The Sensitivity of Population Exposure to Climatic Changes in Flood Magnitude at the Global Scale

Abstract:

Because of global warming and its associated changes in storminess, flood risks in floodplains are expected to rise. These increases in risk are typically expressed as changes in the frequency or magnitude of specific frequency events. However, the impacts of these changes in terms of human exposure to floods are largely unknown.

In this study, we propose a method for analyzing and mapping the sensitivity of population exposure in floodplains to changes in the magnitude of flooding. The method is based on downward counterfactuals, namely perturbations of a selected flood scenario by increasing its magnitude, interpreted in this case as the worsening of a today’s design flood event as a result of climatic changes. The increase in the impact of a current design flood compared to its counterfactual illustrates the sensitivity to changes in hazard.

We test the applicability of the method on a global scale by using existing data sets, including flood hazard maps, flood protection standards, floodplain delineation, river network definition, and spatial population distribution. The return periods were used as indicators for flood magnitude, and five flood magnitudes were considered for the counterfactual analysis. We calculated the normalized gradients, that is, the increase in the exposure and magnitude of the perturbed event relative to the exposure and magnitude of the current scenario. The gradients were found to vary remarkably across the globe and are overall smaller in the upper range of flood magnitudes that in the lower range.

Based on these results, we compare the drivers of the sensitivity in different parts of the world and identify river reaches with the highest relative gradients. These river reaches might be the most affected by climate change and thus deserve an in-depth investigation of the underlying characteristics of the floodplains and the potential for climate change adaptation. Even if climate change predictions are significantly uncertain, this analysis of sensitivity can offer valuable insights on the types of change that could lead to higher impacts and the adaptation strategies that are best suited to manage the risk.