Franšais      print      e-mail    


Arnaud, P. and J. Lavabre (2000). A Stochastic Model of Hourly Rainfall with Rainfall-Runoff Transformation for Predicting Flood Frequency. Rev. Sci. Eau 13 (4) : 441-462. [article in French]

Original title: La modélisation stochastique des pluies horaires et leur transformation en débits pour la prédétermination des crues.

Full text (PDF)


A statistical approach encompassing a stochastic model to generate hourly rainfall and rainfall runoff was used to study frequency distributions of hydrologic variables. The method generates numerous different flood events over a given simulation period to evaluate hydrologic risks. Entitled Simulated HYdrographs for flood PRobability Estimation (SHYPRE), it makes use of observed values to describe hydrological phenomena and successfully reproduces observed-value statistics. Frequency distributions of hydrologic variables are built empirically from model-generated rainfall and flood events. Extrapolation of these frequency distributions to rare frequencies is performed by simulation over longer periods, rather than by direct fit of theoretical probability distributions over observed values. This approach yields different estimations of flood quantiles for common to rare frequencies as well as complete temporal flood data. Moreover, SHYPRE estimates of flood quantiles are more stable than statistical distributions fitted onto observed values, even for frequent events. The improvement stems from better use of rainfall data and from the parametric stability of the rainfall model and rainfall-runoff model.


Stochastic model, rainfall-runoff model, hourly time step, flood frequency estimation, French Mediterranean seaboard.

Corresponding author

Patrick Arnaud, Université Montpellier II, Place Eugène Bataillon, cc MSE, 34 095 Montpellier cedex 5, FRANCE

Email : parnaud@msem.univ-montp2.fr
Telephone : + 33 (0)4 67 14 46 35 / Fax : + 33 (0)4 67 14 47 74

Franšais      print      e-mail    

Update: 2006-12-19
© INRS Eau, Terre et Environnement