Atmospheric and Water Sciences, Bedlewo, ICM, pp. 150-159.
  • Strupczewski, W.G., V.P. Singh and W. Feluch, 2001: "Non-stationary approach to at-site flood frequency modelling. I. Maximum likelihood estimation", J. of Hydrol. 248, pp. 123-142.
  • Strupczewski, W.G., V.P. Singh and S. Węglarczyk, 2002a: "Asymptotic bias of estimation methods caused by the assumption of false probability distribution", J. of Hydrol. 258 (1-4), pp. 122-148.
  • Strupczewski, W.G., S. Węglarczyk and V.P. Singh, 2002b: "Model error in flood frequency
  • estimation". Acta Geophys. Pol. 50 (2), pp. 279-319. Strupczewski, W.G., V.P. Singh, S. Węglarczyk and H.T. Mitosek, 2003: "Physics of flood frequency analysis. II. Convective diffusion model versus lognormal model". Acta Geophys. Pol. 51 (1), pp. 85-106.
  • Strupczewski, W.G., H.T. Mitosek, K. Kochanek, V.P. Singh and S. Węglarczyk, 2005: "Probability of correct selection from lognormal and convective diffusion models based on the likelihood ratio", SERRA, approaching.
  • Wallis, J.R., N.C. Matalas and J.R. Slack, 1974: "Just a moment!" Water Resour. Res. 10 (2), pp. 211-219.
  • Węglarczyk, S., W.G. Strupczewski and V.P. Singh, 2002: "A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: II. Assumed pdf, Hydrol. Sc. J. 47 (1), pp. 123-137.
  • Węglarczyk, S., and W.G. Strupczewski, 2002: "Asymptotic relative bias of mean, δ(EFX) variance,δ(varFX) and quantile, δ(xpF) resulting from improper pdf assumption F with respect to estimation method when True pdf is known". Internal Report of Institute of Geophysics, Polish Academy of Sciences (in Polish, unpublished).
  • Ye, W., B.C. Bates, N.R. Viney, M. Silvapan and A.J. Jakeman, 1997: "Performance of con¬ceptual rainfall-runoff models in low-yielding ephemeral catchments". Water Resour. Res. 33(1), pp. 153-166.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL7-0009-0042
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