Abstract：
BACKGROUND: Countries seeking to mitigate climate change through forests require suitable modelling approaches to predict carbon (C) budget dynamics in forests and their responses to disturbance and management. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a feasible and comprehensive tool for simulating forest C stock dynamics across broad levels, but discrepancies remain to be addressed in China. Taking Guizhou as the case study, we customised the CBM-CFS3 model according to China\'s context, including the modification of aboveground biomass C stock algorithm, addition of C budget accounting for bamboo forests, economic forests, and shrub forests, improvement of non-forest land belowground slow dead organic matter (DOM) pool initialisation, and other model settings.
RESULTS: The adequate linear relationship between the estimated and measured C densities (R2 = 0.967, P < 0.0001, slope = 0.904) in the model validation demonstrated the high accuracy and reliability of our customised model. We further simulated the spatiotemporal dynamics of forest C stocks and disturbance impacts in Guizhou for the period 1990-2016 using our customised model. Results shows that the total ecosystem C stock and C density, and C stocks in biomass, litter, dead wood, and soil in Guizhou increased continuously and significantly, while the soil C density decreased over the whole period, which could be attributed to deforestation history and climate change. The total ecosystem C stock increased from 1220 Tg C in 1990 to 1684 Tg C in 2016 at a rate of 18 Tg C yr-1, with significant enhancement in most areas, especially in the south and northwest. The total decrease in ecosystem C stock and C expenditure caused by disturbances reached 97.6 Tg C and 120.9 Tg C, respectively, but both represented significant decreasing trends owing to the decline of disturbed forest area during 1990-2016. Regeneration logging, deforestation for agriculture, and harvest logging caused the largest C stock decrease and C expenditure, while afforestation and natural expansion of forest contributed the largest increases in C stock.
CONCLUSIONS: The forests in Guizhou were a net carbon sink under large-scale afforestation throughout the study period; Our customised CBM-CFS3 model can serve as a more effective and accurate method for estimating forest C stock and disturbance impacts in China and further enlightens model customisation to other areas.
RESULTS: The adequate linear relationship between the estimated and measured C densities (R2 = 0.967, P < 0.0001, slope = 0.904) in the model validation demonstrated the high accuracy and reliability of our customised model. We further simulated the spatiotemporal dynamics of forest C stocks and disturbance impacts in Guizhou for the period 1990-2016 using our customised model. Results shows that the total ecosystem C stock and C density, and C stocks in biomass, litter, dead wood, and soil in Guizhou increased continuously and significantly, while the soil C density decreased over the whole period, which could be attributed to deforestation history and climate change. The total ecosystem C stock increased from 1220 Tg C in 1990 to 1684 Tg C in 2016 at a rate of 18 Tg C yr-1, with significant enhancement in most areas, especially in the south and northwest. The total decrease in ecosystem C stock and C expenditure caused by disturbances reached 97.6 Tg C and 120.9 Tg C, respectively, but both represented significant decreasing trends owing to the decline of disturbed forest area during 1990-2016. Regeneration logging, deforestation for agriculture, and harvest logging caused the largest C stock decrease and C expenditure, while afforestation and natural expansion of forest contributed the largest increases in C stock.
CONCLUSIONS: The forests in Guizhou were a net carbon sink under large-scale afforestation throughout the study period; Our customised CBM-CFS3 model can serve as a more effective and accurate method for estimating forest C stock and disturbance impacts in China and further enlightens model customisation to other areas.
摘要：
背景：寻求通过森林缓解气候变化的国家需要适当的建模方法来预测森林中的碳（C）预算动态及其对干扰和管理的响应。加拿大森林部门的碳预算模型（CBM-CFS3）是一种可行且全面的工具，用于模拟广泛水平的森林C储量动态，但中国的差异仍有待解决。以贵州为例,我们根据中国的情况定制了CBM-CFS3模型，包括对地上生物量C库存算法的修改，增加竹林的C预算核算，经济林,和灌木森林，改善地下缓慢死亡有机质(DOM)池初始化的非林地，和其他模型设置。
结果：模型验证中估计和测量的C密度之间的适当线性关系（R2=0.967，P<0.0001，斜率=0.904）证明了我们定制模型的高准确性和可靠性。我们使用我们的定制模型进一步模拟了1990-2016年期间贵州森林C种群和干扰影响的时空动态。结果表明，生态系统总的碳储量和碳密度，和生物质中的C库存，垃圾,枯木,贵州土壤持续显著增加，而土壤碳密度在整个时期都在下降，这可能归因于森林砍伐历史和气候变化。总生态系统C存量从1990年的1220TgC增加到2016年的1684TgC，速度为18TgCyr-1，大部分地区显着增强，尤其是在南部和西北部。扰动引起的生态系统C存量和C支出的总减少达到97.6TgC和120.9TgC，分别,但由于1990-2016年期间受干扰森林面积的减少，两者都表现出显着下降趋势。再生测井,为农业砍伐森林，收获伐木导致最大的C库存减少和C支出，而植树造林和森林自然扩张对碳储量的增长贡献最大。
结论：在整个研究期间，贵州森林是大规模造林的净碳汇；我们定制的CBM-CFS3模型可以作为估算中国森林碳储量和扰动影响的更有效和准确的方法，并进一步启发其他地区的模型定制。
结果：模型验证中估计和测量的C密度之间的适当线性关系（R2=0.967，P<0.0001，斜率=0.904）证明了我们定制模型的高准确性和可靠性。我们使用我们的定制模型进一步模拟了1990-2016年期间贵州森林C种群和干扰影响的时空动态。结果表明，生态系统总的碳储量和碳密度，和生物质中的C库存，垃圾,枯木,贵州土壤持续显著增加，而土壤碳密度在整个时期都在下降，这可能归因于森林砍伐历史和气候变化。总生态系统C存量从1990年的1220TgC增加到2016年的1684TgC，速度为18TgCyr-1，大部分地区显着增强，尤其是在南部和西北部。扰动引起的生态系统C存量和C支出的总减少达到97.6TgC和120.9TgC，分别,但由于1990-2016年期间受干扰森林面积的减少，两者都表现出显着下降趋势。再生测井,为农业砍伐森林，收获伐木导致最大的C库存减少和C支出，而植树造林和森林自然扩张对碳储量的增长贡献最大。
结论：在整个研究期间，贵州森林是大规模造林的净碳汇；我们定制的CBM-CFS3模型可以作为估算中国森林碳储量和扰动影响的更有效和准确的方法，并进一步启发其他地区的模型定制。
