Using global temperature as a covariate to project flood risk

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfall. Under global warming, extreme rainfall is increasing, threatening the capacity of existing infrastructure. Hence, there is a need to update our methods of engineering design, namely our design rainfall intensities, for climate change.

One way of adjusting our design inputs for climate change is to incorporate covariates into the fitted probability distributions that describe extreme rainfall. To this end, here we evaluate which large-scale climate driver is best for modelling non-stationarity in IDF curves up to the 100-year design return level. The climate drivers we evaluate include global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode.

Based on the Akaike Information Criteria, precipitable water is the superior covariate, irrespective of storm duration. However, when quantile changes across the historical period are inspected, we find that global temperature is best able to adequately capture the variability in changes across both storm duration and annual exceedance probability. We finish with presenting a case study where extreme rainfalls are projected using a global mean temperature covariate. The implications for flood risk are that, under 4ºC of global warming, flood risk increases by a multiple of eight.