Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa.
School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Glob Chang Biol. 2019 Nov;25(11):3741-3752. doi: 10.1111/gcb.14768. Epub 2019 Aug 10.
Carbon (C) emission and uptake due to land use and land cover change (LULCC) are the most uncertain term in the global carbon budget primarily due to limited LULCC data and inadequate model capability (e.g., underrepresented agricultural managements). We take the commonly used FAOSTAT-based global Land Use Harmonization data (LUH2) and a new high-resolution multisource harmonized national LULCC database (YLmap) to drive a land ecosystem model (DLEM) in the conterminous United States. We found that recent cropland abandonment and forest recovery may have been overestimated in the LUH2 data derived from national statistics, causing previously reported C emissions from land use have been underestimated due to the definition of cropland and aggregated LULCC signals at coarse resolution. This overestimation leads to a strong C sink (30.3 ± 2.5 Tg C/year) in model simulations driven by LUH2 in the United States during the 1980-2016 period, while we find a moderate C source (13.6 ± 3.5 Tg C/year) when using YLmap. This divergence implies that previous C budget analyses based on the global LUH2 dataset have underestimated C emission in the United States owing to the delineation of suitable cropland and aggregated land conversion signals at coarse resolution which YLmap overcomes. Thus, to obtain more accurate quantification of LULCC-induced C emission and better serve global C budget accounting, it is urgently needed to develop fine-scale country-specific LULCC data to characterize the details of land conversion.
土地利用和土地覆盖变化(LULCC)引起的碳(C)排放和吸收是全球碳预算中最不确定的因素,主要是由于 LULCC 数据有限和模型能力不足(例如,代表性不足的农业管理)。我们采用常用的基于 FAOSTAT 的全球土地利用协调数据(LUH2)和新的高分辨率多源协调国家 LULCC 数据库(YLmap)来驱动美国的陆地生态系统模型(DLEM)。我们发现,源自国家统计数据的 LUH2 数据中,最近的耕地废弃和森林恢复可能被高估了,这导致以前报告的土地利用 C 排放被低估,原因是耕地的定义以及在粗分辨率下对 LULCC 信号的综合。这种高估导致模型模拟中 LUH2 驱动的美国在 1980-2016 年期间出现强烈的 C 汇(30.3±2.5TgC/年),而当使用 YLmap 时,我们发现一个适度的 C 源(13.6±3.5TgC/年)。这种差异意味着,以前基于全球 LUH2 数据集的 C 预算分析低估了美国的 C 排放,这是由于在粗分辨率下,适宜耕地和综合土地转换信号的划定,而 YLmap 则克服了这一问题。因此,为了更准确地量化 LULCC 引起的 C 排放,并更好地服务于全球 C 预算核算,迫切需要开发精细尺度的特定国家的 LULCC 数据,以描述土地转换的细节。