Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

Abstract

With accelerating global sea level rise driven by climate change, accurate estimates of potential population exposure (PPE) within the low-elevation coastal zones (LECZ) are critical for coastal planning and assessing the benefits of climate mitigation. Multiple digital elevation models (DEM) and population grid datasets have been used for the PPE assessment of coastal lowlands. However, the uncertainty arising from differences in data sources and production methods results in poorly guided estimates. In this study, four global DEM and five population datasets were used to estimate the PPE in the LECZ of China and to assess the uncertainty of PPE estimation. Based on the DEM and population grid with the best accuracy, we found that more than 13.82% of China’s residents lived in the LECZ in 2010. Different DEM-population combinations yielded significantly different PPE estimates, ranging between 3.59–24.61 million and 31.56–112.24 million people in the LECZ below 1 m and 4 m elevation, respectively. The satellite Lidar-based DEM improves the estimates of the LECZ and obtains the PPE within LECZ below 4 m elevation that far exceeds those of other DEM datasets. The usage of WorldPop and LandScan population datasets leads to an underestimation of PPE within the LECZ of China. In contrast, integrating more geospatial big data helps generate better population grids, thus reducing the uncertainty of coastal PPE estimates. There is still a need to improve the availability and accuracy of coastal geospatial data and to deepen the understanding of coastal vulnerability.