New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Poleward shifts in the maximum of spring phenological responsiveness of Ginkgo biloba to temperature in China

Global warming is advancing the timing of spring leaf-out in temperate and boreal plants, affecting biological interactions and global biogeochemical cycles. However, spatial variation in spring phenological responsiveness to climate change within species remains poorly understood. Here, we investigated variation in the responsiveness of spring phenology to temperature (RSP; days to leaf-out at a given temperature) in 2754 Ginkgo biloba twigs of trees distributed across subtropical and temperate regions in China from 24°N to 44°N. We found a nonlinear effect of mean annual temperature on spatial variation in RSP, with the highest response rate at c. 12°C and lower response rates at warmer or colder temperatures due to declines in winter chilling accumulation. We then predicted the spatial maxima in RSP under current and future climate scenarios, and found that trees are currently most responsive in central China, which corresponds to the species' main distribution area. Under a high-emission scenario, we predict a 4-degree latitude shift in the responsiveness maximum toward higher latitudes over the rest of the century. The identification of the nonlinear responsiveness of spring phenology to climate gradients and the spatial shifts in phenological responsiveness expected under climate change represent new mechanistic insights that can inform models of spring phenology and ecosystem functioning.

Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem

China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO₂ to mitigate climate change caused by the increase of anthropogenic CO₂. It is essential to evaluate the carbon sink potential (CSP) of China's forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China's forest ecosystem in historical (1982-2021) and future (2022-2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (CSindex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO₂, which is approximately 52.03% of the cumulative increased CO₂ emissions in China from 1959 to 2018. In the next 60 years, China's forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO₂. Compared with the carbon stock in the historical period, the cumulative absorption of CO₂ by China's forest ecosystem in 2032-2036, 2062-2066, and 2077-2081 are approximately 11.25-39.68, 110.66-121.49 and 101.31-111.11 Bt CO_2, respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical CSindex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO₂, respectively. In the future, China's forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.

The spatial and temporal distribution of China’s forest carbon

Introduction

China’s forests have sequestrated a significant amount of carbon over the past two decades. However, it is not clear whether China’s forests will be able to continue to have as much carbon sequestration potential capacity in the future.

Methods

In order to research China’s forest carbon storage and carbon sequestration potential capacities at spatial and temporal scales, we built a digital forest model for each province of China using the data from The China Forest Resources Report (2014– 2018) and calculated the carbon storage capacity and sequestration potential capacity of each province with the current management practices without considering natural successions.

Results

The results showed that the current forest carbon storage is 10.0 Pg C, and the carbon sequestration potential in the next 40 years (from year 2019 to 2058) will be 5.04 Pg C. Since immature forests account for the majority of current forests, the carbon sequestration capacity of the forest was also high (0.202 Pg C year−1). However, the forest carbon storage reached the maximum with the increase of stand maturity. At this time, if scenarios such as afforestation and reforestation, human and natural disturbances, and natural succession are not considered, the carbon sequestration capacity of forests will continue to decrease. After 90 years, all stands will develop into mature and over-mature forests, and the forest carbon sequestration capacity is 0.008 Pg year−1; and the carbon sequestration rate is ~4% of what it is nowadays. The change trend of forest carbon in each province is consistent with that of the country. In addition, considering the large forest coverage area in China, the differences in tree species and growing conditions, the forest carbon storage and carbon sequestration capacities among provinces were different. The growth rate of carbon density in high-latitude provinces (such as Heilongjiang, Jilin, and Inner Mongolia) was lower than that in the south (Guangdong, Guangxi, or Hunan), but the forest carbon potential was higher.

Discussion

Planning and implementing targeted forest management strategies is the key to increasing forest carbon storage and extending the service time of forest carbon sinks in provinces. In order to reach the national carbon neutrality goals, we recommend that each province have an informative strategic forest management plan.

Estimation and Simulation of Forest Carbon Stock in Northeast China Forestry Based on Future Climate Change and LUCC

Forest carbon sinks (FCS) play an important role in mitigating global climate change, but there is a lack of more accurate, comprehensive, and efficient forest carbon stock estimates and projections for larger regions. By combining 1980–2020 land use data from the Northeast China Forestry (NCF) and climate change data under the Shared Socioeconomic Pathway (SSP), the land use and cover change (LUCC) of NCF in 2030 and 2050 and the FCS of NCF were estimated based on the measured data of forest carbon density. In general, the forest area of NCF has not yet recovered to the level of 1980. The temporal change in the FCS experienced a U-shaped trend of sharp decline to slow increase, with the inflection point occurring in 2010. If strict ecological conservation measures are implemented, the FCS of the NCF is expected to recover to the 1980 levels by 2050. We believe that the ecological priority (EP) scenario is the most likely and suitable direction for future development of the NCF. We also advocate for more scientific and stringent management measures for NCF natural forests to unlock the huge potential for forest carbon sequestration, which is important for China to meet its carbon neutrality commitments.

Assessing scale-dependent effects on Forest biomass productivity based on machine learning

Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.