Skip to main content
Log in

Regional L-Moment-Based Flood Frequency Analysis in the Upper Vistula River Basin, Poland

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

The Upper Vistula River basin was divided into pooling groups with similar dimensionless frequency distributions of annual maximum river discharge. The cluster analysis and the Hosking and Wallis (HW) L-moment-based method were used to divide the set of 52 mid-sized catchments into disjoint clusters with similar morphometric, land use, and rainfall variables, and to test the homogeneity within clusters. Finally, three and four pooling groups were obtained alternatively. Two methods for identification of the regional distribution function were used, the HW method and the method of Kjeldsen and Prosdocimi based on a bivariate extension of the HW measure. Subsequently, the flood quantile estimates were calculated using the index flood method. The ordinary least squares (OLS) and the generalised least squares (GLS) regression techniques were used to relate the index flood to catchment characteristics. Predictive performance of the regression scheme for the southern part of the Upper Vistula River basin was improved by using GLS instead of OLS. The results of the study can be recommended for the estimation of flood quantiles at ungauged sites, in flood risk mapping applications, and in engineering hydrology to help design flood protection structures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adamowski, K. (2000), Regional analysis of annual maximum and partial duration flood data by nonparametric and L-moment methods, J. Hydrol. 229, 219–231.

  • Akaike, H. (1974), A new look at the statistical model identification, IEEE T. Automat. Contr. 19(6), 716–723. doi:10.1109/TAC.1974.1100705.

  • Aziz, K., Rahman, A., Fang, G., and Shrestha, S. (2014), Application of artificial neural networks in regional flood frequency analysis : a case study for Australia, Stoch. Env. Res. Risk A. 28(3), 541–554. doi:10.1007/s00477-013-0771-5

  • Brath, A., Castellarin, A., Franchini, M., and Galeati, G. (2001), Estimating the index flood using indirect methods, Hydrolog. Sci. J. 46(3), 399–418.

  • Bryndal, T. (2011), The identification of small drainage basins prone to flash-flood creation (as exemplified by the Dynów, Strzyżów and Przemyśl foothill areas, Przegląd Geograficzny. 83, 27–49. (In Polish)

  • Burn, D.H., and Goel, N.K. (2000), The formation of groups for regional flood frequency analysis, Hydrolog. Sci. J. 45, 97–112.

  • Castellarin, A., Burn, D.H., and Brath A. (2001), Assessing the effectiveness of hydrological similarity measures for flood frequency analysis, J. Hydrol. 241, 270–285.

  • Castellarin, A., Kohnová, S., Gaál, L. Fleig, A., Salinas, J.L., Toumazis, A., Kjeldsen, T.R., and Macdonald, N. (2012), Review of Applied Statistical Methods For Flood-Frequency Analysis in Europe (Centre for Ecology & Hydrology on behalf of COST, ISBN: 978-1-906698-32-4), 122 pp.

  • Castellarin, A., Galeati, G. Brandimarte, L., Montanari, A., and Brath, A. (2004), Regional flow-duration curves: reliability for ungauged basins, Adv. Water Resour. 27, 953965.

  • Chasalow, S. (2012), combinat: combinatorics utilities. R package version 0.0-8. https://CRAN.R-project.org/package=combinat

  • Cunnane, C. (1988), Methods and merits of regional flood frequency analysis, J. Hydrol. 100(1–3), 269–290.

  • Dalrymple, T. (1960), Flood Frequency Analyses, Water Supply Paper 1543-A, U.S. Geological Survey, Reston, Va.

  • Dȩbski, K., Hydrologic characteristic of Poland (PWN, Warszawa, 1961), 159 pp. (In Polish)

  • Dobija, A. (1981), Seasonal variability of the runoff in the Upper Vistula river basin (up to the Zawichost gauging station), Zesz. Nauk. UJ, Prace Geogr., 53: 51–112.

  • Dynowska I., and Pociask-Karteczka J., (1999) Water circulation, in: L. Starkel (Ed.), Geography of Poland—the natural environment, PWN Scientific Publishing, Warsaw, 343–373. (In Polish)

  • Efron, B., and Tibshirani, R.J., (1993) An introduction to the bootstrap (New York: Chapman & Hall).

  • FEH: Flood Estimation Handbook. Vol. 1–5. (Institute of Hydrology, Wallingford, 1999).

  • Gaál, L., Lapin, M., Szolgay, J., and Faško, P. (2009), Hybrid approach to delineation of homogeneous regions for regional precipitation frequency analysis, J. Hydrol. Hydromechan., 57, 226–249. doi:10.2478/v10098-009-0021-1.

  • Gaál, L., Kohnová, S., and Szolgay, J. (2013), Regional flood frequency analysis in Slovakia: Which pooling approach suits better? In Comprehensive Flood Risk Management: Research for policy and practice, 27–30.

  • Greenwood J.A., Landwehr J.M., Matalas N.C., and Wallis J.R. (1979), Probability Weighted Moments: Definition and Relation to Parameters of Several Distribution Expresible in Inverse Form, Wat. Resour. Res. 15(5), 1049–1054.

  • Griffis, V.W., and Stedinger, J. R. (2007), The use of GLS regression in regional hydrologic analyses, Journal of Hydrology (2007) 344, 82–95. doi:10.1016/j.jhydrol.2007.06.023

  • Haddad, K., Rahman, A., and Green, J. (2010), Design rainfall estimation in Australia: A case study using L moments and Generalized Least Squares Regression, Stoch. Env. Res. Risk A. 25(6), 815–825.

  • Hosking, J.R., and Wallis, J.R., Regional Frequency Analysis. An Approach Based on L-moments, (Cambridge University Press, Cambridge, 1997).

  • Hosking, J.R. (2013), Regional frequency analysis using L-moments. R package, version 2.5. https://CRAN.R-project.org/package=lmomRFA.

  • Ishak, E. H., Haddad, K., Zaman, M., and Rahman, A. (2011), Scaling property of regional floods in New South Wales, Australia, Nat. Hazards 58(3), 1155–1167. doi:10.1007/s11069-011-9719-6.

  • ISOK Topographic Object Database, http://www.isok.gov.pl/en/topographic-objects-database-bdot, accessed Feb 20, 2014

  • Jain, A.K., and Dubes, R.C., Algorithms for Clustering Data. Prentice Hall Advanced Reference Series (Prentice Hall, New Jersey 1988).

  • Jakob, D., Reed, D.W., and Robson, A.J., Choosing a pooling-group. Flood Estimation Handbook, vol. 3. (Institute of Hydrology, Wallingford, 1999).

  • Johnston, J., Econometric Methods, (McGraw-Hill, New York,1984).

  • Kaczmarek, Z., and Trykozko, E. (1964) Application of the method of quantiles to estimation of the Pearson distribution. Acta Geoph. Pol. XII(1):5–12.

  • Kaufman, L., and Rousseeuw, P.J., Finding groups in data. An introduction to cluster analysis. (Wiley Series in Probability and Statistics, New York, 2005).

  • Kjeldsen, T. R., and D. A. Jones (2009), An exploratory analysis of error components in hydrological regression modeling, Water Resour. Res., 45, W02407, doi:10.1029/2007WR006283.

  • Kjeldsen, T. R., and Prosdocimi, I (2015), A bivariate extension of the Hosking and Wallis goodness-of-fit measure for regional distributions, Water Resour. Res., 51(2), 896–907.

  • Kochanek, K., Strupczewski, W.G., Singh, V.P., and Wȩglarczyk S. (2008), The PWM large quantile estimates of heavy tailed distributions from samples deprived of their largest element, Estimation des grands quantiles de distributions queue dcroissance lente par la mthode des moments pondrs par les probabilits partir d’chantillons amputs de leur plus grande valeur, Hydrological Sciences Journal, 53:2, 367–386,doi:10.1623/hysj.53.2.367.

  • Kochanek, K., Strupczewski, W.G., and Bogdanowicz, E. (2012), On seasonal approach to flood frequency modelling. Part II: flood frequency analysis of Polish rivers, Hydrol. Process. 26, 717–730.

  • Kohnová, S., Szolgay, J., Solin, L., and Hlavčová, K. (2006), Regional Methods for Prediction in Ungauged Basins. Case Studies. (KEY Publishing, Ostrava-Přívoz, 2006).

  • Kondracki, J. Regional Geography of Poland (2014), PWN, Warszawa, 444 pp. (In Polish)

  • Kroll, C.N., and Stedinger, J.R. (1998), Regional hydrologic analysis: Ordinary and generalized least squares revisited. Water Resources Research 34(1), 121–128. doi:10.1029/97WR02685.

  • Landwehr, J.M., Matalas, N.C., and Wallis, J.R. (1979), Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles. Wat. Resour. Res. 15, 1055–1064.

  • Lu, L.H., and Stedinger, J. R. (1992), Sampling variance of normalized GEV/PWM quantile estimators and a regional homogeneity test. J. Hydrol. 138, 223–245.

  • Madsen, H., Mikkelsen, P.S., Rosbjerg, D., and Harremos, P. (1998), Estimation of regional intensity-duration-frequency curves for extreme precipitation. Water Sci. Technol. 37(11), 29–36.

  • KZGW, Map of Hydrological Division of Poland in scale 1:10000. (2010)

  • McCuen, R.H. Modeling Hydrologic Change. (Lewis Publishers, Boca Raton Florida, 2003).

  • Mediero, L., and Kjeldsen, T. R. (2014), Regional flood hydrology in a semi-arid catchment using a GLS regression model. Journal of Hydrology, 514, 158–171. doi:10.1016/j.jhydrol.2014.04.007

  • Merz, R., and Blöschl, G. (2005), Flood frequency regionalization—spatial proximity vs. catchment attributes, J. Hydrol. 302(1–4), 283–306.

  • Miziński, B., Niedzielski, T., Kryza, M., and Szymanowski, M. (2013), Automatic removal of outliers in hydrologic time series and quality control of rainfall data: processing a real-time database of the Local System for Flood Monitoring in Klodzko County, Poland, EGU General Assembly 2013, held 7–12 April, Vienna, Austria, id. EGU2013-12579.

  • Montgomery, D. C., and Peck, E. A., Introduction to Linear Regression Analysis, Second Edition, (Wiley, New York, 1992).

  • Moran, P.A.P. (1950), Notes on continuous stochastic phenomena, Biometrika 37.

  • Noto, L.V., and La Loggia, G. (2009), Use of L-moments Approach for Regional Flood Frequency Analysis in Sicily, Italy, Water Resour. Manag. 23, 2207–2229.

  • Ouarda, T.B.M.J., Girard, C., Cavadias, G.S., and Bobée, B. (2001), Regional flood frequency estimation with canonical correlation analysis, J. Hydrol. 254, 157–173.

  • Pociask-Karteczka J. (1995), Principles of hydrologic regionalization on example of the Upper Vistula Basin, Habilitation Dissertation 291, Jagiellonian University, Kraków), 95 pp. (In Polish)

  • Punzet, J. (1978), Water resources of the upper Vistula river basin. Maximum water discharge, their spatial variability and occurrence probability. IMGW-PIB, Warszawa, 138 pp. (In Polish)

  • Punzet, J. (1991), Characteristic flows, In: Upper Vistula River Basin, part I (eds: Dynowska, Maciejewski). PWN, Warszawa-Kraków) 167–215. (In Polish)

  • R Core Team (2013), R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org)

  • Reed, D. W., Jakob, D., Robinson, A.,J., Faulkner, D. S., and Stewart, E. J. (1999) Regional frequency analysis: a new vocabulary, Hydrological Extremes: Understanding, Predicting, Mitigating, Proc. IUGG 99 Symposium, Birmingham, IAHS Publ. 255, 237–243.

  • Rossi, F., and Villani, P. (1994), Regional Flood Estimation Methods, University of Salerno, Proc. Coping with Floods, Ed. G. Rossi et al. Kluwer Academic Publishers, pp. 135–169.

  • Salinas, J.L., Castellarin, A., Viglione, A., Kohnová, S., and Kjeldsen, T.R. (2014) Regional parent flood frequency distributions in Europe—Part 1: Is the GEV model suitable as a pan-European parent? Hydrol. and Earth Syst. Sci., 18, 43814389. doi:10.5194/hess-18-4381-2014

  • Salinas, J.L., Castellarin, A., Kohnová, S., and Kjeldsen, T.R. (2014) Regional parent flood frequency distributions in Europe—Part 2: Climate and scale controls, Hydrol. and Earth Syst. Sci., 18, 43914401. doi:10.5194/hess-18-4391-2014

  • Sahinler, S., and Topuz, D. (2007), Bootstrap and jackknife resampling algorithms for estimation of regression parameters, J. of Applied Quantitative Methods, 2(2), 188–199.

  • Setmajer J., Skarżyńska K., Michalski P., and Burda H. (1971) Mapping study of the coefficient of soil impermeability index of the left bank of the Upper Vistula River Basin, Wyższa Szkoła Rolnicza w Krakowie, Instytut Budownictwa Wodnego i Ziemnego, 15 pp.(In Polish)

  • Shapiro, S. S., and Wilk, M. B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52(34), 591611.

  • Soczyńska, U. (1977), Methodological principles of a regional catchment model in polish conditions, Materiały Badawcze—Instytut Meteorologii i Gospodarki Wodnej. Seria: Hydrologia, IMiGW, Warszawa, 128 pp. (In Polish)

  • Stachy, J. (1966), Distribution of the mean runoff in Poland (Rozmieszczenie odpywu średniego na obszarze Polski), Prace PIHM, 88:3–42. (In Polish)

  • Stachy J., and Fal B. (1986), Rules for derivations of Flood Frequency (Zasady obliczania maskymalnych przepyww prawdopodobnych), Prace Instytutu Badawczego Dróg i Mostów, 3–4: 91–147. (In Polish)

  • Stedinger, J. R., and Tasker, G. D. (1985), Regional Hydrologic Analysis: 1. Ordinary, Weighted, and Generalized Least Squares Compared, Water Resour. Res., 21(9), 1421–1432.

  • Stedinger, J. R., Vogel, R.M., and Foufoula-Georgiou, E., Frequency analysis of extreme events. In: Handbook of Hydrology, (ed. by D. R. Maidment) (McGraw-Hill, New York, 1993) p. 18.1–18.66.

  • Strupczewski, W.G., Kochanek, K. Bogdanowicz, E., and Markiewicz, I. (2012), On seasonal approach to flood frequency modelling. Part I: Two-component distribution revisited, Hydrol. Process. 26, 705–716.

  • Taylor, M., Haddad, M., Zaman, M., and Rahman, A. (2011), Regional flood modelling in Western Australia: Application of regression based methods using ordinary least squares, 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011.

  • Tasker, G. D., and Stedinger, J. R., (1989) An operational GLS model for hydrologic regression, J. Hydrol., 111, 361–375.

  • Vogel, R.M., and Fennessey, N.M. (1993), L-Moment diagrams should replace product moment diagrams, Wat. Resour. Res. 29(6), 1745–1752.

  • Wałȩga, A., Krzanowski, S., and Chmielowski K. (2009), Method of cluster analysis in identification of homogenous catchments, considering flood indexes and selected physiographic characteristics), Infrastruktura i Ekologia Terenów Wiejskich, 6, 67–81. (In Polish).

  • Ward, J.H. (1963), Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc. 58(301), 236–244.

  • Wang, Q.J. (1997), LH moments for statistical analysis of extreme events, Water Resour. Res. 33, 2841–2848.

  • Wiesberg, S. (1985), Applied linear regression. 2nd ed.. New York: John Wiley.

  • Yang T., Xu, C-Y., Shao Q-X., and Chen, X. (2010), Regional flood frequency and spatial patterns analysis in the Pearl River Delta region using L-moments approach, Stoch. Env. Res. Risk A. 24, 165–182.

  • Viglione, A. (2014), nsRFA: Non-supervised Regional Frequency Analysis. R package version 0.7-12. http://CRAN.R-project.org/package=nsRFA

  • Ziemońska Z. (1973), Hydrographic conditions in the Polish West Carpathians, Prace Geograficzne—Polska Akademia Nauk, 103, 126 pp. (In Polish)

Download references

Acknowledgments

The authors wish to thank the reviewers for their helpful comments, which assisted in the overall improvement of the paper. We also thank the native speakers Mr. Grzegorz Zȩbik and Ms. Madeline Olszak for linguistic support. This research was partially supported by the Ministry of Science and Higher Education of the Republic of Poland. The investigation described in the contribution was also partially financed by the Slovak Grant Agency under VEGA Projects Nos. 1/0776/13 and 1/0710/15.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Rutkowska.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rutkowska, A., Żelazny, M., Kohnová, S. et al. Regional L-Moment-Based Flood Frequency Analysis in the Upper Vistula River Basin, Poland. Pure Appl. Geophys. 174, 701–721 (2017). https://doi.org/10.1007/s00024-016-1298-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-016-1298-8

Keywords

Navigation