The flood probability distribution tail: how heavy is it?

This paper empirically investigates the asymptotic behaviour of the flood probability distribution and more precisely the possible occurrence of heavy tail distributions, generally predicted by multiplicative cascades. Since heavy tails considerably increase the frequency of extremes, they have many practical and societal consequences. A French database of 173 daily discharge time series is analyzed. These series correspond to various climatic and hydrological conditions, drainage areas ranging from 10 to 105 km2, and are from 22 to 95 years long. The peaks-over-threshold method has been used with a set of semi-parametric estimators (Hill and Generalized Hill estimators), and parametric estimators (maximum likelihood and L-moments). We discuss the respective interest of the estimators and compare their respective estimates of the shape parameter of the probability distribution of the peaks. We emphasize the influence of the selected number of the highest observations that are used in the estimation procedure and in this respect the particular interest of the semi-parametric estimators. Nevertheless, the various estimators agree on the prevalence of heavy tails and we point out some links between their presence and hydrological and climatic conditions.

[1]  Shaun Lovejoy,et al.  Multifractals and extreme rainfall events , 1993 .

[2]  D. Schertzer,et al.  Multifractal analysis of daily river flows including extremes for basins of five to two million square kilometres, one day to 75 years , 1998 .

[3]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[4]  Shaun Lovejoy,et al.  Hard and soft multifractal processes , 1992 .

[5]  V. Gupta,et al.  Multiscaling properties of spatial rain-fall and river flow distributions , 1990 .

[6]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[7]  Per Bak,et al.  How Nature Works: The Science of Self-Organised Criticality , 1997 .

[8]  P. Willems Compound intensity/duration/frequency-relationships of extreme precipitation for two seasons and two storm types , 2000 .

[9]  B. Renard Détection et prise en compte d’éventuels impacts du changement climatique sur les extrêmes hydrologiques en France , 2008 .

[10]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[11]  Shaun Lovejoy,et al.  Non-Linear Variability in Geophysics , 1991 .

[12]  Laurens de Haan,et al.  On regular variation and its application to the weak convergence of sample extremes , 1973 .

[13]  Bernard Bobée,et al.  Towards operational guidelines for over-threshold modeling , 1999 .

[14]  Shaun Lovejoy,et al.  Multifractals, universality classes and satellite and radar measurements of cloud and rain fields , 1990 .

[15]  Michel Lang,et al.  Development of regional flood-duration–frequency curves based on the index-flood method , 2002 .

[16]  J. Vrijling,et al.  The estimation of extreme quantiles of wind velocity using L-moments in the peaks-over-threshold approach , 2001 .

[17]  J. R. Wallis,et al.  Noah, Joseph, and Operational Hydrology , 1968 .

[18]  M. Lang,et al.  Collaboration between Historians and Hydrologists on the Ardeche River (France) , 2001 .

[19]  J. Hosking,et al.  Parameter and quantile estimation for the generalized pareto distribution , 1987 .

[20]  J. R. Wallis,et al.  Probability Weighted Moments: Definition and Relation to Parameters of Several Distributions Expressable in Inverse Form , 1979 .

[21]  H. Andrieu,et al.  Use of historical data to assess the occurrence of floods in small watersheds in the French Mediterranean area , 2005 .

[22]  V. Barnett Probability Plotting Methods and Order Statistics , 1975 .

[23]  C. De Michele,et al.  Some hydrological applications of small sample estimators of Generalized Pareto and Extreme Value distributions , 2005 .

[24]  H. Madsen,et al.  The partial duration series method in regional index‐flood modeling , 1997 .

[25]  J. Teugels,et al.  Practical Analysis of Extreme Values , 1996 .

[26]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[27]  Michel Lang,et al.  Flood frequency analysis on the Ardèche river using French documentary sources from the last two centuries , 2005 .

[28]  D. Labat,et al.  Rainfall–runoff relations for karstic springs: multifractal analyses , 2002 .

[29]  B. Gnedenko Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire , 1943 .

[30]  John M. Chambers,et al.  Computational Methods for Data Analysis. , 1978 .

[31]  D. Turcotte,et al.  The applicability of power-law frequency statistics to floods. , 2006 .

[32]  R. Fisher,et al.  Limiting forms of the frequency distribution of the largest or smallest member of a sample , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.

[33]  P. Hubert,et al.  Multiscaling geophysics and sustainable development , 2004 .

[34]  C. Cunnane,et al.  A particular comparison of annual maxima and partial duration series methods of flood frequency prediction , 1973 .

[35]  D. Schertzer,et al.  Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes , 1987 .

[36]  P. Hubert,et al.  Multifractal analysis and modeling of rainfall and river flows and scaling, causal transfer functions , 1996 .

[37]  D. Schertzer,et al.  New Uncertainty Concepts in Hydrology and Water Resources: Multifractals and rain , 1995 .

[38]  Patrick Willems,et al.  Hydrological applications of extreme value analysis , 1998 .

[39]  D. Schertzer,et al.  Non-Linear Variability in Geophysics : Scaling and Fractals , 1990 .

[40]  J. Pickands Statistical Inference Using Extreme Order Statistics , 1975 .

[41]  C. Cunnane Methods and merits of regional flood frequency analysis , 1988 .

[42]  Renzo Rosso,et al.  Statistics, Probability and Reliability for Civil and Environmental Engineers , 1997 .

[43]  A. Barros,et al.  Probable Maximum Precipitation Estimation Using Multifractals: Application in the Eastern United States , 2003 .

[44]  Runoff generation in karst catchments: multifractal analysis , 2004 .

[45]  Upmanu Lall,et al.  A comparison of tail probability estimators for flood frequency analysis , 1993 .

[46]  A. Jenkinson The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[47]  Paul Deheuvels,et al.  Kernel Estimates of the Tail Index of a Distribution , 1985 .

[48]  D. L. Turcotte,et al.  A scale-invariant approach to flood-frequency analysis , 1993 .

[49]  Mark M. Meerschaert,et al.  Modeling river flows with heavy tails , 1998 .

[50]  Using higher probability weighted moments for flood frequency analysis , 1997 .

[51]  J. Stedinger,et al.  Flood Frequency Analysis With Historical and Paleoflood Information , 1986 .

[52]  G. Blöschl,et al.  Flood frequency regionalisation—spatial proximity vs. catchment attributes , 2005 .