Partitioning uncertainties of extreme flood estimates using long continuous simulations - Eleni Kritidou; Martina Kauzlaric; Maria Staudinger; Guillaume Evin; Benoit Hingray; Marc Vis; Daniel Viviroli (Journal of Hydrology, 2026)

Impact of different weather generator scenarios on extreme flood estimates in Switzerland - Eleni Kritidou; Martina Kauzlaric; Maria Staudinger; Guillaume Evin; Benoit Hingray; Marc Vis; Jan Seibert; Daniel Viviroli (Stochastic Environmental Research and Risk Assessment, 2025)
The estimation of extreme floods using long continuous simulations is linked to uncertainties which are inherent in different components of the modeling chain. The main objective of this study was to investigate the role of precipitation input data from a weather generator for extreme flood estimates. A hydrometeorological modeling chain consisting of a multi-site weather generator (GWEX) at an hourly time scale, a rainfall-runoff model (HBV) and a hydrologic routing model (RS Minerve), was implemented, using different parameterizations of GWEX. While the sensitivity to the altered precipitation inputs was not uniform across the selected catchments due to their different physiographic characteristics, we found that the uncertainty of flood estimates increased with increasing return period. In addition, the flood peaks were strongly affected when a bootstrapping of precipitation was performed and to a lesser extent when weather types (WT) were used to condition the parameters of GWEX. However, the latter seemed to reduce the spread of the uncertainty both in generated precipitation and simulated floods. Therefore, results suggested that precipitation inputs strongly contribute to the uncertainties of extreme floods. Accounting for uncertainty information enhances the usefulness of long continuous simulations and is essential as a context for applications including hydraulic engineering, spatial planning and safety assessments.

Generating hourly mean areal precipitation times series with an at-site weather generator in Switzerland - Kaltrina Maloku; Guillaume Evin; Benoit Hingray (Stochastic Environmental Research and Risk Assessment, 2024)

Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA - Adeline Bichet; Arona Diedhiou; Benoit Hingray; Guillaume Evin; N’Datchoh Evelyne Touré; Klutse Nana Ama Browne; Kouakou Kouadio (Climatic Change, 2020)
Over the past decades, large variations of precipitation were observed in Africa, which often led to dramatic consequences for local society and economy. To avoid such disasters in the future, it is crucial to better anticipate the expected changes, especially in the current context of climate change and population growth. To this date, however, projections of precipitation over Africa are still associated with very large uncertainties. To better understand how this uncertainty can be reduced, this study uses an advanced Bayesian analysis of variance (ANOVA) method to characterize, for the first time in the regional climate projections of CORDEX-AFRICA, the different sources of uncertainty associated with the projections of precipitation over Africa. By 2090, the ensemble mean precipitation is projected to increase over the Horn of Africa from September to May and over the eastern Sahel and Guinea Coast from June to November. It is projected to decrease over the northern coast and southern Africa all year long, over western Sahel from March to August, and over the Sahel and Guinea Coast from March to May. Most of these projections however are not robust, i.e., the magnitude of change is smaller than the associated uncertainty. Over time, the relative contribution of internal variability (excluding interannual variability) to total uncertainty is moderate and quickly falls below 10%. By 2090, it is found that over the Horn of Africa, northern coast, southern Africa, and Sahel, most of the uncertainty results from a large dispersion across the driving Global Climate Models (in particular MIROC, CSIRO, CCCma, and IPSL), whereas over the tropics and parts of eastern Africa, most of the uncertainty results from a large dispersion across Regional Climate Models (in particular CLMcom).

Uncertainty component estimates in transient climate projections - Benoit Hingray; Juliette Blanchet; Guillaume Evin; Jean-Philippe Vidal (Climate Dynamics, 2019)

Stochastic generators of multi-site daily temperature: comparison of performances in various applications - Guillaume Evin; Anne-Catherine Favre; Benoit Hingray (Theoretical and Applied Climatology, 2019)

Atmospheric analogues for physically consistent scenarios of surface weather in Europe and Maghreb - Damien Raynaud; Benoit Hingray; Isabella Zin; Sandrine Anquetin; Samuel Debionne; Robert Vautard (International Journal of Climatology, 2017)
This study compares the multivariate predictions of daily temperature, temperature range, precipitation, surface wind and solar radiation of a single‐model analogue approach with an original multi‐model analogy over 12 regions in Europe and Maghreb. Both approaches are based on two‐level analogue models where atmospheric predictors are either dynamic or thermodynamic. In the multi‐model approach, independent analogue models with predictand‐specific predictors are used. In the single‐model one, a unique analogue model and its associated set of predictors is applied to all predictands. Testing numerous large‐scale predictors, we first identify the best predictor sets for each modelling strategy. Those obtained for the single‐model approach are significantly different from those of the predictand‐specific models. This is especially the case for local temperature and wind speed. Both methods perform similarly for precipitation, temperature range and radiation. We next assess the ability of both approaches to simulate physically coherent multivariate weather scenarios. With the single‐model method, weather scenarios are obtained for each prediction day from observations sampled simultaneously on one analogue day. The physical consistency between variables is thus automatically fulfilled each day. This allows the single‐model method to reproduce well the observed inter‐predictand correlations, especially the significant correlations between radiation and precipitation and between radiation and temperature range. These results suggest a hybrid analogue model using a single‐model for radiation, temperature range and precipitation, combined with a univariate approach for wind. Two options are proposed for temperature for which either the predictand‐specific method or a single‐model approach with an additional correction are conceivable. This hybrid approach leads to a possible compromise between reasonable univariate prediction skills and realistic inter‐predictands correlations, both classically required for many impact studies.