Forest Research and Training Center, Kathmandu, Nepal.
Hawkesbury Institute for the Environment, Western Sydney University, Sydney, Australia.
Sci Rep. 2023 May 19;13(1):8090. doi: 10.1038/s41598-023-34247-z.
Comprehensive forest carbon accounting requires reliable estimation of soil organic carbon (SOC) stocks. Despite being an important carbon pool, limited information is available on SOC stocks in global forests, particularly for forests in mountainous regions, such as the Central Himalayas. The availability of consistently measured new field data enabled us to accurately estimate forest soil organic carbon (SOC) stocks in Nepal, addressing a previously existing knowledge gap. Our method involved modelling plot-based estimates of forest SOC using covariates related to climate, soil, and topographic position. Our quantile random forest model resulted in the high spatial resolution prediction of Nepal's national forest SOC stock together with prediction uncertainties. Our spatially explicit forest SOC map showed the high SOC levels in high-elevation forests and a significant underrepresentation of these stocks in global-scale assessments. Our results offer an improved baseline on the distribution of total carbon in the forests of the Central Himalayas. The benchmark maps of predicted forest SOC and associated errors, along with our estimate of 494 million tonnes (SE = 16) of total SOC in the topsoil (0-30 cm) of forested areas in Nepal, carry important implications for understanding the spatial variability of forest SOC in mountainous regions with complex terrains.
全面的森林碳核算需要可靠地估计土壤有机碳 (SOC) 储量。尽管 SOC 是一个重要的碳库,但全球森林,特别是喜马拉雅山脉等山区森林的 SOC 储量信息有限。新的实地测量数据的可用性使我们能够准确估计尼泊尔的森林土壤有机碳 (SOC) 储量,填补了之前存在的知识空白。我们的方法涉及使用与气候、土壤和地形位置相关的协变量来对基于样地的森林 SOC 估计值进行建模。我们的分位数随机森林模型对尼泊尔的国家森林 SOC 储量及其预测不确定性进行了高空间分辨率的预测。我们的空间显式森林 SOC 图显示了高海拔森林中的高 SOC 水平,以及这些储量在全球规模评估中的显著代表性不足。我们的结果为喜马拉雅山脉中部森林的总碳分布提供了一个改进的基准。预测的森林 SOC 基准图及其相关误差,以及我们对尼泊尔森林地区表层土壤(0-30 厘米)中总 SOC 估计为 4.94 亿吨(SE=16),对理解具有复杂地形的山区森林 SOC 的空间变异性具有重要意义。