First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble

Abstract

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20-45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5-30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

Keywords: climate impacts; extremes; river flows.

Fig. 1.

(Upper) Number of experiments (out of 45 in total) showing an increase (Left) or decrease (Right) in the magnitude of Q30 of more than 10% in 2070–2099 under RCP8.5, compared with 1971–2000. (Lower Left) Average change in the magnitude of Q30 across all experiments. (Lower Left) Ratio of GCM variance to IM variance. GCM variance was computed as the variance of the change in Q30 across all GCMs for each individual IM, and then averaged over the nine IMs; IM variance was computed as the variance of the change in Q30 across all IMs for each individual GCM, and then averaged over the nine GCMs. In dark green (purple) areas GCM (IM) variance predominates.

Fig. 2.

Fraction of land grid points where the estimated future return period of the historical Q30 is more than 40 y (red colors), less than 20 y (light green), less than 10 y (cyan), and less than 5 y (blue), grouped by driving GCM (horizontal axis). Results from individual impact models (denoted by their first three letters) are indicated with different symbols. Note that the exact number of land points can be different for each GCM/IM combination.