海平面上升风险中国大陆沿海地区人口与经济暴露度

基于全球潮汐和浪涌再分析数据集,利用数字高程模型在ArcGIS空间分析工具支持下,提取了中国大陆沿海11个省市海平面上升可能淹没范围,结合人口、经济、土地利用数据构建海平面上升风险暴露度评估模型,评估中国大陆沿海地区海平面上升风险的人口和经济暴露度。结果表明：① 海平面上升风险可能淹没范围主要集中在长三角及苏北沿海、珠三角和环渤海等滨海平原地区,其中可能淹没范围最大的是江苏、上海等长三角地区;土地利用类型中受影响最严重的为耕地,约占受影响总面积的65%,其次为建设用地;② 基于2015年人口、经济统计数据,随着海平面上升水位值重现期的变化（10 a一遇至1000 a一遇）,海平面上升风险影响的人口和经济总量逐渐增加;对海平面上升风险暴露度而言,广州是暴露度等级最高的城市,高暴露度等级的区域仍集中于长三角及苏北沿海、珠三角、环渤海地区;③ 基于5种共享社会经济路径预估的人口和经济统计数据,随着海平面上升水位值重现期的变化,2025年和2100年受影响的人口、经济总量在不同路径下均呈增长趋势,常规发展路径下受影响的人口和经济总量最大,局部或不一致发展路径和不均衡发展路径下受影响的人口、经济低于其他3种路径;从重现期角度看,10 a一遇至1000 a一遇海平面上升风险的人口、经济暴露度向高暴露度等级转移;对比2025和2100年两时段的人口、经济暴露度,2100年的暴露度等级整体低于2025年。

Population and economic risk exposure in coastal regionof China under sea level rise

Based on the Global Tide and Surge Reanalysis (GTSR) dataset, the relevant Digital Elevation Model (DEM) was imported into ArcGIS and spatial analysis tools were used to extract possible flooding ranges due to sea level rise in 11 provinces and cities of coastal China. Combined with population, economic, and land use data, a sea level rise risk exposure assessment model is constructed and the population and economic risk exposure from sea level rise in coastal region of China are assessed. Three primary conclusions are drawn from the evaluation: (1) The impact of sea level rise on the coast areas is mainly concentrated in the Yangtze River Delta and northern Jiangsu coastal plains, as well as the Pearl River Delta and Bohai Rim, and other coastal plain areas. The most seriously affected land use types are arable land, which accounts for 65% of affected areas, followed by construction land; (2) Based on population and economic statistics from 2015 and 10 to 1000 years return periods, sea level rise increases risks to populations and the aggregated economy. In terms of sea level rise risk exposure, Guangzhou is most exposed and other areas with high exposure are concentrated in the Yangtze River Delta, northern Jiangsu coastal plains, the Pearl River Delta and Bohai Rim Region; (3) Based on population and economic statistics estimated using five shared social economic paths (SSPs), and variations in return periods from 10 to 1000 years, the gross population and the aggregated economies for 2025 and 2100 tended to increase in different paths. The conventional development path (SSP5) resulted in the largest affected population and aggregated economy while the local or inconsistent development path (SSP3) and unbalanced development path (SSP4) resulted in lower impacts than those of the other three paths. Changes in the return period from 10 to 1000 years can shift the risk exposure. Comparing the exposure risk to the population and economies, we found that the exposure degree in 2100 will be lower than that in 2025.