Jacques Lavabre

Changes in the hydrological response of a small Mediterranean basin a year after a wildfire

This paper presents the results of a study of the hydrological consequences of a wildfire in a small Mediterranean basin during the first 12 months after the event. The Rimbaad basin is a nested sub-basin of the Real Collobrier research and experimental network, which has been continuously monitored since 1967. In August 1990, 85% of its surface area was burnt providing an interesting case study to analyse the hydrological consequences of fire. Models have been calibrated using a 23 year period before the fire and they have been used to simulate what the response would have been if the wildfire had not taken place. Differences between observed and simulated data are analysed, giving special importance to the changes in the annual runoff yield and in the flood regimes. There is a 30% increase in the annual runoff yield related to the reduction in evapotranspiration due to the destruction of the vegetation cover. On the other hand, there are pronounced changes in the shape of the flood hydrographs, and the flood frequency is greatly increased. The 10-year return period flow estimated before the fire was exceeded three times in the year although the rainfall events did not exceed the 1-year return period 12 h rainfall. The quantity and quality of the data collected during the pre-fire period and the possibility of comparing the hydrological responses of similar basins differently affected by fire, give a special relevance to this case study.

Using a stochastic model for generating hourly hyetographs to study extreme rainfalls

Abstract A stochastic model for generating hourly hyetographs has been developed using data from observed precipitation records to simulate rainfall patterns. This model makes it possible to study maximum precipitation distributions for normal or exceptional frequencies over long periods of time. The modelling provides observations (up to 10-year frequency) of quantiles similar to those observed by directly fitting a law of statistical distribution onto an observed distribution. Differences occur for rare frequency quantiles. Modelled rainfall frequency distributions behave in an over-exponential way (i.e. greater than strictly exponential) at infinity, yielding higher results than those obtained using standard fittings. One factor that is considered in modelling can explain this behaviour: the persistence of storms within a rainfall episode which causes high rainfall accumulation. Thus modelling the observed phenomenon provides an innovative approach in studying extreme occurrences.

Sensitivity of hydrological models to uncertainty in rainfall input

Abstract Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km2. The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall–runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning. Citation Arnaud, P., Lavabre, J., Fouchier, C., Diss, S. & Javelle, P. (2011) Sensitivity of hydrological models to uncertainty of rainfall input. Hydrol. Sci. J. 56(3), 397–410.

Uncertainties of extreme rainfall quantiles estimated by a stochastic rainfall model and by a generalized Pareto distribution.

Abstract The hourly rainfall stochastic model SHYPRE generates long hourly rainfall series and enables the estimation of distribution quantiles. Two different uncertainty analyses are proposed, based on frequential and Bayesian methods, to quantify the effect of sampling distribution and parameter uncertainties on the quantile estimations. The results are compared with those of a regional generalized Pareto distribution (GPD) based on extreme value analysis, with a regionally fixed value of the shape parameter. The GPD and SHYPRE are shown to have similar uncertainties. The application of regional approaches is shown to reduce sampling sensitivity in estimations, especially when few data are available. The study is based on a 122-year daily rainfall series in Marseille, France.

Nouvelle approche de la prédétermination des pluies extrêmes

A stochastic model generating hourly hyetographs uses information contained in observed rainfall in order to reproduce it. When used over very long simulation periods, it makes it possible to study rainfall on all frequency scales. The modelling of hyetographs leads to a distribution of maximum rainfall with a behaviour that differs from that obtained with more classical models using a simple exponential law. The modelling of the internal structure of the rainy episodes conditions this behaviour by randomly generating strong rainfall events. Moreover, the modelling appears robust and not very sensitive to sampling. This new approach of predetermining the rainfall remains however statistical: its results are conditioned by the hypotheses of the model, as validation is impossible beyond the observed frequencies.