Theoretical and Applied Climatology

, Volume 135, Issue 3–4, pp 1243–1257 | Cite as

Return period curves for extreme 5-min rainfall amounts at the Barcelona urban network

  • X. LanaEmail author
  • M. C. Casas-Castillo
  • C. Serra
  • R. Rodríguez-Solà
  • A. Redaño
  • A. Burgueño
  • M. D. Martínez
Original Paper


Heavy rainfall episodes are relatively common in the conurbation of Barcelona and neighbouring cities (NE Spain), usually due to storms generated by convective phenomena in summer and eastern and south-eastern advections in autumn. Prevention of local flood episodes and right design of urban drainage have to take into account the rainfall intensity spread instead of a simple evaluation of daily rainfall amounts. The database comes from 5-min rain amounts recorded by tipping buckets in the Barcelona urban network along the years 1994–2009. From these data, extreme 5-min rain amounts are selected applying the peaks-over-threshold method for thresholds derived from both 95% percentile and the mean excess plot. The return period curves are derived from their statistical distribution for every gauge, describing with detail expected extreme 5-min rain amounts across the urban network. These curves are compared with those derived from annual extreme time series. In this way, areas in Barcelona submitted to different levels of flood risk from the point of view of rainfall intensity are detected. Additionally, global time trends on extreme 5-min rain amounts are quantified for the whole network and found as not statistically significant.


Extreme 5-min rain amounts Peaks-over-threshold Mean excess plot Annual extreme time series Return periods GP GLO and GEV distributions Significant time trends Barcelona urban network 


  1. Acero FJ, García JA, Gallego MC (2011) Peaks-over-threshold study of trends in extreme rainfall over the Iberian Peninsula. J Clim 24:1089–1105CrossRefGoogle Scholar
  2. Alhakim A, Hooper W (2008) A non-parametric test for several independent samples. Journal of Nonparametric Statistics 20(3):253–261. CrossRefGoogle Scholar
  3. Anagnostopoulou C, Tolika K (2012) Extreme precipitation in Europe: statistical threshold selection based on climatological criteria. Theor Appl Climatol 107:479–489CrossRefGoogle Scholar
  4. Beguería-Portugués S (2005) Uncertainties in partial duration series modelling of extremes related to the choice of the threshold value. J Hydrol 303:215–230CrossRefGoogle Scholar
  5. Beguería S, Vicente-Serrano SM, López-Moreno JI, García-Ruíz JM (2009) Annual and seasonal mapping of peak intensity, magnitude and duration of extreme precipitation events across a climatic gradient, North-East Spain. Int J Climatol 29:1759–1779CrossRefGoogle Scholar
  6. Beguería S, Angulo-Martínez M, Vicente-Serrano SM, López-Moreno JI, El-Kenawi A (2011) Assessing trends in extreme precipitation events intensity and magnitude using non-stationary peaks-over-threshold analysis: a case study in North-East Spain from 1930 to 2006. Int J Climatol 31:2102–2114CrossRefGoogle Scholar
  7. Benjamin JR, Cornell CA (1970) Probability, statistics and decision for civil engineers. McGraw-Hill Inc., New York NYGoogle Scholar
  8. Burgueño A, Serra C, Lana X (2004) Monthly and annual statistical distributions of the daily rainfall at the Fabra Observatory (Barcelona, NE Spain) for the years 1917–1999. Theor Appl Climatol 77:57–75CrossRefGoogle Scholar
  9. Casas MC, Codina B, Redaño A, Lorente J (2004) A methodology to classify extreme rainfall events in the western Mediterranean area. Theor Appl Climatol 77:139–150. CrossRefGoogle Scholar
  10. Casas MC, Rodríguez R, Redaño A (2010) Analysis of extreme rainfall in Barcelona using a microscale rain gauge network. Meteorol Appl 17:117–123. Google Scholar
  11. Chen YR, Chu P-S (2014) Trends in precipitation extremes and return levels in the Hawaiian Islands and their changing climate. Int J Climatol 34:3913–3925CrossRefGoogle Scholar
  12. Claps P, Laio F (2003) Can continuous streamflow data support flood frequency analysis? An alternative to the partial duration series approach. Water Resour Res 39:1216CrossRefGoogle Scholar
  13. Coles S (2001) An introduction to statistical modeling of extreme values. Springer Science and Business Media, 208 ppGoogle Scholar
  14. Ferguson TS, Genest C, Hallin M (2000) Kendall’s tau for serial dependence. Canadian Journal of Statistics 28:587–604CrossRefGoogle Scholar
  15. Hosking JRM, Wallis JR, Wood EF (1985) Estimation of the generalised extreme value distributions by the method of probability-weighted moments. Techtonometrics 27:251–261CrossRefGoogle Scholar
  16. Hosking JRM, Wallis JR (1997) Regional frequency analysis. Cambridge University Press, Cambridge, 224 ppCrossRefGoogle Scholar
  17. Kendall MG, Stuart A (1967) The advanced theory of statistics, vol 2. Inference and relationship, 2nd edn. Griffin, LondonGoogle Scholar
  18. Kysely J, Picek J, Veranová R (2010) Estimating extremes in climate change simulations using the peaks-over-threshold method with non-stationary threshold. Glob Planet Chang 72:55–68CrossRefGoogle Scholar
  19. Lana X, Burgueño A, Martínez MD, Serra C (2006) Statistical distributions and sampling strategies for the analysis of extreme dry spells in Catalonia (NE Spain). J Hydrol 324:94–114CrossRefGoogle Scholar
  20. Lana X, Martínez MD, Serra C, Burgueño A (2005) Periodicities and irregularities of indices describing the daily pluviometric regime of the Fabra Observatory (NE Spain) for the years 1917–1999. Theor Appl Climatol 82:183–198CrossRefGoogle Scholar
  21. Lana X, Serra C, Casas-Castillo MC, Rodríguez-Solà R, Redaño A, Burgueño A (2017) Rainfall intensity patterns derived for the urban network of Barcelona (NE Spain). Theor Appl Climatol.
  22. Lorente J, Redaño A (1990) Rainfall rate distribution in a local scale: the case of Barcelona City. Theor Appl Climatol 41:23–32CrossRefGoogle Scholar
  23. Mitchell JM, Dzerdzeevskii B, Flohn H, Hofmeyr WL, Lamb HH, Rao KN, Wall’en CC. 1966. Climatic change. Technical Note, No. 79. World Meteorological Organization: Geneva, Switzerland, 99Google Scholar
  24. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C. The art of scientific computing. Cambridge University Press, Cambridge, 994 ppGoogle Scholar
  25. Rodríguez, R., Navarro, X., Casas, M.C., Redaño, A. (2013a). Rainfall spatial organization and areal reduction factors in the metropolitan area of Barcelona (Spain). Theor Appl Climatol, 114, 1–8. DOI:
  26. Rodríguez, R., Casas, M.C., Redaño, A. (2013b). Multifractal analysis of the rainfall time distribution on the metropolitan area of Barcelona (Spain). Meteorol Atmospheric Physics, 121(3–4): 181–187, doi
  27. Rodríguez-Solà R, Casas-Castillo MC, Navarro X, Redaño A (2016) A study of the scaling properties of rainfall in Spain and its appropriateness to generate intensity-duration-frequency curves from daily records. Int J Climatol 37:770–780. CrossRefGoogle Scholar
  28. Sneyers R (1990) On the statistical analysis of series of observations. In: Technical note 415. WMO, GenevaGoogle Scholar
  29. Vicente-Serrano SM, Beguería-Portugués S (2003) Estimating extreme dry-spell risk in the middle Ebro valley (NE Spain): a comparative analysis of partial duration series with general Pareto distribution and annual maxima series with a Gumbel distribution. Int J Climatol 23:1103–1118CrossRefGoogle Scholar
  30. Villarini G, Smith JA, Ntelekos AA, Schwartz U (2011) Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria. J Geophys Res 116:D05103Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • X. Lana
    • 1
    Email author
  • M. C. Casas-Castillo
    • 1
  • C. Serra
    • 1
  • R. Rodríguez-Solà
    • 1
  • A. Redaño
    • 2
  • A. Burgueño
    • 2
  • M. D. Martínez
    • 1
  1. 1.Departament de FísicaUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Departament de Física Aplicada – MeteorologiaUniversitat de BarcelonaBarcelonaSpain

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