HR: 1340h
AN: U43B-1128    [Abstracts]
TI: Multifractal Flood Frequency Analysis
AU: Tchiguirinskaia, I
EM: ioulia@cereve.enpc.fr
AF: CEREVE, Ecole Nationale des Ponts et Chaussees, 6-8, avenue Blaise Pascal, Cite Descartes, MARNE-la-VALLEE, 77455, France
AU: * Schertzer, D
EM: Daniel.Schertzer@enpc.fr
AF: CEREVE, Ecole Nationale des Ponts et Chaussees, 6-8, avenue Blaise Pascal, Cite Descartes, MARNE-la-VALLEE, 77455, France
AU: * Schertzer, D
EM: Daniel.Schertzer@enpc.fr
AF: Meteo-France, 6-8, avenue Blaise Pascal, Cite Descartes, Paris, 75006, France
AU: Lovejoy, S
EM: lovejoy@physics.mcgill.ca
AF: Physics dept., McGill U., 3600 University st., Montréal, H3A2T5, Canada
AB: Hydrology and more generally sciences involved in water resources management, researches and technological or operational development face a fundamental difficulty: the extreme variability of hydrological fields. It clearly appears today that this variability is a function of the observation scale and yield natural hazards such as floods or droughts. The estimation of return periods for extreme precipitation and flooding events requires a model of the natural (unperturbed) statistical behaviour of the probability tails and the possible clustering (including possible long-range dependencies) of the extremes. Appropriate approaches for handling such non classical variability over wide ranges of time and space scale do exist. They are based on a fundamental property of the non-linear equations: scale invariance. Its specific framework is that of multifractals. In this framework hydrological variability builds up scale by scale leading to non-classical statistics; this provides the key element needed to better understand and predict floods. Scaling is a verifiable physical principle which can be exploited to model hydrological processes and estimate their statistics over wide ranges of space-time scales. We first present the Multifractal Flood Frequency Analysis (MFFA) tool and illustrate some results of its application to a large database (for more than 16000 selected stations over USA and Canada). We then discuss its efficiency by showing how the mean flow information – coupled with universal multifractal parametrizations with power law tails – can be used to estimate return times for extreme flood events.
DE: 1808 Dams
DE: 1817 Extreme events
DE: 1821 Floods
DE: 4400 NONLINEAR GEOPHYSICS (3200, 6944, 7839)
DE: 4468 Probability distributions, heavy and fat-tailed (3265)
SC: Union [U]
MN: 2007 Fall Meeting