La crue du Loing de Juin 2016 était-elle exceptionnelle?

C. Rebolho, V. Andréassian, I. Tsoukalas, et A. Efstratiadis, La crue du Loing de Juin 2016 était-elle exceptionnelle?, De la prévision des crues à la gestion de crise, Avignon, Société Hydrotechnique de France, 2018.

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[French]

A heavy rainfall event affected the northern center part of France from May 30 to June 6, 2016, leading to a general overflowing of rivers in the Seine and Loire catchments. The resulting inundations exceeded the previous records on some catchments, such as the River Loing where the water height of January 1910 was outreached for the first time. This event results from the combination of an extremely wet month of May and a rainfall accumulation of 130 mm in one week which led to a daily peak flow of 450 m3/s on this catchment. The main goal of this study is to show the limitations of standard methods for the estimation of return periods of extreme events. Usually, statistic laws such as Gumbel of GEV are used to calculate such return periods. However, various fitting methods exist and can be used to assess the parameters of the theoretical laws. In this study, we found that depending on the methodology, the return period varies from 260 to 2 400 years when using the observed discharges. To address this issue we simulated a long series of streamflows by coupling a rainfall generator and the conceptual hydrological model GR4J. The empirical return period given by the models is 1 000 years. But in this case, we also have the uncertainties of the two models, particularly the hydrological model which struggles reproducing the non-linearities of the catchment behaviour especially when modelling extreme events. This is why it is difficult to assign a single value to the return period of extreme events when only a range is available.

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See also: http://www.shf-hydro.org/223-1-events-16.html

Our works referenced by this work:

1. I. Tsoukalas, C. Makropoulos, and A. Efstratiadis, Stochastic simulation of periodic processes with arbitrary marginal distributions, 15th International Conference on Environmental Science and Technology (CEST2017), Rhodes, Global Network on Environmental Science and Technology, 2017.
2. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.

Tagged under: Floods, Stochastics, Uncertainty