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Flood hazard assessment of the Rhône river revisited with reconstructed discharges from lake sediments

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Jenny,  Jean-Philippe
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Citation

Evin, G., Wilhelm, B., & Jenny, J.-P. (2019). Flood hazard assessment of the Rhône river revisited with reconstructed discharges from lake sediments. Global and Planetary Change, 172, 114-123. doi:10.1016/j.gloplacha.2018.09.010.


Cite as: http://hdl.handle.net/21.11116/0000-0002-F5B5-D
Abstract
Accurate flood hazard assessments are crucial for adequate flood hazard mapping and hydraulic infrastructure design. The choice of an acceptable and cost-effective solution for such assessments depends upon the estimation of quantiles for different characteristics of floods, usually maximum discharges. However, gauge series usually have a limited time length and, thereby, quantile estimates associated to high return periods are subject to large uncertainties. To overcome this limitation, reconstructed flood series from historical, botanical or geological archives can be incorporated. In this study, we propose a novel approach that i) combines classic series of observations with paleodischarges (of the Rhône River) reconstructed from open lake sediments (Lake Bourget, Northwestern Alps, France) and ii) propagates uncertainties related to the reconstruction method during the estimation of extreme quantiles. A Bayesian approach is adopted in order to properly treat the non-systematic nature of the reconstructed flow data, as well as the uncertainties related to the reconstruction method. While this methodology has already been applied to reconstruct maximum discharges from historical documents, tree rings or fluvial sediments, similar applications need to be tested today on open lake sediments as they are one of the only archives that provide long and continuous paleoflood series. Reconstructed sediment volumes being subject to measurement errors, we evaluate and account for this uncertainty, along with the uncertainty related to the reconstruction method, the parametric uncertainty, and the rating-curve errors for systematic gauged flows by propagating these uncertainties through the modeling chain. Reconstructed maximum discharges appear to largely overcome values of observations, reaching values of approximately 2,600, 4,200, 2,450 and 2,500 m3/s in 1689, 1711, 1733 and 1737 respectively, which correspond to historically-known catastrophic floods. Extreme quantiles are estimated using direct measurements of maximum discharges (1853-2004) only and then combined to the sedimentary information (1650-2013). The comparison of the resulting estimates demonstrates the added value of the sedimentary information. In particular, the four historical catastrophic floods are very unlikely if only direct observations are considered for quantile estimations.