PNNL

Abstract

As China has urbanized, flooding has become a regular feature in major cities. Assessing the potential urban floods with climate change is important for better managing such risks given the severity of the devastating disasters (e.g. the current 2016 flooding across China). While many studies have well investigated the impacts of future climate change on urban flood risks, relatively few has accounted for the effects of both climate mitigation and adaptations in a consistent assessment framework. In this study, we assess the benefits of avoided greenhouse gas (GHG) emissions on urban flood risks, and that by drainage system adaptation within the context of global warming choosing a study region in the Northern China. Climate modelling uncertainty was taken into account by applying the bias-corrected output of five CMIP5 global climate models (GCMs) to the urban drainage model (Storm Water Management Model, SWMM). Our results show that due to climate change the percentage of urban flooding volume is projected to increase by 52% by the 2020-2040 as compared to 1971–2000 under the business-as-usual scenario (RCP8.5). The magnitudes of increases in urban floods are found to be nonlinear with the changes in precipitation intensity, featured with highest risks in smaller return periods below 10 years. Despite large uncertainty, the benefits of avoided emissions with respect to urban flood damage and risks are obvious. In average, the magnitude of projected urban floods is 13% less under RCP2.6 than that under RCP8.5, demonstrating the importance of global-scale efforts on GHG emission reduction. This study stands out from previous studies by further designing two feasible adaptation strategies, which are shown to be more effective in reducing urban flood risks, double that by climate mitigation across all extreme events. This study highlights the importance of local adaptation strategies, and emphasizes the need to account for human adaptations in assessing future changes in urban flood risks with global warming.