Simulating catchment scale river discharges and flood events from large scale atmospheric information: Example of the Upper Rhône River (European Alps)

Abstract

<p>Floods are highly destructive natural hazards causing widespread impacts on socio-ecosystems. This hazard could be further amplified with the ongoing climate change, which will likely alter magnitude and frequency of floods. Estimating how flood-rich periods could change in the future is however challenging (UPH 9). The classical approach is to estimate future changes in floods from hydrological simulations forced by time series scenarii of weather variables for different future climate scenarii. The development of relevant weather scenarii for this is often critical. To be adapted to the critical space and time scales of the considered basins, weather scenarii are thus typically produced from climate models with downscaling models, either dynamic or statistical.</p><p>In this study, we aim to evaluate the capacity of such a simulation chain to reproduce changes in flood-rich periods of the upper Rhône River (10900 km², European Alps) over the last century (1902-2009). The modeling chain is made up of (i) the atmospheric reanalysis ERA-20C, (ii) either the statistical downscaling model SCAMP (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions; Raynaud et al., 2020) or the dynamical downscaling model MAR (Modèle Atmosphérique Régional; Ménégoz et al., 2020), and (iii) the glacio-hydrological model GSM-SOCONT (Glacier and Snowmelt SOil CONTribution model; Schaefli et al., 2005).</p><p>To assess the performance of each modeling chain, several hydro-meteorological variables are analysed. The simulated scenarii of mean areal precipitation and temperature are compared to the observed time series over the common period (1961-2009), whereas the discharge scenarii and the simulated flood-rich periods are compared to the reference ones (1920-2009). The flood-rich periods are derived from the discharge time series using the Peak Over Threshold (POT) method.</p><p>Preliminary results suggest that flood events are better reproduced with the statistical downscaling model. This may result from the biases in the MAR simulations for both the mean areal precipitation and the mean areal temperature. Further work will explore the interest for a quantile-quantile correction of MAR precipitation and temperature. The interest for a bias correction will be also explored for SCAMP temperature.</p><p>