Maximizing carbon sequestration potential in Chinese forests through optimal management

Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement.

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Figures S1 to S18
Tables S1 to S9 SI References Supporting Information Text Spatial distribution of the forest plots surveyed during national forest inventory About 415, 000 permanent plots were set up in China for routine survey each five years.Among these plots, nearly 30,000 to 50,000 surveyed plots were covered with trees during the period of the 6 th (1999-2003) to 9 th NFI (2014-2018) (Figure S1).For each forest plot, trees with diameter at breast height (DBH) ≥ 5cm were labeled, measured, and recorded.In total of 18,116,071 tree were recorded, and the tree volume was calculated by referring to the one-variable tree volume tables for each species specifically developed in each province (Ministry of Agriculture and Forestry of China, 1978).The stand volumes were summarized to plot level from all recorded trees.

Forest carbon stock and age
We converted the tree volume data into biomass carbon stock using the continuous biomass expansion factor method (Table S1) and the species-specific carbon conversion parameters (Tables S2).
We examined the biomass carbon stock changes with stand age in natural and planted forests (Figure S2).As expected, forest carbon stocks increase with stand age but saturate as they proceed to mature (Figures S2A-C).The relationships of forest biomass carbon and age were used to estimate the species-specific carbon stock in 2018, which serve as the reference for calibration and validation of the simulations using process-based model (DLEM).
Besides, the age and biomass carbon for each major tree species were showed in Figure S3.

Forest carbon stock and sink calibration and validation at species level
We compared the forest carbon stock derived from inventory data and process-based biogeochemical model (DLEM) for each species of natural forest and planted forest (Figure S6).

Fig. S1 .
Fig. S1.Locations of the national forest inventory plots in China.Red and gray dots indicate plots of planted and natural forests.

Fig. S2 .
Fig. S2.Relationships between forest biomass carbon accumulations and ages.Panels A-C: relationships of forest stock volume and stand age; error bars indicate 1 standard error from the mean; * indicate significant at p<0.05 level.

Fig. S10 .
Fig. S10.The modeled biomass carbon sink and the year of sink peak under scenarios assuming no harvest and annual forest age accrual at 1 year per year.Dash line and solid line indicate the carbon sink under the scenarios of current tree survival rate (47%) and elevated tree survival rate (85%); ssp126, ssp245, ssp370, and ssp585 indicate simulation of climate and CO2 under the scenarios of SSP1-2.6,SSP2-4.5, SSP3-7.0, and SSP5-8.5;Dotted circle and closed circle indicate the biomass carbon sink peak under the scenarios of current tree survival rate (47%) and elevated tree survival rate (85%), respectively.Results were derived from group 3 experiments.

Fig. S12 .
Fig. S12.The projected biomass carbon stock in China's forests.NF non-timber, NF timber, PF non-timber, and PF timber indicate non-timber natural forest, timber natural forest, nontimber planted forest, and timber planted forest; panels a-d, e-h, and i-l indicate the spatial distribution in 2020, 2060, and 2100, respectively; unit: g C m -2 .Results were derived from group 1 experiments.

Fig. S14 .
Fig. S14.Comparison of carbon sink derived from statistical model and process-based model at species level in 2020.Panel a: natural forest; Panel b: planted forest.
Peak year of the forest biomass carbon sink under different SSPs assuming no harvest and annual age accrual at 1 year per year survival rate (47%) and elevated tree survival rate (85%); ssp126, ssp245, ssp370, and ssp585 indicate simulation of climate and CO2 under the scenarios of SSP1-2.6,SSP2-4.5,SSP3-7.0,andSSP5-8.5;Dottedcircleandclosedcircleindicatethebiomasscarbonsink peak under the scenarios of current tree survival rate (47%) and elevated tree survival rate (85%), respectively.Results were derived from group 1 experiments.Diagram showing forest age change and age impacts on carbon accumulationStand age is the average age of trees at canopy level, but average age was derived from all trees in the plot (FigureS9).Generally, stand age was more widely used in statistical model, while process-based models rely on average age.However, process-based model using average age may cause bias in representing the growth rate (Figure4).For example, Cohort1 and Cohort2 in Figure4indicate a young age plot and an aged plot, in which the carbon accumulation rate Fave (instantaneous growth rate, the first-order derivative of the growth curve) at the average age is high, but in reality, the average growth rate (average of F1 and F2) is very low.Thus, age dynamics should be particularly tracked in modeling of tree growth.Fig. S9.The dynamic of the stand age and average age in a plot due to wood harvest, mortality, natural growth, and tree-planting.Stand age: average age of the trees in the overstory level; average age: averaged age of all trees in the plot.The numbers above trees indicate the tree ages in the plot.
Species-specific carbon sink derived from statistical model and process-based model

Table S1 .
Projected carbon stock and sequestration rate reported in China's forests *Initial carbon stock in 2020; **Carbon stock increment from 2020 to the last year/decade reported.Carbon stock Increment in Zhang et al (2022) is abnormally high due to unrealistic increase of forest area; ***Carbon sinks of the periods between 2020 and the last reported year were listed.

Table S2 .
Biomass expansion factor (BEF) and its parameters for China's major tree 224 species.( =  + /, where a and b are constant, x is mean stock volume per hectare).

Table S4 .
The tree species and the number of forest site-records (plots) used in this

Table S5 .
Experiments designed for model simulations

Table S6 .
Harvest age of different tree species/species groups

Table S7 .
Parameter a of different tree species/species groups in the logistic BMF species indicates Broadleaf mixed forest species, which are indigenous species of southern China, these species were grouped into one type in model simulations. *