On the independence from the emission pathway of the projected changes of river runoff

After the Paris Agreement of 2015 many studies on climate impact assessment, e.g. of floods, water resources and droughts, focused on understanding the projected changes at the time frame when a specific warming level is reached. The results of these studies assume that the pathway to reach a certain greenhouse concentration and corresponding warming level plays a minor role in the change of the physical variables that define the hazard. However, this hypothesis should be verified for each variable, as the links between the timing of the warming levels and the projected changes of the geophysical variables are not yet fully understood. To address this gap, in this contribution we compared the projected changes of annual mean, extreme high and extreme low river discharges in Europe at 1.5°C and 2°C under scenarios RCP8.5 and RCP4.5 from an ensemble of Regional Climate Model simulations. The statistical significance of the difference between the two scenarios for both warming levels has been then evaluated versus the other sources of uncertainty, through an Analysis of Variance (ANOVA). The results show that in the majority of Europe (>95% of the surface area for the annual mean discharge, >98% for high and low extremes), the differences in the changes projected in the two pathways are statistically small. These results suggest that in studies of changes at specific warming levels the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the merged ensemble, findings show that the projected changes of annual mean, extreme high and extreme low river discharges are statistically significant in large portions of Europe. Merging the 2 pathways comes with a two-fold advantage with respect to the separate treatment of the 2 scenarios. On the one hand, it improves the estimation of the statistical significance of the projected change, by increasing its size and by better taking into account the pathway-related uncertainty (the emission pathways are set ex-ante as a hypothesis for the CMIP experiment, and the related uncertainty is usually neglected). On the other hand, a multi-pathway ensemble can simplify the discussion of the projected changes by removing from the analysis the dependency from the emission pathway, and making the results clearer and more understandable by a non-scientific public.