RESUMEN
Forest age serves as an essential factor in determining the accuracy of historical and future carbon (C) uptake quantifications, which is especially critical for China since the forest C stock dynamics are sensitive to the fast-growing, young-age plantations. However, a spatially explicit forest age maps with specific focus on forest plantations is not available yet. In this study, we developed a 1-km resolution age and type maps of forest plantations, and quantified their uncertainties spatially using field-measured data, national forest inventory data, digitalized forest maps, and remote sensing-based forest height maps. Simulation results showed forest plantations were 16.5 years old at national scale in 2005, which is close to the age of 16.6 years old derived from the 7th national inventory data using medium age in each forest plantation group with weighted area. Interestingly, we found that human management played an important role in forest age map reconstruction, which has not yet been considered in former studies. We also suggest that forest age and type maps should be used consistently in C stock simulations to avoid biases from mismatch information. Large uncertainty found in this study suggests further endeavors are required for improving the forest age and type maps.
RESUMEN
Soil organic carbon (SOC) plays critical roles in stabilizing atmospheric CO2 concentration, but the mechanistic controls on the amount and distribution of SOC on global scales are not well understood. In turn, this has hampered the ability to model global C budgets and to find measures to mitigate climate change. Here, based on the data from a large field survey campaign with 2600 plots across China's forest ecosystems and a global collection of published data from forested land, we find that a low litter carbon-to-nitrogen ratio (C/N) and high wetness index (P/PET, precipitation-to-potential-evapotranspiration ratio) are the two factors that promote SOC accumulation, with only minor contributions of litter quantity and soil texture. The field survey data demonstrated that high plant diversity decreased litter C/N and thus indirectly promoted SOC accumulation by increasing the litter quality. We conclude that any changes in plant-community composition, plant-species richness and environmental factors that can reduce the litter C/N ratio, or climatic changes that increase wetness index, may promote SOC accumulation. The study provides a guideline for modeling the carbon cycle of various ecosystem scales and formulates the principle for land-based actions for mitigating the rising atmospheric CO2 concentration.
RESUMEN
China's terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China's forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country's total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9-3.4 Pg C in forest biomass in the next 10-20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China's terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally.
